[
    {
        "text": "Planning with partial observation is a central challenge in embodied AI. A\nmajority of prior works have tackled this challenge by developing agents that\nphysically explore their environment to update their beliefs about the world\nstate.In contrast, humans can $\\textit{imagine}$ unseen parts of the world\nthrough a mental exploration and $\\textit{revise}$ their beliefs with imagined\nobservations. Such updated beliefs can allow them to make more informed\ndecisions, without necessitating the physical exploration of the world at all\ntimes. To achieve this human-like ability, we introduce the $\\textit{Generative\nWorld Explorer (Genex)}$, an egocentric world exploration framework that allows\nan agent to mentally explore a large-scale 3D world (e.g., urban scenes) and\nacquire imagined observations to update its belief. This updated belief will\nthen help the agent to make a more informed decision at the current step. To\ntrain $\\textit{Genex}$, we create a synthetic urban scene dataset, Genex-DB.\nOur experimental results demonstrate that (1) $\\textit{Genex}$ can generate\nhigh-quality and consistent observations during long-horizon exploration of a\nlarge virtual physical world and (2) the beliefs updated with the generated\nobservations can inform an existing decision-making model (e.g., an LLM agent)\nto make better plans.",
        "split": "arXiv"
    },
    {
        "text": "I model a rational agent who spends resources between the current time and\nsome fixed future deadline. Opportunities to spend resources arise randomly\naccording to a Poisson process, and the quality of each opportunity follows a\nuniform distribution. The agent values their current resource stock at exactly\nthe sum of expected utility from all future spending opportunities. Unlike in\ntraditional discounted expected utility models, the agent exhibits correlation\naversion, static (but not dynamic) preference reversals, and monotonicity with\nrespect to payment timing. Connecting the agent's risk and time preference is\nintuitive, and doing so leads to a new model of procrastination where the agent\nmisperceives their general attitude toward spending resources.",
        "split": "arXiv"
    },
    {
        "text": "A full set of optimized observables is measured in an angular analysis of the\ndecay B$^0$ $\\to$ K$^*$(892)$^0\\mu^+\\mu^-$ using a sample of proton-proton\ncollisions at $\\sqrt{s}$ = 13 TeV, collected with the CMS detector at the LHC,\ncorresponding to an integrated luminosity of 140 fb$^{-1}$. The analysis is\nperformed in six bins of the squared invariant mass of the dimuon system,\n$q^2$, over the range 1.1 $\\lt$ $q^2$ $\\lt$ 16 GeV$^2$. The results are among\nthe most precise experimental measurements of the angular observables for this\ndecay and are compared to a variety of predictions based on the standard model.",
        "split": "arXiv"
    },
    {
        "text": "Differential equations are derived which show how generalized Euler vector\nrepresentations of the Euler rotation axis and angle for a rigid body evolve in\ntime; the Euler vector is also known as a rotation vector or axis-angle vector.\nThe solutions can exhibit interesting rotational features in this non-abstract,\nvisualizable setting, including spinor-like behavior and quasiperiodicity. The\nequations are well-behaved at zero, reducing to the simple infinitesimal case\nthere. One of them is equivalent to a known quaternion differential equation.\nThe simple geometric derivation does not depend on Euler's rotation theorem,\nand yields a proof of Euler's theorem using only infinitesimal motions. With\nmild regularity conditions on the angular velocity function, there is a\ncontinuous evolution of the normalized axis and angle for all time. Dynamical\nsystems properties are discussed, and numerical solutions are used to\ninvestigate them when the angular velocity is itself rotating, and the Euler\nvector trajectory traces out a torus-like shape, with a strobe plot that\ndensely fills in a closed curve.",
        "split": "arXiv"
    },
    {
        "text": "This paper describes two C++/Open Motion Planning Library implementations of\nthe recently developed motion planning algorithms HyRRT arXiv:2210.15082v1\n[cs.RO] and HySST arXiv:2305.18649v1 [cs.RO]. Specifically, cHyRRT, an\nimplementation of the HyRRT algorithm, is capable of generating a solution to a\nmotion planning problem for hybrid systems with probabilistically completeness,\nwhile cHySST, an implementation of the asymptotically near-optimal HySST\nalgorithm, is capable of computing a trajectory to solve the optimal motion\nplanning problem for hybrid systems. cHyRRT is suitable for motion planning\nproblems where an optimal solution is not required, whereas cHySST is suitable\nfor such problems that prefer optimal solutions, within all feasible solutions.\nThe structure, components, and usage of the two tools are described. Examples\nare included to illustrate the main capabilities of the toolbox.",
        "split": "arXiv"
    },
    {
        "text": "Federated learning (FL) is an emerging paradigm for training machine learning\nmodels across distributed clients. Traditionally, in FL settings, a central\nserver assigns training efforts (or strategies) to clients. However, from a\nmarket-oriented perspective, clients may independently choose their training\nefforts based on rational self-interest. To explore this, we propose a\npotential game framework where each client's payoff is determined by their\nindividual efforts and the rewards provided by the server. The rewards are\ninfluenced by the collective efforts of all clients and can be modulated\nthrough a reward factor. Our study begins by establishing the existence of Nash\nequilibria (NEs), followed by an investigation of uniqueness in homogeneous\nsettings. We demonstrate a significant improvement in clients' training efforts\nat a critical reward factor, identifying it as the optimal choice for the\nserver. Furthermore, we prove the convergence of the best-response algorithm to\ncompute NEs for our FL game. Finally, we apply the training efforts derived\nfrom specific NEs to a real-world FL scenario, validating the effectiveness of\nthe identified optimal reward factor.",
        "split": "arXiv"
    },
    {
        "text": "High penetration from volatile renewable energy resources in the grid and the\nvarying nature of loads raise the need for frequent line switching to ensure\nthe efficient operation of electrical distribution networks. Operators must\nensure maximum load delivery, reduced losses, and the operation between voltage\nlimits. However, computations to decide the optimal feeder configuration are\noften computationally expensive and intractable, making it unfavorable for\nreal-time operations. This is mainly due to the existence of binary variables\nin the network reconfiguration optimization problem. To tackle this issue, we\nhave devised an approach that leverages machine learning techniques to reshape\ndistribution networks featuring multiple substations. This involves predicting\nthe substation responsible for serving each part of the network. Hence, it\nleaves simple and more tractable Optimal Power Flow problems to be solved. This\nmethod can produce accurate results in a significantly faster time, as\ndemonstrated using the IEEE 37-bus distribution feeder. Compared to the\ntraditional optimization-based approaches, a feasible solution is achieved\napproximately ten times faster for all the tested scenarios.",
        "split": "arXiv"
    },
    {
        "text": "Tuning the density of resident electrons or holes in semiconductors provides\ncrucial insight into the composition of excitonic complexes that are observed\nas absorption or photoluminescence resonances in optical studies. Moreover, we\ncan change the way these resonances shift and broaden in energy by controlling\nthe quantum numbers of the resident carriers with magnetic fields and doping\nlevels, and by selecting the quantum numbers of the photoexcited or recombining\nelectron-hole (e-h) pair through optical polarization. We discuss the roles of\ndistinguishability and optimality of excitonic complexes, showing them to be\nkey ingredients that determine the energy shifts and broadening of optical\nresonances in charge-tunable semiconductors. A distinguishable e-h pair means\nthat the electron and hole undergoing photoexcitation or recombination have\nquantum numbers that are not shared by any of the resident carriers. An optimal\nexcitonic complex refers to a complex whose particles come with all available\nquantum numbers of the resident carriers. All optical resonances may be\nclassified as either distinct or indistinct depending on the distinguishability\nof the e-h pair, and the underlying excitonic complex can be classified as\neither optimal or suboptimal. The universality of these classifications,\ninherited from the fundamental Pauli exclusion principle, allows us to\nunderstand how optical resonances shift in energy and whether they should\nbroaden as doping is increased. This understanding is supported by conclusive\nevidence that the decay of optical resonances cannot be simply attributed to\nenhanced screening when resident carriers are added to a semiconductor.\nFinally, applying the classification scheme in either monolayer or moire\nheterobilayer systems, we relate the energy shift and amplitude of the neutral\nexciton resonance to the compressibility of the resident carrier gas.",
        "split": "arXiv"
    },
    {
        "text": "Blockchain networks are facing increasingly heterogeneous computational\ndemands, and in response, protocol designers have started building specialized\ninfrastructure to supply that demand. This paper introduces Resoonance: a new\nkind of transaction fee mechanism that operates in a general two-sided\nmarketplace setting with extreme preference heterogeneity on both sides of the\nmarket. We allow users submitting transactions to have arbitrary valuations for\ninclusion, nodes responsible for executing transactions to incur arbitrary net\ncosts for executing any bundle, and further allow for arbitrary constraints in\nallocation validity. These constraints, for example, may range from\nrepresenting an individual node's specialized hardware constraints to denoting\nthe fact that transactions may not be executed in parallel across different\nnodes if they utilize the same part of the network's state. Transactions may\neven require multiple nodes for execution.\n  Resonance's design utilizes competition among sophisticated brokers to find\nidiosyncratic prices. We show that at pure Nash equilibria, Resonance finds an\nefficient outcome and minimizes the need for strategization by users and nodes.\nIt is also budget-balanced, individually rational for all parties, and\ncomputationally tractable.",
        "split": "arXiv"
    },
    {
        "text": "In this paper we prove that Schr\\\"{o}dinger's equation with a Hamiltonian of\nthe form $H=-\\Delta+i(A \\nabla + \\nabla A) + V$, which includes a magnetic\npotential $A$, has the same dispersive and solution decay properties as the\nfree Schr\\\"{o}dinger equation. In particular, we prove $L^1 \\to L^\\infty$ decay\nand some related estimates for the wave equation.\n  The potentials $A$ and $V$ are short-range and $A$ has four derivatives, but\nthey can be arbitrarily large. All results hold in three space dimensions.",
        "split": "arXiv"
    },
    {
        "text": "A generative adversarial network (GAN) has been a representative backbone\nmodel in generative artificial intelligence (AI) because of its powerful\nperformance in capturing intricate data-generating processes. However, the GAN\ntraining is well-known for its notorious training instability, usually\ncharacterized by the occurrence of mode collapse. Through the lens of\ngradients' variance, this work particularly analyzes the training instability\nand inefficiency in the presence of mode collapse by linking it to\nmultimodality in the target distribution. To ease the raised training issues\nfrom severe multimodality, we introduce a novel GAN training framework that\nleverages a series of tempered distributions produced via convex interpolation.\nWith our newly developed GAN objective function, the generator can learn all\nthe tempered distributions simultaneously, conceptually resonating with the\nparallel tempering in Statistics. Our simulation studies demonstrate the\nsuperiority of our approach over existing popular training strategies in both\nimage and tabular data synthesis. We theoretically analyze that such\nsignificant improvement can arise from reducing the variance of gradient\nestimates by using the tempered distributions. Finally, we further develop a\nvariant of the proposed framework aimed at generating fair synthetic data which\nis one of the growing interests in the field of trustworthy AI.",
        "split": "arXiv"
    },
    {
        "text": "In 1975, Erd\\H{o}s and Sauer asked to estimate, for any constant $r$, the\nmaximum number of edges an $n$-vertex graph can have without containing an\n$r$-regular subgraph. In a recent breakthrough, Janzer and Sudakov proved that\nany $n$-vertex graph with no $r$-regular subgraph has at most $C_r n \\log \\log\nn$ edges, matching an earlier lower bound by Pyber, R\\\"odl and Szemer\\'edi and\nthereby resolving the Erd\\H{o}s-Sauer problem up to a constant depending on\n$r$. We prove that every $n$-vertex graph without an $r$-regular subgraph has\nat most $Cr^2 n \\log \\log n$ edges. This bound is tight up to the value of $C$\nfor $n\\geq n_0(r)$ and hence resolves the Erd\\H{o}s-Sauer problem up to an\nabsolute constant.\n  Moreover, we obtain similarly tight results for the whole range of possible\nvalues of $r$ (i.e., not just when $r$ is a constant), apart from a small error\nterm at a transition point near $r\\approx \\log n$, where, perhaps surprisingly,\nthe answer changes. More specifically, we show that every $n$-vertex graph with\naverage degree at least $\\min(Cr\\log(n/r),Cr^2 \\log\\log n)$ contains an\n$r$-regular subgraph. The bound $Cr\\log(n/r)$ is tight for $r\\geq \\log n$,\nwhile the bound $Cr^2 \\log \\log n$ is tight for $r<(\\log n)^{1-\\Omega(1)}$.\nThese results resolve a problem of R\\\"odl and Wysocka from 1997 for almost all\nvalues of $r$.\n  Among other tools, we develop a novel random process that efficiently finds a\nvery nearly regular subgraph in any almost-regular graph. A key step in our\nproof uses this novel random process to show that every $K$-almost-regular\ngraph with average degree $d$ contains an $r$-regular subgraph for some\n$r=\\Omega_K(d)$, which is of independent interest.",
        "split": "arXiv"
    },
    {
        "text": "Quantum computing architectures based on neutral atoms offer large scales and\nhigh-fidelity operations. They can be heterogeneous, with different zones for\nstorage, entangling operations, and readout. Zoned architectures improve\ncomputation fidelity by shielding idling qubits in storage from side-effect\nnoise, unlike monolithic architectures where all operations occur in a single\nzone. However, supporting these flexible architectures with efficient\ncompilation remains challenging. In this paper, we propose ZAC, a scalable\ncompiler for zoned architectures. ZAC minimizes data movement overhead between\nzones with qubit reuse, i.e., keeping them in the entanglement zone if an\nimmediate entangling operation is pending. Other innovations include novel data\nplacement and instruction scheduling strategies in ZAC, a flexible\nspecification of zoned architectures, and an intermediate representation for\nzoned architectures, ZAIR. Our evaluation shows that zoned architectures\nequipped with ZAC achieve a 22x improvement in fidelity compared to monolithic\narchitectures. Moreover, ZAC is shown to have a 10% fidelity gap on average\ncompared to the ideal solution. This significant performance enhancement\nenables more efficient and reliable quantum circuit execution, enabling\nadvancements in quantum algorithms and applications. ZAC is open source at\nhttps://github.com/UCLA-VAST/ZAC",
        "split": "arXiv"
    },
    {
        "text": "Motivated by knot theory, it is natural to define the orientation-reversal of\na quandle orbit by inverting all the translations given by elements of that\norbit. In this short note we observe that this natural notion is unsuited to\nmedial quandles.",
        "split": "arXiv"
    },
    {
        "text": "We consider the dynamics of a continuously monitored qubit in the limit of\nstrong measurement rate where the quantum trajectory is described by a\nstochastic master equation with Poisson noise. Such limits are expected to give\nrise to quantum jumps between the pointer states associated with the\nnon-demolition measurement. A surprising discovery in earlier work [Tilloy et\nal., Phys. Rev. A 92, 052111 (2015)] on quantum trajectories with Brownian\nnoise was the phenomena of spikes observed in between the quantum jumps. Here,\nwe show that spikes are observed also for Poisson noise. We consider three\ncases where the non-demolition is broken by adding, to the basic strong\nmeasurement dynamics, either unitary evolution or thermal noise or additional\nmeasurements. We present a complete analysis of the spike and jump statistics\nfor all three cases using the fact that the dynamics effectively corresponds to\nthat of stochastic resetting. We provide numerical results to support our\nanalytic results.",
        "split": "arXiv"
    },
    {
        "text": "Coherent perfect absorption (CPA) has been a topic of considerable\ncontemporary research interest. However, its implementation in practical\napplications has been limited, since it has been demonstrated only for plane\nwaves till now. The issue for beams with finite confinement -- characterized by\na collection of plane waves -- is that complete destructive interference is not\nfeasible for all the plane waves simultaneously. In this paper, we study the\nabsorption characteristics of two counter-propagating structured beams, e.g.,\nGaussian and Laguerre-Gaussian (LG) beams with and without orbital angular\nmomentum respectively, incident normally on a composite slab from both sides by\nfulfilling the CPA condition exclusively for the central plane waves. We show\nthat though perfect absorption is not achievable, there can be a substantial\nreduction of the scattered light. We also consider CPA for oblique incidence\nand discuss the difficulties.",
        "split": "arXiv"
    },
    {
        "text": "We investigate prophet inequalities with competitive ratios approaching $1$,\nseeking to generalize $k$-uniform matroids. We first show that large girth does\nnot suffice: for all $k$, there exists a matroid of girth $\\geq k$ and a\nprophet inequality instance on that matroid whose optimal competitive ration is\n$\\frac{1}{2}$. Next, we show $k$-fold matroid unions do suffice: we provide a\nprophet inequality with competitive ratio $1-O(\\sqrt{\\frac{\\log k}{k}})$ for\nany $k$-fold matroid union. Our prophet inequality follows from an online\ncontention resolution scheme.\n  The key technical ingredient in our online contention resolution scheme is a\nnovel bicriterion concentration inequality for arbitrary monotone $1$-Lipschitz\nfunctions over independent items which may be of independent interest. Applied\nto our particular setting, our bicriterion concentration inequality yields\n\"Chernoff-strength\" concentration for a $1$-Lipschitz function that is not\n(approximately) self-bounding.",
        "split": "arXiv"
    },
    {
        "text": "We prove upper bounds which are independent of the dimension of the ambient\nspace, on the number of realizable zero-nonzero patterns as well as sign\nconditions (when the field of coefficients is ordered) of a finite set of\npolynomials $\\mathcal{P}$ restricted to some algebraic subset $V$.Our bounds\n(which are tight) depend on the number and the degrees of the polynomials in\n$\\mathcal{P}$, as well as the degree (of the embedding) of $V$ and the\ndimension of $V$, but are independent of the dimension of the space in which\n$V$ is embedded. This last feature of our bounds is useful in situations where\nthe ambient dimension could be much larger than $\\dim V$.\n  We give several applications of our results. We generalize existing results\non bounding the speeds of algebraically defined classes of graphs, as well as\nlower bounds in terms of the number of connected components for testing\nmembership in semi-algebraic sets using algebraic computation trees. Motivated\nby quantum complexity theory we introduce a notion of relative rank (additive\nas well as multiplicative) in finite dimensional vector spaces and algebras\nrelative to a fixed algebraic subset in the vector space or algebra -- which\ngeneralizes the classical definition of ranks of tensors. We prove a very\ngeneral lower bound on the maximum relative rank of finite subsets relative to\nalgebraic subsets of bounded degree and dimension which is independent of the\ndimension of the vector space or algebra. We show how our lower bound implies a\nquantum analog of a classical lower bound result of Shannon for Boolean\ncircuits -- that almost all Boolean functions require (classical) circuits of\nsize at least $\\Omega(2^n/n)$.",
        "split": "arXiv"
    },
    {
        "text": "Aligning diffusion models with downstream objectives is essential for their\npractical applications. However, standard alignment methods often struggle with\nstep generalization when directly applied to few-step diffusion models, leading\nto inconsistent performance across different denoising step scenarios. To\naddress this, we introduce Stepwise Diffusion Policy Optimization (SDPO), a\nnovel alignment method tailored for few-step diffusion models. Unlike prior\napproaches that rely on a single sparse reward from only the final step of each\ndenoising trajectory for trajectory-level optimization, SDPO incorporates dense\nreward feedback at every intermediate step. By learning the differences in\ndense rewards between paired samples, SDPO facilitates stepwise optimization of\nfew-step diffusion models, ensuring consistent alignment across all denoising\nsteps. To promote stable and efficient training, SDPO introduces an online\nreinforcement learning framework featuring several novel strategies designed to\neffectively exploit the stepwise granularity of dense rewards. Experimental\nresults demonstrate that SDPO consistently outperforms prior methods in\nreward-based alignment across diverse step configurations, underscoring its\nrobust step generalization capabilities. Code is avaliable at\nhttps://github.com/ZiyiZhang27/sdpo.",
        "split": "arXiv"
    },
    {
        "text": "The Vol-Det Conjecture, formulated by Champanerkar, Kofman and Purcell,\nstates that there exists a specific inequality connecting the hyperbolic volume\nof an alternating link and its determinant. Among the classes of links for\nwhich this conjecture holds are all knots with at most 16 crossings, 2-bridge\nlinks, and links that are closures of 3-strand braids. In the present paper,\nBurton's bound on the number of crossings for which the Vol-Det Conjecture\nholds is improved for links with more than eight twists. In addition,\nStoimenow's inequalities between hyperbolic volumes and determinants are\nimproved for alternating and alternating arborescent links with more than eight\ntwists.",
        "split": "arXiv"
    },
    {
        "text": "We prove that for every compact, convex subset $K\\subset\\mathbb{R}^2$ the\noperator system $A(K)$, consisting of all continuous affine functions on $K$,\nis hyperrigid in the C*-algebra $C(\\mathrm{ex}(K))$. In particular, this result\nimplies that the weak and strong operator topologies coincide on the set $$\n  \\{ T\\in\\mathcal{B}(H);\\ T\\ \\mathrm{normal}\\ \\mathrm{and}\\ \\sigma(T)\\subset\n\\mathrm{ex}(K) \\}.\n  $$ Our approach relies on geometric properties of $K$ and generalizes\nprevious results by Brown.",
        "split": "arXiv"
    },
    {
        "text": "This paper explores quadratic forms over finite fields with associated\nArtin-Schreier curves. Specifically, we investigate quadratic forms of $\\mathbb\nF_{q^n}/\\mathbb F_q$ represented by polynomials over $\\mathbb F_{q^n}$ with $q$\nodd, characterizing them using certain matrices defined by coefficients of the\npolynomials. In particular, a comprehensive treatment will be given for those\npolynomials whose coefficients all lie in $\\mathbb F_q$. Afterwards, the\nresults on quadratic forms will be applied to get maximal and minimal\nArtin-Schreier curves explicitly.",
        "split": "arXiv"
    },
    {
        "text": "We are interested in the nonlinear damped Klein-Gordon equation \\[\n\\partial_t^2 u+2\\alpha \\partial_t u-\\Delta u+u-|u|^{p-1}u=0 \\] on\n$\\mathbb{R}^d$ for $2\\le d\\le 5$ and energy sub-critical exponents $2 < p <\n\\frac{d+2}{d-2}$.\n  We construct multi-solitons, that is, solutions which behave for large times\nas a sum of decoupled solitons, in various configurations with symmetry: this\nincludes multi-solitons whose soliton centers lie at the vertices of an\nexpanding regular polygon (with or without a center), of a regular polyhedron\n(with a center), or of a higher dimensional regular polytope. We give a precise\ndescription of these multi-solitons: in particular the interaction between\nnearest neighbour solitons is asymptotic to $\\ln (t)$ as $t \\to +\\infty$.\n  We also prove that in any multi-soliton, the solitons can not all share the\nsame sign.\n  Both statements generalize and precise results from \\cite{F98}, \\cite{Nak}\nand are based on the analysis developed in \\cite{CMYZ,CMY}.",
        "split": "arXiv"
    },
    {
        "text": "The personalization techniques of diffusion models succeed in generating\nspecific concepts but also pose threats to copyright protection and illegal\nuse. Model Watermarking is an effective method to prevent the unauthorized use\nof subject-driven or style-driven image generation, safeguarding concept\ncopyrights. However, under the goal of concept-oriented protection, current\nwatermarking schemes typically add watermarks to all images rather than\napplying them in a refined manner targeted at specific concepts. Additionally,\nthe personalization techniques of diffusion models can easily remove\nwatermarks. Existing watermarking methods struggle to achieve fine-grained\nwatermark embedding with a few images of specific concept and prevent removal\nof watermarks through personalized fine-tuning. Therefore, we introduce a novel\nconcept-oriented watermarking framework that seamlessly embeds imperceptible\nwatermarks into the concept of diffusion models. We conduct extensive\nexperiments and ablation studies to verify our framework. Our code is available\nat https://anonymous.4open.science/r/Conceptwm-4EB3/.",
        "split": "arXiv"
    },
    {
        "text": "We critically review the evidence for time-varying dark energy from recent\nBaryon Acoustic Oscillations (BAO) and Supernova (SN) observations. First, we\nshow that such evidence is present at the 3$\\sigma$ level, even without the new\nBAO data from the dark energy Spectroscopic Instrument (DESI), by instead using\nBAO data from the dark energy Survey (DES), combined with the DES5Y supernovae\nand Planck CMB data. Next, we examine the role of the DES5Y supernova dataset,\nshowing that the preference for time-varying dark energy is driven by the low\nredshift supernovae common to both the DES5Y and Pantheon+ compilations. We\nfind that combining Pantheon+ and DES5Y supernovae by removing the common\nsupernovae leads to two different results, depending on whether they are\nremoved from the DES5Y or the Pantheon+ catalog, leading to stronger or weaker\nexclusion of $\\Lambda$CDM, at the (3.8$\\sigma$) and (2.5$\\sigma$) level,\nrespectively. These common supernovae have smaller error bars in DES5Y compared\nto Pantheon+, and, as recently pointed out, there is an offset in magnitude in\nDES5Y between supernovae at ($z > 0.1$), where almost all the measurements\ntaken during the full five years of DES are, and the low-redshift ones ($z <\n0.1$), where all the historical set of nearby supernovae lies. We show that\nmarginalizing over such an offset in DES5Y would lead to significantly weaker\nevidence for evolving dark energy.",
        "split": "arXiv"
    },
    {
        "text": "The abundant chemical compositions in ternary hydrides bring much more\npossibility to explore high temperature superconductors under lower pressure.\nHere we constructed 115 ternary hydrides on the basis of the elements\nsubstitution using 16 metal elements within 5 reported prototype structures. We\nconducted a three-step approach to screen and study these candidate structures\nin the aspects of dynamical stability, formation energy and relative enthalpy,\nrespectively. Based on this approach, we found three meta-stable compounds with\nhydrogen clathrate cages in the space group of P-3m1, including Y2CdH18,\nY2InH18 and Ca2SnH18. All of the structures are superconductive under high\npressure with Tc above 110 K, which is larger than the superconductive\ntemperature of liquid nitrogen. Our study enriches the database of novel\nternary hydrides under high pressure, and provides insight for future\ntheoretical and experimental researches.",
        "split": "arXiv"
    },
    {
        "text": "The variation of the physical conditions across the three dimensions of our\nGalaxy is a major source of complexity for the modelling of the foreground\nsignal facing the cosmic microwave background (CMB). In the present work, we\ndemonstrate that the spin-moment expansion formalism provides a powerful\nframework to model and understand this complexity, with a special focus on that\narising from variations of the physical conditions along each line-of-sight on\nthe sky. We perform the first application of the moment expansion to reproduce\na thermal dust model largely used by the CMB community, demonstrating its power\nas a minimal tool to compress, understand and model the information contained\nwithin any foreground model. Furthermore, we use this framework to produce new\nmodels of thermal dust emission containing the maximal amount of complexity\nallowed by the current data, remaining compatible with the observed angular\npower-spectra by the $Planck$ mission. By assessing the impact of these models\non the performance of component separation methodologies, we conclude that the\nadditional complexity contained within the third dimension could represent a\nsignificant challenge for future CMB experiments and that different component\nseparation approaches are sensitive to different properties of the moments.",
        "split": "arXiv"
    },
    {
        "text": "Ashkin-Teller model is a two-layer lattice model where spins in each layer\ninteract ferromagnetically with strength $J$, and the spin-dipoles (product of\nspins) interact with neighbors with strength $\\lambda.$ The model exhibits\nsimultaneous magnetic and electric transitions along a self-dual line on the\n$\\lambda$-$J$ plane with continuously varying critical exponents. In this\narticle, we investigate the percolation of geometric clusters of spins and\nspin-dipoles denoted respectively as magnetic and electric clusters. We find\nthat the largest cluster in both cases becomes macroscopic in size and spans\nthe lattice when interaction exceeds a critical threshold given by the same\nself-dual line where magnetic and electric transitions occur. The fractal\ndimension of the critical spanning clusters is related to order parameter\nexponent $\\beta_{m,e}$ as $D_{m,e}=d-\\frac{5}{12}\\frac{\\beta_{m,e}}\\nu,$ where\n$d=2$ is the spatial dimension and $\\nu$ is the correlation length exponent.\nThis relation determines all other percolation exponents and their variation\nwrt $\\lambda.$ We show that for magnetic Percolation, the Binder cumulant, as a\nfunction of $\\xi_2/L$ with $\\xi_2$ being the second-moment correlation length,\nremains invariant all along the critical line and matches with that of the\nspin-percolation in the usual Ising model. The function also remains invariant\nfor the electric percolation, forming a new superuniversality class of\npercolation transition.",
        "split": "arXiv"
    },
    {
        "text": "We study the dynamics of synthetic molecules whose architectures are\ngenerated by space transformations from a point group acting on seed\nresonators. We show that the dynamical matrix of any such molecule can be\nreproduced as the left regular representation of a self-adjoint element from\nthe stabilized group's algebra. Furthermore, we use elements of representation\ntheory and K-theory to rationalize the dynamical features supported by such\nsynthetic molecules up to topological equivalences. These tools enable us to\nidentify a set of fundamental models which generate by superposition all\npossible dynamical matrices up to homotopy equivalences. Interpolations between\nthese fundamental models give rise to topological spectral flows.",
        "split": "arXiv"
    },
    {
        "text": "We prove a version of Jordan's classification theorem for finite subgroups of\n$\\mathrm{GL}_{n}(K)$ that is at the same time quantitatively explicit,\nCFSG-free, and valid for arbitrary $K$. This is the first proof to satisfy all\nthree properties at once. Our overall strategy follows Larsen and Pink [24],\nwith explicit computations based on techniques developed by the authors and\nHelfgott [2, 3], particularly in relation to dimensional estimates.",
        "split": "arXiv"
    },
    {
        "text": "We consider two supersymmetric M5 brane probe solutions in $\\textrm{AdS}_7\n\\times S^4$ and one in $\\textrm{AdS}_4 \\times S^7$ that all have the\n$\\textrm{AdS}_3 \\times S^3$ world-volume geometry. The values of the classical\naction of the first two M5 probes (with $S^3$ in $\\textrm{AdS}_7$ or in $S^4$)\nare related to the leading $N^2$ parts in the anomaly b-coefficient in the\n(2,0) theory corresponding to a spherical surface defect in symmetric or\nantisymmetric $SU(N)$ representations. We present a detailed computation of the\ncorresponding one-loop M5 brane partition functions finding that they vanish\n(in a particular regularization). This implies the vanishing of the order $N^0$\npart in the b-anomaly coefficients, in agreement with earlier predictions for\ntheir exact values. It remains, however, a puzzle of how to reproduce the\nnon-vanishing order $N$ terms in these coefficients within the semiclassical\nM5-brane probe setup.",
        "split": "arXiv"
    },
    {
        "text": "Determining potential probability distributions with a given causal graph is\nvital for causality studies. To bypass the difficulty in characterizing latent\nvariables in a Bayesian network, the nested Markov model provides an elegant\nalgebraic approach by listing exactly all the equality constraints on the\nobserved variables. However, this algebraically motivated causal model\ncomprises distributions outside Bayesian networks, and its physical\ninterpretation remains vague. In this work, we inspect the nested Markov model\nthrough the lens of generalized probabilistic theory, an axiomatic framework to\ndescribe general physical theories. We prove that all the equality constraints\ndefining the nested Markov model hold valid theory-independently. Yet, we show\nthis model generally contains distributions not implementable even within such\nrelaxed physical theories subjected to merely the relativity principles and\nmild probabilistic rules. To interpret the origin of such a gap, we establish a\nnew causal model that defines valid distributions as projected from a\nhigh-dimensional Bell-type causal structure. The new model unveils inequality\nconstraints induced by relativity principles, or equivalently high-dimensional\nconditional independences, which are absent in the nested Markov model.\nNevertheless, we also notice that the restrictions on states and measurements\nintroduced by the generalized probabilistic theory framework can pose\nadditional inequality constraints beyond the new causal model. As a by-product,\nwe discover a new causal structure exhibiting strict gaps between the\ndistribution sets of a Bayesian network, generalized probabilistic theories,\nand the nested Markov model. We anticipate our results will enlighten further\nexplorations on the unification of algebraic and physical perspectives of\ncausality.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we construct new t-server Private Information Retrieval (PIR)\nschemes with communication complexity subpolynomial in the previously best\nknown, for all but finitely many t. Our results are based on combining\nderivatives (in the spirit of Woodruff-Yekhanin) with the Matching Vector based\nPIRs of Yekhanin and Efremenko. Previously such a combination was achieved in\nan ingenious way by Dvir and Gopi, using polynomials and derivatives over\ncertain exotic rings, en route to their fundamental result giving the first\n2-server PIR with subpolynomial communication.\n  Our improved PIRs are based on two ingredients:\n  - We develop a new and direct approach to combine derivatives with Matching\nVector based PIRs. This approach is much simpler than that of Dvir-Gopi: it\nworks over the same field as the original PIRs, and only uses elementary\nproperties of polynomials and derivatives.\n  - A key subproblem that arises in the above approach is a higher-order\npolynomial interpolation problem. We show how \"sparse S-decoding polynomials\",\na powerful tool from the original constructions of Matching Vector PIRs, can be\nused to solve this higher-order polynomial interpolation problem using\nsurprisingly few higer-order evaluations.\n  Using the known sparse S-decoding polynomials, in combination with our ideas\nleads to our improved PIRs. Notably, we get a 3-server PIR scheme with\ncommunication $2^{O^{\\sim}( (\\log n)^{1/3}) }$, improving upon the previously\nbest known communication of $2^{O^{\\sim}( \\sqrt{\\log n})}$ due to Efremenko.",
        "split": "arXiv"
    },
    {
        "text": "Large double-logarithmic corrections are induced by soft gluon emissions near\nthreshold in the semi-inclusive $e^+e^-$ annihilation (SIA) distributions, and\nmust be resummed to all-orders in perturbation theory for reliable theoretical\npredictions. Building on strategy developed for threshold resummation for DIS\nstructure function in momentum space using soft-collinear effective theory\n(SCET), we present the explicit formalism for SIA cross section. We then\nperform the resummation directly in momentum space for $\\gamma^* \\to q \\bar q$,\n$H \\to gg$ and $H \\to b\\bar b$ to N$^4$LL accuracy and demonstrate good\nconvergence. We anticipate that these results will benefit the extraction of\nthe light-quark, the heavy-quark as well as the gluon fragmentation functions.",
        "split": "arXiv"
    },
    {
        "text": "This paper studies the distributed bandit convex optimization problem with\ntime-varying inequality constraints, where the goal is to minimize network\nregret and cumulative constraint violation. To calculate network cumulative\nconstraint violation, existing distributed bandit online algorithms solving\nthis problem directly use the clipped constraint function to replace its\noriginal constraint function. However, the use of the clipping operation\nrenders Slater condition (i.e, there exists a point that strictly satisfies the\ninequality constraints at all iterations) ineffective to achieve reduced\nnetwork cumulative constraint violation. To tackle this challenge, we propose a\nnew distributed bandit online primal-dual algorithm. If local loss functions\nare convex, we show that the proposed algorithm establishes sublinear network\nregret and cumulative constraint violation bounds. When Slater condition holds,\nthe network cumulative constraint violation bound is reduced. In addition, if\nlocal loss functions are strongly convex, for the case where strongly convex\nparameters are unknown, the network regret bound is reduced. For the case where\nstrongly convex parameters are known, the network regret and cumulative\nconstraint violation bounds are further reduced. To the best of our knowledge,\nthis paper is among the first to establish reduced (network) cumulative\nconstraint violation bounds for (distributed) bandit convex optimization with\ntime-varying constraints under Slater condition. Finally, a numerical example\nis provided to verify the theoretical results.",
        "split": "arXiv"
    },
    {
        "text": "This paper studies observability inequalities for heat equations on both\nbounded domains and the whole space $\\mathbb{R}^d$. The observation sets are\nmeasured by log-type Hausdorff contents, which are induced by certain log-type\ngauge functions closely related to the heat kernel. On a bounded domain, we\nderive the observability inequality for observation sets of positive log-type\nHausdorff content. Notably, the aforementioned inequality holds not only for\nall sets with Hausdorff dimension $s$ for any $s\\in (d-1,d]$, but also for\ncertain sets of Hausdorff dimension $d-1$. On the whole space $\\mathbb{R}^d$,\nwe establish the observability inequality for observation sets that are thick\nat the scale of the log-type Hausdorff content. Furthermore, we prove that for\nthe 1-dimensional heat equation on an interval, the Hausdorff content we have\nchosen is an optimal scale for the observability inequality.\n  To obtain these observability inequalities, we use the adapted Lebeau-Robiano\nstrategy from \\cite{Duyckaerts2012resolvent}. For this purpose, we prove the\nfollowing results at scale of the log-type Hausdorff content, the former being\nderived from the latter: We establish a spectral inequality/a Logvinenko-Sereda\nuncertainty principle; we set up a quantitative propagation of smallness of\nanalytic functions; we build up a Remez' inequality; and more fundamentally, we\nprovide an upper bound for the log-type Hausdorff content of a set where a\nmonic polynomial is small, based on an estimate in Lubinsky\n\\cite{Lubinsky1997small}, which is ultimately traced back to the classical\nCartan Lemma. In addition, we set up a capacity-based slicing lemma (related to\nthe log-type gauge functions) and establish a quantitative relationship between\nHausdorff contents and capacities. These tools are crucial in the studies of\nthe aforementioned propagation of smallness in high-dimensional situations.",
        "split": "arXiv"
    },
    {
        "text": "Bonne and Censor-Hillel (ICALP 2019) initiated the study of distributed\nsubgraph finding in dynamic networks of limited bandwidth. For the case where\nthe target subgraph is a clique, they determined the tight bandwidth complexity\nbounds in nearly all settings. However, several open questions remain, and very\nlittle is known about finding subgraphs beyond cliques. In this work, we\nconsider these questions and explore subgraphs beyond cliques.\n  For finding cliques, we establish an $\\Omega(\\log \\log n)$ bandwidth lower\nbound for one-round membership-detection under edge insertions only and an\n$\\Omega(\\log \\log \\log n)$ bandwidth lower bound for one-round detection under\nboth edge insertions and node insertions. Moreover, we demonstrate new\nalgorithms to show that our lower bounds are tight in bounded-degree networks\nwhen the target subgraph is a triangle. Prior to our work, no lower bounds were\nknown for these problems.\n  For finding subgraphs beyond cliques, we present a complete characterization\nof the bandwidth complexity of the membership-listing problem for every target\nsubgraph, every number of rounds, and every type of topological change: node\ninsertions, node deletions, edge insertions, and edge deletions. We also show\npartial characterizations for one-round membership-detection and listing.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we study the componentwise linearity of symbolic powers of\nedge ideals. We propose the conjecture that all symbolic powers of the edge\nideal of a cochordal graph are componentwise linear. This conjecture is\nverified for some families of cochordal graphs, including complements of block\ngraphs and complements of proper interval graphs. As a corollary, Minh's\nconjecture is established for such families. Moreover, we show that\n$I(G)^{(2)}$ is componentwise linear, for any cochordal graph $G$.",
        "split": "arXiv"
    },
    {
        "text": "The Schrieffer-Wolff transformation (SWT) is an important perturbative method\nin quantum mechanics used to simplify Hamiltonians by decoupling low- and\nhigh-energy subspaces. Existing methods for implementing the SWT often lack\ngeneral applicability to arbitrary perturbative systems or fail to provide a\nclosed-form solution for the SWT generator. In this article, we present a\nsystematic and unified framework for the SWT that addresses these shortcomings.\nSpecifically, we derive a closed-form solution for the SWT generator that is\nuniversally applicable to any system that satisfies the conditions required to\nbe perturbatively treated. Furthermore, we extend this solution to\ntime-dependent systems with periodic perturbations, covering all frequency\nregimes. The effectiveness of this approach is then demonstrated by applying it\nto analyze the dispersive shift of an anharmonic resonator coupled to a\ntwo-level system with time-dependent coupling.",
        "split": "arXiv"
    },
    {
        "text": "The James Webb Space Telescope has uncovered a puzzling population of\nUV-faint broad-line active galactic nuclei (AGN), nicknamed ``Little Red Dots''\n(LRD) owing to their compact morphology and red rest-frame optical colours.\nInterpreted as dust attenuated AGN, their inferred intrinsic luminosities and\nsupermassive black hole (SMBH) masses rival those of UV-luminous quasars,\nalthough they are $>100$ times more abundant. If LRDs and quasars are members\nof the same underlying population, they should inhabit comparable mass dark\nmatter halos, traced by similar overdensities of galaxies. Otherwise, they\nrepresent distinct populations with different physical properties and formation\nhistories. Characterizing LRD environments thus provides a critical test of\ntheir nature. Here, we report the discovery of a LRD at $z=7.3$, attenuated by\nmoderate amounts of dust, $A_V = {3.26}\\,\\rm{mag}$, with an intrinsic\nbolometric luminosity of $10^{46.7}\\,\\rm{erg}\\,\\rm{s}^{-1}$ and a SMBH mass of\n$7\\times10^8\\,\\rm{M}_\\odot$. Most notably, this object is embedded in an\noverdensity of eight nearby galaxies, allowing us to calculate the first\nspectroscopic estimate of the clustering of galaxies around LRDs. We find a\nLRD-galaxy cross-correlation length of $r_0\\!=\\!9\\pm2\\,\\rm{h}^{-1}\\,\\rm{cMpc}$,\ncomparable to that of $z\\!\\sim\\!6$ UV-luminous quasars. The resulting estimate\nof their minimum dark matter halo mass of $\\log_{10}(M_{\\rm{halo,\nmin}}/\\rm{M}_{\\odot})= 12.3_{-0.8}^{+0.7}$ indicates that nearly all halos\nabove this mass must host actively accreting SMBHs at $z\\approx7$, in strong\ncontrast with the far smaller duty cycle of luminous quasars ($<1\\%$). Our\nresults, taken at face value, motivate a picture in which LRDs are the obscured\ncounterparts of UV-luminous quasars, which provides a natural explanation for\nthe short UV-luminous lifetimes inferred from both quasar clustering and quasar\nproximity zones.",
        "split": "arXiv"
    },
    {
        "text": "The rapid development of large language models (LLMs) with advanced\nprogramming capabilities has paved the way for innovative approaches in\nsoftware testing. Fuzz testing, a cornerstone for improving software\nreliability and detecting vulnerabilities, often relies on manually written\nfuzz drivers, limiting scalability and efficiency. To address this challenge,\nwe propose CodeGraphGPT, a novel system that integrates code knowledge graphs\nwith an LLM-powered intelligent agent to automate the fuzz driver generation\nprocess. By framing fuzz driver creation as a code generation task,\nCodeGraphGPT leverages program analysis to construct a knowledge graph of code\nrepositories, where nodes represent code entities, such as functions or files,\nand edges capture their relationships. This enables the system to generate\ntailored fuzz drivers and input seeds, resolve compilation errors, and analyze\ncrash reports, all while adapting to specific API usage scenarios.\nAdditionally, querying the knowledge graph helps identify precise testing\ntargets and contextualize the purpose of each fuzz driver within the fuzzing\nloop. We evaluated CodeGraphGPT on eight open-source software projects,\nachieving an average improvement of 8.73\\% in code coverage compared to\nstate-of-the-art methods. Moreover, it reduced the manual workload in crash\ncase analysis by 84.4\\% and identified 11 real-world bugs, including nine\npreviously unreported ones. This work highlights how integrating LLMs with code\nknowledge graphs enhances fuzz driver generation, offering an efficient\nsolution for vulnerability detection and software quality improvement.",
        "split": "arXiv"
    },
    {
        "text": "In this paper we present an approach to reduce hallucinations in Large\nLanguage Models (LLMs) by incorporating Knowledge Graphs (KGs) as an additional\nmodality. Our method involves transforming input text into a set of KG\nembeddings and using an adapter to integrate these embeddings into the language\nmodel space, without relying on external retrieval processes.\n  To facilitate this, we created WikiEntities, a dataset containing over 3\nmillion Wikipedia texts annotated with entities from Wikidata and their\ncorresponding embeddings from PyTorch-BigGraph. This dataset serves as a\nvaluable resource for training Entity Linking models and adapting the described\nmethod to various LLMs using specialized adapters.\n  Our method does not require fine-tuning of the language models themselves;\ninstead, we only train the adapter. This ensures that the model's performance\non other tasks is not affected. We trained an adapter for the Mistral 7B, LLaMA\n2-7B (chat), and LLaMA 3-8B (instruct) models using this dataset and\ndemonstrated that our approach improves performance on the HaluEval, True-False\nbenchmarks and FEVER dataset. The results indicate that incorporating KGs as a\nnew modality can effectively reduce hallucinations and improve the factual\naccuracy of language models, all without the need for external retrieval.",
        "split": "arXiv"
    },
    {
        "text": "Massive Open Online Courses (MOOCs) have greatly contributed to making\neducation more accessible.However, many MOOCs maintain a rigid,\none-size-fits-all structure that fails to address the diverse needs and\nbackgrounds of individual learners.Learning path personalization aims to\naddress this limitation, by tailoring sequences of educational content to\noptimize individual student learning outcomes.Existing approaches, however,\noften require either massive student interaction data or extensive expert\nannotation, limiting their broad application.In this study, we introduce a\nnovel data-efficient framework for learning path personalization that operates\nwithout expert annotation.Our method employs a flexible recommender system\npre-trained with reinforcement learning on a dataset of raw course\nmaterials.Through experiments on semi-synthetic data, we show that this\npre-training stage substantially improves data-efficiency in a range of\nadaptive learning scenarios featuring new educational materials.This opens up\nnew perspectives for the design of foundation models for adaptive learning.",
        "split": "arXiv"
    },
    {
        "text": "E-commerce app users exhibit behaviors that are inherently logically\nconsistent. A series of multi-scenario user behaviors interconnect to form the\nscene-level all-domain user moveline, which ultimately reveals the user's true\nintention. Traditional CTR prediction methods typically focus on the item-level\ninteraction between the target item and the historically interacted items.\nHowever, the scene-level interaction between the target item and the user\nmoveline remains underexplored. There are two challenges when modeling the\ninteraction with preceding all-domain user moveline: (i) Heterogeneity between\nitems and scenes: Unlike traditional user behavior sequences that utilize items\nas carriers, the user moveline utilizes scenes as carriers. The heterogeneity\nbetween items and scenes complicates the process of aligning interactions\nwithin a unified representation space. (ii) Temporal misalignment of linked\nscene-level and item-level behaviors: In the preceding user moveline with a\nfixed sampling length, certain critical scene-level behaviors are closely\nlinked to subsequent item-level behaviors. However, it is impossible to\nestablish a complete temporal alignment that clearly identifies which specific\nscene-level behaviors correspond to which item-level behaviors. To address\nthese challenges and pioneer modeling user intent from the perspective of the\nall-domain moveline, we propose All-domain Moveline Evolution Network (AMEN).\nAMEN not only transfers interactions between items and scenes to homogeneous\nrepresentation spaces, but also introduces a Temporal Sequential Pairwise (TSP)\nmechanism to understand the nuanced associations between scene-level and\nitem-level behaviors, ensuring that the all-domain user moveline differentially\ninfluences CTR predictions for user's favored and unfavored items. Online A/B\ntesting demonstrates that our method achieves a +11.6% increase in CTCVR.",
        "split": "arXiv"
    },
    {
        "text": "While AI models have demonstrated remarkable capabilities in constrained\ndomains like game strategy, their potential for genuine creativity in\nopen-ended domains like art remains debated. We explore this question by\nexamining how AI can transcend human cognitive limitations in visual art\ncreation. Our research hypothesizes that visual art contains a vast unexplored\nspace of conceptual combinations, constrained not by inherent incompatibility,\nbut by cognitive limitations imposed by artists' cultural, temporal,\ngeographical and social contexts.\n  To test this hypothesis, we present the Alien Recombination method, a novel\napproach utilizing fine-tuned large language models to identify and generate\nconcept combinations that lie beyond human cognitive availability. The system\nmodels and deliberately counteracts human availability bias, the tendency to\nrely on immediately accessible examples, to discover novel artistic\ncombinations.\n  This system not only produces combinations that have never been attempted\nbefore within our dataset but also identifies and generates combinations that\nare cognitively unavailable to all artists in the domain. Furthermore, we\ntranslate these combinations into visual representations, enabling the\nexploration of subjective perceptions of novelty. Our findings suggest that\ncognitive unavailability is a promising metric for optimizing artistic novelty,\noutperforming merely temperature scaling without additional evaluation\ncriteria. This approach uses generative models to connect previously\nunconnected ideas, providing new insight into the potential of framing\nAI-driven creativity as a combinatorial problem.",
        "split": "arXiv"
    },
    {
        "text": "We establish the profound equivalence between measures of genuine\nmultipartite entanglement(GME) and their corresponding coherence measures.\nInitially we construct two distinct classes of measures for genuine\nmultipartite entanglement utilizing real symmetric concave functions and the\nconvex roof technique. We then demonstrate that all coherence measures for any\nqudit states, defined through the convex roof approach, are identical to our\ntwo classes of GME measures of the states combined with an incoherent ancilla\nunder a unitary incoherent operation. This relationship implies that genuine\nmultipartite entanglement can be generated from the coherence inherent in an\ninitial state through the unitary incoherent operations. Furthermore, we\nexplore the interplay between coherence and other forms of genuine quantum\ncorrelations, specifically genuine multipartite steering and genuine\nmultipartite nonlocality. In the instance of special three-qubit X-states (only\nnonzero elements of X-state are diagonal or antidiagonal when written in an\northonormal basis), we find that genuine multipartite steering and nonlocality\nare present if and only if the coherence exists in the corresponding qubit\nstates.",
        "split": "arXiv"
    },
    {
        "text": "Uncertainty persists over how and why some countries become democratic and\nothers do not, or why some countries remain democratic and others 'backslide'\ntoward autocracy. Furthermore, while scholars generally agree on the nature of\n'democracy' and 'autocracy', the nature of regimes in-between, and changes\nbetween them, are much less clear. By applying the spectral\ndimensionality-reduction technique Diffusion Map to political-science data from\nthe V-Dem project for the period 1900 to 2021, we identify a low-dimensional\nnon-linear manifold on which all electoral regimes move. Using the diffusion\nequation from statistical physics, we measure the time scale on which countries\nchange their degree of electoral quality, freedom of association, and freedom\nof expression depending on their position on the manifold. By quantifying the\ncoefficients of the diffusion equation for each country and over time, we show\nthat democracies behave like sub-diffusive (i.e. slow spreading) particles and\nthat autocracies on the verge of collapse behave like super-diffusive (i.e.\nfast spreading) particles. We show that regimes in-between exhibit diffusion\ndynamics distinct from autocracies and democracies, and an overall higher\ninstability. Furthermore, we show that a country's position on the manifold and\nits dynamics are linked to its propensity for civil conflict. Our study\npioneers the use of statistical physics in the analysis of political regimes.\nOur results provide a quantitative foundation for developing theories about\nwhat changes during democratization and democratic backsliding, as well as a\nnew framework for regime-transformation and risk-of-conflict assessment.",
        "split": "arXiv"
    },
    {
        "text": "Given a closed subset $K$ in $\\mathbb{R}$, the rational $K$-truncated moment\nproblem ($K$-RTMP) asks to characterize the existence of a positive Borel\nmeasure $\\mu$, supported on $K$, such that a linear functional $\\mathcal{L}$,\ndefined on all rational functions of the form $\\frac{f}{q}$, where $q$ is a\nfixed polynomial with all real zeros of even order and $f$ is any real\npolynomial of degree at most $2k$, is an integration with respect to $\\mu$. The\ncase of a compact set $K$ was solved by Chandler in 1994, but there is no\nargument that ensures that $\\mu$ vanishes on all real zeros of $q$. An obvious\nnecessary condition for the solvability of the $K$-RTMP is that $\\mathcal{L}$\nis nonnegative on every $f$ satisfying $f|_{K}\\geq 0$. If $\\mathcal{L}$ is\nstrictly positive on every $0\\neq f|_{K}\\geq 0$, we add the missing argument\nfrom Chandler's solution and also bound the number of atoms in a minimal\nrepresenting measure. We show by an example that nonnegativity of $\\mathcal{L}$\nis not sufficient and add the missing conditions to the solution. We also solve\nthe $K$-RTMP for unbounded $K$ and derive the solutions to the strong truncated\nHamburger moment problem and the truncated moment problem on the unit circle as\nspecial cases.",
        "split": "arXiv"
    },
    {
        "text": "This work investigates the effects of tangent polar activity on the\nconformational and dynamic properties of entangled polymer melts through\nLangevin molecular dynamics simulations. We examine systems composed of all\nself-propelled, monodisperse linear chains, so that constraint release is\nconsidered. The range of activities explored here includes values where the\nactive reptation theory is applicable, as well as higher activities that\nchallenge the validity of the theory. Chain conformations exhibit a moderate\nincrease in coil size increase, which becomes more pronounced at higher\nactivity levels. Under these conditions, a local bond alignment along the chain\ncontour appears together with a non-homogeneous segmental stretching, and\norientation and stretching of the tube. Dynamically, polar activity induces a\nmolecular-weight-independent diffusion coefficient, a transient superdiffusive\nbehavior, and an end-to-end relaxation time inversely proportional to the\nmolecular weight. Finally, our results are summarized in a diagram that\nclassifies the various regimes of behavior observed in the simulations.\nOverall, these findings provide valuable insights into the complex interplay\nbetween activity and entanglements, advancing our understanding of active\npolymer systems and their potential applications across various fields.",
        "split": "arXiv"
    },
    {
        "text": "Depth estimation is an essential task toward full scene understanding since\nit allows the projection of rich semantic information captured by cameras into\n3D space. While the field has gained much attention recently, datasets for\ndepth estimation lack scene diversity or sensor modalities. This work presents\nthe ADUULM-360 dataset, a novel multi-modal dataset for depth estimation. The\nADUULM-360 dataset covers all established autonomous driving sensor modalities,\ncameras, lidars, and radars. It covers a frontal-facing stereo setup, six\nsurround cameras covering the full 360-degree, two high-resolution long-range\nlidar sensors, and five long-range radar sensors. It is also the first depth\nestimation dataset that contains diverse scenes in good and adverse weather\nconditions. We conduct extensive experiments using state-of-the-art\nself-supervised depth estimation methods under different training tasks, such\nas monocular training, stereo training, and full surround training. Discussing\nthese results, we demonstrate common limitations of state-of-the-art methods,\nespecially in adverse weather conditions, which hopefully will inspire future\nresearch in this area. Our dataset, development kit, and trained baselines are\navailable at https://github.com/uulm-mrm/aduulm_360_dataset.",
        "split": "arXiv"
    },
    {
        "text": "Migration is a key ingredient for the formation of close-in super-Earth and\nmini-Neptune systems, as it sets in which resonances planets can be trapped.\nSlower migration rates result in wider resonance configurations compared to\nhigher migration rates. We investigate the influence of different migration\nrates, set by the disc's viscosity, on the structure of multi-planet systems\ngrowing by pebble accretion via N-body simulations. Planets in low viscosity\nenvironments migrate slower due to partial gap opening. Thus systems formed in\nlow viscosity environments tend to have planets trapped in wider resonant\nconfigurations (typically 4:3, 3:2 and 2:1), compared to their high viscosity\ncounterparts (mostly 7:6, 5:4 and 4:3 resonances). After gas disc dissipation,\nthe damping forces cease and the systems can undergo instabilities, rearranging\ntheir configurations and breaking the resonance chains. The low viscosity discs\nnaturally account for the resonant chains like Trappist-1, TOI-178 and\nKepler-223, unlike high viscosity simulations which produce relatively more\ncompact chains. About 95% of our low viscosity resonant chains became unstable,\nexperiencing giant impacts. Dynamical instabilities in our low viscosity\nsimulations are more violent than those of high viscosity simulations due to\nthe effects of leftover external perturbers (P>200 days). About 50% of our\nfinal system ended with no planets within 200 days, while all our systems have\nremaining outer planets. We speculate that this process could be qualitatively\nconsistent with the lack of inner planets in a large fraction of Sun-like\nstars. Systems produced in low viscosity simulations alone do not match the\noverall period ratio distribution of observations, but give a better match to\nthe period distributions of chains, which may suggest that systems of\nsuper-Earths and mini-Neptunes form in natal discs with a diversity of\nviscosities.",
        "split": "arXiv"
    },
    {
        "text": "Recently, the Large High-Altitude Air Shower Observatory (LHAASO)\ncollaboration has obtained a measurement of the gamma-ray diffuse emission in\nthe ultra-high energy range, $10-10^3$ TeV after masking the contribution of\nknown sources. The measurement appears to be 2-3 times higher than the\ngamma-ray signal expected from the hadronic interactions of diffuse cosmic rays\nwith the interstellar medium, potentially suggesting a contribution from\nunresolved sources. However, estimates of the diffuse emission are affected by\nlarge uncertainties that must be accounted for. In this work, we calculate the\nhadronic gamma-ray diffuse emission including uncertainties in the gas content\nof the Galactic disk, in the energy and spatial distribution of cosmic rays as\nwell as in the hadronic interaction cross-section. We show that the LHAASO data\nabove $\\sim 30$ TeV are consistent with the gamma-ray diffuse emission model\nwhen all these uncertainties are taken into account. This implies that, with\nthe current data in this energy range, there is no need to invoke a cosmic ray\nspectral variation toward the Galactic center, nor a dominant contribution from\nunresolved sources.",
        "split": "arXiv"
    },
    {
        "text": "A large host of scientific journals and conferences solicit peer reviews from\nmultiple reviewers for the same submission, aiming to gather a broader range of\nperspectives and mitigate individual biases. In this work, we reflect on the\nrole of diversity in the slate of reviewers assigned to evaluate a submitted\npaper as a factor in diversifying perspectives and improving the utility of the\npeer-review process. We propose two measures for assessing review utility:\nreview coverage -- reviews should cover most contents of the paper -- and\nreview redundancy -- reviews should add information not already present in\nother reviews. We hypothesize that reviews from diverse reviewers will exhibit\nhigh coverage and low redundancy. We conduct a causal study of different\nmeasures of reviewer diversity on review coverage and redundancy using\nobservational data from a peer-reviewed conference with approximately 5,000\nsubmitted papers. Our study reveals disparate effects of different diversity\nmeasures on review coverage and redundancy. Our study finds that assigning a\ngroup of reviewers that are topically diverse, have different seniority levels,\nor have distinct publication networks leads to broader coverage of the paper or\nreview criteria, but we find no evidence of an increase in coverage for\nreviewer slates with reviewers from diverse organizations or geographical\nlocations. Reviewers from different organizations, seniority levels, topics, or\npublications networks (all except geographical diversity) lead to a decrease in\nredundancy in reviews. Furthermore, publication network-based diversity alone\nalso helps bring in varying perspectives (that is, low redundancy), even within\nspecific review criteria. Our study adopts a group decision-making perspective\nfor reviewer assignments in peer review and suggests dimensions of diversity\nthat can help guide the reviewer assignment process.",
        "split": "arXiv"
    },
    {
        "text": "Graph augmentation is a fundamental and well-studied problem that arises in\nnetwork optimization. We consider a new variant of this model motivated by\nreconfigurable communication networks. In this variant, we consider a given\nphysical network and the measured communication demands between the nodes. Our\ngoal is to augment the given physical network with a matching, so that the\nshortest path lengths in the augmented network, weighted with the demands, are\nminimal.We prove that this problem is NP-hard, even if the physical network is\na cycle. We then use results from demand-aware network design to provide a\nconstant-factor approximation algorithm for adding a matching in case that only\na few nodes in the network cause almost all the communication. For general\nreal-world communication patterns, we design and evaluate a series of\nheuristics that can deal with arbitrary graphs as the underlying network\nstructure. Our algorithms are validated experimentally using real-world traces\n(from e.g., Facebook) of data centers.",
        "split": "arXiv"
    },
    {
        "text": "Recent advances in Large Language Models (LLMs) have enabled them to overcome\ntheir context window limitations, and demonstrate exceptional retrieval and\nreasoning capacities on longer context. Quesion-answering systems augmented\nwith Long-Context Language Models (LCLMs) can automatically search massive\nexternal data and incorporate it into their contexts, enabling faithful\npredictions and reducing issues such as hallucinations and knowledge staleness.\nExisting studies targeting LCLMs mainly concentrate on addressing the so-called\nlost-in-the-middle problem or improving the inference effiencicy, leaving their\nprivacy risks largely unexplored. In this paper, we aim to bridge this gap and\nargue that integrating all information into the long context makes it a\nrepository of sensitive information, which often contains private data such as\nmedical records or personal identities. We further investigate the membership\nprivacy within LCLMs external context, with the aim of determining whether a\ngiven document or sequence is included in the LCLMs context. Our basic idea is\nthat if a document lies in the context, it will exhibit a low generation loss\nor a high degree of semantic similarity to the contents generated by LCLMs. We\nfor the first time propose six membership inference attack (MIA) strategies\ntailored for LCLMs and conduct extensive experiments on various popular models.\nEmpirical results demonstrate that our attacks can accurately infer membership\nstatus in most cases, e.g., 90.66% attack F1-score on Multi-document QA\ndatasets with LongChat-7b-v1.5-32k, highlighting significant risks of\nmembership leakage within LCLMs input contexts. Furthermore, we examine the\nunderlying reasons why LCLMs are susceptible to revealing such membership\ninformation.",
        "split": "arXiv"
    },
    {
        "text": "We elaborate on and further develop an approach to determining the\nteleparallel analogue of spacetimes in General Relativity (GR) by studying the\nTeleparallel analogue of pp-Wave (TppW) spacetimes. This relies on using the\nfact that these solutions belong to the Vanishing Scalar Invariant (VSI)\nsubclass for which the explicit forms of the frame and spin-connection are\nknown. By identifying the pp-wave (ppW) metric within this class, we are able\nto use frame based symmetry methods and the Cartan-Karlhede (CK) algorithm to\ndetermine the necessary form for the frame. Through this analysis we find two\noverlooked solutions that are permitted in teleparallel gravity (TPG) and in\nGR.",
        "split": "arXiv"
    },
    {
        "text": "Let $k$ be an uncountable algebraically closed filed of positive\ncharacteristic and let $S_0$ be a connected smooth projective surface over $k$.\nWe extend the theorem on the Gysin kernel from [28, Theorem 5.1] to also be\ntrue over $k$, where it was proved over $\\mathbb{C}$. This is done by showing\nthat almost all results still hold true over $k$ via the same argument or by\nusing \\'{e}tale base arguments and then use a lift with the Comparison theorem\n[22, Theorems 21.1 & 20.5] as needed.",
        "split": "arXiv"
    },
    {
        "text": "In this study, we introduced a simple yet innovative method to trigger\nturbulence in a channel flow to achieve statistically stationary flow\nconditions. We compare this new method based on synthetically generated\nthree-dimensional turbulence with two other well-established methods, namely,\nlinear profile superposed with random noise and descending counter-rotating\nvortices and log-law profile superposed with random noise and descending\ncounter-rotating vortices. We found that synthetically generated\nthree-dimensional turbulence provides a computationally cheap and effective way\nto reduce simulation spin-up to achieve statistically stationary flow\nconditions when a precursor turbulent initial condition is not available. At a\none-time cost of less than 1 CPU hour to generate the synthetic turbulent\ninitial condition, the flow becomes statistically stationary within 3 eddy\nturnovers for all the parameters of interest in wall-bounded pressure-driven\nchannel flow simulations when compared to other alternatives that can take more\nthan 10 eddy turnovers resulting in substantial savings in the computational\ncost.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we prove several new infinite families of Ramanujan--like\ncongruences satisfied by the coefficients of the generating function $U_t(a,q)$\nwhich is an extension of MacMahon's generalized sum-of-divisors function. As a\nby-product, we also show that, for all $n\\geq 0$, $\\overline{B}_3(15n+7)\\equiv\n0 \\pmod{5}$ where $\\overline{B}_3(n)$ is the number of almost $3$-regular\noverpartitions of $n$.",
        "split": "arXiv"
    },
    {
        "text": "Bell scenarios are multipartite scenarios that exclude any communication\nbetween parties. This constraint leads to a strict hierarchy of correlation\nsets in such scenarios, namely, classical, quantum, and nonsignaling. However,\nwithout any constraints on communication between the parties, they can realize\narbitrary correlations by exchanging only classical systems. Here we consider a\nmultipartite scenario where the parties can engage in at most a single round of\ncommunication, i.e., each party is allowed to receive a system once, implement\nany local intervention on it, and send out the resulting system once. While no\nglobal assumption about causal relations between parties is assumed in this\nscenario, we do make a causal assumption local to each party, i.e., the input\nreceived by it causally precedes the output it sends out. We then introduce\nantinomicity, a notion of nonclassicality for correlations in such scenarios,\nand prove the existence of a strict hierarchy of correlation sets classified by\ntheir antinomicity. Antinomicity serves as a generalization of Bell\nnonlocality: when all the parties discard their output systems (i.e., in a\nnonsignaling scenario), it is mathematically equivalent to Bell nonlocality.\nLike Bell nonlocality, it can be understood as an instance of fine-tuning, one\nthat is necessary in any classical model of cyclic causation that avoids\ntime-travel antinomies but allows antinomic correlations. Furthermore,\nantinomicity resolves a long-standing puzzle, i.e., the failure of causal\ninequality violations as device-independent witnesses of nonclassicality.\nAntinomicity implies causal inequality violations, but not conversely.",
        "split": "arXiv"
    },
    {
        "text": "The rapid advancement of face forgery techniques has introduced a growing\nvariety of forgeries. Incremental Face Forgery Detection (IFFD), involving\ngradually adding new forgery data to fine-tune the previously trained model,\nhas been introduced as a promising strategy to deal with evolving forgery\nmethods. However, a naively trained IFFD model is prone to catastrophic\nforgetting when new forgeries are integrated, as treating all forgeries as a\nsingle ''Fake\" class in the Real/Fake classification can cause different\nforgery types overriding one another, thereby resulting in the forgetting of\nunique characteristics from earlier tasks and limiting the model's\neffectiveness in learning forgery specificity and generality. In this paper, we\npropose to stack the latent feature distributions of previous and new tasks\nbrick by brick, $\\textit{i.e.}$, achieving $\\textbf{aligned feature\nisolation}$. In this manner, we aim to preserve learned forgery information and\naccumulate new knowledge by minimizing distribution overriding, thereby\nmitigating catastrophic forgetting. To achieve this, we first introduce Sparse\nUniform Replay (SUR) to obtain the representative subsets that could be treated\nas the uniformly sparse versions of the previous global distributions. We then\npropose a Latent-space Incremental Detector (LID) that leverages SUR data to\nisolate and align distributions. For evaluation, we construct a more advanced\nand comprehensive benchmark tailored for IFFD. The leading experimental results\nvalidate the superiority of our method.",
        "split": "arXiv"
    },
    {
        "text": "From an architectural perspective with the main goal of reducing the\neffective traffic load in the network and thus gaining more operational\nefficiency, optical networks have been essentially remained the same in the\nrecent two decades since the year 2000s with the success and then dominance of\noptical-bypass mode. In the optical-bypass-enabled network, the add/drop and\ncross-connect functions constitute the fundamental operations in handling the\ntraffic at the optical layer, whose the underlying principle lies in the fact\nthat in cross-connecting in-transit lightpaths over an intermediate node, such\nlightpaths must be guarded from each other in a certain dimension, be it the\ntime, frequency or spatial domain, to avoid interference, which is treated as\ndestructive. In view of the rapid progresses in the realm of optical computing\nenabling the precisely controlled interference between optical channels for\nvarious computing capabilities, we envision a different perspective to turn the\nlong-established wisdom in optical-bypass network around by putting the optical\nchannel interference to a good use, resulting into the so-called\noptical-computing-enabled network. This paper presents two illustrative\nexamples based on the optical aggregation and optical XOR operations which have\nbeen progressively maturing and thus, could be feasibly integrated into the\ncurrent legacy infrastructure with possibly minimal disruptions. We then\npropose a detailed case study in formulating and solving the network\ncoding-enabled optical networks, demonstrating the efficacy of the\noptical-computing-enabled network, and highlighting the unique challenges tied\nwith greater complexities in network design problems, compared to\noptical-bypass counterpart",
        "split": "arXiv"
    },
    {
        "text": "Recent advancements in medical image analysis have predominantly relied on\nConvolutional Neural Networks (CNNs), achieving impressive performance in chest\nX-ray classification tasks, such as the 92% AUC reported by AutoThorax-Net and\nthe 88% AUC achieved by ChexNet in classifcation tasks. However, in the medical\nfield, even small improvements in accuracy can have significant clinical\nimplications. This study explores the application of Vision Transformers (ViT),\na state-of-the-art architecture in machine learning, to chest X-ray analysis,\naiming to push the boundaries of diagnostic accuracy. I present a comparative\nanalysis of two ViT-based approaches: one utilizing full chest X-ray images and\nanother focusing on segmented lung regions. Experiments demonstrate that both\nmethods surpass the performance of traditional CNN-based models, with the\nfull-image ViT achieving up to 97.83% accuracy and the lung-segmented ViT\nreaching 96.58% accuracy in classifcation of diseases on three label and AUC of\n94.54% when label numbers are increased to eight. Notably, the full-image\napproach showed superior performance across all metrics, including precision,\nrecall, F1 score, and AUC-ROC. These findings suggest that Vision Transformers\ncan effectively capture relevant features from chest X-rays without the need\nfor explicit lung segmentation, potentially simplifying the preprocessing\npipeline while maintaining high accuracy. This research contributes to the\ngrowing body of evidence supporting the efficacy of transformer-based\narchitectures in medical image analysis and highlights their potential to\nenhance diagnostic precision in clinical settings.",
        "split": "arXiv"
    },
    {
        "text": "Nuclear magnetic resonance instruments are becoming available to the\ndo-it-yourself community. The challenges encountered in the endeavor to build a\nmagnetic resonance imaging instrument from scratch were confronted in a\nfour-day hackathon at Singapore University of Technology and Design in spring\n2024. One day was devoted to educational lectures and three days to system\nconstruction and testing. Seventy young researchers from all parts of the world\nformed six teams focusing on magnet, gradient coil, RF coil, console, system\nintegration, and design, which together produced a working MRI instrument in\nthree days. The different steps, encountered challenges, and their solutions\nare reported.",
        "split": "arXiv"
    },
    {
        "text": "We describe an algorithm to compute the stable multiplicity of a family of\nirreducible representations in the cohomology of ordered configuration space of\nthe plane. Using this algorithm, we compute the stable multiplicities of all\nfamilies of irreducibles given by Young diagrams with $23$ boxes or less up to\ncohomological degree $50$. In particular, this determines the stable cohomology\nin cohomological degrees $0 \\leq i \\leq 11$. We prove related qualitative\nresults and formulate some conjectures.",
        "split": "arXiv"
    },
    {
        "text": "Amongst the issues plaguing the Standard Model (SM) are questions pertaining\nto neutrino masses and mixings, the anomalous magnetic moment of the electron\nand muon and the problem of a suitable dark matter (DM) candidate. All the\nthree issues can be addressed at once by extending the SM with two generations\nof vector-like fermions and an inert scalar doublet, all odd under a Z2\nsymmetry. The light neutrino masses and mixings are generated radiatively while\nmaintaining consistency with bounds on lepton flavor violation. Loop diagrams\nwith the very same fields also serve to explain the anomalous magnetic moments.\nSimilarly, the correct dark matter relic abundance is reproduced without coming\ninto conflict with direct detection constraints, or those from big bang\nnucleosynthesis or the cosmic microwave observations. Finally, prospective\nsignatures at the LHC are discussed.",
        "split": "arXiv"
    },
    {
        "text": "Machine learning is an important tool for analyzing high-dimension\nhyperspectral data; however, existing software solutions are either\nclosed-source or inextensible research products. In this paper, we present\ncuvis.ai, an open-source and low-code software ecosystem for data acquisition,\npreprocessing, and model training. The package is written in Python and\nprovides wrappers around common machine learning libraries, allowing both\nclassical and deep learning models to be trained on hyperspectral data. The\ncodebase abstracts processing interconnections and data dependencies between\noperations to minimize code complexity for users. This software package\ninstantiates nodes in a directed acyclic graph to handle all stages of a\nmachine learning ecosystem, from data acquisition, including live or static\ndata sources, to final class assignment or property prediction. User-created\nmodels contain convenient serialization methods to ensure portability and\nincrease sharing within the research community. All code and data are available\nonline: https://github.com/cubert-hyperspectral/cuvis.ai",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we propose a conceptual framework for personalized\nbrain-computer interface (BCI) applications, which can offer an enhanced user\nexperience by customizing services to individual preferences and needs, based\non endogenous electroencephalography (EEG) paradigms including motor imagery\n(MI), speech imagery (SI), and visual imagery. The framework includes two\nessential components: user identification and intention classification, which\nenable personalized services by identifying individual users and recognizing\ntheir intended actions through EEG signals. We validate the feasibility of our\nframework using a private EEG dataset collected from eight subjects, employing\nthe ShallowConvNet architecture to decode EEG features. The experimental\nresults demonstrate that user identification achieved an average classification\naccuracy of 0.995, while intention classification achieved 0.47 accuracy across\nall paradigms, with MI demonstrating the best performance. These findings\nindicate that EEG signals can effectively support personalized BCI\napplications, offering robust identification and reliable intention decoding,\nespecially for MI and SI.",
        "split": "arXiv"
    },
    {
        "text": "This paper presents an accelerated spherical K-means clustering algorithm for\nlarge-scale and high-dimensional sparse document data sets. We design an\nalgorithm working in an architecture-friendly manner (AFM), which is a\nprocedure of suppressing performance-degradation factors such as the numbers of\ninstructions, branch mispredictions, and cache misses in CPUs of a modern\ncomputer system. For the AFM operation, we leverage unique universal\ncharacteristics (UCs) of a data-object and a cluster's mean set, which are\nskewed distributions on data relationships such as Zipf's law and a\nfeature-value concentration phenomenon. The UCs indicate that the most part of\nthe number of multiplications for similarity calculations is executed regarding\nterms with high document frequencies (df) and the most part of a similarity\nbetween an object- and a mean-feature vector is obtained by the multiplications\nregarding a few high mean-feature values. Our proposed algorithm applies an\ninverted-index data structure to a mean set, extracts the specific region with\nhigh-df terms and high mean-feature values in the mean-inverted index by newly\nintroduced two structural parameters, and exploits the index divided into three\nparts for efficient pruning. The algorithm determines the two structural\nparameters by minimizing the approximate number of multiplications related to\nthat of instructions, reduces the branch mispredictions by sharing the index\nstructure including the two parameters with all the objects, and suppressing\nthe cache misses by keeping in the caches the frequently used data in the\nforegoing specific region, resulting in working in the AFM. We experimentally\ndemonstrate that our algorithm efficiently achieves superior speed performance\nin large-scale documents compared with algorithms using the state-of-the-art\ntechniques.",
        "split": "arXiv"
    },
    {
        "text": "Quantum secure direct communication (QSDC) enables the message sender to\ndirectly send secure messages to the receiver through the quantum channel\nwithout keys. Device-independent (DI) and measurement-device-independent (MDI)\nQSDC protocols can enhance QSDC's practical security in theory. DI QSDC\nrequires extremely high global detection efficiency and has quite low secure\ncommunication distance. DI and MDI QSDC both require high-quality entanglement.\nCurrent entanglement sources prepare entangled photon pairs with low\nefficiency, largely reducing their practical communication efficiency. In the\npaper, we propose a single-photon-based receiver-device-independent (RDI) QSDC\nprotocol. It only relies on the trusted single-photon source, which is nearly\non-demand under current technology, and treats all the receiving devices in\nboth communication parties as ``black-boxes''. The parties ensure the message\nsecurity only from the observed statistics. We develop a numerical method to\nsimulate its performance in practical noisy communication situation. RDI QSDC\nprovides the same security level as MDI QSDC. Compared with DI and MDI QSDC,\nRDI QSDC has some advantages. First, it uses the single-photon source and\nsingle-photon measurement, which makes it obtain the practical communication\nefficiency about 3415 times of that in DI QSDC and easy to implement. The whole\nprotocol is feasible with current technology. Second, it has higher photon loss\nrobustness and noise tolerance than DI QSDC, which enables it to have a secure\ncommunication distance about 26 times of that in DI QSDC. Based on above\nfeatures, the RDI QSDC protocol makes it possible to achieve highly-secure and\nhigh-efficient QSDC in the near future.",
        "split": "arXiv"
    },
    {
        "text": "It is proved that the Chebyshev's method applied to an entire function $f$ is\na rational map if and only if $f(z) = p(z) e^{q(z)}$, for some polynomials $p$\nand $q$. These are referred to as rational Chebyshev maps, and their fixed\npoints are discussed in this article. It is seen that $\\infty$ is a parabolic\nfixed point with multiplicity one bigger than the degree of $q$. Considering\n$q(z)=p(z)^n+c$, where $p$ is a linear polynomial, $n \\in \\mathbb{N}$ and $c$\nis a non-zero constant, we show that the Chebyshev's method applied to $pe^q$\nis affine conjugate to that applied to $z e^{z^n}$. We denote this by $C_n$.\nAll the finite extraneous fixed points of $C_n$ are shown to be repelling. The\nJulia set $\\mathcal{J}(C_n)$ of $C_n$ is found to be preserved under rotations\nof order $n$ about the origin. For each $n$, the immediate basin of $0$ is\nproved to be simply connected. For all $n \\leq 16$, we prove that\n$\\mathcal{J}(C_n)$ is connected. The Newton's method applied to $ze^{z^n}$ is\nfound to be conjugate to a polynomial, and its dynamics is also completely\ndetermined.",
        "split": "arXiv"
    },
    {
        "text": "Currently, deep learning-based instance segmentation for various applications\n(e.g., Agriculture) is predominantly performed using a labor-intensive process\ninvolving extensive field data collection using sophisticated sensors, followed\nby careful manual annotation of images, presenting significant logistical and\nfinancial challenges to researchers and organizations. The process also slows\ndown the model development and training process. In this study, we presented a\nnovel method for deep learning-based instance segmentation of apples in\ncommercial orchards that eliminates the need for labor-intensive field data\ncollection and manual annotation. Utilizing a Large Language Model (LLM), we\nsynthetically generated orchard images and automatically annotated them using\nthe Segment Anything Model (SAM) integrated with a YOLO11 base model. This\nmethod significantly reduces reliance on physical sensors and manual data\nprocessing, presenting a major advancement in \"Agricultural AI\". The synthetic,\nauto-annotated dataset was used to train the YOLO11 model for Apple instance\nsegmentation, which was then validated on real orchard images. The results\nshowed that the automatically generated annotations achieved a Dice Coefficient\nof 0.9513 and an IoU of 0.9303, validating the accuracy and overlap of the mask\nannotations. All YOLO11 configurations, trained solely on these synthetic\ndatasets with automated annotations, accurately recognized and delineated\napples, highlighting the method's efficacy. Specifically, the YOLO11m-seg\nconfiguration achieved a mask precision of 0.902 and a mask mAP@50 of 0.833 on\ntest images collected from a commercial orchard. Additionally, the YOLO11l-seg\nconfiguration outperformed other models in validation on 40 LLM-generated\nimages, achieving the highest mask precision and mAP@50 metrics.\n  Keywords: YOLO, SAM, SAMv2, YOLO11, YOLOv11, Segment Anything, YOLO-SAM",
        "split": "arXiv"
    },
    {
        "text": "A \\emph{conforming partition} of a rectilinear $ n $-gon\\bastien{I change\nfrom ``a polygon'', otherwise $ n $ is not defined.} $ P $ is a partition of $\nP $ into rectangles without using Steiner points (i.e., all corners of all\nrectangles must lie on\\bastien{Maybe add: the boundary of} $ P $). The stabbing\nnumber of such a partition is the maximum number of rectangles intersected by\nan axis-aligned segment lying in the interior of $ P $. In this paper, we\nexamine the problem of computing conforming partitions with low stabbing\nnumber. We show that computing a conforming partition with stabbing number at\nmost~$ 4 $ is $ NP $-hard, which strengthens a previously known hardness result\n[Durocher \\& Mehrabi, Theor. Comput. Sci. 689: 157-168 (2017)] and eliminates\nthe possibility for fixed-parameter-tractable algorithms parameterized by the\nstabbing number unless $ P = NP $. In contrast, we give (i) an $ O ( n \\log n )\n$-time\\bastien{Reviewer request: changed from \"linearithmic\".} algorithm to\ndecide whether a conforming partition with stabbing number~$ 2 $ exists, (ii) a\nfixed-parameter-tractable algorithm parameterized by both the stabbing number\nand treewidth of the pixelation of the polygon, and (iii) a\nfixed-parameter-tractable algorithm parameterized by the stabbing number for\nsimple polygons in general position.",
        "split": "arXiv"
    },
    {
        "text": "We consider estimating the shared mean of a sequence of heavy-tailed random\nvariables taking values in a Banach space. In particular, we revisit and extend\na simple truncation-based mean estimator by Catoni and Giulini. While existing\ntruncation-based approaches require a bound on the raw (non-central) second\nmoment of observations, our results hold under a bound on either the central or\nnon-central $p$th moment for some $p > 1$. In particular, our results hold for\ndistributions with infinite variance. The main contributions of the paper\nfollow from exploiting connections between truncation-based mean estimation and\nthe concentration of martingales in 2-smooth Banach spaces. We prove two types\nof time-uniform bounds on the distance between the estimator and unknown mean:\nline-crossing inequalities, which can be optimized for a fixed sample size $n$,\nand non-asymptotic law of the iterated logarithm type inequalities, which match\nthe tightness of line-crossing inequalities at all points in time up to a\ndoubly logarithmic factor in $n$. Our results do not depend on the dimension of\nthe Banach space, hold under martingale dependence, and all constants in the\ninequalities are known and small.",
        "split": "arXiv"
    },
    {
        "text": "In Radio Super Novae (RSNe) a magnetic field of $(B \\, \\times \\, r) \\, = \\,\n10^{16.0 \\pm 0.12} \\, {\\rm Gauss \\, \\times \\, cm}$ is observed; these are the\nsame numbers for Blue Super Giant (BSG) star explosions as for Red Super Giant\n(RSG) star explosions, despite their very different wind properties. The EHT\ndata for M87 as well for low power radio galaxies all show consistency with\njust this value of the quantity $(B \\, \\times \\, r )$, key for angular momentum\nand energy transport, and can be derived from the radio jet data. We interpret\nthis as a property of the near surroundings of a black hole (BH) at near\nmaximal rotation, independent of BH mass. In the commonly used green onion\nmodel, in which a $2 \\, \\pi$ flow changes over to a jet flow we interpret this\nas a wind emanating from the BH/accretion disk system and its surroundings.\nNear the BH collisions in the wind can produce a large fraction of\nanti-protons. In this scenario the cosmic Ray (CR) population from the wind/jet\nis proposed to be visible as EeV protons and anti-protons in the CR data to EeV\nenergy, with a $E^{-7/3}$ spectrum. This can be connected to a concept of inner\nand outer Penrose zones in the ergo-region. The observed numbers for the\nmagnetic field imply the Planck time as the governing time scale: A BH rotating\nnear maximum can accept a proton per log bin of energy in an extended spectrum\nwith the associated pions every Planck time.",
        "split": "arXiv"
    },
    {
        "text": "Aggregators of distributed energy resources are increasingly encouraged to\nparticipate in wholesale market bidding. However, the delivery of the power\nthey are awarded can result in over-voltage or congestion issues within the\ndistribution network (DN). The opportunity to lease energy storage from the\nutility that manages the DN provides the aggregator with a means to mitigate\nthese issues, while also benefiting the utility in terms of additional lease\nrevenue. Nevertheless, this leasing opportunity considerably complicates the\naggregator's offer-making process, as it requires the consideration of market\nuncertainties, uncertain power injection at DN buses, and the strategic\ninteractions between the aggregator and the utility. This paper presents a\nstochastic Stackelberg game model that effectively captures the interactions\nbetween the aggregator and the utility, ensuring DN security across all\npotential uncertainty scenarios. Furthermore, in light of the privacy concerns\nof both the aggregator and the utility, two distributed solution methods are\nproposed. The first method follows a traditional predict-then-optimize\nframework and has been validated to achieve the game equilibrium. The second\nmethod employs an end-to-end framework, which has been empirically shown to\nyield superior economic results. Case studies conducted on 69 and 533-bus DNs\nillustrate the efficacy of the proposed methods.",
        "split": "arXiv"
    },
    {
        "text": "Convection in planets and stars is predicted to occur in the \"ultimate\nregime'' of diffusivity-free, rapidly rotating turbulence, in which flows are\ncharacteristically unaffected by viscous and thermal diffusion. Boundary layer\ndiffusion, however, has historically hindered experimental study of this\nregime. Here, we utilize the boundary-independent oscillatory thermal-inertial\nmode of rotating convection to realize the diffusivity-free scaling in liquid\nmetal laboratory experiments. This oscillatory style of convection arises in\nrotating liquid metals (low Prandtl number fluids) and is driven by the\ntemperature gradient in the fluid bulk, thus remaining independent of diffusive\nboundary dynamics. We triply verify the existence of the diffusivity-free\nregime via measurements of heat transfer efficiency $Nu$, dimensionless flow\nvelocities $Re$, and internal temperature anomalies $\\theta$, all of which are\nin quantitative agreement with planar asymptotically-reduced models. Achieving\nthe theoretical diffusivity-free scalings in desktop-sized laboratory\nexperiments provides the validation necessary to extrapolate and predict the\nconvective flows in remote geophysical and astrophysical systems.",
        "split": "arXiv"
    },
    {
        "text": "Pre-trained vision-language models provide a robust foundation for efficient\ntransfer learning across various downstream tasks. In the field of video action\nrecognition, mainstream approaches often introduce additional parameter modules\nto capture temporal information. While the increased model capacity brought by\nthese additional parameters helps better fit the video-specific inductive\nbiases, existing methods require learning a large number of parameters and are\nprone to catastrophic forgetting of the original generalizable knowledge. In\nthis paper, we propose a simple yet effective Multi-modal Spatio-Temporal\nAdapter (MSTA) to improve the alignment between representations in the text and\nvision branches, achieving a balance between general knowledge and\ntask-specific knowledge. Furthermore, to mitigate over-fitting and enhance\ngeneralizability, we introduce a spatio-temporal description-guided consistency\nconstraint. This constraint involves feeding template inputs (i.e., ``a video\nof $\\{\\textbf{cls}\\}$'') into the trainable language branch, while\nLLM-generated spatio-temporal descriptions are input into the pre-trained\nlanguage branch, enforcing consistency between the outputs of the two branches.\nThis mechanism prevents over-fitting to downstream tasks and improves the\ndistinguishability of the trainable branch within the spatio-temporal semantic\nspace. We evaluate the effectiveness of our approach across four tasks:\nzero-shot transfer, few-shot learning, base-to-novel generalization, and\nfully-supervised learning. Compared to many state-of-the-art methods, our MSTA\nachieves outstanding performance across all evaluations, while using only 2-7\\%\nof the trainable parameters in the original model. Code will be avaliable at\nhttps://github.com/chenhaoxing/ETL4Video.",
        "split": "arXiv"
    },
    {
        "text": "Most robotics applications are typically accompanied with safety restrictions\nthat need to be satisfied with a high degree of confidence even in environments\nunder uncertainty. Controlling the state distribution of a system and enforcing\nsuch specifications as distribution constraints is a promising approach for\nmeeting such requirements. In this direction, covariance steering (CS) is an\nincreasingly popular stochastic optimal control (SOC) framework for designing\nsafe controllers via explicit constraints on the system covariance.\nNevertheless, a major challenge in applying CS methods to systems with the\nnonlinear dynamics and chance constraints common in robotics is that the\napproximations needed are conservative and highly sensitive to the point of\napproximation. This can cause sequential convex programming methods to converge\nto poor local minima or incorrectly report problems as infeasible due to\nshifting constraints. This paper presents a novel algorithm for solving\nchance-constrained nonlinear CS problems that directly addresses this\nchallenge. Specifically, we propose an operator-splitting approach that\ntemporarily separates the main problem into subproblems that can be solved in\nparallel. The benefit of this relaxation lies in the fact that it does not\nrequire all iterates to satisfy all constraints simultaneously prior to\nconvergence, thus enhancing the exploration capabilities of the algorithm for\nfinding better solutions. Simulation results verify the ability of the proposed\nmethod to find higher quality solutions under stricter safety constraints than\nstandard methods on a variety of robotic systems. Finally, the applicability of\nthe algorithm on real systems is confirmed through hardware demonstrations.",
        "split": "arXiv"
    },
    {
        "text": "In this article, we study the FitzHugh-Nagumo $(1,1)$--fast-slow system where\nthe vector fields associated to the slow/fast equations come from the reduction\nof the Hodgin-Huxley model for the nerve impulse. After deriving dynamical\nproperties of the singular and regular cases, we perform a bifurcation analysis\nand we investigate how the parameters (of the affine slow equation) impact the\ndynamics of the system. The study of codimension one bifurcations and the\nnumerical locus of canards concludes this case-study. All theoretical results\nare numerically illustrated.",
        "split": "arXiv"
    },
    {
        "text": "Let $p$ be an odd prime and $k$ be an algebraically closed field with\ncharacteristic $p$. Booher and Cais showed that the $a$-number of a $\\mathbb\nZ/p \\mathbb Z$-Galois cover of curves $\\phi: Y \\to X$ must be greater than a\nlower bound determined by the ramification of $\\phi$. In this paper, we provide\nevidence that the lower bound is optimal by finding examples of Artin-Schreier\ncurves that have $a$-number equal to its lower bound for all $p$. Furthermore\nwe use formal patching to generate infinite families of Artin-Schreier curves\nwith $a$-number equal to the lower bound in any characteristic.",
        "split": "arXiv"
    },
    {
        "text": "Because of the rapid development and increasing public availability of\nGenerative Artificial Intelligence (GenAI) models and tools, educational\ninstitutions and educators must immediately reckon with the impact of students\nusing GenAI. There is limited prior research on computing students' use and\nperceptions of GenAI. In anticipation of future advances and evolutions of\nGenAI, we capture a snapshot of student attitudes towards and uses of yet\nemerging GenAI, in a period of time before university policies had reacted to\nthese technologies. We surveyed all computer science majors in a small\nengineering-focused R1 university in order to: (1) capture a baseline\nassessment of how GenAI has been immediately adopted by aspiring computer\nscientists; (2) describe computing students' GenAI-related needs and concerns\nfor their education and careers; and (3) discuss GenAI influences on CS\npedagogy, curriculum, culture, and policy. We present an exploratory\nqualitative analysis of this data and discuss the impact of our findings on the\nemerging conversation around GenAI and education.",
        "split": "arXiv"
    },
    {
        "text": "In a system of two-dimensional electrons, a combination of broken symmetry,\ninteractions, and nontrivial topology can conspire to give rise to a nonlinear\ntransport regime, where electric current density scales as the square of\nelectric field. This regime has become a venue for exciting discoveries such as\nthe nonlinear Hall effect and diode-like nonreciprocal transport. However,\ninterpretation of experimental data is challenging in the nonlinear regime as\nDC transport is described by a rank-3 conductivity tensor with 6 free\nparameters. Here, we resolve this challenge by analytically solving for the\nnonlinear potential distribution across the disk sample for an arbitrary linear\nand nonlinear conductivity tensors. This allows us to unambiguously extract all\ncomponents of the nonlinear tensor from experimental measurement. Using this\nnovel tool, we identify giant nonlinear Hall effect in Bernal bilayer graphene.\nOur methodology provides the first systematic framework for interpreting\nnonlinear transport and uncovers a new route towards understanding quasi-2D\nmaterials.",
        "split": "arXiv"
    },
    {
        "text": "This work investigates the self-organization of multi-agent systems into\nclosed trajectories, a common requirement in unmanned aerial vehicle (UAV)\nsurveillance tasks. In such scenarios, smooth, unbiased control signals save\nenergy and mitigate mechanical strain. We propose a decentralized control\nsystem architecture that produces a globally stable emergent structure from\nlocal observations only; there is no requirement for agents to share a global\nplan or follow prescribed trajectories. Central to our approach is the\nformulation of an injective virtual embedding induced by rotations from the\nactual agent positions. This embedding serves as a structure-preserving map\naround which all agent stabilize their relative positions and permits the use\nof well-established linear control techniques. We construct the embedding such\nthat it is topologically equivalent to the desired trajectory (i.e., a\nhomeomorphism), thereby preserving the stability characteristics. We\ndemonstrate the versatility of this approach through implementation on a swarm\nof Quanser QDrone quadcopters. Results demonstrate the quadcopters\nself-organize into the desired trajectory while maintaining even separation.",
        "split": "arXiv"
    },
    {
        "text": "For a positive integer $k \\ge 1$, a $k$-star ($k^+$-star, $k^-$-star,\nrespectively) is a connected graph containing a degree-$\\ell$ vertex and $\\ell$\ndegree-$1$ vertices, where $\\ell = k$ ($\\ell \\ge k$, $1 \\le \\ell \\le k$,\nrespectively). The $k^+$-star packing problem is to cover as many vertices of\nan input graph $G$ as possible using vertex-disjoint $k^+$-stars in $G$; and\ngiven $k > t \\ge 1$, the $k^-/t$-star packing problem is to cover as many\nvertices of $G$ as possible using vertex-disjoint $k^-$-stars but no $t$-stars\nin $G$. Both problems are NP-hard for any fixed $k \\ge 2$. We present a $(1 +\n\\frac {k^2}{2k+1})$- and a $\\frac 32$-approximation algorithms for the\n$k^+$-star packing problem when $k \\ge 3$ and $k = 2$, respectively, and a $(1\n+ \\frac 1{t + 1 + 1/k})$-approximation algorithm for the $k^-/t$-star packing\nproblem when $k > t \\ge 2$. They are all local search algorithms and they\nimprove the best known approximation algorithms for the problems, respectively.",
        "split": "arXiv"
    },
    {
        "text": "We consider the problem of allocating heterogeneous and indivisible goods\namong strategic agents, with preferences over subsets of goods, when there is\nno medium of exchange. This model captures the well studied problem of fair\nallocation of indivisible goods. Serial-quota mechanisms are allocation\nmechanisms where there is a predefined order over agents, and each agent in her\nturn picks a predefined number of goods from the remaining goods. These\nmechanisms are clearly strategy-proof, non-bossy, and neutral. Are there other\nmechanisms with these properties? We show that for important classes of strict\nordinal preferences (as lexicographic preferences, and as the class of all\nstrict preferences), these are the only mechanisms with these properties.\nImportantly, unlike previous work, we can prove the claim even for mechanisms\nthat are not Pareto-efficient. Moreover, we generalize these results to\npreferences that are cardinal, including any valuation class that contains\nadditive valuations. We then derive strong negative implications of this result\non truthful mechanisms for fair allocation of indivisible goods to agents with\nadditive valuations.",
        "split": "arXiv"
    },
    {
        "text": "We participated in track 2 of the VoiceMOS Challenge 2024, which aimed to\npredict the mean opinion score (MOS) of singing samples. Our submission secured\nthe first place among all participating teams, excluding the official baseline.\nIn this paper, we further improve our submission and propose a novel\nPitch-and-Spectrum-aware Singing Quality Assessment (PS-SQA) method. The PS-SQA\nis designed based on the self-supervised-learning (SSL) MOS predictor,\nincorporating singing pitch and spectral information, which are extracted using\npitch histogram and non-quantized neural codec, respectively. Additionally, the\nPS-SQA introduces a bias correction strategy to address prediction biases\ncaused by low-resource training samples, and employs model fusion technology to\nfurther enhance prediction accuracy. Experimental results confirm that our\nproposed PS-SQA significantly outperforms all competing systems across all\nsystem-level metrics, confirming its strong sing quality assessment\ncapabilities.",
        "split": "arXiv"
    },
    {
        "text": "We investigate the degrees of freedom of New General Relativity. This theory\nis a three-parameter theory and is classified into nine irreducible types\naccording to the rotation symmetry of $SO(3)$ on each leaf of ADM-foliation. In\nthe previous work~[{\\it 2410.15056[gr-qc]}], we investigated the degrees of\nfreedom in types of NGR that are interests for describing gravity: Type 2, Type\n3, Type 5, and Type 8. In this work, we focus on unveiling those numbers in all\nother types to complete the analysis of NGR. After providing the Hamiltonian\nformulation of NGR, we perform the analysis on Type 4, Type 7, and Type 9\naccording to the method that is provided in the previous work~[{\\it\n2410.15056[gr-qc]}]. Then we unveil that the degrees of freedom of Type 4, Type\n7, and Type 9 are five, null, and three, respectively. Type 4 and Type 9 have\nsecond-class constraint densities only. Type 7 has first-class constraint\ndensities only, but which is over constraint. In every type, no bifurcation\noccurs unlikely to Type 8 in the previous work~[2410.15056[gr-qc]]. Finally, we\nsummarize this work and give a concluding remark for this series of works.",
        "split": "arXiv"
    },
    {
        "text": "Abundant geomorphological and geochemical evidence of liquid water on the\nsurface of early Mars during the late Noachian and early Hesperian periods\nneeds to be reconciled with a fainter young Sun. While a dense CO2 atmosphere\nand related warming mechanisms are potential solutions to the early Mars\nclimate problem, further investigation is warranted. Here, we complete a\ncomprehensive survey of the warming potential of all known greenhouse gases and\nperform detailed calculations for 15 different minor gas species under early\nMartian conditions. We find that of these 15 species, H2O2, HNO3, NH3, SO2, and\nC2H4 cause significant greenhouse warming at concentrations of ~0.1 ppmv or\ngreater. However, the most highly effective greenhouse gas species also tend to\nbe more condensable, soluble and vulnerable to photolytic destruction. To\nprovide a reference for future atmospheric evolution and photochemical studies,\nwe have made our warming potential database freely available online.",
        "split": "arXiv"
    },
    {
        "text": "The accurate segmentation of retinal blood vessels plays a crucial role in\nthe early diagnosis and treatment of various ophthalmic diseases. Designing a\nnetwork model for this task requires meticulous tuning and extensive\nexperimentation to handle the tiny and intertwined morphology of retinal blood\nvessels. To tackle this challenge, Neural Architecture Search (NAS) methods are\ndeveloped to fully explore the space of potential network architectures and go\nafter the most powerful one. Inspired by neuronal diversity which is the\nbiological foundation of all kinds of intelligent behaviors in our brain, this\npaper introduces a novel and foundational approach to neural network design,\ntermed ``neuron programming'', to automatically search neuronal types into a\nnetwork to enhance a network's representation ability at the neuronal level,\nwhich is complementary to architecture-level enhancement done by NAS.\nAdditionally, to mitigate the time and computational intensity of neuron\nprogramming, we develop a hypernetwork that leverages the search-derived\narchitectural information to predict optimal neuronal configurations.\nComprehensive experiments validate that neuron programming can achieve\ncompetitive performance in retinal blood segmentation, demonstrating the strong\npotential of neuronal diversity in medical image analysis.",
        "split": "arXiv"
    },
    {
        "text": "In a setting where segmentation models have to be built for multiple\ndatasets, each with its own corresponding label set, a straightforward way is\nto learn one model for every dataset and its labels. Alternatively, multi-task\narchitectures with shared encoders and multiple segmentation heads or shared\nweights with compound labels can also be made use of. This work proposes a\nnovel label sharing framework where a shared common label space is constructed\nand each of the individual label sets are systematically mapped to the common\nlabels. This transforms multiple datasets with disparate label sets into a\nsingle large dataset with shared labels, and therefore all the segmentation\ntasks can be addressed by learning a single model. This eliminates the need for\ntask specific adaptations in network architectures and also results in\nparameter and data efficient models. Furthermore, label sharing framework is\nnaturally amenable for incremental learning where segmentations for new\ndatasets can be easily learnt. We experimentally validate our method on various\nmedical image segmentation datasets, each involving multi-label segmentation.\nFurthermore, we demonstrate the efficacy of the proposed method in terms of\nperformance and incremental learning ability vis-a-vis alternative methods.",
        "split": "arXiv"
    },
    {
        "text": "The coefficient algebra of a finite-dimensional Lie algebra with respect to a\nfaithful representation is defined as the subalgebra generated by all\ncoefficients of the corresponding characteristic polynomial. We establish a\nconnection between classical invariant theory and the coefficient algebras of\nfinite-dimensional complex Lie algebras. Specifically, we prove that with\nrespect to any symmetric power of the standard representation: (1) the\ncoefficient algebra of the upper triangular solvable complex Lie algebra is\nisomorphic to the algebra of symmetric polynomials; (2) the coefficient algebra\nof the general linear complex Lie algebra is the invariant ring of the general\nlinear group with the conjugacy action on the full space of matrices; and (3)\nthe coefficient algebra of the special linear complex Lie algebra can be\ngenerated by classical trace functions. As an application, we exactly exhibit\nthe characteristic polynomial of the special linear complex Lie algebra.",
        "split": "arXiv"
    },
    {
        "text": "Let $M_n$ be the algebra of $n \\times n$ complex matrices and $\\mathcal{T}_n\n\\subseteq M_n$ the corresponding upper-triangular subalgebra. In their\ninfluential work, Petek and \\v{S}emrl characterize Jordan automorphisms of\n$M_n$ and $\\mathcal{T}_n$, when $n \\geq 3$, as (injective in the case of\n$\\mathcal{T}_n$) continuous commutativity and spectrum preserving maps $\\phi :\nM_n \\to M_n$ and $\\phi : \\mathcal{T}_n \\to \\mathcal{T}_n$. Recently, in a joint\nwork with Petek, the authors extended this characterization to the maps $\\phi :\n\\mathcal{A} \\to M_n$, where $\\mathcal{A}$ is an arbitrary subalgebra of $M_n$\nthat contains $\\mathcal{T}_n$. In particular, any such map $\\phi$ is a Jordan\nembedding and hence of the form $\\phi(X)=TXT^{-1}$ or $\\phi(X)=TX^tT^{-1}$, for\nsome invertible matrix $T\\in M_n$. In this paper we further extend the\naforementioned results in the context of structural matrix algebras (SMAs),\ni.e. subalgebras $\\mathcal{A}$ of $M_n$ that contain all diagonal matrices.\nMore precisely, we provide both a necessary and sufficient condition for an SMA\n$\\mathcal{A}\\subseteq M_n$ such that any injective continuous commutativity and\nspectrum preserving map $\\phi: \\mathcal{A} \\to M_n$ is necessarily a Jordan\nembedding. In contrast to the previous cases, such maps $\\phi$ no longer need\nto be multiplicative/antimultiplicative, nor rank-one preservers.",
        "split": "arXiv"
    },
    {
        "text": "Expanding reinforcement learning (RL) to offline domains generates promising\nprospects, particularly in sectors where data collection poses substantial\nchallenges or risks. Pivotal to the success of transferring RL offline is\nmitigating overestimation bias in value estimates for state-action pairs absent\nfrom data. Whilst numerous approaches have been proposed in recent years, these\ntend to focus primarily on continuous or small-scale discrete action spaces.\nFactorised discrete action spaces, on the other hand, have received relatively\nlittle attention, despite many real-world problems naturally having\nfactorisable actions. In this work, we undertake a formative investigation into\noffline reinforcement learning in factorisable action spaces. Using\nvalue-decomposition as formulated in DecQN as a foundation, we present the case\nfor a factorised approach and conduct an extensive empirical evaluation of\nseveral offline techniques adapted to the factorised setting. In the absence of\nestablished benchmarks, we introduce a suite of our own comprising datasets of\nvarying quality and task complexity. Advocating for reproducible research and\ninnovation, we make all datasets available for public use alongside our code\nbase.",
        "split": "arXiv"
    },
    {
        "text": "The prevailing of artificial intelligence-of-things calls for higher\nenergy-efficient edge computing paradigms, such as neuromorphic agents\nleveraging brain-inspired spiking neural network (SNN) models based on\nspatiotemporally sparse binary activations. However, the lack of efficient and\nhigh-accuracy deep SNN learning algorithms prevents them from practical edge\ndeployments with a strictly bounded cost. In this paper, we propose a\nspatiotemporal orthogonal propagation (STOP) algorithm to tack this challenge.\nOur algorithm enables fully synergistic learning of synaptic weights as well as\nfiring thresholds and leakage factors in spiking neurons to improve SNN\naccuracy, while under a unified temporally-forward trace-based framework to\nmitigate the huge memory requirement for storing neural states of all\ntime-steps in the forward pass. Characteristically, the spatially-backward\nneuronal errors and temporally-forward traces propagate orthogonally to and\nindependently of each other, substantially reducing computational overhead. Our\nSTOP algorithm obtained high recognition accuracies of 99.53%, 94.84%, 74.92%,\n98.26% and 77.10% on the MNIST, CIFAR-10, CIFAR-100, DVS-Gesture and\nDVS-CIFAR10 datasets with adequate SNNs of intermediate scales from LeNet-5 to\nResNet-18. Compared with other deep SNN training works, our method is more\nplausible for edge intelligent scenarios where resources are limited but\nhigh-accuracy in-situ learning is desired.",
        "split": "arXiv"
    },
    {
        "text": "High annotation costs from hiring or crowdsourcing complicate the creation of\nlarge, high-quality datasets needed for training reliable text classifiers.\nRecent research suggests using Large Language Models (LLMs) to automate the\nannotation process, reducing these costs while maintaining data quality. LLMs\nhave shown promising results in annotating downstream tasks like hate speech\ndetection and political framing. Building on the success in these areas, this\nstudy investigates whether LLMs are viable for annotating the complex task of\nmedia bias detection and whether a downstream media bias classifier can be\ntrained on such data. We create annolexical, the first large-scale dataset for\nmedia bias classification with over 48000 synthetically annotated examples. Our\nclassifier, fine-tuned on this dataset, surpasses all of the annotator LLMs by\n5-9 percent in Matthews Correlation Coefficient (MCC) and performs close to or\noutperforms the model trained on human-labeled data when evaluated on two media\nbias benchmark datasets (BABE and BASIL). This study demonstrates how our\napproach significantly reduces the cost of dataset creation in the media bias\ndomain and, by extension, the development of classifiers, while our subsequent\nbehavioral stress-testing reveals some of its current limitations and\ntrade-offs.",
        "split": "arXiv"
    },
    {
        "text": "The demand for deploying deep convolutional neural networks (DCNNs) on\nresource-constrained devices for real-time applications remains substantial.\nHowever, existing state-of-the-art structured pruning methods often involve\nintricate implementations, require modifications to the original network\narchitectures, and necessitate an extensive fine-tuning phase. To overcome\nthese challenges, we propose a novel method that, for the first time,\nincorporates the concepts of charge and electrostatic force from physics into\nthe training process of DCNNs. The magnitude of this force is directly\nproportional to the product of the charges of the convolution filter and the\nsource filter, and inversely proportional to the square of the distance between\nthem. We applied this electrostatic-like force to the convolution filters,\neither attracting filters with opposite charges toward non-zero weights or\nrepelling filters with like charges toward zero weights. Consequently, filters\nsubject to repulsive forces have their weights reduced to zero, enabling their\nremoval, while the attractive forces preserve filters with significant weights\nthat retain information. Unlike conventional methods, our approach is\nstraightforward to implement, does not require any architectural modifications,\nand simultaneously optimizes weights and ranks filter importance, all without\nthe need for extensive fine-tuning. We validated the efficacy of our method on\nmodern DCNN architectures using the MNIST, CIFAR, and ImageNet datasets,\nachieving competitive performance compared to existing structured pruning\napproaches.",
        "split": "arXiv"
    },
    {
        "text": "We report the detection of an extreme stellar prominence eruption on the M\ndwarf LAMOST J044431.62+235627.9, observed through time-domain H$\\alpha$\nspectroscopy with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope\n(LAMOST). This prominence eruption was accompanied by a superflare lasting over\n160.4 minutes. The H$\\alpha$ line profile exhibits significant blue-wing\nenhancement during the impulsive phase and near the flare peak, with a\nprojected bulk blueshift velocity of $-228\\pm11$~km~s$^{-1}$ and a maximum\nblueshift velocity reaching $-605\\pm15$~km~s$^{-1}$. Velocity analysis of the\neruptive prominence at various heights above the stellar surface indicates that\nsome of the projected ejection velocities along the line of sight exceed the\ncorresponding escape velocities, suggesting a potential coronal mass ejection\n(CME). The equivalent width (EW) of the H$\\alpha$ blue-wing enhancement in this\neruption appears to be the largest observed to date and is comparable to the EW\nof the H$\\alpha$ line profile during the quiescent phase of the host star. We\nperformed a two-cloud modeling for the prominence and the associated flare,\nwhich suggests that the eruptive prominence has a mass ranging from $1.6 \\times\n10^{19}~\\text{g}$ to $7.2 \\times 10^{19}~\\text{g}$. More importantly, the mass\nratio of the erupting prominence to its host star is the largest among all\nreported stellar prominence eruptions/CMEs.",
        "split": "arXiv"
    },
    {
        "text": "Subspace clustering seeks to identify subspaces that segment a set of n data\npoints into k (k<<n) groups, which has emerged as a powerful tool for analyzing\ndata from various domains, especially images and videos. Recently, several\nstudies have demonstrated the great potential of subspace clustering models for\npartitioning vertices in attributed graphs, referred to as SCAG. However, these\nworks either demand significant computational overhead for constructing the nxn\nself-expressive matrix, or fail to incorporate graph topology and attribute\ndata into the subspace clustering framework effectively, and thus, compromise\nresult quality.\n  Motivated by this, this paper presents two effective and efficient\nalgorithms, S2CAG and M-S2CAG, for SCAG computation. Particularly, S2CAG\nobtains superb performance through three major contributions. First, we\nformulate a new objective function for SCAG with a refined representation model\nfor vertices and two non-trivial constraints. On top of that, an efficient\nlinear-time optimization solver is developed based on our theoretically\ngrounded problem transformation and well-thought-out adaptive strategy. We then\nconduct an in-depth analysis to disclose the theoretical connection of S2CAG to\nconductance minimization, which further inspires the design of M-S2CAG that\nmaximizes the modularity. Our extensive experiments, comparing S2CAG and\nM-S2CAG against 17 competitors over 8 benchmark datasets, exhibit that our\nsolutions outperform all baselines in terms of clustering quality measured\nagainst the ground truth while delivering high efficiency",
        "split": "arXiv"
    },
    {
        "text": "By the end of 2023, renewable sources cover 63.4% of the total electric power\ndemand of Chile, and in line with the global trend, photovoltaic (PV) power\nshows the most dynamic increase. Although Chile's Atacama Desert is considered\nthe sunniest place on Earth, PV power production, even in this area, can be\nhighly volatile. Successful integration of PV energy into the country's power\ngrid requires accurate short-term PV power forecasts, which can be obtained\nfrom predictions of solar irradiance and related weather quantities. Nowadays,\nin weather forecasting, the state-of-the-art approach is the use of ensemble\nforecasts based on multiple runs of numerical weather prediction models.\nHowever, ensemble forecasts still tend to be uncalibrated or biased, thus\nrequiring some form of post-processing. The present work investigates\nprobabilistic forecasts of solar irradiance for Regions III and IV in Chile.\nFor this reason, 8-member short-term ensemble forecasts of solar irradiance for\ncalendar year 2021 are generated using the Weather Research and Forecasting\n(WRF) model, which are then calibrated using the benchmark ensemble model\noutput statistics (EMOS) method based on a censored Gaussian law, and its\nmachine learning-based distributional regression network (DRN) counterpart.\nFurthermore, we also propose a neural network-based post-processing method\nresulting in improved 8-member ensemble predictions. All forecasts are\nevaluated against station observations for 30 locations, and the skill of\npost-processed predictions is compared to the raw WRF ensemble. Our case study\nconfirms that all studied post-processing methods substantially improve both\nthe calibration of probabilistic- and the accuracy of point forecasts. Among\nthe methods tested, the corrected ensemble exhibits the best overall\nperformance. Additionally, the DRN model generally outperforms the\ncorresponding EMOS approach.",
        "split": "arXiv"
    },
    {
        "text": "Cell-free (CF) massive multiple-input multiple-output (mMIMO) and\nreconfigurable intelligent surface (RIS) are two advanced transceiver\ntechnologies for realizing future sixth-generation (6G) networks. In this\npaper, we investigate the joint precoding and access point (AP) selection for\nenergy efficient RIS-aided CF mMIMO system. To address the associated\ncomputational complexity and communication power consumption, we advocate for\nuser-centric dynamic networks in which each user is served by a subset of APs\nrather than by all of them. Based on the user-centric network, we formulate a\njoint precoding and AP selection problem to maximize the energy efficiency (EE)\nof the considered system. To solve this complex nonconvex problem, we propose\nan innovative double-layer multi-agent reinforcement learning (MARL)-based\nscheme. Moreover, we propose an adaptive power threshold-based AP selection\nscheme to further enhance the EE of the considered system. To reduce the\ncomputational complexity of the RIS-aided CF mMIMO system, we introduce a fuzzy\nlogic (FL) strategy into the MARL scheme to accelerate convergence. The\nsimulation results show that the proposed FL-based MARL cooperative\narchitecture effectively improves EE performance, offering a 85\\% enhancement\nover the zero-forcing (ZF) method, and achieves faster convergence speed\ncompared with MARL. It is important to note that increasing the transmission\npower of the APs or the number of RIS elements can effectively enhance the\nspectral efficiency (SE) performance, which also leads to an increase in power\nconsumption, resulting in a non-trivial trade-off between the quality of\nservice and EE performance.",
        "split": "arXiv"
    },
    {
        "text": "Transition region explosive events are characterized by non-Gaussian profiles\nof the emission lines formed at transition region temperatures, and they are\nbelieved to be manifestations of small-scale reconnection events in the\ntransition region. We took a 3D self-consistent quiet-Sun model extending from\nthe upper convection zone to the lower corona calculated using the MURaM code.\nWe first synthesized the Si IV line profiles from the model and then located\nthe profiles which show signatures of bi-directional flows. These tend to\nappear along network lanes, and most do not reach coronal temperatures. We\nisolated two hot (around 1 MK) events and one cool (order of 0.1 MK) event and\nexamined the magnetic field evolution in and around these selected events.\nFurthermore, we investigated why some explosive events reach coronal\ntemperatures while most remain cool. The field lines around two events\nreconnect at small angles, i.e., they undergo component reconnection. The third\ncase is associated with the relaxation of a highly twisted flux rope. All of\nthe three events reveal signatures in the synthesized EUI 174 {\\AA} images. The\nintensity variations in two events are dominated by variations of the coronal\nemissions, while the cool component seen in the respective channel contributes\nsignificantly to the intensity variation in one case. Comparing to the cool\nevent, one hot event is embedded in regions with higher magnetic field strength\nand heating rates while the densities are comparable, and the other hot event\nis heated to coronal temperatures mainly because of the low density.\nSmall-scale heating events seen in EUV channels of AIA or EUI might be hot or\ncool. Our results imply that the major difference between the events in which\ncoronal counterparts dominate or not is the amount of converted magnetic energy\nand/or density in and around the reconnection region.",
        "split": "arXiv"
    },
    {
        "text": "Recent advances in multimodal Large Language Models (LLMs) have shown great\nsuccess in understanding multi-modal contents. For video understanding tasks,\ntraining-based video LLMs are difficult to build due to the scarcity of\nhigh-quality, curated video-text paired data. In contrast, paired image-text\ndata are much easier to obtain, and there is substantial similarity between\nimages and videos. Consequently, extending image LLMs for video understanding\ntasks presents an appealing alternative. Developing effective strategies for\ncompressing visual tokens from multiple frames is a promising way to leverage\nthe powerful pre-trained image LLM. In this work, we explore the limitations of\nthe existing compression strategies for building a training-free video LLM. The\nfindings lead to our method TS-LLaVA, which constructs visual tokens through a\nThumbnail-and-Sampling strategy. Given a video, we select few equidistant\nframes from all input frames to construct a Thumbnail image as a detailed\nvisual cue, complemented by Sampled visual tokens from all input frames. Our\nmethod establishes the new state-of-the-art performance among training-free\nvideo LLMs on various benchmarks. Notably, our 34B model outperforms GPT-4V on\nthe MVBench benchmark, and achieves performance comparable to the 72B\ntraining-based video LLM, Video-LLaMA2, on the challenging MLVU benchmark. Code\nis available at https://github.com/tingyu215/TS-LLaVA.",
        "split": "arXiv"
    },
    {
        "text": "We prove that for all constants $a\\in\\N$, $b\\in\\Z$, $c,d\\in\\R$, $c\\neq 0$,\nthe fractions $\\phi(an+b)/(cn+d)$ lie dense in the interval $]0,D]$\n(respectively $[D,0[$ if $c<0$), where $D=a\\phi(\\gcd(a,b))/(c\\gcd(a,b))$. This\ninterval is the largest possible, since it may happen that isolated fractions\nlie outside of the interval: we prove a complete determination of the case\nwhere this happens, which yields an algorithm that calculates the amount of $n$\nsuch that $\\rad(an+b)|g$ for coprime $a,b$ and any $g$. Furthermore, this leads\nto an interesting open question which is a generalization of a famous problem\nraised by V.~Arnold. For the fractions $\\phi(an+b)/\\phi(cn+d)$ with constants\n$a,c\\in\\N,b,d\\in\\Z$, we prove that they lie dense in $]0,\\infty[$ exactly if\n$ad\\neq bc$.",
        "split": "arXiv"
    },
    {
        "text": "In the theory of dynamic programming, an optimal policy is a policy whose\nlifetime value dominates that of all other policies at every point in the state\nspace. This raises a natural question: under what conditions does optimality at\na single state imply optimality at every state? We show that, in a general\nsetting, the irreducibility of the transition kernel under a feasible policy is\na sufficient condition for extending optimality from one state to all states.\nThese results have important implications for dynamic optimization algorithms\nbased on gradient methods, which are routinely applied in reinforcement\nlearning and other large scale applications.",
        "split": "arXiv"
    },
    {
        "text": "Recent advancements of generative AI have significantly promoted content\ncreation and editing, where prevailing studies further extend this exciting\nprogress to video editing. In doing so, these studies mainly transfer the\ninherent motion patterns from the source videos to the edited ones, where\nresults with inferior consistency to user prompts are often observed, due to\nthe lack of particular alignments between the delivered motions and edited\ncontents. To address this limitation, we present a shape-consistent video\nediting method, namely StableV2V, in this paper. Our method decomposes the\nentire editing pipeline into several sequential procedures, where it edits the\nfirst video frame, then establishes an alignment between the delivered motions\nand user prompts, and eventually propagates the edited contents to all other\nframes based on such alignment. Furthermore, we curate a testing benchmark,\nnamely DAVIS-Edit, for a comprehensive evaluation of video editing, considering\nvarious types of prompts and difficulties. Experimental results and analyses\nillustrate the outperforming performance, visual consistency, and inference\nefficiency of our method compared to existing state-of-the-art studies.",
        "split": "arXiv"
    },
    {
        "text": "Population size estimation is a major challenge in official statistics,\nsocial sciences, and natural sciences. The problem can be tackled by applying\ncapture-recapture methods, which vary depending on the number of sources used,\nparticularly on whether a single or multiple sources are involved. This paper\nfocuses on the first group of methods and introduces a novel R package:\nsingleRcapture. The package implements state-of-the-art single-source\ncapture-recapture (SSCR) models (e.g.zero-truncated one-inflated regression)\ntogether with new developments proposed by the authors, and provides a\nuser-friendly application programming interface (API). This self-contained\npackage can be used to produce point estimates and their variance and\nimplements several bootstrap variance estimators or diagnostics to assess\nquality and conduct sensitivity analysis. It is intended for users interested\nin estimating the size of populations, particularly those that are difficult to\nreach or measure, for which information is available only from one source and\ndual/multiple system estimation is not applicable. Our package serves to bridge\na significant gap, as the SSCR methods are either not available at all or are\nonly partially implemented in existing R packages and other open-source\nsoftware. Furthermore, since many R users are familiar with countreg or VGAM\npackages, we have implemented a lightweight extension called\nsingleRcaptureExtra which can be used to integrate singleRcapture with these\npackages.",
        "split": "arXiv"
    },
    {
        "text": "Quantum Local Area Networks (QLANs) represent a promising building block for\nlarger scale quantum networks with the ambitious goal -- in a long time horizon\n-- of realizing a Quantum Internet. Surprisingly, the physical topology of a\nQLAN can be enriched by a set of artificial links, enabled by shared\nmultipartite entangled states among the nodes of the network. This novel\nconcept of artificial topology revolutionizes the possibilities of connectivity\nwithin the local network, enabling an on-demand manipulation of the artificial\nnetwork topology. In this paper, we discuss the implementation of the QLAN\nmodel in SeQUeNCe, a discrete-event simulator of quantum networks.\nSpecifically, we provide an analysis of how network nodes interact, with an\nemphasis on the interplay between quantum operations and classical signaling\nwithin the network. Remarkably, through the modeling of a measurement protocol\nand a correction protocol, our QLAN model implementation enables the simulation\nof the manipulation process of a shared entangled quantum state, and the\nsubsequent engineering of the entanglement-based connectivity. Our simulations\ndemonstrate how to obtain different virtual topologies with different\nmanipulations of the shared resources and with all the possible measurement\noutcomes, with an arbitrary number of nodes within the network.",
        "split": "arXiv"
    },
    {
        "text": "We propose and study a minimalist approach towards synthetic tabular data\ngeneration. The model consists of a minimalistic unsupervised SparsePCA encoder\n(with contingent clustering step or log transformation to handle nonlinearity)\nand XGboost decoder which is SOTA for structured data regression and\nclassification tasks. We study and contrast the methodologies with\n(variational) autoencoders in several toy low dimensional scenarios to derive\nnecessary intuitions. The framework is applied to high dimensional simulated\ncredit scoring data which parallels real-life financial applications. We\napplied the method to robustness testing to demonstrate practical use cases.\nThe case study result suggests that the method provides an alternative to raw\nand quantile perturbation for model robustness testing. We show that the method\nis simplistic, guarantees interpretability all the way through, does not\nrequire extra tuning and provide unique benefits.",
        "split": "arXiv"
    },
    {
        "text": "Future satellite missions are expected to perform all-sky surveys, thus\nproviding the entire sky near-infrared spectral data and consequently opening a\nnew window to investigate the evolution of galaxies. Specifically, the infrared\nspectral data facilitate the precise estimation of stellar masses of numerous\nlow-redshift galaxies. We utilize the synthetic spectral energy distribution\n(SED) of 2853 nearby galaxies drawn from the DustPedia (435) and Stripe 82\nregions (2418). The stellar mass-to-light ratio ($M_*/L$) estimation accuracy\nover a wavelength range of $0.75-5.0$ $\\mu$m is computed through the SED\nfitting of the multi-wavelength photometric dataset, which has not yet been\nintensively explored in previous studies. We find that the scatter in $M_*/L$\nis significantly larger in the shorter and longer wavelength regimes due to the\neffect of the young stellar population and the dust contribution, respectively.\nWhile the scatter in $M_*/L$ approaches its minimum ($\\sim0.10$ dex) at\n$\\sim1.6$ $\\mu$m, it remains sensitive to the adopted star formation history\nmodel. Furthermore, $M_*/L$ demonstrates weak and strong correlations with the\nstellar mass and the specific star formation rate (SFR), respectively. Upon\nadequately correcting the dependence of $M_*/L$ on the specific SFR, the\nscatter in the $M_*/L$ further reduces to $0.02$ dex at $\\sim1.6$ $\\mu$m. This\nindicates that the stellar mass can be estimated with an accuracy of $\\sim0.02$\ndex with a prior knowledge of SFR, which can be estimated using the infrared\nspectra obtained with future survey missions.",
        "split": "arXiv"
    },
    {
        "text": "The majority of atomic nuclei have deformed shapes and nearly all these\nshapes are symmetric with respect to reflection. There are only a few\nreflection asymmetric pear-shaped nuclei that have been found in actinide and\nlanthanide regions, which have static octupole deformation. These nuclei\npossess an intrinsic electric dipole moment due to the shift between the center\nof charge and the center of mass. This manifests in the enhancement of the\nelectric dipole transition rates. In this article, we report on the measurement\nof the lifetimes of the high spin levels of the two alternate parity bands in\n$^{100}$Ru through the Doppler Shift Attenuation Method. The estimated electric\ndipole transition rates have been compared with the calculated transition rates\nusing the triaxial projected shell model without octupole deformation, and are\nfound to be an order of magnitude enhanced. Thus, the observation of seven\ninter-leaved electric dipole transitions with enhanced rates establish\n$^{100}$Ru as possibly the first octupole deformed nucleus reported in the A\n$\\approx$ 100 mass region.",
        "split": "arXiv"
    },
    {
        "text": "Random walks and related spatial stochastic models have been used in a range\nof application areas including animal and plant ecology, infectious disease\nepidemiology, developmental biology, wound healing, and oncology. Classical\nrandom walk models assume that all individuals in a population behave\nindependently, ignoring local physical and biological interactions. This\nassumption simplifies the mathematical description of the population\nconsiderably, enabling continuum-limit descriptions to be derived and used in\nmodel analysis and fitting. However, interactions between individuals can have\na crucial impact on population-level behaviour. In recent decades, research has\nincreasingly been directed towards models that include interactions, including\nphysical crowding effects and local biological processes such as adhesion,\ncompetition, dispersal, predation and adaptive directional bias. In this\narticle, we review the progress that has been made with models of interacting\nindividuals. We aim to provide an overview that is accessible to researchers in\napplication areas, as well as to specialist modellers. We focus particularly on\nderivation of asymptotically exact or approximate continuum-limit descriptions\nand simplified deterministic models of mean-field behaviour and resulting\nspatial patterns. We provide worked examples and illustrative results of\nselected models. We conclude with a discussion of current areas of focus and\nfuture challenges.",
        "split": "arXiv"
    },
    {
        "text": "We show how curing an anomaly of the twistor uplift of self-dual Yang-Mills\ntheory implies linear relations among one-loop, $n$-gluon, color-ordered\nsubamplitudes in QCD, when all $n$ gluon helicities are positive, or when\nexactly one is negative. We compute the number of linearly independent\nsubamplitudes as determined by these relations, in terms of unsigned Stirling\nnumbers. Then we use a momentum-twistor parametrization to show that there are\nno further linear dependencies.",
        "split": "arXiv"
    },
    {
        "text": "Ultra-high-definition (UHD) image restoration is vital for applications\ndemanding exceptional visual fidelity, yet existing methods often face a\ntrade-off between restoration quality and efficiency, limiting their practical\ndeployment. In this paper, we propose TSFormer, an all-in-one framework that\nintegrates \\textbf{T}rusted learning with \\textbf{S}parsification to boost both\ngeneralization capability and computational efficiency in UHD image\nrestoration. The key is that only a small amount of token movement is allowed\nwithin the model. To efficiently filter tokens, we use Min-$p$ with random\nmatrix theory to quantify the uncertainty of tokens, thereby improving the\nrobustness of the model. Our model can run a 4K image in real time (40fps) with\n3.38 M parameters. Extensive experiments demonstrate that TSFormer achieves\nstate-of-the-art restoration quality while enhancing generalization and\nreducing computational demands. In addition, our token filtering method can be\napplied to other image restoration models to effectively accelerate inference\nand maintain performance.",
        "split": "arXiv"
    },
    {
        "text": "We study the problem of maximizing a function that is approximately\nsubmodular under a cardinality constraint. Approximate submodularity implicitly\nappears in a wide range of applications as in many cases errors in evaluation\nof a submodular function break submodularity. Say that $F$ is\n$\\varepsilon$-approximately submodular if there exists a submodular function\n$f$ such that $(1-\\varepsilon)f(S) \\leq F(S)\\leq (1+\\varepsilon)f(S)$ for all\nsubsets $S$. We are interested in characterizing the query-complexity of\nmaximizing $F$ subject to a cardinality constraint $k$ as a function of the\nerror level $\\varepsilon>0$. We provide both lower and upper bounds: for\n$\\varepsilon>n^{-1/2}$ we show an exponential query-complexity lower bound. In\ncontrast, when $\\varepsilon< {1}/{k}$ or under a stronger bounded curvature\nassumption, we give constant approximation algorithms.",
        "split": "arXiv"
    },
    {
        "text": "In recent years, with the rapid development of augmented reality (AR)\ntechnology, there is an increasing demand for multi-user collaborative\nexperiences. Unlike for single-user experiences, ensuring the spatial\nlocalization of every user and maintaining synchronization and consistency of\npositioning and orientation across multiple users is a significant challenge.\nIn this paper, we propose a multi-user localization system based on ORB-SLAM2\nusing monocular RGB images as a development platform based on the Unity 3D game\nengine. This system not only performs user localization but also places a\ncommon virtual object on a planar surface (such as table) in the environment so\nthat every user holds a proper perspective view of the object. These generated\nvirtual objects serve as reference points for multi-user position\nsynchronization. The positioning information is passed among every user's AR\ndevices via a central server, based on which the relative position and movement\nof other users in the space of a specific user are presented via virtual\navatars all with respect to these virtual objects. In addition, we use deep\nlearning techniques to estimate the depth map of an image from a single RGB\nimage to solve occlusion problems in AR applications, making virtual objects\nappear more natural in AR scenes.",
        "split": "arXiv"
    },
    {
        "text": "We present a novel digital humanities method for representing our Twitch\nchatters as user embeddings created by a large language model (LLM). We cluster\nthese embeddings automatically using affinity propagation and further narrow\nthis clustering down through manual analysis. We analyze the chat of one stream\nby each Twitch streamer: SmallAnt, DougDoug and PointCrow. Our findings suggest\nthat each streamer has their own type of chatters, however two categories\nemerge for all of the streamers: supportive viewers and emoji and reaction\nsenders. Repetitive message spammers is a shared chatter category for two of\nthe streamers.",
        "split": "arXiv"
    },
    {
        "text": "Non-gravitational forces play surprising and, sometimes, centrally important\nroles in shaping the motions and properties of small planetary bodies. In the\nsolar system, the morphologies of comets, the delivery of meteorites and the\nshapes and dynamics of asteroids are all affected by non-gravitational forces.\nAround other stars, non-gravitational forces affect the lifetimes of particles\nand their rates of radial transport within circumstellar disks. Unlike the\ngravitational force, which is a simple function of the well known separations\nand masses of bodies, the non-gravitational forces are frequently functions of\npoorly known or even unmeasurable physical properties. Here, we present\norder-of-magnitude descriptions of non-gravitational forces, with examples of\ntheir application.",
        "split": "arXiv"
    },
    {
        "text": "Understanding the performance of electrochemical energy storage systems\nrequires probing the electrochemical properties at each layer and interface\nduring cell operation. While traditional onboard and operando methods can\nmeasure impedance, voltage, or capacity, they lack spatial resolution to\npinpoint the properties to specific layers and interfaces. In this work, we\ndescribe an approach of using thermal waves to measure entropy change,\ntransport resistance, and charge-transfer resistance with depth resolution of a\nfew microns within an electrochemical cell. We achieve this by relating heat\ngeneration at multiple harmonics of an AC current to electrochemical processes\nand leveraging frequency dependence of thermal penetration depth for spatial\nresolution. We name this frequency domain spectroscopy of the thermal\nsignatures of the electrochemical processes measured at multiple harmonics of\nthe alternating current as Multi-harmonic ElectroThermal Spectroscopy (METS).\nThis technique enables isolation and measurement of solvation entropy at\nindividual electrode-electrolyte interfaces from the first harmonic (1{\\omega})\nthermal signature and resolution of the overall interfacial impedance into\ncharge-transfer and interface transport resistance components from the second\nharmonic (2{\\omega}) thermal signature. From this, we also demonstrate an\noperando measurement of the growth of the solid-electrolyte interphase (SEI)\nlayer at the lithium-electrolyte interface and show that two chemically similar\nelectrodes can have significantly different interfacial transport resistance\nbased on the preparation of the electrodes. Additionally, the method is not\nspecific to lithium-ion chemistry and can therefore be generalized for all\nelectrochemical systems of interest.",
        "split": "arXiv"
    },
    {
        "text": "We establish a fundamental theorem of orders (FTO) which allows us to express\nall orders uniquely as an intersection of `irreducible orders' along which the\nindex and the conductor distributes multiplicatively.\n  We define a subclass of Irreducible orders named Pseudo maximal orders. We\nthen consider orders (called Sudo maximal orders) whose decomposition under FTO\ncontains only Pseudo maximal orders. These rings can be seen as being ``close\"\nto being maximal (ring of integers) and thus there is a limited number of them\nwith bounded index (by X). We give an upper bound for this quantity. We then\nshow that all polynomials which can be sieved using only the Ekedahl sieve\ncorrespond to Sudo Maximal Orders. We use this understanding to get a weighted\ncount for the number of number-fields with fixed degree and bounded\ndiscriminant using the concept of weakly divisible rings.",
        "split": "arXiv"
    },
    {
        "text": "Alignment of large language models (LLMs) to societal values should account\nfor pluralistic values from diverse groups. One technique uses in-context\nlearning for inference-time alignment, but only considers similarity when\ndrawing few-shot examples, not accounting for cross-group differences in value\nprioritization. We propose SPICA, a framework for pluralistic alignment that\naccounts for group-level differences during in-context example retrieval. SPICA\nintroduces three designs to facilitate pluralistic alignment: scenario banks,\ngroup-informed metrics, and in-context alignment prompts. From an evaluation of\nSPICA on an alignment task collecting inputs from four demographic groups ($n =\n544$), our metrics retrieve in-context examples that more closely match\nobserved preferences, with the best prompt configuration using multiple\ncontrastive responses to demonstrate examples. In an end-to-end evaluation ($n\n= 80$), we observe that SPICA-aligned models are higher rated than a baseline\nsimilarity-only retrieval approach, with groups seeing up to a +0.16 point\nimprovement on a 5 point scale. Additionally, gains from SPICA were more\nuniform, with all groups benefiting from alignment rather than only some.\nFinally, we find that while a group-agnostic approach can effectively align to\naggregated values, it is not most suited for aligning to divergent groups.",
        "split": "arXiv"
    },
    {
        "text": "A fundamental problem in network experiments is selecting an appropriate\nexperimental design in order to precisely estimate a given causal effect of\ninterest. In fact, optimal rates of estimation remain unknown for essentially\nall causal effects in network experiments. In this work, we propose a general\napproach for constructing experiment designs under network interference with\nthe goal of precisely estimating a pre-specified causal effect. A central\naspect of our approach is the notion of a conflict graph, which captures the\nfundamental unobservability associated with the casual effect and the\nunderlying network. We refer to our experimental design as the Conflict Graph\nDesign. In order to estimate effects, we propose a modified Horvitz--Thompson\nestimator. We show that its variance under the Conflict Graph Design is bounded\nas $O(\\lambda(H) / n )$, where $\\lambda(H)$ is the largest eigenvalue of the\nadjacency matrix of the conflict graph. These rates depend on both the\nunderlying network and the particular causal effect under investigation. Not\nonly does this yield the best known rates of estimation for several\nwell-studied causal effects (e.g. the global and direct effects) but it also\nprovides new methods for effects which have received less attention from the\nperspective of experiment design (e.g. spill-over effects). Our results\ncorroborate two implicitly understood points in the literature: (1) that in\norder to increase precision, experiment designs should be tailored to specific\ncausal effects of interest and (2) that \"more local\" effects are easier to\nestimate than \"more global\" effects. In addition to point estimation, we\nconstruct conservative variance estimators which facilitate the construction of\nasymptotically valid confidence intervals for the casual effect of interest.",
        "split": "arXiv"
    },
    {
        "text": "Distributed phased arrays have recently garnered interest in applications\nsuch as satellite communications and high-resolution remote sensing.\nHigh-performance coherent distributed operations such as distributed\nbeamforming are dependent on the ability to synchronize the spatio-electrical\nstates of the elements in the array to the order of the operational wavelength,\nso that coherent signal summation can be achieved at any arbitrary target\ndestination. In this paper, we address the fundamental challenge of precise\ndistributed array element localization to enable coherent operation, even in\ncomplex environments where the array may not be capable of directly estimating\nall nodal link distances. We employ a two-way time transfer technique to\nsynchronize the nodes of the array and perform internode ranging. We implement\nthe classical multidimensional scaling algorithm to recover a decentralized\narray geometry from a set of range estimates. We also establish the incomplete\nset of range estimates as a multivariable non-convex optimization problem, and\ndefine the differential evolution algorithm which searches the solution space\nto complete the set of ranges. We experimentally demonstrate wireless\nlocalization using a spectrally-sparse pulsed two-tone waveform with 40 MHz\ntone separation in a laboratory environment, achieving a mean localization\nerror vector magnitude of 0.82 mm in an environment with an average link SNR of\n34 dB, theoretically supporting distributed beamforming operation up to 24.3\nGHz.",
        "split": "arXiv"
    },
    {
        "text": "Recently the galaxy matter density 4-point correlation function has been\nlooked at to investigate parity violation in large scale structure surveys. The\n4-point correlation function is the lowest order statistic which is sensitive\nto parity violation, since a tetrahedron is the simplest shape that cannot be\nsuperimposed on its mirror image by a rotation. If the parity violation is\nintrinsic in nature, this could give us a window into inflationary physics.\nHowever, we need to exhaust all other contaminations before we consider them to\nbe intrinsic. Even though the standard Newtonian redshift-space distortions are\nparity symmetric, the full relativistic picture is not. Therefore, we expect a\nparity-odd trispectrum when observing in redshift space. We calculate the\ntrispectrum with the leading-order relativistic effects and investigate in\ndetail the parameter space of the trispectrum and the effects of these\nrelativistic corrections for different parameter values and configurations. We\nalso look at different surveys and how the evolution and magnification biases\ncan be affected by different parameter choices.",
        "split": "arXiv"
    },
    {
        "text": "While state-of-the-art models for breast cancer detection leverage multi-view\nmammograms for enhanced diagnostic accuracy, they often focus solely on visual\nmammography data. However, radiologists document valuable lesion descriptors\nthat contain additional information that can enhance mammography-based breast\ncancer screening. A key question is whether deep learning models can benefit\nfrom these expert-derived features. To address this question, we introduce a\nnovel multi-modal approach that combines textual BI-RADS lesion descriptors\nwith visual mammogram content. Our method employs iterative attention layers to\neffectively fuse these different modalities, significantly improving\nclassification performance over image-only models. Experiments on the CBIS-DDSM\ndataset demonstrate substantial improvements across all metrics, demonstrating\nthe contribution of handcrafted features to end-to-end.",
        "split": "arXiv"
    },
    {
        "text": "We study the classic single-choice prophet secretary problem through a\nresource augmentation lens. Our goal is to bound the $(1-\\epsilon)$-competition\ncomplexity for different classes of online algorithms. This metric asks for the\nsmallest $k$ such that the expected value of the online algorithm on $k$ copies\nof the original instance, is at least a $(1 - \\epsilon)$-approximation to the\nexpected offline optimum on the original instance (without added copies).\n  We consider four natural classes of online algorithms: single-threshold,\ntime-based threshold, activation-based, and general algorithms. We show that\nfor single-threshold algorithms the $(1-\\epsilon)$-competition complexity is\n$\\Theta(\\ln(\\frac{1}{\\epsilon}))$ (as in the i.i.d. case). Additionally, we\ndemonstrate that time-based threshold and activation-based algorithms (which\ncover all previous approaches for obtaining competitive-ratios for the classic\nprophet secretary problem) yield a sub-optimal $(1-\\epsilon)$-competition\ncomplexity of\n$\\Theta\\left(\\frac{\\ln(\\frac{1}{\\epsilon})}{\\ln\\ln(\\frac{1}{\\epsilon})}\\right)$,\nwhich is strictly better than the class of single-threshold algorithms.\nFinally, we find that the $(1-\\epsilon)$-competition complexity of general\nadaptive algorithms is $\\Theta(\\sqrt{\\ln(\\frac{1}{\\epsilon})})$, which is in\nsharp contrast to $\\Theta(\\ln\\ln(\\frac{1}{\\epsilon}))$ in the i.i.d. case.",
        "split": "arXiv"
    },
    {
        "text": "Scientific Workflow Systems (SWSs) are advanced software frameworks that\ndrive modern research by orchestrating complex computational tasks and managing\nextensive data pipelines. These systems offer a range of essential features,\nincluding modularity, abstraction, interoperability, workflow composition\ntools, resource management, error handling, and comprehensive documentation.\nUtilizing these frameworks accelerates the development of scientific computing,\nresulting in more efficient and reproducible research outcomes. However,\ndeveloping a user-friendly, efficient, and adaptable SWS poses several\nchallenges. This study explores these challenges through an in-depth analysis\nof interactions on Stack Overflow (SO) and GitHub, key platforms where\ndevelopers and researchers discuss and resolve issues. In particular, we\nleverage topic modeling (BERTopic) to understand the topics SWSs developers\ndiscuss on these platforms. We identified 10 topics developers discuss on SO\n(e.g., Workflow Creation and Scheduling, Data Structures and Operations,\nWorkflow Execution) and found that workflow execution is the most challenging.\nBy analyzing GitHub issues, we identified 13 topics (e.g., Errors and Bug\nFixing, Documentation, Dependencies) and discovered that data structures and\noperations is the most difficult. We also found common topics between SO and\nGitHub, such as data structures and operations, task management, and workflow\nscheduling. Additionally, we categorized each topic by type (How, Why, What,\nand Others). We observed that the How type consistently dominates across all\ntopics, indicating a need for procedural guidance among developers. The\ndominance of the How type is also evident in domains like Chatbots and Mobile\ndevelopment. Our study will guide future research in proposing tools and\ntechniques to help the community overcome the challenges developers face when\ndeveloping SWSs.",
        "split": "arXiv"
    },
    {
        "text": "We study the contact geometry of the connected components of the energy\nhypersurface, in the symmetric restricted 3-body problem on $\\mathbb{S}^2$, for\na specific type of motion of the primaries. In particular, we show that these\ncomponents are of contact type for all energies below the first critical value\nand slightly above it. We prove that these components, suitably compactified\nusing a Moser-type regularization are contactomorphic to $R\\mathbb{P}^3$ with\nits unique tight contact structure or to the connected sum of two copies of it,\ndepending on the value of the energy. We exploit Taubes' solution of the\nWeinstein conjecture in dimension three, to infer the existence of periodic\norbits in all these cases.",
        "split": "arXiv"
    },
    {
        "text": "Operating Systems enforce logical isolation using abstractions such as\nprocesses, containers, and isolation technologies to protect a system from\nmalicious or buggy code. In this paper, we show new types of side channels\nthrough the file system that break this logical isolation. The file system\nplays a critical role in the operating system, managing all I/O activities\nbetween the application layer and the physical storage device. We observe that\nthe file system implementation is shared, leading to timing leakage when using\ncommon I/O system calls. Specifically, we found that modern operating systems\ntake advantage of any flush operation (which saves cached blocks in memory to\nthe SSD or disk) to flush all of the I/O buffers, even those used by other\nisolation domains. Thus, by measuring the delay of syncfs, the attacker can\ninfer the I/O behavior of victim programs. We then demonstrate a syncfs covert\nchannel attack on multiple file systems, including both Linux native file\nsystems and the Windows file system, achieving a maximum bandwidth of 5 Kbps\nwith an error rate of 0.15% on Linux and 7.6 Kbps with an error rate of 1.9% on\nWindows. In addition, we construct three side-channel attacks targeting both\nLinux and Android devices. On Linux devices, we implement a website\nfingerprinting attack and a video fingerprinting attack by tracking the write\npatterns of temporary buffering files. On Android devices, we design an\napplication fingerprinting attack that leaks application write patterns during\nboot-up. The attacks achieve over 90% F1 score, precision, and recall. Finally,\nwe demonstrate that these attacks can be exploited across containers\nimplementing a container detection technique and a cross-container covert\nchannel attack.",
        "split": "arXiv"
    },
    {
        "text": "Giant radio galaxies (GRGs), a minority among the extended-jetted population,\nform in a wide range of jet and environmental configurations, complicating the\nidentification of the growth factors that facilitate their attainment of\nmegaparsec scales. This study aims to numerically investigate the hypothesized\nformation mechanisms of GRGs extending $\\gtrsim 1$ Mpc to assess their general\napplicability. We employ triaxial ambient medium settings to generate varying\nlevels of jet frustration and simulate jets with low and high power from\ndifferent locations in the environment, formulating five representations. The\nemergence of distinct giant phases in all five simulated scenarios suggests\nthat GRGs may be more common than previously believed, a prediction to be\nverified with contemporary radio telescopes. We find that different\ncombinations of jet morphology, power, and the evolutionary age of the formed\nstructure hold the potential to elucidate different formation scenarios. The\nsimulated lobes are overpressured, prompting further investigation into\npressure profiles when jet activity ceases, potentially distinguishing between\nrelic and active GRGs. We observed a potential phase transition in giant radio\ngalaxies, marked by differences in lobe expansion speed and pressure variations\ncompared to their smaller evolutionary phases. This suggests the need for\nfurther investigation across a broader parameter space to determine if GRGs\nfundamentally differ from smaller RGs. Axial ratio analysis reveals\nself-similar expansion in rapidly propagating jets, with notable deviations\nwhen the jet forms wider lobes. Overall, this study emphasizes that multiple\ngrowth factors at work can better elucidate the current-day population of GRGs,\nincluding scenarios e.g., growth of GRGs in dense environments, GRGs of several\nmegaparsecs, GRG development in low-powered jets, and the formation of X-shaped\nGRGs.",
        "split": "arXiv"
    },
    {
        "text": "The generation of complex, large-scale code projects using generative AI\nmodels presents challenges due to token limitations, dependency management, and\niterative refinement requirements. This paper introduces the See-Saw generative\nmechanism, a novel methodology for dynamic and recursive code generation. The\nproposed approach alternates between main code updates and dependency\ngeneration to ensure alignment and functionality. By dynamically optimizing\ntoken usage and incorporating key elements of the main code into the generation\nof dependencies, the method enables efficient and scalable code generation for\nprojects requiring hundreds of interdependent files. The mechanism ensures that\nall code components are synchronized and functional, enabling scalable and\nefficient project generation. Experimental validation demonstrates the method's\ncapability to manage dependencies effectively while maintaining coherence and\nminimizing computational overhead.",
        "split": "arXiv"
    },
    {
        "text": "Many computational problems can be modelled as the class of all finite\nrelational structures $\\mathbb A$ that satisfy a fixed first-order sentence\n$\\phi$ hereditarily, i.e., we require that every substructure of $\\mathbb A$\nsatisfies $\\phi$. In this case, we say that the class is in HerFO. The problems\nin HerFO are always in coNP, and sometimes coNP-complete. HerFO also contains\nmany interesting computational problems in P, including many constraint\nsatisfaction problems (CSPs). We show that HerFO captures the class of\ncomplements of CSPs for reducts of finitely bounded structures, i.e., every\nsuch CSP is polynomial-time equivalent to the complement of a problem in HerFO.\nHowever, we also prove that HerFO does not have the full computational power of\ncoNP: there are problems in coNP that are not polynomial-time equivalent to a\nproblem in HerFO, unless E=NE. Another main result is a description of the\nquantifier-prefixes for $\\phi$ such that hereditarily checking $\\phi$ is in P;\nwe show that for every other quantifier-prefix there exists a formula $\\phi$\nwith this prefix such that hereditarily checking $\\phi$ is coNP-complete.",
        "split": "arXiv"
    },
    {
        "text": "Data contamination presents a critical barrier preventing widespread\nindustrial adoption of advanced software engineering techniques that leverage\ncode language models (CLMs). This phenomenon occurs when evaluation data\ninadvertently overlaps with the public code repositories used to train CLMs,\nseverely undermining the credibility of performance evaluations. For software\ncompanies considering the integration of CLM-based techniques into their\ndevelopment pipeline, this uncertainty about true performance metrics poses an\nunacceptable business risk. Code refactoring, which comprises code\nrestructuring and variable renaming, has emerged as a promising measure to\nmitigate data contamination. It provides a practical alternative to the\nresource-intensive process of building contamination-free evaluation datasets,\nwhich would require companies to collect, clean, and label code created after\nthe CLMs' training cutoff dates. However, the lack of automated code\nrefactoring tools and scientifically validated refactoring techniques has\nhampered widespread industrial implementation. To bridge the gap, this paper\npresents the first systematic study to examine the efficacy of code refactoring\noperators at multiple scales (method-level, class-level, and cross-class level)\nand in different programming languages. In particular, we develop an\nopen-sourced toolkit, CODECLEANER, which includes 11 operators for Python, with\nnine method-level, one class-level, and one cross-class-level operator. A drop\nof 65% overlap ratio is found when applying all operators in CODECLEANER,\ndemonstrating their effectiveness in addressing data contamination.\nAdditionally, we migrate four operators to Java, showing their generalizability\nto another language. We make CODECLEANER online available to facilitate further\nstudies on mitigating CLM data contamination.",
        "split": "arXiv"
    },
    {
        "text": "We employ Maximum Likelihood Estimators to examine the Pantheon+ catalogue of\nType Ia supernovae for large scale anisotropies in the expansion rate of the\nUniverse. The analyses are carried out in the heliocentric frame, the CMB\nframe, as well as the Local Group frame. In all frames, the Hubble expansion\nrate in the redshift range 0.023 < z < 0.15 is found to have a statistically\nsignificant dipolar variation exceeding 1.5 km/s/Mpc, i.e. bigger than the\nclaimed 1% uncertainty in the SH0ES measurement of the Hubble parameter H_0.\nThe deceleration parameter too has a redshift-dependent dipolar modulation at\n>5 sigma significance, consistent with previous findings using the SDSSII/SNLS3\nJoint Lightcurve Analysis catalogue. The inferred cosmic acceleration cannot\ntherefore be due to a Cosmological Constant, but is probably an apparent\n(general relativistic) effect due to the anomalous bulk flow in our local\nUniverse.",
        "split": "arXiv"
    },
    {
        "text": "We present a unified controllable video generation approach AnimateAnything\nthat facilitates precise and consistent video manipulation across various\nconditions, including camera trajectories, text prompts, and user motion\nannotations. Specifically, we carefully design a multi-scale control feature\nfusion network to construct a common motion representation for different\nconditions. It explicitly converts all control information into frame-by-frame\noptical flows. Then we incorporate the optical flows as motion priors to guide\nfinal video generation. In addition, to reduce the flickering issues caused by\nlarge-scale motion, we propose a frequency-based stabilization module. It can\nenhance temporal coherence by ensuring the video's frequency domain\nconsistency. Experiments demonstrate that our method outperforms the\nstate-of-the-art approaches. For more details and videos, please refer to the\nwebpage: https://yu-shaonian.github.io/Animate_Anything/.",
        "split": "arXiv"
    },
    {
        "text": "Given the inherent non-stationarity prevalent in real-world applications,\ncontinual Reinforcement Learning (RL) aims to equip the agent with the\ncapability to address a series of sequentially presented decision-making tasks.\nWithin this problem setting, a pivotal challenge revolves around\n\\textit{catastrophic forgetting} issue, wherein the agent is prone to\neffortlessly erode the decisional knowledge associated with past encountered\ntasks when learning the new one. In recent progresses, the \\textit{generative\nreplay} methods have showcased substantial potential by employing generative\nmodels to replay data distribution of past tasks. Compared to storing the data\nfrom past tasks directly, this category of methods circumvents the growing\nstorage overhead and possible data privacy concerns. However, constrained by\nthe expressive capacity of generative models, existing \\textit{generative\nreplay} methods face challenges in faithfully reconstructing the data\ndistribution of past tasks, particularly in scenarios with a myriad of tasks or\nhigh-dimensional data. Inspired by the success of diffusion models in various\ngenerative tasks, this paper introduces a novel continual RL algorithm DISTR\n(Diffusion-based Trajectory Replay) that employs a diffusion model to memorize\nthe high-return trajectory distribution of each encountered task and wakeups\nthese distributions during the policy learning on new tasks. Besides,\nconsidering the impracticality of replaying all past data each time, a\nprioritization mechanism is proposed to prioritize the trajectory replay of\npivotal tasks in our method. Empirical experiments on the popular continual RL\nbenchmark \\texttt{Continual World} demonstrate that our proposed method obtains\na favorable balance between \\textit{stability} and \\textit{plasticity},\nsurpassing various existing continual RL baselines in average success rate.",
        "split": "arXiv"
    },
    {
        "text": "Taking inspiration from [1, 21, 24], we develop a general framework to deal\nwith the model theory of open incidence structures. In this first paper we\nfocus on the study of systems of points and lines (rank $2$). This has a number\nof applications, in particular we show that for any of the following classes\nall the non-degenerate free structures are elementarily equivalent, and their\ncommon theory is decidable, stricly stable, and with no prime model: $(k,\nn)$-Steiner systems (for $2 \\leq k < n$); generalised $n$-gons (for $n \\geq\n3$); $k$-nets (for $k \\geq 3$); affine planes; projective M\\\"obius, Laguerre\nand Minkowski planes.",
        "split": "arXiv"
    },
    {
        "text": "Consider a trade market with one seller and multiple buyers. The seller aims\nto sell an indivisible item and maximize their revenue. This paper focuses on a\nsimple and popular mechanism--the fixed-price mechanism. Unlike the standard\nsetting, we assume there is information asymmetry between buyers and the\nseller. Specifically, we allow the seller to design information before setting\nthe fixed price, which implies that we study the mechanism design problem in a\nbroader space. We call this mechanism space the fixed-price signaling\nmechanism.\n  We assume that buyers' valuation of the item depends on the quality of the\nitem. The seller can privately observe the item's quality, whereas buyers only\nsee its distribution. In this case, the seller can influence buyers' valuations\nby strategically disclosing information about the item's quality, thereby\nadjusting the fixed price. We consider two types of buyers with different\nlevels of rationality: ex-post individual rational (IR) and ex-interim\nindividual rational. We show that when the market has only one buyer, the\noptimal revenue generated by the fixed-price signaling mechanism is identical\nto that of the fixed-price mechanism, regardless of the level of rationality.\nFurthermore, when there are multiple buyers in the market and all of them are\nex-post IR, we show that there is no fixed-price mechanism that is obedient for\nall buyers. However, if all buyers are ex-interim IR, we show that the optimal\nfixed-price signaling mechanism will generate more revenue for the seller than\nthe fixed-price mechanism.",
        "split": "arXiv"
    },
    {
        "text": "We conduct a systematic investigation of the role of Hubbard U corrections in\nelectronic structure calculations of two-dimensional (2D) materials containing\n3d transition metals. Specifically, we use density functional theory (DFT) with\nthe PBE and PBE+U approximations to calculate the crystal structure, band gaps,\nand magnetic parameters of 638 monolayers. Based on a comprehensive comparison\nto experiments we first establish that inclusion of the U correction worsens\nthe accuracy for the lattice constant. Consequently, PBE structures are used\nfor subsequent property evaluations. The band gaps show significant dependence\non the U-parameter. In particular, for 134 (21%) of the materials the U\nparameter leads to a metal-insulator transition. For the magnetic materials we\ncalculate the magnetic moment, magnetic exchange coupling, and magnetic\nanisotropy parameters. In contrast to the band gaps, the size of the magnetic\nmoments shows only weak dependence on U. Both the exchange energies and\nmagnetic anisotropy parameters are systematically reduced by the U correction.\nOn this basis we conclude that the Hubbard U correction will lead to lower\npredicted Curie temperatures in 2D materials. All the calculated properties are\navailable in the Computational 2D Materials Database (C2DB).",
        "split": "arXiv"
    },
    {
        "text": "Cardiovascular magnetic resonance (CMR) imaging is the gold standard for\ndiagnosing several heart diseases due to its non-invasive nature and proper\ncontrast. MR imaging is time-consuming because of signal acquisition and image\nformation issues. Prolonging the imaging process can result in the appearance\nof artefacts in the final image, which can affect the diagnosis. It is possible\nto speed up CMR imaging using image reconstruction based on deep learning. For\nthis purpose, the high-quality clinical interpretable images can be\nreconstructed by acquiring highly undersampled k-space data, that is only\npartially filled, and using a deep learning model. In this study, we proposed a\nstepwise reconstruction approach based on the Patch-GAN structure for highly\nundersampled k-space data compatible with the multi-contrast nature, various\nanatomical views and trajectories of CMR imaging. The proposed approach was\nvalidated using the CMRxRecon2024 challenge dataset and outperformed previous\nstudies. The structural similarity index measure (SSIM) values for the first\nand second tasks of the challenge are 99.07 and 97.99, respectively. This\napproach can accelerate CMR imaging to obtain high-quality images, more\naccurate diagnosis and a pleasant patient experience.",
        "split": "arXiv"
    },
    {
        "text": "We study the following one-dimensional cubic nonlinear Schr\\\"{o}dinger\nsystem: \\[ u_i''+2\\Big(\\sum_{k=1}^Nu_k^2\\Big)u_i=-\\mu_iu_i \\ \\,\\ \\mbox{in}\\, \\\n\\mathbb{R} , \\ \\ i=1, 2, \\cdots, N, \\] where\n$\\mu_1\\leq\\mu_2\\leq\\cdots\\leq\\mu_N<0$ and $N\\ge 2$. In this paper, we mainly\nfocus on the case $N=3$ and prove the following results: (i). The solutions of\nthe system can be completely classified; (ii). Depending on the explicit values\nof $\\mu_1\\leq\\mu_2\\leq\\mu_3<0$, there exist two different classes of normalized\nsolutions $u=(u_1, u_2, u_3)$ satisfying $\\int _{R}u_i^2dx=1$ for all $i=1, 2,\n3$, which are completely different from the case $N=2$; (iii). The linearized\noperator at any nontrivial solution of the system is non-degenerate. The\nconjectures on the explicit classification and nondegeneracy of solutions for\nthe system are also given for the case $N>3$. These address the questions of\n[R. Frank, D. Gontier and M. Lewin, CMP, 2021], where the complete\nclassification and uniqueness results for the system were already proved for\nthe case $N=2$.",
        "split": "arXiv"
    },
    {
        "text": "Chest X-rays (CXRs) often display various diseases with disparate class\nfrequencies, leading to a long-tailed, multi-label data distribution. In\nresponse to this challenge, we explore the Pruned MIMIC-CXR-LT dataset, a\ncurated collection derived from the MIMIC-CXR dataset, specifically designed to\nrepresent a long-tailed and multi-label data scenario. We introduce LTCXNet, a\nnovel framework that integrates the ConvNeXt model, ML-Decoder, and strategic\ndata augmentation, further enhanced by an ensemble approach. We demonstrate\nthat LTCXNet improves the performance of CXR interpretation across all classes,\nespecially enhancing detection in rarer classes like `Pneumoperitoneum' and\n`Pneumomediastinum' by 79\\% and 48\\%, respectively. Beyond performance metrics,\nour research extends into evaluating fairness, highlighting that some methods,\nwhile improving model accuracy, could inadvertently affect fairness across\ndifferent demographic groups negatively. This work contributes to advancing the\nunderstanding and management of long-tailed, multi-label data distributions in\nmedical imaging, paving the way for more equitable and effective diagnostic\ntools.",
        "split": "arXiv"
    },
    {
        "text": "A popular poster from Myanmar lists food pairings that should be avoided,\nsometimes at all costs. Coconut and honey taken together, for example, are\nbelieved to cause nausea, while pork and curdled milk will induce diarrhea.\nWorst of all, according to the poster, many seemingly innocuous combinations\nthat include jelly and coffee, beef and star fruit, or pigeon and pumpkin, are\nlikely to kill the unwary consumer. But why are these innocuous combinations\nconsidered dangerous, even fatal? The answer is relevant, not just to food\nbeliefs, but to social beliefs of many kinds. Here we describe the prevalence\nof food combination superstitions, and an opinion formation model simulating\ntheir emergence and fixation. We find that such food norms are influenced, not\njust by actual risks, but also by strong forces of cultural learning that can\ndrive and lock in arbitrary rules, even in the face of contrary evidence.",
        "split": "arXiv"
    },
    {
        "text": "Various linear complexity models, such as Linear Transformer (LinFormer),\nState Space Model (SSM), and Linear RNN (LinRNN), have been proposed to replace\nthe conventional softmax attention in Transformer structures. However, the\noptimal design of these linear models is still an open question. In this work,\nwe attempt to answer this question by finding the best linear approximation to\nsoftmax attention from a theoretical perspective. We start by unifying existing\nlinear complexity models as the linear attention form and then identify three\nconditions for the optimal linear attention design: 1) Dynamic memory ability;\n2) Static approximation ability; 3) Least parameter approximation. We find that\nnone of the current linear models meet all three conditions, resulting in\nsuboptimal performance. Instead, we propose Meta Linear Attention (MetaLA) as a\nsolution that satisfies these conditions. Our experiments on Multi-Query\nAssociative Recall (MQAR) task, language modeling, image classification, and\nLong-Range Arena (LRA) benchmark demonstrate that MetaLA is more effective than\nthe existing linear models.",
        "split": "arXiv"
    },
    {
        "text": "We developed a shoe-mounted gait monitoring system capable of tracking up to\n17 gait parameters, including gait length, step time, stride velocity, and\nothers. The system employs a stereo camera mounted on one shoe to track a\nmarker placed on the opposite shoe, enabling the estimation of spatial gait\nparameters. Additionally, a Force Sensitive Resistor (FSR) affixed to the heel\nof the shoe, combined with a custom-designed algorithm, is utilized to measure\ntemporal gait parameters. Through testing on multiple participants and\ncomparison with the gait mat, the proposed gait monitoring system exhibited\nnotable performance, with the accuracy of all measured gait parameters\nexceeding 93.61%. The system also demonstrated a low drift of 4.89% during\nlong-distance walking. A gait identification task conducted on participants\nusing a trained Transformer model achieved 95.7% accuracy on the dataset\ncollected by the proposed system, demonstrating that our hardware has the\npotential to collect long-sequence gait data suitable for integration with\ncurrent Large Language Models (LLMs). The system is cost-effective,\nuser-friendly, and well-suited for real-life measurements.",
        "split": "arXiv"
    },
    {
        "text": "Finding ground state solutions of diagonal Hamiltonians is relevant for both\ntheoretical as well as practical problems of interest in many domains such as\nfinance, physics and computer science. These problems are typically very hard\nto tackle by classical computing and quantum computing could help in speeding\nup computations and efficiently tackling larger problems. Here we use imaginary\ntime evolution through a new block encoding scheme to obtain the ground state\nof such problems and apply our method to MaxCut as an illustration. Our method,\nwhich for simplicity we call ITE-BE, requires no variational parameter\noptimization as all the parameters in the procedure are expressed as analytical\nfunctions of the couplings of the Hamiltonian. We demonstrate that our method\ncan be successfully combined with other quantum algorithms such as quantum\napproximate optimization algorithm (QAOA). We find that the QAOA ansatz\nincreases the post-selection success of ITE-BE, and shallow QAOA circuits, when\nboosted with ITE-BE, achieve better performance than deeper QAOA circuits. For\nthe special case of the transverse initial state, we adapt our block encoding\nscheme to allow for a deterministic application of the first layer of the\ncircuit.",
        "split": "arXiv"
    },
    {
        "text": "Gamma-ray bursts (GRBs) are intense pulses of high-energy emission associated\nwith massive stars' death or compact objects' coalescence. Their\nmulti-wavelength observations help verify the reliability of the standard\nfireball model. We analyze 14 GRBs observed contemporaneously in gamma-rays by\nthe \\textit{Fermi} Large Area Telescope (LAT), in X-rays by the \\textit{Swift}\nTelescope, and in the optical bands by \\textit{Swift} and many ground-based\ntelescopes. We study the correlation between the spectral and temporal indices\nusing closure relations according to the synchrotron forward-shock model in the\nstratified medium ($n \\propto r^{-k}$) with $k$ ranging from 0 to 2.5. We find\nthat the model without energy injection is preferred over the one with energy\ninjection in all the investigated wavelengths. In gamma-rays, we only explored\nthe $\\nu > $ max\\{$\\nu_c,\\nu_m$\\} (SC/FC) cooling condition (where $\\nu_c$ and\n$\\nu_m$ are the cooling and characteristic frequencies, namely the frequencies\nat the spectral break). In the X-ray and optical bands, we explored all the\ncooling conditions, including $\\nu_m < \\nu < \\nu_c$ (SC), $\\nu_c < \\nu < \\nu_m$\n(FC), and SC/FC, and found a clear preference for SC for X-rays and SC/FC for\noptical. Within these cooling conditions, X-rays exhibit the highest rate of\noccurrence for the density profile with $k = 0$, while the optical band has the\nhighest occurrence for $k$ = 2.5 when considering no energy injection. Although\nwe can pinpoint a definite environment for some GRBs, we find degeneracies in\nother GRBs.",
        "split": "arXiv"
    },
    {
        "text": "We explore Mahler numbers originating from functions $f(z)$ that satisfy the\nfunctional equation $f(z) = (A(z)f(z^d) + C(z))/B(z)$. A procedure to compute\nthe irrationality exponents of such numbers is developed using continued\nfractions for formal Laurent series, and the form of all such irrationality\nexponents is investigated. This serves to extend Dmitry Badziahin's paper, On\nthe Spectrum of Irrationality Exponents of Mahler Numbers, where he does the\nsame under the condition that $C(z) = 0$. Furthermore, we cover the required\nbackground of continued fractions in detail for unfamiliar readers. This essay\nwas submitted as a thesis in the Pure Mathematics Honours program at the\nUniversity of Sydney.",
        "split": "arXiv"
    },
    {
        "text": "The management of type 1 diabetes has been revolutionized by the artificial\npancreas system (APS), which automates insulin delivery based on continuous\nglucose monitor (CGM). While conventional closed-loop systems rely on CGM data,\nwhich leads to higher energy consumption at the sensors and increased data\nredundancy in the underlying communication network. In contrast, this paper\nproposes a self-triggered control mechanism that can potentially achieve lower\nlatency and energy efficiency. The model for the APS consists of a state and\ninput-constrained dynamical system affected by exogenous meal disturbances. Our\nself-triggered mechanism relies on restricting the state evolution within the\nrobust control invariant of such a system at all times. To that end, using\ntools from reachability, we associate a safe time interval with such invariant\nsets, which denotes the maximum time for which the invariant set remains\ninvariant, even without transmission of CGM data at all times.",
        "split": "arXiv"
    },
    {
        "text": "Image restoration models often face the simultaneous interaction of multiple\ndegradations in real-world scenarios. Existing approaches typically handle\nsingle or composite degradations based on scene descriptors derived from text\nor image embeddings. However, due to the varying proportions of different\ndegradations within an image, these scene descriptors may not accurately\ndifferentiate between degradations, leading to suboptimal restoration in\npractical applications. To address this issue, we propose a novel\nTransformer-based restoration framework, AllRestorer. In AllRestorer, we enable\nthe model to adaptively consider all image impairments, thereby avoiding errors\nfrom scene descriptor misdirection. Specifically, we introduce an All-in-One\nTransformer Block (AiOTB), which adaptively removes all degradations present in\na given image by modeling the relationships between all degradations and the\nimage embedding in latent space. To accurately address different variations\npotentially present within the same type of degradation and minimize ambiguity,\nAiOTB utilizes a composite scene descriptor consisting of both image and text\nembeddings to define the degradation. Furthermore, AiOTB includes an adaptive\nweight for each degradation, allowing for precise control of the restoration\nintensity. By leveraging AiOTB, AllRestorer avoids misdirection caused by\ninaccurate scene descriptors, achieving a 5.00 dB increase in PSNR compared to\nthe baseline on the CDD-11 dataset.",
        "split": "arXiv"
    },
    {
        "text": "The Gutzwiller trace formula establishes a profound connection between the\nquantum spectrum and classical periodic orbits. However, its application is\nlimited by its reliance on the semiclassical saddle point approximation. In\nthis work, we explore the full quantum version of the trace formula using the\nLefschetz thimble method by incorporating complexified periodic orbits. Upon\ncomplexification, classical real periodic orbits are transformed into cycles on\ncompact Riemann surfaces. Our key innovation lies in the simultaneous\ncomplexification of the periods of cycles, resulting in a fully quantum trace\nformula that accounts for all contributions classified by the homology classes\nof the associated Riemann surfaces. This formulation connects the quantum\nspectrum to contributions across all complex time directions, encompassing all\nrelevant homology classes. Our approach naturally unifies and extends two\nestablished methodologies: periodic orbits in real time, as in Gutzwiller's\noriginal work, and quantum tunneling in imaginary time, as in the instanton\nmethod.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we analyze the feature-based knowledge distillation for\nrecommendation from the frequency perspective. By defining knowledge as\ndifferent frequency components of the features, we theoretically demonstrate\nthat regular feature-based knowledge distillation is equivalent to equally\nminimizing losses on all knowledge and further analyze how this equal loss\nweight allocation method leads to important knowledge being overlooked. In\nlight of this, we propose to emphasize important knowledge by redistributing\nknowledge weights. Furthermore, we propose FreqD, a lightweight knowledge\nreweighting method, to avoid the computational cost of calculating losses on\neach knowledge. Extensive experiments demonstrate that FreqD consistently and\nsignificantly outperforms state-of-the-art knowledge distillation methods for\nrecommender systems. Our code is available at\n\\url{https://anonymous.4open.science/r/FreqKD/}",
        "split": "arXiv"
    },
    {
        "text": "Federated learning (FL) is vulnerable to model poisoning attacks due to its\ndistributed nature. The current defenses start from all user gradients (model\nupdates) in each communication round and solve for the optimal aggregation\ngradients (horizontal solution). This horizontal solution will completely fail\nwhen facing large-scale (>50%) model poisoning attacks. In this work, based on\nthe key insight that the convergence process of the model is a highly\npredictable process, we break away from the traditional horizontal solution of\ndefense and innovatively transform the problem of solving the optimal\naggregation gradients into a vertical solution problem. We propose VERT, which\nuses global communication rounds as the vertical axis, trains a predictor using\nhistorical gradients information to predict user gradients, and compares the\nsimilarity with actual user gradients to precisely and efficiently select the\noptimal aggregation gradients. In order to reduce the computational complexity\nof VERT, we design a low dimensional vector projector to project the user\ngradients to a computationally acceptable length, and then perform subsequent\npredictor training and prediction tasks. Exhaustive experiments show that VERT\nis efficient and scalable, exhibiting excellent large-scale (>=80%) model\npoisoning defense effects under different FL scenarios. In addition, we can\ndesign projector with different structures for different model structures to\nadapt to aggregation servers with different computing power.",
        "split": "arXiv"
    },
    {
        "text": "As the research of Multimodal Large Language Models (MLLMs) becomes popular,\nan advancing MLLM model is typically required to handle various textual and\nvisual tasks (e.g., VQA, Detection, OCR, and ChartQA) simultaneously for\nreal-world applications. However, due to the significant differences in\nrepresentation and distribution among data from various tasks, simply mixing\ndata of all tasks together leads to the well-known``multi-task conflict\" issue,\nresulting in performance degradation across various tasks. To address this\nissue, we propose Awaker2.5-VL, a Mixture of Experts~(MoE) architecture\nsuitable for MLLM, which acquires the multi-task capabilities through multiple\nsparsely activated experts. To speed up the training and inference of\nAwaker2.5-VL, each expert in our model is devised as a low-rank adaptation\n(LoRA) structure. Extensive experiments on multiple latest benchmarks\ndemonstrate the effectiveness of Awaker2.5-VL. The code and model weight are\nreleased in our Project Page: https://github.com/MetabrainAGI/Awaker.",
        "split": "arXiv"
    },
    {
        "text": "The discovery of the Dead Sea Scrolls over 60 years ago is widely regarded as\none of the greatest archaeological breakthroughs in modern history. Recent\nstudy of the scrolls presents ongoing computational challenges, including\ndetermining the provenance of fragments, clustering fragments based on their\ndegree of similarity, and pairing fragments that originate from the same\nmanuscript -- all tasks that require focusing on individual letter and fragment\nshapes. This paper presents a computational method for segmenting ink and\nparchment regions in multispectral images of Dead Sea Scroll fragments. Using\nthe newly developed Qumran Segmentation Dataset (QSD) consisting of 20\nfragments, we apply multispectral thresholding to isolate ink and parchment\nregions based on their unique spectral signatures. To refine segmentation\naccuracy, we introduce an energy minimization technique that leverages ink\ncontours, which are more distinguishable from the background and less noisy\nthan inner ink regions. Experimental results demonstrate that this\nMultispectral Thresholding and Energy Minimization (MTEM) method achieves\nsignificant improvements over traditional binarization approaches like Otsu and\nSauvola in parchment segmentation and is successful at delineating ink borders,\nin distinction from holes and background regions.",
        "split": "arXiv"
    },
    {
        "text": "Large Language Models (LLMs) have revolutionized natural language processing\nby unifying tasks into text generation, yet their large parameter sizes and\nautoregressive nature limit inference speed. SAM-Decoding addresses this by\nintroducing a novel retrieval-based speculative decoding method that uses a\nsuffix automaton for efficient and accurate draft generation. Unlike n-gram\nmatching used by the existing method, SAM-Decoding finds the longest suffix\nmatch in generating text and text corpuss, achieving an average time complexity\nof $O(1)$ per generation step. SAM-Decoding constructs static and dynamic\nsuffix automatons for the text corpus and input prompts, respectively, enabling\nfast and precise draft generation. Meanwhile, it is designed as an approach\nthat can be combined with existing methods, allowing SAM-Decoding to adaptively\nselect a draft generation strategy based on the matching length, thus\nincreasing the inference speed of the LLM. When combined with Token Recycling,\nevaluations show SAM-Decoding outperforms existing model-free methods,\nachieving a speedup of $2.27\\times$ over autoregressive decoding on Spec-Bench.\nWhen combined with EAGLE2, it reaches a speedup of $2.49\\times$, surpassing all\ncurrent approaches. Our code is available at\nhttps://github.com/hyx1999/SAM-Decoding.",
        "split": "arXiv"
    },
    {
        "text": "Latency is a major concern for web rendering engines like those in Chrome,\nSafari, and Firefox. These engines reduce latency by using an incremental\nlayout algorithm to redraw the page when the user interacts with it. In such an\nalgorithm, elements that change frame-to-frame are marked dirty; only the dirty\nelements need be processed to draw the next frame, dramatically reducing\nlatency. However, the standard incremental layout algorithm must search the\npage for dirty elements, accessing a number of auxiliary elements in the\nprocess. These auxiliary elements add cache misses and stalled cycles, and are\nresponsible for a sizable fraction of all layout latency. We introduce a new,\nfaster incremental layout algorithm called Spineless Traversal. Spineless\nTraversal uses a more computationally demanding priority queue algorithm to\navoid the need to access auxiliary nodes and thus reduces cache traffic and\nstalls. This leads to dramatic speedups on the most latency-critical\ninteractions such as hovering, typing, or animations. Moreover, thanks to\nnumerous low-level optimizations, we are able to make Spineless Traversal\ncompetitive across the whole spectrum of incremental layout workloads. As a\nresult, across 2216 benchmarks, Spineless Traversal is faster on 78.2% of the\nbenchmark, with a mean speedup of 3.23x concentrated in the most\nlatency-critical interactions such as hovering, typing, and animations.",
        "split": "arXiv"
    },
    {
        "text": "As technology advances, conceptualizations of effective strategies for\nteaching and learning shift. Due in part to their facilitation of unique\naffordances for learning, mobile devices, augmented reality, and games are all\nbecoming more prominent elements in learning environments. In this work, we\nexamine mobile augmented reality serious games (MARSGs) as the intersection of\nthese technology-based experiences and to what effect their combination can\nyield even greater learning outcomes. We present a PRISMA review of 23 papers\n(from 610) spanning the entire literature timeline from 2002 to 2023. Among\nthese works, there is wide variability in the realized application of game\nelements and pedagogical theories underpinning the game experience. For an\neducational tool to be effective, it must be designed to facilitate learning\nwhile anchored by pedagogical theory. Given that most MARSG developers are not\npedagogical experts, this review further provides design considerations\nregarding which game elements might proffer the best of three major pedagogical\ntheories for modern learning (cognitive constructivism, social constructivism,\nand behaviorism) based on existing applications. We will also briefly touch on\nradical constructivism and the instructional elements embedded within MARSGs.\nLastly, this work offers a synthesis of current MARSG findings and extended\nfuture directions for MARSG development.",
        "split": "arXiv"
    },
    {
        "text": "Generalizations of plain strings have been proposed as a compact way to\nrepresent a collection of nearly identical sequences or to express uncertainty\nat specific text positions by enumerating all possibilities. While a plain\nstring stores a character at each of its positions, generalizations consider a\nset of characters (indeterminate strings), a set of strings of equal length\n(generalized degenerate strings, or shortly GD strings), or a set of strings of\narbitrary lengths (elastic-degenerate strings, or shortly ED strings). These\ngeneralizations are of importance to compactly represent such type of data, and\nfind applications in bioinformatics for representing and maintaining a set of\ngenetic sequences of the same taxonomy or a multiple sequence alignment. To be\nof use, attention has been drawn to answering various query types such as\npattern matching or measuring similarity of ED strings by generalizing\ntechniques known to plain strings. However, for some types of queries, it has\nbeen shown that a generalization of a polynomial-time solvable query on classic\nstrings becomes NP-hard on ED strings, e.g. [Russo et al.,2022]. In that light,\nwe wonder about other types of queries, which are of particular interest to\nbioinformatics: the search for the longest repeating factor, unique substrings,\nabsent words, anti-powers, and longest previous factors. While we obtain a\npolynomial time algorithm for the first problem on ED strings, we show that all\nothers are NP-hard to compute, some of them even under the restriction that the\ninput can be modelled as an indeterminate or GD string.",
        "split": "arXiv"
    },
    {
        "text": "The practical applications of Wasserstein distances (WDs) are constrained by\ntheir sample and computational complexities. Sliced-Wasserstein distances\n(SWDs) provide a workaround by projecting distributions onto one-dimensional\nsubspaces, leveraging the more efficient, closed-form WDs for one-dimensional\ndistributions. However, in high dimensions, most random projections become\nuninformative due to the concentration of measure phenomenon. Although several\nSWD variants have been proposed to focus on \\textit{informative} slices, they\noften introduce additional complexity, numerical instability, and compromise\ndesirable theoretical (metric) properties of SWD. Amidst the growing literature\nthat focuses on directly modifying the slicing distribution, which often face\nchallenges, we revisit the classical Sliced-Wasserstein and propose instead to\nrescale the 1D Wasserstein to make all slices equally informative. Importantly,\nwe show that with an appropriate data assumption and notion of \\textit{slice\ninformativeness}, rescaling for all individual slices simplifies to \\textbf{a\nsingle global scaling factor} on the SWD. This, in turn, translates to the\nstandard learning rate search for gradient-based learning in common machine\nlearning workflows. We perform extensive experiments across various machine\nlearning tasks showing that the classical SWD, when properly configured, can\noften match or surpass the performance of more complex variants. We then answer\nthe following question: \"Is Sliced-Wasserstein all you need for common learning\ntasks?\"",
        "split": "arXiv"
    },
    {
        "text": "In wireless communications, efficient image transmission must balance\nreliability, throughput, and latency, especially under dynamic channel\nconditions. This paper presents an adaptive and progressive pipeline for\nlearned image compression (LIC)-based architectures tailored to such\nenvironments. We investigate two state-of-the-art learning-based models: the\nhyperprior model and Vector Quantized Generative Adversarial Network (VQGAN).\nThe hyperprior model achieves superior compression performance through lossless\ncompression in the bottleneck but is susceptible to bit errors, necessitating\nthe use of error correction or retransmission mechanisms. In contrast, the\nVQGAN decoder demonstrates robust image reconstruction capabilities even in the\nabsence of channel coding, enhancing reliability in challenging transmission\nscenarios. We propose progressive versions of both models, enabling partial\nimage transmission and decoding under imperfect channel conditions. This\nprogressive approach not only maintains image integrity under poor channel\nconditions but also significantly reduces latency by allowing immediate partial\nimage availability. We evaluate our pipeline using the Kodak high-resolution\nimage dataset under a Rayleigh fading wireless channel model simulating dynamic\nconditions. The results indicate that the progressive transmission framework\nenhances reliability and latency while maintaining or improving throughput\ncompared to non-progressive counterparts across various Signal-to-Noise Ratio\n(SNR) levels. Specifically, the progressive-hyperprior model consistently\noutperforms others in latency metrics, particularly in the 99.9th percentile\nwaiting time-a measure indicating the maximum waiting time experienced by 99.9%\nof transmission instances-across all SNRs, and achieves higher throughput in\nlow SNR scenarios. where Adaptive WebP fails.",
        "split": "arXiv"
    },
    {
        "text": "Iterative methods such as iterative closest point (ICP) for point cloud\nregistration often suffer from bad local optimality (e.g. saddle points), due\nto the nature of nonconvex optimization. To address this fundamental challenge,\nin this paper we propose learning to form the loss landscape of a deep\niterative method w.r.t. predictions at test time into a convex-like shape\nlocally around each ground truth given data, namely Deep Loss Convexification\n(DLC), thanks to the overparametrization in neural networks. To this end, we\nformulate our learning objective based on adversarial training by manipulating\nthe ground-truth predictions, rather than input data. In particular, we propose\nusing star-convexity, a family of structured nonconvex functions that are\nunimodal on all lines that pass through a global minimizer, as our geometric\nconstraint for reshaping loss landscapes, leading to (1) extra novel hinge\nlosses appended to the original loss and (2) near-optimal predictions. We\ndemonstrate the state-of-the-art performance using DLC with existing network\narchitectures for the tasks of training recurrent neural networks (RNNs), 3D\npoint cloud registration, and multimodel image alignment.",
        "split": "arXiv"
    },
    {
        "text": "The pseudogap state of high-$T_{\\rm c}$ cuprates, known for its partial\ngapping of the Fermi surface above the superconducting transition temperature\n$T_{\\rm c}$, is believed to hold the key to understanding the origin of\nPlanckian relaxation and quantum criticality. However, the nature of the Fermi\nsurface in the pseudogap state has remained a fundamental open question. Here,\nwe report the observation of the Yamaji effect above $T_{\\rm c}$ in the single\nlayer cuprate HgBa$_2$CuO$_{4+\\delta}$. This observation is direct evidence of\nclosed Fermi surface pockets in the normal state of the pseudogap phase. The\nsmall size of the pockets determined from the Yamaji effect (occupying\napproximately $1.3\\%$ of the Brillouin zone area) is all the more surprising\ngiven the absence of evidence for long-range broken translational symmetry that\ncan reconstruct the Fermi-surface.",
        "split": "arXiv"
    },
    {
        "text": "Efforts are needed to identify and measure both communities' exposure to\nclimate hazards and the social vulnerabilities that interact with these\nhazards, but the science of validating hazard vulnerability indicators is still\nin its infancy. Progress is needed to improve: 1) the selection of variables\nthat are used as proxies to represent hazard vulnerability; 2) the\napplicability and scale for which these indicators are intended, including\ntheir transnational applicability. We administered an international urban\nsurvey in Buenos Aires, Argentina; Johannesburg, South Africa; London, United\nKingdom; New York City, United States; and Seoul, South Korea in order to\ncollect data on exposure to various types of extreme weather events,\nsocioeconomic characteristics commonly used as proxies for vulnerability (i.e.,\nincome, education level, gender, and age), and additional characteristics not\noften included in existing composite indices (i.e., queer identity, disability\nidentity, non-dominant primary language, and self-perceptions of both\ndiscrimination and vulnerability to flood risk). We then use feature importance\nanalysis with gradient-boosted decision trees to measure the importance that\nthese variables have in predicting exposure to various types of extreme weather\nevents. Our results show that non-traditional variables were more relevant to\nself-reported exposure to extreme weather events than traditionally employed\nvariables such as income or age. Furthermore, differences in variable relevance\nacross different types of hazards and across urban contexts suggest that\nvulnerability indicators need to be fit to context and should not be used in a\none-size-fits-all fashion.",
        "split": "arXiv"
    },
    {
        "text": "Pressure injury (PI) detection is challenging, especially in dark skin tones,\ndue to the unreliability of visual inspection. Thermography has been suggested\nas a viable alternative as temperature differences in the skin can indicate\nimpending tissue damage. Although deep learning models have demonstrated\nconsiderable promise toward reliably detecting PI, the existing work fails to\nevaluate the performance on darker skin tones and varying data collection\nprotocols. In this paper, we introduce a new thermal and optical imaging\ndataset of 35 participants focused on darker skin tones where temperature\ndifferences are induced through cooling and cupping protocols. We vary the\nimage collection process to include different cameras, lighting, patient pose,\nand camera distance. We compare the performance of a small convolutional neural\nnetwork (CNN) trained on either the thermal or the optical images on all skin\ntones. Our preliminary results suggest that thermography-based CNN is robust to\ndata collection protocols for all skin tones.",
        "split": "arXiv"
    },
    {
        "text": "We address the issue of the exploding computational requirements of recent\nState-of-the-art (SOTA) open set multimodel 3D mapping (dense 3D mapping)\nalgorithms and present Voxel-Aggregated Feature Synthesis (VAFS), a novel\napproach to dense 3D mapping in simulation. Dense 3D mapping involves\nsegmenting and embedding sequential RGBD frames which are then fused into 3D.\nThis leads to redundant computation as the differences between frames are small\nbut all are individually segmented and embedded. This makes dense 3D mapping\nimpractical for research involving embodied agents in which the environment,\nand thus the mapping, must be modified with regularity. VAFS drastically\nreduces this computation by using the segmented point cloud computed by a\nsimulator's physics engine and synthesizing views of each region. This reduces\nthe number of features to embed from the number of captured RGBD frames to the\nnumber of objects in the scene, effectively allowing a \"ground truth\" semantic\nmap to be computed an order of magnitude faster than traditional methods. We\ntest the resulting representation by assessing the IoU scores of semantic\nqueries for different objects in the simulated scene, and find that VAFS\nexceeds the accuracy and speed of prior dense 3D mapping techniques.",
        "split": "arXiv"
    },
    {
        "text": "Resilient divertor features connected to open chaotic edge structures in the\nHelically Symmetric Experiment (HSX) are investigated. For the first time, an\nexpanded vessel wall was considered that would give space for implementation of\na physical divertor target structure. The analysis was done for four different\nmagnetic configurations with very different chaotic plasma edges. A resilient\nplasma wall interaction pattern was identified across all configurations. This\nmanifests as qualitatively very similar footprint behavior across the different\nplasma equilibria. Overall, the resilient field lines of interest with high\nconnection length $L_C$ lie within a helical band along the wall for all\nconfigurations. This resiliency can be used to identify the best location of a\ndivertor. The details of the magnetic footprint's resilient helical band is\nsubject to specific field line structures which are linked to the penetration\ndepth of field lines into the plasma and directly influence the heat and\nparticle flux patterns. The differences arising from these details are\ncharacterized by introducing a new metric, the minimum radial connection\n$\\text{min}(\\delta_N)$ of a field line from the last closed flux surface. The\nrelationship, namely the deviation from a scaling law, between\n$\\text{min}(\\delta_N)$ and $L_C$ of the field lines in the plasma edge field\nline behavior suggests that the field lines are associated with structures such\nas resonant islands, cantori, and turnstiles. This helps determine the relevant\nmagnetic flux channels based on the radial location of these chaotic edge\nstructures and the divertor target footprint. These details will need to be\ntaken into account for resilient divertor design.",
        "split": "arXiv"
    },
    {
        "text": "In this work we establish under certain hypotheses the $N \\to +\\infty$\nasymptotic expansion of integrals of the form $$\\mathcal{Z}_{N,\\Gamma}[V] \\, =\n\\, \\int_{\\Gamma^N} \\prod_{ a < b}^{N}(z_a - z_b)^\\beta \\, \\prod_{k=1}^{N}\n\\mathrm{e}^{ - N \\beta V(z_k) } \\, \\mathrm{d}\\mathbf{z}$$ where $V \\in\n\\mathbb{C}[X]$, $\\beta \\in 2 \\mathbb{N}^*$ is an even integer and $\\Gamma\n\\subset \\mathbb{C}$ is an unbounded contour such that the integral converges.\nFor even degree, real valued $V$s and when $\\Gamma = \\mathbb{R}$, it is well\nknown that the large-$N$ expansion is characterised by an equilibrium measure\ncorresponding to the minimiser of an appropriate energy functional. This method\nbears a structural resemblance with the Laplace method. By contrast, in the\ncomplex valued setting we are considering, the analysis structurally resembles\nthe classical steepest-descent method, and involves finding a critical point\n\\textit{and} a steepest descent curve, the latter being a deformation of the\noriginal integration contour. More precisely, one minimises a curve-dependent\nenergy functional with respect to measures on the curve and then maximises the\nenergy over an appropriate space of curves. Our analysis deals with the one-cut\nregime of the associated equilibrium measure. We establish the existence of an\nall order asymptotic expansion for $\\ln \\mathcal{Z}_{N,\\Gamma}[V]$ and\nexplicitly identify the first few terms.",
        "split": "arXiv"
    },
    {
        "text": "We investigate the dynamic behavior of spin reversal events in the dilute\nIsing model, focusing on the influence of static disorder introduced by pinned\nspins. Our Monte Carlo simulations reveal that in a homogeneous, defect-free\nsystem, the inter-event time (IET) between local spin flips follows an\nexponential distribution, characteristic of Poissonian processes. However, in\nheterogeneous systems where defects are present, we observe a significant\ndeparture from this behavior. At high temperatures, the IET exhibits a\npower-law distribution resulting from the interplay of spins located in varying\npotential environments, where defect density influences reversal probabilities.\nAt low temperatures, all site classes converge to a unique power-law\ndistribution, regardless of their potential, leading to distinct critical\nexponents for the high- and low-temperature regimes. This transition from\nexponential to power-law behavior underscores the critical response features of\nmagnetic systems with defects, suggesting analogies to glassy dynamics. Our\nfindings highlight the complex mechanisms governing spin dynamics in disordered\nsystems, with implications for understanding the universal aspects of\nrelaxation in glassy materials.",
        "split": "arXiv"
    },
    {
        "text": "Federated Learning (FL) enables collaborative, personalized model training\nacross multiple devices without sharing raw data, making it ideal for pervasive\ncomputing applications that optimize user-centric performances in diverse\nenvironments. However, data heterogeneity among clients poses a significant\nchallenge, leading to inconsistencies among trained client models and reduced\nperformance. To address this, we introduce the Alignment with Prototypes (ALP)\nlayers, which align incoming embeddings closer to learnable prototypes through\nan optimal transport plan. During local training, the ALP layer updates local\nprototypes and aligns embeddings toward global prototypes aggregated from all\nclients using our novel FL framework, Federated Alignment (FedAli). For model\ninferences, embeddings are guided toward local prototypes to better reflect the\nclient's local data distribution. We evaluate FedAli on heterogeneous\nsensor-based human activity recognition and vision benchmark datasets,\ndemonstrating that it outperforms existing FL strategies. We publicly release\nour source code to facilitate reproducibility and furthered research.",
        "split": "arXiv"
    },
    {
        "text": "Price forecasting for used construction equipment is a challenging task due\nto spatial and temporal price fluctuations. It is thus of high interest to\nautomate the forecasting process based on current market data. Even though\napplying machine learning (ML) to these data represents a promising approach to\npredict the residual value of certain tools, it is hard to implement for small\nand medium-sized enterprises due to their insufficient ML expertise. To this\nend, we demonstrate the possibility of substituting manually created ML\npipelines with automated machine learning (AutoML) solutions, which\nautomatically generate the underlying pipelines. We combine AutoML methods with\nthe domain knowledge of the companies. Based on the CRISP-DM process, we split\nthe manual ML pipeline into a machine learning and non-machine learning part.\nTo take all complex industrial requirements into account and to demonstrate the\napplicability of our new approach, we designed a novel metric named method\nevaluation score, which incorporates the most important technical and\nnon-technical metrics for quality and usability. Based on this metric, we show\nin a case study for the industrial use case of price forecasting, that domain\nknowledge combined with AutoML can weaken the dependence on ML experts for\ninnovative small and medium-sized enterprises which are interested in\nconducting such solutions.",
        "split": "arXiv"
    },
    {
        "text": "Rocky planets in our Solar System, namely Mercury, Venus, Earth, Mars, and\nthe Moon, which is generally added to this group due to its geological\ncomplexity, possess a solid surface and share a common structure divided into\nmajor layers, namely a silicate crust, a silicate mantle, and an iron-rich\ncore. However, while all terrestrial planets share a common structure, the\nthickness of their interior layers, their bulk chemical composition, and\nsurface expressions of geological processes are often unique to each of them.\nIn this chapter we provide an overview of the surfaces and interiors of rocky\nplanets in the Solar System. We list some of the major discoveries in planetary\nexploration and discuss how they have helped to answer fundamental questions\nabout planetary evolution while at the same time opening new avenues. For each\nof the major planetary layers, i.e., the surface, the crust and lithosphere,\nthe mantle, and the core, we review key geological and geophysical processes\nthat have shaped the planets that we observe today. Understanding the\nsimilarities and differences between the terrestrial planets in the Solar\nSystem will teach us about the diversity of evolutionary paths a planet could\nfollow, helping us to better understand our own home, the Earth.",
        "split": "arXiv"
    },
    {
        "text": "We prove that for $1\\le p,q\\le\\infty$ the mixed-norm spaces $L_q(L_p)$ are\nmutually non-isomorphic, with the only exception that $L_q(L_2)$ is isomorphic\nto $L_q(L_q)$ for all $1<q<\\infty$.",
        "split": "arXiv"
    },
    {
        "text": "Identifying predictive features from high-dimensional datasets is a major\ntask in biomedical research. However, it is difficult to determine the\nrobustness of selected features. Here, we investigate the performance of\nrandomly chosen features, what we term \"random feature baselines\" (RFBs), in\nthe context of disease risk prediction from blood plasma proteomics data in the\nUK Biobank. We begin with two published case studies predicting diagnosis of\n(1) dementia and (2) hip fracture. RFBs perform similarly to published features\nof interest (using the same number of proteins, but randomly chosen). We then\nmeasure the performance of RFBs for all 607 disease outcomes in the UK Biobank,\nwith various numbers of randomly chosen features, as well as all proteins in\nthe dataset. 114/607 outcomes showed a higher mean AUROC when choosing 5 random\nfeatures than using all proteins, and the absolute difference in mean AUC was\n0.075. 163 outcomes showed a higher mean AUROC when choosing 1000 random\nfeatures than using all proteins, and the absolute difference in mean AUC was\n0.03. Incorporating RFBs should become part of ML practice when feature\nselection or target discovery is a goal.",
        "split": "arXiv"
    },
    {
        "text": "In computer vision tasks, the ability to focus on relevant regions within an\nimage is crucial for improving model performance, particularly when key\nfeatures are small, subtle, or spatially dispersed. Convolutional neural\nnetworks (CNNs) typically treat all regions of an image equally, which can lead\nto inefficient feature extraction. To address this challenge, I have introduced\nVision Eagle Attention, a novel attention mechanism that enhances visual\nfeature extraction using convolutional spatial attention. The model applies\nconvolution to capture local spatial features and generates an attention map\nthat selectively emphasizes the most informative regions of the image. This\nattention mechanism enables the model to focus on discriminative features while\nsuppressing irrelevant background information. I have integrated Vision Eagle\nAttention into a lightweight ResNet-18 architecture, demonstrating that this\ncombination results in an efficient and powerful model. I have evaluated the\nperformance of the proposed model on three widely used benchmark datasets:\nFashionMNIST, Intel Image Classification, and OracleMNIST, with a primary focus\non image classification. Experimental results show that the proposed approach\nimproves classification accuracy. Additionally, this method has the potential\nto be extended to other vision tasks, such as object detection, segmentation,\nand visual tracking, offering a computationally efficient solution for a wide\nrange of vision-based applications. Code is available at:\nhttps://github.com/MahmudulHasan11085/Vision-Eagle-Attention.git",
        "split": "arXiv"
    },
    {
        "text": "This paper introduces a large-scale multi-modal dataset captured in and\naround well-known landmarks in Oxford using a custom-built multi-sensor\nperception unit as well as a millimetre-accurate map from a Terrestrial LiDAR\nScanner (TLS). The perception unit includes three synchronised global shutter\ncolour cameras, an automotive 3D LiDAR scanner, and an inertial sensor - all\nprecisely calibrated. We also establish benchmarks for tasks involving\nlocalisation, reconstruction, and novel-view synthesis, which enable the\nevaluation of Simultaneous Localisation and Mapping (SLAM) methods,\nStructure-from-Motion (SfM) and Multi-view Stereo (MVS) methods as well as\nradiance field methods such as Neural Radiance Fields (NeRF) and 3D Gaussian\nSplatting. To evaluate 3D reconstruction the TLS 3D models are used as ground\ntruth. Localisation ground truth is computed by registering the mobile LiDAR\nscans to the TLS 3D models. Radiance field methods are evaluated not only with\nposes sampled from the input trajectory, but also from viewpoints that are from\ntrajectories which are distant from the training poses. Our evaluation\ndemonstrates a key limitation of state-of-the-art radiance field methods: we\nshow that they tend to overfit to the training poses/images and do not\ngeneralise well to out-of-sequence poses. They also underperform in 3D\nreconstruction compared to MVS systems using the same visual inputs. Our\ndataset and benchmarks are intended to facilitate better integration of\nradiance field methods and SLAM systems. The raw and processed data, along with\nsoftware for parsing and evaluation, can be accessed at\nhttps://dynamic.robots.ox.ac.uk/datasets/oxford-spires/.",
        "split": "arXiv"
    },
    {
        "text": "We study the binary discrimination problem of identification of boosted $H\\to\ngg$ decays from massive QCD jets in a systematic expansion in the strong\ncoupling. Though this decay mode of the Higgs is unlikely to be discovered at\nthe LHC, we analytically demonstrate several features of the likelihood ratio\nfor this problem through explicit analysis of signal and background matrix\nelements. Through leading-order, we prove that by imposing a constraint on the\njet mass and measuring the energy fraction of the softer subjet an improvement\nof signal to background ratio that is independent of the kinematics of the jets\nat high boosts can be obtained, and is approximately equal to the inverse of\nthe strong coupling evaluated at the Higgs mass. At next-to-leading order, we\nconstruct a powerful discrimination observable through a sort of anomaly\ndetection approach by simply inverting the next-to-leading order $H\\to gg$\nmatrix element with soft gluon emission, which is naturally infrared and\ncollinear safe. Our analytic conclusions are validated in simulated data from\nall-purpose event generators and subsequent parton showering and demonstrate\nthat the signal-to-background ratio can be improved by a factor of several\nhundred at high, but accessible, jet energies at the LHC.",
        "split": "arXiv"
    },
    {
        "text": "We present a detailed analysis of EELG1002: a $z = 0.8275$ EELG identified\nwithin archival Gemini/GMOS spectroscopy as part of the COSMOS Spectroscopic\nArchive. Combining GMOS spectra and available multi-wavelength photometry, we\nfind EELG1002 is a low-mass ($10^{7 - 8}$ M$_\\odot$), compact ($\\sim 530$ pc),\nand bursty star-forming galaxy with mass doubling timescales of $\\sim 5 - 15$\nMyr. EELG1002 has record-breaking rest-frame [OIII]+H$\\beta$ EW of $\\sim 2800 -\n3700$\\AA~which is $\\sim 16 - 35 \\times$ higher than typical $z \\sim 0.8$ [OIII]\nemitters with similar stellar mass and even higher than typical $z > 5$\ngalaxies. We find no clear evidence of an AGN suggesting the emission lines are\nstar formation driven. EELG1002 is chemically unevolved (direct $T_e$;\n$12+\\log_{10} (\\textrm{O/H}) \\sim 7.5$ consistent with $z > 5$ galaxies at\nfixed stellar mass) and may be undergoing a first intense, bursty star\nformation phase analogous to conditions expected of galaxies in the early\nUniverse. We find evidence for a highly energetic ISM ([OIII]/[OII] $\\sim 11$)\nand hard ionizing radiation field (elevated [NeIII]/[OII] at fixed\n[OIII]/[OII]). Coupled with its compact, metal-poor, and actively star-forming\nnature, EELG1002 is found to efficiently produce ionizing photons with\n$\\xi_{ion} \\sim 10^{25.70 - 25.75}$ erg$^{-1}$ Hz and may have $\\sim 10 - 20\\%$\nLyC escape fraction suggesting such sources may be important reionization-era\nanalogs. We find dynamical mass of $\\sim 10^9$ M$_\\odot$ suggesting copious\namounts of gas to support intense star-formation activity as also suggested by\nanalogs identified in Illustris-TNG. EELG1002 may be an ideal low-$z$\nlaboratory of galaxies in the early Universe and demonstrates how archival\ndatasets can support high-$z$ science and next-generation surveys planned with\n\\textit{Euclid} and \\textit{Roman}.",
        "split": "arXiv"
    },
    {
        "text": "Nearly all cool, evolved stars are solar-like oscillators, and fundamental\nstellar properties can be inferred from these oscillations with\nasteroseismology. Scaling relations are commonly used to relate global\nasteroseismic properties, the frequency of maximum power $\\nu_{max}$ and the\nlarge frequency separation $\\Delta \\nu$, to stellar properties. Mass, radius,\nand age can then be inferred with the addition of stellar spectroscopy. There\nis excellent agreement between seismic radii and fundamental data on the lower\nred giant branch and red clump. However, the scaling relations appear to\nbreakdown in luminous red giant stars. We attempt to constrain the\ncontributions of the asteroseismic parameters to the observed breakdown. We\ntest the $\\nu_{max}$ and $\\Delta \\nu$ scaling relations separately, by using\nstars of known mass and radius in star clusters and the Milky Way's\nhigh-$\\alpha$ sequence. We find evidence that the $\\Delta \\nu$-scaling relation\ncontributes to the observed breakdown in luminous giants more than the\n$\\nu_{max}$ relation. We test different methods of mapping the observed $\\Delta\n\\nu$ to the mean density via a correction factor, $F_{\\Delta \\nu}$ and find a\n$\\approx 1 - 3\\%$ difference in the radii in the luminous giant regime\ndepending on the technique used to measure $F_{\\Delta \\nu}$. The differences\nbetween the radii inferred by these two techniques are too small on the\nluminous giant branch to account for the inflated seismic radii observed in\nevolved giant stars. Finally, we find that the $F_{\\Delta \\nu}$ correction is\ninsensitive to the adopted mixing length, chosen by calibrating the models to\nobservations of $T_{eff}$.",
        "split": "arXiv"
    },
    {
        "text": "We explore the possibility that exotic forms of dark matter could expose\nhumans on Earth or on prolonged space travel to a significant radiation dose.\nThe radiation exposure from dark matter interacting with nuclei in the human\nbody is generally assumed to be negligible compared to other sources of\nbackground radiation. However, as we discuss here, current data allow for dark\nmatter models where this is not necessarily true. In particular, if dark matter\nis heavier and more strongly interacting than weakly interacting massive\nparticle dark matter, it could act as ionizing radiation and deposit a\nsignificant amount of radiation energy in all or part of the human population,\nsimilar to or even exceeding the known radiation exposure from other background\nsources. Conversely, the non-observation of such an exposure can be used to\nconstrain this type of heavier and more strongly interacting dark matter. We\nfirst consider the case where dark matter scatters elastically and identify the\nrelevant parameter space in a model-independent way. We also discuss how\nprevious bounds from cosmological probes, as well as atmospheric and\nspace-based detectors, might be avoided, and how a re-analysis of existing\nradiation data, along with a simple experiment monitoring ionizing radiation in\nspace with a lower detection threshold, could help constrain part of this\nparameter space. We finally propose a hypothetical dark matter candidate that\nscatters inelastically and argue that, in principle, one per mille of the\nEarth's population could attain a significant radiation dose from such a dark\nmatter exposure in their lifetime.",
        "split": "arXiv"
    },
    {
        "text": "Quenching of star-formation plays a fundamental role in galaxy evolution.\nThis process occurs due to the removal of the cold interstellar medium (ISM) or\nstabilization against collapse, so that gas cannot be used in the formation of\nnew stars. In this paper, we study the effect of different mechanisms of ISM\nremoval. In particular, we revised the well-known Baldwin-Philips-Terlevich\n(BPT) and $\\mathrm{EW_{H\\alpha}}$ vs. $\\mathrm{[NII]/H\\alpha}$ (WHAN)\nemission-line ratio diagnostics, so that we could classify all galaxies, even\nthose not detected at some emission lines, introducing several new spectral\nclasses. We use spectroscopic data and several physical parameters of 2409\ndusty early-type galaxies in order to find out the dominant ionization source\n[active galactic nuclei (AGNs), young massive stars, hot low-mass evolved stars\n(HOLMES)] and its effect on the ISM. We find that strong AGNs can play a\nsignificant role in the ISM removal process only for galaxies with ages lower\nthan $10^{9.4}$ yr, but we cannot rule out the influence of weak AGNs at any\nage. For older galaxies, HOLMES/planetary nebulae contribute significantly to\nthe ISM removal process. Additionally, we provide the BPT and WHAN\nclassifications not only for the selected sample but also for all 300000\ngalaxies in the GAMA fields.",
        "split": "arXiv"
    },
    {
        "text": "The paper is concerned with a scalar conservation law with discontinuous\ngradient-dependent flux. Namely, the flux is described by two different\nfunctions $f(u)$ or $g(u)$, when the gradient $u_x$ of the solution is positive\nor negative, respectively. We study here the stable case where $f(u)<g(u)$ for\nall $u\\in {\\mathbb R}$, with $f,g$ smooth but possibly not convex. A front\ntracking algorithm is introduced, proving that piecewise constant\napproximations converge to the trajectories of a contractive semigroup on\n$\\mathbf{L}^1({\\mathbb R})$. In the spatially periodic case, we prove that\nsemigroup trajectories coincide with the unique limits of a suitable class of\nvanishing viscosity approximations.",
        "split": "arXiv"
    },
    {
        "text": "Single-photon emission from a two-level system offers promising perspectives\nfor the development of quantum technologies, where multiphotons are generally\nregarded as accidental, undesired and should be suppressed. In quantum\nmechanics, however, multiphoton emission can turn out to be even more\nfundamental and interesting than the single-photon emission, since in a\ncoherently driven system, the multiphoton suppression arises from quantum\ninterferences between virtual multiphoton fluctuations and the mean field in a\nPoisson superposition of all number states. Here, we demonstrate how one can\ncontrol the multiphoton dynamics of a two-level system by disrupting these\nquantum interferences through a precise and independent homodyne control of the\nmean field. We show that, counterintuitively, quantum fluctuations always play\na major qualitative role, even and in fact especially, when their quantitative\ncontribution is vanishing as compared to that of the mean field. Our findings\nprovide new insights into the paradoxical character of quantum mechanics and\nopen pathways for mean-field engineering as a tool for precision multiphoton\ncontrol.",
        "split": "arXiv"
    },
    {
        "text": "Applied macroeconomists frequently use impulse response estimators motivated\nby linear models. We study whether the estimands of such procedures have a\ncausal interpretation when the true data generating process is in fact\nnonlinear. We show that vector autoregressions and linear local projections\nonto observed shocks or proxies identify weighted averages of causal effects\nregardless of the extent of nonlinearities. By contrast, identification\napproaches that exploit heteroskedasticity or non-Gaussianity of latent shocks\nare highly sensitive to departures from linearity. Our analysis is based on new\nresults on the identification of marginal treatment effects through weighted\nregressions, which may also be of interest to researchers outside\nmacroeconomics.",
        "split": "arXiv"
    },
    {
        "text": "We present an approach that can be utilized in order to account for the\ncovariate shift between two datasets of the same observable with different\ndistributions, so as to improve the generalizability of a neural network model\ntrained on in-distribution samples (IDs) when inferring cosmology at the field\nlevel on out-of-distribution samples (OODs) of {\\it unknown labels}. We make\nuse of HI maps from the two simulation suites in CAMELS, IllustrisTNG and\nSIMBA. We consider two different techniques, namely adversarial approach and\noptimal transport, to adapt a target network whose initial weights are those of\na source network pre-trained on a labeled dataset. Results show that after\nadaptation, salient features that are extracted by source and target encoders\nare well aligned in the embedding space, indicating that the target encoder has\nlearned the representations of the target domain via the adversarial training\nand optimal transport. Furthermore, in all scenarios considered in our\nanalyses, the target encoder, which does not have access to any labels\n($\\Omega_{\\rm m}$) during adaptation phase, is able to retrieve the underlying\n$\\Omega_{\\rm m}$ from out-of-distribution maps to a great accuracy of $R^{2}$\nscore $\\ge$ 0.9, comparable to the performance of the source encoder trained in\na supervised learning setup. We further test the viability of the techniques\nwhen only a few out-of-distribution instances are available and find that the\ntarget encoder still reasonably recovers the matter density. Our approach is\ncritical in extracting information from upcoming large scale surveys.",
        "split": "arXiv"
    },
    {
        "text": "Galaxies at redshift $z\\sim 1-2$ display high star formation rates (SFRs)\nwith elevated cold gas fractions and column densities. Simulating a\nself-regulated ISM in a hydrodynamical, self-consistent context, has proven\nchallenging due to strong outflows triggered by supernova (SN) feedback. At\nsufficiently high gas column densities, and in the absence of magnetic fields,\nthese outflows prevent a quasi-steady disk from forming at all. To this end, we\npresent GHOSDT, a suite of magneto-hydrodynamical simulations that implement\nISM physics at high resolution. We demonstrate the importance of magnetic\npressure in the stabilization of gas-rich star-forming disks. We show that a\nrelation between the magnetic field and gas surface density emerges naturally\nfrom our simulations. We argue that the magnetic field in the dense,\nstar-forming gas, may be set by the SN-driven turbulent gas motions. When\ncompared to pure hydrodynamical runs, we find that the inclusion of magnetic\nfields increases the cold gas fraction and reduces the disc scale height, both\nby up to a factor of $\\sim 2$, and reduces the star formation burstiness. In\ndense ($n>100\\;\\rm{cm}^{-3}$) gas, we find steady-state magnetic field\nstrengths of 10--40 $\\mu$G, comparable to those observed in molecular clouds.\nFinally, we demonstrate that our simulation framework is consistent with the\nOstriker & Kim (2022) Pressure Regulated Feedback Modulated Theory of star\nformation and stellar Feedback.",
        "split": "arXiv"
    },
    {
        "text": "We study vertical resonant trapping and resonant heating of orbits. These two\nprocesses both lead to the growth of a boxy/peanut-shaped bulge in a typical\n$N$-body model. For the first time, we study this by means of the action\nvariables and resonant angles of the actual orbits that compose the model\nitself. We used the resonant angle instead of the frequency ratio, which\nallowed us to clearly distinguish between these two processes in numerical\nsimulations. We show that trapping and heating occur simultaneously, at least\nat the stage of a mature bar, that is, some orbits quickly pass through\nvertical resonance while at the same time, a substantial number of orbits\nremains trapped into this stage for a long time. Half of all bar orbits spend\nmore than 2.5 Gyr in vertical resonance over an interval of 4 Gyr. Half of the\norbits trapped into the bar over the last 3 Gyr of simulation remain captured\nin vertical resonance for more than 2 Gyr. We conclude that in the later stages\nof the bar evolution, the process of vertical trapping dominates in the ongoing\nprocess that causes the boxy/peanut shape of a bar in a typical $N$-body model.\nThis contradicts the results of several recent works.",
        "split": "arXiv"
    },
    {
        "text": "Internal crack detection has been a subject of focus in structural health\nmonitoring. By focusing on crack detection in structural datasets, it is\ndemonstrated that deep learning (DL) methods can effectively analyze seismic\nwave fields interacting with micro-scale cracks, which are beyond the\nresolution of conventional visual inspection. This work explores a novel\napplication of DL-based key point detection technique, where cracks are\nlocalized by predicting the coordinates of four key points that define a\nbounding region of the crack. The study not only opens new research directions\nfor non-visual applications but also effectively mitigates the impact of\nimbalanced data which poses a challenge for previous DL models, as it can be\nbiased toward predicting the majority class (non-crack regions). Popular DL\ntechniques, such as the Inception blocks, are used and investigated. The model\nshows an overall reduction in loss when applied to micro-scale crack detection\nand is reflected in the lower average deviation between the location of actual\nand predicted cracks, with an average Intersection over Union (IoU) being 0.511\nfor all micro cracks (greater than 0.00 micrometers) and 0.631 for larger micro\ncracks (greater than 4 micrometers).",
        "split": "arXiv"
    },
    {
        "text": "Studies investigating the causal effects of spatially varying exposures on\nhealth$\\unicode{x2013}$such as air pollution, green space, or\ncrime$\\unicode{x2013}$often rely on observational and spatially indexed data. A\nprevalent challenge is unmeasured spatial confounding, where an unobserved\nspatially varying variable affects both exposure and outcome, leading to biased\ncausal estimates and invalid confidence intervals. In this paper, we introduce\na general framework based on instrumental variables (IV) that encompasses and\nunites most of the existing methods designed to account for an unmeasured\nspatial confounder. We show that a common feature of all existing methods is\ntheir reliance on small-scale variation in exposure, which functions as an IV.\nIn this framework, we outline the underlying assumptions and the estimation\nstrategy of each method. Furthermore, we demonstrate that the IV can be used to\nidentify and estimate the exposure-response curve under more relaxed\nassumptions. We conclude by estimating the exposure-response curve between\nlong-term exposure to fine particulate matter and all-cause mortality among\n33,454 zip codes in the United States while adjusting for unmeasured spatial\nconfounding.",
        "split": "arXiv"
    },
    {
        "text": "Different methods can be employed to render virtual reverberation, often\nrequiring substantial information about the room's geometry and the acoustic\ncharacteristics of the surfaces. However, fully comprehensive approaches that\naccount for all aspects of a given environment may be computationally costly\nand redundant from a perceptual standpoint. For these methods, achieving a\ntrade-off between perceptual authenticity and model's complexity becomes a\nrelevant challenge.\n  This study investigates this compromise through the use of geometrical\nacoustics to render Ambisonics-based binaural reverberation. Its precision is\ndetermined, among other factors, by its fidelity to the room's geometry and to\nthe acoustic properties of its materials.\n  The purpose of this study is to investigate the impact of simplifying the\nroom geometry and the frequency resolution of absorption coefficients on the\nperception of reverberation within a virtual sound scene. Several decimated\nmodels based on a single room were perceptually evaluated using the a\nmulti-stimulus comparison method. Additionally, these differences were\nnumerically assessed through the calculation of acoustic parameters of the\nreverberation.\n  According to numerical and perceptual evaluations, lowering the frequency\nresolution of absorption coefficients can have a significant impact on the\nperception of reverberation, while a less notable impact was observed when\ndecimating the geometry of the model.",
        "split": "arXiv"
    },
    {
        "text": "Simulations and observations suggest that galaxy interactions may enhance the\nstar formation rate (SFR) in merging galaxies. One proposed mechanism is the\ntorque exerted on the gas and stars in the larger galaxy by the smaller galaxy.\nWe analyze the interaction torques and star formation activity on six galaxies\nfrom the FIRE-2 simulation suite with masses comparable to the Milky Way galaxy\nat redshift $z=0$. We trace the halos from $z = 3.6$ to $z=0$, calculating the\ntorque exerted by the nearby galaxies on the gas in the central galaxy. We\ncalculate the correlation between the torque and the SFR across the simulations\nfor various mass ratios. For near-equal-stellar-mass-ratio interactions in the\ngalaxy sample, occurring between $z=1.2-3.6$, there is a positive and\nstatistically significant correlation between the torque from nearby galaxies\non the gas of the central galaxies and the SFR. For all other samples, no\nstatistically significant correlation is found between the torque and the SFR.\nOur analysis shows that some, but not all, major interactions cause starbursts\nin the simulated Milky Way-mass galaxies, and that most starbursts are not\ncaused by galaxy interactions. The transition from `bursty' at high redshift\n($z\\gtrsim1$) to `steady' star-formation state at later times is independent of\nthe interaction history of the galaxies, and most of the interactions do not\nleave significant imprints on the overall trend of the star formation history\nof the galaxies.",
        "split": "arXiv"
    },
    {
        "text": "False data injection attacks (FDIAs) on smart inverters are a growing concern\nlinked to increased renewable energy production. While data-based FDIA\ndetection methods are also actively developed, we show that they remain\nvulnerable to impactful and stealthy adversarial examples that can be crafted\nusing Reinforcement Learning (RL). We propose to include such adversarial\nexamples in data-based detection training procedure via a continual adversarial\nRL (CARL) approach. This way, one can pinpoint the deficiencies of data-based\ndetection, thereby offering explainability during their incremental\nimprovement. We show that a continual learning implementation is subject to\ncatastrophic forgetting, and additionally show that forgetting can be addressed\nby employing a joint training strategy on all generated FDIA scenarios.",
        "split": "arXiv"
    },
    {
        "text": "The current vision-based aphid counting methods in water traps suffer from\nundercounts caused by occlusions and low visibility arising from dense\naggregation of insects and other objects. To address this problem, we propose a\nnovel aphid counting method through interactive stirring actions. We use\ninteractive stirring to alter the distribution of aphids in the yellow water\ntrap and capture a sequence of images which are then used for aphid detection\nand counting through an optimized small object detection network based on\nYolov5. We also propose a counting confidence evaluation system to evaluate the\nconfidence of count-ing results. The final counting result is a weighted sum of\nthe counting results from all sequence images based on the counting confidence.\nExperimental results show that our proposed aphid detection network\nsignificantly outperforms the original Yolov5, with improvements of 33.9% in\nAP@0.5 and 26.9% in AP@[0.5:0.95] on the aphid test set. In addition, the aphid\ncounting test results using our proposed counting confidence evaluation system\nshow significant improvements over the static counting method, closely aligning\nwith manual counting results.",
        "split": "arXiv"
    },
    {
        "text": "We analyze the recent MIT lattice data for the gravitational form factors\n(GFFs) of the pion which extend up to $Q^2= 2~{\\rm GeV}^2$ for $m_\\pi=170$~MeV.\nWe show that simple monopole fits comply with the old idea of meson dominance.\nWe use Chiral Perturbation theory ($\\chi$PT) to next-to-leading order (NLO) to\ntransform the MIT data to the physical world with $m_\\pi=140~$MeV and find that\nthe spin-0 GFF is effectively saturated with the $f_0(600)$ and the spin-2 with\nthe $f_2(1270)$, with monopole masses $m_\\sigma= 630(60)$~MeV and $m_{f_2}=\n1270(40)$~MeV. We determine in passing the chiral low energy constants (LECs)\nfrom the MIT lattice data alone\n  $$\n  10^3 \\cdot L_{11} (m_\\rho^2)=1.06(15) \\, , \\qquad 10^3 \\cdot L_{12}\n(m_\\rho^2)= -2.2(1) \\, ,\n  \\qquad 10^3 \\cdot L_{13} (m_\\rho^2) = -0.7(1.1).\n  $$ which agree in sign and order of magnitude with the original estimates by\nDonoghue and Leutwyler. We also analyze the sum rules based on perturbative QCD\n(pQCD) that imply that the corresponding spectral functions are not positive\ndefinite. We show that these sum rules are strongly violated in a variety of\n$\\pi\\pi-K \\bar K$ coupled channel Omn\\`es-Muskhelishvili calculations. This is\nnot mended by the inclusion of the pQCD tail, suggesting the need for an extra\nnegative spectral strength. Using a simple model implementing all sum rules, we\nfind the expected onset of pQCD at very high momenta.",
        "split": "arXiv"
    },
    {
        "text": "Large language models (LLMs) have significantly advanced the field of\nautomated code generation. However, a notable research gap exists in the\nevaluation of social biases that may be present in the code produced by LLMs.\nTo solve this issue, we propose a novel fairness framework, i.e., Solar, to\nassess and mitigate the social biases of LLM-generated code. Specifically,\nSolar can automatically generate test cases for quantitatively uncovering\nsocial biases of the auto-generated code by LLMs. To quantify the severity of\nsocial biases in generated code, we develop a dataset that covers a diverse set\nof social problems. We applied Solar and the crafted dataset to four\nstate-of-the-art LLMs for code generation. Our evaluation reveals severe bias\nin the LLM-generated code from all the subject LLMs. Furthermore, we explore\nseveral strategies for bias mitigation, including Chain-of-Thought (CoT)\nprompting, combining positive role-playing with CoT prompting and iterative\nprompting. Our experiments show that iterative prompting can effectively reduce\nsocial bias in LLM-generated code by up to 90%. Solar is highly extensible to\nevaluate new social problems.",
        "split": "arXiv"
    },
    {
        "text": "In machine learning (ML), the inference phase is the process of applying\npre-trained models to new, unseen data with the objective of making\npredictions. During the inference phase, end-users interact with ML services to\ngain insights, recommendations, or actions based on the input data. For this\nreason, serving strategies are nowadays crucial for deploying and managing\nmodels in production environments effectively. These strategies ensure that\nmodels are available, scalable, reliable, and performant for real-world\napplications, such as time series forecasting, image classification, natural\nlanguage processing, and so on. In this paper, we evaluate the performances of\nfive widely-used model serving frameworks (TensorFlow Serving, TorchServe,\nMLServer, MLflow, and BentoML) under four different scenarios (malware\ndetection, cryptocoin prices forecasting, image classification, and sentiment\nanalysis). We demonstrate that TensorFlow Serving is able to outperform all the\nother frameworks in serving deep learning (DL) models. Moreover, we show that\nDL-specific frameworks (TensorFlow Serving and TorchServe) display\nsignificantly lower latencies than the three general-purpose ML frameworks\n(BentoML, MLFlow, and MLServer).",
        "split": "arXiv"
    },
    {
        "text": "We present Y-MAP-Net, a Y-shaped neural network architecture designed for\nreal-time multi-task learning on RGB images. Y-MAP-Net, simultaneously predicts\ndepth, surface normals, human pose, semantic segmentation and generates\nmulti-label captions, all from a single network evaluation. To achieve this, we\nadopt a multi-teacher, single-student training paradigm, where task-specific\nfoundation models supervise the network's learning, enabling it to distill\ntheir capabilities into a lightweight architecture suitable for real-time\napplications. Y-MAP-Net, exhibits strong generalization, simplicity and\ncomputational efficiency, making it ideal for robotics and other practical\nscenarios. To support future research, we will release our code publicly.",
        "split": "arXiv"
    },
    {
        "text": "Bitcoin, launched in 2008 by Satoshi Nakamoto, established a new digital\neconomy where value can be stored and transferred in a fully decentralized\nmanner - alleviating the need for a central authority. This paper introduces a\nlarge scale dataset in the form of a transactions graph representing\ntransactions between Bitcoin users along with a set of tasks and baselines. The\ngraph includes 252 million nodes and 785 million edges, covering a time span of\nnearly 13 years of and 670 million transactions. Each node and edge is\ntimestamped. As for supervised tasks we provide two labeled sets i. a 33,000\nnodes based on entity type and ii. nearly 100,000 Bitcoin addresses labeled\nwith an entity name and an entity type. This is the largest publicly available\ndata set of bitcoin transactions designed to facilitate advanced research and\nexploration in this domain, overcoming the limitations of existing datasets.\nVarious graph neural network models are trained to predict node labels,\nestablishing a baseline for future research. In addition, several use cases are\npresented to demonstrate the dataset's applicability beyond Bitcoin analysis.\nFinally, all data and source code is made publicly available to enable\nreproducibility of the results.",
        "split": "arXiv"
    },
    {
        "text": "The recently released model, Claude 3.5 Computer Use, stands out as the first\nfrontier AI model to offer computer use in public beta as a graphical user\ninterface (GUI) agent. As an early beta, its capability in the real-world\ncomplex environment remains unknown. In this case study to explore Claude 3.5\nComputer Use, we curate and organize a collection of carefully designed tasks\nspanning a variety of domains and software. Observations from these cases\ndemonstrate Claude 3.5 Computer Use's unprecedented ability in end-to-end\nlanguage to desktop actions. Along with this study, we provide an\nout-of-the-box agent framework for deploying API-based GUI automation models\nwith easy implementation. Our case studies aim to showcase a groundwork of\ncapabilities and limitations of Claude 3.5 Computer Use with detailed analyses\nand bring to the fore questions about planning, action, and critic, which must\nbe considered for future improvement. We hope this preliminary exploration will\ninspire future research into the GUI agent community. All the test cases in the\npaper can be tried through the project:\nhttps://github.com/showlab/computer_use_ootb.",
        "split": "arXiv"
    },
    {
        "text": "Let $\\mathcal{G}$ be the set of all the planar embeddings of a (not\nnecessarily connected) $n$-vertex graph $G$. We present a bijection $\\Phi$ from\n$\\mathcal{G}$ to the natural numbers in the interval $[0 \\dots |\\mathcal{G}| -\n1]$. Given a planar embedding $\\mathcal{E}$ of $G$, we show that\n$\\Phi(\\mathcal{E})$ can be decomposed into a sequence of $O(n)$ natural numbers\neach describing a specific feature of $\\mathcal{E}$. The function $\\Phi$, which\nis a ranking function for $\\mathcal{G}$, can be computed in $O(n)$ time, while\nits inverse unranking function $\\Phi^{-1}$ can be computed in $O(n \\alpha(n))$\ntime. The results of this paper can be of practical use to uniformly at random\ngenerating the planar embeddings of a graph $G$ or to enumerating such\nembeddings with amortized constant delay. Also, they can be used to counting,\nenumerating or uniformly at random generating constrained planar embeddings of\n$G$.",
        "split": "arXiv"
    },
    {
        "text": "Autonomous vehicles require road information for their operation, usually in\nform of HD maps. Since offline maps eventually become outdated or may only be\npartially available, online HD map construction methods have been proposed to\ninfer map information from live sensor data. A key issue remains how to exploit\nsuch partial or outdated map information as a prior. We introduce M3TR\n(Multi-Masking Map Transformer), a generalist approach for HD map construction\nboth with and without map priors. We address shortcomings in ground truth\ngeneration for Argoverse 2 and nuScenes and propose the first realistic\nscenarios with semantically diverse map priors. Examining various query\ndesigns, we use an improved method for integrating prior map elements into a HD\nmap construction model, increasing performance by +4.3 mAP. Finally, we show\nthat training across all prior scenarios yields a single Generalist model,\nwhose performance is on par with previous Expert models that can handle only\none specific type of map prior. M3TR thus is the first model capable of\nleveraging variable map priors, making it suitable for real-world deployment.\nCode is available at https://github.com/immel-f/m3tr",
        "split": "arXiv"
    },
    {
        "text": "In this work, we propose novel offline and online Inverse Differential Game\n(IDG) methods for nonlinear Differential Games (DG), which identify the cost\nfunctions of all players from control and state trajectories constituting a\nfeedback Nash equilibrium. The offline approach computes the sets of all\nequivalent cost function parameters that yield the observed trajectories. Our\nonline method is guaranteed to converge to cost function parameters of the\noffline calculated sets. For both methods, we additionally analyze the case\nwhere the cost and value functions are not given by known parameterized\nstructures and approximation structures, like polynomial basis functions, need\nto be chosen. Here, we found that for guaranteeing a bounded error between the\ntrajectories resulting from the offline and online IDG solutions and the\nobserved trajectories an appropriate selection of the cost function structures\nis required. They must be aligned to assumed value function structures such\nthat the coupled Hamilton-Jacobi-Bellman equations can be fulfilled. Finally,\nthe theoretical results and the effectiveness of our new methods are\nillustrated with a numerical example.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we investigate the parking process on a uniform random rooted\nbinary tree with $n$ vertices. Viewing each vertex as a single parking space, a\nrandom number of cars independently arrive at and attempt to park on each\nvertex one at a time. If a car attempts to park on an occupied vertex, it\ntraverses the unique path on the tree towards the root, parking at the first\nempty vertex it encounters. If this is not possible, the car exits the tree at\nthe root.\n  We shall investigate the limit of the probability of the event that all cars\ncan park when $\\lfloor \\alpha n \\rfloor$ cars arrive, with $\\alpha > 0$. We\nfind that there is a phase transition at $\\alpha_c = 2 - \\sqrt{2}$, with this\nevent having positive limiting probability when $\\alpha < \\alpha_c$, and the\nprobability tending to 0 as $n \\rightarrow \\infty$ for $\\alpha > \\alpha_c$.\n  This is analogous to the work done by Goldschmidt and Przykucki\n(arXiv:1610.08786) and Goldschmidt and Chen (arXiv:1911.03816), while agreeing\nwith the general result proven by Curien and H\\'enard (arXiv:2205.15932).",
        "split": "arXiv"
    },
    {
        "text": "We study the problem of clock synchronization in a networked system with\narbitrary starts for all nodes. We consider a synchronous network of $n$ nodes,\nwhere each node has a local clock that is an integer counter. Eventually,\nclocks must be all equal and increase by one in each round modulo some period\n$P$. The purpose of this paper is to study whether clock synchronization can be\nachieved with bounded memory, that is every node maintains a number of states\nthat does not depend on the network size. In particular, we are interested in\nclock synchronization algorithms which work in dynamic networks, i.e., tolerate\nthat communication links continuously fail and come-up.\n  We first focus on self-stabilizing solutions for clock synchronization, and\nprove that there is no such algorithm that is bounded memory, even in the case\nof static networks. More precisely, we show a lower bound of $n+1$ states at\neach node required to achieve clock synchronization in static strongly\nconnected networks with at most $n$ nodes, and derive a lower bound of $n-2$\nrounds on synchronization time, in the worst case. We then prove that, when the\nself-stabilizing requirement is removed, the impossibility of clock\nsynchronization with bounded memory still holds in the dynamic setting: every\nsolution for the clock synchronization problem in dynamic networks with at most\n$n$ nodes requires each node to have $\\Omega(\\log n)$ states.",
        "split": "arXiv"
    },
    {
        "text": "We consider the Hospital/Residents (HR) problem in the presence of ties in\npreference lists. Among the three notions of stability, viz. weak, strong, and\nsuper stability, we focus on the notion of strong stability. Strong stability\nhas many desirable properties both theoretically and practically; however, its\nexistence is not guaranteed.\n  In this paper, our objective is to optimally increase the quotas of hospitals\nto ensure that a strongly stable matching exists in the modified instance.\nFirst, we show that if ties are allowed in residents' preference lists, it may\nnot be possible to augment the hospital quotas to obtain an instance that\nadmits a strongly stable matching. When residents' preference lists are strict,\nwe explore two natural optimization criteria: (i) minimizing the maximum\ncapacity increase for any hospital (MINMAX), and (ii) minimizing the total\ncapacity increase across all hospitals (MINSUM). We show that the MINMAX\nproblem is NP-hard in general. When hospital preference lists can have ties of\nlength at most $\\ell+1$, we give a polynomial-time algorithm that increases\neach hospital's quota by at most $\\ell$, ensuring the resulting instance admits\na strongly stable matching.\n  We show that the MINSUM problem admits a polynomial-time algorithm. However,\nwhen each hospital incurs a cost for each capacity increase, the problem\nbecomes NP-hard, even if the costs are 0 or 1. This also implies that the\nproblem cannot be approximated to any multiplicative factor. We also consider a\nrelated problem under the MINSUM objective. Given an HR instance and a forced\npair $(r^*,h^*)$, the goal is to decide if it is possible to increase hospital\nquotas (if necessary) to obtain a strongly stable matching that matches the\npair $(r^*,h^*)$. We show a polynomial-time algorithm for this problem.",
        "split": "arXiv"
    },
    {
        "text": "We produce twisted derived equivalences between torsors under abelian\nvarieties and their moduli spaces of simple semi-homogeneous sheaves. We also\nestablish the natural converse to this result and show that a large class of\ntwisted derived equivalences, including all derived equivalences, between\ntorsors arise in this way. As corollaries, we obtain partial extensions of the\nusual derived equivalence criterion for abelian varieties established by Orlov\nand Polishchuk.",
        "split": "arXiv"
    },
    {
        "text": "We consider $\\mathbb{Z}_q$-valued clock models on a regular tree, for general\nclasses of ferromagnetic nearest neighbor interactions which have a discrete\nrotational symmetry. It has been proved recently that, at strong enough\ncoupling, families of homogeneous Markov chain Gibbs states $\\mu_A$ coexist\nwhose single-site marginals concentrate on $A\\subset \\mathbb{Z}_q$, and which\nare not convex combinations of each other [AbHeKuMa24]. In this note, we aim at\na description of the extremal decomposition of $\\mu_A$ for $|A|\\geq 2$ into all\nextremal Gibbs measures, which may be spatially inhomogeneous. First, we show\nthat in regimes of very strong coupling, $\\mu_A$ is not extremal. Moreover,\n$\\mu_A$ possesses a single-site reconstruction property which holds for spin\nvalues sent from the origin to infinity, when these initial values are chosen\nfrom $A$. As our main result, we show that $\\mu_A$ decomposes into uncountably\nmany extremal inhomogeneous states. The proof is based on multi-site\nreconstruction which allows to derive concentration properties of branch\noverlaps. Our method is based on a new good site/bad site decomposition adapted\nto the $A$-localization property, together with a coarse graining argument in\nlocal state space.",
        "split": "arXiv"
    },
    {
        "text": "Many magnetic white dwarfs exhibit a polarised spectrum that periodically\nvaries as the star rotates because the magnetic field is not symmetric about\nthe rotation axis. In this work, we report the discovery that while weakly\nmagnetic white dwarfs of all ages with M < 1Mo show polarimetric variability\nwith a period between hours and several days, the large majority of magnetic\nwhite dwarfs in the same mass range with cooling ages older than 2 Gyr and\nfield strengths > 10 MG show little or no polarimetric variability. This could\nbe interpreted as extremely slow rotation, but a lack of known white dwarfs\nwith measured periods longer than two weeks means that we do not see white\ndwarfs slowing their rotation. We therefore suggest a different interpretation:\nold strongly magnetic white dwarfs do not vary because their fields are roughly\nsymmetric about the rotation axes. Symmetry may either be a consequence of\nfield evolution or a physical characteristic intrinsic to the way strong fields\nare generated in older stars. Specifically, a strong magnetic field could\ndistort the shape of a star, forcing the principal axis of maximum inertia away\nfrom the spin axis. Eventually, as a result of energy dissipation, the magnetic\naxis will align with the angular momentum axis. We also find that the\nhigher-mass strongly magnetised white dwarfs, which are likely the products of\nthe merging of two white dwarfs, may appear as either polarimetrically variable\nor constant. This may be the symptom of two different formation channels or the\nconsequence of the fact that a dynamo operating during a merger may produce\ndiverse magnetic configurations. Alternatively, the massive white dwarfs with\nconstant polarisation may be rotating with periods much shorter than the\ntypical exposure times of the observations.",
        "split": "arXiv"
    },
    {
        "text": "Super $L_\\infty$-algebras unify extended super-symmetry with rational\nclassifying spaces for higher flux densities: The super-invariant super-fluxes\nwhich control super $p$-branes and their supergravity target super-spaces are,\ntogether with their (non-linear) Bianchi identities, neatly encoded in\n(non-abelian) super-$L_\\infty$ cocycles. These are the rational shadows of\nflux-quantization laws (in ordinary cohomology, K-theory, Cohomotopy, iterated\nK-theory, etc).\n  We first review, in streamlined form while filling some previous gaps,\ndouble-dimensional reduction/oxidation and 10D superspace T-duality along\nhigher-dimensional super-tori. We do so tangent super-space wise, by viewing it\nas an instance of adjunctions (dualities) between super-$L_\\infty$-extensions\nand -cyclifications, applied to the avatar super-flux densities of 10D\nsupergravity. In particular, this yields a derivation, at the rational level,\nof the traditional laws of \"topological T-duality\" from the super-$L_\\infty$\nstructure of type II superspace. At this level, we also discuss a higher\ncategorical analog of T-duality involving M-branes.\n  Then, by considering super-space T-duality along all 1+9 spacetime dimensions\nwhile retaining the 11th dimension as in F-theory, we find the M-algebra\nappearing as the complete brane-charge extension of the fully\nT-doubled/correspondence super-spacetime. On this backdrop, we recognize the\n\"decomposed\" M-theory 3-form on the \"hidden M-algebra\" as an M-theoretic lift\nof the Poincar\\'e super 2-form that controls superspace T-duality as the\nintegral kernel of the super Fourier-Mukai transform. This provides the\nsuper-space structure of an M-theory lift of the doubled/correspondence space\ngeometry, which controls T-duality.",
        "split": "arXiv"
    },
    {
        "text": "A well-known result of Shalom says that lattices in SO$(n,1)$ are $L^p$\nmeasure equivalent for all $p<n-1$. His proof actually yields the following\nstronger statement: the natural coupling resulting from a suitable choice of\nfundamental domains from a uniform lattice $\\Lambda$ to a uniform one $\\Gamma$\nis $(L^{\\infty},L^p)$. Moreover, the fundamental domain of $\\Gamma$ is\ncontained in a union of finitely many translates of the fundamental domain of\n$\\Lambda$. The purpose of this note is to prove a converse statement. More\ngenerally, it is proved that if a ME-coupling from a non-hyperbolic group\n$\\Lambda$ to a hyperbolic group $\\Gamma$ is $(L^{\\infty},L^p)$ and the\nfundamental domain of $\\Gamma$ is contained in a union of finitely many\ntranslates of the fundamental domain of $\\Lambda$, then $p$ must be less than\nsome $p_0$ only depending on $\\Gamma$.",
        "split": "arXiv"
    },
    {
        "text": "A central computational task in database theory, finite model theory, and\ncomputer science at large is the evaluation of a first-order sentence on a\nfinite structure. In the context of this task, the \\emph{width} of a sentence,\ndefined as the maximum number of free variables over all subformulas, has been\nestablished as a crucial measure, where minimizing width of a sentence (while\nretaining logical equivalence) is considered highly desirable. An\nundecidability result rules out the possibility of an algorithm that, given a\nfirst-order sentence, returns a logically equivalent sentence of minimum width;\nthis result motivates the study of width minimization via syntactic rewriting\nrules, which is this article's focus. For a number of common rewriting rules\n(which are known to preserve logical equivalence), including rules that allow\nfor the movement of quantifiers, we present an algorithm that, given a positive\nfirst-order sentence $\\phi$, outputs the minimum-width sentence obtainable from\n$\\phi$ via application of these rules. We thus obtain a complete algorithmic\nunderstanding of width minimization up to the studied rules; this result is the\nfirst one -- of which we are aware -- that establishes this type of\nunderstanding in such a general setting. Our result builds on the theory of\nterm rewriting and establishes an interface among this theory, query\nevaluation, and structural decomposition theory.",
        "split": "arXiv"
    },
    {
        "text": "We evaluate the consistency of hadronic interaction models in the CORSIKA\nsimulation package with publicly available fluorescence telescope data from the\nPierre Auger Observatory. By comparing the first few central moments of the\nextended air shower depth maximum distributions, as extracted from measured\nevents, to those predicted by the best-fit inferred compositions, we derive a\nstatistical measure of the consistency of a given hadronic model with data. To\nmitigate possible systematic biases, we include all primaries up to iron,\ncompensate for the differences between the measured and simulated energy\nspectra of cosmic rays and account for other known systematic effects.\nAdditionally, we study the effects of including higher central moments in the\nfit and project our results to larger statistics.",
        "split": "arXiv"
    },
    {
        "text": "An angular analysis of the $B_s^0 \\rightarrow \\phi e^+e^-$ decay is performed\nusing the proton-proton collision dataset collected between 2011 and 2018 by\nthe LHCb experiment, corresponding to an integrated luminosity of $9\\,{\\rm\nfb}^{-1}$ at centre-of-mass energies of 7, 8 and $13\\,{\\rm TeV}$. The analysis\nis performed in the very low dielectron invariant mass-squared region between\n$0.0009$ and $0.2615\\,{\\rm GeV}^2\\!/c^4$. The longitudinal polarisation\nfraction of the $\\phi$ meson is measured to be less than $11.5\\%$ at $90\\%$\nconfidence level. The $A_{\\mathrm{T}}^{\\mathcal{R}e C\\!P}$ observable, which is\nrelated to the lepton forward-backward asymmetry, is measured to be $0.116 \\pm\n0.155 \\pm 0.006$, where the first uncertainty is statistical and the second\nsystematic. The transverse asymmetries, $A_{\\mathrm{T}}^{(2)}$ and\n$A_{\\mathrm{T}}^{\\mathcal{I}m C\\!P}$ , which are sensitive to the virtual\nphoton polarisation, are found to be $-0.045 \\pm 0.235 \\pm 0.014$ and $0.002\n\\pm 0.247 \\pm 0.016$, respectively. The results are consistent with Standard\nModel predictions.",
        "split": "arXiv"
    },
    {
        "text": "Large language models (LLMs) and LLM-based Agents have been applied to fix\nbugs automatically, demonstrating the capability in addressing software defects\nby engaging in development environment interaction, iterative validation and\ncode modification. However, systematic analysis of these agent and non-agent\nsystems remain limited, particularly regarding performance variations among\ntop-performing ones. In this paper, we examine seven proprietary and\nopen-source systems on the SWE-bench Lite benchmark for automated bug fixing.\nWe first assess each system's overall performance, noting instances solvable by\nall or none of these sytems, and explore why some instances are uniquely solved\nby specific system types. We also compare fault localization accuracy at file\nand line levels and evaluate bug reproduction capabilities, identifying\ninstances solvable only through dynamic reproduction. Through analysis, we\nconcluded that further optimization is needed in both the LLM itself and the\ndesign of Agentic flow to improve the effectiveness of the Agent in bug fixing.",
        "split": "arXiv"
    },
    {
        "text": "Can outreach inspire and lead to research and vice versa? In this work, we\nintroduce our approach to the gamification of research in mathematics and\ncomputer science through three illustrative examples. We discuss our primary\nmotivations and provide insights into what makes our proposed gamification\neffective for three research topics in discrete and computational geometry and\ntopology: (1) DominatriX, an art gallery problem involving polyominoes with\nrooks and queens; (2) Cubical Sliding Puzzles, an exploration of the discrete\nconfiguration spaces of sliding puzzles on the $d$-cube with topological\nobstructions; and (3) The Fence Challenge, a participatory isoperimetric\nproblem based on polyforms. Additionally, we report on the collaborative\ndevelopment of the game Le Carr\\'e du Diable, inspired by The Fence Challenge\nand created during the workshop Let's talk about outreach!, held in October\n2022 in Les Diablerets, Switzerland. All of our outreach encounters and\ncreations are designed and curated with an inclusive culture and a strong\ncommitment to welcoming the most diverse audience possible.",
        "split": "arXiv"
    },
    {
        "text": "The symmetrized Asymptotic Mean Value Laplacian $\\tilde{\\Delta}$, obtained as\nlimit of approximating operators $\\tilde{\\Delta}_r$, is an extension of the\nclassical Euclidean Laplace operator to the realm of metric measure spaces. We\nshow that, as $r \\downarrow 0$, the operators $\\tilde{\\Delta}_r$ eventually\nadmit isolated eigenvalues defined via min-max procedure on any compact locally\nAhlfors regular metric measure space. Then we prove $L^2$ and spectral\nconvergence of $\\tilde{\\Delta}_r$ to the Laplace--Beltrami operator of a\ncompact Riemannian manifold, imposing Neumann conditions when the manifold has\na non-empty boundary.",
        "split": "arXiv"
    },
    {
        "text": "Spatio-Temporal predictive Learning is a self-supervised learning paradigm\nthat enables models to identify spatial and temporal patterns by predicting\nfuture frames based on past frames. Traditional methods, which use recurrent\nneural networks to capture temporal patterns, have proven their effectiveness\nbut come with high system complexity and computational demand. Convolutions\ncould offer a more efficient alternative but are limited by their\ncharacteristic of treating all previous frames equally, resulting in poor\ntemporal characterization, and by their local receptive field, limiting the\ncapacity to capture distant correlations among frames. In this paper, we\npropose STLight, a novel method for spatio-temporal learning that relies solely\non channel-wise and depth-wise convolutions as learnable layers. STLight\novercomes the limitations of traditional convolutional approaches by\nrearranging spatial and temporal dimensions together, using a single\nconvolution to mix both types of features into a comprehensive spatio-temporal\npatch representation. This representation is then processed in a purely\nconvolutional framework, capable of focusing simultaneously on the interaction\namong near and distant patches, and subsequently allowing for efficient\nreconstruction of the predicted frames. Our architecture achieves\nstate-of-the-art performance on STL benchmarks across different datasets and\nsettings, while significantly improving computational efficiency in terms of\nparameters and computational FLOPs. The code is publicly available",
        "split": "arXiv"
    },
    {
        "text": "In many situations humans have to reason with inconsistent knowledge. These\ninconsistencies may occur due to not fully reliable sources of information. In\norder to reason with inconsistent knowledge, it is not possible to view a set\nof premisses as absolute truths as is done in predicate logic. Viewing the set\nof premisses as a set of assumptions, however, it is possible to deduce useful\nconclusions from an inconsistent set of premisses. In this paper a logic for\nreasoning with inconsistent knowledge is described. This logic is a\ngeneralization of the work of N. Rescher [15]. In the logic a reliability\nrelation is used to choose between incompatible assumptions. These choices are\nonly made when a contradiction is derived. As long as no contradiction is\nderived, the knowledge is assumed to be consistent. This makes it possible to\ndefine an argumentation-based deduction process for the logic. For the logic a\nsemantics based on the ideas of Y. Shoham [22, 23], is defined. It turns out\nthat the semantics for the logic is a preferential semantics according to the\ndefinition S. Kraus, D. Lehmann and M. Magidor [12]. Therefore the logic is a\nlogic of system P and possesses all the properties of an ideal non-monotonic\nlogic.",
        "split": "arXiv"
    },
    {
        "text": "The obtention of quantum-grade rare-earth doped oxide thin films that can be\nintegrated with optical cavities and microwave resonators is of great interest\nfor the development of scalable quantum devices. Among the different growth\nmethods, Chemical Vapour Deposition (CVD) offers high flexibility and has\ndemonstrated the ability to produce oxide films hosting rare-earth ions with\nnarrow linewidths. However, growing epitaxial films directly on silicon is\nchallenging by CVD due to a native amorphous oxide layer formation at the\ninterface. In this manuscript, we investigate the CVD growth of erbium-doped\nyttrium oxide (Er:Y2O3) thin films on different substrates, including silicon,\nsapphire, quartz or yttria stabilized zirconia (YSZ). Alternatively, growth was\nalso attempted on an epitaxial Y2O3 template layer on Si (111) prepared by\nmolecular beam epitaxy (MBE) in order to circumvent the issue of the amorphous\ninterlayer. We found that the substrate impacts the film morphology and the\ncrystalline orientations, with different textures observed for the CVD film on\nthe MBE-oxide/Si template (111) and epitaxial growth on YSZ (001). In terms of\noptical properties, Er3+ ions exhibit visible and IR emission features that are\ncomparable for all samples, indicating a high-quality local crystalline\nenvironment regardless of the substrate. Our approach opens interesting\nprospects to integrate such films into scalable devices for optical quantum\ntechnologies.",
        "split": "arXiv"
    },
    {
        "text": "Top-quark pair production is observed in lead-lead (Pb+Pb) collisions at\n$\\sqrt{s_\\mathrm{NN}}=5.02$ TeV at the Large Hadron Collider with the ATLAS\ndetector. The data sample was recorded in 2015 and 2018, amounting to an\nintegrated luminosity of 1.9 nb$^{-1}$. Events with exactly one electron and\none muon and at least two jets are selected. Top-quark pair production is\nmeasured with an observed (expected) significance of 5.0 (4.1) standard\ndeviations. The measured top-quark pair production cross-section is\n$\\sigma_{t\\bar{t}} =\n3.6\\;^{+1.0}_{-0.9}\\;\\mathrm{(stat.)}\\;^{+0.8}_{-0.5}\\;\\mathrm{(syst.)}\n~\\mathrm{\\mu b}$, with a total relative uncertainty of 31%, and is consistent\nwith theoretical predictions using a range of different nuclear parton\ndistribution functions. The observation of this process consolidates the\nevidence of the existence of all quark flavors in the pre-equilibrium stage of\nthe quark-gluon plasma at very high energy densities, similar to the conditions\npresent in the early universe.",
        "split": "arXiv"
    },
    {
        "text": "Text-to-image generation and text-guided image manipulation have received\nconsiderable attention in the field of image generation tasks. However, the\nmainstream evaluation methods for these tasks have difficulty in evaluating\nwhether all the information from the input text is accurately reflected in the\ngenerated images, and they mainly focus on evaluating the overall alignment\nbetween the input text and the generated images. This paper proposes new\nevaluation metrics that assess the alignment between input text and generated\nimages for every individual object. Firstly, according to the input text,\nchatGPT is utilized to produce questions for the generated images. After that,\nwe use Visual Question Answering(VQA) to measure the relevance of the generated\nimages to the input text, which allows for a more detailed evaluation of the\nalignment compared to existing methods. In addition, we use Non-Reference Image\nQuality Assessment(NR-IQA) to evaluate not only the text-image alignment but\nalso the quality of the generated images. Experimental results show that our\nproposed evaluation approach is the superior metric that can simultaneously\nassess finer text-image alignment and image quality while allowing for the\nadjustment of these ratios.",
        "split": "arXiv"
    },
    {
        "text": "We construct a holographic model to study the striped superconductor on ionic\nlattices. This model features a phase diagram with three distinct phases,\nnamely the charge density wave (CDW) phase, ordinary superconducting phase (SC)\nand the striped superconducting phase (SSC). The effect of the ionic lattices\non the phase diagram is investigated in detail. First, due to the periodic\nnature of the background, different types of CDW solutions can be found below\nthe critical temperature. Furthermore, with the increase of the lattice\namplitude these solutions are locked in different commensurate states. Second,\nwe find that the critical temperature of CDW phase decreases with the increase\nof the lattice amplitude, while that of the SC phase increases. Additionally,\nthe background solutions are obtained for different phases, and it is verified\nthat the SSC phase has the lowest free energy among all three phases.",
        "split": "arXiv"
    },
    {
        "text": "During the past decade, Deep Neural Networks (DNNs) proved their value on a\nlarge variety of subjects. However despite their high value and public\naccessibility, the protection of the intellectual property of DNNs is still an\nissue and an emerging research field. Recent works have successfully extracted\nfully-connected DNNs using cryptanalytic methods in hard-label settings,\nproving that it was possible to copy a DNN with high fidelity, i.e., high\nsimilitude in the output predictions. However, the current cryptanalytic\nattacks cannot target complex, i.e., not fully connected, DNNs and are limited\nto special cases of neurons present in deep networks.\n  In this work, we introduce a new end-to-end attack framework designed for\nmodel extraction of embedded DNNs with high fidelity. We describe a new\nblack-box side-channel attack which splits the DNN in several linear parts for\nwhich we can perform cryptanalytic extraction and retrieve the weights in\nhard-label settings. With this method, we are able to adapt cryptanalytic\nextraction, for the first time, to non-fully connected DNNs, while maintaining\na high fidelity. We validate our contributions by targeting several\narchitectures implemented on a microcontroller unit, including a Multi-Layer\nPerceptron (MLP) of 1.7 million parameters and a shortened MobileNetv1. Our\nframework successfully extracts all of these DNNs with high fidelity (88.4% for\nthe MobileNetv1 and 93.2% for the MLP). Furthermore, we use the stolen model to\ngenerate adversarial examples and achieve close to white-box performance on the\nvictim's model (95.8% and 96.7% transfer rate).",
        "split": "arXiv"
    },
    {
        "text": "Let $\\overline X$ be a smooth rigid variety over $C=\\mathbb C_p$ admitting a\nlift $X$ over $B_{dR}^+$. In this paper, we use the stacky language to prove a\nnilpotent $p$-adic Riemann-Hilbert correspondence. After introducing the moduli\nstack of $\\mathbb B^+_{dR}$-local systems and $t$-connections, we prove that\nthere is an equivalence of the nilpotent locus of the two stacks: $RH^0:LS^0_X\n\\to tMIC^0_X$, where $LS^0_X$ is the stack of nilpotent $\\mathbb\nB^+_{dR}$-local systems on $\\overline X_{1,v}$ and $tMIC^0_X$ is the stack of\n$\\mathcal{O}_X$-bundles with integrable $t$-connection on $X_{et}$.",
        "split": "arXiv"
    },
    {
        "text": "Multimodal learning, which involves integrating information from various\nmodalities such as text, images, audio, and video, is pivotal for numerous\ncomplex tasks like visual question answering, cross-modal retrieval, and\ncaption generation. Traditional approaches rely on modality-specific encoders\nand late fusion techniques, which can hinder scalability and flexibility when\nadapting to new tasks or modalities. To address these limitations, we introduce\na novel framework that extends the concept of task reformulation beyond natural\nlanguage processing (NLP) to multimodal learning. We propose to reformulate\ndiverse multimodal tasks into a unified next-frame prediction problem, allowing\na single model to handle different modalities without modality-specific\ncomponents. This method treats all inputs and outputs as sequential frames in a\nvideo, enabling seamless integration of modalities and effective knowledge\ntransfer across tasks. Our approach is evaluated on a range of tasks, including\ntext-to-text, image-to-text, video-to-video, video-to-text, and audio-to-text,\ndemonstrating the model's ability to generalize across modalities with minimal\nadaptation. We show that task reformulation can significantly simplify\nmultimodal model design across various tasks, laying the groundwork for more\ngeneralized multimodal foundation models.",
        "split": "arXiv"
    },
    {
        "text": "Various technologies, including computer vision models, are employed for the\nautomatic monitoring of manual assembly processes in production. These models\ndetect and classify events such as the presence of components in an assembly\narea or the connection of components. A major challenge with detection and\nclassification algorithms is their susceptibility to variations in\nenvironmental conditions and unpredictable behavior when processing objects\nthat are not included in the training dataset. As it is impractical to add all\npossible subjects in the training sample, an alternative solution is necessary.\nThis study proposes a model that simultaneously performs classification and\nanomaly detection, employing metric learning to generate vector representations\nof images in a multidimensional space, followed by classification using\ncross-entropy. For experimentation, a dataset of over 327,000 images was\nprepared. Experiments were conducted with various computer vision model\narchitectures, and the outcomes of each approach were compared.",
        "split": "arXiv"
    },
    {
        "text": "In the past few years, Artificial Intelligence (AI)-based weather forecasting\nmethods have widely demonstrated strong competitiveness among the weather\nforecasting systems. However, these methods are insufficient for\nhigh-spatial-resolution short-term nowcasting within 6 hours, which is crucial\nfor warning short-duration, mesoscale and small-scale weather events.\nGeostationary satellite remote sensing provides detailed, high spatio-temporal\nand all-day observations, which can address the above limitations of existing\nmethods. Therefore, this paper proposed an advanced data-driven thermal\ninfrared cloud images forecasting model, \"DaYu.\" Unlike existing data-driven\nweather forecasting models, DaYu is specifically designed for geostationary\nsatellite observations, with a temporal resolution of 0.5 hours and a spatial\nresolution of ${0.05}^\\circ$ $\\times$ ${0.05}^\\circ$. DaYu is based on a\nlarge-scale transformer architecture, which enables it to capture fine-grained\ncloud structures and learn fast-changing spatio-temporal evolution features\neffectively. Moreover, its attention mechanism design achieves a balance in\ncomputational complexity, making it practical for applications. DaYu not only\nachieves accurate forecasts up to 3 hours with a correlation coefficient higher\nthan 0.9, 6 hours higher than 0.8, and 12 hours higher than 0.7, but also\ndetects short-duration, mesoscale, and small-scale weather events with enhanced\ndetail, effectively addressing the shortcomings of existing methods in\nproviding detailed short-term nowcasting within 6 hours. Furthermore, DaYu has\nsignificant potential in short-term climate disaster prevention and mitigation.",
        "split": "arXiv"
    },
    {
        "text": "Blazars are a subclass of active galactic nuclei (AGNs) with relativistic\njets pointing toward the observer. They are notable for their flux variability\nat all observed wavelengths and timescales. Together with simultaneous\nmeasurements at lower energies, the very-high-energy (VHE) emission observed\nduring blazar flares may be used to probe the population of accelerated\nparticles. However, optimally triggering observations of blazar high states can\nbe challenging. Notable examples include identifying a flaring episode in real\ntime and predicting VHE flaring activity based on lower energy observables. For\nthis purpose, we have developed a novel deep learning analysis framework, based\non data-driven anomaly detection techniques. It is capable of detecting various\ntypes of anomalies in real-world, multiwavelength light curves, ranging from\nclear high states to subtle correlations across bands. Based on unsupervised\nanomaly detection and clustering methods, we differentiate source variability\nfrom noisy background activity, without the need for a labeled training dataset\nof flaring states. The framework incorporates measurement uncertainties and is\nrobust given data quality challenges, such as varying cadences and\nobservational gaps. We evaluate our approach using both historical data and\nsimulations of blazar light curves in two energy bands, corresponding to\nsources observable with the Fermi Large Area Telescope, and the upcoming\nCherenkov Telescope Array Observatory (CTAO). In a statistical analysis, we\nshow that our framework can reliably detect known historical flares.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we analyse an extension of the children's higher-or-lower\nnumber guessing game with two guessing players, where players alternate\nguessing a secret integer between 1 and n, and it is revealed whether these\nguesses are higher or lower than the secret number, with the first player to\nguess the number being the loser. We describe and prove the solution when both\nplayers are rational, which involves different guessing strategies dependent on\nthe value of n modulo 4. We then consider the case where one player is not\nrational but instead makes all their guesses uniformly at random, while the\nother player plays to exploit this. We show that, in this case, the probability\nthat the exploitative player wins approaches a constant (approximately 0.599)\nas n increases, and that the numbers 2 and n-1 are always optimal guesses for\nthem.",
        "split": "arXiv"
    },
    {
        "text": "Important advances have recently been made in the search for materials with\ncomplex multi-phase landscapes that host photoinduced metastable collective\nstates with exotic functionalities. In almost all cases so far, the desired\nphases are accessed by exploiting light-matter interactions via the imaginary\npart of the dielectric function through above-bandgap or resonant mode\nexcitation. Nonresonant Raman excitation of coherent modes has been\nexperimentally observed and proposed for dynamic material control, but the\nresulting atomic excursion has been limited to perturbative levels. Here, we\ndemonstrate that it is possible to overcome this challenge by employing\nnonresonant ultrashort pulses with low photon energies well below the bandgap.\nUsing mid-infrared pulses, we induce ferroelectric reversal in lithium niobate\nand phase switching in tin selenide and characterize the large-amplitude mode\ndisplacements through femtosecond Raman scattering, second harmonic generation,\nand x-ray diffraction. This approach, validated by first-principle\ncalculations, defines a novel method for synthesizing hidden phases with unique\nfunctional properties and manipulating complex energy landscapes at reduced\nenergy consumption and ultrafast speeds.",
        "split": "arXiv"
    },
    {
        "text": "Longitudinal analyses are increasingly used in clinical studies as they allow\nthe study of subtle changes over time within the same subjects. In most of\nthese studies, it is necessary to align all the images studied to a common\nreference by registering them to a template. In the study of white matter using\nthe recently developed fixel-based analysis (FBA) method, this registration is\nimportant, in particular because the fiber bundle cross-section metric is a\ndirect measure of this registration. In the vast majority of longitudinal FBA\nstudies described in the literature, sessions acquired for a same subject are\ndirectly independently registered to the template. However, it has been shown\nin T1-based morphometry that a 2-step registration through an intra-subject\naverage can be advantageous in longitudinal analyses. In this work, we propose\nan implementation of this 2-step registration method in a typical longitudinal\nFBA aimed at investigating the evolution of white matter changes in Alzheimer's\ndisease (AD). We compared at the fixel level the mean absolute effect and\nstandard deviation yielded by this registration method and by a direct\nregistration, as well as the results obtained with each registration method for\nthe study of AD in both fixelwise and tract-based analyses. We found that the\n2-step method reduced the variability of the measurements and thus enhanced\nstatistical power in both types of analyses.",
        "split": "arXiv"
    },
    {
        "text": "We study internal diffusion limited aggregation on $\\mathbb{Z}$, where a\ncluster is grown by sequentially adding the first site outside the cluster\nvisited by each random walk dispatched from the origin. We assume that the\nincrement distribution $X$ of the driving random walks has $\\mathbb{E} X =0$,\nbut may be neither simple nor symmetric, and can have $\\mathbb{E} (X^2) =\n\\infty$, for example. For the case where $\\mathbb{E} (X^2) < \\infty$, we prove\nthat after $m$ walks have been dispatched, all but $o(m)$ sites in the cluster\nform an approximately symmetric contiguous block around the origin. This\nextends known results for simple random walk. On the other hand, if~$X$ is in\nthe domain of attraction of a symmetric $\\alpha$-stable law, $1 < \\alpha <2$,\nwe prove that the cluster contains a contiguous block of $\\delta m +o(m)$\nsites, where $0 < \\delta < 1$, but, unlike the finite-variance case, one may\nnot take $\\delta=1$.",
        "split": "arXiv"
    },
    {
        "text": "We study functions $f : [0, 1]^d \\rightarrow [0, 1]^d$ that are both monotone\nand contracting, and we consider the problem of finding an\n$\\varepsilon$-approximate fixed point of $f$. We show that the problem lies in\nthe complexity class UEOPL. We give an algorithm that finds an\n$\\varepsilon$-approximate fixed point of a three-dimensional monotone\ncontraction using $O(\\log (1/\\varepsilon))$ queries to $f$. We also give a\ndecomposition theorem that allows us to use this result to obtain an algorithm\nthat finds an $\\varepsilon$-approximate fixed point of a $d$-dimensional\nmonotone contraction using $O((c \\cdot \\log (1/\\varepsilon))^{\\lceil d / 3\n\\rceil})$ queries to $f$ for some constant $c$. Moreover, each step of both of\nour algorithms takes time that is polynomial in the representation of $f$.\nThese results are strictly better than the best-known results for functions\nthat are only monotone, or only contracting.\n  All of our results also apply to Shapley stochastic games, which are known to\nbe reducible to the monotone contraction problem. Thus we put Shapley games in\nUEOPL, and we give a faster algorithm for approximating the value of a Shapley\ngame.",
        "split": "arXiv"
    },
    {
        "text": "We evaluate the Green's function for the insertion of the second moment of\nthe twist-$2$ flavour nonsinglet Wilson operator in a quark $2$-point function\nin all three different single scale external momentum configurations at four\nloops in the MSbar scheme and the chiral limit. One configuration is where the\noperator is inserted at zero momentum while the other two are where a non-zero\nmomentum flows out through the operator itself with one external quark momentum\nnullified. In the latter two configurations mixing of the operator with a total\nderivative twist-$2$ operator is included for renormalization group\nconsistency. In addition we compute the correlation functions of both gauge\ninvariant operators to four loops in the same scheme.",
        "split": "arXiv"
    },
    {
        "text": "Although image-based virtual try-on has made considerable progress, emerging\napproaches still encounter challenges in producing high-fidelity and robust\nfitting images across diverse scenarios. These methods often struggle with\nissues such as texture-aware maintenance and size-aware fitting, which hinder\ntheir overall effectiveness. To address these limitations, we propose a novel\ngarment perception enhancement technique, termed FitDiT, designed for\nhigh-fidelity virtual try-on using Diffusion Transformers (DiT) allocating more\nparameters and attention to high-resolution features. First, to further improve\ntexture-aware maintenance, we introduce a garment texture extractor that\nincorporates garment priors evolution to fine-tune garment feature,\nfacilitating to better capture rich details such as stripes, patterns, and\ntext. Additionally, we introduce frequency-domain learning by customizing a\nfrequency distance loss to enhance high-frequency garment details. To tackle\nthe size-aware fitting issue, we employ a dilated-relaxed mask strategy that\nadapts to the correct length of garments, preventing the generation of garments\nthat fill the entire mask area during cross-category try-on. Equipped with the\nabove design, FitDiT surpasses all baselines in both qualitative and\nquantitative evaluations. It excels in producing well-fitting garments with\nphotorealistic and intricate details, while also achieving competitive\ninference times of 4.57 seconds for a single 1024x768 image after DiT structure\nslimming, outperforming existing methods.",
        "split": "arXiv"
    },
    {
        "text": "Jointly optimizing power allocation and device association is crucial in\nInternet-of-Things (IoT) networks to ensure devices achieve their data\nthroughput requirements. Device association, which assigns IoT devices to\nspecific access points (APs), critically impacts resource allocation. Many\nexisting works often assume all data throughput requirements are satisfied,\nwhich is impractical given resource limitations and diverse demands. When\nrequirements cannot be met, the system becomes infeasible, causing congestion\nand degraded performance. To address this problem, we propose a novel framework\nto enhance IoT system robustness by solving two problems, comprising maximizing\nthe number of satisfied IoT devices and jointly maximizing both the number of\nsatisfied devices and total network throughput. These objectives often conflict\nunder infeasible circumstances, necessitating a careful balance. We thus\npropose a modified branch-and-bound (BB)-based method to solve the first\nproblem. An iterative algorithm is proposed for the second problem that\ngradually increases the number of satisfied IoT devices and improves the total\nnetwork throughput. We employ a logarithmic approximation for a lower bound on\ndata throughput and design a fixed-point algorithm for power allocation,\nfollowed by a coalition game-based method for device association. Numerical\nresults demonstrate the efficiency of the proposed algorithm, serving fewer\ndevices than the BB-based method but with faster running time and higher total\nthroughput.",
        "split": "arXiv"
    },
    {
        "text": "Multi-view learning often faces challenges in effectively leveraging images\ncaptured from different angles and locations. This challenge is particularly\npronounced when addressing inconsistencies and uncertainties between views. In\nthis paper, we propose a novel Multi-View Uncertainty-Weighted Mutual\nDistillation (MV-UWMD) method. Our method enhances prediction consistency by\nperforming hierarchical mutual distillation across all possible view\ncombinations, including single-view, partial multi-view, and full multi-view\npredictions. This introduces an uncertainty-based weighting mechanism through\nmutual distillation, allowing effective exploitation of unique information from\neach view while mitigating the impact of uncertain predictions. We extend a\nCNN-Transformer hybrid architecture to facilitate robust feature learning and\nintegration across multiple view combinations. We conducted extensive\nexperiments using a large, unstructured dataset captured from diverse,\nnon-fixed viewpoints. The results demonstrate that MV-UWMD improves prediction\naccuracy and consistency compared to existing multi-view learning approaches.",
        "split": "arXiv"
    },
    {
        "text": "We introduce FedEvPrompt, a federated learning approach that integrates\nprinciples of evidential deep learning, prompt tuning, and knowledge\ndistillation for distributed skin lesion classification. FedEvPrompt leverages\ntwo sets of prompts: b-prompts (for low-level basic visual knowledge) and\nt-prompts (for task-specific knowledge) prepended to frozen pre-trained Vision\nTransformer (ViT) models trained in an evidential learning framework to\nmaximize class evidences. Crucially, knowledge sharing across federation\nclients is achieved only through knowledge distillation on attention maps\ngenerated by the local ViT models, ensuring enhanced privacy preservation\ncompared to traditional parameter or synthetic image sharing methodologies.\nFedEvPrompt is optimized within a round-based learning paradigm, where each\nround involves training local models followed by attention maps sharing with\nall federation clients. Experimental validation conducted in a real distributed\nsetting, on the ISIC2019 dataset, demonstrates the superior performance of\nFedEvPrompt against baseline federated learning algorithms and knowledge\ndistillation methods, without sharing model parameters. In conclusion,\nFedEvPrompt offers a promising approach for federated learning, effectively\naddressing challenges such as data heterogeneity, imbalance, privacy\npreservation, and knowledge sharing.",
        "split": "arXiv"
    },
    {
        "text": "Federated domain generalization (FedDG) aims to improve the global model\ngeneralization in unseen domains by addressing data heterogeneity under\nprivacy-preserving constraints. A common strategy in existing FedDG studies\ninvolves sharing domain-specific knowledge among clients, such as spectrum\ninformation, class prototypes, and data styles. However, this knowledge is\nextracted directly from local client samples, and sharing such sensitive\ninformation poses a potential risk of data leakage, which might not fully meet\nthe requirements of FedDG. In this paper, we introduce prompt learning to adapt\npre-trained vision-language models (VLMs) in the FedDG scenario, and leverage\nlocally learned prompts as a more secure bridge to facilitate knowledge\ntransfer among clients. Specifically, we propose a novel FedDG framework\nthrough Prompt Learning and AggregatioN (PLAN), which comprises two training\nstages to collaboratively generate local prompts and global prompts at each\nfederated round. First, each client performs both text and visual prompt\nlearning using their own data, with local prompts indirectly synchronized by\nregarding the global prompts as a common reference. Second, all domain-specific\nlocal prompts are exchanged among clients and selectively aggregated into the\nglobal prompts using lightweight attention-based aggregators. The global\nprompts are finally applied to adapt VLMs to unseen target domains. As our PLAN\nframework requires training only a limited number of prompts and lightweight\naggregators, it offers notable advantages in computational and communication\nefficiency for FedDG. Extensive experiments demonstrate the superior\ngeneralization ability of PLAN across four benchmark datasets.",
        "split": "arXiv"
    },
    {
        "text": "Quantitative Information Flow (QIF) provides a robust information-theoretical\nframework for designing secure systems with minimal information leakage. While\nprevious research has addressed the design of such systems under hard\nconstraints (e.g. application limitations) and soft constraints (e.g. utility),\nscenarios often arise where the core system's behavior is considered fixed. In\nsuch cases, the challenge is to design a new component for the existing system\nthat minimizes leakage without altering the original system. In this work we\naddress this problem by proposing optimal solutions for constructing a new row,\nin a known and unmodifiable information-theoretic channel, aiming at minimizing\nthe leakage. We first model two types of adversaries: an exact-guessing\nadversary, aiming to guess the secret in one try, and a s-distinguishing one,\nwhich tries to distinguish the secret s from all the other secrets.Then, we\ndiscuss design strategies for both fixed and unknown priors by offering, for\neach adversary, an optimal solution under linear constraints, using Linear\nProgramming.We apply our approach to the problem of website fingerprinting\ndefense, considering a scenario where a site administrator can modify their own\nsite but not others. We experimentally evaluate our proposed solutions against\nother natural approaches. First, we sample real-world news websites and then,\nfor both adversaries, we demonstrate that the proposed solutions are effective\nin achieving the least leakage. Finally, we simulate an actual attack by\ntraining an ML classifier for the s-distinguishing adversary and show that our\napproach decreases the accuracy of the attacker.",
        "split": "arXiv"
    },
    {
        "text": "Having a better understanding of how locational marginal prices (LMPs) change\nhelps in price forecasting and market strategy making. This paper investigates\nthe fundamental distribution of the congestion part of LMPs in high-dimensional\nEuclidean space using an unsupervised approach. LMP models based on the\nlossless and lossy DC optimal power flow (DC-OPF) are analyzed to show the\noverlapping subspace property of the LMP data. The congestion part of LMPs is\nspanned by certain row vectors of the power transfer distribution factor (PTDF)\nmatrix, and the subspace attributes of an LMP vector uniquely are found to\nreflect the instantaneous congestion status of all the transmission lines. The\nproposed method searches for the basis vectors that span the subspaces of\ncongestion LMP data in hierarchical ways. In the bottom-up search, the data\nbelonging to 1-dimensional subspaces are detected, and other data are projected\non the orthogonal subspaces. This procedure is repeated until all the basis\nvectors are found or the basis gap appears. Top-down searching is used to\naddress the basis gap by hyperplane detection with outliers. Once all the basis\nvectors are detected, the congestion status can be identified. Numerical\nexperiments based on the IEEE 30-bus system, IEEE 118-bus system, Illinois\n200-bus system, and Southwest Power Pool are conducted to show the performance\nof the proposed method.",
        "split": "arXiv"
    },
    {
        "text": "In 2009, Calegari constructed smooth homotopy 4-spheres from monodromies of\nfibered knots. We prove that all these are diffeomorphic to the standard\n4-sphere. Our method uses 5-dimensional handlebody techniques and results on\nmapping class groups of 3-dimensional handlebodies. As an application, we\npresent potential counterexamples to the smooth 4-dimensional Schoenflies\nconjecture which are related to the work of Casson and Gordon on fibered ribbon\nknots.",
        "split": "arXiv"
    },
    {
        "text": "Physics-Informed Neural Networks (PINNs) have emerged as an influential\ntechnology, merging the swift and automated capabilities of machine learning\nwith the precision and dependability of simulations grounded in theoretical\nphysics. PINNs are often employed to solve algebraic or differential equations\nto replace some or even all steps of multi-stage computational workflows,\nleading to their significant speed-up. However, wide adoption of PINNs is still\nhindered by reliability issues, particularly at extreme ends of the input\nparameter ranges. In this study, we demonstrate this in the context of a system\nof coupled non-linear differential reaction-diffusion and heat transfer\nequations related to Fischer-Tropsch synthesis, which are solved by a\nfinite-difference method with a PINN used in evaluating their source terms. It\nis shown that the testing strategies traditionally used to assess the accuracy\nof neural networks as function approximators can overlook the peculiarities\nwhich ultimately cause instabilities of the finite-difference solver. We\npropose a domain knowledge-based modifications to the PINN architecture\nensuring its correct asymptotic behavior. When combined with an improved\nnumerical scheme employed as an initial guess generator, the proposed\nmodifications are shown to recover the overall stability of the simulations,\nwhile preserving the speed-up brought by PINN as the workflow component. We\ndiscuss the possible applications of the proposed hybrid transport equation\nsolver in context of chemical reactors simulations.",
        "split": "arXiv"
    },
    {
        "text": "We report the discovery of a rare isolated group of five dwarf galaxies\nlocated at z = 0.0086 ($D$ = 36 Mpc). All member galaxies are star-forming,\nblue, and gas-rich with $g-r$ indices ranging from 0.2 to 0.6 mag, and two of\nthem show signs of ongoing mutual interaction. The most massive member of the\ngroup has a stellar mass that is half of the Small Magellanic Cloud stellar\nmass, and the median stellar mass of the group members is 7.87 $\\times$\n10$^{7}$ M$_{\\odot}$. The derived total dynamical mass of the group is $M_{\\rm\ndyn}$ = 6.02$\\times$10$^{10}$ M$_{\\odot}$, whereas its total baryonic mass\n(stellar + HI) is 2.6$\\times$10$^{9}$ M$_{\\odot}$, which gives us the dynamical\nto baryonic mass ratio of 23. Interestingly, all galaxies found in the group\nare aligned along a straight line in the plane of the sky. The observed spatial\nextent of the member galaxies is 154 kpc, and their relative line-of-sight\nvelocity span is within 75 km s$^{-1}$. Using the spatially resolved optical\nspectra provided by DESI EDR, we find that three group members share a common\nrotational direction. With these unique properties of the group and its member\ngalaxies, we discuss the possible importance of such a system in the formation\nand evolution of dwarf galaxy groups and in testing the theory of large-scale\nstructure formation.",
        "split": "arXiv"
    },
    {
        "text": "Determining the creep compliances of orthotropic composite materials requires\nexperiments in at least three different uniaxial and biaxial loading\ndirections. Up to date, data respecting multiple climates and all anatomical\ndirections are sparse for hygro-responsive materials like Norway spruce.\nConsequently, simulation models of wood frequently over-simplify creep, e.g.,\nby proportionally scaling missing components or neglecting climatic influences.\nTo overcome such simplifications, an automated computer-controlled climatized\ncreep rack was developed, that experimentally assesses moisture-dependent\nviscoelasticity and mechanosorption in all anatomical directions. The device\nsimultaneously measures the creep strains of three dogbone tension samples,\nthree flat compression samples, and six Arcan shear samples via Digital Image\nCorrelation. This allows for ascertaining the complete orthotropic compliance\ntensors while accounting for loading direction asymmetries. This paper explains\nthe creep rack's structure and demonstrates its use by determining all nine\nindependent creep compliance components of Norway spruce at 65% relative\nhumidity. The data shows that loading asymmetry effects amount up to 16%.\nFurthermore, the found creep compliance tensor is not proportional to the\nelastic compliance tensor. By clustering the compliance components, we identify\nfour necessary components to represent the full orthotropy of the compliance\ntensor, obtainable from not less than two experiments.",
        "split": "arXiv"
    },
    {
        "text": "The constant workspace algorithms use a constant number of words in addition\nto the read-only input to the algorithm stored in an array. In this paper, we\ndevise algorithms to efficiently compute relative hulls in the plane using a\nconstant workspace. Specifically, we devise algorithms for the following three\nproblems: \\newline (i) Given two simple polygons $P$ and $Q$ with $P \\subseteq\nQ$, compute a simple polygon $P'$ with a perimeter of minimum length such that\n$P \\subseteq P' \\subseteq Q$. \\newline (ii) Given two simple polygons $P$ and\n$Q$ such that $Q$ does not intersect the interior of $P$ but it does intersects\nwith the interior of the convex hull of $P$, compute a weakly simple polygon\n$P'$ contained in the convex hull of $P$ such that the perimeter of $P'$ is of\nminimum length. \\newline (iii) Given a set $S$ of points located in a simple\npolygon $P$, compute a weakly simple polygon $P' \\subseteq P$ with a perimeter\nof minimum length such that $P'$ contains all the points in $S$. \\newline To\nour knowledge, no prior works devised algorithms to compute relative hulls\nusing a constant workspace and this work is the first such attempt.",
        "split": "arXiv"
    },
    {
        "text": "Effective usage of approximate circuits for various performance trade-offs\nrequires accurate computation of error. Several average and worst case error\nmetrics have been proposed in the literature. We propose a framework for exact\ncomputation of these error metrics, including the error rate (ER), mean\nabsolute error (MAE), mean squared error (MSE) and the worst-case error (WCE).\nWe use a combination of SAT and message-passing algorithms. Our algorithm takes\nas input the CNF formula for the exact and approximate circuits followed by a\nsubtractor that finds the difference of the two outputs. This is converted into\na tree, with each vertex of the tree associated with a sub-formulas and all\nsatisfying solutions to it. Once this is done, any probability can be computed\nby setting appropriate error bits and using a message passing algorithm on the\ntree. Since message-passing is fast, besides ER and MAE, computation of metrics\nlike MSE is also very efficient. In fact, it is possible to get the entire\nprobability distribution of the error. Besides standard benchmarks, we could\ncompute the error metrics exactly for approximate Gaussian and Sobel filters,\nwhich has not been done previously.",
        "split": "arXiv"
    },
    {
        "text": "The goal of multi-object tracking (MOT) is to detect and track all objects in\na scene across frames, while maintaining a unique identity for each object.\nMost existing methods rely on the spatial motion features and appearance\nembedding features of the detected objects in consecutive frames. Effectively\nand robustly representing the spatial and appearance features of long\ntrajectories has become a critical factor affecting the performance of MOT. We\npropose a novel approach for appearance and spatial feature representation,\nimproving upon the clustering association method MOT\\_FCG. For spatial motion\nfeatures, we propose Diagonal Modulated GIoU, which more accurately represents\nthe relationship between the position and shape of the objects. For appearance\nfeatures, we utilize a dynamic appearance representation that incorporates\nconfidence information, enabling the trajectory appearance features to be more\nrobust and global. Based on the baseline model MOT\\_FCG, we achieved 76.1 HOTA,\n80.4 MOTA and 81.3 IDF1 on the MOT17 validation set, and also achieved\ncompetitive performance on the MOT20 and DanceTrack validation sets.",
        "split": "arXiv"
    },
    {
        "text": "The NSGA-II is the most prominent multi-objective evolutionary algorithm\n(cited more than 50,000 times). Very recently, a mathematical runtime analysis\nhas proven that this algorithm can have enormous difficulties when the number\nof objectives is larger than two (Zheng, Doerr. IEEE Transactions on\nEvolutionary Computation (2024)). However, this result was shown only for the\nOneMinMax benchmark problem, which has the particularity that all solutions are\non the Pareto front, a fact heavily exploited in the proof of this result.\n  In this work, we show a comparable result for the LeadingOnesTrailingZeroes\nbenchmark. This popular benchmark problem appears more natural in that most of\nits solutions are not on the Pareto front. With a careful analysis of the\npopulation dynamics of the NGSA-II optimizing this benchmark, we manage to show\nthat when the population grows on the Pareto front, then it does so much faster\nby creating known Pareto optima than by spreading out on the Pareto front.\nConsequently, already when still a constant fraction of the Pareto front is\nunexplored, the crowding distance becomes the crucial selection mechanism, and\nthus the same problems arise as in the optimization of OneMinMax. With these\nand some further arguments, we show that the NSGA-II, with a population size by\nat most a constant factor larger than the Pareto front, cannot compute the\nPareto front in less than exponential time.",
        "split": "arXiv"
    },
    {
        "text": "We aim to provide more insights into the applicability to solar coronal\nseismology of the much-studied discrete leaky modes (DLMs) in classic analyses.\nUnder linear ideal pressureless MHD, we examine two-dimensional (2D) axial\nfundamental kink motions that arise when localized velocity exciters impact\nsome symmetric slab equilibria. Continuous structuring is allowed for. A 1D\ninitial value problem (IVP) is formulated in conjunction with an eigenvalue\nproblem (EVP) for laterally open systems, with no strict boundary conditions\n(BCs) at infinity. The IVP is solved by eigenfunction expansion, allowing a\nclear distinction between the contributions from proper eigenmodes and improper\ncontinuum eigenmodes. Example solutions are offered for parameters typical of\nactive region loops. Our solutions show that the system evolves towards long\nperiodicities due to proper eigenmodes (of order the axial Alfven time),\nwhereas the interference of the improper continuum may lead to short\nperiodicities initially (of order the lateral Alfven time). Specializing to the\nslab axis, we demonstrate that the proper contribution strengthens with the\ndensity contrast, but may occasionally be stronger for less steep density\nprofiles. Short periodicities are not guaranteed in the improper contribution,\nthe details of the initial exciter being key. When identifiable, these\nperiodicities tend to agree with the oscillation frequencies expected for DLMs,\ndespite the differences in the BCs between our EVP and classic analyses. The\neigenfunction expansion approach enables all qualitative features to be\ninterpreted as the interplay between the initial exciter and some response\nfunction, the latter solely determined by the equilibria. Classic theories for\nDLMs can find seismological applications, with time-dependent studies offering\nadditional ways for constraining initial exciters.",
        "split": "arXiv"
    },
    {
        "text": "We present analytical and numerical calculations for the photon polarization\ntensor at finite temperature and density in a constant magnetic field. We first\ndiscuss the tensor decomposition in the presence of the magnetic field which\nbreaks rotational symmetry. Then, we analytically perform all the momentum\nintegrations and numerically take the Landau level sum. We present the real and\nimaginary parts of the photon polarization tensor as functions of the momenta,\nthe chemical potential, and the finite temperature. As an application, we\nconsider the real photon limit and estimate the photon decay rate in the hot\nand dense medium. We specifically quantify the difference between the X-mode\nand the O-mode with the polarization orthogonal and parallel to the magnetic\nfield. As long as the magnetic field is weak, the decay rate of the X-mode\nphoton is larger than that of the O-mode photon, while the O-mode becomes\ndominant due to the Landau level suppression of the X-mode at strong magnetic\nfield.",
        "split": "arXiv"
    },
    {
        "text": "The installation of high-capacity fast chargers for electric vehicles (EVs)\nis posing a significant risk to the distribution grid as the increased demand\nfrom widespread residential EV charging could exceed the technical limits of\nthe distribution system. Addressing this issue is critical, given that current\ninfrastructure upgrades to enhance EV hosting capacity are both costly and\ntime-consuming. Moreover, the inherent uncertainties associated with EV\ncharging parameters make it challenging for power utilities to accurately\nassess the impact of EVs added to specific locations. To address these\nknowledge gaps, this study (a) introduces an algorithm to coordinate\nresidential EV charging, and (b) proposes a comprehensive framework that\nevaluates all transformers within a feeder. The proposed method is applied to a\nreal-world feeder, which includes 120 transformers of varying capacities. The\nresults demonstrate that this approach effectively manages a substantial number\nof EVs without overloading any of the transformers, while also pinpointing\nlocations that must be prioritized for future upgrades. This framework can\nserve as a valuable reference for utilities when conducting distribution system\nevaluations for supporting the growing EV penetration.",
        "split": "arXiv"
    },
    {
        "text": "We explore birational geometry of matroids by investigating automorphisms of\ntheir coarse Bergman fans. Combinatorial Cremona maps provide such\nautomorphisms of Bergman fans which are not induced by matroid automorphisms.\nWe investigate the structure of matroids allowing combinatorial Cremona maps\nand prove a realizability criterion in the presence of two different Cremonas.\nWe also prove that for all matroids associated to Coxeter arrangements the\ngroup of coarse automorphisms of the Bergman fan is generated by the matroid\nautomorphisms and at most one combinatorial Cremona map.",
        "split": "arXiv"
    },
    {
        "text": "Purpose: Journal Impact Factors and other citation-based indicators are\nwidely used and abused to help select journals to publish in or to estimate the\nvalue of a published article. Nevertheless, citation rates primarily reflect\nscholarly impact rather than other quality dimensions, including societal\nimpact, originality, and rigour. In contrast, Journal Quality Factors (JQFs)\nare average quality score estimates given to a journal's articles by ChatGPT.\nDesign: JQFs were compared with Polish, Norwegian and Finnish journal ranks and\nwith journal citation rates for 1,300 journals with 130,000 articles from 2021\nin large monodisciplinary journals in the 25 out of 27 Scopus broad fields of\nresearch for which it was possible. Outliers were also examined. Findings: JQFs\ncorrelated positively and mostly strongly (median correlation: 0.641) with\njournal ranks in 24 out of the 25 broad fields examined, indicating a nearly\nscience-wide ability for ChatGPT to estimate journal quality. Journal citation\nrates had similarly high correlations with national journal ranks, however, so\nJQFs are not a universally better indicator. An examination of journals with\nJQFs not matching their journal ranks suggested that abstract styles may affect\nthe result, such as whether the societal contexts of research are mentioned.\nLimitations: Different journal rankings may have given different findings\nbecause there is no agreed meaning for journal quality. Implications: The\nresults suggest that JQFs are plausible as journal quality indicators in all\nfields and may be useful for the (few) research and evaluation contexts where\njournal quality is an acceptable proxy for article quality, and especially for\nfields like mathematics for which citations are not strong indicators of\nquality. Originality: This is the first attempt to estimate academic journal\nvalue with a Large Language Model.",
        "split": "arXiv"
    },
    {
        "text": "A measurement of the $B^{0}$ meson lifetime and related properties using $B^0\n\\to J/\\psi K^{*0}$ decays in data from 13 TeV proton-proton collisions with an\nintegrated luminosity of 140 fb$^{-1}$ recorded by the ATLAS detector at the\nLHC is presented. The measured effective lifetime is $$ \\tau = 1.5053 \\pm\n0.0012 ~\\mathrm{(stat.)} \\pm 0.0035 ~\\mathrm{(syst.)~ps}. $$ The average decay\nwidth extracted from the effective lifetime, using parameters from external\nsources, is $$ \\Gamma_d = 0.6639 \\pm 0.0005 ~\\mathrm{(stat.)} \\pm 0.0016\n~\\mathrm{(syst.)}\\pm 0.0038 ~\\textrm{(ext.)} \\textrm{ ps}^{-1}, $$ where the\nuncertainties are statistical, systematic and from external sources. The\nearlier ATLAS measurement of $\\Gamma_s$ in the $B^0_s \\to J/\\psi\\phi$ decay was\nused to derive a value for the ratio of the average decay widths $\\Gamma_d$ and\n$\\Gamma_s$ for $B^{0}$ and $B_s^{0}$ mesons respectively, of $$ \\frac{\\Gamma_d\n}{\\Gamma_s } = 0.9905 \\pm 0.0022 ~\\textrm{(stat.)} \\pm 0.0036 ~\\textrm{(syst.)}\n\\pm 0.0057 ~\\textrm{(ext.)}. $$ The measured lifetime, average decay width and\ndecay width ratio are in agreement with theoretical predictions and with\nmeasurements by other experiments. This measurement provides the most precise\nresult of the effective lifetime of the $B^{0}$ meson to date.",
        "split": "arXiv"
    },
    {
        "text": "Classification in the sense of similarity is an important issue. In this\npaper, we study similarity classification in Topological Data Analysis. We\ndefine a pseudometric $d_{S}^{(p)}$ to measure the distance between barcodes\ngenerated by persistent homology groups of topological spaces, and we provide\nthat our pseudometric $d_{S}^{(2)}$ is a similarity invariant. Thereby, we\nestablish a connection between Operator Theory and Topological Data Analysis.\nWe give the calculation formula of the pseudometric $d_{S}^{(2)}$\n$(d_{S}^{(1)})$ by arranging all eigenvalues of matrices determined by barcodes\nin descending order to get the infimum over all matchings. Since conformal\nlinear transformation is one representative type of similarity transformations,\nwe construct comparative experiments on both synthetic datasets and waves from\nan online platform to demonstrate that our pseudometric $d_{S}^{(2)}$\n$(d_{S}^{(1)})$ is stable under conformal linear transformations, whereas the\nbottleneck and Wasserstein distances are not. In particular, our pseudometric\non waves is only related to the waveform but is independent on the frequency\nand amplitude. Furthermore, the computation time for $d_{S}^{(2)}$\n$(d_{S}^{(1)})$ is significantly less than the computation time for bottleneck\ndistance and is comparable to the computation time for accelerated Wasserstein\ndistance between barcodes.",
        "split": "arXiv"
    },
    {
        "text": "This paper addresses the collision detection problem in population protocols.\nThe network consists of state machines called agents. At each time step,\nexactly one pair of agents is chosen uniformly at random to have an\ninteraction, changing the states of the two agents. The collision detection\nproblem involves each agent starting with an input integer between $1$ and $n$,\nwhere $n$ is the number of agents, and requires those agents to determine\nwhether there are any duplicate input values among all agents. Specifically,\nthe goal is for all agents to output false if all input values are distinct,\nand true otherwise.\n  In this paper, we present an algorithm that requires a polynomial number of\nstates per agent and solves the collision detection problem with probability\none in sub-linear parallel time, both with high probability and in expectation.\nTo the best of our knowledge, this algorithm is the first to solve the\ncollision detection problem using a polynomial number of states within\nsublinear parallel time, affirmatively answering the question raised by Burman,\nChen, Chen, Doty, Nowak, Severson, and Xu [PODC 2021] for the first time.",
        "split": "arXiv"
    },
    {
        "text": "Autonomous manipulation in everyday tasks requires flexible action generation\nto handle complex, diverse real-world environments, such as objects with\nvarying hardness and softness. Imitation Learning (IL) enables robots to learn\ncomplex tasks from expert demonstrations. However, a lot of existing methods\nrely on position/unilateral control, leaving challenges in tasks that require\nforce information/control, like carefully grasping fragile or varying-hardness\nobjects. As the need for diverse controls increases, there are demand for\nlow-cost bimanual robots that consider various motor inputs. To address these\nchallenges, we introduce Bilateral Control-Based Imitation Learning via Action\nChunking with Transformers(Bi-ACT) and\"A\" \"L\"ow-cost \"P\"hysical \"Ha\"rdware\nConsidering Diverse Motor Control Modes for Research in Everyday Bimanual\nRobotic Manipulation (ALPHA-$\\alpha$). Bi-ACT leverages bilateral control to\nutilize both position and force information, enhancing the robot's adaptability\nto object characteristics such as hardness, shape, and weight. The concept of\nALPHA-$\\alpha$ is affordability, ease of use, repairability, ease of assembly,\nand diverse control modes (position, velocity, torque), allowing\nresearchers/developers to freely build control systems using ALPHA-$\\alpha$. In\nour experiments, we conducted a detailed analysis of Bi-ACT in unimanual\nmanipulation tasks, confirming its superior performance and adaptability\ncompared to Bi-ACT without force control. Based on these results, we applied\nBi-ACT to bimanual manipulation tasks. Experimental results demonstrated high\nsuccess rates in coordinated bimanual operations across multiple tasks. The\neffectiveness of the Bi-ACT and ALPHA-$\\alpha$ can be seen through\ncomprehensive real-world experiments. Video available at:\nhttps://mertcookimg.github.io/alpha-biact/",
        "split": "arXiv"
    },
    {
        "text": "This article is concerned with ``up to $C^{2, \\alpha}$-regularity results''\nabout a mixed local-nonlocal nonlinear elliptic equation which is driven by the\nsuperposition of Laplacian and fractional Laplacian operators.\n  First of all, an estimate on the $L^\\infty$ norm of weak solutions is\nestablished for more general cases than the ones present in the literature,\nincluding here critical nonlinearities.\n  We then prove the interior $C^{1,\\alpha}$-regularity and the\n$C^{1,\\alpha}$-regularity up to the boundary of weak solutions, which extends\nprevious results by the authors [X. Su, E. Valdinoci, Y. Wei and J. Zhang,\nMath. Z. (2022)], where the nonlinearities considered were of subcritical type.\n  In addition, we establish the interior $C^{2,\\alpha}$-regularity of solutions\nfor all $s\\in(0,1)$ and the $C^{2,\\alpha}$-regularity up to the boundary for\nall $s\\in(0,\\frac{1}{2})$, with sharp regularity exponents.\n  For further perusal, we also include a strong maximum principle and some\nproperties about the principal eigenvalue.",
        "split": "arXiv"
    },
    {
        "text": "Variable Subset Forecasting (VSF) refers to a unique scenario in multivariate\ntime series forecasting, where available variables in the inference phase are\nonly a subset of the variables in the training phase. VSF presents significant\nchallenges as the entire time series may be missing, and neither inter- nor\nintra-variable correlations persist. Such conditions impede the effectiveness\nof traditional imputation methods, primarily focusing on filling in individual\nmissing data points. Inspired by the principle of feature engineering that not\nall variables contribute positively to forecasting, we propose Task-Oriented\nImputation for VSF (TOI-VSF), a novel framework shifts the focus from accurate\ndata recovery to directly support the downstream forecasting task. TOI-VSF\nincorporates a self-supervised imputation module, agnostic to the forecasting\nmodel, designed to fill in missing variables while preserving the vital\ncharacteristics and temporal patterns of time series data. Additionally, we\nimplement a joint learning strategy for imputation and forecasting, ensuring\nthat the imputation process is directly aligned with and beneficial to the\nforecasting objective. Extensive experiments across four datasets demonstrate\nthe superiority of TOI-VSF, outperforming baseline methods by $15\\%$ on\naverage.",
        "split": "arXiv"
    },
    {
        "text": "A $k$-star is a complete bipartite graph $K_{1,k}$. A partial $k$-star design\nof order $n$ is a pair $(V,\\mathcal{A})$ where $V$ is a set of $n$ vertices and\n$\\mathcal{A}$ is a set of edge-disjoint $k$-stars whose vertex sets are subsets\nof $V$. If each edge of the complete graph with vertex set $V$ is in some star\nin $\\mathcal{A}$, then $(V,\\mathcal{A})$ is a (complete) $k$-star design. We\nsay that $(V,\\mathcal{A})$ is completable if there is a $k$-star design\n$(V,\\mathcal{B})$ such that $\\mathcal{A} \\subseteq \\mathcal{B}$. In this paper\nwe determine, for all $k$ and $n$, the minimum number of stars in an\nuncompletable partial $k$-star design of order $n$.",
        "split": "arXiv"
    },
    {
        "text": "We present the first HI mass function (HIMF) measurement for the recent FAST\nAll Sky HI (FASHI) survey and the most complete measurements of HIMF in the\nlocal universe so far by combining the HI catalogues from HI Parkes All Sky\nSurvey (HIPASS), Arecibo Legacy Fast ALFA (ALFALFA) and FASHI surveys at\nredshift 0 < z < 0.05, covering 76% of the entire sky. We adopt the same\nmethods to estimate distances, calculate sample completeness, and determine the\nHIMF for all three surveys. The best-fitting Schechter function for the total\nHIMF has a low-mass slope parameter alpha = -1.30 and a knee mass log(Ms) =\n9.86 and a normalization phi_s = 0.00658. This gives the cosmic HI abundance\nomega_HI= 0.000454. We find that a double Schechter function with the same\nslope alpha better describes our HIMF, and the two different knee masses are\nlog(Ms1) = 9.96 and log(Ms2) = 9.65. We verify that the measured HIMF is\nmarginally affected by the choice of distance estimates. The effect of cosmic\nvariance is significantly suppressed by combining the three surveys and it\nprovides a unique opportunity to obtain an unbiased estimate of the HIMF in the\nlocal universe.",
        "split": "arXiv"
    },
    {
        "text": "Visual navigation takes inspiration from humans, who navigate in previously\nunseen environments using vision without detailed environment maps. Inspired by\nthis, we introduce a novel no-RL, no-graph, no-odometry approach to visual\nnavigation using feudal learning to build a three tiered agent. Key to our\napproach is a memory proxy map (MPM), an intermediate representation of the\nenvironment learned in a self-supervised manner by the high-level manager agent\nthat serves as a simplified memory, approximating what the agent has seen. We\ndemonstrate that recording observations in this learned latent space is an\neffective and efficient memory proxy that can remove the need for graphs and\nodometry in visual navigation tasks. For the mid-level manager agent, we\ndevelop a waypoint network (WayNet) that outputs intermediate subgoals, or\nwaypoints, imitating human waypoint selection during local navigation. For the\nlow-level worker agent, we learn a classifier over a discrete action space that\navoids local obstacles and moves the agent towards the WayNet waypoint. The\nresulting feudal navigation network offers a novel approach with no RL, no\ngraph, no odometry, and no metric map; all while achieving SOTA results on the\nimage goal navigation task.",
        "split": "arXiv"
    },
    {
        "text": "Today navigation applications (e.g., Waze and Google Maps) enable human users\nto learn and share the latest traffic observations, yet such information\nsharing simply aids selfish users to predict and choose the shortest paths to\njam each other. Prior routing game studies focus on myopic users in\noversimplified one-shot scenarios to regulate selfish routing via information\nhiding or pricing mechanisms. For practical human-in-the-loop learning (HILL)\nin repeated routing games, we face non-myopic users of differential past\nobservations and need new mechanisms (preferably non-monetary) to persuade\nusers to adhere to the optimal path recommendations. We model the repeated\nrouting game in a typical parallel transportation network, which generally\ncontains one deterministic path and $N$ stochastic paths. We first prove that\nno matter under the information sharing mechanism in use or the latest routing\nliterature's hiding mechanism, the resultant price of anarchy (PoA) for\nmeasuring the efficiency loss from social optimum can approach infinity,\ntelling arbitrarily poor exploration-exploitation tradeoff over time. Then we\npropose a novel user-differential probabilistic recommendation (UPR) mechanism\nto differentiate and randomize path recommendations for users with differential\nlearning histories. We prove that our UPR mechanism ensures interim individual\nrationality for all users and significantly reduces $\\text{PoA}=\\infty$ to\nclose-to-optimal $\\text{PoA}=1+\\frac{1}{4N+3}$, which cannot be further reduced\nby any other non-monetary mechanism. In addition to theoretical analysis, we\nconduct extensive experiments using real-world datasets to generalize our\nrouting graphs and validate the close-to-optimal performance of UPR mechanism.",
        "split": "arXiv"
    },
    {
        "text": "Compute-and-forward (CF) is a relaying strategy which allows the relay to\ndecode a linear combination of the transmitted messages. This work studies the\noptimal power allocation problem for the CF scheme in fast fading channels for\nmaximizing the symmetric computation rate, which is a non-convex optimization\nproblem with no simple analytical or numerical solutions. In the first part of\nthe paper, we investigate the problem when there are finitely many channel\nstates (discrete case). We establish several important properties of the\noptimal solutions and show that if all users share the same power allocation\npolicy (symmetric policy), the optimal solution takes the form of a\nwater-filling type when the power constraint exceeds a certain threshold.\nHowever, if asymmetric policies are allowed, the optimal solution does not take\nthis form for any power constraint. We propose a low-complexity order-based\nalgorithm for both scenarios and compare its performance with baseline\nalgorithms. In the second part of the paper, we state relevant results when the\nchannel coefficients are modelled as continuous random variables (continuous\ncase) and propose a similar low-complexity iterative algorithm for the\nsymmetric policy scenario. Numerical results are provided for both discrete and\ncontinuous cases. It is shown that in general our proposed algorithm finds good\nsuboptimal solutions with low complexity, and for some examples considered,\nfinds an exact optimal solution.",
        "split": "arXiv"
    },
    {
        "text": "Mutation testing was proposed to identify weaknesses in test suites by\nrepeatedly generating artificially faulty versions of the software (mutants)\nand determining if the test suite is sufficient to detect them (kill them).\nWhen the tests are insufficient, each surviving mutant provides an opportunity\nto improve the test suite. We conducted a study and found that many such\nsurviving mutants (up to 84% for the subjects of our study) are detectable by\nsimply augmenting existing tests with additional assertions, or assertion\namplification. Moreover, we find that many of these mutants are detectable by\nmultiple existing tests, giving developers options for how to detect them. To\nhelp with these challenges, we created a technique that performs memory-state\nanalysis to identify candidate assertions that developers can use to detect the\nsurviving mutants. Additionally, we build upon prior research that identifies\n``crossfiring'' opportunities -- tests that coincidentally kill multiple\nmutants. To this end, we developed a theoretical model that describes the\nvarying granularities that crossfiring can occur in the existing test suite,\nwhich provide opportunities and options for how to kill surviving mutants. We\noperationalize this model to an accompanying technique that optimizes the\nassertion amplification of the existing tests to crossfire multiple mutants\nwith fewer added assertions, optionally concentrated within fewer tests. Our\nexperiments show that we can kill all surviving mutants that are detectable\nwith existing test data with only 1.1% of the identified assertion candidates,\nand increasing by a factor of 6x, on average, the number of killed mutants from\namplified tests, over tests that do not crossfire.",
        "split": "arXiv"
    },
    {
        "text": "We study the booklink, a braid-like embedding with local maxima and minima,\nand the bridge-braid spectrum of a link, which captures the smallest number of\nbraid-strands in a booklink with a prescribed number of critical points. This\nspectrum spans the gap between the classical bridge and braid indices. We apply\na foliation theory argument to provide a formula for the spectra of both split\nand composite links. We then generate a table for the spectra of all prime\nknots up to 9 crossings.",
        "split": "arXiv"
    },
    {
        "text": "Let ${\\mathcal M}_g$ be the moduli space of compact connected Riemann\nsurfaces of genus $g\\geq 2$ and let $\\widehat{{\\mathcal M}_g}$ be its\nDeligne-Mumford compactification, which is stratified by the topological type\nof the stable Riemann surfaces. We consider the equisymmetric loci in $\\mathcal\nM_g$ corresponding to Riemann surfaces whose automorphism group is abelian and\ndetermine the topological type of the maximal dimension strata at their\nboundary. For the particular cases of the hyperelliptic and the cyclic\n$p$-gonal actions, we describe all the topological strata at the boundary in\nterms of trees with a fixed number of edges.",
        "split": "arXiv"
    },
    {
        "text": "For a hypergraph $\\mathbb{H}$ on $[n]$, the hypergraphic poset $P_\\mathbb{H}$\nis the transitive closure of the oriented skeleton of the hypergraphic polytope\n$\\triangle_\\mathbb{H}$ (the Minkowski sum of the standard simplices\n$\\triangle_H$ for all $H \\in \\mathbb{H}$). Hypergraphic posets include the weak\norder for the permutahedron (when $\\mathbb{H}$ is the complete graph on $[n]$)\nand the Tamari lattice for the associahedron (when $\\mathbb{H}$ is the set of\nall intervals of $[n]$), which motivates the study of lattice properties of\nhypergraphic posets. In this paper, we focus on interval hypergraphs, where all\nhyperedges are intervals of $[n]$. We characterize the interval hypergraphs\n$\\mathbb{I}$ for which $P_\\mathbb{I}$ is a lattice, a distributive lattice, a\nsemidistributive lattice, and a lattice quotient of the weak order.",
        "split": "arXiv"
    },
    {
        "text": "Optimal control theory extending from the calculus of variations has not been\nused to study the wind turbine power system (WTPS) control problem, which aims\nat achieving two targets: (i) maximizing power generation in lower wind speed\nconditions; and (ii) maintaining the output power at the rated level in high\nwind speed conditions. A lack of an optimal control framework for the WTPS\n(i.e., no access to actual optimal control trajectories) reduces optimal\ncontrol design potential and prevents competing control methods of WTPSs to\nhave a reference control solution for comparison. In fact, the WTPS control\nliterature often relies on reduced and linearized models of WTPSs, and avoids\nthe nonsmoothness present in the system during transitions between different\nconditions of operation. In this paper, we introduce a novel optimal control\nframework for the WTPS control problem. We use in our formulation a recent\naccurate, nonlinear differential-algebraic equation (DAE) model of WTPSs, which\nwe then generalize over all wind speed ranges using non-smooth functions. We\nalso use developments in nonsmooth optimal control theory to take into account\nnonsmoothness present in the system. We implement this new WTPS optimal control\napproach to solve the problem numerically, including (i) different wind speed\nprofiles for testing the system response; (ii) real-world wind data; and (iii)\na comparison with smoothing and naive approaches. Results show the\neffectiveness of the proposed approach.",
        "split": "arXiv"
    },
    {
        "text": "While deep learning has revolutionized computer-aided drug discovery, the AI\ncommunity has predominantly focused on model innovation and placed less\nemphasis on establishing best benchmarking practices. We posit that without a\nsound model evaluation framework, the AI community's efforts cannot reach their\nfull potential, thereby slowing the progress and transfer of innovation into\nreal-world drug discovery. Thus, in this paper, we seek to establish a new gold\nstandard for small molecule drug discovery benchmarking, WelQrate.\nSpecifically, our contributions are threefold: WelQrate Dataset Collection - we\nintroduce a meticulously curated collection of 9 datasets spanning 5\ntherapeutic target classes. Our hierarchical curation pipelines, designed by\ndrug discovery experts, go beyond the primary high-throughput screen by\nleveraging additional confirmatory and counter screens along with rigorous\ndomain-driven preprocessing, such as Pan-Assay Interference Compounds (PAINS)\nfiltering, to ensure the high-quality data in the datasets; WelQrate Evaluation\nFramework - we propose a standardized model evaluation framework considering\nhigh-quality datasets, featurization, 3D conformation generation, evaluation\nmetrics, and data splits, which provides a reliable benchmarking for drug\ndiscovery experts conducting real-world virtual screening; Benchmarking - we\nevaluate model performance through various research questions using the\nWelQrate dataset collection, exploring the effects of different models, dataset\nquality, featurization methods, and data splitting strategies on the results.\nIn summary, we recommend adopting our proposed WelQrate as the gold standard in\nsmall molecule drug discovery benchmarking. The WelQrate dataset collection,\nalong with the curation codes, and experimental scripts are all publicly\navailable at WelQrate.org.",
        "split": "arXiv"
    },
    {
        "text": "We computed for the first time the $\\tau$ data-driven Euclidean windows for\nthe hadronic vacuum polarization contribution to the muon g-2. We showed that\n$\\tau$-based results agree with the available lattice window evaluations and\nwith the full result. On the intermediate window, where all lattice evaluations\nare rather precise and agree, $\\tau$-based results are compatible with them.\nThis is particularly interesting, given that the disagreement of the $e^+e^-$\ndata-driven result with the lattice values in this window is the main cause for\ntheir discrepancy, affecting the interpretation of the $a_\\mu$ measurement in\nterms of possible new physics.",
        "split": "arXiv"
    },
    {
        "text": "A recent result by Kardo\\v{s}, M\\'a\\v{c}ajov\\'a and Zerafa [J. Comb. Theory,\nSer. B. 160 (2023) 1--14] related to the famous Berge-Fulkerson conjecture\nimplies that given an arbitrary set of odd pairwise edge-disjoint cycles, say\n$\\mathcal O$, in a bridgeless cubic graph, there exists a $1$-factor\nintersecting all cycles in $\\mathcal O$ in at least one edge. This remarkable\nresult opens up natural generalizations in the case of an $r$-regular graph $G$\nand a $t$-factor $F$, with $r$ and $t$ being positive integers. In this paper,\nwe start the study of this problem by proving necessary and sufficient\nconditions on $G$, $t$ and $r$ to assure the existence of a suitable $F$ for\nany possible choice of the set $\\mathcal O$. First of all, we show that $G$\nneeds to be $2$-connected. Under this additional assumption, we highlight how\nthe ratio $\\frac{t}{r}$ seems to play a crucial role in assuring the existence\nof a $t$-factor $F$ with the required properties by proving that $\\frac{t}{r}\n\\geq \\frac{1}{3}$ is a further necessary condition. We suspect that this\ncondition is also sufficient, and we confirm it in the case\n$\\frac{t}{r}=\\frac{1}{3}$, generalizing the case $t=1$ and $r=3$ proved by\nKardo\\v{s}, M\\'a\\v{c}ajov\\'a, Zerafa, and in the case $\\frac{t}{r}=\\frac{1}{2}$\nwith $t$ even. Finally, we provide further results in the case of cycles of\narbitrary length.",
        "split": "arXiv"
    },
    {
        "text": "We consider fair resource allocation in sequential decision-making\nenvironments modeled as weakly coupled Markov decision processes, where\nresource constraints couple the action spaces of $N$ sub-Markov decision\nprocesses (sub-MDPs) that would otherwise operate independently. We adopt a\nfairness definition using the generalized Gini function instead of the\ntraditional utilitarian (total-sum) objective. After introducing a general but\ncomputationally prohibitive solution scheme based on linear programming, we\nfocus on the homogeneous case where all sub-MDPs are identical. For this case,\nwe show for the first time that the problem reduces to optimizing the\nutilitarian objective over the class of \"permutation invariant\" policies. This\nresult is particularly useful as we can exploit Whittle index policies in the\nrestless bandits setting while, for the more general setting, we introduce a\ncount-proportion-based deep reinforcement learning approach. Finally, we\nvalidate our theoretical findings with comprehensive experiments, confirming\nthe effectiveness of our proposed method in achieving fairness.",
        "split": "arXiv"
    },
    {
        "text": "Guessing Codeword Decoding (GCD) is a recently proposed soft-input forward\nerror correction decoder for arbitrary linear forward error correction codes.\nInspired by recent proposals that leverage binary linear codebook structure to\nreduce the number of queries made by Guessing Random Additive Noise Decoding\n(GRAND), for binary linear codes that include one full single parity-check\n(SPC) bit, we show that it is possible to reduce the number of queries made by\nGCD by a factor of up to 2 without impacting decoding precision. The greatest\nguesswork reduction is realized at lower SNRs, where the decoder output is\nusually correct but guesswork is most burdensome. Codes without a SPC can be\nmodified to include one by swapping a column of the generator matrix for an\nall-ones column to obtain a decoding complexity advantage, and we demonstrate\nthat this can often be done without losing decoding precision. To practically\navail of the complexity advantage, a noise effect pattern generator capable of\nproducing sequences for given Hamming weights, such as the one underlying\nORBGRAND, is necessary.",
        "split": "arXiv"
    },
    {
        "text": "Self-assembly of nanoscale synthetic subunits is a promising bottom-up\nstrategy for fabrication of functional materials. Here, we introduce a design\nprinciple for DNA origami nanoparticles of 50-nm size, exploiting modularity,\nto make a family of versatile subunits that can target an abundant variety of\nself-assembled structures. The subunits are based on a core module that remains\nconstant among all the subunits. Variable bond modules and angle modules are\nadded to the exterior of the core to control interaction specificity, strength\nand structural geometry. A series of subunits with designed bond/angle modules\nare demonstrated to self-assemble into a rich variety of structures with\ndifferent Gaussian curvatures, exemplified by sheets, spherical shells, and\ntubes. The design features flexible joints implemented using single-stranded\nangle modules between adjacent subunits whose mechanical properties, such as\nbending elastic moduli, are inferred from cryo-EM. Our findings suggest that\nincorporating a judicious amount of flexibility in the bond provides error\ntolerances in design and fabrication while still guaranteeing target fidelity.\nLastly, while increasing flexibility could introduce greater variability and\npotential errors in assembly, these effects can be counterbalanced by\nincreasing the number of distinct bonds, thereby allowing for precise targeting\nof specific structural binding angles within a broad range of configurations.",
        "split": "arXiv"
    },
    {
        "text": "In a recent paper (Gonzalez et al., 2023), we investigated the motion of\ngrains within a granular bed sheared by a viscous fluid, and showed how\nsegregation and hardening occur in the fluid- (bedload) and solid-like (creep)\nregions. In this paper, we inquire further into the mechanisms leading to grain\nsegregation in a bidisperse bed, and how the forces are distributed. For that,\nwe carried out numerical simulations at the grain scale by using CFD-DEM\n(computational fluid dynamics-discrete element method), with which we were able\nto track the positions, velocities, forces, and solid contacts underwent by\neach grain. We show that during the upward motion of large grains the direct\naction of fluid forces is significant in the middle and upper parts of the\nbedload layer, while only contact forces are significant in the creep layer and\nlower part of the bedload layer. We also show that in all cases the particles\nexperience a moment about a -45 degrees contact point (with respect to the\nhorizontal plane) when migrating upward, whether entrained by other contacts or\ndirectly by the fluid. In addition, we show the variations in the average\nsolid-solid contacts, and how forces caused either by solid-solid contacts or\ndirectly by the fluid are distributed within the bed. Our results provide the\nrelationship between force propagation and reorganization of grains in sheared\nbeds, explaining mechanisms found, for example, in river beds and landslides.",
        "split": "arXiv"
    },
    {
        "text": "This study aimed to identify and analyze the characteristics of highly cited\npublications in the field of artificial intelligence within the Science\nCitation Index Expanded from 1991 to 2022. The assessment focused on documents\nthat garnered 100 citations or more from the Web of Science Core Collection,\nspanning from their publication date to the end of 2022. Various aspects of\nthese documents were analyzed, encompassing document types, the distribution of\nannual production, the average number of citations per publication, Web of\nScience categories, and journals. Moreover, the publication performance of\ncountries, institutions, and authors underwent evaluation through six\npublication indicators and associated citation metrics. To facilitate a\ncomprehensive comparison of the authors research performance, the Y-index was\nemployed. The outcomes of the analysis revealed that a majority of the highly\ncited articles were published within the Web of Science categories of\n\"artificial intelligence computer science\" and \"electrical and electronic\nengineering\". Notably, the United States exhibited dominance across all six\npublication indicators. Within the realm of average citations per publication,\nthe United Kingdom emerged as a leader for independent articles, first-author\narticles, and corresponding-author articles. Exceptionally, the Chinese Academy\nof Sciences in China and the Massachusetts Institute of Technology (MIT) in the\nUSA, contributed significantly. The significant impact of highly cited articles\nextended to the output of Stanford University in the USA. B.L. Bassler\npublished the most highly cited articles. Upon employing the Y-index analysis,\nJ.E.P. Santos was identified as having the highest potential for publication.\nIn addition to the primary analysis, this study also presented nine classic\narticles that have left an indelible mark on artificial intelligence research.",
        "split": "arXiv"
    },
    {
        "text": "Machine learning (ML) defenses protect against various risks to security,\nprivacy, and fairness. Real-life models need simultaneous protection against\nmultiple different risks which necessitates combining multiple defenses. But\ncombining defenses with conflicting interactions in an ML model can be\nineffective, incurring a significant drop in the effectiveness of one or more\ndefenses being combined. Practitioners need a way to determine if a given\ncombination can be effective. Experimentally identifying effective combinations\ncan be time-consuming and expensive, particularly when multiple defenses need\nto be combined. We need an inexpensive, easy-to-use combination technique to\nidentify effective combinations.\n  Ideally, a combination technique should be (a) accurate (correctly identifies\nwhether a combination is effective or not), (b) scalable (allows combining\nmultiple defenses), (c) non-invasive (requires no change to the defenses being\ncombined), and (d) general (is applicable to different types of defenses).\nPrior works have identified several ad-hoc techniques but none satisfy all the\nrequirements above. We propose a principled combination technique, Def\\Con, to\nidentify effective defense combinations. Def\\Con meets all requirements,\nachieving 90% accuracy on eight combinations explored in prior work and 81% in\n30 previously unexplored combinations that we empirically evaluate in this\npaper.",
        "split": "arXiv"
    },
    {
        "text": "We build and discuss a low energy effective field theory for anisotropic\nanti-ferromagnets in presence of an external magnetic field. Such an effective\ntheory is simple yet rich, and features a number of phenomena such as the\nappearance of gapped Goldstones, pseudo-Goldstones and a \"spin flop\" phase\ntransition, all within the regime of validity of the theory. We also discuss in\ndetail, the quantization procedure of the free theory in the presence of a\nmagnetic field, which is made non-trivial by the presence of a single-time\nderivative term. This class of materials make a precious test field for exotic\nphenomena in quantum field theory. Moreover, we explicitly perform the matching\nof the effective theory to the short distance theory of a specific\nanti-ferromagnet, namely, nickel oxide. The latter is particularly relevant in\nlight of recent proposals of employing this material towards the hunt for light\ndark matter. As a byproduct of our study, we also re-evaluate the role played\nby discrete symmetries in magnetic materials, presenting it in a way that is\ncompletely consistent with the proper low energy EFT ideology.",
        "split": "arXiv"
    },
    {
        "text": "Given a source image of a clothed person (an image subject), AI-based\nnudification applications can produce nude (undressed) images of that person.\nMoreover, not only do such applications exist, but there is ample evidence of\nthe use of such applications in the real world and without the consent of an\nimage subject. Still, despite the growing awareness of the existence of such\napplications and their potential to violate the rights of image subjects and\ncause downstream harms, there has been no systematic study of the nudification\napplication ecosystem across multiple applications. We conduct such a study\nhere, focusing on 20 popular and easy-to-find nudification websites. We study\nthe positioning of these web applications (e.g., finding that most sites\nexplicitly target the nudification of women, not all people), the features that\nthey advertise (e.g., ranging from undressing-in-place to the rendering of\nimage subjects in sexual positions, as well as differing user-privacy options),\nand their underlying monetization infrastructure (e.g., credit cards and\ncryptocurrencies). We believe this work will empower future, data-informed\nconversations -- within the scientific, technical, and policy communities -- on\nhow to better protect individuals' rights and minimize harm in the face of\nmodern (and future) AI-based nudification applications. Content warning: This\npaper includes descriptions of web applications that can be used to create\nsynthetic non-consensual explicit AI-created imagery (SNEACI). This paper also\nincludes an artistic rendering of a user interface for such an application.",
        "split": "arXiv"
    },
    {
        "text": "The quantum approximate optimization algorithm (QAOA) is a near-term quantum\nalgorithm aimed at solving combinatorial optimization problems. Since its\nintroduction, various generalizations have emerged, spanning modifications to\nthe initial state, phase unitaries, and mixer unitaries. In this work, we\npresent an analytical study of broad families of QAOA variants. We begin by\nexamining a family of QAOA with product mixers, which includes single-body\nmixers parametrized by multiple variational angles, and derive exact analytical\nexpressions for the cost expectation on weighted problem graphs in the\nsingle-layer ansatz setting. We then analyze a family of QAOA that employs\nmany-body Grover-type mixers, deriving analogous analytical expressions for\nweighted problem hypergraphs in the setting of arbitrarily many circuit ansatz\nlayers. For both families, we allow individual phase angles for each node and\nedge (hyperedge) in the problem graph (hypergraph). Our results reveal that, in\ncontrast to product mixers, the Grover mixer is sensitive to contributions from\ncycles of all lengths in the problem graph, exhibiting a form of non-locality.\nOur study advances the understanding of QAOA's behavior in general scenarios,\nproviding a foundation for further theoretical exploration.",
        "split": "arXiv"
    },
    {
        "text": "We present the first set of high-resolution, hydrodynamical cosmological\nsimulations of galaxy formation in a Fuzzy Dark Matter (FDM) framework. These\nsimulations were performed with a new version of the GASOLINE2 code, known as\nFUZZY-GASOLINE, which can simulate quantum FDM effects alongside a\ncomprehensive baryonic model that includes metal cooling, star formation,\nsupernova feedback, and black hole physics, previously used in the NIHAO\nsimulation suite. Using thirty zoom-in simulations of galaxies with halo masses\nin the range $10^9 \\lesssim M_{\\text{halo}}/M_{\\odot} \\lesssim 10^{11}$, we\nexplore how the interplay between FDM quantum potential and baryonic processes\ninfluences dark matter distributions and observable galaxy properties. Our\nfindings indicate that both baryons and low-mass FDM contribute to core\nformation within dark matter profiles, though through distinct mechanisms:\nFDM-induced cores emerge in all haloes, particularly within low-mass systems at\nhigh redshift, while baryon-driven cores form within a specific mass range and\nat low redshift. Despite these significant differences in dark matter\nstructure, key stellar observables such as star formation histories and\nvelocity dispersion profiles remain remarkably similar to predictions from the\nCold Dark Matter (CDM) model, making it challenging to distinguish between CDM\nand FDM solely through stellar observations.",
        "split": "arXiv"
    },
    {
        "text": "The mass of galaxy clusters is a critical quantity for probing cluster\ncosmology and testing theories of gravity, but its measurement could be biased\ngiven assumptions are inevitable. In this paper, we employ and compare two mass\nproxies for galaxy clusters: thermodynamics of the intracluster medium and\nkinematics of member galaxies. We select 22 galaxy clusters from the cluster\ncatalog in the first SRG/eROSITA All-Sky Survey (eRASS1) that have sufficient\noptical and near-infrared observations. We generate multi-band images in the\nenergy range of (0.3, 7) keV for each cluster, and derive their temperature\nprofiles, gas mass profiles and hydrostatic mass profiles using a parametric\napproach that does not assume dark matter halo models. With spectroscopically\nconfirmed member galaxies collected from multiple surveys, we numerically solve\nthe spherical Jeans equation for their dynamical mass profiles. Our results\nquantify the correlation between dynamical mass and line-of-sight velocity\ndispersion with an rms scatter of 0.14 dex. We find the two mass proxies lead\nto roughly the same total mass, with no observed systematic bias. As such, the\n$\\sigma_8$ tension is not specific to hydrostatic mass or weak lensing shears,\nbut also appears with galaxy kinematics. We also compare our hydrostatic masses\nwith the latest weak lensing masses inferred with scaling relations. The\ncomparison shows the weak lensing mass is significantly higher than our\nhydrostatic mass by $\\sim$110%. This might explain the significantly larger\nvalue of $\\sigma_8$ from the latest measurement using eRASS1 clusters than\nalmost all previous estimates in the literature. Finally, we test the radial\nacceleration relation (RAR) established in disk galaxies. We confirm the\nmissing baryon problem in the inner region of galaxy clusters using three\nindependent mass proxies for the first time.",
        "split": "arXiv"
    },
    {
        "text": "We propose a model for a finite-size particle detector, which allows us to\nderive its stress-energy tensor. This tensor is obtained from a covariant\nLagrangian that describes not only the quantum field that models the detector,\n$\\phi_{\\text{d}}$, but also the systems responsible for its localization: a\ncomplex scalar field, $\\psi_{\\text{c}}$, and a perfect fluid. The local\ninteraction between the detector and the complex field ensures the square\nintegrability of the detector modes, while the fluid serves to define the\nspatial profile of $\\psi_{\\text{c}}$, localizing it in space. We then\ndemonstrate that, under very general conditions, the resulting energy tensor --\nincorporating all components of the system -- is physically reasonable and\nsatisfies the energy conditions.",
        "split": "arXiv"
    },
    {
        "text": "Given any positive integer $r$, Nahm's problem is to determine all $r\\times\nr$ rational positive definite matrix $A$, $r$-dimensional rational vector $B$\nand rational scalar $C$ such that the rank $r$ Nahm sum associated with\n$(A,B,C)$ is modular. Around 2007, Zagier conjectured that if the rank $r$ Nahm\nsum for $(A,B,C)$ is modular, then so is the dual Nahm sum associated with\n$(A^{-1},A^{-1}B,B^\\mathrm{T} A^{-1}B/2-{r}/{24}-C)$. We construct some\nexplicit rank four Nahm sums which are modular while their duals are not\nmodular. This provides counterexamples to Zagier's duality conjecture.",
        "split": "arXiv"
    },
    {
        "text": "One potential route toward fault-tolerant universal quantum computation is to\nuse non-Abelian topological codes. In this work, we investigate how to achieve\nthis goal with the quantum double model $\\mathcal{D}(S_3)$ -- a specific\nnon-Abelian topological code. By embedding each on-site Hilbert space into a\nqubit-qutrit pair, we give an explicit construction of the circuits for\ncreating, moving, and locally measuring all non-trivial anyons. We also design\na specialized anyon interferometer to measure the total charge remotely for\nwell-separated anyons; this avoids fusing them together. These protocols enable\nthe implementation of a universal gate set proposed by Cui et al. and active\nquantum error correction of the circuit-level noise during the computation\nprocess. To further reduce the error rate and facilitate error correction, we\nencode each physical degree of freedom of $\\mathcal{D}(S_3)$ into a novel,\nquantum, error-correcting code, enabling fault-tolerant realization, at the\nlogical level, of all gates in the anyon manipulation circuits. Our proposal\noffers a promising path to realize universal topological quantum computation in\nthe NISQ era.",
        "split": "arXiv"
    },
    {
        "text": "In the high energy limit, $s\\gg -t$, amplitudes in planar gauge theories\nReggeize, with power law behavior $\\big( \\frac{s}{-t} \\big)^{\\alpha(t)}$\ngoverned by the Regge trajectory $\\alpha(t)$. Beyond the planar limit this\nsimplicity is violated by \"Regge cuts\", for which practical organizational\nprinciples are still being developed. We use a top-down effective field theory\norganization based on color projection in the $t$ channel and rapidity\nevolution equations for collinear impact factors, to sum large $s\\gg -t$\nlogarithms for Regge cut contributions. The results are matrix equations which\nare closed within a given color channel. To illustrate the method we derive in\nQCD with $SU(N_c)$ for the first time a closed 6$\\times$6 evolution equation\nfor the \"decupletons\" in the $\\text{10}\\oplus\\overline{\\text{10}}$ Regge color\nchannel, a 2$\\times$2 evolution equation for the \"triantapentons\" in the\n$\\text{35}\\oplus\\overline{\\text{35}}$ color channel, and a scalar evolution\nequation for the \"tetrahexaconton\" in the 64 color channel. More broadly, our\napproach allows us to describe generic Reggeization phenomena in non-planar\ngauge theories, providing valuable data for the all loop structure of\namplitudes beyond the planar limit.",
        "split": "arXiv"
    },
    {
        "text": "In this article, we introduce and analyse some statistical properties of a\nclass of models of random landscapes of the form ${\\cal H}({\\bf\nx})=\\frac{\\mu}{2}{\\bf x}^2+\\sum_{l=1}^M \\phi_l({\\bf k}_l\\cdot {\\bf x}), \\, \\,\n{\\bf x}\\in \\mathbb{R}^N,\\,\\, \\mu>0 $ where both the functions $\\phi_l(z)$ and\nvectors ${\\bf k}_l$ are random. An important example of such landscape\ndescribes superposition of $M$ plane waves with random amplitudes, directions\nof the wavevectors, and phases, further confined by a parabolic potential of\ncurvature $\\mu$.\n  Our main efforts are directed towards analysing the landscape features in the\nlimit $N\\to \\infty, M\\to \\infty$ keeping $\\alpha=M/N$ finite. In such a limit\nwe find (i) the rates of asymptotic exponential growth with $N$ of the mean\nnumber of all critical points and of local minima known as the annealed\ncomplexities and (ii) the expression for the mean value of the deepest\nlandscape minimum (the ground-state energy). In particular, for the latter we\nderive the Parisi-like optimisation functional and analyse conditions for the\noptimiser to reflect various phases for different values of $\\mu$ and $\\alpha$:\nreplica-symmetric, one-step and full replica symmetry broken, as well as\ncriteria for continuous, Gardner and random first order transitions between\ndifferent phases.",
        "split": "arXiv"
    },
    {
        "text": "Several statistical models for regression of a function $F$ on $\\mathbb{R}^d$\nwithout the statistical and computational curse of dimensionality exist, for\nexample by imposing and exploiting geometric assumptions on the distribution of\nthe data (e.g. that its support is low-dimensional), or strong smoothness\nassumptions on $F$, or a special structure $F$. Among the latter, compositional\nmodels assume $F=f\\circ g$ with $g$ mapping to $\\mathbb{R}^r$ with $r\\ll d$,\nhave been studied, and include classical single- and multi-index models and\nrecent works on neural networks. While the case where $g$ is linear is rather\nwell-understood, much less is known when $g$ is nonlinear, and in particular\nfor which $g$'s the curse of dimensionality in estimating $F$, or both $f$ and\n$g$, may be circumvented. In this paper, we consider a model\n$F(X):=f(\\Pi_\\gamma X) $ where $\\Pi_\\gamma:\\mathbb{R}^d\\to[0,\\rm{len}_\\gamma]$\nis the closest-point projection onto the parameter of a regular curve $\\gamma:\n[0,\\rm{len}_\\gamma]\\to\\mathbb{R}^d$ and $f:[0,\\rm{len}_\\gamma]\\to\\mathbb{R}^1$.\nThe input data $X$ is not low-dimensional, far from $\\gamma$, conditioned on\n$\\Pi_\\gamma(X)$ being well-defined. The distribution of the data, $\\gamma$ and\n$f$ are unknown. This model is a natural nonlinear generalization of the\nsingle-index model, which corresponds to $\\gamma$ being a line. We propose a\nnonparametric estimator, based on conditional regression, and show that under\nsuitable assumptions, the strongest of which being that $f$ is coarsely\nmonotone, it can achieve the $one$-$dimensional$ optimal min-max rate for\nnon-parametric regression, up to the level of noise in the observations, and be\nconstructed in time $\\mathcal{O}(d^2n\\log n)$. All the constants in the\nlearning bounds, in the minimal number of samples required for our bounds to\nhold, and in the computational complexity are at most low-order polynomials in\n$d$.",
        "split": "arXiv"
    },
    {
        "text": "Background: Open-Source Pre-Trained Models (PTMs) and datasets provide\nextensive resources for various Machine Learning (ML) tasks, yet these\nresources lack a classification tailored to Software Engineering (SE) needs.\nAims: We apply an SE-oriented classification to PTMs and datasets on a popular\nopen-source ML repository, Hugging Face (HF), and analyze the evolution of PTMs\nover time. Method: We conducted a repository mining study. We started with a\nsystematically gathered database of PTMs and datasets from the HF API. Our\nselection was refined by analyzing model and dataset cards and metadata, such\nas tags, and confirming SE relevance using Gemini 1.5 Pro. All analyses are\nreplicable, with a publicly accessible replication package. Results: The most\ncommon SE task among PTMs and datasets is code generation, with a primary focus\non software development and limited attention to software management. Popular\nPTMs and datasets mainly target software development. Among ML tasks, text\ngeneration is the most common in SE PTMs and datasets. There has been a marked\nincrease in PTMs for SE since 2023 Q2. Conclusions: This study underscores the\nneed for broader task coverage to enhance the integration of ML within SE\npractices.",
        "split": "arXiv"
    },
    {
        "text": "In this article, we study the effect of invisible neutrino decay of the third\nneutrino state for accelerator neutrino experiments at two different baselines,\n1300 km with a liquid argon time projection chamber (LArTPC) detector (similar\nto DUNE) and 2588 km with a water Cherenkov detector (similar to P2O). For such\nbaselines, the matter effect starts to become important. Our aim is to\nascertain the sensitivity to mass hierarchy and octant of $\\theta_{23}$ in\nthese two experiments in the presence of a decaying neutrino state. We compare\nand contrast the results of the two experimental setups. We find that, in\ngeneral, hierarchy sensitivity decreases in the presence of decay. However, if\nwe consider decay only in the opposite hierarchy (test scenario), in the 2588\nkm setup, the hierarchy sensitivity with the true hierarchy as IH is larger\nthan the no decay case. We also study the dependence of hierarchy sensitivity\nwith true $\\theta_{23}$. We find that the dominant muon background in P2O plays\nan important role in how the hierarchy sensitivity depends on $\\theta_{23}$.\nThe octant sensitivity for both setups increases in the presence of decay\nexcept for the LArTPC setup in case true $\\theta_{23}=49^\\circ$. To understand\nthe octant sensitivity results in the two setups, we check the synergy in\nsensitivity between electron and muon channels as a function of test\n$\\theta_{23}$. We also study the degeneracies in the test\n$\\theta_{23}-\\delta_{CP}$ plane and find that combined analysis of the two\nsetups removes all the degeneracies in the test $\\theta_{23}-\\delta_{CP}$ plane\nat $5\\sigma$ significance.",
        "split": "arXiv"
    },
    {
        "text": "The concept of Vapnik-Chervonenkis (VC) density is pivotal across various\nmathematical fields, including model theory, discrete geometry, and probability\ntheory. In this paper, we introduce a topological generalization of VC-density.\nLet $Y$ be a topological space and\n$\\mathcal{X},\\mathcal{Z}^{(0)},\\ldots,\\mathcal{Z}^{(q-1)}$ be families of\nsubspaces of $Y$. We define a two parameter family of numbers,\n$\\mathrm{vcd}^{p,q}_{\\mathcal{X},\\overline{\\mathcal{Z}}}$, which we refer to as\nthe degree $p$, order $q$, VC-density of the pair \\[\n(\\mathcal{X},\\overline{\\mathcal{Z}} =\n(\\mathcal{Z}^{(0)},\\ldots,\\mathcal{Z}^{(q-1)}). \\] The classical notion of\nVC-density within this topological framework can be recovered by setting $p=0,\nq=1$. For $p=0, q > 0$, we recover Shelah's notion of higher-order VC-density\nfor $q$-dependent families. Our definition introduces a new notion when $p>0$.\nOur main result establishes that that in any model of these theories \\[\n\\mathrm{vcd}^{p,q}_{\\mathcal{X},\\overline{\\mathcal{Z}}} \\leq (p+q) \\dim X. \\]\nThis result generalizes known VC-density bounds in these structures, extending\nthem in multiple ways, as well as providing a uniform proof paradigm applicable\nto all of them. We give examples to show that our bounds are optimal. We\npresent combinatorial applications of our higher-degree VC-density bounds,\nderiving topological analogs of well-known results such as the existence of\n$\\varepsilon$-nets and the fractional Helly theorem. We show that with certain\nrestrictions, these results extend to our higher-degree topological setting.",
        "split": "arXiv"
    },
    {
        "text": "We holographically study quantum chaos in hyperscaling-violating Lifshitz\n(HVL) theories (with charge). Particularly, we present a detailed computation\nof the out-of-time ordered correlator (OTOC) via the shockwave analysis in the\nbulk HVL geometry with a planar horizon topology. We also compute the butterfly\nvelocity ($v_{B}$) using the entanglement wedge reconstruction and find that\nthe result matches the one obtained from shockwave analysis. Furthermore, we\nanalyze in detail, the behavior of $v_{B}$ with respect to the dynamical\ncritical exponent (z), hyperscaling-violating parameter ($\\theta$), charge (Q)\nand the horizon radius ($r_{h}$). We interestingly find non-monotonic behavior\nof $v_{B}$ with respect to z (in the allowed region and for certain (not all)\nfixed, permissible values of $\\theta$, Q and $r_{h}$) and $\\theta$ (in the\nallowed region and for certain (not all) fixed, permissible values of z, Q and\n$r_{h}$). Moreover, $v_{B}$ is found to monotonically decrease with an increase\nin charge (for all permissible, fixed values of z, $\\theta$ and $r_{h}$),\nwhereas it is found to monotonically increase with $r_{h}$ (for all fixed,\npermissible values of z, $\\theta$ and Q). Unpacking these features can offer\nsome valuable insights into the chaotic nature of HVL theories.",
        "split": "arXiv"
    },
    {
        "text": "The ATLAS collaboration at the LHC has published inclusive cross-section\nmeasurements for the single-top and \\ttbar production modes at center-of-mass\nenergies of $\\sqrt{s} = 5.02, 8.16$, $13$, and $13.6$ TeV. Single-top\nmeasurements are conducted in the $t$-channel and $tW$ channel. In addition to\nthe nominal cross-section measurements, various measurements of other\ninteresting observables such as the $V_{tb}$ element of the Cabibbo Kobayashi\nMaskawa (CKM) quark-mixing matrix, the ratio of the inclusive cross-sections\nbetween $tq$ and $t\\overline{q}$, the ratio of inclusive cross-sections between\n$t\\overline{t}$ and $Z\\rightarrow \\ell\\ell$, and the nuclear modification\nfactor (defined as the ratio of the inclusive $t\\overline{t}$ cross section in\nheavy-ion collisions to the inclusive $t\\overline{t}$ cross-section in $pp$\ncollisions) are also reported. These results are compared to their\ncorresponding SM predictions, calculated at (N)NLO in QCD. All results are in\ngood agreement with SM predictions.",
        "split": "arXiv"
    },
    {
        "text": "During language model decoding, it is known that using higher temperature\nsampling gives more creative responses, while lower temperatures are more\nfactually accurate. However, such models are commonly applied to general\ninstruction following, which involves both creative and fact seeking tasks,\nusing a single fixed temperature across all examples and tokens. In this work,\nwe introduce Adaptive Decoding, a layer added to the model to select the\nsampling temperature dynamically at inference time, at either the token or\nexample level, in order to optimize performance. To learn its parameters we\nintroduce Latent Preference Optimization (LPO) a general approach to train\ndiscrete latent variables such as choices of temperature. Our method\noutperforms all fixed decoding temperatures across a range of tasks that\nrequire different temperatures, including UltraFeedback, Creative Story\nWriting, and GSM8K.",
        "split": "arXiv"
    },
    {
        "text": "We present complete results for the hadronic vacuum polarization (HVP)\ncontribution to the muon anomalous magnetic moment $a_\\mu$ in the short- and\nintermediate-distance window regions, which account for roughly 10% and 35% of\nthe total HVP contribution to $a_\\mu$, respectively. In particular, we perform\nlattice-QCD calculations for the isospin-symmetric connected and disconnected\ncontributions, as well as corrections due to strong isospin-breaking. For the\nshort-distance window observables, we investigate the so-called log-enhancement\neffects as well as the significant oscillations associated with staggered\nquarks in this region. For the dominant, isospin-symmetric light-quark\nconnected contribution, we obtain $a^{ll,\\,{\\mathrm{SD}}}_{\\mu}(\\mathrm{conn.})\n= 48.116(16)(94)[96] \\times 10^{-10}$ and\n$a^{ll,\\,{\\mathrm{W}}}_{\\mu}(\\mathrm{conn.}) = 207.06(17)(63)[66] \\times\n10^{-10}$. We use Bayesian model averaging combined with a global bootstrap to\nfully estimate the covariance matrix between the individual contributions. Our\ndeterminations of the complete window contributions are\n$a^{{\\mathrm{SD}}}_{\\mu} = 69.01(2)(21)[21] \\times 10^{-10}$ and\n$a^{{\\mathrm{W}}}_{\\mu} = 236.57(20)(94)[96] \\times 10^{-10}$. This work is\npart of our ongoing effort to compute all contributions to HVP with an overall\nuncertainty at the few permille level.",
        "split": "arXiv"
    },
    {
        "text": "Supersymmetry (SUSY) addresses several problems of the Standard Model, such\nas the naturalness problem and gauge coupling unification, and can provide\ncosmologically viable dark matter candidates. SUSY must be broken at high\nenergy scales with mechanisms like gravity, anomaly, gauge mediation, etc. This\npaper revisits the Gauge Mediated SUSY Breaking (GMSB) scenarios in the context\nof data from the Large Hadron Collider (LHC) experiment. The ATLAS mono-photon\nsearch at 139 inverse femtobarn integrated luminosity at the 13 TeV LHC, in the\ncontext of a simplified General Gauge Mediation (GGM) scenario (which is a\nphenomenological version of GMSB with an agnostic approach to the nature of the\nhidden sector), relies on assumptions that do not hold across the entire\nparameter space. We identify a few crucial assumptions regarding the decay\nwidths of SUSY particles into final states with gravitinos that affect the LHC\nlimits on the masses of the SUSY particles. Our study aims to reinterpret the\nATLAS constraints on the gluino-NLSP mass plane, considering all possible decay\nmodes of SUSY particles in a realistic GGM model.",
        "split": "arXiv"
    },
    {
        "text": "We present a polynomial-time reduction from max-average constraints to the\nfeasibility problem for semidefinite programs. This shows that Condon's simple\nstochastic games, stochastic mean payoff games, and in particular mean payoff\ngames and parity games can all be reduced to semidefinite programming.",
        "split": "arXiv"
    },
    {
        "text": "Specifying all desirable properties of a language model is challenging, but\ncertain requirements seem essential. Given samples from an unknown language,\nthe trained model should produce valid strings not seen in training and be\nexpressive enough to capture the language's full richness. Otherwise,\noutputting invalid strings constitutes \"hallucination,\" and failing to capture\nthe full range leads to \"mode collapse.\" We ask if a language model can meet\nboth requirements.\n  We investigate this within a statistical language generation setting building\non Gold and Angluin. Here, the model receives random samples from a\ndistribution over an unknown language K, which belongs to a possibly infinite\ncollection of languages. The goal is to generate unseen strings from K. We say\nthe model generates from K with consistency and breadth if, as training size\nincreases, its output converges to all unseen strings in K.\n  Kleinberg and Mullainathan [KM24] asked if consistency and breadth in\nlanguage generation are possible. We answer this negatively: for a large class\nof language models, including next-token prediction models, this is impossible\nfor most collections of candidate languages. This contrasts with [KM24]'s\nresult, showing consistent generation without breadth is possible for any\ncountable collection of languages. Our finding highlights that generation with\nbreadth fundamentally differs from generation without breadth.\n  As a byproduct, we establish near-tight bounds on the number of samples\nneeded for generation with or without breadth.\n  Finally, our results offer hope: consistent generation with breadth is\nachievable for any countable collection of languages when negative examples\n(strings outside K) are available alongside positive ones. This suggests that\npost-training feedback, which encodes negative examples, can be crucial in\nreducing hallucinations while limiting mode collapse.",
        "split": "arXiv"
    },
    {
        "text": "Causal concept effect estimation is gaining increasing interest in the field\nof interpretable machine learning. This general approach explains the behaviors\nof machine learning models by estimating the causal effect of\nhuman-understandable concepts, which represent high-level knowledge more\ncomprehensibly than raw inputs like tokens. However, existing causal concept\neffect explanation methods assume complete observation of all concepts involved\nwithin the dataset, which can fail in practice due to incomplete annotations or\nmissing concept data. We theoretically demonstrate that unobserved concepts can\nbias the estimation of the causal effects of observed concepts. To address this\nlimitation, we introduce the Missingness-aware Causal Concept Explainer (MCCE),\na novel framework specifically designed to estimate causal concept effects when\nnot all concepts are observable. Our framework learns to account for residual\nbias resulting from missing concepts and utilizes a linear predictor to model\nthe relationships between these concepts and the outputs of black-box machine\nlearning models. It can offer explanations on both local and global levels. We\nconduct validations using a real-world dataset, demonstrating that MCCE\nachieves promising performance compared to state-of-the-art explanation methods\nin causal concept effect estimation.",
        "split": "arXiv"
    },
    {
        "text": "In this study, we examine two important new physics scenarios, \\textit{i.e},\nthe theory of Large Extra Dimension (LED) and the theory of neutrino decay. We\nstudy LED in the context of P2SO, DUNE, and T2HK with emphasis on P2SO, whereas\ndecay has been studied solely in the context of P2SO. For LED, in our study we\nfind that the combination of P2SO, DUNE, and T2HK can provide a better bound\nthan the current one only if all the oscillation parameters are measured with\nabsolute certainty. However, for decay, one can obtain a better bound with P2SO\nas compared to ESSnuSB and MOMENT, but the bound obtained by P2SO is weak as\ncompared to DUNE and T2HK. Regarding sensitivities to the current unknowns, if\nLED exists in nature, its impact on mass ordering, octant, and CP violation is\nvery mild; however, decay can alter the sensitivities related to CP violation\nand octant in a non-trivial way.",
        "split": "arXiv"
    },
    {
        "text": "We employ techniques from group theory to show that, in many cases, counting\nproblems on graphs are almost as hard to solve in a small number of instances\nas they are in all instances. Specifically, we show the following results.\n  1. Goldreich (2020) asks if, for every constant $\\delta < 1 / 2$, there is an\n$\\tilde{O} \\left( n^2 \\right)$-time randomized reduction from computing the\nnumber of $k$-cliques modulo $2$ with a success probability of greater than $2\n/ 3$ to computing the number of $k$-cliques modulo $2$ with an error\nprobability of at most $\\delta$.\n  In this work, we show that for almost all choices of the $\\delta 2^{n \\choose\n2}$ corrupt answers within the average-case solver, we have a reduction taking\n$\\tilde{O} \\left( n^2 \\right)$-time and tolerating an error probability of\n$\\delta$ in the average-case solver for any constant $\\delta < 1 / 2$. By\n\"almost all\", we mean that if we choose, with equal probability, any subset $S\n\\subset \\{0,1\\}^{n \\choose 2}$ with $|S| = \\delta2^{n \\choose 2}$, then with a\nprobability of $1-2^{-\\Omega \\left( n^2 \\right)}$, we can use an average-case\nsolver corrupt on $S$ to obtain a probabilistic algorithm.\n  2. Inspired by the work of Goldreich and Rothblum in FOCS 2018 to take the\nweighted versions of the graph counting problems, we prove that if the RETH is\ntrue, then for a prime $p = \\Theta \\left( 2^n \\right)$, the problem of counting\nthe number of unique Hamiltonian cycles modulo $p$ on $n$-vertex directed\nmultigraphs and the problem of counting the number of unique half-cliques\nmodulo $p$ on $n$-vertex undirected multigraphs, both require exponential time\nto compute correctly on even a $1 / 2^{n/\\log n}$-fraction of instances.\nMeanwhile, simply printing $0$ on all inputs is correct on at least a $\\Omega\n\\left( 1 / 2^n \\right)$-fraction of instances.",
        "split": "arXiv"
    },
    {
        "text": "By augmenting Large Language Models (LLMs) with external tools, their\ncapacity to solve complex problems has been significantly enhanced. However,\ndespite ongoing advancements in the parsing capabilities of LLMs, incorporating\nall available tools simultaneously in the prompt remains impractical due to the\nvast number of external tools. Consequently, it is essential to provide LLMs\nwith a precise set of tools tailored to the specific task, considering both\nquantity and quality. Current tool retrieval methods primarily focus on\nrefining the ranking list of tools and directly packaging a fixed number of\ntop-ranked tools as the tool set. However, these approaches often fail to equip\nLLMs with the optimal set of tools prior to execution, since the optimal number\nof tools for different tasks could be different, resulting in inefficiencies\nsuch as redundant or unsuitable tools, which impede immediate access to the\nmost relevant tools. This paper addresses the challenge of recommending precise\ntoolsets for LLMs. We introduce the problem of tool recommendation, define its\nscope, and propose a novel Precision-driven Tool Recommendation (PTR) approach.\nPTR captures an initial, concise set of tools by leveraging historical tool\nbundle usage and dynamically adjusts the tool set by performing tool matching,\nculminating in a multi-view-based tool addition. Additionally, we present a new\ndataset, RecTools, and a metric, TRACC, designed to evaluate the effectiveness\nof tool recommendation for LLMs. We further validate our design choices through\ncomprehensive experiments, demonstrating promising accuracy across two open\nbenchmarks and our RecTools dataset.",
        "split": "arXiv"
    },
    {
        "text": "In recent years, attention mechanisms have significantly enhanced the\nperformance of object detection by focusing on key feature information.\nHowever, prevalent methods still encounter difficulties in effectively\nbalancing local and global features. This imbalance hampers their ability to\ncapture both fine-grained details and broader contextual information-two\ncritical elements for achieving accurate object detection.To address these\nchallenges, we propose a novel attention mechanism, termed Local-Global\nAttention, which is designed to better integrate both local and global\ncontextual features. Specifically, our approach combines multi-scale\nconvolutions with positional encoding, enabling the model to focus on local\ndetails while concurrently considering the broader global context.\nAdditionally, we introduce a learnable parameters, which allow the model to\ndynamically adjust the relative importance of local and global attention,\ndepending on the specific requirements of the task, thereby optimizing feature\nrepresentations across multiple scales.We have thoroughly evaluated the\nLocal-Global Attention mechanism on several widely used object detection and\nclassification datasets. Our experimental results demonstrate that this\napproach significantly enhances the detection of objects at various scales,\nwith particularly strong performance on multi-class and small object detection\ntasks. In comparison to existing attention mechanisms, Local-Global Attention\nconsistently outperforms them across several key metrics, all while maintaining\ncomputational efficiency.",
        "split": "arXiv"
    },
    {
        "text": "We say that a function is rare-case hard against a given class of algorithms\n(the adversary) if all algorithms in the class can compute the function only on\nan $o(1)$-fraction of instances of size $n$ for large enough $n$. Starting from\nany NP-complete language, for each $k > 0$, we construct a function that cannot\nbe computed correctly on even a $1/n^k$-fraction of instances for\npolynomial-sized circuit families if NP $\\not \\subset$ P/POLY and by\npolynomial-time algorithms if NP $\\not \\subset$ BPP - functions that are\nrare-case hard against polynomial-time algorithms and polynomial-sized\ncircuits. The constructed function is a number-theoretic polynomial evaluated\nover specific finite fields. For NP-complete languages that admit parsimonious\nreductions from all of NP (for example, SAT), the constructed functions are\nhard to compute on even a $1/n^k$-fraction of instances by polynomial-time\nalgorithms and polynomial-sized circuit families simply if $P^{\\#P} \\not\n\\subset$ BPP and $P^{\\#P} \\not \\subset$ P/POLY, respectively. We also show that\nif the Randomized Exponential Time Hypothesis (RETH) is true, none of these\nconstructed functions can be computed on even a $1/n^k$-fraction of instances\nin subexponential time. These functions are very hard, almost always.\n  While one may not be able to efficiently compute the values of these\nconstructed functions themselves, in polynomial time, one can verify that the\nevaluation of a function, $s = f(x)$, is correct simply by asking a prover to\ncompute $f(y)$ on targeted queries.",
        "split": "arXiv"
    },
    {
        "text": "Propensity Score Matching (PSM) stands as a widely embraced method in\ncomparative effectiveness research. PSM crafts matched datasets, mimicking some\nattributes of randomized designs, from observational data. In a valid PSM\ndesign where all baseline confounders are measured and matched, the confounders\nwould be balanced, allowing the treatment status to be considered as if it were\nrandomly assigned. Nevertheless, recent research has unveiled a different facet\nof PSM, termed \"the PSM paradox.\" As PSM approaches exact matching by\nprogressively pruning matched sets in order of decreasing propensity score\ndistance, it can paradoxically lead to greater covariate imbalance, heightened\nmodel dependence, and increased bias, contrary to its intended purpose.\nMethods: We used analytic formula, simulation, and literature to demonstrate\nthat this paradox stems from the misuse of metrics for assessing chance\nimbalance and bias. Results: Firstly, matched pairs typically exhibit different\ncovariate values despite having identical propensity scores. However, this\ndisparity represents a \"chance\" difference and will average to zero over a\nlarge number of matched pairs. Common distance metrics cannot capture this\n``chance\" nature in covariate imbalance, instead reflecting increasing\nvariability in chance imbalance as units are pruned and the sample size\ndiminishes. Secondly, the largest estimate among numerous fitted models,\nbecause of uncertainty among researchers over the correct model, was used to\ndetermine statistical bias. This cherry-picking procedure ignores the most\nsignificant benefit of matching design-reducing model dependence based on its\nrobustness against model misspecification bias. Conclusions: We conclude that\nthe PSM paradox is not a legitimate concern and should not stop researchers\nfrom using PSM designs.",
        "split": "arXiv"
    },
    {
        "text": "Let $\\mathcal{H}$ be a hyperplane arrangement in $\\mathbb{CP}^n$. We define a\nquadratic form $Q$ on $\\mathbb{R}^{\\mathcal{H}}$ that is entirely determined by\nthe intersection poset of $\\mathcal{H}$. Using the Bogomolov-Gieseker\ninequality for parabolic bundles, we show that if $\\mathbf{a} \\in\n\\mathbb{R}^{\\mathcal{H}}$ is such that the weighted arrangement $(\\mathcal{H},\n\\mathbf{a})$ is \\emph{stable}, then $Q(\\mathbf{a}) \\leq 0$.\n  As an application, we consider the symmetric case where all the weights are\nequal. The inequality $Q(a, \\ldots, a) \\leq 0$ gives a lower bound for the\ntotal sum of multiplicities of codimension $2$ intersection subspaces of\n$\\mathcal{H}$. The lower bound is attained when every $H \\in \\mathcal{H}$\nintersects all the other members of $\\mathcal{H} \\setminus \\{H\\}$ along\n$(1-2/(n+1))|\\mathcal{H}| + 1$ codimension $2$ subspaces; extending from $n=2$\nto higher dimensions a condition found by Hirzebruch for line arrangements in\nthe complex projective plane.",
        "split": "arXiv"
    },
    {
        "text": "The Event Horizon Telescope Collaboration (EHTC) observed the Galactic centre\nsource Sgr A* and used emission models primarily based on single ion\ntemperature (1T) general relativistic magnetohydrodynamic (GRMHD) simulations.\nThis predicted emission is strongly dependent on a modelled prescription of the\nion-to-electron temperature ratio. The two most promising models are\nmagnetically arrested disk (MAD) states. However, these and nearly all MAD\nmodels exhibit greater light-curve variability at 230 GHz compared to\nhistorical observations. Moreover, no model successfully passes all the\nvariability and multiwavelength constraints. This limitation possibly stems\nfrom the fact that the actual temperature ratio depends on microphysical\ndissipation, radiative processes and other effects not captured in ideal fluid\nsimulations. Therefore, we investigate the effects of two-temperature (2T)\nthermodynamics in MAD GRMHD simulations of Sgr A*, where the temperatures of\nboth species are evolved more self-consistently. We include Coulomb coupling,\nradiative cooling of electrons, and model heating via magnetic reconnection. We\nfind that the light-curve variability more closely matches historical\nobservations when we include the 2T treatment and variable adiabatic indices,\ncompared to 1T simulations. Contrary to the common assumption of neglecting\nradiative cooling for the low accretion rates of Sgr A*, we also find that\nradiative cooling still affects the accretion flow, reducing the electron\ntemperature in the inner disk by about 10%, which in turn lowers both the\naverage flux and variability at 230 GHz by roughly 10%.",
        "split": "arXiv"
    },
    {
        "text": "Existing claim verification datasets often do not require systems to perform\ncomplex reasoning or effectively interpret multimodal evidence. To address\nthis, we introduce a new task: multi-hop multimodal claim verification. This\ntask challenges models to reason over multiple pieces of evidence from diverse\nsources, including text, images, and tables, and determine whether the combined\nmultimodal evidence supports or refutes a given claim. To study this task, we\nconstruct MMCV, a large-scale dataset comprising 16k multi-hop claims paired\nwith multimodal evidence, generated and refined using large language models,\nwith additional input from human feedback. We show that MMCV is challenging\neven for the latest state-of-the-art multimodal large language models,\nespecially as the number of reasoning hops increases. Additionally, we\nestablish a human performance benchmark on a subset of MMCV. We hope this\ndataset and its evaluation task will encourage future research in multimodal\nmulti-hop claim verification.",
        "split": "arXiv"
    },
    {
        "text": "While general-purpose computing follows Von Neumann's architecture, the data\nmovement between memory and processor elements dictates the processor's\nperformance. The evolving compute-in-memory (CiM) paradigm tackles this issue\nby facilitating simultaneous processing and storage within static random-access\nmemory (SRAM) elements. Numerous design decisions taken at different levels of\nhierarchy affect the figure of merits (FoMs) of SRAM, such as power,\nperformance, area, and yield. The absence of a rapid assessment mechanism for\nthe impact of changes at different hierarchy levels on global FoMs poses a\nchallenge to accurately evaluating innovative SRAM designs. This paper presents\nan automation tool designed to optimize the energy and latency of SRAM designs\nincorporating diverse implementation strategies for executing logic operations\nwithin the SRAM. The tool structure allows easy comparison across different\narray topologies and various design strategies to result in energy-efficient\nimplementations. Our study involves a comprehensive comparison of over 6900+\ndistinct design implementation strategies for EPFL combinational benchmark\ncircuits on the energy-recycling resonant compute-in-memory (rCiM) architecture\ndesigned using TSMC 28 nm technology. When provided with a combinational\ncircuit, the tool aims to generate an energy-efficient implementation strategy\ntailored to the specified input memory and latency constraints. The tool\nreduces 80.9% of energy consumption on average across all benchmarks while\nusing the six-topology implementation compared to baseline implementation of\nsingle-macro topology by considering the parallel processing capability of rCiM\ncache size ranging from 4KB to 192KB.",
        "split": "arXiv"
    },
    {
        "text": "Theoretical methods based on the density matrix are powerful tools to\ndescribe open quantum systems. However, such methods are complicated and\nintricate to be used analytically. Here we present an object-oriented framework\nfor constructing the equation of motion of the correlation matrix at a given\norder in the quantum chain of BBGKY hierarchy used to describe the interaction\nof many-particle systems. The algorithm of machine derivation of equations\nincludes the implementation of the principles of quantum mechanics and operator\nalgebra. It is based on the description and use of classes in the Python\nprogramming environment. Class objects correspond to the elements of the\nequations that are derived: density matrix, correlation matrix, energy\noperators, commutator and several operators indexing systems. The program\ncontains a special class that allows one to define a statistical ensemble with\nan infinite number of subsystems. For all classes, methods implementing the\nactions of the operator algebra are specified. The number of subsystems of the\nstatistical ensemble for the physical problem and the types of subsystems\nbetween which pairwise interactions are possible are specified as an input\ndata. It is shown that this framework allows one to derive the equations of\nmotion of the fourth-order correlation matrix in less than a minute.",
        "split": "arXiv"
    },
    {
        "text": "According to the asymptotically safe gravity, black holes can have\ncharacteristics different from those described according to general relativity.\nParticularly, they are more compact, with a smaller event horizon, which in\nturn affects the other quantities dependent on it, like the photon ring and the\nsize of the innermost stable circular orbit. We decided to test the latter by\nsearching in the literature for observational measurements of the emission from\naccretion disk around stellar-mass black holes. All published values of the\nradius of the inner accretion disk were made homogeneous by taking into account\nthe most recent and more reliable values of mass, spin, viewing angle, and\ndistance from the Earth. We do not find any significant deviation from the\nexpectations of general relativity. Some doubtful cases can be easily\nunderstood as due to specific states of the object during the observation or\ninstrumental biases.",
        "split": "arXiv"
    },
    {
        "text": "The role of internal and environmental factors in the star formation activity\nof galaxies is still a matter of debate, particularly at higher redshifts.\nLeveraging the most recent release of the COSMOS catalog, COSMOS2020, and\ndensity measurements from our previous study we disentangle the impact of\nenvironment and stellar mass on the star formation rate (SFR), and specific SFR\n(sSFR) of a sample of $\\sim 210,000$ galaxies within redshift range $0.4< z <\n4$ and present our findings in three cosmic epochs: 1) out to $z\\sim 1$, the\naverage SFR and sSFR decline at extremely dense environments and high mass end\nof the distribution which is mostly due to the presence of the massive\nquiescent population; 2) at $1<z<2$, the environmental dependence diminishes,\nwhile mass is still the dominant factor in star formation activity; 3) beyond\n$z\\sim 2$, our sample is dominated by star-forming galaxies and we observe a\nreversal of the trends seen in the local universe: the average SFR increases\nwith increasing environmental density. Our analysis shows that both\nenvironmental and mass quenching efficiencies increase with stellar mass at all\nredshifts, with mass being the dominant quenching factor in massive galaxies\ncompared to environmental effects. At $2<z<4$, negative values of environmental\nquenching efficiency suggest that the fraction of star-forming galaxies in\ndense environments exceeds that in less dense regions, likely due to the\ngreater availability of cold gas, higher merger rates, and tidal effects that\ntrigger star formation activity.",
        "split": "arXiv"
    },
    {
        "text": "Technical carbon dioxide removal through bioenergy with carbon capture or\ndirect air capture plays a role in virtually all climate mitigation scenarios.\nBoth of these technologies rely on the use of chemical solvents or sorbents in\norder to capture CO$_2$. Lately, concerns have surfaced about the cost and\nenergy implications of producing solvents and sorbents at scale. Here, we show\nthat the production of chemical sorbents could have significant implications on\nsystem cost, energy use and material use depending on how much they are\nconsumed. Among the three chemical sorbents investigated, namely\nmonoethanolamine (MEA) for post-combustion carbon capture, potassium hydroxide\nfor liquid direct air capture and polyethylenimine-silica (PEI) for solid\nsorbent direct air capture, we found that the production of the compound for\nsolid sorbent direct air capture represent the highest uncertainties for the\nsystem. At the high range of solid sorbent consumption, total energy system\ncost increased by up to 6.5\\%, while effects for other options were small to\nnegligible. Scale-up of material production capacities was also substantial for\nMEA and PEI. Implications of sorbent consumption for carbon capture\ntechnologies should be considered more thoroughly in scenarios relying on\ndirect air capture using a solid sorbent.",
        "split": "arXiv"
    },
    {
        "text": "Magnetic imaging with ultra-high spatial resolution is crucial to exploring\nthe magnetic textures of emerging quantum materials. We propose a novel\nmagnetic imaging protocol that achieves Angstrom-scale resolution by combining\nspin defects in van der Waals materials and terahertz scattering scanning\nnear-field optical microscopy (THz s-SNOM). Spin defects in the atomic\nmonolayer enable the probe-to-sample distance diving into the Angstrom range\nwhere the exchange interactions between the probe and sample spins become\npredominant. This exchange interaction leads to energy splitting of the probe\nspin in the order of millielectronvolts, corresponding to THz frequencies. With\nTHz optics and the spin-dependent fluorescence of the probe spin, the\ninteraction energy can be resolved entirely through optical methods. Our\nproposed all-optical magnetic imaging protocol holds significant promise for\ninvestigating magnetic textures in condensed matter physics due to its\nexcellent compatibility and high spatial resolution.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we present a framework for learning the solution map of a\nbackward parabolic Cauchy problem. The solution depends continuously but\nnonlinearly on the final data, source, and force terms, all residing in Banach\nspaces of functions. We utilize Fr\\'echet space neural networks (Benth et al.\n(2023)) to address this operator learning problem. Our approach provides an\nalternative to Deep Operator Networks (DeepONets), using basis functions to\nspan the relevant function spaces rather than relying on finite-dimensional\napproximations through censoring. With this method, structural information\nencoded in the basis coefficients is leveraged in the learning process. This\nresults in a neural network designed to learn the mapping between\ninfinite-dimensional function spaces. Our numerical proof-of-concept\ndemonstrates the effectiveness of our method, highlighting some advantages over\nDeepONets.",
        "split": "arXiv"
    },
    {
        "text": "This paper compares the HLLEM and HLL-CPS schemes for Euler equations and\nproposes improvements for all Mach number flows. Enhancements to the HLLEM\nscheme involve adding anti-diffusion terms in the face normal direction and\nmodifying anti-diffusion coefficients for linearly degenerate waves near\nshocks. The HLL-CPS scheme is improved by adjusting anti-diffusion coefficients\nfor the face normal direction and linearly degenerate waves. Matrix stability,\nlinear perturbation, and asymptotic analyses demonstrate the robustness of the\nproposed schemes and their ability to capture low Mach flow features. Numerical\ntests confirm that the schemes are free from shock instabilities at high speeds\nand accurately resolve low Mach number flow features.",
        "split": "arXiv"
    },
    {
        "text": "Turbulent kinetic energy (TKE) is a measure for unsteady loads and important\nregarding the design of e.g. propellers or energy-saving devices. While\nsimulations are often done for a double-body, using a symmetry condition,\nexperiments and the final product have a free surface. Simulations with and\nwithout free surface are carried out for the Japan Bulk Carrier, comparing TKE\nin the vortex cores. The reliability of finding the vortex centers is\ndiscussed. As the fine meshes show an unexpected trend for the TKE, a detailed\ninvestigation is done, mainly to exclude method-related drawbacks from using a\nhybrid URANS/ LES model. It is found that a shift in vortex-core positions\ndistorts the results whereby the experimental center positions which are\nreferenced are questionable. Using a fixed position for all cases improves\ncomparability and gives a different picture. Thereupon the medium meshes were\nenhanced in such a way that one of the refinement boxes was extended further\nforward, now showing much better agreement with the fine meshes. TKE is then\nportrayed as integral quantity and shows no significant difference between the\nsimulations with and without free surface. However, the structure itself is\ninfluenced by the surface in a way which alters local characteristics.",
        "split": "arXiv"
    },
    {
        "text": "Understanding the text in legal documents can be challenging due to their\ncomplex structure and the inclusion of domain-specific jargon. Laws and\nregulations are often crafted in such a manner that engagement with them\nrequires formal training, potentially leading to vastly different\ninterpretations of the same texts. Linguistic complexity is an important\ncontributor to the difficulties experienced by readers. Simplifying texts could\nenhance comprehension across a broader audience, not just among trained\nprofessionals. Various metrics have been developed to measure document\nreadability. Therefore, we adopted a systematic review approach to examine the\nlinguistic and readability metrics currently employed for legal and regulatory\ntexts. A total of 3566 initial papers were screened, with 34 relevant studies\nfound and further assessed. Our primary objective was to identify which current\nmetrics were applied for evaluating readability within the legal field. Sixteen\ndifferent metrics were identified, with the Flesch-Kincaid Grade Level being\nthe most frequently used method. The majority of studies (73.5%) were found in\nthe domain of \"informed consent forms\". From the analysis, it is clear that not\nall legal domains are well represented in terms of readability metrics and that\nthere is a further need to develop more consensus on which metrics should be\napplied for legal documents.",
        "split": "arXiv"
    },
    {
        "text": "Large language models (LLMs) excel in high-resource languages but face\nnotable challenges in low-resource languages like Mongolian. This paper\naddresses these challenges by categorizing capabilities into language abilities\n(syntax and semantics) and cognitive abilities (knowledge and reasoning). To\nsystematically evaluate these areas, we developed MM-Eval, a specialized\ndataset based on Modern Mongolian Language Textbook I and enriched with WebQSP\nand MGSM datasets.\n  Preliminary experiments on models including Qwen2-7B-Instruct, GLM4-9b-chat,\nLlama3.1-8B-Instruct, GPT-4, and DeepseekV2.5 revealed that: 1) all models\nperformed better on syntactic tasks than semantic tasks, highlighting a gap in\ndeeper language understanding; and 2) knowledge tasks showed a moderate\ndecline, suggesting that models can transfer general knowledge from\nhigh-resource to low-resource contexts.\n  The release of MM-Eval, comprising 569 syntax, 677 semantics, 344 knowledge,\nand 250 reasoning tasks, offers valuable insights for advancing NLP and LLMs in\nlow-resource languages like Mongolian. The dataset is available at\nhttps://github.com/joenahm/MM-Eval.",
        "split": "arXiv"
    },
    {
        "text": "Two families $\\mathcal{F}$ and $\\mathcal{G}$ are called cross-intersecting if\nfor every $F\\in \\mathcal{F}$ and $G\\in \\mathcal{G}$, the intersection $F\\cap G$\nis non-empty. For any positive integers $n$ and $k$, let $\\binom{[n]}{k}$\ndenote the family of all $k$-element subsets of $\\{1,2,\\ldots,n\\}$. Let $t, s,\nk, n$ be non-negative integers with $k \\geq s+1$ and $n \\geq 2 k+t$. In 2016,\nFrankl proved that if $\\mathcal{F} \\subseteq\\binom{[n]}{k+t}$ and $\\mathcal{G}\n\\subseteq\\binom{[n]}{k}$ are cross-intersecting families, and $\\mathcal{F}$ is\n$(t+1)$-intersecting and $|\\mathcal{F}| \\geq 1$, then\n$|\\mathcal{F}|+|\\mathcal{G}| \\leq\\binom{n}{k}-\\binom{n-k-t}{k}+1$. Furthermore,\nFrankl conjectured that under an additional condition $\\binom{[k+t+s]}\n{k+t}\\subseteq\\mathcal{F}$, the following inequality holds: $$\n|\\mathcal{F}|+|\\mathcal{G}|\n\\leq\\binom{k+t+s}{k+t}+\\binom{n}{k}-\\sum_{i=0}^s\\binom{k+t+s}{i}\\binom{n-k-t-s}{k-i}.\n$$ In this paper, we prove this conjecture. The key ingredient is to establish\na theorem for cross-intersecting families with a restricted universe. Moreover,\nwe derive an analogous result for this conjecture.",
        "split": "arXiv"
    },
    {
        "text": "In this work, we propose an all-optical stroboscopic scheme to simulate an\nopen quantum system. By incorporating the tritter, consisting of a group of\nbeam splitters, we find the emergence of spontaneous anti-phase synchronization\nin the steady state. To better understand the synchronization and entanglement\nproperties within the system, we utilize the relative error measure and find\nthe distribution of logarithmic negativity in parameter space shows similar\nstructures with the results of synchronization measure. Finally, we derive the\nadjoint master equation corresponding to the system when the synchronization\ncondition is satisfied and explain the existence of oscillations. In addition,\nwe explore the effect of non-Markovianity on synchronization, and we find that\nit only slows down the time for the system to reach the steady state but does\nnot change the synchronization properties of the steady state. Our work\nprovides a promising scheme for experimental studies focused on synchronization\nand other nonequilibrium steady states.",
        "split": "arXiv"
    },
    {
        "text": "Program decomposition is essential for developing maintainable and efficient\nsoftware, yet it remains a challenging skill to teach and learn in introductory\nprogramming courses. What does program decomposition for procedural CS1\nprograms entail? How can CS1 students improve the decomposition of their\nprograms? What scaffolded exercises can instructors use to teach program\ndecomposition skills? We aim to answer all these questions by presenting a\nconceptual framework that (1) is grounded in the established code style\nprinciples, (2) provides a systematic approach that can be taught to students\nas an actionable strategy to improve the program decomposition of their\nprograms, and (3) includes scaffolded exercises to be used in classroom\nactivities. In addition, this systematic approach is automatable and can\nfurther be used to implement visualizers, automated feedback generators and\ndigital tutors.",
        "split": "arXiv"
    },
    {
        "text": "We study the nonleptonic decays $\\bar{B}_s^0 \\to D_s^{(*)+} \\pi^-$ and\n$\\bar{B}^0 \\to D^{(*)+} K^-$ within the Weak Effective Theory (WET) up to\nmass-dimension six. We revisit the calculation of the hadronic matrix elements\nwithin QCD Factorization including the full set of WET operators. We\nrecalculate the two-particle contributions to the hard-scattering kernels at\nnext-to-leading order in $\\alpha_s$, confirming recent results in the\nliterature. We also calculate the three-particle contributions at leading order\nin $\\alpha_s$, clarifying the procedure, refining the SM results in the\nliterature, and providing for the first time the complete set of contributions\nwithin the WET. We use these results to perform a global phenomenological study\nof the effective couplings, putting bounds on the size of the WET Wilson\ncoefficients in four distinct fit models. The fits include constraints from the\nnonleptonic $B$-meson decay width, which we calculate at the leading order for\nthe full set of WET operators for the first time. This study is the first one\nto account for simultaneous variation of up to six effective couplings. We\nidentify two distinct modes in all fit models and discuss how future\nmeasurements can be used to distinguish between them.",
        "split": "arXiv"
    },
    {
        "text": "We have analytically explored both the zero temperature and the finite\ntemperature scaling theory for the collapse of an attractively interacting 3-D\nharmonically trapped Bose gas in a synthetic magnetic field. We have considered\nshort ranged (contact) attractive inter-particle interactions and Hartree-Fock\napproximation for the same. We have separately studied the collapse of both the\ncondensate and the thermal cloud below and above the condensation point,\nrespectively. We have obtained an anisotropy, artificial magnetic field, and\ntemperature dependent critical number of particles for the collapse of the\ncondensate. We have found a dramatic change in the critical exponent (from\n$\\alpha=1$ to $0$) of the specific heat ($C_v\\propto|T-T_c|^{\\alpha}$) when the\nthermal cloud is about to collapse with the critical number of particles\n($N=N_c$) just below and above the condensation point. All the results obtained\nby us are experimentally testable within the present-day experimental set-up\nfor the ultracold systems in the magneto-optical traps.",
        "split": "arXiv"
    },
    {
        "text": "Recent research has shown that state-of-the-art (SotA) Automatic Speech\nRecognition (ASR) systems, such as Whisper, often exhibit predictive biases\nthat disproportionately affect various demographic groups. This study focuses\non identifying the performance disparities of Whisper models on Dutch speech\ndata from the Common Voice dataset and the Dutch National Public Broadcasting\norganisation. We analyzed the word error rate, character error rate and a\nBERT-based semantic similarity across gender groups. We used the moral\nframework of Weerts et al. (2022) to assess quality of service harms and\nfairness, and to provide a nuanced discussion on the implications of these\nbiases, particularly for automatic subtitling. Our findings reveal substantial\ndisparities in word error rate (WER) among gender groups across all model\nsizes, with bias identified through statistical testing.",
        "split": "arXiv"
    },
    {
        "text": "Let $\\mathcal{X} = \\{ X_{\\gamma} \\}_{\\gamma \\in \\Gamma}$ be a family of\nBanach spaces and let $\\mathcal{E}$ be a Banach sequence space defined on\n$\\Gamma$. The main aim of this work is to investigate the abstract Kadets--Klee\nproperties, that is, the Kadets--Klee type properties in which the weak\nconvergence of sequences is replaced by the convergence with respect to some\nlinear Hausdorff topology, for the direct sum construction $(\\bigoplus_{\\gamma\n\\in \\Gamma} X_{\\gamma})_{\\mathcal{E}}$. As we will show, and this seems to be\nquite atypical behavior when compared to some other geometric properties, to\nlift the Kadets--Klee properties from the components to whole direct sum it is\nnot enough to assume that all involved spaces have the appropriate Kadets--Klee\nproperty. Actually, to complete the picture one must add a dichotomy in the\nform of the Schur type properties for $X_{\\gamma}$'s supplemented by the\nvariant of strict monotonicity for $\\mathcal{E}$. Back down to earth, this\ngeneral machinery naturally provides a blue print for other topologies like,\nfor example, the weak topology or the topology of local convergence in measure,\nthat are perhaps more commonly associated with this type of considerations.\nFurthermore, by limiting ourselves to direct sums in which the family\n$\\mathcal{X}$ is constant, that is, $X_{\\gamma} = X$ for all $\\gamma \\in\n\\Gamma$ and some Banach space $X$, we return to the well-explored ground of\nK{\\\"o}the--Bochner sequence spaces $\\mathcal{E}(X)$. Doing all this, we will\nreproduce, but sometimes also improve, essentially all existing results about\nthe classical Kadets--Klee properties in K{\\\"o}the--Bochner sequence spaces.",
        "split": "arXiv"
    },
    {
        "text": "Integrated sensing and communication (ISAC) is envisioned as a key technology\nfor future sixth-generation (6G) networks. Classical ISAC system considering\nmonostatic and/or bistatic settings will inevitably degrade both communication\nand sensing performance due to the limited service coverage and easily blocked\ntransmission paths. Besides, existing ISAC studies usually focus on downlink\n(DL) or uplink (UL) communication demands and unable to achieve the systematic\nDL and UL communication tasks. These challenges can be overcome by networked FD\nISAC framework. Moreover, ISAC generally considers the trade-off between\ncommunication and sensing, unavoidably leading to a loss in communication\nperformance. This shortcoming can be solved by the emerging movable antenna\n(MA) technology. In this paper, we utilize the MA to promote communication\ncapability with guaranteed sensing performance via jointly designing\nbeamforming, power allocation, receiving filters and MA configuration towards\nmaximizing sum rate. The optimization problem is highly difficult due to the\nunique channel model deriving from the MA. To resolve this challenge, via\nleveraging the cutting-the-edge majorization-minimization (MM) method, we\ndevelop an efficient solution that optimizes all variables via convex\noptimization techniques. Extensive simulation results verify the effectiveness\nof our proposed algorithms and demonstrate the substantial performance\npromotion by deploying MA in the networked FD ISAC system.",
        "split": "arXiv"
    },
    {
        "text": "We study in-depth those rings $R$ for which, there exists a fixed $n\\geq 1$,\nsuch that $u^n-1$ lies in the subring $\\Delta(R)$ of $R$ for every unit $u\\in\nR$. We succeeded to describe for any $n\\geq 1$ all reduced $\\pi$-regular\n$(2n-1)$-$\\Delta$U rings by showing that they satisfy the equation $x^{2n}=x$\nas well as to prove that the property of being exchange and clean are\ntantamount in the class of $(2n-1)$-$\\Delta$U rings. These achievements\nconsiderably extend results established by Danchev (Rend. Sem. Mat. Univ. Pol.\nTorino, 2019) and Ko\\c{s}an et al. (Hacettepe J. Math. \\& Stat., 2020). Some\nother closely related results of this branch are also established.",
        "split": "arXiv"
    },
    {
        "text": "Generative artificial intelligence is now a widely used tool in molecular\nscience. Despite the popularity of probabilistic generative models, numerical\nexperiments benchmarking their performance on molecular data are lacking. In\nthis work, we introduce and explain several classes of generative models,\nbroadly sorted into two categories: flow-based models and diffusion models. We\nselect three representative models: Neural Spline Flows, Conditional Flow\nMatching, and Denoising Diffusion Probabilistic Models, and examine their\naccuracy, computational cost, and generation speed across datasets with tunable\ndimensionality, complexity, and modal asymmetry. Our findings are varied, with\nno one framework being the best for all purposes. In a nutshell, (i) Neural\nSpline Flows do best at capturing mode asymmetry present in low-dimensional\ndata, (ii) Conditional Flow Matching outperforms other models for\nhigh-dimensional data with low complexity, and (iii) Denoising Diffusion\nProbabilistic Models appears the best for low-dimensional data with high\ncomplexity. Our datasets include a Gaussian mixture model and the dihedral\ntorsion angle distribution of the Aib\\textsubscript{9} peptide, generated via a\nmolecular dynamics simulation. We hope our taxonomy of probabilistic generative\nframeworks and numerical results may guide model selection for a wide range of\nmolecular tasks.",
        "split": "arXiv"
    },
    {
        "text": "In this article we study a reaction diffusion system with $m$ unknown\nconcentration. The non-linearity in our study comes from an underlying\nreversible chemical reaction and triangular in nature. Our objective is to\nunderstand the large time behaviour of solution where there are degeneracies.\nIn particular we treat those cases when one of the diffusion coefficient is\nzero and others are strictly positive. We prove convergence to equilibrium type\nof results under some condition on stoichiometric coefficients in dimension\n$1$,$2$ and $3$ in correspondence with the existence of classical solution. For\ndimension greater than 3 we prove similar result under certain closeness\ncondition on the non-zero diffusion coefficients and with the same condition\nimposed on stoichiometric coefficients. All the constant occurs in the decay\nestimates are explicit.",
        "split": "arXiv"
    },
    {
        "text": "Domain generalisation in computational histopathology is challenging because\nthe images are substantially affected by differences among hospitals due to\nfactors like fixation and staining of tissue and imaging equipment. We\nhypothesise that focusing on nuclei can improve the out-of-domain (OOD)\ngeneralisation in cancer detection. We propose a simple approach to improve OOD\ngeneralisation for cancer detection by focusing on nuclear morphology and\norganisation, as these are domain-invariant features critical in cancer\ndetection. Our approach integrates original images with nuclear segmentation\nmasks during training, encouraging the model to prioritise nuclei and their\nspatial arrangement. Going beyond mere data augmentation, we introduce a\nregularisation technique that aligns the representations of masks and original\nimages. We show, using multiple datasets, that our method improves OOD\ngeneralisation and also leads to increased robustness to image corruptions and\nadversarial attacks. The source code is available at\nhttps://github.com/undercutspiky/SFL/",
        "split": "arXiv"
    },
    {
        "text": "We study operator algebraic and function theoretic aspects of algebras of\nbounded nc functions on subvarieties of the nc domain determined by all levels\nof the unit ball of an operator space (nc operator balls). Our main result is\nthe following classification theorem: under very mild assumptions on the\nvarieties, two such algebras $H^\\infty(\\mathfrak{V})$ and\n$H^\\infty(\\mathfrak{W})$ are completely isometrically and weak-* isomorphic if\nand only if there is a nc biholomorphism between the varieties. For matrix\nspanning homogeneous varieties in injective operator balls, we further sharpen\nthis equivalence, showing that there exists a linear isomorphism between the\nrespective balls that maps one variety onto the other; in general, we show, the\nhomogeneity condition cannot be dropped. We highlight some difficulties and\nopen problems, contrasting with the well studied case of row ball.",
        "split": "arXiv"
    },
    {
        "text": "A full set of vibrationally-resolved cross sections for electron impact\nexcitation of NO(X2{\\Pi}, v) molecules is calculated from ab initio molecular\ndynamics, in the framework of the local-complex-potential approach.\nElectron-vibration energy exchanges in non-equilibrium thermodynamic conditions\nare studied from a state-to-state model accounting for all electron impact\nexcitation and de-excitation processes of the nitric oxide vibration manifold,\nand it is shown that the calculated vibration relaxation times are in good\nagreement with the experimental data. The new vibrational excitation cross\nsections are used in a complete electron impact cross section set in order to\nobtain non-equilibrium electron energy distributions functions and to calculate\nelectron transport parameters in NO. It is verified that the new cross sections\nbring a significant improvement between simulations and experimental swarm\ndata, providing an additional validation of the calculations, when used within\nthe complete set of cross sections investigated in this work.",
        "split": "arXiv"
    },
    {
        "text": "This paper describes an algorithm for reconstructing and identifying a highly\ncollimated hadronically decaying $\\tau$-lepton pair with low transverse\nmomentum. When two $\\tau$-leptons are highly collimated, their visible decay\nproducts might overlap, degrading the reconstruction performance for each of\nthe $\\tau$-leptons. This requires a dedicated treatment that attempts to tag it\nas a single object. The reconstruction algorithm is based on a large radius jet\nand its associated two leading subjets, and the identification uses a boosted\ndecision tree to discriminate between signatures from $\\tau^+\\tau^-$ systems\nand those arising from QCD jets. The efficiency of the identification algorithm\nis measured in $Z\\gamma$ events using proton-proton collision data at\n$\\sqrt{s}=13$ TeV collected by the ATLAS experiment at the Large Hadron\nCollider between 2015 and 2018, corresponding to an integrated luminosity of\n139 $\\mbox{fb}^{-1}$. The resulting data-to-simulation scale factors are close\nto unity with uncertainties ranging from 26% to 37%.",
        "split": "arXiv"
    },
    {
        "text": "We study the design of iterative combinatorial auctions (ICAs). The main\nchallenge in this domain is that the bundle space grows exponentially in the\nnumber of items. To address this, several papers have recently proposed machine\nlearning (ML)-based preference elicitation algorithms that aim to elicit only\nthe most important information from bidders to maximize efficiency. The SOTA\nML-based algorithms elicit bidders' preferences via value queries (i.e., \"What\nis your value for the bundle $\\{A,B\\}$?\"). However, the most popular iterative\ncombinatorial auction in practice elicits information via more practical\n\\emph{demand queries} (i.e., \"At prices $p$, what is your most preferred bundle\nof items?\"). In this paper, we examine the advantages of value and demand\nqueries from both an auction design and an ML perspective. We propose a novel\nML algorithm that provably integrates the full information from both query\ntypes. As suggested by our theoretical analysis, our experimental results\nverify that combining demand and value queries results in significantly better\nlearning performance. Building on these insights, we present MLHCA, the most\nefficient ICA ever designed. MLHCA substantially outperforms the previous SOTA\nin realistic auction settings, delivering large efficiency gains. Compared to\nthe previous SOTA, MLHCA reduces efficiency loss by up to a factor of 10, and\nin the most challenging and realistic domain, MLHCA outperforms the previous\nSOTA using 30% fewer queries. Thus, MLHCA achieves efficiency improvements that\ntranslate to welfare gains of hundreds of millions of USD, while also reducing\nthe cognitive load on the bidders, establishing a new benchmark both for\npracticability and for economic impact.",
        "split": "arXiv"
    },
    {
        "text": "We consider the initial-boundary value problem in the quarter space for the\nsystem of equations of ideal Magneto-Hydrodynamics for compressible fluids with\nperfectly conducting wall boundary conditions. On the two parts of the boundary\nthe solution satisfies different boundary conditions, which make the problem an\ninitial-boundary value problem with non-uniformly characteristic boundary.\n  We identify a subspace ${{\\mathcal H}}^3(\\Omega)$ of the Sobolev space\n$H^3(\\Omega)$, obtained by addition of suitable boundary conditions on one\nportion of the boundary, such that for initial data in ${{\\mathcal\nH}}^3(\\Omega)$ there exists a solution in the same space ${{\\mathcal\nH}}^3(\\Omega)$, for all times in a small time interval. This yields the\nwell-posedness of the problem combined with a persistence property of full\n$H^3$-regularity, although in general we expect a loss of normal regularity\nnear the boundary. Thanks to the special geometry of the quarter space the\nproof easily follows by the \"reflection technique\".",
        "split": "arXiv"
    },
    {
        "text": "Starting with an $n$-dimensional oriented Riemannian manifold with a Spin-c\nstructure, we describe an elliptic system of equations which recover the\nSeiberg-Witten equations when $n=3,4$. The equations are for a U(1)-connection\n$A$ and spinor $\\phi$, as usual, and also an odd degree form $\\beta$ (generally\nof inhomogeneous degree). From $A$ and $\\beta$ we define a Dirac operator\n$D_{A,\\beta}$ using the action of $\\beta$ and $*\\beta$ on spinors (with\ncarefully chosen coefficients) to modify $D_A$. The first equation in our\nsystem is $D_{A,\\beta}(\\phi)=0$. The left-hand side of the second equation is\nthe principal part of the Weitzenb\\\"ock remainder for\n$D^*_{A,\\beta}D_{A,\\beta}$. The equation sets this equal to $q(\\phi)$, the\ntrace-free part of projection against $\\phi$, as is familiar from the cases\n$n=3,4$. In dimensions $n=4m$ and $n=2m+1$, this gives an elliptic system\nmodulo gauge. To obtain a system which is elliptic modulo gauge in dimensions\n$n=4m+2$, we use two spinors and two connections, and so have two Dirac and two\ncurvature equations, that are then coupled via the form $\\beta$. We also prove\na collection of a priori estimates for solutions to these equations.\nUnfortunately they are not sufficient to prove compactness modulo gauge,\ninstead leaving the possibility that bubbling may occur.",
        "split": "arXiv"
    },
    {
        "text": "The first measurement of $\\phi(1020)$ meson production in fixed-target $p$Ne\ncollisions at $\\sqrt{s_{NN}}=68.5$ GeV is presented. The $\\phi(1020)$ mesons\nare reconstructed in their $K^{+}K^{-}$ decay in a data sample consisting of\nproton collisions on neon nuclei at rest, corresponding to an integrated\nluminosity of $21.7 \\pm 1.4$ nb$^{-1}$, collected by the LHCb detector at CERN.\nThe $\\phi(1020)$ production cross-section in the centre-of-mass rapidity range\nof $-1.8<y^*<0$ and transverse momentum range of $800<p_{T}<6500$ MeV/c is\nfound to be\n$\\sigma=182.7\\pm2.7~\\text{(stat.)}\\pm14.1~\\text{(syst)}~\\mu$b/nucleon. A\ndouble-differential measurement of the cross-section is also provided in four\nregions of rapidity and six regions of transverse momentum of the $\\phi(1020)$\nmeson and compared with the predictions from Pythia and EPOS4, which are found\nto underestimate the experimental values.",
        "split": "arXiv"
    },
    {
        "text": "Event-by-event fluctuations of the event-wise mean transverse momentum,\n$\\langle p_{\\mathrm{T}}\\rangle$, of charged particles produced in proton-proton\n(pp) collisions at $\\sqrt{s}$ = 5.02 TeV, Xe-Xe collisions at\n$\\sqrt{s_{\\mathrm{NN}}}$ = 5.44 TeV, and Pb-Pb collisions at\n$\\sqrt{s_{\\mathrm{NN}}}$ = 5.02 TeV are studied using the ALICE detector based\non the integral correlator $\\langle\\langle \\Delta p_{\\rm T}\\Delta p_{\\rm\nT}\\rangle\\rangle $. The correlator strength is found to decrease monotonically\nwith increasing produced charged-particle multiplicity measured at midrapidity\nin all three systems. In Xe-Xe and Pb-Pb collisions, the multiplicity\ndependence of the correlator deviates significantly from a simple power-law\nscaling as well as from the predictions of the HIJING and AMPT models. The\nobserved deviation from power-law scaling is expected from transverse radial\nflow in semicentral to central Xe-Xe and Pb-Pb collisions. In pp collisions,\nthe correlation strength is also studied by classifying the events based on the\ntransverse spherocity, $S_0$, of the particle production at midrapidity, used\nas a proxy for the presence of a pronounced back-to-back jet topology.\nLow-spherocity (jetty) events feature a larger correlation strength than those\nwith high spherocity (isotropic). The strength and multiplicity dependence of\njetty and isotropic events are well reproduced by calculations with the PYTHIA\n8 and EPOS LHC models.",
        "split": "arXiv"
    },
    {
        "text": "We propose a novel generalized framework for grant-free random-access (GFRA)\nin cell-free massive multiple input multiple-output systems where multiple\ngeographically separated access points (APs) or base stations (BSs) aim to\ndetect sporadically active user-equipment (UEs). Unlike a conventional\narchitecture in which all the active UEs transmit their signature or pilot\nsequences of equal length, we admit a flexible pilot length for each UE, which\nalso enables a seamless integration into conventional grant-based wireless\nsystems. We formulate the joint UE activity detection and the distributed\nchannel estimation as a sparse support and signal recovery problem, and\ndescribe a Bayesian learning procedure to solve it. We develop a scheme to fuse\nthe posterior statistics of the latent variables inferred by each AP to jointly\ndetect the UEs' activities, and utilize them to further refine the channel\nestimates. In addition, we allude to an interesting point which enables this\nflexible GFRA framework to encode the information bits from the active UEs. We\nnumerically evaluate the normalized mean square error and the probability of\nmiss-detection performances obtained by the Bayesian algorithm and show that\nthe latent-variable fusion enhances the detection and the channel estimation\nperformances by a large margin. We also benchmark against a genie-aided\nalgorithm which has a prior knowledge of the UEs' activities.",
        "split": "arXiv"
    },
    {
        "text": "The $k$d-tree is one of the most widely used data structures to manage\nmulti-dimensional data. Due to the ever-growing data volume, it is imperative\nto consider parallelism in $k$d-trees. However, we observed challenges in\nexisting parallel kd-tree implementations, for both constructions and updates.\n  The goal of this paper is to develop efficient in-memory $k$d-trees by\nsupporting high parallelism and cache-efficiency. We propose the Pkd-tree\n(Parallel $k$d-tree), a parallel $k$d-tree that is efficient both in theory and\nin practice. The Pkd-tree supports parallel tree construction, batch update\n(insertion and deletion), and various queries including k-nearest neighbor\nsearch, range query, and range count. We proved that our algorithms have strong\ntheoretical bounds in work (sequential time complexity), span (parallelism),\nand cache complexity. Our key techniques include 1) an efficient construction\nalgorithm that optimizes work, span, and cache complexity simultaneously, and\n2) reconstruction-based update algorithms that guarantee the tree to be\nweight-balanced. With the new algorithmic insights and careful engineering\neffort, we achieved a highly optimized implementation of the Pkd-tree.\n  We tested Pkd-tree with various synthetic and real-world datasets, including\nboth uniform and highly skewed data. We compare the Pkd-tree with\nstate-of-the-art parallel $k$d-tree implementations. In all tests, with better\nor competitive query performance, Pkd-tree is much faster in construction and\nupdates consistently than all baselines. We released our code.",
        "split": "arXiv"
    },
    {
        "text": "The dominant accretion process leading to the formation of the terrestrial\nplanets of the Solar System is a subject of intense scientific debate. Two\nradically different scenarios have been proposed. The classic scenario starts\nfrom a disk of planetesimals which, by mutual collisions, produce a set of Moon\nto Mars-mass planetary embryos. After the removal of gas from the disk, the\nembryos experience mutual giant impacts which, together with the accretion of\nadditional planetesimals, lead to the formation of the terrestrial planets on a\ntimescale of tens of millions of years. In the alternative, pebble accretion\nscenario, the terrestrial planets grow by accreting sunward-drifting mm-cm\nsized particles from the outer disk. The planets all form within the lifetime\nof the disk, with the sole exception of Earth, which undergoes a single\npost-disk giant impact with Theia (a fifth protoplanet formed by pebble\naccretion itself) to form the Moon. To distinguish between these two scenarios,\nwe revisit all available constraints: compositional (in terms of\nnucleosynthetic isotope anomalies and chemical composition), dynamical and\nchronological. We find that the pebble accretion scenario is unable to match\nthese constraints in a self-consistent manner, unlike the classic scenario.",
        "split": "arXiv"
    },
    {
        "text": "A major challenge in nuclear fusion research is the coherent combination of\ndata from heterogeneous diagnostics and modelling codes for machine control and\nsafety as well as physics studies. Measured data from different diagnostics\noften provide information about the same subset of physical parameters.\nAdditionally, information provided by some diagnostics might be needed for the\nanalysis of other diagnostics. A joint analysis of complementary and redundant\ndata allows, e.g., to improve the reliability of parameter estimation, to\nincrease the spatial and temporal resolution of profiles, to obtain synergistic\neffects, to consider diagnostics interdependencies and to find and resolve data\ninconsistencies. Physics-based modelling and parameter relationships provide\nadditional information improving the treatment of ill-posed inversion problems.\nA coherent combination of all kind of available information within a\nprobabilistic framework allows for improved data analysis results.\n  The concept of Integrated Data Analysis (IDA) in the framework of Bayesian\nprobability theory is outlined and contrasted with conventional data analysis.\nComponents of the probabilistic approach are summarized and specific\ningredients beneficial for data analysis at fusion devices are discussed.",
        "split": "arXiv"
    },
    {
        "text": "The large sieve is used to estimate the density of integral quadratic\npolynomials $Q$, such that there exists an odd degree integral polynomial which\nhas resultant $\\pm 1$ with $Q$. The proof uses properties of cyclotomic\npolynomials and the Chebotarev density theorem. Given a monic integral\npolynomial $R$ of odd degree, this is used to show that for almost all integral\nquadratic polynomials $Q$, there exists a prime $p$ such that $Q$ and $R$ share\na common root in the algebraic closure of the finite field with $p$ elements.\nUsing recent work of Landesman, an application to the average size of the\n$n$-torsion of the class group of quadratic number fields is also given.",
        "split": "arXiv"
    },
    {
        "text": "We determine the spectroscopic properties of ~1000 ostensibly star-forming\ngalaxies at redshifts (z=4-10) using prism spectroscopy from JWST/NIRSpec. With\nrest-wavelength coverage between Lya and [S II] in the optical, we stack\nspectra as a function of nebular conditions, and compare UV spectral properties\nwith stellar age. This reveals UV lines of N III], N IV], C III], C IV, He II,\nand O III] in the average high-z galaxy. All UV lines are more intense in\nyounger starbursts. We measure electron temperatures from the collisionally\nexcited [O III] line ratios, finding Te=18000-22000 K for the O++ regions. We\nalso detect a significant nebular Balmer Jump from which we estimate only\nTe=8000-13000 K. Accounting for typical temperature offsets between zones\nbearing doubly and singly ionized oxygen, these two temperatures remain\ndiscrepant by around 40%. We use the [O III] temperatures to estimate\nabundances of carbon, nitrogen, and oxygen. We find that log(C/O) is\nconsistently ~-1, with no evolution of C/O with metallicity or stellar age. The\naverage spectra are mildly enhanced in Nitrogen, with higher N/O than low-z\nstarbursts, but are less enhanced than samples of high-z galaxies with visible\nUV N III] and N IV]. Whatever processes produce the N-enhancement in the\nindividual galaxies must also be ongoing, at lower levels, in the median galaxy\nin the early Universe. The strongest starbursts are a source of significant\nionizing emission: ionizing photon production efficiencies reach 10^25.7\nHz/erg, and show multiple signatures of high Lyman continuum escape, including\nMg II escape fractions nearing 100%, significant deficits in [S II] emission,\nhigh degrees of ionization, and blue UV colors.",
        "split": "arXiv"
    },
    {
        "text": "Few-shot class-incremental learning (FSCIL) aims to continually learn new\nclasses from only a few samples without forgetting previous ones, requiring\nintelligent agents to adapt to dynamic environments. FSCIL combines the\ncharacteristics and challenges of class-incremental learning and few-shot\nlearning: (i) Current classes occupy the entire feature space, which is\ndetrimental to learning new classes. (ii) The small number of samples in\nincremental rounds is insufficient for fully training. In existing mainstream\nvirtual class methods, for addressing the challenge (i), they attempt to use\nvirtual classes as placeholders. However, new classes may not necessarily align\nwith the virtual classes. For the challenge (ii), they replace trainable fully\nconnected layers with Nearest Class Mean (NCM) classifiers based on cosine\nsimilarity, but NCM classifiers do not account for sample imbalance issues. To\naddress these issues in previous methods, we propose the class-center guided\nembedding Space Allocation with Angle-Norm joint classifiers (SAAN) learning\nframework, which provides balanced space for all classes and leverages norm\ndifferences caused by sample imbalance to enhance classification criteria.\nSpecifically, for challenge (i), SAAN divides the feature space into multiple\nsubspaces and allocates a dedicated subspace for each session by guiding\nsamples with the pre-set category centers. For challenge (ii), SAAN establishes\na norm distribution for each class and generates angle-norm joint logits.\nExperiments demonstrate that SAAN can achieve state-of-the-art performance and\nit can be directly embedded into other SOTA methods as a plug-in, further\nenhancing their performance.",
        "split": "arXiv"
    },
    {
        "text": "Solving multiscale diffusion problems is often computationally expensive due\nto the spatial and temporal discretization challenges arising from\nhigh-contrast coefficients. To address this issue, a partially explicit\ntemporal splitting scheme is proposed. By appropriately constructing multiscale\nspaces, the spatial multiscale property is effectively managed, and it has been\ndemonstrated that the temporal step size is independent of the contrast. To\nenhance simulation speed, we propose a parallel algorithm for the multiscale\nflow problem that leverages the partially explicit temporal splitting scheme.\nThe idea is first to evolve the partially explicit system using a coarse time\nstep size, then correct the solution on each coarse time interval with a fine\npropagator, for which we consider both the sequential solver and all-at-once\nsolver. This procedure is then performed iteratively till convergence. We\nanalyze the stability and convergence of the proposed algorithm. The numerical\nexperiments demonstrate that the proposed algorithm achieves high numerical\naccuracy for high-contrast problems and converges in a relatively small number\nof iterations. The number of iterations stays stable as the number of coarse\nintervals increases, thus significantly improving computational efficiency\nthrough parallel processing.",
        "split": "arXiv"
    },
    {
        "text": "Global attention has been focused on extreme climatic changes. This paper\ninvestigates the relationship between different phases of solar activity and\nextreme precipitation events in Kerala, India. Sunspot number and rainfall data\nwere analysed over 122 years (1901-2022) on an annual scale. A negative\ncorrelation was observed in the winter and post-monsoon seasons, while positive\ncorrelations were seen in the pre-monsoon and monsoon seasons, all of which\nwere statistically significant. Using cross-wavelet transform, the temporal\nrelationship between sunspot number and rainfall values was investigated,\nrevealing significant cross-power at an 8-12 year scale across all seasons.\nWavelet coherence between the two data sets demonstrated significant\ncorrelation at the 2-4 and 4-8 year scales throughout the four seasons. The\nresults show that the seasonal rainfall over Kerala is related to solar\nactivity. The solar phases of Solar Cycles 14-24 were determined for all\nseasons, and the years with excessive and insufficient rainfall were\nidentified. It was observed that the descending phase had an impact on excess\nrainfall events during the winter and pre-monsoon seasons, while the ascending\nphase notably affected the monsoon and post-monsoon seasons. The study\nspecifically examined the different magnetic polarities of sunspots in\nalternating solar cycles, focusing on even and odd cycles. It was found that\nextreme rainfall events were more frequent during the winter and pre-monsoon\nseasons in the even cycles, whereas in the odd cycles, they were more prevalent\nduring the monsoon and post-monsoon seasons. These findings are presented for\nthe first time and may offer new perspectives on how different phases affect\nrainfall. This study suggests a physical link between solar activity and\nextreme precipitation in Kerala, which could increase predictability.",
        "split": "arXiv"
    },
    {
        "text": "Pandora temporal fault tree, as one notable extension of the fault tree,\nintroduces temporal gates and temporal laws. Pandora Temporal Fault Tree(TFT)\nenhances the capability of fault trees and enables the modeling of system\nfailure behavior that depends on sequences. The calculation of system failure\nprobability in Pandora TFT relies on precise probabilistic information on\ncomponent failures. However, obtaining such precise failure data can often be\nchallenging. The data may be uncertain as historical records are used to derive\nfailure data for system components. To mitigate this uncertainty, in this\nstudy, we proposed a method that integrates fuzzy set theory with Pandora TFT.\nThis integration aims to enable dynamic analysis of complex systems, even in\ncases where quantitative failure data of components is unreliable or imprecise.\nThe proposed work introduces the development of Fuzzy AND, Fuzzy OR, Fuzzy\nPAND, and Fuzzy POR logic gates for Pandora TFT. We also introduce a fuzzy\nimportance measure for criticality analysis of basic events. All events in our\nanalysis are assumed to have exponentially distributed failures, with their\nfailure rates represented as triangular fuzzy numbers. We illustrate the\nproposed method through a case study of the Aircraft Fuel Distribution System\n(AFDS), highlighting its practical application and effectiveness in analyzing\ncomplex systems. The results are compared with existing results from Petri net\nand Bayesian network techniques to validate the findings.",
        "split": "arXiv"
    },
    {
        "text": "The widespread use of social media platforms like Twitter and Facebook has\nenabled people of all ages to share their thoughts and experiences, leading to\nan immense accumulation of user-generated content. However, alongside the\nbenefits, these platforms also face the challenge of managing hate speech and\noffensive content, which can undermine rational discourse and threaten\ndemocratic values. As a result, there is a growing need for automated methods\nto detect and mitigate such content, especially given the complexity of\nconversations that may require contextual analysis across multiple languages,\nincluding code-mixed languages like Hinglish, German-English, and Bangla. We\nparticipated in the English task where we have to classify English tweets into\ntwo categories namely Hate and Offensive and Non Hate-Offensive. In this work,\nwe experiment with state-of-the-art large language models like GPT-3.5 Turbo\nvia prompting to classify tweets into Hate and Offensive or Non Hate-Offensive.\nIn this study, we evaluate the performance of a classification model using\nMacro-F1 scores across three distinct runs. The Macro-F1 score, which balances\nprecision and recall across all classes, is used as the primary metric for\nmodel evaluation. The scores obtained are 0.756 for run 1, 0.751 for run 2, and\n0.754 for run 3, indicating a high level of performance with minimal variance\namong the runs. The results suggest that the model consistently performs well\nin terms of precision and recall, with run 1 showing the highest performance.\nThese findings highlight the robustness and reliability of the model across\ndifferent runs.",
        "split": "arXiv"
    },
    {
        "text": "Interpreting human neural signals to decode static speech intentions such as\ntext or images and dynamic speech intentions such as audio or video is showing\ngreat potential as an innovative communication tool. Human communication\naccompanies various features, such as articulatory movements, facial\nexpressions, and internal speech, all of which are reflected in neural signals.\nHowever, most studies only generate short or fragmented outputs, while\nproviding informative communication by leveraging various features from neural\nsignals remains challenging. In this study, we introduce a dynamic neural\ncommunication method that leverages current computer vision and brain-computer\ninterface technologies. Our approach captures the user's intentions from neural\nsignals and decodes visemes in short time steps to produce dynamic visual\noutputs. The results demonstrate the potential to rapidly capture and\nreconstruct lip movements during natural speech attempts from human neural\nsignals, enabling dynamic neural communication through the convergence of\ncomputer vision and brain--computer interface.",
        "split": "arXiv"
    },
    {
        "text": "We investigate the consequences of temporal reflection on wave propagation\nand transformation in systems governed by a pseudospin-1/2 Dirac equation.\nThese systems are spatially uniform but are subject to random temporal\nvariations in mass, which correspond to the energy gap between the Dirac cones.\nBy employing the invariant imbedding method on two complementary random models,\nwe accurately compute all moments of temporal reflectance and derive their\nanalytical expressions in short- and long-time regimes. In the long-time\nregime, the reflectance probability density is a constant equal to one,\nindicating uniform probability for any reflectance value. The group velocity of\nthe wave decays to zero with time, signifying spatial localization induced by\ntemporal variations. Numerical simulations of a wave pulse show that the\ninitially narrow pulse evolves into a precisely Gaussian shape over time. In\nthe long-time regime, the pulse center exhibits spatial localization, while its\nwidth shows ordinary diffusive behavior, increasing without limit. This\nbehavior is universal, persisting regardless of the initial pulse shape or the\nprobability distribution of the random mass. Our findings suggest that\ninsulating behavior can be induced in Dirac materials by random temporal\nvariations of the medium parameters. We discuss the possibilities of verifying\nour predictions in various experimental systems.",
        "split": "arXiv"
    },
    {
        "text": "Fast radio bursts (FRBs) are radio burst signals that lasting milliseconds.\nThey originate from cosmological distances and have relatively high dispersion\nmeasures (DMs), making them being excellent distance indicators. However, there\nare many important questions about FRBs remain us to resolve. With its wide\nfield of view and excellent sensitivity, CHIME/FRB has discovered more than\nhalf of all known FRBs. As more and more FRBs are located within or connected\nwith their host galaxies, the study of FRB progenitors is becoming more\nimportant. In this work, we collect the currently available information related\nto the host galaxies of FRBs, and the MCMC analysis about limited localized\nsamples reveals no significant difference in the $\\mathrm{DM_{host}}$ between\nrepeaters and non-repeaters. After examining CHIME/FRB samples, we estimated\nthe volumetric rates of repeaters and non-repeaters, accounting for\n$\\mathrm{DM_{host}}$ contributions. We compare event rate with rates of\npredicted origin models and transient events. Our results indicate that\n$\\mathrm{DM_{host}}$ significantly affects volumetric rates and offer insights\ninto the origin mechanisms of FRB populations.",
        "split": "arXiv"
    },
    {
        "text": "Efficient multiple setpoint tracking can enable advanced biotechnological\napplications, such as maintaining desired population levels in co-cultures for\noptimal metabolic division of labor. In this study, we employ reinforcement\nlearning as a control method for population setpoint tracking in co-cultures,\nfocusing on policy-gradient techniques where the control policy is\nparameterized by neural networks. However, achieving accurate tracking across\nmultiple setpoints is a significant challenge in reinforcement learning, as the\nagent must effectively balance the contributions of various setpoints to\nmaximize the expected system performance. Traditional return functions, such as\nthose based on a quadratic cost, often yield suboptimal performance due to\ntheir inability to efficiently guide the agent toward the simultaneous\nsatisfaction of all setpoints. To overcome this, we propose a novel return\nfunction that rewards the simultaneous satisfaction of multiple setpoints and\ndiminishes overall reward gains otherwise, accounting for both stage and\nterminal system performance. This return function includes parameters to\nfine-tune the desired smoothness and steepness of the learning process. We\ndemonstrate our approach considering an $\\textit{Escherichia coli}$ co-culture\nin a chemostat with optogenetic control over amino acid synthesis pathways,\nleveraging auxotrophies to modulate growth.",
        "split": "arXiv"
    },
    {
        "text": "Imagine searching a collection of coins for quarters ($0.25$), dimes\n($0.10$), nickels ($0.05$), and pennies ($0.01$)-a hybrid foraging task where\nobservers look for multiple instances of multiple target types. In such tasks,\nhow do target values and their prevalence influence foraging and eye movement\nbehaviors (e.g., should you prioritize rare quarters or common nickels)? To\nexplore this, we conducted human psychophysics experiments, revealing that\nhumans are proficient reward foragers. Their eye fixations are drawn to regions\nwith higher average rewards, fixation durations are longer on more valuable\ntargets, and their cumulative rewards exceed chance, approaching the upper\nbound of optimal foragers. To probe these decision-making processes of humans,\nwe developed a transformer-based Visual Forager (VF) model trained via\nreinforcement learning. Our VF model takes a series of targets, their\ncorresponding values, and the search image as inputs, processes the images\nusing foveated vision, and produces a sequence of eye movements along with\ndecisions on whether to collect each fixated item. Our model outperforms all\nbaselines, achieves cumulative rewards comparable to those of humans, and\napproximates human foraging behavior in eye movements and foraging biases\nwithin time-limited environments. Furthermore, stress tests on\nout-of-distribution tasks with novel targets, unseen values, and varying set\nsizes demonstrate the VF model's effective generalization. Our work offers\nvaluable insights into the relationship between eye movements and\ndecision-making, with our model serving as a powerful tool for further\nexploration of this connection. All data, code, and models will be made\npublicly available.",
        "split": "arXiv"
    },
    {
        "text": "We consider the simulation of isentropic flow in pipelines and pipe networks.\nStandard operating conditions in pipe networks suggest an emphasis to simulate\nlow Mach and high friction regimes -- however, the system is stiff in these\nregimes and conventional explicit approximation techniques prove quite costly\nand often impractical. To combat these inefficiencies, we develop a novel\nasymptotic-preserving scheme that is uniformly consistent and stable for all\nMach regimes. The proposed method for a single pipeline follows the flux\nsplitting suggested in [Haack et al., Commun. Comput. Phys., 12 (2012), pp.\n955--980], in which the flux is separated into stiff and non-stiff portions\nthen discretized in time using an implicit-explicit approach. The non-stiff\npart is advanced in time by an explicit hyperbolic solver; we opt for the\nsecond-order central-upwind finite volume scheme. The stiff portion is advanced\nin time implicitly using an approach based on Rosenbrock-type Runge-Kutta\nmethods, which ultimately reduces this implicit stage to a discretization of a\nlinear elliptic equation.\n  To extend to full pipe networks, the scheme on a single pipeline is paired\nwith coupling conditions defined at pipe-to-pipe intersections to ensure a\nmathematically well-posed problem. We show that the coupling conditions remain\nwell-posed in the low Mach/high friction limit -- which, when used to define\nthe ghost cells of each pipeline, results in a method that is accurate across\nthese intersections in all regimes. The proposed method is tested on several\nnumerical examples and produces accurate, non-oscillatory results with run\ntimes independent of the Mach number.",
        "split": "arXiv"
    },
    {
        "text": "The observation of a Hall effect, a finite transverse voltage induced by a\nlongitudinal current, usually requires the breaking of time-reversal symmetry,\nfor example through the application of an external magnetic field or the\npresence of long range magnetic order in a sample. Recently it was suggested\nthat under certain symmetry conditions, the presence of finite Berry curvatures\nin the band structure of a system with time-reversal symmetry but without\ninversion symmetry can give rise to a nonlinear Hall effect in the presence of\na probe current. In order to observe the nonlinear Hall effect, one requires a\nfinite component of a so-called Berry dipole along the direction of the probe\ncurrent. We report here measurements of the nonlinear Hall effect in\ntwo-dimensional electron gases fabricated on the surface of KTaO$_3$ with\ndifferent surface crystal orientations as a function of the probe current, a\ntransverse electric field and back gate voltage. For all three crystal\norientations, the transverse electric field modifies the nonlinear Hall effect.\nWe discuss our results in the context of the current understanding of the\nnonlinear Hall effect as well as potential experimental artifacts that may give\nrise to the same effects.",
        "split": "arXiv"
    },
    {
        "text": "Recent advancements in session-based recommendation models using deep\nlearning techniques have demonstrated significant performance improvements.\nWhile they can enhance model sophistication and improve the relevance of\nrecommendations, they also make it challenging to implement a scalable\nreal-time solution. To addressing this challenge, we propose GRAINRec: a Graph\nand Attention Integrated session-based recommendation model that generates\nrecommendations in real-time. Our scope of work is item recommendations in\nonline retail where a session is defined as an ordered sequence of digital\nguest actions, such as page views or adds to cart. The proposed model generates\nrecommendations by considering the importance of all items in the session\ntogether, letting us predict relevant recommendations dynamically as the\nsession evolves. We also propose a heuristic approach to implement real-time\ninferencing that meets Target platform's service level agreement (SLA). The\nproposed architecture lets us predict relevant recommendations dynamically as\nthe session evolves, rather than relying on pre-computed recommendations for\neach item. Evaluation results of the proposed model show an average improvement\nof 1.5% across all offline evaluation metrics. A/B tests done over a 2 week\nduration showed an increase of 10% in click through rate and 9% increase in\nattributable demand. Extensive ablation studies are also done to understand our\nmodel performance for different parameters.",
        "split": "arXiv"
    },
    {
        "text": "Egocentric Hand Object Interaction (HOI) videos provide valuable insights\ninto human interactions with the physical world, attracting growing interest\nfrom the computer vision and robotics communities. A key task in fully\nunderstanding the geometry and dynamics of HOI scenes is dense pointclouds\nsequence reconstruction. However, the inherent motion of both hands and the\ncamera makes this challenging. Current methods often rely on time-consuming\ntest-time optimization, making them impractical for reconstructing\ninternet-scale videos. To address this, we introduce UniHOI, a model that\nunifies the estimation of all variables necessary for dense 4D reconstruction,\nincluding camera intrinsic, camera poses, and video depth, for egocentric HOI\nscene in a fast feed-forward manner. We end-to-end optimize all these variables\nto improve their consistency in 3D space. Furthermore, our model could be\ntrained solely on large-scale monocular video dataset, overcoming the\nlimitation of scarce labeled HOI data. We evaluate UniHOI with both in-domain\nand zero-shot generalization setting, surpassing all baselines in pointclouds\nsequence reconstruction and long-term 3D scene flow recovery. UniHOI is the\nfirst approach to offer fast, dense, and generalizable monocular egocentric HOI\nscene reconstruction in the presence of motion. Code and trained model will be\nreleased in the future.",
        "split": "arXiv"
    },
    {
        "text": "We introduce a set of useful expressions of Differential Privacy (DP) notions\nin terms of the Laplace transform of the privacy loss distribution. Its bare\nform expression appears in several related works on analyzing DP, either as an\nintegral or an expectation. We show that recognizing the expression as a\nLaplace transform unlocks a new way to reason about DP properties by exploiting\nthe duality between time and frequency domains. Leveraging our interpretation,\nwe connect the $(q, \\rho(q))$-R\\'enyi DP curve and the $(\\epsilon,\n\\delta(\\epsilon))$-DP curve as being the Laplace and inverse-Laplace transforms\nof one another. This connection shows that the R\\'enyi divergence is\nwell-defined for complex orders $q = \\gamma + i \\omega$. Using our Laplace\ntransform-based analysis, we also prove an adaptive composition theorem for\n$(\\epsilon, \\delta)$-DP guarantees that is exactly tight (i.e., matches even in\nconstants) for all values of $\\epsilon$. Additionally, we resolve an issue\nregarding symmetry of $f$-DP on subsampling that prevented equivalence across\nall functional DP notions.",
        "split": "arXiv"
    },
    {
        "text": "ABCI 3.0 is the latest version of the ABCI, a large-scale open AI\ninfrastructure that AIST has been operating since August 2018 and will be fully\noperational in January 2025. ABCI 3.0 consists of computing servers equipped\nwith 6128 of the NVIDIA H200 GPUs and an all-flash storage system. Its peak\nperformance is 6.22 exaflops in half precision and 3.0 exaflops in single\nprecision, which is 7 to 13 times faster than the previous system, ABCI 2.0. It\nalso more than doubles both storage capacity and theoretical read/write\nperformance. ABCI 3.0 is expected to accelerate research and development,\nevaluation, and workforce development of cutting-edge AI technologies, with a\nparticular focus on generative AI.",
        "split": "arXiv"
    },
    {
        "text": "While contrastive pre-training is widely employed, its data efficiency\nproblem has remained relatively under-explored thus far. Existing methods often\nrely on static coreset selection algorithms to pre-identify important data for\ntraining. However, this static nature renders them unable to dynamically track\nthe data usefulness throughout pre-training, leading to subpar pre-trained\nmodels. To address this challenge, our paper introduces a novel dynamic\nbootstrapping dataset pruning method. It involves pruning data preparation\nfollowed by dataset mutation operations, both of which undergo iterative and\ndynamic updates. We apply this method to two prevalent contrastive pre-training\nframeworks: \\textbf{CLIP} and \\textbf{MoCo}, representing vision-language and\nvision-centric domains, respectively. In particular, we individually pre-train\nseven CLIP models on two large-scale image-text pair datasets, and two MoCo\nmodels on the ImageNet dataset, resulting in a total of 16 pre-trained models.\nWith a data pruning rate of 30-35\\% across all 16 models, our method exhibits\nonly marginal performance degradation (less than \\textbf{1\\%} on average)\ncompared to corresponding models trained on the full dataset counterparts\nacross various downstream datasets, and also surpasses several baselines with a\nlarge performance margin. Additionally, the byproduct from our method, \\ie\ncoresets derived from the original datasets after pre-training, also\ndemonstrates significant superiority in terms of downstream performance over\nother static coreset selection approaches.",
        "split": "arXiv"
    },
    {
        "text": "We present a verifier of quantum programs called AutoQ 2.0. Quantum programs\nextend quantum circuits (the domain of AutoQ 1.0) by classical control flow\nconstructs, which enable users to describe advanced quantum algorithms in a\nformal and precise manner. The extension is highly non-trivial, as we needed to\ntackle both theoretical challenges (such as the treatment of measurement, the\nnormalization problem, and lifting techniques for verification of classical\nprograms with loops to the quantum world), and engineering issues (such as\nextending the input format with a~support for specifying loop invariants). We\nhave successfully used AutoQ 2.0 to verify two types of advanced quantum\nprograms that cannot be expressed using only quantum circuits: the\n\\emph{repeat-until-success} (RUS) algorithm and the weak-measurement-based\nversion of Grover's search algorithm. AutoQ 2.0 can efficiently verify all our\nbenchmarks: all RUS algorithms were verified instantly and, for the\nweak-measurement-based version of Grover's search, we were able to handle the\ncase of 100 qubits in $\\sim$20 minutes.",
        "split": "arXiv"
    },
    {
        "text": "Interstellar objects (ISOs), astronomical objects not gravitationally bound\nto the sun, could present valuable opportunities to advance our understanding\nof the universe's formation and composition. In response to the unpredictable\nnature of their discoveries that inherently come with large and rapidly\nchanging uncertainty in their state, this paper proposes a novel\nmulti-spacecraft framework for locally maximizing information to be gained\nthrough ISO encounters with formal probabilistic guarantees. Given some\napproximated control and estimation policies for fully autonomous spacecraft\noperations, we first construct an ellipsoid around its terminal position, where\nthe ISO would be located with a finite probability. The large state uncertainty\nof the ISO is formally handled here through the hierarchical property in\nstochastically contracting nonlinear systems. We then propose a method to find\nthe terminal positions of the multiple spacecraft optimally distributed around\nthe ellipsoid, which locally maximizes the information we can get from all the\npoints of interest (POIs). This utilizes a probabilistic information cost\nfunction that accounts for spacecraft positions, camera specifications, and ISO\nposition uncertainty, where the information is defined as visual data collected\nby cameras. Numerical simulations demonstrate the efficacy of this approach\nusing synthetic ISO candidates generated from quasi-realistic empirical\npopulations. Our method allows each spacecraft to optimally select its terminal\nstate and determine the ideal number of POIs to investigate, potentially\nenhancing the ability to study these rare and fleeting interstellar visitors\nwhile minimizing resource utilization.",
        "split": "arXiv"
    },
    {
        "text": "A number of models have been developed for information spread through\nnetworks, often for solving the Influence Maximization (IM) problem. IM is the\ntask of choosing a fixed number of nodes to \"seed\" with information in order to\nmaximize the spread of this information through the network, with applications\nin areas such as marketing and public health. Most methods for this problem\nrely heavily on the assumption of known strength of connections between network\nmembers (edge weights), which is often unrealistic. In this paper, we develop a\nlikelihood-based approach to estimate edge weights from the fully and partially\nobserved information diffusion paths. We also introduce a broad class of\ninformation diffusion models, the general linear threshold (GLT) model, which\ngeneralizes the well-known linear threshold (LT) model by allowing arbitrary\ndistributions of node activation thresholds. We then show our weight estimator\nis consistent under the GLT and some mild assumptions. For the special case of\nthe standard LT model, we also present a much faster expectation-maximization\napproach for weight estimation. Finally, we prove that for the GLT models, the\nIM problem can be solved by a natural greedy algorithm with standard optimality\nguarantees if all node threshold distributions have concave cumulative\ndistribution functions. Extensive experiments on synthetic and real-world\nnetworks demonstrate that the flexibility in the choice of threshold\ndistribution combined with the estimation of edge weights significantly\nimproves the quality of IM solutions, spread prediction, and the estimates of\nthe node activation probabilities.",
        "split": "arXiv"
    },
    {
        "text": "We carried out a project involving the systematic analysis of microlensing\ndata from the Korea Microlensing Telescope Network survey. The aim of this\nproject is to identify lensing events with complex anomaly features that are\ndifficult to explain using standard binary-lens or binary-source models. Our\ninvestigation reveals that the light curves of microlensing events\nKMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412 display highly\ncomplex patterns with three or more anomaly features. These features cannot be\nadequately explained by a binary-lens (2L1S) model alone. However, the 2L1S\nmodel can effectively describe certain segments of the light curve. By\nincorporating an additional source into the modeling, we identified a\ncomprehensive model that accounts for all the observed anomaly features.\nBayesian analysis, based on constraints provided by lensing observables,\nindicates that the lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are binary\nsystems composed of M dwarfs. For KMT-2022-BLG-2480, the primary lens is an\nearly K-type main-sequence star with an M dwarf companion. The lenses of\nKMT-2021-BLG-0284 and KMT-2024-BLG-0412 are likely located in the bulge,\nwhereas the lens of KMT-2022-BLG-2480 is more likely situated in the disk. In\nall events, the binary stars of the sources have similar magnitudes due to a\ndetection bias favoring binary source events with a relatively bright secondary\nsource star, which increases detection efficiency.",
        "split": "arXiv"
    },
    {
        "text": "Retrograde analysis is used in game-playing programs to solve states at the\nend of a game, working backwards toward the start of the game. The algorithm\niterates through and computes the perfect-play value for as many states as\nresources allow. We introduce setrograde analysis which achieves the same\nresults by operating on sets of states that have the same game value. The\nalgorithm is demonstrated by computing exact solutions for Bridge double dummy\ncard-play. For deals with 24 cards remaining to be played ($10^{27}$ states,\nwhich can be reduced to $10^{15}$ states using preexisting techniques), we\nstrongly solve all deals. The setrograde algorithm performs a factor of $10^3$\nfewer search operations than a standard retrograde algorithm, producing a\ndatabase with a factor of $10^4$ fewer entries. For applicable domains, this\nallows retrograde searching to reach unprecedented search depths.",
        "split": "arXiv"
    },
    {
        "text": "In this paper we address the reverse isoperimetric inequality for convex\nbodies with uniform curvature constraints in the hyperbolic plane\n$\\mathbb{H}^2$. We prove that thethick $\\lambda$-sausage body, that is, the\nconvex domain bounded by two equal circular arcs of curvature $\\lambda$ and two\nequal arcs of hypercircle of curvature $1 / \\lambda$, is the unique minimizer\nof area among all bodies $K \\subset \\mathbb{H}^2$ with a given length and with\ncurvature of $\\partial K$ satisfying $1 / \\lambda \\leq \\kappa \\leq \\lambda$ (in\na weak sense). We call this class of bodies thick $\\lambda$-concave bodies, in\nanalogy to the Euclidean case where a body is $\\lambda$-concave if $0 \\leq\n\\kappa \\leq \\lambda$. The main difficulty in the hyperbolic setting is that the\ninner parallel bodies of a convex body are not necessarily convex. To overcome\nthis difficulty, we introduce an extra assumption of thickness $\\kappa \\geq\n1/\\lambda$. In addition, we prove the Blaschke's rolling theorem for\n$\\lambda$-concave bodies under the thickness assumption. That is, we prove that\na ball of curvature $\\lambda$ can roll freely inside a thick $\\lambda$-concave\nbody. In striking contrast to the Euclidean setting, Blaschke's rolling theorem\nfor $\\lambda$-concave domains in $\\mathbb{H}^2$ does not hold in general, and\nthus has not been studied in literature before. We address this gap, and show\nthat the thickness assumption is necessary and sufficient for such a theorem to\nhold.",
        "split": "arXiv"
    },
    {
        "text": "We propose a quantum error mitigation scheme for single-qubit measurement\nerrors, particularly suited for one-way quantum computation. Contrary to well\nestablished error mitigation methods for circuit-based quantum computation,\nthat require to run the circuits several times, our method is capable of\nmitigating measurement errors in real-time, during the processing measurements\nof the one-way computation. For that, an ancillary qubit register is entangled\nwith the to-be-measured qubit and additionally measured afterwards. By using a\nvoting protocol on all measurement outcomes, occurring measurement errors can\nbe mitigated in real-time while the one-way computation continues. We provide\nan analytical expression for the probability to detect a measurement error in\ndependency of the error rate and the number of ancilla qubits. From this, we\nderive an estimate of the ancilla register size for a given measurement error\nrate and a required success probability to detect a measurement error.\nAdditionally, we also consider the CNOT gate error in our mitigation method and\ninvestigate how this influences the probability to detect a measurement error.\nFinally, we show in proof-of-principle simulations, also considering a hardware\nnoise model, that our method is capable of reducing the measurement errors\nsignificantly in a one-way quantum computation with only a small number of\nancilla qubits.",
        "split": "arXiv"
    },
    {
        "text": "A common approach to detecting weak signals or minute quantities involves\nleveraging on the localized spectral features of resonant modes, where sharper\nlines (i.e. high Q-factors) enhance transduction sensitivity. However,\nmaximizing the Q-factor often introduced technical challenges in fabrication\nand design. In this work, we propose an alternative strategy to achieve sharper\nspectral features by using interference and nonlinearity, all while maintaining\na constant dissipation rate. Using far-infrared thermomechanical detectors as a\ntest case, we demonstrate that signal transduction along an engineered response\ncurve slope effectively reduces the detector's noise equivalent power (NEP).\nThis method, combined with an optimized absorbing layer, achieves sub-pW NEP\nfor electrical read-out detectors operating in the sub-THz range.",
        "split": "arXiv"
    },
    {
        "text": "For an atomic domain $D$, the $elasticity$ $\\rho(D)$ of $D$ is defined as\n$\\sup\\{r/s: \\pi_1\\cdots \\pi_r = \\rho_1 \\cdots \\rho_s,~ \\text{where each $\\pi_i,\n\\rho_j$ is irreducible}\\}$; the elasticity provides a concrete measure of the\nfailure of unique factorization in $D$. Fix a quadratic number field $K$ with\ndiscriminant $\\Delta_K$, and for each positive integer $f$, let $\\mathcal{O}_f\n= \\mathbb{Z} + f\\mathcal{O}_K$ denote the order of conductor $f$ in $K$.\nResults of Halter-Koch imply that $\\mathcal{O}_f$ has finite elasticity\nprecisely when $f$ is $\\textit{split-free}$, meaning not divisible by any\nrational prime $p$ with $(\\Delta_K/p)=1$. When $K$ is imaginary, we show that\nfor almost all split-free $f$, \\[ \\rho(\\mathcal{O}_f) =\nf/(\\log{f})^{\\frac{1}{2}\\log\\log\\log{f} + \\frac{1}{2}C_K+o(1)}, \\] for a\nconstant $C_K$ depending on $K$. When $K$ is real, we prove under the\nassumption of the Generalized Riemann Hypothesis that \\[ \\rho(\\mathcal{O}_f)=\n(\\log{f})^{\\frac12 +o(1)} \\] for almost all split-free $f$. Underlying these\nestimates are new statistical theorems about class groups of orders in\nquadratic fields, whose proofs borrow ideas from investigations of Erd\\H{o}s,\nHooley, Li, Pomerance, Schmutz, and others into the multiplicative groups\n$(\\mathbb{Z}/m\\mathbb{Z})^\\times$. One novelty of the argument is the\ndevelopment of a weighted version of the Tur\\'{a}n--Kubilius inequality to\nhandle a variety of sums over split-free integers.",
        "split": "arXiv"
    },
    {
        "text": "We study the metric Steiner tree problem in the sublinear query model. In\nthis problem, for a set of $n$ points $V$ in a metric space given to us by\nmeans of query access to an $n\\times n$ matrix $w$, and a set of terminals\n$T\\subseteq V$, the goal is to find the minimum-weight subset of the edges that\nconnects all the terminal vertices.\n  Recently, Chen, Khanna and Tan [SODA'23] gave an algorithm that uses\n$\\widetilde{O}(n^{13/7})$ queries and outputs a $(2-\\eta)$-estimate of the\nmetric Steiner tree weight, where $\\eta>0$ is a universal constant. A key\ncomponent in their algorithm is a sublinear algorithm for a particular set\ncover problem where, given a set system $(U, F)$, the goal is to provide a\nmultiplicative-additive estimate for $|U|-\\textsf{SC}(U, F)$. Here $U$ is the\nset of elements, $F$ is the collection of sets, and $\\textsf{SC}(U, F)$ denotes\nthe optimal set cover size of $(U, F)$. In particular, their algorithm returns\na $(1/4, \\varepsilon\\cdot|U|)$-multiplicative-additive estimate for this set\ncover problem using $\\widetilde{O}(|F|^{7/4})$ membership oracle queries\n(querying whether a set $S$ contains an $e$), where $\\varepsilon$ is a fixed\nconstant.\n  In this work, we improve the query complexity of $(2-\\eta)$-estimating the\nmetric Steiner tree weight to $\\widetilde{O}(n^{5/3})$ by showing a $(1/2,\n\\varepsilon \\cdot |U|)$-estimate for the above set cover problem using\n$\\widetilde{O}(|F|^{5/3})$ membership queries. To design our set cover\nalgorithm, we estimate the size of a random greedy maximal matching for an\nauxiliary multigraph that the algorithm constructs implicitly, without access\nto its adjacency list or matrix.",
        "split": "arXiv"
    },
    {
        "text": "Fainter standard stars are essential for the calibration of larger\ntelescopes. This work adds to the CALSPEC (calibration spectra) database 19\nfaint white dwarfs (WDs) with all-sky coverage and V magnitudes between 16.5\nand 18.7. Included for these stars is new UV (ultraviolet) HST (Hubble Space\nTelescope) STIS (Space Telescope Imaging Spectrometer) spectrophotometry\nbetween 1150 and 3000~\\AA\\ with a resolution of $\\sim$500. Pure hydrogen WD\nmodels are fit to these UV spectra and to six-band HST/WFC3 (Wide Field Camera\n3) photometry at 0.28 to 1.6~\\micron\\ to construct predicted model SEDs\n(spectral energy distributions) covering wavelengths from 900~\\AA\\ to the JWST\n(James Webb Space Telescope) limit of 30~\\micron\\ using well-established\nCALSPEC procedures for producing flux standards with the goal of 1\\% accuracy.",
        "split": "arXiv"
    },
    {
        "text": "The objective assessment of gait kinematics is crucial in evaluating human\nmovement, informing clinical decisions, and advancing rehabilitation and\nassistive technologies. Assessing gait symmetry, in particular, holds\nsignificant importance in clinical rehabilitation, as it reflects the intricate\ncoordination between nerves and muscles during human walking. In this research,\na dataset has been compiled to improve the understanding of gait kinematics.\nThe dataset encompasses motion capture data of the walking patterns of eleven\nhealthy participants who were tasked with completing various activities on a\ncircular path. These activities included normal walking, walking with a\nweighted dominant hand, walking with a braced dominant leg, and walking with\nboth weight and brace. The walking tasks involving weight and brace were\ndesigned to emulate the asymmetry associated with common health conditions,\nshedding light on irregularities in individuals' walking patterns and\nreflecting the coordination between nerves and muscles. All tasks were\nperformed at regular and fast speeds, offering valuable insights into upper and\nlower body kinematics. The dataset comprises raw sensor data, providing\ninformation on joint dynamics, angular velocities, and orientation changes\nduring walking, as well as analyzed data, including processed data, Euler\nangles, and joint kinematics spanning various body segments. This dataset will\nserve as a valuable resource for researchers, clinicians, and engineers,\nfacilitating the analysis of gait patterns and extracting relevant indices on\nmobility and balance.",
        "split": "arXiv"
    },
    {
        "text": "As part of the Canadian Hydrogen Intensity Mapping Experiment Fast Radio\nBurst (CHIME/FRB) project, we report 41 new Rotation Measures (RMs) from 20\nrepeating Fast Radio Bursts (FRBs) obtained between 2019 and 2023 for which no\nprevious RM was determined. We also report 22 additional RM measurements for\neight further repeating FRBs. We observe temporal RM variations in practically\nall repeating FRBs. Repeaters appear to be separated into two categories: those\nwith dynamic and those with stable RM environments, differentiated by the\nratios of RM standard deviations over the averaged RM magnitudes. Sources from\nstable RM environments likely have little RM contributions from the\ninterstellar medium (ISM) of their host galaxies, whereas sources from dynamic\nRM environments share some similarities with Galactic pulsars in eclipsing\nbinaries but appear distinct from Galactic centre solitary pulsars. We observe\na new stochastic, secular, and again stochastic trend in the temporal RM\nvariation of FRB 20180916B, which does not support binary orbit modulation\nbeing the reason for this RM changes. We highlight two more repeaters that show\nRM sign change, namely FRBs 20290929C and 20190303A. We perform an updated\ncomparison of polarization properties between repeating and non-repeating FRBs,\nwhich show a marginal dichotomy in their distribution of\nelectron-density-weighted parallel-component line-of-sight magnetic fields.",
        "split": "arXiv"
    },
    {
        "text": "The Max-Flow/Min-Cut problem is a fundamental tool in graph theory, with\napplications in many domains, including data mining, image segmentation,\ntransportation planning, and many types of assignment problems, in addition to\nbeing an essential building block for many other algorithms. The Ford-Fulkerson\nAlgorithm for Max-Flow/Min-Cut and its variants are therefore commonly taught\nin undergraduate and beginning graduate algorithms classes. However, these\nalgorithms -- and in particular the so-called residual graphs they utilize --\noften pose significant challenges for students.\n  To help students achieve a deeper understanding, we developed iFlow, an\ninteractive visualization tool for the Ford-Fulkerson Algorithm and its\nvariants. iFlow lets users design or import flow networks, and execute the\nalgorithm by hand. In particular, the user can select an augmentation path and\namount, and then update the residual graph. The user is given detailed feedback\non mistakes, and can also have iFlow auto-complete each step, to use it as a\ndemonstration tool while still in the initial learning stages. iFlow has been\nmade publicly available and open-sourced.\n  We deployed iFlow in an undergraduate algorithms class, and collected\nstudents' self-reported learning benefits via an optional survey. All\nrespondents considered the tool at least somewhat useful and engaging, with\nmost rating it either as useful/engaging or very useful/engaging. Students also\ngenerally reported a significant increase in understanding of the algorithm.",
        "split": "arXiv"
    },
    {
        "text": "This work explores the implications of the Exclusivity Principle (EP) in the\ncontext of quantum and post-quantum correlations. We first establish a key\ntechnical result demonstrating that given the set of correlations for a\ncomplementary experiment, the EP restricts the maximum set of correlations for\nthe original experiment to the anti-blocking set. Based on it, we can prove our\ncentral result: if all quantum behaviors are accessible in Nature, the EP\nguarantees that no post-quantum behaviors can be realized. This can be seen as\na generalization of the result of [Phys. Rev. A 89, 030101(R)], to a wider\nrange of scenarios. It also provides novel insights into the structure of\nquantum correlations and their limitations.",
        "split": "arXiv"
    },
    {
        "text": "We study the long-time behaviour of solutions to some classes of fourth-order\nnonlinear PDEs with non-monotone nonlinearities, which include the\nLandau--Lifshitz--Baryakhtar (LLBar) equation (with all relevant fields and\nspin torques) and the convective Cahn--Hilliard/Allen--Cahn (CH-AC) equation\nwith a proliferation term, in dimensions $d=1,2,3$. Firstly, we show the global\nwell-posedness, as well as the existence of global and exponential attractors\nwith finite fractal dimensions for these problems. In the case of the\nexchange-dominated LLBar equation and the CH-AC equation without convection, an\nestimate for the rate of convergence of the solution to the corresponding\nstationary state is given. Finally, we show the existence of a robust family of\nexponential attractors when $d\\leq 2$. As a corollary, exponential attractor of\nthe LLBar equation is shown to converge to that of the Landau--Lifshitz--Bloch\nequation in the limit of vanishing exchange damping, while exponential\nattractor of the convective CH-AC equation is shown to converge to that of the\nconvective Allen--Cahn equation in the limit of vanishing diffusion\ncoefficient.",
        "split": "arXiv"
    },
    {
        "text": "A discrete analog of quantum unique ergodicity was proved for Cayley graphs\nof quasirandom groups by Magee, Thomas and Zhao. They show that for large\ngraphs there exist real orthonormal basis of eigenfunctions of the adjacency\nmatrix such that quantum probability measures of the eigenfunctions put\napproximately the correct proportion of their mass on subsets of the vertices\nthat are not too small. We investigate this property for Cayley graphs of\ncyclic groups (circulant graphs). We observe that there exist sequences of\northonormal eigenfunction bases which are perfectly equidistributed. However,\nfor sequences of 4-regular circulant graphs of prime order, we show that there\nare no sequences of real orthonormal bases where all sequences of\neigenfunctions equidistribute. To obtain this result, we also prove that, for\nlarge 4-regular circulant graphs of prime order, the maximum multiplicity of\nthe eigenvalues of the adjacency matrix is two.",
        "split": "arXiv"
    },
    {
        "text": "Improving hyperspectral image (HSI) semantic segmentation by exploiting\ncomplementary information from a supplementary data type (referred to\nX-modality) is promising but challenging due to differences in imaging sensors,\nimage content, and resolution. Current techniques struggle to enhance\nmodality-specific and modality-shared information, as well as to capture\ndynamic interaction and fusion between different modalities. In response, this\nstudy proposes CoMiX, an asymmetric encoder-decoder architecture with\ndeformable convolutions (DCNs) for HSI-X semantic segmentation. CoMiX is\ndesigned to extract, calibrate, and fuse information from HSI and X data. Its\npipeline includes an encoder with two parallel and interacting backbones and a\nlightweight all-multilayer perceptron (ALL-MLP) decoder. The encoder consists\nof four stages, each incorporating 2D DCN blocks for the X model to accommodate\ngeometric variations and 3D DCN blocks for HSIs to adaptively aggregate\nspatial-spectral features. Additionally, each stage includes a Cross-Modality\nFeature enhancement and eXchange (CMFeX) module and a feature fusion module\n(FFM). CMFeX is designed to exploit spatial-spectral correlations from\ndifferent modalities to recalibrate and enhance modality-specific and\nmodality-shared features while adaptively exchanging complementary information\nbetween them. Outputs from CMFeX are fed into the FFM for fusion and passed to\nthe next stage for further information learning. Finally, the outputs from each\nFFM are integrated by the ALL-MLP decoder for final prediction. Extensive\nexperiments demonstrate that our CoMiX achieves superior performance and\ngeneralizes well to various multimodal recognition tasks. The CoMiX code will\nbe released.",
        "split": "arXiv"
    },
    {
        "text": "We revisit the calculation of classical observables from causal response\nfunctions, following up on recent work by Caron-Huot at al. [JHEP 01 (2024)\n139]. We derive a formula to compute asymptotic in-in observables from a\nparticular soft limit of five-point amputated response functions. Using such\nformula, we re-derive the formulas by Kosower, Maybee and O'Connell (KMOC) for\nthe linear impulse and radiated linear momentum of particles undergoing\nscattering, and we present an unambiguous calculation of the radiated angular\nmomentum at leading order. Then, we explore the consequences of manifestly\ncausal Feynman rules in the calculation of classical observables by employing\nthe causal (Keldysh) basis in the in-in formalism. We compute the linear\nimpulse, radiated waveform and its variance at leading and/or next-to-leading\norder in the causal basis, and find that all terms singular in the $\\hbar \\to\n0$ limit cancel manifestly at the integrand level. We also find that the\ncalculations simplify considerably and classical properties such as\nfactorization of six-point amplitudes are more transparent in the causal basis.",
        "split": "arXiv"
    },
    {
        "text": "We investigate the use of labelled graphs as a Morita equivalence invariant\nfor inverse semigroups. We construct a labelled graph from a combinatorial\ninverse semigroup $S$ with $0$ admitting a special set of idempotent\n$\\mathcal{D}$-class representatives and show that $S$ is Morita equivalent to a\nlabelled graph inverse semigroup. For the inverse hull $S$ of a Markov shift,\nwe show that the labelled graph determines the Morita equivalence class of $S$\namong all other inverse hulls of Markov shifts.",
        "split": "arXiv"
    },
    {
        "text": "Quark stars are challenging to confirm or exclude observationally because\nthey can have similar masses and radii as neutron stars. By performing the\nfirst calculation of the non-equilibrium equation of state of decompressed\nquark matter at finite temperature, we determine the properties of the ejecta\nfrom binary quark-star or quark star-black hole mergers. We account for all\nrelevant physical processes during the ejecta evolution, including quark nugget\nevaporation and cooling, and weak interactions. We find that these merger\nejecta can differ significantly from those in neutron star mergers, depending\non the binding energy of quark matter. For relatively high binding energies,\nquark star mergers are unlikely to produce r-process elements and kilonova\nsignals. We propose that future observations of binary mergers and kilonovae\ncould impose stringent constraints on the binding energy of quark matter and\nthe existence of quark stars.",
        "split": "arXiv"
    },
    {
        "text": "AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant\ntailored for research in astronomy, astrophysics, and cosmology. Trained on the\ncomplete collection of astronomy-related arXiv papers from 2007-2024 along with\nmillions of synthetically-generated question-answer pairs and other\nastronomical literature, AstroSage-Llama-3.1-8B demonstrates remarkable\nproficiency on a wide range of questions. AstroSage-Llama-3.1-8B scores 80.9%\non the AstroMLab-1 benchmark, greatly outperforming all models -- proprietary\nand open-weight -- in the 8-billion parameter class, and performing on par with\nGPT-4o. This achievement demonstrates the potential of domain specialization in\nAI, suggesting that focused training can yield capabilities exceeding those of\nmuch larger, general-purpose models. AstroSage-Llama-3.1-8B is freely\navailable, enabling widespread access to advanced AI capabilities for\nastronomical education and research.",
        "split": "arXiv"
    },
    {
        "text": "As language models grow ever larger, so do their vocabularies. This has\nshifted the memory footprint of LLMs during training disproportionately to one\nsingle layer: the cross-entropy in the loss computation. Cross-entropy builds\nup a logit matrix with entries for each pair of input tokens and vocabulary\nitems and, for small models, consumes an order of magnitude more memory than\nthe rest of the LLM combined. We propose Cut Cross-Entropy (CCE), a method that\ncomputes the cross-entropy loss without materializing the logits for all tokens\ninto global memory. Rather, CCE only computes the logit for the correct token\nand evaluates the log-sum-exp over all logits on the fly. We implement a custom\nkernel that performs the matrix multiplications and the log-sum-exp reduction\nover the vocabulary in flash memory, making global memory consumption for the\ncross-entropy computation negligible. This has a dramatic effect. Taking the\nGemma 2 (2B) model as an example, CCE reduces the memory footprint of the loss\ncomputation from 24 GB to 1 MB, and the total training-time memory consumption\nof the classifier head from 28 GB to 1 GB. To improve the throughput of CCE, we\nleverage the inherent sparsity of softmax and propose to skip elements of the\ngradient computation that have a negligible (i.e., below numerical precision)\ncontribution to the gradient. Experiments demonstrate that the dramatic\nreduction in memory consumption is accomplished without sacrificing training\nspeed or convergence.",
        "split": "arXiv"
    },
    {
        "text": "One way to define a sub-Riemannian metric is as the limit of a Riemannian\nmetric. Consider a Riemannian structure depending on a parameter $s$ such that\nits limit defines a sub-Riemannian metric when $s \\to \\infty$, assuming that\nthe Riemannian geodesic flow is integrable for all $s$. An interesting question\nis: Can we determine the integrability of the sub-Riemannian geodesic flow as\nthe limit of the integrals of motion of the Riemannian geodesic flow? The\npaper's main contribution is to provide a positive answer to this question in\nthe special orthogonal group. Theorem 1.1 states that the Riemannian geodesic\nflow is Liuville integrable: The Manakov integrals' limit suggests the\nexistence of a Lax pair formulation of the Riemannian geodesic equations, and\nthe proof of Theorem 1.1 relies on this Lax pair.",
        "split": "arXiv"
    },
    {
        "text": "We undertake a comprehensive analysis of the supersymmetric partition\nfunction of the $\\text{U}(N)_k\\times\\text{U}(N)_{-k}$ ABJM theory on a Seifert\nmanifold, evaluating it to all orders in the $1/N$-perturbative expansion up to\nexponentially suppressed corrections. Through holographic duality, our\nperturbatively exact result is successfully matched with the regularized\non-shell action of a dual Euclidean AdS$_4$-Taub-Bolt background incorporating\n4-derivative corrections, and also provides valuable insights into the\nlogarithmic corrections that emerge from the 1-loop calculations in M-theory\npath integrals. In this process, we revisit the Euclidean AdS$_4$-Taub-Bolt\nbackground carefully, elucidating the flat connection in the background\ngraviphoton field. This analysis umambiguously determines the U(1)$_R$ holonomy\nalong the Seifert fiber, thereby solidifying the holographic comparison\nregarding the partition function on a large class of Seifert manifolds.",
        "split": "arXiv"
    },
    {
        "text": "In comparative studies of progressive diseases, such as randomized controlled\ntrials (RCTs), the mean Change From Baseline (CFB) of a continuous outcome at a\npre-specified follow-up time across subjects in the target population is a\nstandard estimand used to summarize the overall disease progression. Despite\nits simplicity in interpretation, the mean CFB may not efficiently capture\nimportant features of the trajectory of the mean outcome relevant to the\nevaluation of the treatment effect of an intervention. Additionally, the\nestimation of the mean CFB does not use all longitudinal data points. To\naddress these limitations, we propose a class of estimands called Principal\nProgression Rate (PPR). The PPR is a weighted average of local or instantaneous\nslope of the trajectory of the population mean during the follow-up. The\nflexibility of the weight function allows the PPR to cover a broad class of\nintuitive estimands, including the mean CFB, the slope of ordinary least-square\nfit to the trajectory, and the area under the curve. We showed that properly\nchosen PPRs can enhance statistical power over the mean CFB by amplifying the\nsignal of treatment effect and/or improving estimation precision. We evaluated\ndifferent versions of PPRs and the performance of their estimators through\nnumerical studies. A real dataset was analyzed to demonstrate the advantage of\nusing alternative PPR over the mean CFB.",
        "split": "arXiv"
    },
    {
        "text": "Mixture-of-Experts (MoE) architectures have recently gained popularity in\nenabling efficient scaling of large language models. However, we uncover a\nfundamental tension: while MoEs are designed for selective expert activation,\nproduction serving requires request batching, which forces the activation of\nall experts and negates MoE's efficiency benefits during the decode phase. We\npresent Lynx, a system that enables efficient MoE inference through dynamic,\nbatch-aware expert selection. Our key insight is that expert importance varies\nsignificantly across tokens and inference phases, creating opportunities for\nruntime optimization. Lynx leverages this insight through a lightweight\nframework that dynamically reduces active experts while preserving model\naccuracy. Our evaluations show that Lynx achieves up to 1.55x reduction in\ninference latency while maintaining negligible accuracy loss from baseline\nmodel across complex code generation and mathematical reasoning tasks.",
        "split": "arXiv"
    },
    {
        "text": "Dust polarization, which comes from the alignment of aspherical grains to\nmagnetic fields, has been widely employed to study the interstellar medium\n(ISM) dust properties. The wavelength dependence of the degree of optical\npolarization, known as the Serkowski relation, was a key observational\ndiscovery that advanced grain modeling and alignment theories. However, it was\nrecently shown that line-of-sight (LOS) variations in the structure of the ISM\nor the magnetic field morphology contaminate the constraints extracted from\nfits to the Serkowski relation. These cases can be identified by the\nwavelength-dependent variability in the polarization angles. We aim to\ninvestigate the extent to which we can constrain the intrinsic dust properties\nand alignment efficiency from dust polarization data, by accounting for LOS\nvariations of the magnetic field morphology. We employed archival data to fit\nthe Serkowski relation and constrain its free parameter. We explored potential\nimprints of LOS variations of the magnetic field morphology in these\nconstraints. We found that these LOS integration effects contaminate the\nmajority of the existing dataset, thus biasing the obtained Serkowski\nparameters by approximately 10%. The constancy of the polarization angles with\nwavelength does not necessarily guarantee the absence of 3D averaging effects.\nWe examined the efficiency of dust grains in polarizing starlight, as probed by\nthe ratio of the degree of polarization to dust reddening, E(B-V). We found\nthat all measurements respect the limit established by polarized dust emission\ndata. A suppression in polarization efficiencies occurs at E(B-V) close to 0.5\nmag, which we attribute to projection effects and may be unrelated to the\nintrinsic alignment of dust grains.",
        "split": "arXiv"
    },
    {
        "text": "Exploring nuclear physics through the fundamental constituents of the strong\nforce -- quarks and gluons -- is a formidable challenge. While numerical\ncalculations using lattice quantum chromodynamics offer the most promising\napproach for this pursuit, practical implementation is arduous, especially due\nto the uncontrollable growth of quark-combinatorics, the so-called\nWick-contraction problem of nuclei. We present here two novel methods providing\na state-of-the-art solution to this problem. In the first, we exploit\nrandomized algorithms inspired from computational number theory to detect and\neliminate redundancies that arise in Wick contraction computations. Our second\nmethod explores facilities for automation of tensor computations -- in terms of\nefficient utilization of specialized hardware, algorithmic optimizations, as\nwell as ease of programming and the potential for automatic code generation --\nthat are offered by new programming models inspired by applications in machine\nlearning (e.g., TensorFlow). We demonstrate the efficacy of our methods by\ncomputing two-point correlation functions for Deuteron, Helium-3, Helium-4 and\nLithium-7, achieving at least an order of magnitude improvement over existing\nalgorithms with efficient implementation on GPU-accelerators. Additionally, we\ndiscover an intriguing characteristic shared by all the nuclei we study:\nspecific spin-color combinations dominate the correlation functions, hinting at\na potential connection to an as-yet-unidentified symmetry in nuclei. Moreover\nfinding them beforehand can reduce the computing time further and\nsubstantially. Our results, with the efficiency that we achieved, suggest the\npossibility of extending the applicability of our methods for calculating\nproperties of light nuclei, potentially up to A ~12 and beyond.",
        "split": "arXiv"
    },
    {
        "text": "Disks around intermediate mass stars called Herbig disks are the formation\nsites of giant exoplanets. Obtaining a complete inventory of these disks will\ntherefore give insights into giant planet formation. However, until now no\ncomplete disk survey has been done on Herbig disks in a single star-forming\nregion. Orion is the only nearby region with a significant number of Herbig\ndisks (N=35) to carry out such a survey. Using new NOEMA observations of 25\nHerbig disks, in combination with ALMA archival data of 10 Herbig disks,\nresults in a complete sample of all know Herbig disks in Orion. Using uv-plane\nanalysis for the NOEMA observed disks, and literature values of the ALMA\nobserved disks, we obtain the dust masses of all Herbig disks and obtain a\ncumulative dust mass distribution. Additionally, six disks with new CO\nisotopologues detections are presented, one of which is detected in C17O. We\ncalculate the external ultraviolet (UV) irradiance on each disk and compare the\ndust mass to it. We find a median disk dust mass of 11.7 M_\\oplus for the\nHerbig disks. Comparing the Herbig disks in Orion to previous surveys for\nmainly T Tauri disks in Orion, we find that while ~50% of the Herbig disks have\na mass higher than 10 M_\\oplus, this is at most 25% for the T~Tauri disks. This\ndifference is especially striking when considering that the Herbig disks are\naround a factor of two older than the T Tauri disks. Comparing to the Herbig\ndisks observed with ALMA from a previous study, no significant difference is\nfound between the distributions. We find a steeper (slope of -7.6) relationship\nbetween the dust mass and external UV irradation compared to that of the\nT~Tauri disks (slope of -1.3). This work shows the importance of complete\nsamples, giving rise to the need of a complete survey of the Herbig disk\npopulation.",
        "split": "arXiv"
    },
    {
        "text": "Charge density waves (CDW) in single-layer 1$T$-MTe$_2$ (M= Nb, Ta) recently\nraised large attention because of the contrasting structural and physical\nbehavior with the sulfide and selenide analogues. A first-principles study of\nfourteen different 1$T$-type TaTe$_2$ single-layers is reported. The importance\nof Te to Ta electron transfer and multicenter metal-metal bonding in\nstabilizing different structural modulations is highlighted. Analysis of the\nelectronic structure of the optimized structures provides a rationale for what\ndistinguishes 1$T$-TaTe$_2$ from the related disulfide and diselenide, what are\nthe more stable structural modulations for 1$T$-type TaTe$_2$ single-layers,\nthe possible role of Fermi surface nesting on some of these CDW instabilities,\nhow the CDW affects the metallic properties of the non-distorted lattice and\nthe possibility that some of these CDW phases exhibit exotic properties. All\nCDW phases studied exhibit band structures typical of metallic systems although\nsome exhibit both very flat and dispersive bands at the Fermi level so that\nMott effects could develop; one of the (4$\\times$4) phases studied exhibits a\nDirac cone at the Fermi level.",
        "split": "arXiv"
    },
    {
        "text": "We adapt the topological quantum chemistry formalism to layer groups, and\napply it to study the band topology of 8,872 entries from the computational\ntwo-dimensional (2D) materials databases C2DB and MC2D. In our analysis, we\nfind 4,073 topologically non-trivial or obstructed atomic insulator entries,\nincluding 905 topological insulators, 602 even-electron number topological\nsemimetals, and 1,003 obstructed atomic insulators. We thus largely expand the\nlibrary of known topological or obstructed materials in two dimensions, beyond\nthe few hundreds known to date. We additionally classify the materials into\nfour categories: experimentally existing, stable, computationally exfoliated,\nand not stable. We present a detailed analysis of the edge states emerging in a\nnumber of selected new materials, and compile a Topological 2D Materials\nDatabase (2D-TQCDB) containing the band structures and detailed topological\nproperties of all the materials studied in this work. The methodology here\ndeveloped is implemented in new programs available to the public, designed to\nstudy the topology of any non-magnetic monolayer or multilayer 2D material.",
        "split": "arXiv"
    },
    {
        "text": "We show that the anisotropies in the spectrum of gravitational waves induced\nby scalar modes after the end of inflation in canonical, single-field models\nare completely determined by the tilt of the scalar and tensor power spectra.\nThe latter contains information about anisotropies produced due to the\npropagation of the tensor modes in an inhomogeneous Universe, whereas the\nformer represents the anisotropies generated at the time of production and\narise only when non-Gaussian corrections to the angular power spectrum are\nconsidered. Our proof takes into account all scalar interactions in the cubic\ninflaton Lagrangian.",
        "split": "arXiv"
    },
    {
        "text": "Several recent works seek to develop foundation models specifically for\nmedical applications, adapting general-purpose large language models (LLMs) and\nvision-language models (VLMs) via continued pretraining on publicly available\nbiomedical corpora. These works typically claim that such domain-adaptive\npretraining (DAPT) improves performance on downstream medical tasks, such as\nanswering medical licensing exam questions. In this paper, we compare ten\npublic \"medical\" LLMs and two VLMs against their corresponding base models,\narriving at a different conclusion: all medical VLMs and nearly all medical\nLLMs fail to consistently improve over their base models in the zero-/few-shot\nprompting and supervised fine-tuning regimes for medical question-answering\n(QA). For instance, across all tasks and model pairs we consider in the 3-shot\nsetting, medical LLMs only outperform their base models in 22.7% of cases,\nreach a (statistical) tie in 36.8% of cases, and are significantly worse than\ntheir base models in the remaining 40.5% of cases. Our conclusions are based on\n(i) comparing each medical model head-to-head, directly against the\ncorresponding base model; (ii) optimizing the prompts for each model separately\nin zero-/few-shot prompting; and (iii) accounting for statistical uncertainty\nin comparisons. While these basic practices are not consistently adopted in the\nliterature, our ablations show that they substantially impact conclusions.\nMeanwhile, we find that after fine-tuning on specific QA tasks, medical LLMs\ncan show performance improvements, but the benefits do not carry over to tasks\nbased on clinical notes. Our findings suggest that state-of-the-art\ngeneral-domain models may already exhibit strong medical knowledge and\nreasoning capabilities, and offer recommendations to strengthen the conclusions\nof future studies.",
        "split": "arXiv"
    },
    {
        "text": "When the coupling of a quantum system to its environment is non-negligible,\nits steady state is known to deviate from the textbook Gibbs state. The\nBloch-Redfield quantum master equation, one of the most widely adopted\nequations to solve the open quantum dynamics, cannot predict all the deviations\nof the steady state of a quantum system from the Gibbs state. In this paper,\nfor a generic spin-boson model, we use a higher-order quantum master equation\n(in system environment coupling strength) to analytically calculate all the\ndeviations of the steady state of the quantum system up to second order in the\ncoupling strength. We also show that this steady state is exactly identical to\nthe corresponding generalized Gibbs state, the so-called quantum mean force\nGibbs state, at arbitrary temperature. All these calculations are highly\ngeneral, making them immediately applicable to a wide class of systems well\nmodeled by the spin-Boson model, ranging from various condensed phase processes\nto quantum thermodynamics. As an example, we use our results to study the\ndynamics and the steady state of a double quantum dot system under physically\nrelevant choices of parameters.",
        "split": "arXiv"
    },
    {
        "text": "French language models, such as CamemBERT, have been widely adopted across\nindustries for natural language processing (NLP) tasks, with models like\nCamemBERT seeing over 4 million downloads per month. However, these models face\nchallenges due to temporal concept drift, where outdated training data leads to\na decline in performance, especially when encountering new topics and\nterminology. This issue emphasizes the need for updated models that reflect\ncurrent linguistic trends. In this paper, we introduce two new versions of the\nCamemBERT base model-CamemBERTav2 and CamemBERTv2-designed to address these\nchallenges. CamemBERTav2 is based on the DeBERTaV3 architecture and makes use\nof the Replaced Token Detection (RTD) objective for better contextual\nunderstanding, while CamemBERTv2 is built on RoBERTa, which uses the Masked\nLanguage Modeling (MLM) objective. Both models are trained on a significantly\nlarger and more recent dataset with longer context length and an updated\ntokenizer that enhances tokenization performance for French. We evaluate the\nperformance of these models on both general-domain NLP tasks and\ndomain-specific applications, such as medical field tasks, demonstrating their\nversatility and effectiveness across a range of use cases. Our results show\nthat these updated models vastly outperform their predecessors, making them\nvaluable tools for modern NLP systems. All our new models, as well as\nintermediate checkpoints, are made openly available on Huggingface.",
        "split": "arXiv"
    },
    {
        "text": "This paper presents the GRBSN webtool, a public facing application which\nhosts the most complete list of GRB-SN associations to date. In contrast to\nother repositories of supernova or gamma-ray burst data, this tool brings\ntogether all of the information required to study a GRB-SN association. GRBSN\nallows users to view and interact with plots of the data; search and filter the\nwhole database; and download all multi-wavelength data related to a GRB-SN\nassociation, including radio, X-ray, optical/NIR photometric and spectroscopic\ndata. The tool is fully open source and is hosted on a public GitHub\nrepository, meaning users can upload their own data, flag missing data and\nsuggest improvements. As the number of confirmed GRB-SN associations increases,\nthe webtool will provide a robust framework in which to catalogue these\nassociations and their associated data. The web application is freely available\nand publicly accessible at https://grbsn.watchertelescope.ie.",
        "split": "arXiv"
    },
    {
        "text": "This note discusses some of the aspects of a model for the covariance of\nequity returns based on a simple \"isotropic\" structure in which all pairwise\ncorrelations are taken to be the same value. The effect of the structure on\nfeasible values for the common correlation of returns and on the \"effective\ndegrees of freedom\" within the equity cross-section are discussed, as well as\nthe impact of this constraint on the asymptotic Normality of portfolio returns.\nAn eigendecomposition of the covariance matrix is presented and used to\npartition variance into that from a common \"market\" factor and\n\"non-diversifiable\" idiosyncratic risk. A empirical analysis of the recent\nhistory of the returns of S&P 500 Index members is presented and compared to\nthe expectations from both this model and linear factor models. This analysis\nsupports the isotropic covariance model and does not seem to provide evidence\nin support of linear factor models. Analysis of portfolio selection under\nisotropic correlation is presented using mean-variance optimization for both\nheteroskedastic and homoskedastic cases. Portfolio selection for negative\nexponential utility maximizers is also discussed for the general case of\ndistributions of returns with elliptical symmetry. The fact that idiosyncratic\nrisk may not be removed by diversification in a model that the data supports\nundermines the basic premises of structures such as the C.A.P.M. and A.P.T. If\nthe cross-section of equity returns is more accurately described by this\nstructure then an inevitable consequence is that picking stocks is not a\n\"pointless\" activity, as the returns to residual risk would be non-zero.",
        "split": "arXiv"
    },
    {
        "text": "The minimum separation between reconnecting vortices in fluids and\nsuperfluids obeys a universal scaling law with respect to time. The\npre-reconnection and the post-reconnection prefactors of this scaling law are\ndifferent, a property related to irreversibility and to energy transfer and\ndissipation mechanisms. In the present work, we determine the temperature\ndependence of these prefactors in superfluid helium from experiments and a\nnumeric model which fully accounts for the coupled dynamics of the superfluid\nvortex lines and the thermal normal fluid component. At all temperatures, we\nobserve a pre- and post-reconnection asymmetry similar to that observed in\nother superfluids and in classical viscous fluids, indicating that vortex\nreconnections display a universal behaviour independent of the small-scale\nregularising dynamics. We also numerically show that each vortex reconnection\nevent represents a sudden injection of energy in the normal fluid. Finally we\nargue that in a turbulent flow, these punctuated energy injections can sustain\nthe normal fluid in a perturbed state, provided that the density of superfluid\nvortices is large enough.",
        "split": "arXiv"
    },
    {
        "text": "We present detailed elaboration and first generally applicable linearization\nroutines of the \\textit{Parameter Space Concept} (PSC) for determining\n1-dimensionally projected structures of $m$ independent scatterers. This\ncrystal determination approach does not rely on Fourier inversion but rather\nconsiders all structure parameter combinations consistent with available\ndiffraction data in a parameter space of dimension $m$. The method utilizes $m$\nstructure factor amplitudes or intensities represented by piece-wise analytic\nhyper-surfaces, to define the acceptable parameter regions. By employing the\nisosurfaces, the coordinates of the point scatterers are obtained through the\nintersection of multiple isosurfaces. This approach allows for the detection of\nall possible solutions for the given structure factor amplitudes in a single\nderivation. Taking the resonant contrast into account, the spatial resolution\nachieved by the presented method may exceed that of traditional Fourier\ninversion, and the algorithms can be significantly optimized by exploiting the\nsymmetry properties of the isosurfaces. The applied 1-dimensional projection\ndemonstrates the efficiency of the PSC linearization approach based on fewer\nreflections than Fourier sums. The Monte-Carlo simulations, using the\nprojections of various random two- and three-atom structure examples, are\npresented to illustrate the universal applicability of the proposed method.\nFurthermore, ongoing efforts aim to enhance the efficiency of data handling and\nto overcome current constraints, promising further advancements in the\ncapabilities and accuracy of the PSC framework.",
        "split": "arXiv"
    },
    {
        "text": "The effect of replacing individual contributions to an empirical energy\nfunction are assessed for halogenated benzenes (X-Bz, X = H, F, Cl, Br) and\nchlorinated phenols (Cl-PhOH). Introducing electrostatic models based on\ndistributed charges (MDCM) instead of usual atom-centered point charges yields\noverestimated hydration free energies unless the van der Waals parameters are\nreparametrized. Scaling van der Waals ranges by 10 \\% to 20 \\% for three\nCl-PhOH and most X-Bz yield results within experimental error bars, which is\nencouraging, whereas for benzene (H-Bz) point charge-based models are\nsufficient. Replacing the bonded terms by a neural network-trained energy\nfunction with either fluctuating charges or MDCM electrostatics also yields\nqualitatively correct hydration free energies which still require adaptation of\nthe van der Waals parameters. The infrared spectroscopy of Cl-PhOH is rather\nwell predicted by all models although the ML-based energy function performs\nsomewhat better in the region of the framework modes. It is concluded that\nrefinements of empirical energy functions for targeted applications is a\nmeaningful way towards more quantitative simulations.",
        "split": "arXiv"
    },
    {
        "text": "Since the initial discovery of two-dimensional van der Waals (vdW) materials,\nsignificant effort has been made to incorporate the three properties of\nmagnetism, band structure topology, and strong electron correlations $-$ to\nleverage emergent quantum phenomena and expand their potential applications.\nHowever, the discovery of a single vdW material that intrinsically hosts all\nthree ingredients has remained an outstanding challenge. Here we report the\ndiscovery of a Kondo-interacting topological antiferromagnet in the vdW 5$f$\nelectron system UOTe. It has a high antiferromagnetic (AFM) transition\ntemperature of 150 K, with a unique AFM configuration that breaks the combined\nparity and time reversal ($PT$) symmetry in an even number of layers while\nmaintaining zero net magnetic moment. Our angle-resolved photoemission\nspectroscopy (ARPES) measurements reveal Dirac bands near the Fermi level,\nwhich combined with our theoretical calculations demonstrate UOTe as an AFM\nDirac semimetal. Within the AFM order, we observed the presence of the Kondo\ninteraction, as evidenced by the emergence of a 5$f$ flat band near the Fermi\nlevel below 100 K and hybridization between the Kondo band and the Dirac band.\nOur density functional theory calculations in its bilayer form predict UOTe as\na rare example of a fully-compensated AFM Chern insulator.",
        "split": "arXiv"
    },
    {
        "text": "This study presents an \\textit{ab initio} investigation of the XANES spectra\nat the aluminum K edge for three compounds: Al$_2$O$_3$, AlF$_3$ and AlCl$_3$,\nwhere the Al atoms share the same oxidation state~(III) and are coordinated in\nan octahedral symmetry. The XANES spectra calculated within the\nindependent-particle approximation reveal significant differences, including\nshifts in the spectrum onset, variations in the spectral shapes, and the\npresence of a pre-peak in the case of AlCl$_3$, all in correspondence with the\nbehavior of the PDOS of the absorbing atom in the different materials. The\norigin of the features stems from the specific band structure of each compound.\nWhen electron--hole interactions are taken into account through the solution of\nthe Bethe-Salpeter equation, a series of dark and bright excitons with large\nbinding energies and Frenkel character is obtained. The strong excitonic\neffects lead to the suppression of the pre-peak in AlCl$_3$ and further\naccentuate the differences among the three Al K-edge spectra.",
        "split": "arXiv"
    },
    {
        "text": "This work focuses on the gradient flow dynamics of a neural network model\nthat uses correlation loss to approximate a multi-index function on\nhigh-dimensional standard Gaussian data. Specifically, the multi-index function\nwe consider is a sum of neurons $f^*(x) \\!=\\! \\sum_{j=1}^k \\! \\sigma^*(v_j^T\nx)$ where $v_1, \\dots, v_k$ are unit vectors, and $\\sigma^*$ lacks the first\nand second Hermite polynomials in its Hermite expansion. It is known that, for\nthe single-index case ($k\\!=\\!1$), overcoming the search phase requires\npolynomial time complexity. We first generalize this result to multi-index\nfunctions characterized by vectors in arbitrary directions. After the search\nphase, it is not clear whether the network neurons converge to the index\nvectors, or get stuck at a sub-optimal solution. When the index vectors are\northogonal, we give a complete characterization of the fixed points and prove\nthat neurons converge to the nearest index vectors. Therefore, using $n \\!\n\\asymp \\! k \\log k$ neurons ensures finding the full set of index vectors with\ngradient flow with high probability over random initialization. When $ v_i^T\nv_j \\!=\\! \\beta \\! \\geq \\! 0$ for all $i \\neq j$, we prove the existence of a\nsharp threshold $\\beta_c \\!=\\! c/(c+k)$ at which the fixed point that computes\nthe average of the index vectors transitions from a saddle point to a minimum.\nNumerical simulations show that using a correlation loss and a mild\noverparameterization suffices to learn all of the index vectors when they are\nnearly orthogonal, however, the correlation loss fails when the dot product\nbetween the index vectors exceeds a certain threshold.",
        "split": "arXiv"
    },
    {
        "text": "Local differential privacy (LDP) is increasingly employed in\nprivacy-preserving machine learning to protect user data before sharing it with\nan untrusted aggregator. Most LDP methods assume that users possess only a\nsingle data record, which is a significant limitation since users often gather\nextensive datasets (e.g., images, text, time-series data) and frequently have\naccess to public datasets. To address this limitation, we propose a locally\nprivate sampling framework that leverages both the private and public datasets\nof each user. Specifically, we assume each user has two distributions: $p$ and\n$q$ that represent their private dataset and the public dataset, respectively.\nThe objective is to design a mechanism that generates a private sample\napproximating $p$ while simultaneously preserving $q$. We frame this objective\nas a minimax optimization problem using $f$-divergence as the utility measure.\nWe fully characterize the minimax optimal mechanisms for general\n$f$-divergences provided that $p$ and $q$ are discrete distributions.\nRemarkably, we demonstrate that this optimal mechanism is universal across all\n$f$-divergences. Experiments validate the effectiveness of our minimax optimal\nsampler compared to the state-of-the-art locally private sampler.",
        "split": "arXiv"
    },
    {
        "text": "We consider a stylized formal model of public transportation, where a set of\nagents need to travel along a given road, and there is a bus that runs the\nlength of this road. Each agent has a left terminal and a right terminal\nbetween which they wish to travel; they can walk all the way, or walk to/from\nthe nearest stop and use the bus for the rest of their journey. The bus can\nmake a fixed number of stops, and the planner needs to select locations for\nthese stops. We study notions of efficiency and fairness for this setting.\nFirst, we give a polynomial-time algorithm for computing a solution that\nminimizes the total travel time; our approach can capture further extensions of\nthe base model, such as more general cost functions or existing infrastructure.\nSecond, we develop a polynomial-time algorithm that outputs solutions with\nprovable fairness guarantees (such as a variant of the justified representation\naxiom or $2$-approximate core) as long as the agents' costs only depend on the\ndistance they need to walk. Our simulations indicate that our algorithm almost\nalways outputs fair solutions, even for parameter regimes that do not admit\ntheoretical guarantees.",
        "split": "arXiv"
    },
    {
        "text": "Purpose: Radiotherapy commonly relies on CT, but there is growing interest in\nusing hybrid PET/MR. Therefore, dedicated hardware setups have been proposed\nfor PET/MR systems which enable imaging in radiotherapy treatment position.\nThese radiotherapy setups typically include a flat tabletop, positioning tools\nand coil holders specifically tailored to the devices. However, reduced MR\nimage quality has been reported. Especially in neck and upper thorax,\nconventional radiotherapy setups are not optimal as they consist of head-only\ncoil configurations. The purpose was to develop a novel PET/MR radiotherapy\nsetup for improved MR image quality in head, neck and thorax and to test\ncompliance in a multicenter setting. Methods: A novel radiotherapy setup was\ndesigned, prototyped and tested on a 3T PET/MR system in three different\ncenters. Imaging experiments were conducted in phantoms and healthy volunteers\nto compare against a standard radiotherapy setup. Imaging protocols included\nT1-, T2-, and diffusion-weighted MR (DWI). Finally, compliance with American\nCollege of Radiology (ACR) and the Quantitative Imaging Biomarker Alliance\n(QIBA) acceptance criteria was evaluated. Results: SNR in neck/thorax was\nincreased by a factor of 1.6 in phantom (p = 0.031) and volunteer images alike.\nThe new setup passed ACR detectability and QIBA SNR tests, which the standard\nsetup failed. The new setup passed all but two ACR test criteria in the three\ncenters, presented repeatability and reproducibility variations of 4.9% and\n7.8% and met all QIBA criteria for DWI except ADC precision. Conclusion: The\nproposed setup yielded significantly higher SNR, better detectability, and\ncomplied with nearly all ACR and QIBA image quality criteria. It may thus\nadvance the usage of PET/MR for radiotherapy purposes.",
        "split": "arXiv"
    },
    {
        "text": "An oblivious subspace embedding is a random $m\\times n$ matrix $\\Pi$ such\nthat, for any $d$-dimensional subspace, with high probability $\\Pi$ preserves\nthe norms of all vectors in that subspace within a $1\\pm\\epsilon$ factor. In\nthis work, we give an oblivious subspace embedding with the optimal dimension\n$m=\\Theta(d/\\epsilon^2)$ that has a near-optimal sparsity of $\\tilde\nO(1/\\epsilon)$ non-zero entries per column of $\\Pi$. This is the first result\nto nearly match the conjecture of Nelson and Nguyen [FOCS 2013] in terms of the\nbest sparsity attainable by an optimal oblivious subspace embedding, improving\non a prior bound of $\\tilde O(1/\\epsilon^6)$ non-zeros per column [Chenakkod et\nal., STOC 2024]. We further extend our approach to the non-oblivious setting,\nproposing a new family of Leverage Score Sparsified embeddings with Independent\nColumns, which yield faster runtimes for matrix approximation and regression\ntasks.\n  In our analysis, we develop a new method which uses a decoupling argument\ntogether with the cumulant method for bounding the edge universality error of\nisotropic random matrices. To achieve near-optimal sparsity, we combine this\ngeneral-purpose approach with new traces inequalities that leverage the\nspecific structure of our subspace embedding construction.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we establish the existence of positive density collection of\n$d\\in\\mathbb{N}$ such that class numbers of $\\mathbb{Q}(\\sqrt{d}), \\\n\\mathbb{Q}(\\sqrt{d+1})\\dots\\mathbb{Q}(\\sqrt{d+n})$ are not divisible by $3^k$\nfor $n=3^{k+1}-5$ for any $k\\in\\mathbb{N}$. This result constitutes the\nindivisibility counterpart of Iizuka's conjecture. For the same choice of $n$,\nwe prove the existence of positive density collection of $d$, in the set of\nnegative integers, such that the class numbers of $\\mathbb{Q}(\\sqrt{d}), \\\n\\mathbb{Q}(\\sqrt{d+1}),\\dots,\\mathbb{Q}(\\sqrt{d+n})$ are not divisible by\n$3^{k+1}$. Further, we write the set of all square-free natural numbers as an\nincreasing sequence $(d_n)$ and prove the existence of positive density\ncollection of $i$ in the set of natural numbers such that the class numbers of\nthe number fields $\\mathbb{Q}(\\sqrt{d_i}), \\ \\mathbb{Q}(\\sqrt{d_{i+1}}),\\\n\\mathbb{Q}(\\sqrt{d_{i+2}}),$ $ \\mathbb{Q}(\\sqrt{d_{i+3}}),\\dots,\n\\mathbb{Q}(\\sqrt{d_{i+n}})$ are not divisible by $3^k$ for $n=3^{k+1}-5$. For\nhigher degree, we show that certain limit of the collection of imaginary\nbi-quadratic fields whose class number is not divisible by $3$ over all the\nimaginary biquadratic fields is positive.",
        "split": "arXiv"
    },
    {
        "text": "We study the problem of tolerant testing of stabilizer states. In particular,\nwe give the first such algorithm that accepts mixed state inputs. Formally,\ngiven a mixed state $\\rho$ that either has fidelity at least $\\varepsilon_1$\nwith some stabilizer pure state or fidelity at most $\\varepsilon_2$ with all\nsuch states, where $\\varepsilon_2 \\leq \\varepsilon_1^{O(1)}$, our algorithm\ndistinguishes the two cases with sample complexity\n$\\text{poly}(1/\\varepsilon_1)$ and time complexity $O(n \\cdot\n\\text{poly}(1/\\varepsilon_1))$.",
        "split": "arXiv"
    },
    {
        "text": "We apply the singular sequence method to investigate the finiteness problem\nfor stationary configurations of the planar five-vortex problem. The initial\nstep of the singular sequence method involves identifying all two-colored\ndiagrams. These diagrams represent potential scenarios where finiteness may\nfail. We determined all such diagrams for the planar five-vortex problem.",
        "split": "arXiv"
    },
    {
        "text": "Within the context of the ALICE ITS3 collaboration, a set of MAPS small-scale\ntest structures were developed using the 65 nm TPSCo CMOS imaging process with\nthe upgrade of the ALICE inner tracking system as its primary focus. One such\nsensor, the Circuit Exploratoire 65 nm (CE-65), and its evolution the CE-65v2,\nwere developed to explore charge collection properties for varying\nconfigurations including collection layer process (standard, blanket, modified\nwith gap), pixel pitch (15, 18, \\SI{22.5}{\\micro\\meter}), and pixel geometry\n(square vs hexagonal/staggered). In this work the characterisation of the\nCE-65v2 chip, based on $^{55}$Fe lab measurements and test beams at CERN SPS,\nis presented. Matrix gain uniformity up to the $\\mathcal{O}$(5\\%) level was\ndemonstrated for all considered chip configurations. The CE-65v2 chip achieves\na spatial resolution of under \\SI{2}{\\micro\\meter} during beam tests. Process\nmodifications allowing for faster charge collection and less charge sharing\nresult in decreased spatial resolution, but a considerably wider range of\noperation, with both the \\SI{15}{\\micro\\meter} and \\SI{22.5}{\\micro\\meter}\nchips achieving over 99\\% efficiency up to a $\\sim$180 e$^{-}$ seed threshold.\nThe results serve to validate the 65 nm TPSCo CMOS process, as well as to\nmotivate design choices in future particle detection experiments.",
        "split": "arXiv"
    },
    {
        "text": "We investigate the formation of bound states of non-relativistic dark matter\nparticles subject to long-range interactions through radiative capture. The\ninitial scattering and final bound states are described by Coulomb potentials\nwith different strengths, as relevant for non-abelian gauge interactions or\ntheories featuring charged scalars. For bound states with generic quantum\nnumbers $n$ and $\\ell$, we provide closed-form expressions for the bound-state\nformation (BSF) cross sections of monopole, dipole and quadrupole transitions,\nand of arbitrary multipole order when $\\ell=n-1$. This allows us to investigate\nin detail a strong enhancement of BSF that occurs for initial states in a\nrepulsive potential. For $\\ell=n-1\\gg 1$, we show that the BSF cross section\nfor each single bound state violates the perturbative unitarity bound in the\nvicinity of a certain critical initial velocity, and provide an interpretation\nin terms of a smooth matching of classical trajectories. When summing the BSF\ncross section over all possible bound states in the final state, this leads to\na unitarity violation below a certain velocity, but within the validity range\nof the weakly coupled non-relativistic description. We identify an effectively\nstrong interaction as the origin of this unitarity violation, which is caused\nby an \"anomalously\" large overlap of scattering and bound-state wave functions\nin Coulomb potentials of different strength.",
        "split": "arXiv"
    },
    {
        "text": "Aligning Large Language Models (LLMs) traditionally relies on costly training\nand human preference annotations. Self-alignment seeks to reduce these expenses\nby enabling models to align themselves. To further lower costs and achieve\nalignment without any expensive tuning or annotations, we introduce a new\ntuning-free approach for self-alignment, Dynamic Rewarding with Prompt\nOptimization (DRPO). Our approach leverages a search-based optimization\nframework that allows LLMs to iteratively self-improve and craft the optimal\nalignment instructions, all without additional training or human intervention.\nThe core of DRPO is a dynamic rewarding mechanism, which identifies and\nrectifies model-specific alignment weaknesses, allowing LLMs to adapt\nefficiently to diverse alignment challenges. Empirical evaluations on eight\nrecent LLMs, both open- and closed-sourced, demonstrate that DRPO significantly\nenhances alignment performance, with base models outperforming their\nSFT/RLHF-tuned counterparts. Moreover, the prompts automatically optimized by\nDRPO surpass those curated by human experts, further validating the\neffectiveness of our approach. Our findings highlight the great potential of\ncurrent LLMs to achieve adaptive self-alignment through inference-time\noptimization, complementing tuning-based alignment methods.",
        "split": "arXiv"
    },
    {
        "text": "In this article, we apply the techniques developed in our previous article\n``Local generation of tilings'', in which we introduced two definitions\ncapturing the intuitive idea that some subshifts admit a procedure that can\ngenerate any tiling and working in a local way. We classify all the Wang\ntilesets with two colors in which each tile has an even number of each color.",
        "split": "arXiv"
    },
    {
        "text": "We provide a new existence result for abstract nonlinear operator systems in\nnormed spaces, by means of topological methods. The solution is located within\nthe product of annular regions and conical shells. The theoretical result\npossesses a wide range of applicability, which, for concreteness, we illustrate\nin the context of systems of nonlinear Poisson equations subject to homogeneous\nDirichlet boundary conditions. For the latter problem we obtain existence and\nlocalization of solutions having all components nontrivial. This is also\nillustrated with an explicit example in which we also furnish a numerically\napproximated solution, consistent with the theoretical results.",
        "split": "arXiv"
    },
    {
        "text": "The rovibrational level populations, and subsequent emission in various\nastrophysical environments, is driven by inelastic collision processes. The\navailable rovibrational rate coefficients for water have been calculated using\na number of approximations. We present a numerically exact calculation for the\nrovibrational quenching for all water vibrational modes due to collisions with\natomic hydrogen. The scattering theory implements a quantum close-coupling (CC)\nmethod on a high level ab initio six-dimensional (6D) potential energy surface\n(PES). Total rovibrational quenching cross sections for excited bending levels\nwere compared with earlier results on a 4D PES with the rigid-bender\nclose-coupling (RBCC) approximation. General agreement between 6D-CC and\n4D-RBCC calculations are found, but differences are evident including the\nenergy and amplitude of low-energy orbiting resonances. Quenching cross\nsections from the symmetric and asymmetric stretch modes are provided for the\nfirst time. The current 6D-CC calculation provides accurate inelastic data\nneeded for astrophysical modeling.",
        "split": "arXiv"
    },
    {
        "text": "In this article, we investigate the possibility of generating all the\nconfigurations of a subshift in a local way. We propose two definitions of\nlocal generation, explore their properties and develop techniques to determine\nwhether a subshift satisfies these definitions. We illustrate the results with\nseveral examples.",
        "split": "arXiv"
    },
    {
        "text": "Billiard models of single particles moving freely in two-dimensional regions\nenclosed by hard walls, have long provided ideal toy models for the\ninvestigation of dynamical systems and chaos. Recently, billiards with\n(semi-)permeable walls and internal holes have been used to study open systems.\nHere we introduce a billiard model containing an internal region with partial\nabsorption. The absorption does not change the trajectories, but instead\nreduces an intensity variable associated with each trajectory. The value of the\nintensity can be tracked as a function of the initial configuration and the\nnumber of reflections from the wall and depicted in intensity landscapes over\nthe Poincar\\'e phase space. This is similar in spirit to escape time diagrams\nthat are often considered in dynamical systems with holes. We analyse the\nresulting intensity landscapes for three different geometries; a circular,\nelliptic, and oval billiard, respectively, all with a centrally placed circular\nabsorbing region. The intensity landscapes feature increasingly more complex\nstructures, organised around the sets of points that are a particular number of\niteration away from the absorbing region, and enriched by effects arising from\nmultiple absorption events for a given trajectory.",
        "split": "arXiv"
    },
    {
        "text": "Recently, the temporal evolution of the angles characterizing the spatial\nconfiguration of the jet in the supermassive black hole M87$^\\ast$ was measured\nexhibiting a precessional pattern around the hole's spin axis. It would be due\nto the dragging induced by the fact that the hole's external spacetime is\ndescribed by the Kerr metric. Here, it is shown that the Lense-Thirring orbital\nprecessions of a test particle moving about a rotating massive object,\ncalculated perturbatively to the first post-Newtonian order, are able to fully\nreproduce all the measured features of the jet axis of M87$^\\ast$. In\nparticular, by assuming that the latter is aligned with the angular momentum of\nthe accretion disk, modelled as an effective particle moving along a circular\norbit, the condition that the absolute value of the predicted Lense-Thirring\nprecessional frequency of the disk agrees with the measured value of $0.56\\pm\n0.02$ radians per year of the jet's one is satisfied for a range of physically\nmeaningful values of the hole's spin parameter, close to unity, and of the\neffective disk radius, of the order of just over a dozen gravitational radii.\nRelying upon such assumptions and results, it is possible to predict that the\nangle between the hole's spin axis and the jet's one stays constant over the\nyears amounting to $1.16^\\circ$, in agreement with its measured value of\n$1.25^\\circ\\pm 0.18^\\circ$. Furthermore, also the temporal pattern and the\namplitudes of the time series of the jet's angles are reproduced by the\naforementioned Lense-Thirring precessional model.",
        "split": "arXiv"
    },
    {
        "text": "Changing-look active galactic nuclei (CLAGNs) show the appearance and\ndisappearance of broad emission lines in their UV/optical spectra on timescales\nof months to decades. Here, we investigate how CL transitions depend on several\nAGN parameters such as accretion rate, obscuration properties and black hole\nmass. We study a sample of 20 nearby optically-identified CLAGNs from the BAT\nAGN Spectroscopic Survey (BASS), using quasi-simultaneous optical and X-ray\nobservations taken in the last $\\sim 40$ years. We find that for all CLAGNs,\nthe transition is accompanied by a change in Eddington ratio. The CL\ntransitions are not associated with changes in the obscuration properties of\nthe AGN. CLAGNs are found to have a median Eddington ratio lower than the AGNs\nin the BASS sample in which CL transitions were not detected. The median of the\ntransition Eddington ratio (Eddington ratio at which AGN changes its state) is\nfound to be $\\sim 0.01$ for type 1 $\\leftrightarrow$ 1.8/1.9/2 transition,\nwhich is consistent with the hard $\\leftrightarrow$ soft state transition in\nblack hole X-ray binaries. Most CL events are constrained to occur within 3-4\nyears, which is considerably shorter than the expected viscous timescale in AGN\naccretion disk. The transitions of the optical CLAGNs studied here are likely\nassociated to state changes in the accretion flow, possibly driven by\ndisk-instability.",
        "split": "arXiv"
    },
    {
        "text": "We study the twisted Kitaev quantum double model within the framework of\nLocal Topological Order (LTO). We extend its definition to arbitrary 2D\nlattices, enabling an explicit characterization of the ground state space\nthrough invariant spaces of monomial representations. We reformulate the LTO\nconditions for including general lattices and prove that the twisted model\nsatisfies all four LTO axioms on any 2D lattice. As a corollary, we show that\nits ground state space is a quantum error-correcting code.",
        "split": "arXiv"
    },
    {
        "text": "Byte-Pair Encoding (BPE) is a widely used method for subword tokenization,\nwith origins in grammar-based text compression. It is employed in a variety of\nlanguage processing tasks such as machine translation or large language model\n(LLM) pretraining, to create a token dictionary of a prescribed size. Most\nevaluations of BPE to date are empirical, and the reasons for its good\npractical performance are not well understood.\n  In this paper we focus on the optimization problem underlying BPE: finding a\npair encoding that achieves optimal compression utility. We show that this\nproblem is APX-complete, indicating that it is unlikely to admit a\npolynomial-time approximation scheme. This answers, in a stronger form, a\nquestion recently raised by Zouhar et al.\n  On the positive side, we show that BPE approximates the compression utility\nof the optimal pair encoding to a worst-case factor between $0.333$ and\n$0.625$. Our results aim to explain the ongoing success of BPE and are, to our\nknowledge, the first rigorous guarantees on its compression utility that hold\nfor all inputs.",
        "split": "arXiv"
    },
    {
        "text": "One of the most important and topical challenges of quantum circuits is their\nscalability. Rapid Single Flux Quantum (RSFQ) technology is at the forefront of\nreplacing current standard CMOS-based control architectures for a number of\napplications, including quantum computing and quantum sensor arrays. By\ncondensing the control and readout to SFQ-based on-chip devices that are\ndirectly connected to the quantum systems, it is possible to minimise the total\nsystem overhead, improving scalability and integration. In this work, we\npresent a novel RSFQ device that generates multi tone digital signals, based on\ncomplex pulse train sequences using a Circular Shift Register (CSR) and a comb\nfilter stage. We show that the frequency spectrum of the pulse trains is\ndependent on a preloaded pattern on the CSR, as well as on the delay line of\nthe comb filter stage. By carefully selecting both the pattern and delay, the\ndesired tones can be isolated and amplified as required. Finally, we propose\narchitectures where this device can be implemented to control and readout\narrays of quantum devices, such as qubits and single photon detectors.",
        "split": "arXiv"
    },
    {
        "text": "Advanced Air Mobility aircraft require energy efficient flight plans to be\neconomically viable. This paper defines minimum energy direct trajectories\nbetween waypoints for Lift+Cruise electric Vertical Take-Off and Landing\n(eVTOL) aircraft. Energy consumption is optimized over accelerated and cruise\nflight profiles with consideration of mode transitions. Because eVTOL\noperations start and end in hover for vertical take-off and landing, hover\nwaypoints are utilized. Energy consumption is modeled as a function of airspeed\nfor each flight mode, providing the basis to prove energy optimality for\nmulti-mode traversal. Wind magnitude and direction dictate feasibility of\nstraight-line traversal because Lift+Cruise aircraft point into the relative\nwind direction while hovering but also have a maximum heading rate constraint.\nEnergy and power use for an experimentally validated QuadPlane small eVTOL\naircraft are characterized with respect to airspeed and acceleration in all\nflight modes. Optimal QuadPlane traversals are presented. Constraints on\nacceleration and wind are derived for straight-line QuadPlane traversal.\nResults show an optimal QuadPlane $500m$ traversal between hover waypoints\nsaves $71\\%$ energy compared to pure vertical flight traversal for a\nrepresentative case study with a direct $4m/s$ crosswind. Energy optimal eVTOL\ndirect trajectory definition with transitions to and from hover is novel to\nthis work. Future work should model three-dimensional flight and wind as well\nas optimize maneuver primitives when required.",
        "split": "arXiv"
    },
    {
        "text": "By observing binary black hole (BBH) mergers out to the edge of the Universe,\nnext-generation (XG) ground-based gravitational-wave (GW) detectors like Cosmic\nExplorer and Einstein Telescope will map the BBH merger rate across all of\ncosmic history. This merger rate traces the formation rate of their progenitor\nstars convolved with a delay time distribution. Given theoretically-motivated\npriors on the delay time distribution, we show how XG observations can measure\nthe BBH progenitor formation rate, probing the star formation rate (SFR) up to\n$z > 15$. However, the progenitor formation rate does not directly give a\nmeasurement of the SFR, but rather a combination of the SFR and its metallicity\ndistribution as a function of redshift. Fortunately, the metallicity-dependence\nof BBH formation likely varies as a function of BBH mass and/or formation\nchannel. We find that if different BBH subpopulations with distinct metallicity\nbiases can be identified, comparing their rates as a function of redshift\nyields a simultaneous measurement of the SFR and its metallicity distribution.\nGiven optimistic theoretical priors and one year of observation, this may\nprovide a $\\sim10\\%$ measurement of the SFR at its peak and a 0.2 dex (0.7 dex)\nmeasurement of the median metallicity out to $z = 10$ ($z = 15$) at 90\\%\ncredibility, although the uncertainties scale with theoretical uncertainties on\nBBH delay times and formation efficiencies.",
        "split": "arXiv"
    },
    {
        "text": "Quasi-static time series (QSTS) simulations have great potential for\nevaluating the grid's ability to accommodate the large-scale integration of\ndistributed energy resources. However, as grids expand and operate closer to\ntheir limits, iterative power flow solvers, central to QSTS simulations, become\ncomputationally prohibitive and face increasing convergence issues. Neural\npower flow solvers provide a promising alternative, speeding up power flow\ncomputations by 3 to 4 orders of magnitude, though they are costly to train. In\nthis paper, we envision how recently introduced grid foundation models could\nimprove the economic viability of neural power flow solvers. Conceptually,\nthese models amortize training costs by serving as a foundation for a range of\ngrid operation and planning tasks beyond power flow solving, with only minimal\nfine-tuning required. We call for collaboration between the AI and power grid\ncommunities to develop and open-source these models, enabling all operators,\neven those with limited resources, to benefit from AI without building\nsolutions from scratch.",
        "split": "arXiv"
    },
    {
        "text": "The evolution of wireless communication systems will be fundamentally\nimpacted by an open radio access network (O-RAN), a new concept defining an\nintelligent architecture with enhanced flexibility, openness, and the ability\nto slice services more efficiently. For all its promises, and like any\ntechnological advancement, O-RAN is not without risks that need to be carefully\nassessed and properly addressed to accelerate its wide adoption in future\nmobile networks. In this paper, we present an in-depth security analysis of the\nO-RAN architecture, discussing the potential threats that may arise in the\ndifferent O-RAN architecture layers and their impact on the Confidentiality,\nIntegrity, and Availability (CIA) triad. We also promote the potential of zero\ntrust, Moving Target Defense (MTD), blockchain, and large language models(LLM)\ntechnologies in fortifying O-RAN's security posture. Furthermore, we\nnumerically demonstrate the effectiveness of MTD in empowering robust deep\nreinforcement learning methods for dynamic network slice admission control in\nthe O-RAN architecture. Moreover, we examine the effect of explainable AI (XAI)\nbased on LLMs in securing the system.",
        "split": "arXiv"
    },
    {
        "text": "Near-eye display plays an important role in emerging spatial computing\nsystems, providing a distinctive visual effect of virtual-real fusion. However,\nits application for all-day wear is greatly limited by the bulky structure,\nenergy expenditure, and continuous battery heating. Here, we propose a\nlightweight holographic near-eye display system that takes advantage of solar\nenergy for self-charging. To achieve the collection of solar energy and\nnear-eye display without crosstalk, holographic optical elements (HOE) are used\nto diffract sunlight and signal light into a common waveguide. Then, small-area\nsolar cells convert the collected solar energy and power the system. Compact\npower supply components replace heavy batteries, contributing to the\nlightweight design. The simple acquisition of solar energy provides the system\nwith sustainable self-charging capability. We believe that the lightweight\ndesign and continuous energy input solution will significantly promote the\npopularity of near-eye display in our daily lives.",
        "split": "arXiv"
    },
    {
        "text": "This paper considers the application of Model Predictive Control (MPC) to a\nweighted coverage path planning (WCPP) problem. The problem appears in a wide\nrange of practical applications, such as search and rescue (SAR) missions. The\nbasic setup is that one (or multiple) agents can move around a given search\nspace and collect rewards from a given spatial distribution. Unlike an\nartificial potential field, each reward can only be collected once. In contrast\nto a Traveling Salesman Problem (TSP), the agent moves in a continuous space.\nMoreover, he is not obliged to cover all locations and/or may return to\npreviously visited locations. The WCPP problem is tackled by a new Model\nPredictive Control (MPC) formulation with so-called Coverage Constraints (CCs).\nIt is shown that the solution becomes more effective if the solver is\ninitialized with a TSP-based heuristic. With and without this initialization,\nthe proposed MPC approach clearly outperforms a naive MPC formulation, as\ndemonstrated in a small simulation study.",
        "split": "arXiv"
    },
    {
        "text": "The Segment Anything Model (SAM) and similar models build a family of\npromptable foundation models (FMs) for image and video segmentation. The object\nof interest is identified using prompts, such as bounding boxes or points. With\nthese FMs becoming part of medical image segmentation, extensive evaluation\nstudies are required to assess their strengths and weaknesses in clinical\nsetting. Since the performance is highly dependent on the chosen prompting\nstrategy, it is important to investigate different prompting techniques to\ndefine optimal guidelines that ensure effective use in medical image\nsegmentation. Currently, no dedicated evaluation studies exist specifically for\nbone segmentation in CT scans, leaving a gap in understanding the performance\nfor this task. Thus, we use non-iterative, ``optimal'' prompting strategies\ncomposed of bounding box, points and combinations to test the zero-shot\ncapability of SAM-family models for bone CT segmentation on three different\nskeletal regions. Our results show that the best settings depend on the model\ntype and size, dataset characteristics and objective to optimize. Overall, SAM\nand SAM2 prompted with a bounding box in combination with the center point for\nall the components of an object yield the best results across all tested\nsettings. As the results depend on multiple factors, we provide a guideline for\ninformed decision-making in 2D prompting with non-interactive, ''optimal''\nprompts.",
        "split": "arXiv"
    },
    {
        "text": "$F(R)$ models for dark energy generally exhibit a weak curvature singularity,\nwhich can be cured by adding an $R^2$ term. This correction allows for a\nunified description of primordial and late-time accelerated expansions.\nHowever, most existing models struggle to achieve this, as they become unstable\nover certain negative ranges of the Ricci scalar, where either the first or\nsecond derivative of $F(R)$ turns negative. These instabilities may disrupt the\npost-inflationary evolution when the Ricci scalar oscillates about the vacuum\nstate after the $R^2$ inflation. In this work, we introduce a new\nmodel-building to guarantee global stability, i.e., the first and second\nderivatives are positive for all real Ricci scalars. By extending the idea from\nAppleby and Battye, we demonstrate that viable models can be constructed by\nimposing a positive, bounded first derivative of $F(R)$ with a sigmoid shape.\nAs examples, we first reformulate and generalize the original Appleby-Battye\nmodel. Then, we propose a new dark energy model, which successfully explains\nthe acceleration of cosmic expansion and passes local gravity tests.",
        "split": "arXiv"
    },
    {
        "text": "The May 10, 2024 space weather event stands out as the most powerful storm\nrecorded during the current solar cycle. This study employs a numerical\nframework utilizing a semi-empirical coronal model, along with HUXt\n(Heliospheric Upwind eXtrapolation with time-dependence) and cone-CME models\nfor the inner heliosphere, to forecast solar wind velocity and the arrival of\nCMEs associated with this event. The simulations were also carried out using\nSpace Weather Adaptive SimulaTion (SWASTi) and a drag-based model (DBM) for\nthis complex event of multiple CMEs. Predicted arrival times and velocities\nfrom these models are compared with actual observations at the Sun-Earth L1\npoint. These simulations reveal that three coronal mass ejections (CMEs)\nreached Earth nearly simultaneously, resulting in the extreme space weather\nevent, followed by the arrival of a few more eruptions. The simulations\naccurately predicted arrival times with a discrepancy of approximately 5 hours\nor less for these CMEs. Further, the ensemble study of DBM shows the\nsensitivity of the CME arrival time to the background solar wind speed and drag\nparameters. All three models have done fairly well in reproducing the arrival\ntime closely to the actual observation of the CMEs responsible for the extreme\ngeomagnetic storm of May 10, 2024. These rare solar storms offered a unique\nopportunity to thoroughly evaluate and validate our advanced models for\npredicting their arrival on the Earth.",
        "split": "arXiv"
    },
    {
        "text": "Vibrating systems can respond to an infinite number of initial conditions and\nthe overall dynamics of the system can be strongly affected by them. Therefore,\nit is of practical importance to have methods by which we can determine the\ndamping that is in some sense optimal for all initial conditions, or for a\ngiven set of initial conditions. For a single and multi degree of freedom\nsystems, we determine the optimal damping coefficients adapted to different\nsets of initial conditions using the known method of minimizing the (zero to\ninfinity) time integral of the energy of the system, averaged over a set of\ninitial conditions, and using two new methods that we introduce. One method is\nbased on determining the damping for which the energy of the system, averaged\nover a set of initial conditions, drops the fastest to a given threshold value.\nThe other method is based on determining the damping that gives minimal average\nsettling time of the system, where we take that the system settled when its\nenergy dropped to a given threshold value. We show that the two new methods\ngive results for optimal damping that are in excellent agreement with each\nother, but are significantly different from the results given by the\nminimization of the average energy integral. More precisely, for considered\nmulti degree of freedom systems and sets of initial conditions, the two new\nmethods give optimal damping coefficients that converge to the critical damping\nof the first mode as the target energy threshold decreases. On the other hand,\nfor these same systems and sets of initial conditions, the method of minimizing\nthe average energy integral gives optimal damping coefficients which are deep\nin the overdamped regime with respect to the first mode.",
        "split": "arXiv"
    },
    {
        "text": "Let $M$ be a compact complex manifold, and $D\\, \\subset\\, M$ a reduced normal\ncrossing divisor on it, such that the logarithmic tangent bundle $TM(-\\log D)$\nis holomorphically trivial. Let ${\\mathbb A}$ denote the maximal connected\nsubgroup of the group of all holomorphic automorphisms of $M$ that preserve the\ndivisor $D$. Take a holomorphic Cartan geometry $(E_H,\\,\\Theta)$ of type $(G,\\,\nH)$ on $M$, where $H\\, \\subset\\, G$ are complex Lie groups. We prove that\n$(E_H,\\,\\Theta)$ is isomorphic to $(\\rho^* E_H,\\,\\rho^* \\Theta)$ for every\n$\\rho\\, \\in\\, \\mathbb A$ if and only if the principal $H$--bundle $E_H$ admits\na logarithmic connection $\\Delta$ singular on $D$ such that $\\Theta$ is\npreserved by the connection $\\Delta$.",
        "split": "arXiv"
    },
    {
        "text": "Associative memory models, such as Hopfield networks and their modern\nvariants, have garnered renewed interest due to advancements in memory capacity\nand connections with self-attention in transformers. In this work, we introduce\na unified framework-Hopfield-Fenchel-Young networks-which generalizes these\nmodels to a broader family of energy functions. Our energies are formulated as\nthe difference between two Fenchel-Young losses: one, parameterized by a\ngeneralized entropy, defines the Hopfield scoring mechanism, while the other\napplies a post-transformation to the Hopfield output. By utilizing Tsallis and\nnorm entropies, we derive end-to-end differentiable update rules that enable\nsparse transformations, uncovering new connections between loss margins,\nsparsity, and exact retrieval of single memory patterns. We further extend this\nframework to structured Hopfield networks using the SparseMAP transformation,\nallowing the retrieval of pattern associations rather than a single pattern.\nOur framework unifies and extends traditional and modern Hopfield networks and\nprovides an energy minimization perspective for widely used\npost-transformations like $\\ell_2$-normalization and layer normalization-all\nthrough suitable choices of Fenchel-Young losses and by using convex analysis\nas a building block. Finally, we validate our Hopfield-Fenchel-Young networks\non diverse memory recall tasks, including free and sequential recall.\nExperiments on simulated data, image retrieval, multiple instance learning, and\ntext rationalization demonstrate the effectiveness of our approach.",
        "split": "arXiv"
    },
    {
        "text": "Assessing the quality of aleatoric uncertainty estimates from uncertainty\nquantification (UQ) deep learning methods is important in scientific contexts,\nwhere uncertainty is physically meaningful and important to characterize and\ninterpret exactly. We systematically compare aleatoric uncertainty measured by\ntwo UQ techniques, Deep Ensembles (DE) and Deep Evidential Regression (DER).\nOur method focuses on both zero-dimensional (0D) and two-dimensional (2D) data,\nto explore how the UQ methods function for different data dimensionalities. We\ninvestigate uncertainty injected on the input and output variables and include\na method to propagate uncertainty in the case of input uncertainty so that we\ncan compare the predicted aleatoric uncertainty to the known values. We\nexperiment with three levels of noise. The aleatoric uncertainty predicted\nacross all models and experiments scales with the injected noise level.\nHowever, the predicted uncertainty is miscalibrated to $\\rm{std}(\\sigma_{\\rm\nal})$ with the true uncertainty for half of the DE experiments and almost all\nof the DER experiments. The predicted uncertainty is the least accurate for\nboth UQ methods for the 2D input uncertainty experiment and the high-noise\nlevel. While these results do not apply to more complex data, they highlight\nthat further research on post-facto calibration for these methods would be\nbeneficial, particularly for high-noise and high-dimensional settings.",
        "split": "arXiv"
    },
    {
        "text": "We present a semi-algorithm which for any rational function\n$r\\in\\mathbb{K}(x,y)$ and any irreducible polynomial $p\\in\\mathbb{K}[x,y]$\ndecides whether the restriction of $r$ to the curve defined by $p$ is the\nrestriction of an element of $\\mathbb{K}(x)+\\mathbb{K}(y)$. In case it is, it\nfinds all such elements.",
        "split": "arXiv"
    },
    {
        "text": "We arrange the orders in an algebraic number field in a tree. This tree can\nbe used to enumerate all orders of bounded index in the maximal order as well\nas the orders over some given order.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we developed a spectral emulator based on the Mapping Nearby\nGalaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping\noptimization strategy to estimate effective temperature (T_eff), surface\ngravity (log g), metallicity ([Fe/H]) and the abundance of alpha elements with\nrespect to iron ([alpha/Fe]) for O-M-type stars within the Large Sky Area\nMulti-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution spectra. The\nprimary aim is to use a rapid spectral-fitting method, specifically the\nspectral emulator with the grouping optimization strategy, to create a\ncomprehensive catalog for stars of all types within LAMOST, addressing the\nshortcomings in parameter estimations for both cold and hot stars present in\nthe official LAMOST AFGKM-type catalog. This effort is part of our series of\nstudies dedicated to establishing an empirical spectral library for LAMOST.\nExperimental results demonstrate that our method is effectively applicable to\nparameter prediction for LAMOST, with the single-machine processing time within\n$70$ hr. We observed that the internal error dispersions for T_eff, log g,\n[Fe/H], and [alpha/Fe] across different spectral types lie within the ranges of\n$15-594$ K, $0.03-0.27$ dex, $0.02-0.10$ dex, and $0.01-0.04$ dex,\nrespectively, indicating a good consistency. A comparative analysis with\nexternal data highlighted deficiencies in the official LAMOST catalog and\nissues with MaStar parameters, as well as potential limitations of our method\nin processing spectra with strong emission lines and bad pixels. The derived\natmospheric parameters as a part of this work are available at\nhttps://nadc.china-vo.org/res/r101402/ .",
        "split": "arXiv"
    },
    {
        "text": "We extend the theory of quantum time loops introduced by Greenberger and\nSzovil [1] from the scalar situation (where paths have just an associated\ncomplex amplitude) to the general situation where the time traveling system has\nmulti-dimensional underlying Hilbert space. The main mathematical tool which\nemerges is the noncommutative M\\{o}bius Transformation and this affords a\nformalism similar to the modular structure well known to feedback control\nproblems. We argue that a sum-over-all-paths approach may be carried out in the\nscalar case, but quickly becomes unwieldy in the general case. It is natural to\nreplace the beamsplitters of [1] with more general components having their own\nquantum structure, in which case the theory starts to resemble the quantum\nfeedback networks theory for open quantum optical models and indeed we exploit\nthis to look at more realistic physical models of time loops. We analyze some\nGrandfather paradoxes in the new setting",
        "split": "arXiv"
    },
    {
        "text": "The shuffle product on positive integer points, which corresponds to the\nshuffle algebra for multiple zeta values, is extended uniquely to all integer\npoints, by making the linear operator which decreases the first entry by one a\ndifferential operator. We then show that all convergent integer points form a\nsubalgebra under this extended shuffle product. By lifting the extended shuffle\nproduct to the locality algebra of Chen symbols, we prove that the multiple\nzeta series defines an algebra homomorphism from the subalgebra of convergent\npoints to real numbers, which shows that the extended shuffle product is a\nstructure for convergent integer points.",
        "split": "arXiv"
    },
    {
        "text": "Functional simulation is an essential step in digital hardware design.\nRecently, there has been a growing interest in leveraging Large Language Models\n(LLMs) for hardware testbench generation tasks. However, the inherent\ninstability associated with LLMs often leads to functional errors in the\ngenerated testbenches. Previous methods do not incorporate automatic functional\ncorrection mechanisms without human intervention and still suffer from low\nsuccess rates, especially for sequential tasks. To address this issue, we\npropose CorrectBench, an automatic testbench generation framework with\nfunctional self-validation and self-correction. Utilizing only the RTL\nspecification in natural language, the proposed approach can validate the\ncorrectness of the generated testbenches with a success rate of 88.85%.\nFurthermore, the proposed LLM-based corrector employs bug information obtained\nduring the self-validation process to perform functional self-correction on the\ngenerated testbenches. The comparative analysis demonstrates that our method\nachieves a pass ratio of 70.13% across all evaluated tasks, compared with the\nprevious LLM-based testbench generation framework's 52.18% and a direct\nLLM-based generation method's 33.33%. Specifically in sequential circuits, our\nwork's performance is 62.18% higher than previous work in sequential tasks and\nalmost 5 times the pass ratio of the direct method. The codes and experimental\nresults are open-sourced at the link: https://github.com/AutoBench/CorrectBench",
        "split": "arXiv"
    },
    {
        "text": "The SPEC CPU2017 benchmark suite is an industry standard for accessing CPU\nperformance. It adheres strictly to some workload and system configurations -\narbitrary specificity - while leaving other system configurations undefined -\narbitrary ambiguity. This article reveals: (1) Arbitrary specificity proves not\nmeaningful, obscuring many scenarios, as evidenced by significant performance\nvariations, a 74.49x performance difference observed on the same CPU. (2)\nArbitrary ambiguity is unfair as it fails to establish the same configurations\nfor comparing different CPUs.\n  We propose an innovative CPU evaluation methodology. It considers all\nworkload and system configurations valid and mandates each configuration to be\nwell-defined to avoid arbitrary specificity and ambiguity. To reduce the\nevaluation cost, a sampling approach is proposed to select a subset of the\nconfigurations. To expose CPU performance under different scenarios, it treats\nall outcomes under each configuration as equally important. Finally, it\nutilizes confidence level and confidence interval to report the outcomes to\navoid bias.",
        "split": "arXiv"
    },
    {
        "text": "Many studies have predicted SocioEconomic Position (SEP) for aggregated\nspatial units such as villages using satellite data, but SEP prediction at the\nhousehold level and other sources of imagery have not been yet explored. We\nassembled a dataset of 975 households in a semi-rural district in southern\nMozambique, consisting of self-reported asset, expenditure, and income SEP\ndata, as well as multimodal imagery including satellite images and a\nground-based photograph survey of 11 household elements. We fine-tuned a\nconvolutional neural network to extract feature vectors from the images, which\nwe then used in regression analyzes to model household SEP using different sets\nof image types. The best prediction performance was found when modeling\nasset-based SEP using random forest models with all image types, while the\nperformance for expenditure- and income-based SEP was lower. Using SHAP, we\nobserved clear differences between the images with the largest positive and\nnegative effects, as well as identified the most relevant household elements in\nthe predictions. Finally, we fitted an additional reduced model using only the\nidentified relevant household elements, which had an only slightly lower\nperformance compared to models using all images. Our results show how\nground-based household photographs allow to zoom in from an area-level to an\nindividual household prediction while minimizing the data collection effort by\nusing explainable machine learning. The developed workflow can be potentially\nintegrated into routine household surveys, where the collected household\nimagery could be used for other purposes, such as refined asset\ncharacterization and environmental exposure assessment.",
        "split": "arXiv"
    },
    {
        "text": "An edge-colored graph is called \\textit{rainbow graph} if all the colors on\nits edges are distinct. For a given positive integer $n$ and a family of graphs\n$\\mathcal{G}$, the anti-Ramsey number $ar(n, \\mathcal{G})$ is the smallest\nnumber of colors $r$ required to ensure that, no matter how the edges of the\ncomplete graph $K_n$ are colored using exactly $r$ colors, there will always be\na rainbow copy of some graph $G$ from the family $\\mathcal{G}$. A friendship\ngraph $F_k$ is the graph obtained by combining $k$ triangles that share a\ncommon vertex. In this paper, we determine the anti-Ramsey number $ar(n,\n\\{F_k\\})$ for large values of $n$. Additionally, we also determine the $ar(n,\n\\{K_{1,k}, M_k\\}$, where $K_{1,k}$ is a star graph with $ k+1$ vertices and\n$M_k$ is a matching of size $k$.",
        "split": "arXiv"
    },
    {
        "text": "Inertial waves in convective regions of stars exhibit topological properties\nlinked to a Chern number of 1. The first of these is a unique, unidirectional,\nprograde oscillation mode within the cavity, which propagates at arbitrarily\nlow frequencies for moderate azimuthal wavenumbers. The second one are phase\nsingularities around which the phase winds in Fourier space, with winding\nnumbers of $\\pm 1$ depending on the hemisphere. Phase winding is a collective\neffect over waves propagating in all directions that is strongly robust to\nnoise. This suggests a topology-based method for wave detection in noisy\nobservational data.",
        "split": "arXiv"
    },
    {
        "text": "Pick $n$ independent and uniform random points $U_1,\\ldots,U_n$ in a compact\nconvex set $K$ of $\\mathbb{R}^d$ with volume 1, and let $P^{(d)}_K(n)$ be the\nprobability that these points are in convex position. The Sylvester conjecture\nin $\\mathbb{R}^d$ is that $\\min_K P^{(d)}_K(d+2)$ is achieved by the\n$d$-dimensional simplices $K$ (only).\n  In this paper, we focus on a companion model, already studied in the $2d$\ncase, which we define in any dimension $d$: we say that $K$ has $F$ as a flat\nfloor, if $F$ is a subset of $K$, contained in a hyperplan $P$, such that $K$\nlies in one of the half-spaces defined by $P$.\n  We define $Q_K^F(n)$ as the probability that $U_1,\\cdots,U_n$ together with\n$F$ are in convex position (i.e., the $U_i$ are on the boundary of the convex\nhull ${\\sf CH}(\\{U_1,\\cdots,U_n\\}\\cup F\\})$). We prove that, for all fixed $F$,\n  $K\\mapsto Q_K^F(2)$ reaches its minimum on the \"mountains\" with floor $F$\n(mountains are convex hull of $F$ union an additional vertex), while the\nmaximum is not reached, but $K\\mapsto Q_K^F(2)$ has values arbitrary close to\n1. If the optimisation is done on the set of $K$ contained in $F\\times[0,d]$\n(the \"subprism case\"), then the minimum is also reached by the mountains, and\nthe maximum by the \"prism\" $F\\times[0,1]$. Since again, $Q_K^F{(2)}$ relies on\nthe expected volume (of ${\\sf CH}(\\{V_1,V_2\\}\\cup F\\})$), this result can be\nseen as a proof of the Sylvester problem in the floor case.\n  In $2d$, where $F$ can essentially be the segment $[0,1],$ we give a general\ndecomposition formula for $Q_K^F(n)$ so to compute several formulas and bounds\nfor different $K$. In 3D, we give some bounds for $Q_K^F(n)$ for various floors\n$F$ and special cases of $K$.",
        "split": "arXiv"
    },
    {
        "text": "We analyze all individual cosmic strings of various lengths in a large\nensemble of the global cosmic string networks in the post-inflationary\nscenario, obtained from numerical simulations on a discrete lattice with $N^3 =\n4096^3$. A strong evidence for a logarithmically growing spectral index of the\nstring power spectrum during the evolution is newly reported as our main\nresult. The logarithmic scaling is checked against two different approaches for\ngenerating initial random field configurations, namely fat-string type and\nthermal phase transition. We derive the analytic relation between two power\nspectra of cosmic strings and axions which should be valid under some\nassumptions, and the validity of those assumptions is discussed. We argue that\nour analytic result strongly supports the correlated spectra of cosmic strings\nand axions. Additionally, we initiate the statistical analysis of the causal\ndynamics of the cosmic strings.",
        "split": "arXiv"
    },
    {
        "text": "The remarkable advances in deep learning have led to the emergence of many\noff-the-shelf classifiers, e.g., large pre-trained models. However, since they\nare typically trained on clean data, they remain vulnerable to adversarial\nattacks. Despite this vulnerability, their superior performance and\ntransferability make off-the-shelf classifiers still valuable in practice,\ndemanding further work to provide adversarial robustness for them in a post-hoc\nmanner. A recently proposed method, denoised smoothing, leverages a denoiser\nmodel in front of the classifier to obtain provable robustness without\nadditional training. However, the denoiser often creates hallucination, i.e.,\nimages that have lost the semantics of their originally assigned class, leading\nto a drop in robustness. Furthermore, its noise-and-denoise procedure\nintroduces a significant distribution shift from the original distribution,\ncausing the denoised smoothing framework to achieve sub-optimal robustness. In\nthis paper, we introduce Fine-Tuning with Confidence-Aware Denoised Image\nSelection (FT-CADIS), a novel fine-tuning scheme to enhance the certified\nrobustness of off-the-shelf classifiers. FT-CADIS is inspired by the\nobservation that the confidence of off-the-shelf classifiers can effectively\nidentify hallucinated images during denoised smoothing. Based on this, we\ndevelop a confidence-aware training objective to handle such hallucinated\nimages and improve the stability of fine-tuning from denoised images. In this\nway, the classifier can be fine-tuned using only images that are beneficial for\nadversarial robustness. We also find that such a fine-tuning can be done by\nupdating a small fraction of parameters of the classifier. Extensive\nexperiments demonstrate that FT-CADIS has established the state-of-the-art\ncertified robustness among denoised smoothing methods across all\n$\\ell_2$-adversary radius in various benchmarks.",
        "split": "arXiv"
    },
    {
        "text": "Peer review, as a widely used practice to ensure the quality and integrity of\npublications, lacks a well-defined and common mechanism to self-incentivize\nvirtuous behavior across all the conferences and journals. This is because\ninformation about reviewer efforts and author feedback typically remains local\nto a single venue, while the same group of authors and reviewers participate in\nthe publication process across many venues. Previous attempts to incentivize\nthe reviewing process assume that the quality of reviews and papers authored\ncorrelate for the same person, or they assume that the reviewers can receive\nphysical rewards for their work. In this paper, we aim to keep track of\nreviewing and authoring efforts by users (who review and author) across\ndifferent venues while ensuring self-incentivization. We show that our system,\nDecentPeeR, incentivizes reviewers to behave according to the rules, i.e., it\nhas a unique Nash equilibrium in which virtuous behavior is rewarded.",
        "split": "arXiv"
    },
    {
        "text": "Uncertainty visualisation is quickly becomming a hot topic in information\nvisualisation. Exisiting reviews in the field take the definition and purpose\nof an uncertainty visualisation to be self evident which results in a large\namout of conflicting information. This conflict largely stems from a conflation\nbetween uncertainty visualisations designed for decision making and those\ndesigned to prevent false conclusions. We coin the term \"signal suppression\" to\ndescribe a visualisation that is designed for preventing false conclusions, as\nthe approach demands that the signal (i.e. the collective take away of the\nestimates) is suppressed by the noise (i.e. the variance on those estimates).\nWe argue that the current standards in visualisation suggest that uncertainty\nvisualisations designed for decision making should not be considered\nuncertainty visualisations at all. Therefore, future work should focus on\nsignal suppression. Effective signal suppression requires us to communicate the\nsignal and the noise as a single \"validity of signal\" variable, and doing so\nproves to be difficult with current methods. We illustrate current approaches\nto uncertainty visualisation by showing how they would change the visual\napprearance of a choropleth map. These maps allow us to see why some methods\nsucceed at signal suppression, while others fall short. Evaluating\nvisualisations on how well they perform signal suppression also proves to be\ndifficult, as it involves measuring the effect of noise, a variable we\ntypically try to ignore. We suggest authors use qualitative studies or compare\nuncertainty visualisations to the relevant hypothesis tests.",
        "split": "arXiv"
    },
    {
        "text": "Larger transformer models always perform better on various tasks but require\nmore costs to scale up the model size. To efficiently enlarge models, the\nmixture-of-experts (MoE) architecture is widely adopted, which consists of a\ngate network and a series of experts and keep the training cost constant by\nrouting the input data to a fixed number of experts instead of all. In existing\nlarge-scale MoE training systems, experts would be distributed among different\nGPUs for parallelization, and thus input data requires additional all-to-all\ncommunications to access the target experts and conduct corresponding\ncomputations. However, upon evaluating the training process of three mainstream\nMoE models on commonly used GPU clusters, we found that the all-to-all\ncommunication ratio averaged around 45%, which significantly hinders the\nefficiency and scalability of training MoE models.\n  In this paper, we propose LSH-MoE, a communication-efficient MoE training\nframework using locality-sensitive hashing (LSH). We first present the problems\nof scaling MoE training in existing systems and highlight the potential of\nexploiting token similarity to facilitate data compression. Then, we introduce\nan efficient LSH-based compression technique, which utilizes the cross-polytope\nhashing for rapid clustering and implements a residual-based error compensation\nscheme to alleviate the adverse impact of compression. To verify the\neffectiveness of our methods, we conduct experiments on both language models\n(e.g., RoBERTa, GPT, and T5) and vision models (e.g., Swin) for pre-training\nand fine-tuning tasks. The results demonstrate that our method substantially\noutperforms its counterparts across different tasks by 1.28x - 2.2x of speedup.",
        "split": "arXiv"
    },
    {
        "text": "Despite its excellent performance in microelectronic industry, silicon was\nnot able to perform well in photonic devices arena. This is because the silicon\nhas never been a good optical source mainly due to its indirect band gap\nstructure. Many of the device functionalities in silicon have been reported,\nwith an exception of, until recently, a reliable optical source. Silicon is a\nnonlinear material which makes use of its nonlinearities to realize various\nfunctionalities. This paper presents a theoretical treatment of generating and\nenhancing third-harmonic field which may be used as optical source, crystal\nstate monitoring and all-optical signal processing applications.",
        "split": "arXiv"
    },
    {
        "text": "In this paper we investigate the tractability of robust Markov Decision\nProcesses (RMDPs) under various structural assumptions on the uncertainty set.\nSurprisingly, we show that in all generality (i.e. without any assumption on\nthe instantaneous rewards), s-rectangular and sa-rectangular uncertainty sets\nare the only models of uncertainty that are tractable. Our analysis also shows\nthat existing non-rectangular models, including r-rectangular uncertainty and\nnew generalizations, are only weakly tractable in that they require an\nadditional structural assumption that the instantaneous rewards do not depend\non the next state, and in this case they are equivalent to rectangular models,\nwhich severely undermines their significance and usefulness. Interestingly, our\nproof techniques rely on identifying a novel simultaneous solvability property,\nwhich we show is at the heart of several important properties of RMDPs,\nincluding the existence of stationary optimal policies and dynamic\nprogramming-based formulations. The simultaneous solvability property enables a\nunified approach to studying the tractability of all existing models of\nuncertainty, rectangular and non-rectangular alike.",
        "split": "arXiv"
    },
    {
        "text": "In the distributed localization problem (DLP), n anonymous robots (agents)\nA0, A1, ..., A(n-1) begin at arbitrary positions p0, ..., p(n-1) in S, where S\nis a Euclidean space. The primary goal in DLP is for agents to reach a\nconsensus on a unified coordinate system that accurately reflects the relative\npositions of all points, p0, ... , p(n-1), in S. Extensive research on DLP has\nprimarily focused on the feasibility and complexity of achieving consensus when\nagents have limited access to inter-agent distances, often due to missing or\nimprecise data. In this paper, however, we examine a minimalist,\ncomputationally efficient model of distributed computing in which agents have\naccess to all pairwise distances, if needed. Specifically, we introduce a novel\nvariant of population protocols, referred to as the spatial population\nprotocols model. In this variant each agent can memorise one or a fixed number\nof coordinates, and when agents A(i) and A(j) interact, they can not only\nexchange their current knowledge but also either determine the distance d(i,j)\nbetween them in S (distance query model) or obtain the vector v(i,j) spanning\npoints p(i) and p(j) (vector query model).\n  We examine three DLP scenarios:\n  - Self-stabilising localisation protocol with distance queries We propose and\nanalyse self-stabilising localisation protocol based on pairwise distance\nadjustment. We also discuss several hard instances in this scenario, and\nsuggest possible improvements for the considered protocol,\n  - Leader-based localisation protocol with distance queries We propose and\nanalyse several leader-based protocols which stabilise in o(n) parallel time.\nThese protocols rely on efficient solution to multi-contact epidemic, and\n  - Self-stabilising localisation protocol with vector queries We propose and\nanalyse superfast self-stabilising DLP protocol which stabilises in O(log n)\nparallel time.",
        "split": "arXiv"
    },
    {
        "text": "In this paper we consider the existence of standing waves for a coupled\nsystem of $k$ equations with Lotka-Volterra type interaction. We prove the\nexistence of a standing wave solution with all nontrivial components satisfying\na prescribed asymptotic profile. In particular, the $k-1$-last components of\nsuch solution exhibits a concentrating behavior, while the first one keeps a\nquantum nature. We analyze first in detail the result with three equations\nsince this is the first case in which the coupling has a role contrary to what\nhappens when only two densities appear. We also discuss the existence of\nsolutions of this form for systems with other kind of couplings making a\ncomparison with Lotka-Volterra type systems.",
        "split": "arXiv"
    },
    {
        "text": "The aim of this article is to give an expository account of the equivalence\nbetween modest sets and partial equivalence relations. Our proof is entirely\nself-contained in that we do not assume any knowledge of categorical\nrealizability. At the heart of the equivalence lies the subquotient\nconstruction on a partial equivalence relation. The subquotient construction\nembeds the category of partial equivalence relations into the category of\nmodest sets. We show that this embedding is a split essentially surjective\nfunctor, and thereby, an equivalence of categories. Our development is both\nconstructive and predicative, and employs the language of homotopy type theory.\nAll the mathematics presented in this article has been mechanised in Cubical\nAgda.",
        "split": "arXiv"
    },
    {
        "text": "We present a preliminary laboratory test of a setup designed to measure\nHanbury Brown and Twiss-type intensity correlations from a chaotic light source\nusing five spectral channels simultaneously. After averaging the zero-delay\ncorrelation peaks from all channels, we obtain an improvement of the\nsignalto-noise ratio fairly consistent with theory. The goal is to demonstrate\nthe feasibility and scalability of this technique to improve the sensitivity of\nstellar intensity interferometry using optical telescopes.",
        "split": "arXiv"
    },
    {
        "text": "Modern imaging technologies are widely based on classical principles of light\nor electromagnetic wave propagation. They can be remarkably sophisticated, with\nrecent successes ranging from single molecule microscopy to imaging far-distant\ngalaxies. However, new imaging technologies based on quantum principles are\ngradually emerging. They can either surpass classical approaches or provide\nnovel imaging capabilities that would not otherwise be possible. {Here }we\nprovide an overview {of the most recently developed quantum imaging systems,\nhighlighting the non-classical properties of sources such as bright squeezed\nlight, entangled photons, and single-photon emitters that enable their\nfunctionality.} We outline potential upcoming trends and the associated\nchallenges, all driven by a central inquiry, which is to understand whether\nquantum light can make visible the invisible.",
        "split": "arXiv"
    },
    {
        "text": "Fluid antenna system (FAS)/movable antenna (MA) has emerged as a promising\ntechnology to fully exploit the spatial degrees of freedom (DoFs). In this\npaper, we propose a new rotatable antenna (RA) model, as a simplified\nimplementation of six-dimensional movable antenna (6DMA), to improve the\nperformance of wireless communication systems. Different from conventional\nfixed-position antenna (FPA), the proposed RA system can independently and\nflexibly change the three-dimensional (3D) orientation of each antenna by\nadjusting its declination angles to achieve desired channel realizations.\nSpecifically, we study an RA-enabled uplink communication system, where the\nreceive beamforming and the declination angles of all RAs are jointly optimized\nto maximize the minimum signal-to-interference-plus-noise ratio (SINR) among\nall the users. In the special single-user and free-space propagation setup, the\noptimal declination angles are derived in closed form with the maximum-ratio\ncombining (MRC) beamformer applied at the base station (BS). In the general\nmulti-user and multi-path setup, we propose an alternating optimization (AO)\nalgorithm to alternately optimize the receive beamforming and the declination\nangles in an iterative manner. Simulation results are provided to demonstrate\nthat the proposed RA-enabled system can significantly outperform other\nbenchmark schemes.",
        "split": "arXiv"
    },
    {
        "text": "We classify all possible occurrences of Kazama-Suzuki duality between the\n$N=2$ superconformal algebra $L^{N=2}_c$ and the subregular algebra\n$\\mathcal{W}$-algebra $\\mathcal{W}_{k}(sl_4, f_{sub})$. We establish a new\nKazama-Suzuki duality between the subregular $\\mathcal{W}$-algebra\n$\\mathcal{W}^k(sl_4, f_{\\text{sub}})$ and the the $N = 2$ superconformal\nalgebra $L^{N=2}_{c}$ for $c=-15$. As a consequence of duality, we classify the\nirreducible $\\mathcal{W}_{k=-1}(sl_4, f_{\\text{sub}})$-modules.",
        "split": "arXiv"
    },
    {
        "text": "Evolutionary competition often occurs simultaneously at multiple levels of\norganization, in which traits or behaviors that are costly for an individual\ncan provide collective benefits to groups to which the individual belongs.\nBuilding off of recent work that has used ideas from game theory to study\nevolutionary competition within and among groups, we study a PDE model for\nmultilevel selection that considers group-level evolutionary dynamics through a\npairwise conflict depending on the strategic composition of the competing\ngroups. This model allows for incorporation of group-level frequency\ndependence, facilitating the exploration for how the form of probabilities for\nvictory in a group-level conflict can impact the long-time support for\ncooperation via multilevel selection. We characterize well-posedness properties\nfor measure-valued solutions of our PDE model and apply these properties to\nshow that the population will converge to a delta-function at the all-defector\nequilibrium when between-group selection is sufficiently weak. We further\nprovide necessary conditions for the existence of bounded steady state\ndensities for the multilevel dynamics of Prisoners' Dilemma and Hawk-Dove\nscenarios, using a mix of analytical and numerical techniques to characterize\nthe relative strength of between-group selection required to ensure the\nlong-time survival of cooperation via multilevel selection. We also see that\nthe average payoff at steady state appears to be limited by the average payoff\nof the all-cooperator group, even for games in which groups achieve maximal\naverage payoff at intermediate levels of cooperation, generalizing behavior\nthat has previously been observed in PDE models of multilevel selection with\nfrequency-indepdent group-level competition.",
        "split": "arXiv"
    },
    {
        "text": "Video generation has emerged as a promising tool for world simulation,\nleveraging visual data to replicate real-world environments. Within this\ncontext, egocentric video generation, which centers on the human perspective,\nholds significant potential for enhancing applications in virtual reality,\naugmented reality, and gaming. However, the generation of egocentric videos\npresents substantial challenges due to the dynamic nature of egocentric\nviewpoints, the intricate diversity of actions, and the complex variety of\nscenes encountered. Existing datasets are inadequate for addressing these\nchallenges effectively. To bridge this gap, we present EgoVid-5M, the first\nhigh-quality dataset specifically curated for egocentric video generation.\nEgoVid-5M encompasses 5 million egocentric video clips and is enriched with\ndetailed action annotations, including fine-grained kinematic control and\nhigh-level textual descriptions. To ensure the integrity and usability of the\ndataset, we implement a sophisticated data cleaning pipeline designed to\nmaintain frame consistency, action coherence, and motion smoothness under\negocentric conditions. Furthermore, we introduce EgoDreamer, which is capable\nof generating egocentric videos driven simultaneously by action descriptions\nand kinematic control signals. The EgoVid-5M dataset, associated action\nannotations, and all data cleansing metadata will be released for the\nadvancement of research in egocentric video generation.",
        "split": "arXiv"
    },
    {
        "text": "A proper vertex coloring of a graph is equitable if the sizes of all color\nclasses differ by at most $1$. For a list assignment $L$ of $k$ colors to each\nvertex of an $n$-vertex graph $G$, an equitable $L$-coloring of $G$ is a proper\ncoloring of vertices of $G$ from their lists such that no color is used more\nthan $\\lceil n/k\\rceil$ times. Call a graph equitably $k$-choosable if it has\nan equitable $L$-coloring for every $k$-list assignment $L$. A graph $G$ is\n$(a,b)$-sparse if for every $A\\subseteq V(G)$, the number of edges in the\nsubgraph $G[A]$ of $G$ induced by $A$ is at most $a|A|+b$.\n  Our first main result is that every $(\\frac{7}{6},\\frac{1}{3})$-sparse graph\nwith minimum degree at least $2$ is equitably $3$-colorable and equitably\n$3$-choosable. This is sharp. Our second main result is that every\n$(\\frac{5}{4},\\frac{1}{2})$-sparse graph with minimum degree at least $2$ is\nequitably $4$-colorable and equitably $4$-choosable. This is also sharp.\n  One of the tools in the proof is the new notion of strongly equitable (SE)\nlist coloring. This notion is both stronger and more natural than equitable\nlist coloring; and our upper bounds are for SE list coloring.",
        "split": "arXiv"
    },
    {
        "text": "A code ${\\mathcal C}$ is a subset of the vertex set of a Hamming graph\n$H(n,q)$, and ${\\mathcal C}$ is $2$-neighbour-transitive if the automorphism\ngroup $G={\\rm Aut}({\\mathcal C})$ acts transitively on each of the sets\n${\\mathcal C}$, ${\\mathcal C}_1$ and ${\\mathcal C}_2$, where ${\\mathcal C}_1$\nand ${\\mathcal C}_2$ are the (non-empty) sets of vertices that are distances\n$1$ and $2$, respectively, (but no closer) to some element of ${\\mathcal C}$.\n  Suppose that ${\\mathcal C}$ is a $2$-neighbour-transitive code with minimum\ndistance at least $5$. For $q=2$, all `minimal' such ${\\mathcal C}$ have been\nclassified. Moreover, it has previously been shown that a subgroup of the\nautomorphism group of the code induces an affine $2$-transitive group action on\nthe alphabet of the Hamming graph. The main results of this paper are to show\nthat this affine $2$-transitive group must be a subgroup of ${\\rm A}\\Gamma{\\rm\nL}_1(q)$ and to provide a number of infinite families of examples of such\ncodes. These examples are described via polynomial algebras related to\nrepresentations of certain classical groups.",
        "split": "arXiv"
    },
    {
        "text": "The recent discovery of an axial amplitude (Higgs) mode in the long-studied\ncharge density wave (CDW) systems GdTe$_3$ and LaTe$_3$ suggests a heretofore\nunidentified hidden order. A theoretical study proposed that the axial Higgs\nresults from a hidden ferroaxial component of the CDW, which could arise from\nnon-trivial orbital texture. Here, we report extensive experimental studies on\nErTe$_3$ and HoTe$_3$ that possess a high-temperature CDW similar to other\nRTe$_3$ (R = rare earth), along with an additional low-temperature CDW with an\northogonal ordering vector. Combining Raman spectroscopy with large-angle\nconvergent beam electron diffraction (LACBED), rotational anisotropy\nsecond-harmonic generation (RA-SHG), and muon-spin relaxation ($\\mu$SR), we\nprovide unambiguous evidence that the high-temperature CDW breaks translation,\nrotation, and all vertical and diagonal mirror symmetries, but not\ntime-reversal or inversion. In contrast, the low-temperature CDW only\nadditionally breaks translation symmetry. Simultaneously, Raman scattering\nshows the high-temperature CDW produces an axial Higgs mode while the\nlow-temperature mode is scalar. The weak monoclinic structural distortion and\nclear axial response in Raman and SHG are consistent with a ferroaxial phase in\n\\ch{RTe3} driven by coupled orbital and charge orders. Thus, our study provides\na new standard for uncovering unconventional orders and confirms the power of\nHiggs modes to reveal them.",
        "split": "arXiv"
    },
    {
        "text": "Precision medicine leverages patient heterogeneity to estimate individualized\ntreatment regimens, formalized, data-driven approaches designed to match\npatients with optimal treatments. In the presence of competing events, where\nmultiple causes of failure can occur and one cause precludes others, it is\ncrucial to assess the risk of the specific outcome of interest, such as one\ntype of failure over another. This helps clinicians tailor interventions based\non the factors driving that particular cause, leading to more precise treatment\nstrategies. Currently, no precision medicine methods simultaneously account for\nboth survival and competing risk endpoints. To address this gap, we develop a\nnonparametric individualized treatment regime estimator. Our two-phase method\naccounts for both overall survival from all events as well as the cumulative\nincidence of a main event of interest. Additionally, we introduce a\nmulti-utility value function that incorporates both outcomes. We develop random\nsurvival and random cumulative incidence forests to construct individual\nsurvival and cumulative incidence curves. Simulation studies demonstrated that\nour proposed method performs well, which we applied to a cohort of peripheral\nartery disease patients at high risk for limb loss and mortality.",
        "split": "arXiv"
    },
    {
        "text": "This paper examines how the mathematicians and astronomers of the Kerala\nschool tackled the problem of computing the values of the arcsin function. Four\ndifferent approaches are discussed all of which are found in Nilakantha\nSomayaji's (1444 - 1545 CE) Tantrasangraha and the roots of all of which can be\ntraced to ideas originally articulated by Sangamagrama Madhava (c. 1340 - 1425\nCE): (i) a simple method when the argument is small; (ii) an iterative method\nwhen the argument is small; (iii) a method based on a lookup table; (iv) a\nmethod when the argument is large. The paper also contains the original\nSanskrit verses describing the various methods and English translations\nthereof. Moreover, there is a presentation of a novel method for computing the\ncircumference of a circle found in Jyeshthadeva's (c. 1500 - 1575 CE)\nYuktibhasha which is based on method (i) for computing the arcsin function. All\nmethods have been illustrated with numerical examples. A surprising by-product\nof the investigation is a totally unexpected appearance of a core integer\nsequence, namely, the entry A001764 in the Online Encyclopedia of Integer\nSequence, while studying the iterative method for computing the arcsin\nfunction.",
        "split": "arXiv"
    },
    {
        "text": "The relaxed optimal $k$-thresholding pursuit (ROTP) is a recent algorithm for\nlinear inverse problems. This algorithm is based on the optimal\n$k$-thresholding technique which performs vector thresholding and error metric\nreduction simultaneously. Although ROTP can be used to solve small to\nmedium-sized linear inverse problems, the computational cost of this algorithm\nis high when solving large-scale problems. By merging the optimal\n$k$-thresholding technique and iterative method with memory as well as\noptimization with sparse search directions, we propose the so-called dynamic\nthresholding algorithm with memory (DTAM), which iteratively and dynamically\nselects vector bases to construct the problem solution. At every step, the\nalgorithm uses more than one or all iterates generated so far to construct a\nnew search direction, and solves only the small-sized quadratic subproblems at\nevery iteration. Thus the computational complexity of DTAM is remarkably lower\nthan that of ROTP-type methods. It turns out that DTAM can locate the solution\nof linear inverse problems if the matrix involved satisfies the restricted\nisometry property. Experiments on synthetic data, audio signal reconstruction\nand image denoising demonstrate that the proposed algorithm performs comparably\nto several mainstream thresholding and greedy algorithms, and it works much\nfaster than the ROTP-type algorithms especially when the sparsity level of\nsignal is relatively low.",
        "split": "arXiv"
    },
    {
        "text": "Streaming systems are present throughout modern applications, processing\ncontinuous data in real-time. Existing streaming languages have a variety of\nsemantic models and guarantees that are often incompatible. Yet all these\nlanguages are considered \"streaming\" -- what do they have in common? In this\npaper, we identify two general yet precise semantic properties: streaming\nprogress and eager execution. Together, they ensure that streaming outputs are\ndeterministic and kept fresh with respect to streaming inputs. We formally\ndefine these properties in the context of Flo, a parameterized streaming\nlanguage that abstracts over dataflow operators and the underlying structure of\nstreams. It leverages a lightweight type system to distinguish bounded streams,\nwhich allow operators to block on termination, from unbounded ones.\nFurthermore, Flo provides constructs for dataflow composition and nested graphs\nwith cycles. To demonstrate the generality of our properties, we show how key\nideas from representative streaming and incremental computation systems --\nFlink, LVars, and DBSP -- have semantics that can be modeled in Flo and\nguarantees that map to our properties.",
        "split": "arXiv"
    },
    {
        "text": "The main question of this paper is the following: how much cancellation can\nthe partial sums restricted to the $k$-free integers up to $x$ of a $\\pm 1$\nmultiplicative function $f$ be in terms of $x$? Building upon the recent paper\nby Q. Liu, Acta Math. Sin. (Engl. Ser.) 39 (2023), no. 12, 2316-2328, we prove\nthat under the Riemann Hypothesis for quadratic Dirichlet $L$-functions, we can\nget $x^{1/(k+1)}$ cancellation when $f$ is a modified quadratic Dirichlet\ncharacter, i.e., $f$ is completely multiplicative and for some quadratic\nDirichlet character $\\chi$, $f(p)=\\chi(p)$ for all but a finite subset of prime\nnumbers. This improves the conditional results by Aymone, Medeiros and the\nauthor cf. Ramanujan J. 59 (2022), no. 3, 713-728.",
        "split": "arXiv"
    },
    {
        "text": "There has been a recent surge in interest in quantum foundations coming from\nincorporating ideas from general relativity and quantum gravity. In particular,\nthe field of indefinite causal order has emerged and is now an important\nresearch topic in its own right. Many of the tools that we use in quantum\nfoundations and information, are, however, totally agnostic as to the\nunderlying spacetime in which the quantum systems live. To give a practical\nexample, whenever we draw a quantum circuit we are not taking into account the\nconnectivity of the physical qubits which will realize this circuit. In this\nwork, we aim to address this limitation. In particular, we show how to extend\nthe formalism of process theories (a framework to study both quantum and\npost-quantum theories) to incorporate a background causal structure arising\nfrom a fixed spacetime. We discuss when processes are embeddable in spacetime\nunder certain constraints. To this end, we introduce the concept of\nimplementations of a process, which are decompositions of the process. A\nprocess is then embeddable if one of its implementations can be embedded in\nsuch a way that all the processes are localized and all wires follow time-like\npaths. The set of all implementations of a process is a rather unwieldy object\nbut we show that there exists a subset with useful properties which tells us\neverything we need to know about the remaining implementations and the\nembeddability of a process. We call this subset the set of minimal\nrepresentatives. Future directions include defining and analysing the\ncompositional structure of the framework more rigorously, extending the\nframework to indefinite causal structures, studying exotic causal influence,\nand using the minimal representatives to probe the decompositional structure of\nquantum theory and beyond.",
        "split": "arXiv"
    },
    {
        "text": "Modern approaches to perform Bayesian variable selection rely mostly on the\nuse of shrinkage priors. That said, an ideal shrinkage prior should be adaptive\nto different signal levels, ensuring that small effects are ruled out, while\nkeeping relatively intact the important ones. With this task in mind, we\ndevelop the nonparametric Bayesian Lasso, an adaptive and flexible shrinkage\nprior for Bayesian regression and variable selection, particularly useful when\nthe number of predictors is comparable or larger than the number of available\ndata points. We build on spike-and-slab Lasso ideas and extend them by placing\na Dirichlet Process prior on the shrinkage parameters. The result is a prior on\nthe regression coefficients that can be seen as an infinite mixture of Double\nExponential densities, all offering different amounts of regularization,\nensuring a more adaptive and flexible shrinkage. We also develop an efficient\nMarkov chain Monte Carlo algorithm for posterior inference. Through various\nsimulation exercises and real-world data analyses, we demonstrate that our\nproposed method leads to a better recovery of the true regression coefficients,\na better variable selection, and better out-of-sample predictions, highlighting\nthe benefits of the nonparametric Bayesian Lasso over existing shrinkage\npriors.",
        "split": "arXiv"
    },
    {
        "text": "Let $X$ be a connected normal scheme of finite type over $\\mathbf{Z}$, let\n$G$ be a connected reductive group over $\\mathbf{Q}$, and let\n$\\{\\rho_\\ell\\colon\\pi_1(X[1/\\ell])\\to G(\\mathbf{Q}_\\ell)\\}_\\ell$ be a\nFrobenius-compatible collection of continuous homomorphisms indexed by the\nprimes. Assume $\\mathrm{Img}(\\rho_\\ell)$ is Zariski-dense in\n$G_{\\mathbf{Q}_\\ell}$ for all $\\ell$ in a nonempty finite set $\\mathcal{R}$. We\nprove that, under certain hypotheses on $\\mathcal{R}$ (depending only on $G$),\n$\\mathrm{Img}(\\rho_\\ell)$ is Zariski-dense in $G_{\\mathbf{Q}_\\ell}$ for all\n$\\ell$ in a set of Dirichlet density $1$. As an application, we combine this\nresult with a version of Hilbert's irreducibility theorem and recent work of\nKlevdal--Patrikis to obtain new information about the \"canonical\" local systems\nattached to Shimura varieties not of Abelian type.",
        "split": "arXiv"
    },
    {
        "text": "A permutation code is a nonlinear code whose codewords are permutation of a\nset of symbols. We consider the use of permutation code in the deletion\nchannel, and consider the symbol-invariant error model, meaning that the values\nof the symbols that are not removed are not affected by the deletion. In 1992,\nLevenshtein gave a construction of perfect single-deletion-correcting\npermutation codes that attain the maximum code size. Furthermore, he showed in\nthe same paper that the set of all permutations of a given length can be\npartitioned into permutation codes so constructed. This construction relies on\nthe binary Varshamov-Tenengolts codes. In this paper we give an independent and\nmore direct proof of Levenshtein's result that does not depend on the\nVarshamov-Tenengolts code. Using the new approach, we devise efficient encoding\nand decoding algorithms that correct one deletion.",
        "split": "arXiv"
    },
    {
        "text": "Out-of-distribution (OOD) detection is essential for ensuring the robustness\nof machine learning models by identifying samples that deviate from the\ntraining distribution. While traditional OOD detection has primarily focused on\nsingle-modality inputs, such as images, recent advances in multimodal models\nhave demonstrated the potential of leveraging multiple modalities (e.g., video,\noptical flow, audio) to enhance detection performance. However, existing\nmethods often overlook intra-class variability within in-distribution (ID)\ndata, assuming that samples of the same class are perfectly cohesive and\nconsistent. This assumption can lead to performance degradation, especially\nwhen prediction discrepancies are uniformly amplified across all samples. To\naddress this issue, we propose Dynamic Prototype Updating (DPU), a novel\nplug-and-play framework for multimodal OOD detection that accounts for\nintra-class variations. Our method dynamically updates class center\nrepresentations for each class by measuring the variance of similar samples\nwithin each batch, enabling adaptive adjustments. This approach allows us to\namplify prediction discrepancies based on the updated class centers, thereby\nimproving the model's robustness and generalization across different\nmodalities. Extensive experiments on two tasks, five datasets, and nine base\nOOD algorithms demonstrate that DPU significantly improves OOD detection\nperformance, setting a new state-of-the-art in multimodal OOD detection, with\nimprovements of up to 80 percent in Far-OOD detection. To facilitate\naccessibility and reproducibility, our code is publicly available on GitHub.",
        "split": "arXiv"
    },
    {
        "text": "The computation of magnetizability tensors using gauge-including atomic\norbitals is discussed in the context of Cholesky decomposition for the\ntwo-electron repulsion integrals with a focus on the involved doubly\ndifferentiated integrals. Three schemes for their handling are suggested: the\nfirst exploits the DF aspect of Cholesky decomposition, the second uses\nexpressions obtained by differentiating the CD expression for the unperturbed\ntwo electron integrals, while the third addresses the issue that the first two\nschemes are not able to represent the doubly differentiated integrals with\narbitrary accuracy. This scheme uses a separate Cholesky decomposition for the\ncross terms in the doubly differentiated two-electron integrals. Test\ncalculations reveal that all three schemes are able to represent the integrals\nwith similar accuracy and yield indistinguishable results for the values of the\ncomputed magnetizability tensor elements. Thus, we recommend our first scheme\nwhich has the lowest computational cost for routine computations. The\napplicability of our CD schemes is further shown in large-scale Hartree-Fock\ncalculations of the magnetizability tensor of coronene (C24H12) with a doubly\npolarized triple-zeta basis consisting of 684 basis functions.",
        "split": "arXiv"
    },
    {
        "text": "The outcome of continuously measuring a quantum system is a string of data\nwhose intricate correlation properties reflect the underlying quantum dynamics.\nIn this paper we study the role of these correlation in reconstructing the\nprobabilities of finite sequences of outcomes, the so-called empirical\ndistributions. Our approach is cast in terms of generic quantum instruments,\nand therefore encompass all types of sequential and continuous quantum\nmeasurements. We also show how this specializes to important cases, such as\nquantum jumps. To quantify the precise role of correlations, we introduce a\nrelative-entropy based measure that quantifies the range of correlations in the\nstring, and the influence that these correlations have in reconstructing finite\nsequences.",
        "split": "arXiv"
    },
    {
        "text": "Nanophotonic device design aims to optimize photonic structures to meet\nspecific requirements across various applications. Inverse design has unlocked\nnon-intuitive, high-dimensional design spaces, enabling the discovery of\nhigh-performance devices beyond heuristic or analytic methods. The adjoint\nmethod, which calculates gradients for all variables using just two\nsimulations, enables efficient navigation of this complex space. However, many\ninverse-designed structures, while numerically plausible, are difficult to\nfabricate and sensitive to variations, limiting their practical use. The\ndiscrete nature with numerous local-optimal structures also pose significant\noptimization challenges, often causing gradient-based methods to converge on\nsuboptimal designs. In this work, we formulate inverse design as a\nfabrication-restricted, discrete, probabilistic optimization problem and\nintroduce BOSON-1, an end-to-end, variation-aware subspace optimization\nframework to address the challenges of manufacturability, robustness, and\noptimizability. To overcome optimization difficulty, we propose dense\ntarget-enhanced gradient flows to mitigate misleading local optima and\nintroduce a conditional subspace optimization strategy to create\nhigh-dimensional tunnels to escape local optima. Furthermore, we significantly\nreduce the runtime associated with optimizing across exponential variation\nsamples through an adaptive sampling-based robust optimization, ensuring both\nefficiency and variation robustness. On three representative photonic device\nbenchmarks, our proposed inverse design methodology BOSON^-1 delivers\nfabricable structures and achieves the best convergence and performance under\nrealistic variations, outperforming prior arts with 74.3% post-fabrication\nperformance. We open-source our codes at https://github.com/ScopeX-ASU/BOSON.",
        "split": "arXiv"
    },
    {
        "text": "Let $G=GL_n(K)$ be the general linear group defined over an infinite field\n$K$ of positive characteristic $p$ and let $\\Delta(\\lambda)$ be the Weyl module\nof $G$ which corresponds to a partition $\\lambda$. In this paper we classify\nall homomorphisms $\\Delta(\\lambda) \\to \\Delta(\\mu)$ when $\\lambda=(a,b,1^d)$\nand $\\mu=(a+d,b)$, $d>1$. In particular, we show that\n$Hom_G(\\Delta(\\lambda),\\Delta(\\mu))$ is nonzero if and only if $p=2$ and $a$ is\neven. In this case, we show that the dimension of the homomorphism space is\nequal to 1 and we provide an explicit generator whose description depends on\nbinary expansions of various integers. We also show that these generators in\ngeneral are not compositions of Carter-Payne homomorphisms.",
        "split": "arXiv"
    },
    {
        "text": "We characterize the power of constant-depth Boolean circuits in generating\nuniform symmetric distributions. Let $f\\colon\\{0,1\\}^m\\to\\{0,1\\}^n$ be a\nBoolean function where each output bit of $f$ depends only on $O(1)$ input\nbits. Assume the output distribution of $f$ on uniform input bits is close to a\nuniform distribution $D$ with a symmetric support. We show that $D$ is\nessentially one of the following six possibilities: (1) point distribution on\n$0^n$, (2) point distribution on $1^n$, (3) uniform over $\\{0^n,1^n\\}$, (4)\nuniform over strings with even Hamming weights, (5) uniform over strings with\nodd Hamming weights, and (6) uniform over all strings. This confirms a\nconjecture of Filmus, Leigh, Riazanov, and Sokolov (RANDOM 2023).",
        "split": "arXiv"
    },
    {
        "text": "Worldline quantum field theory (WQFT) has proven itself a powerful tool for\nclassical two-body scattering calculations in general relativity. In this paper\nwe develop a new worldline action involving bosonic oscillators, which enables\nthe use of the WQFT formalism to describe massive compact bodies to all orders\nin their spins. Inspired by bosonic string theory in the tensionless limit, we\naugment traditional trajectory variables with bosonic oscillators capturing the\nspin dependence. We show its equivalence to the covariant phase space\ndescription of a spinning body in curved space and clarify the role of the\nspin-supplementary condition in a Hamiltonian treatment. Higher-spin\nHamiltonians are classified to linear and quadratic order in curvature.\nFinally, perturbative computations at 1PM order for arbitrary powers and\norientations of spin and at 2PM up to quartic spin order are performed,\nrecovering results from the literature.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we present a telegraph diffusion model with variable exponents\nfor image despeckling. Moving beyond the traditional assumption of a constant\nexponent in the telegraph diffusion framework, we explore three distinct\nvariable exponents for edge detection. All of these depend on the gray level of\nthe image or its gradient. We rigorously prove the existence and uniqueness of\nweak solutions of our model in a functional setting and perform numerical\nexperiments to assess how well it can despeckle noisy gray-level images. We\nconsider both a range of natural images contaminated by varying degrees of\nartificial speckle noise and synthetic aperture radar (SAR) images. We finally\ncompare our method with the nonlocal speckle removal technique and find that\nour model outperforms the latter at speckle elimination and edge preservation.",
        "split": "arXiv"
    },
    {
        "text": "This study sought to better understand the causes of price disparity in\ncesarean sections, using newly released hospital data. Beginning January 1,\n2021, Centers for Medicare and Medicaid Services (CMS) requires hospitals\nfunctioning in the United States to publish online pricing information for\nitems and services these hospitals provide in a machine-readable format and a\nconsumer friendly shoppable format. Initial analyses of these data have shown\nthat the price for a given procedure can differ in a hospital and across\nhospitals. The cesarean section (C-section) is one of the most common inpatient\nprocedures performed across all hospitals in the United States as of 2018. This\npreliminary study found that for C-section procedures, pricing varied from as\nlittle as \\$162 to as high as \\$115,483 for a single procedure. Overall,\nindicators for quality and whether or not the hospital was a teaching hospital\nwere found to be significantly significant, while variables including median\nincome and the gini coefficient for wealth inequality were not shown to be\nstatistically significant.",
        "split": "arXiv"
    },
    {
        "text": "We study the problem of multi-agent multi-armed bandits with adversarial\ncorruption in a heterogeneous setting, where each agent accesses a subset of\narms. The adversary can corrupt the reward observations for all agents. Agents\nshare these corrupted rewards with each other, and the objective is to maximize\nthe cumulative total reward of all agents (and not be misled by the adversary).\nWe propose a multi-agent cooperative learning algorithm that is robust to\nadversarial corruptions. For this newly devised algorithm, we demonstrate that\nan adversary with an unknown corruption budget $C$ only incurs an additive\n$O((L / L_{\\min}) C)$ term to the standard regret of the model in\nnon-corruption settings, where $L$ is the total number of agents, and\n$L_{\\min}$ is the minimum number of agents with mutual access to an arm. As a\nside-product, our algorithm also improves the state-of-the-art regret bounds\nwhen reducing to both the single-agent and homogeneous multi-agent scenarios,\ntightening multiplicative $K$ (the number of arms) and $L$ (the number of\nagents) factors, respectively.",
        "split": "arXiv"
    },
    {
        "text": "This paper presents for the first time an approach to minimize direct\noperational costs (DOC) for all-electric aircraft during the climb phase,\nintroducing a time-varying cost index (CI). The CI is modeled as a dynamic\nparameter commanded by Air Traffic Control (ATC), allowing the aircraft to\nmaintain a constant airspeed throughout the climb, while respecting the air\ntraffic regulations. This paper also explores the implications of a\ntime-varying CI on the determination of optimal airspeed and climbing time for\nall-electric aircraft. Additionally, it provides the necessary equations to\ncalculate both the optimal climb airspeed and climb duration. The proposed\nmethodology has been validated through a simulated scenario that reflects\nactual operational procedures. As a result, optimal values for climb airspeed,\nclimbing time, and energy consumption have been established, paving the way for\nfuture applications of this methodology to advanced air mobility all-electric\nvehicles.",
        "split": "arXiv"
    },
    {
        "text": "A new model to follow the complete evolution of a drop in Leidenfrost state\nis presented in this work. The main ingredients of the phenomenon were\nconsidered, including: 1) the shape and weight of a sessile drop, according to\nits size, compared to the capillary length, using the Young-Laplace equation;\n2) the evaporation at the entire surface of the drop, due to the heat transfer\nacross the vapor film, to the proximitiy of a hot plate and to the diffusion in\nair; 3) the velocity, pressure and temperature fields at the vapor film,\nbetween the drop and the hot plate, which are recovered by means of a Hankel\ntransform method, being valid for any size of drops and any thickness of vapor\nfilms (below the vapor film stability threshold); 4) an estimation of the\nthermo-capillary Marangoni convection flow, without simulating numerically the\nflow within the drop. The aforementioned features were addressed and\ncalculated, in order to include their effect within a single non-linear ODE,\ndescribing the temporal evolution of the size of the drop, through the Bond\nnumber. Three dimensionless parameters, relating the thermophysical properties\nof the drop fluid and the surrounding air, control the development of the\nphenomenon. All those properties were calculated according to the ideal gas\napproximation and to widely used empirical correlations, without any fitting\nparameter. The model predictions were compared against experimental results,\nusing different organic and inorganic compounds, for which a good agreement has\nbeen found, when no bounce or rotation of the drop spontaneously occurs.",
        "split": "arXiv"
    },
    {
        "text": "Careful design of semiconductor manufacturing equipment is crucial for\nensuring the performance, yield, and reliability of semiconductor devices.\nDespite this, numerical optimization methods are seldom applied to optimize the\ndesign of such equipment due to the difficulty of obtaining accurate simulation\nmodels. In this paper, we address a practical and industrially relevant\nelectrostatic chuck (ESC) design optimization problem by proposing a novel\nmulti-fidelity surrogate modeling approach. The optimization aims to improve\nthe temperature uniformity of the wafer during the etching process by adjusting\nseven parameters associated with the coolant path and embossing. Our approach\ncombines low-fidelity (LF) and high-fidelity (HF) simulation data to\nefficiently predict spatial-field quantities, even with a limited number of\ndata points. We use proper orthogonal decomposition (POD) to project the\nspatially interpolated HF and LF field data onto a shared latent space,\nfollowed by the construction of a multi-fidelity kriging model to predict the\nlatent variables of the HF output field. In the ESC design problem, with\nhundreds or fewer data, our approach achieves a more than 10% reduction in\nprediction error compared to using kriging models with only HF or LF data.\nAdditionally, in the ESC optimization problem, our proposed method yields\nbetter solutions with improvements in all of the quantities of interest, while\nrequiring 20% less data generation cost compared to the HF surrogate modeling\napproach.",
        "split": "arXiv"
    },
    {
        "text": "The properties of a hypergraph explored through the spectrum of its unified\nmatrix was made by the authors in [26]. In this paper, we introduce three\ndifferent hypergraph matrices: unified Laplacian matrix, unified signless\nLaplacian matrix, and unified normalized Laplacian matrix, all defined using\nthe unified matrix. We show that these three matrices of a hypergraph are\nrespectively identical to the Laplacian matrix, signless Laplacian matrix, and\nnormalized Laplacian matrix of the associated graph. This allows us to use the\nspectra of these hypergraph matrices as a means to connect the structural\nproperties of the hypergraph with those of the associated graph. Additionally,\nwe introduce certain hypergraph structures and invariants during this process,\nand relate them to the eigenvalues of these three matrices.",
        "split": "arXiv"
    },
    {
        "text": "Recognizing and identifying human locomotion is a critical step to ensuring\nfluent control of wearable robots, such as transtibial prostheses. In\nparticular, classifying the intended locomotion mode and estimating the gait\nphase are key. In this work, a novel, interpretable, and computationally\nefficient algorithm is presented for simultaneously predicting locomotion mode\nand gait phase. Using able-bodied (AB) and transtibial prosthesis (PR) data,\nseven locomotion modes are tested including slow, medium, and fast level\nwalking (0.6, 0.8, and 1.0 m/s), ramp ascent/descent (5 degrees), and stair\nascent/descent (20 cm height). Overall classification accuracy was 99.1$\\%$ and\n99.3$\\%$ for the AB and PR conditions, respectively. The average gait phase\nerror across all data was less than 4$\\%$. Exploiting the structure of the\ndata, computational efficiency reached 2.91 $\\mu$s per time step. The time\ncomplexity of this algorithm scales as $O(N\\cdot M)$ with the number of\nlocomotion modes $M$ and samples per gait cycle $N$. This efficiency and high\naccuracy could accommodate a much larger set of locomotion modes ($\\sim$ 700 on\nOpen-Source Leg Prosthesis) to handle the wide range of activities pursued by\nindividuals during daily living.",
        "split": "arXiv"
    },
    {
        "text": "We investigate the formation history of intrahalo light (IHL) using the\nhigh-resolution (~1 kpc), large-scale (~Gpc) cosmological hydrodynamical\nsimulation, Horizon Run 5 (HR5). IHL particles are identified by carefully\nconsidering both their binding energies and positions with respect to the tidal\nradii of individual galaxies. By analyzing more than 1,200 galaxy groups and\nclusters with $\\geq 10^{13} M_{\\odot}$ and tracing their individual IHL\nparticles back in time, we classify the origin of each IHL particle at each\nepoch based on the status of the originating galaxy into three categories:\nbrightest halo galaxy (BHG) formation/merger, satellite galaxy stripping, and\npre-processing. Our study reveals that the IHL production through BHG\nformation/merger is the predominant production channel, contributing over 60\\%\nof the total IHL mass across all redshifts. The second most significant IHL\nproduction channel is pre-processing, providing more than 20\\% in the final HR5\nsnapshot. Stripping is negligible at $z>4$ but becomes gradually more important\nas halos mature at $z<4$. Finally, we verify that IHL production through the\ndisruption of dwarf galaxies and in-situ formation is negligible, contributing\nless than ~3\\% and ~0.5\\% to the total IHL production, respectively.",
        "split": "arXiv"
    },
    {
        "text": "We present several nonlinear wavefront sensing techniques for few-mode\nsensors, all of which are empirically calibrated and agnostic to the choice of\nwavefront sensor. The first class of techniques involves a straightforward\nextension of the linear phase retrieval scheme to higher order; the resulting\nTaylor polynomial can then be solved using the method of successive\napproximations, though we discuss alternate methods such as homotopy\ncontinuation. In the second class of techniques, a model of the WFS intensity\nresponse is created using radial basis function interpolation. We consider both\nforward models, which map phase to intensity and can be solved with nonlinear\nleast-squares methods such as the Levenberg-Marquardt algorithm, as well as\nbackwards models which directly map intensity to phase and do not require a\nsolver. We provide demonstrations for both types of techniques in simulation\nusing a quad-cell sensor and a photonic lantern wavefront sensor as examples.\nNext, we demonstrate how the nonlinearity of an arbitrary sensor may studied\nusing the method of numerical continuation, and apply this technique both to\nthe quad-cell sensor and a photonic lantern sensor. Finally, we briefly\nconsider the extension of nonlinear techniques to polychromatic sensors.",
        "split": "arXiv"
    },
    {
        "text": "We develop a comprehensive method to construct analytical continuum models\nfor moir\\'e systems directly from first-principle calculations without any\nparameter fitting. The core idea of this method is to interpret the terms in\nthe continuum model as a basis, allowing us to determine model parameters as\ncoefficients of this basis through Gram-Schmidt orthogonalization. We apply our\nmethod to twisted MoTe$_2$ and WSe$_2$ with twist angles ranging from\n2.13$^\\circ$ to 3.89$^\\circ$, producing continuum models that exhibit excellent\nagreement with both energy bands and wavefunctions obtained from\nfirst-principles calculations. We further propose a strategy to integrate out\nthe higher-energy degrees of freedom to reduce the number of the parameters in\nthe model without sacrificing the accuracy for low-energy bands. Our findings\nreveal that decreasing twist angles typically need an increasing number of\nharmonics in the moir\\'e potentials to accurately replicate first-principles\nresults. We provide parameter values for all derived continuum models,\nfacilitating further robust many-body calculations. Our approach is general and\napplicable to any commensurate moir\\'e materials accessible by first-principles\ncalculations.",
        "split": "arXiv"
    },
    {
        "text": "Quantum memories are a crucial precondition in many protocols for processing\nquantum information. A fundamental problem that illustrates this statement is\ngiven by the task of channel discrimination, in which an unknown channel drawn\nfrom a known random ensemble should be determined by applying it for a single\ntime. In this paper, we characterise the quality of channel discrimination\nprotocols when the quantum memory, quantified by the auxiliary dimension, is\nlimited. This is achieved by formulating the problem in terms of separable\nquantum states with additional affine constraints that all of their factors in\neach separable decomposition obey. We discuss the computation of upper and\nlower bounds to the solutions of such problems which allow for new insights\ninto the role of memory in channel discrimination. In addition to the\nsingle-copy scenario, this methodological insight allows to systematically\ncharacterise quantum and classical memories in adaptive channel discrimination\nprotocols. Especially, our methods enabled us to identify channel\ndiscrimination scenarios where classical or quantum memory is required, and to\nidentify the hierarchical and non-hierarchical relationships within adaptive\nchannel discrimination protocols.",
        "split": "arXiv"
    },
    {
        "text": "We study a class of supersymmetric models where the strong CP problem is\nsolved through spontaneous CP violation, carried out by a complex scalar field\nthat determines the Yukawa couplings of the theory. Assuming that one real\ncomponent of this field - the CPon - is light, we examine the conditions under\nwhich it provides a viable Dark Matter candidate. The CPon couplings to\nfermions are largely determined by the field-dependent Yukawa interactions, and\ninduce couplings to gauge bosons at 1-loop that are suppressed by a special sum\nrule. All couplings are suppressed by an undetermined UV scale, which needs to\nexceed $10^{12}$ GeV in order to satisfy constraints on excessive stellar\ncooling and rare Kaon decays. The CPon mass is limited from below by 5th force\nexperiments and from above by X-ray telescopes looking for CPon decays to\nphotons, leaving a range roughly between 10 meV and 1 MeV. Everywhere in the\nallowed parameter space the CPon can saturate the observed Dark Matter\nabundance through an appropriate balance of misalignment and freeze-in\nproduction from heavy SM fermions.",
        "split": "arXiv"
    },
    {
        "text": "We compute the spectra of anyon quasiparticles in all three super-selection\nsectors of the Kitaev model (i.e., visons, fermions and bosons), perturbed by a\nZeeman field away from its exactly solvable limit, to gain insights on the\ncompetition of its non-abelian spin-liquid with other nearby phases, such as\nthe mysterious intermediate state observed in the antiferromagnetic model. Both\nfor the ferro- and antiferro-magnetic models we find that the fermions and\nvisons become gapless at nearly identical critical Zeeman couplings. In the\nferromagnetic model this is consistent with a direct transition into a\npolarized state. In the anti-ferromagnetic model this implies that previous\ntheories of the intermediate phase viewed as a spin liquid with a different\nfermion Chern number are inadequate, as they presume that the vison gap does\nnot close. In the antiferromagnetic model we also find that a bosonic\nquasiparticle becomes gapless at nearly the same critical field as the fermions\nand visons. This boson carries the quantum numbers of an anti-ferromagnetic\norder parameter, suggesting that the intermediate phase has spontaneously\nbroken symmetry with this order.",
        "split": "arXiv"
    },
    {
        "text": "The fermionic many-body problem in the strong correlation regime is\nnotoriously difficult to tackle. In a previous work (Phys. Rev. B 101, 045109\n(2020)), we have proposed to extend the single-reference coupled-cluster (SRCC)\nmethod to the strong correlation regime using low-rank tensor decompositions\n(LRTD) to express the cluster operator, without truncating it with respect to\nthe number of excitations. For that purpose, we have proposed a new type of\nLRTD called ``superpositions of tree-tensor networks'' (STTN), which use the\nsame set of building blocs to define all the tensors involved in the CC\nequations, and combine different ``channels'', i.e. different types of pairing\namong excited particles and holes, in the decomposition of a given tensor.\nThose two principles are aimed at globally minimizing the total number of free\nparameters required to accurately represent the ground state. In this work, we\nshow that STTN can indeed be compact and accurate representations of strongly\ncorrelated ground states by using them to express the CC cluster operator\namplitudes and wave function coefficients of exact grounds states of small\ntwo-dimensional Hubbard clusters, at half-filling, up to three particle-hole\nexcitations. We show the compactness of STTN by using a number of free\nparameters smaller than the number of equations in the CCSD approximation, i.e.\nmuch smaller than the number of fitted tensor elements. We find that, for the\nsystems considered, the STTN are more accurate as the size of the system\nincreases and that combining different channels in the decompositions of the\nmost strongly correlated tensors is crucial to obtain good accuracy.",
        "split": "arXiv"
    },
    {
        "text": "Physical reasoning is an important skill needed for robotic agents when\noperating in the real world. However, solving such reasoning problems often\ninvolves hypothesizing and reflecting over complex multi-body interactions\nunder the effect of a multitude of physical forces and thus learning all such\ninteractions poses a significant hurdle for state-of-the-art machine learning\nframeworks, including large language models (LLMs). To study this problem, we\npropose a new physical reasoning task and a dataset, dubbed TraySim. Our task\ninvolves predicting the dynamics of several objects on a tray that is given an\nexternal impact -- the domino effect of the ensued object interactions and\ntheir dynamics thus offering a challenging yet controlled setup, with the goal\nof reasoning being to infer the stability of the objects after the impact. To\nsolve this complex physical reasoning task, we present LLMPhy, a zero-shot\nblack-box optimization framework that leverages the physics knowledge and\nprogram synthesis abilities of LLMs, and synergizes these abilities with the\nworld models built into modern physics engines. Specifically, LLMPhy uses an\nLLM to generate code to iteratively estimate the physical hyperparameters of\nthe system (friction, damping, layout, etc.) via an implicit\nanalysis-by-synthesis approach using a (non-differentiable) simulator in the\nloop and uses the inferred parameters to imagine the dynamics of the scene\ntowards solving the reasoning task. To show the effectiveness of LLMPhy, we\npresent experiments on our TraySim dataset to predict the steady-state poses of\nthe objects. Our results show that the combination of the LLM and the physics\nengine leads to state-of-the-art zero-shot physical reasoning performance,\nwhile demonstrating superior convergence against standard black-box\noptimization methods and better estimation of the physical parameters.",
        "split": "arXiv"
    },
    {
        "text": "Last passage percolation (LPP) is a model of a directed metric and a\nzero-temperature polymer where the main observable is a directed path evolving\nin a random environment accruing as energy the sum of the random weights along\nitself. When the environment has light tails and a fast decay of correlation,\nthe fluctuations of LPP are predicted to be explained by the\nKardar-Parisi-Zhang (KPZ) universality theory. However, the KPZ theory is not\nexpected to apply for many natural environments, particularly \"critical\" ones\nexhibiting a hierarchical structure often leading to logarithmic correlations.\n  In this article, we initiate a novel study of LPP in such hierarchical\nenvironments by investigating two particularly interesting examples. The first\nis an i.i.d. environment but with a power-law distribution with an inverse\nquadratic tail decay which is conjectured to be the critical point for the\nvalidity of the KPZ scaling relation. The second is the Branching Random Walk\nwhich is a hierarchical approximation of the two-dimensional Gaussian Free\nField. The second example may be viewed as a high-temperature directed version\nof Liouville Quantum Gravity, which is a model of random geometry driven by the\nexponential of a logarithmically-correlated field. Due to the underlying\nfractal structure, LPP in such environments is expected to exhibit logarithmic\ncorrection terms with novel critical exponents. While discussions about such\ncritical models appear in the physics literature, precise predictions about\nexponents seem to be missing. Developing a framework based on multi-scale\nanalysis, we obtain bounds on such exponents and prove almost optimal\nconcentration results in all dimensions for both models. As a byproduct of our\nanalysis we answer a long-standing question of Martin concerning necessary and\nsufficient conditions for the linear growth of the LPP energy in i.i.d.\nenvironments.",
        "split": "arXiv"
    },
    {
        "text": "Hybrid studies allow investigators to simultaneously study an intervention\neffectiveness outcome and an implementation research outcome. In particular,\ntype 2 hybrid studies support research that places equal importance on both\noutcomes rather than focusing on one and secondarily on the other (i.e., type 1\nand type 3 studies). Hybrid 2 studies introduce the statistical issue of\nmultiple testing, complicated by the fact that they are typically also cluster\nrandomized trials. Standard statistical methods do not apply in this scenario.\nHere, we describe the design methodologies available for validly powering\nhybrid type 2 studies and producing reliable sample size calculations in a\ncluster-randomized design with a focus on binary outcomes. Through a literature\nsearch, 18 publications were identified that included methods relevant to the\ndesign of hybrid 2 studies. Five methods were identified, two of which did not\naccount for clustering but are extended in this article to do so, namely the\ncombined outcomes approach and the single 1-degree of freedom combined test.\nProcedures for powering hybrid 2 studies using these five methods are described\nand illustrated using input parameters inspired by a study from the Community\nIntervention to Reduce CardiovascuLar Disease in Chicago (CIRCL-Chicago)\nImplementation Research Center. In this illustrative example, the intervention\neffectiveness outcome was controlled blood pressure, and the implementation\noutcome was reach. The conjunctive test resulted in higher power than the\npopular p-value adjustment methods, and the newly extended combined outcomes\nand single 1-DF test were found to be the most powerful among all of the tests.",
        "split": "arXiv"
    },
    {
        "text": "We characterise absolutely dilatable completely positive maps on the space of\nall bounded operators on a Hilbert space that are also bimodular over a given\nvon Neumann algebra as rotations by a suitable unitary on a larger Hilbert\nspace followed by slicing along the trace of an additional ancilla. We define\nthe local, quantum and approximately quantum types of absolutely dilatable\nmaps, according to the type of the admissible ancilla. We show that the local\nabsolutely dilatable maps admit an exact factorisation through an abelian\nancilla and show that they are limits in the point weak* topology of\nconjugations by unitaries in the commutant of the given von Neumann algebra. We\nshow that the Connes Embedding Problem is equivalent to deciding if all\nabsolutely dilatable maps are approximately quantum.",
        "split": "arXiv"
    },
    {
        "text": "Improving hydrocarbon production with hydraulic fracturing from\nunconventional reservoirs requires investigating transport phenomena at the\nsingle fracture level. In this study, we simulated geomechanical deformation,\nfluid flow, and reactive transport to understand the effect of hydraulic\nfracturing treatment on permeability evolution in shale rough-walled fractures.\nUsing concepts of fractional Brownian motion and surface roughness\ncharacterizations with laser profilometer, we first generated three\nrough-walled microfractures consistent with three laboratory experiments (i.e.,\nE4, E5 and E6). After that, the generated microfractures were subjected to a\nconfining pressure in accord with experimental conditions, and geomechanical\ndeformation was simulated. We used the OpenFOAM software package to simulate\nthe fluid flow and permeability. By comparing the simulated permeability values\nwith the experimentally measured ones we found relative errors equal to 28, 15\nand 200% respectively for the experiments E4, E5 and E6. After calibration,\nhowever, the relative error dropped below 4%. We next simulated the reactive\ntransport using the GeoChemFOAM solver and investigated permeability evolution\nin the deformed microfractures. We found that after 10 hrs of reactive\ntransport simulations, permeability increased by 47%, on average, in all cases\nstudied here.",
        "split": "arXiv"
    },
    {
        "text": "We demonstrate that Gini coefficients can be used as unified metrics to\nevaluate many-versus-many (all-to-all) similarity in vector spaces. Our\nanalysis of various image datasets shows that images with the highest Gini\ncoefficients tend to be the most similar to one another, while images with the\nlowest Gini coefficients are the least similar. We also show that this\nrelationship holds true for vectorized text embeddings from various corpuses,\nhighlighting the consistency of our method and its broad applicability across\ndifferent types of data. Additionally, we demonstrate that selecting machine\nlearning training samples that closely match the distribution of the testing\ndataset is far more important than ensuring data diversity. Selection of\nexemplary and iconic training samples with higher Gini coefficients leads to\nsignificantly better model performance compared to simply having a diverse\ntraining set with lower Gini coefficients. Thus, Gini coefficients can serve as\neffective criteria for selecting machine learning training samples, with our\nselection method outperforming random sampling methods in very sparse\ninformation settings.",
        "split": "arXiv"
    },
    {
        "text": "In the past few years, the improved sensitivity and cadence of wide-field\noptical surveys have enabled the discovery of several afterglows without\nassociated detected gamma-ray bursts (GRBs). We present the identification,\nobservations, and multiwavelength modeling of a recent such afterglow\n(AT2023lcr), and model three literature events (AT2020blt, AT2021any, and\nAT2021lfa) in a consistent fashion. For each event, we consider the following\npossibilities as to why a GRB was not observed: 1) the jet was off-axis; 2) the\njet had a low initial Lorentz factor; and 3) the afterglow was the result of an\non-axis classical GRB (on-axis jet with physical parameters typical of the GRB\npopulation), but the emission was undetected by gamma-ray satellites. We\nestimate all physical parameters using afterglowpy and Markov Chain Monte Carlo\nmethods from emcee. We find that AT2023lcr, AT2020blt, and AT2021any are\nconsistent with on-axis classical GRBs, and AT2021lfa is consistent with both\non-axis low Lorentz factor ($\\Gamma_0 \\approx 5 - 13$) and off-axis\n($\\theta_\\text{obs}=2\\theta_\\text{jet}$) high Lorentz factor ($\\Gamma_0 \\approx\n100$) jets.",
        "split": "arXiv"
    },
    {
        "text": "We present our lattice QCD result for the long-distance part of the hadronic\nvacuum polarization contribution, $(a_\\mu^{\\rm hvp})^{\\rm LD}$, to the muon\n$g-2$ in the time-momentum representation. This is the numerically dominant,\nand at the same time the most challenging part regarding statistical precision.\nOur calculation is based on ensembles with dynamical up, down and strange\nquarks, employing the O($a$)-improved Wilson fermion action with lattice\nspacings ranging from $0.035-0.099$ fm. In order to reduce statistical noise in\nthe long-distance part of the correlator to the per-mille level, we apply\nlow-mode averaging and combine it with an explicit spectral reconstruction. Our\nresult is $(a_\\mu^{\\rm hvp})^{\\rm LD} = 423.2(4.2)_{\\rm stat}(3.4)_{\\rm\nsyst}\\times 10^{-10}$ in isospin-symmetric QCD, where the pion decay constant\nis used to set the energy scale. When combined with our previous results for\nthe short- and intermediate-distance window observables and after including all\nsub-dominant contributions as well as isospin-breaking corrections, we obtain\nthe total leading-order hadronic vacuum polarization contribution as\n$a_\\mu^{\\rm hvp} = 724.9(5.0)_{\\rm stat}(4.9)_{\\rm syst}\\times 10^{-10}$. Our\nresult displays a tension of 3.9 standard deviations with the data-driven\nestimate published in the 2020 White Paper, but leads to a SM prediction for\nthe total muon anomalous magnetic moment that agrees with the current\nexperimental average.",
        "split": "arXiv"
    },
    {
        "text": "In a recent preprint, we constructed a sesquiharmonic Maass form\n$\\mathcal{G}$ of weight $\\frac{1}{2}$ and level $4N$ with $N$ odd and\nsquarefree. Extending seminal work by Duke, Imamo\\={g}lu, and T\\'{o}th,\n$\\mathcal{G}$ maps to Zagier's non-holomorphic Eisenstein series and a linear\ncombination of Pei and Wang's generalized Cohen--Eisenstein series under the\nBruinier--Funke operator $\\xi_{\\frac{1}{2}}$. In this paper, we realize\n$\\mathcal{G}$ as the output of a regularized Siegel theta lift of $1$ whenever\n$N=p$ is an odd prime building on more general work by Bruinier, Funke and\nImamo\\={g}lu. In addition, we supply the computation of the square-indexed\nFourier coefficients of $\\mathcal{G}$. This yields explicit identities between\nthe Fourier coefficients of $\\mathcal{G}$ and all quadratic traces of $1$.\nFurthermore, we evaluate the Millson theta lift of $1$ and consider spectral\ndeformations of $1$.",
        "split": "arXiv"
    },
    {
        "text": "To date there is little publicly available scientific data on Unidentified\nAerial Phenomena (UAP) whose properties and kinematics purportedly reside\noutside the performance envelope of known phenomena. To address this\ndeficiency, the Galileo Project is designing, building, and commissioning a\nmulti-modal ground-based observatory to continuously monitor the sky and\nconduct a rigorous long-term aerial census of all aerial phenomena, including\nnatural and human-made. One of the key instruments is an all-sky infrared\ncamera array using eight uncooled long-wave infrared FLIR Boson 640 cameras.\nTheir calibration includes a novel extrinsic calibration method using airplane\npositions from Automatic Dependent Surveillance-Broadcast (ADS-B) data. We\nestablish a first baseline for the system performance over five months of field\noperation, using a real-world dataset derived from ADS-B data, synthetic 3-D\ntrajectories, and a hand-labelled real-world dataset. We report acceptance\nrates (e.g. viewable airplanes that are recorded) and detection efficiencies\n(e.g. recorded airplanes which are successfully detected) for a variety of\nweather conditions, range and aircraft size. We reconstruct $\\sim$500,000\ntrajectories of aerial objects from this commissioning period. A toy outlier\nsearch focused on large sinuosity of the 2-D reconstructed trajectories flags\nabout 16% of trajectories as outliers. After manual review, 144 trajectories\nremain ambiguous: they are likely mundane objects but cannot be elucidated at\nthis stage of development without distance and kinematics estimation or other\nsensor modalities. Our observed count of ambiguous outliers combined with\nsystematic uncertainties yields an upper limit of 18,271 outliers count for the\nfive-month interval at a 95% confidence level. This likelihood-based method to\nevaluate significance is applicable to all of our future outlier searches.",
        "split": "arXiv"
    },
    {
        "text": "Modern software for propositional satisfiability problems gives a powerful\nautomated reasoning toolkit, capable of outputting not only a\nsatisfiable/unsatisfiable signal but also a justification of unsatisfiability\nin the form of resolution proof (or a more expressive proof), which is commonly\nused for verification purposes. Empirically, modern SAT solvers produce\nrelatively short proofs, however, there are no inherent guarantees that these\nproofs cannot be significantly reduced. This paper proposes a novel\nbranch-and-bound algorithm for finding the shortest resolution proofs; to this\nend, we introduce a layer list representation of proofs that groups clauses by\ntheir level of indirection. As we show, this representation breaks all\npermutational symmetries, thereby improving upon the state-of-the-art\nsymmetry-breaking and informing the design of a novel workflow for proof\nminimization. In addition to that, we design pruning procedures that reason on\nproof length lower bound, clause subsumption, and dominance. Our experiments\nsuggest that the proofs from state-of-the-art solvers could be shortened by\n30-60% on the instances from SAT Competition 2002 and by 25-50% on small\nsynthetic formulas. When treated as an algorithm for finding the shortest\nproof, our approach solves twice as many instances as the previous work based\non SAT solving and reduces the time to optimality by orders of magnitude for\nthe instances solved by both approaches.",
        "split": "arXiv"
    },
    {
        "text": "Tensor parallelism provides an effective way to increase server large\nlanguage model (LLM) inference efficiency despite adding an additional\ncommunication cost. However, as server LLMs continue to scale in size, they\nwill need to be distributed across more devices, magnifying the communication\ncost. One way to approach this problem is with quantization, but current\nmethods for LLMs tend to avoid quantizing the features that tensor parallelism\nneeds to communicate. Taking advantage of consistent outliers in communicated\nfeatures, we introduce a quantization method that reduces communicated values\non average from 16 bits to 4.2 bits while preserving nearly all of the original\nperformance. For instance, our method maintains around 98.0% and 99.5% of Gemma\n2 27B's and Llama 2 13B's original performance, respectively, averaged across\nall tasks we evaluated on.",
        "split": "arXiv"
    },
    {
        "text": "An $r$-graph is called $t$-cancellative if for arbitrary $t+2$ distinct edges\n$A_1,\\ldots,A_t,B,C$, it holds that $(\\cup_{i=1}^t A_i)\\cup B\\neq (\\cup_{i=1}^t\nA_i)\\cup C$; it is called $t$-union-free if for arbitrary two distinct subsets\n$\\mathcal{A},\\mathcal{B}$, each consisting of at most $t$ edges, it holds that\n$\\cup_{A\\in\\mathcal{A}} A\\neq \\cup_{B\\in\\mathcal{B}} B$. Let $C_t(n,r)$ and\n$U_t(n,r)$ denote the maximum number of edges that can be contained in an\n$n$-vertex $t$-cancellative and $t$-union-free $r$-graph, respectively. The\nstudy of $C_t(n,r)$ and $U_t(n,r)$ has a long history, dating back to the\nclassic works of Erd\\H{o}s and Katona, and Erd\\H{o}s and Moser in the 1970s. In\n2020, Shangguan and Tamo showed that $C_{2(t-1)}(n,tk)=\\Theta(n^k)$ and\n$U_{t+1}(n,tk)=\\Theta(n^k)$ for all $t\\ge 2$ and $k\\ge 2$. In this paper, we\ndetermine the asymptotics of these two functions up to a lower order term, by\nshowing that for all $t\\ge 2$ and $k\\ge 2$,\n  \\begin{align*}\n  \\text{$\\lim_{n\\rightarrow\\infty}\\frac{C_{2(t-1)}(n,tk)}{n^k}=\\lim_{n\\rightarrow\\infty}\\frac{U_{t+1}(n,tk)}{n^k}=\\frac{1}{k!}\\cdot\n\\frac{1}{\\binom{tk-1}{k-1}}$.}\n  \\end{align*}\n  Previously, it was only known by a result of F\\\"uredi in 2012 that\n$\\lim_{n\\rightarrow\\infty}\\frac{C_{2}(n,4)}{n^2}=\\frac{1}{6}$.\n  To prove the lower bounds of the limits, we utilize a powerful framework\ndeveloped recently by Delcourt and Postle, and independently by Glock, Joos,\nKim, K\\\"uhn, and Lichev, which shows the existence of near-optimal hypergraph\npackings avoiding certain small configurations, and to prove the upper bounds,\nwe apply a novel counting argument that connects $C_{2(t-1)}(n,tk)$ to a\nclassic result of Kleitman and Frankl on a special case of the famous Erd\\H{o}s\nMatching Conjecture.",
        "split": "arXiv"
    },
    {
        "text": "How does social network structure amplify or stifle behavior diffusion?\nExisting theory suggests that when social reinforcement makes the adoption of\nbehavior more likely, it should spread more -- both farther and faster -- on\nclustered networks with redundant ties. Conversely, if adoption does not\nbenefit from social reinforcement, then it should spread more on random\nnetworks without such redundancies. We develop a novel model of behavior\ndiffusion with tunable probabilistic adoption and social reinforcement\nparameters to systematically evaluate the conditions under which clustered\nnetworks better spread a behavior compared to random networks. Using both\nsimulations and analytical techniques we find precise boundaries in the\nparameter space where either network type outperforms the other or performs\nequally. We find that in most cases, random networks spread a behavior equally\nas far or farther compared to clustered networks despite strong social\nreinforcement. While there are regions in which clustered networks better\ndiffuse contagions with social reinforcement, this only holds when the\ndiffusion process approaches that of a deterministic threshold model and does\nnot hold for all socially reinforced behaviors more generally. At best,\nclustered networks only outperform random networks by at least a five percent\nmargin in 18\\% of the parameter space, and when social reinforcement is large\nrelative to the baseline probability of adoption.",
        "split": "arXiv"
    },
    {
        "text": "The scarcity of comprehensive datasets in the traffic light detection and\nrecognition domain and the poor performance of state-of-the-art models under\nhostile weather conditions present significant challenges. To address these\nissues, this paper proposes a novel approach by merging two widely used\ndatasets, LISA and S2TLD. The merged dataset is further processed to tackle\nclass imbalance, a common problem in this domain. This merged dataset becomes\nour source domain. Synthetic rain and fog are added to the dataset to create\nour target domain. We employ Fourier Domain Adaptation (FDA) to create a final\ndataset with a minimized domain gap between the two datasets, helping the model\ntrained on this final dataset adapt to rainy and foggy weather conditions.\nAdditionally, we explore Semi-Supervised Learning (SSL) techniques to leverage\nthe available data more effectively. Experimental results demonstrate that\nmodels trained on FDA-augmented images outperform those trained without FDA\nacross confidence-dependent and independent metrics, like mAP50, mAP50-95,\nPrecision, and Recall. The best-performing model, YOLOv8, achieved a Precision\nincrease of 5.1860%, Recall increase of 14.8009%, mAP50 increase of 9.5074%,\nand mAP50-95 increase of 19.5035%. On average, percentage increases of 7.6892%\nin Precision, 19.9069% in Recall, 15.8506% in mAP50, and 23.8099% in mAP50-95\nwere observed across all models, highlighting the effectiveness of FDA in\nmitigating the impact of adverse weather conditions on model performance. These\nimprovements pave the way for real-world applications where reliable\nperformance in challenging environmental conditions is critical.",
        "split": "arXiv"
    },
    {
        "text": "Podcasts provide highly diverse content to a massive listener base through a\nunique on-demand modality. However, limited data has prevented large-scale\ncomputational analysis of the podcast ecosystem. To fill this gap, we introduce\na massive dataset of over 1.1M podcast transcripts that is largely\ncomprehensive of all English language podcasts available through public RSS\nfeeds from May and June of 2020. This data is not limited to text, but rather\nincludes audio features and speaker turns for a subset of 370K episodes, and\nspeaker role inferences and other metadata for all 1.1M episodes. Using this\ndata, we also conduct a foundational investigation into the content, structure,\nand responsiveness of this ecosystem. Together, our data and analyses open the\ndoor to continued computational research of this popular and impactful medium.",
        "split": "arXiv"
    },
    {
        "text": "Machine learning models are often trained on sensitive data (e.g., medical\nrecords and race/gender) that is distributed across different \"silos\" (e.g.,\nhospitals). These federated learning models may then be used to make\nconsequential decisions, such as allocating healthcare resources. Two key\nchallenges emerge in this setting: (i) maintaining the privacy of each person's\ndata, even if other silos or an adversary with access to the central server\ntries to infer this data; (ii) ensuring that decisions are fair to different\ndemographic groups (e.g., race/gender). In this paper, we develop a novel\nalgorithm for private and fair federated learning (FL). Our algorithm satisfies\ninter-silo record-level differential privacy (ISRL-DP), a strong notion of\nprivate FL requiring that silo i's sent messages satisfy record-level\ndifferential privacy for all i. Our framework can be used to promote different\nfairness notions, including demographic parity and equalized odds. We prove\nthat our algorithm converges under mild smoothness assumptions on the loss\nfunction, whereas prior work required strong convexity for convergence. As a\nbyproduct of our analysis, we obtain the first convergence guarantee for\nISRL-DP nonconvex-strongly concave min-max FL. Experiments demonstrate the\nstate-of-the-art fairness-accuracy tradeoffs of our algorithm across different\nprivacy levels.",
        "split": "arXiv"
    },
    {
        "text": "A unified definition for the rotation angle and rotation angular speed of\ngeneral beams, including those with orbital angular momentum (OAM), has been\nlacking until now. The rotation of a general beam is characterized by observing\nthe rotational behavior of the directions of the extreme spot sizes during\npropagation. We introduce the beam quality $M^2(\\psi)$ factor to characterize\nthe unique beam quality of a general beam across all directions, not limited to\nthe $x$- or $y$-axes. Besides that, we present the beam center\n$s_{\\psi}(\\psi,z)$, spot size $w_{\\psi}(\\psi,z)$, waist position, waist radius,\nand divergence angle along the direction that forms an angle $\\psi$ with the\n$x$-axis in the plane perpendicular to the $z$-axis for the general beam.\nFurthermore, this paper presents rapid calculation formulas for these\nparameters, utilizing the mode expansion method (MEM). Subsequently, we prove\nthat only two extreme spot sizes exist in a given detection plane and the angle\nbetween the maximum and minimum spot angles is consistently $90^{\\circ}$ during\nthe propagation. We also prove the spot rotation angles converge as $z$\napproaches either positive or negative infinity. We first show the extreme spot\nsizes, spot rotation angle, and angular speed for the vortex beam. Our formulas\nefficiently differentiate between vortex OAM beams and asymmetry OAM beams.",
        "split": "arXiv"
    },
    {
        "text": "Arch filament systems (AFSs) are chromospheric and coronal manifestations of\nemerging magnetic flux. Using high spatial resolution observations taken at a\nhigh cadence by the Extreme Ultraviolet Imager (EUI) on board Solar Orbiter, we\nidentified small-scale elongated brightenings within the AFSs. These\nbrightenings appear as bidirectional flows along the threads of AFSs. For our\nstudy, we investigated the coordinated observations of the AFSs acquired by the\nEUI and the Atmospheric Imaging Assembly (AIA) on board SDO on 2022 March 4 and\n17. We analyzed 15 bidirectional propagating brightenings from EUI 174 {\\AA}\nimages. These brightenings reached propagating speeds of 100-150 km/s. The\nevent observed on March 17 exhibits blob-like structures, which may be\nsignatures of plasmoids and due to magnetic reconnection. In this case, we also\nobserved counterparts in the running difference slit-jaw images in the 1400\n{\\AA} passbands taken by the Interface Region Imaging Spectrograph (IRIS). Most\nevents show co-temporal intensity variations in all AIA EUV passbands.\nTogether, this implies that these brightenings in the AFSs are dominated by\nemission from cool plasma with temperatures well below 1 MK. The magnetograms\ntaken by the Polarimetric and Helioseismic Imager (PHI) on board Solar Orbiter\nshow signatures of flux emergence beneath the brightenings. This suggests that\nthe events in the AFSs are triggered by magnetic reconnection that may occur\nbetween the newly emerging magnetic flux and the preexisting magnetic field\nstructures in the middle of the AFSs. This would also give a natural\nexplanation for the bidirectional propagation of the brightenings near the apex\nof the AFSs. The interaction of the preexisting field and the emerging flux may\nbe important for mass and energy transfer within the AFSs.",
        "split": "arXiv"
    },
    {
        "text": "Humans excel at discovering regular structures from limited samples and\napplying inferred rules to novel settings. We investigate whether modern\ngenerative models can similarly learn underlying rules from finite samples and\nperform reasoning through conditional sampling. Inspired by Raven's Progressive\nMatrices task, we designed GenRAVEN dataset, where each sample consists of\nthree rows, and one of 40 relational rules governing the object position,\nnumber, or attributes applies to all rows. We trained generative models to\nlearn the data distribution, where samples are encoded as integer arrays to\nfocus on rule learning. We compared two generative model families: diffusion\n(EDM, DiT, SiT) and autoregressive models (GPT2, Mamba). We evaluated their\nability to generate structurally consistent samples and perform panel\ncompletion via unconditional and conditional sampling. We found diffusion\nmodels excel at unconditional generation, producing more novel and consistent\nsamples from scratch and memorizing less, but performing less well in panel\ncompletion, even with advanced conditional sampling methods. Conversely,\nautoregressive models excel at completing missing panels in a rule-consistent\nmanner but generate less consistent samples unconditionally. We observe diverse\ndata scaling behaviors: for both model families, rule learning emerges at a\ncertain dataset size - around 1000s examples per rule. With more training data,\ndiffusion models improve both their unconditional and conditional generation\ncapabilities. However, for autoregressive models, while panel completion\nimproves with more training data, unconditional generation consistency\ndeclines. Our findings highlight complementary capabilities and limitations of\ndiffusion and autoregressive models in rule learning and reasoning tasks,\nsuggesting avenues for further research into their mechanisms and potential for\nhuman-like reasoning.",
        "split": "arXiv"
    },
    {
        "text": "All but the most massive main-sequence stars are expected to have a rarefied\nand hot (million-Kelvin) corona like the Sun. How such a hot corona is formed\nand supported has not been completely understood yet, even in the case of the\nSun. Recently, Barbieri et al. (A&A 2024, J. Plasma Phys. 2024) introduced a\nnew model of a confined plasma atmosphere and applied it to the solar case,\nshowing that rapid, intense, intermittent and short-lived heating events in the\nhigh chromosphere can drive the coronal plasma into a stationary state with\ntemperature and density profiles similar to those observed in the solar\natmosphere. In this paper we apply the model to main-sequence stars, showing\nthat it predicts the presence of a solar-like hot and rarefied corona for all\nsuch stars, regardless of their mass. However, the model is not applicable as\nsuch to the most massive main-sequence stars, because the latter lack the\nconvective layer generating the magnetic field loop structures supporting a\nstationary corona, whose existence is assumed by the model. We also discuss the\nrole of stellar mass in determining the shape of the temperature and density\nprofiles.",
        "split": "arXiv"
    },
    {
        "text": "In an accelerator, the nonlinear behavior near a horizontal resonance line\n($n\\nu_x$) usually involves the appearance of stable fixed points (SFPs) in the\nhorizontal phase space, also referred to as transverse resonance island\n``buckets\" (TRIBs). Specific conditions are required for TRIBs formation. At\nthe Cornell Electron Storage Ring, a new method is developed to improve the\ndynamic and momentum apertures in a 6-GeV lattice as well as to preserve the\nconditions for TRIBs formation. This method reduces the dimension of variables\nfrom 76 sextupoles to 8 group variables and then utilizes the robust conjugate\ndirection search algorithm in optimization. Created with a few harmonic\nsextupoles or octupoles, several knobs that can either rotate the TRIBs in\nphase space or adjust the actions of SFPs are discussed and demonstrated by\nboth tracking simulations and experimental results. In addition, a new scheme\nto drive all particles into one single island is described. Possible\napplications using TRIBs in accelerators are also discussed.",
        "split": "arXiv"
    },
    {
        "text": "When unsure about an answer, humans often respond with more words than\nnecessary, hoping that part of the response will be correct. We observe a\nsimilar behavior in large language models (LLMs), which we term \"Verbosity\nCompensation\" (VC). VC is harmful because it confuses the user understanding,\nleading to low efficiency, and influences the LLM services by increasing the\nlatency and cost of generating useless tokens. In this paper, we present the\nfirst work that defines and analyzes Verbosity Compensation, explores its\ncauses, and proposes a simple mitigating approach. We define Verbosity\nCompensation as the behavior of generating responses that can be compressed\nwithout information loss when prompted to write concisely. Our experiments,\nconducted on five datasets of knowledge and reasoning-based QA tasks with 14\nnewly developed LLMs, reveal three conclusions. 1) We reveal a pervasive\npresence of verbosity compensation across all models and all datasets. Notably,\nGPT-4 exhibits a VC frequency of 50.40%. 2) We reveal the large performance gap\nbetween verbose and concise responses, with a notable difference of 27.61% on\nthe Qasper dataset. We also demonstrate that this difference does not naturally\ndiminish as LLM capability increases. Both 1) and 2) highlight the urgent need\nto mitigate the frequency of VC behavior and disentangle verbosity with\nveracity. We propose a simple yet effective cascade algorithm that replaces the\nverbose responses with the other model-generated responses. The results show\nthat our approach effectively alleviates the VC of the Mistral model from\n63.81% to 16.16% on the Qasper dataset. 3) We also find that verbose responses\nexhibit higher uncertainty across all five datasets, suggesting a strong\nconnection between verbosity and model uncertainty. Our dataset and code are\navailable at https://github.com/psunlpgroup/VerbosityLLM.",
        "split": "arXiv"
    },
    {
        "text": "Significant advances have been made in natural language processing in recent\nyears. However, our current deep learning approach to language modeling\nrequires substantial resources in terms of data and computation. One of the\nside effects of this data-hungry paradigm is the current schism between\nlanguages, separating those considered high-resource, where most of the\ndevelopment happens and resources are available, and the low-resource ones,\nwhich struggle to attain the same level of performance and autonomy. This study\naims to introduce a new set of resources to stimulate the future development of\nneural text generation in Portuguese. In this work, we document the development\nof GigaVerbo, a concatenation of deduplicated Portuguese text corpora amounting\nto 200 billion tokens. Via this corpus, we trained a series of\ndecoder-transformers named Tucano. Our models perform equal or superior to\nother Portuguese and multilingual language models of similar size in several\nPortuguese benchmarks. The evaluation of our models also reveals that model\nperformance on many currently available benchmarks used by the Portuguese NLP\ncommunity has little to no correlation with the scaling of token ingestion\nduring training, highlighting the limitations of such evaluations when it comes\nto the assessment of Portuguese generative language models. All derivatives of\nour study are openly released on GitHub and Hugging Face. See\nhttps://nkluge-correa.github.io/Tucano/",
        "split": "arXiv"
    },
    {
        "text": "We examine the problem of assigning teachers to public schools over time when\nteachers have tenured positions and can work simultaneously in multiple\nschools. To do this, we investigate a dynamic many-to-many school choice\nproblem where public schools have priorities over teachers and teachers hold\nsubstitutable preferences over subsets of schools. We introduce a new concept\nof dynamic stability that recognizes the tenured positions of teachers and we\nprove that a dynamically stable matching always exists. We propose the\nTenured-Respecting Deferred Acceptance $(TRDA)$ mechanism, which produces a\ndynamically stable matching that is constrained-efficient within the class of\ndynamically stable matchings and minimizes unjustified claims. To improve\nefficiency beyond this class, we also propose the Tenured-Respecting\nEfficiency-Adjusted Deferred Acceptance $(TREADA)$ mechanism, an adaptation of\nthe Efficiency-Adjusted Deferred Acceptance mechanism to our dynamic context.\nWe demonstrate that the outcome of the $TREADA$ mechanism Pareto-dominates any\ndynamically stable matching and achieves efficiency when all teachers consent.\nAdditionally, we examine the issue of manipulability, showing that although the\n$TRDA$ and $TREADA$ mechanisms can be manipulated, they remain non-obviously\ndynamically manipulable under specific conditions on schools' priorities.",
        "split": "arXiv"
    },
    {
        "text": "Landmark-based navigation (e.g. go to the wooden desk) and relative\npositional navigation (e.g. move 5 meters forward) are distinct navigation\nchallenges solved very differently in existing robotics navigation methodology.\nWe present a new dataset, OC-VLN, in order to distinctly evaluate grounding\nobject-centric natural language navigation instructions in a method for\nperforming landmark-based navigation. We also propose Natural Language grounded\nSLAM (NL-SLAM), a method to ground natural language instruction to robot\nobservations and poses. We actively perform NL-SLAM in order to follow\nobject-centric natural language navigation instructions. Our methods leverage\npre-trained vision and language foundation models and require no task-specific\ntraining. We construct two strong baselines from state-of-the-art methods on\nrelated tasks, Object Goal Navigation and Vision Language Navigation, and we\nshow that our approach, NL-SLAM, outperforms these baselines across all our\nmetrics of success on OC-VLN. Finally, we successfully demonstrate the\neffectiveness of NL-SLAM for performing navigation instruction following in the\nreal world on a Boston Dynamics Spot robot.",
        "split": "arXiv"
    },
    {
        "text": "With the increase in the number of parameters in large language models, the\nprocess of pre-training and fine-tuning increasingly demands larger volumes of\nGPU memory. A significant portion of this memory is typically consumed by the\noptimizer state. To overcome this challenge, recent approaches such as low-rank\nadaptation (LoRA (Hu et al., 2021)), low-rank gradient projection (GaLore (Zhao\net al., 2024)), and blockwise optimization (BAdam (Luo et al., 2024)) have been\nproposed. However, in all these algorithms, the $\\textit{effective rank of the\nweight updates remains low-rank}$, which can lead to a substantial loss of\ninformation from the gradient. This loss can be critically important,\nespecially during the pre-training stage. In this paper, we introduce\n$\\texttt{FRUGAL}$ ($\\textbf{F}$ull-$\\textbf{R}$ank $\\textbf{U}$pdates with\n$\\textbf{G}$r$\\textbf{A}$dient sp$\\textbf{L}$itting), a new memory-efficient\noptimization framework. $\\texttt{FRUGAL}$ leverages gradient splitting to\nperform low-dimensional updates using advanced algorithms (such as Adam), while\nupdates along the remaining directions are executed via state-free methods like\nSGD or signSGD (Bernstein et al., 2018). Our framework can be integrated with\nvarious low-rank update selection techniques, including GaLore and BAdam. We\nprovide theoretical convergence guarantees for our framework when using SGDM\nfor low-dimensional updates and SGD for state-free updates. Additionally, our\nmethod consistently outperforms concurrent approaches across various fixed\nmemory budgets, achieving state-of-the-art results in pre-training and\nfine-tuning tasks while balancing memory efficiency and performance metrics.",
        "split": "arXiv"
    },
    {
        "text": "This study introduces a novel self-supervised learning approach for\nvolumetric segmentation of defect indications captured by phased array\nultrasonic testing data from Carbon Fiber Reinforced Polymers (CFRPs). By\nemploying this self-supervised method, defect segmentation is achieved\nautomatically without the need for labelled training data or examples of\ndefects. The approach has been tested using artificially induced defects,\nincluding back-drilled holes and Polytetrafluoroethylene (PTFE) inserts, to\nmimic different defect responses. Additionally, it has been evaluated on\nstepped geometries with varying thickness, demonstrating impressive\ngeneralization across various test scenarios. Minimal preprocessing\nrequirements are needed, with no removal of geometric features or\nTime-Compensated Gain (TCG) necessary for applying the methodology. The model's\nperformance was evaluated for defect detection, in-plane and through-thickness\nlocalisation, as well as defect sizing. All defects were consistently detected\nwith thresholding and different processing steps able to remove false positive\nindications for a 100% detection accuracy. Defect sizing aligns with the\nindustrial standard 6 dB amplitude drop method, with a Mean Absolute Error\n(MAE) of 1.41 mm. In-plane and through-thickness localisation yielded\ncomparable results, with MAEs of 0.37 and 0.26 mm, respectively. Visualisations\nare provided to illustrate how this approach can be utilised to generate\ndigital twins of components.",
        "split": "arXiv"
    },
    {
        "text": "We consider a population spreading across a finite number of sites.\nIndividuals can move from one site to the other according to a network\n(oriented links between the sites) that vary periodically over time. On each\nsite, the population experiences a growth rate which is also periodically time\nvarying. Recently, this kind of models have been extensively studied, using\nvarious technical tools to derive precise necessary and sufficient conditions\non the parameters of the system (ie the local growth rate on each site, the\ntime period and the strength of migration between the sites) for the population\nto grow. In the present paper, we take a completely different approach: using\nelementary comparison results between linear systems, we give sufficient\ncondition for the growth of the population This condition is easy to check and\ncan be applied in a broad class of examples. In particular, in the case when\nall sites are sinks (ie, in the absence of migration, the population become\nextinct in each site), we prove that when our condition of growth if satisfied,\nthe population grows when the time period is large and for values of the\nmigration strength that are exponentially small with respect to the time\nperiod, which answers positively to a conjecture stated by Katriel.",
        "split": "arXiv"
    },
    {
        "text": "Federated learning (FL) has become one of the key methods for\nprivacy-preserving collaborative learning, as it enables the transfer of models\nwithout requiring local data exchange. Within the FL framework, an aggregation\nalgorithm is recognized as one of the most crucial components for ensuring the\nefficacy and security of the system. Existing average aggregation algorithms\ntypically assume that all client-trained data holds equal value or that weights\nare based solely on the quantity of data contributed by each client. In\ncontrast, alternative approaches involve training the model locally after\naggregation to enhance adaptability. However, these approaches fundamentally\nignore the inherent heterogeneity between different clients' data and the\ncomplexity of variations in data at the aggregation stage, which may lead to a\nsuboptimal global model.\n  To address these issues, this study proposes a novel dual-criterion weighted\naggregation algorithm involving the quantity and quality of data from the\nclient node. Specifically, we quantify the data used for training and perform\nmultiple rounds of local model inference accuracy evaluation on a specialized\ndataset to assess the data quality of each client. These two factors are\nutilized as weights within the aggregation process, applied through a\ndynamically weighted summation of these two factors. This approach allows the\nalgorithm to adaptively adjust the weights, ensuring that every client can\ncontribute to the global model, regardless of their data's size or initial\nquality. Our experiments show that the proposed algorithm outperforms several\nexisting state-of-the-art aggregation approaches on both a general-purpose\nopen-source dataset, CIFAR-10, and a dataset specific to visual obstacle\navoidance.",
        "split": "arXiv"
    },
    {
        "text": "We develop a novel key routing algorithm for quantum key distribution (QKD)\nnetworks that utilizes a distribution of keys between remote, i.e., not\ndirectly connected by a QKD link, nodes through multiple non-overlapping paths.\nThis approach enchases the security of QKD network by minimizing potential\nvulnerabilities associated with individual trusted nodes. The algorithm ensures\na balanced allocation of the workload across the QKD network links, while\naiming for the target key generation rate between directly connected and remote\nnodes. We present the results of testing the algorithm on two QKD network\nmodels consisting of 6 and 10 nodes. The testing demonstrates the ability of\nthe algorithm to distribute secure keys among the nodes of the network in an\nall-to-all manner, ensuring that the information-theoretic security of the keys\nbetween remote nodes is maintained even when one of the trusted nodes is\ncompromised. These results highlight the potential of the algorithm to improve\nthe performance of QKD networks.",
        "split": "arXiv"
    },
    {
        "text": "Given a digraph $H$, we say a digraph $H^\\prime$ is an $H$-subdivision if\n$H^\\prime$ is obtained from $H$ by replacing one or more arcs from $H$ with\ninternally vertex-disjoint path(s). In this paper, we prove that for any\ndigraph $H$ with $h$ arcs and no isolated vertices, there is a constant $C_0>0$\nsuch that the following hold.\n  $(1)$ For any integer $C\\geq C_0$ and every digraph $D$ on $n\\geq Ch$\nvertices, if the minimum in- and out-degree of $D$ is at least $n/2$, then it\ncontains an $H$-subdivision covering all vertices of $D$.\n  $(2)$ For any integer partition $n=n_1+\\cdots+n_m$ such that the sum of the\n$n_i$ less than $\\alpha n$ is no more than $\\beta n$, if a digraph $D$ has the\norder $n\\geq Cm$ and the minimum in- and out-degree at least\n$\\sum_{i=1}^m\\lceil\\frac{n_i}{2}\\rceil$, then it contains $m$ disjoint\n$H$-subdivisions, where the order of these $H$-subdivisions is $n_1, \\ldots,\nn_m$, respectively.\n  The result of $(1)$ settles the conjecture raised by Pavez-Sign\\'{e}\n\\cite{Pavez} in a stronger form, and ameliorate the result of Lee \\cite{Lee}.\nAlso, the conclusion of $(2)$ partly answers of the conjecture of Lee\n\\cite{Lee1} and generalizes the recent work of Lee \\cite{Lee1}.",
        "split": "arXiv"
    },
    {
        "text": "Let $F$ be any field containing the finite field of order $q$. A\n$q$-polynomial $L$ over $F$ is an element of the polynomial ring $F[x]$ with\nthe property that all powers of $x$ that appear in $L$ with nonzero coefficient\nhave exponent a power of $q$. It is well known that given any ordinary\npolynomial $f$ in $F[x]$, there exists a $q$-polynomial that is divisible by\n$f$. We study the smallest degree of such a $q$-polynomial. This is equivalent\nto studying the $\\mathbb{F}_q$-span of the roots of $f$ in a splitting field.\nWe relate this quantity to the representation theory of the Galois group of\n$f$. As an application we give a simultaneous construction of the binary Golay\ncode of length 24, and the Steiner system on 24 points.",
        "split": "arXiv"
    },
    {
        "text": "An elastic-degenerate (ED) string $T$ is a sequence of $n$ sets\n$T[1],\\ldots,T[n]$ containing $m$ strings in total whose cumulative length is\n$N$. We call $n$, $m$, and $N$ the length, the cardinality and the size of $T$,\nrespectively. The language of $T$ is defined as $L(T)=\\{S_1 \\cdots S_n\\,:\\,S_i\n\\in T[i]$ for all $i\\in[1,n]\\}$. ED strings have been introduced to represent a\nset of closely-related DNA sequences, also known as a pangenome. The basic\nquestion we investigate here is: Given two ED strings, how fast can we check\nwhether the two languages they represent have a nonempty intersection? We call\nthe underlying problem the ED String Intersection (EDSI) problem.For two ED\nstrings $T_1$ and $T_2$ of lengths $n_1$ and $n_2$, cardinalities $m_1$ and\n$m_2$, and sizes $N_1$ and $N_2$, respectively, we show the following:\n  - There is no $O((N_1N_2)^{1-\\epsilon})$-time algorithm, for any constant\n$\\epsilon>0$, for EDSI even when $T_1$ and $T_2$ are over a binary alphabet,\nunless the Strong Exponential-Time Hypothesis is false.\n  - There is no combinatorial $O((N_1+N_2)^{1.2-\\epsilon}f(n_1,n_2))$-time\nalgorithm, for any constant $\\epsilon>0$ and any function $f$, for EDSI even\nwhen $T_1$ and $T_2$ are over a binary alphabet, unless the Boolean Matrix\nMultiplication conjecture is false.\n  - An $O(N_1\\log N_1\\log n_1+N_2\\log N_2\\log n_2)$-time algorithm for\noutputting a compact (RLE) representation of the intersection language of two\nunary ED strings. In the case when $T_1$ and $T_2$ are given in a compact\nrepresentation, we show that the problem is NP-complete.\n  - An $O(N_1m_2+N_2m_1)$-time algorithm for EDSI.\n  - An $\\tilde{O}(N_1^{\\omega-1}n_2+N_2^{\\omega-1}n_1)$-time algorithm for\nEDSI, where $\\omega$ is the exponent of matrix multiplication; the $\\tilde{O}$\nnotation suppresses factors that are polylogarithmic in the input size.",
        "split": "arXiv"
    },
    {
        "text": "Let $s(n)$ denote the number of ones in the binary expansion of the\nnonnegative integer $n$. How does $s$ behave under addition of a constant $t$?\nIn order to study the differences \\[s(n+t)-s(n),\\] for all $n\\ge0$, we consider\nthe associated characteristic function $\\gamma_t$. Our main theorem is a\nstructural result on the decomposition of $\\gamma_t$ into a sum of\n\\emph{components}. We also study in detail the case that $t$ contains at most\ntwo blocks of consecutive $1$s. The results in this paper are motivated by\n\\emph{Cusick's conjecture} on the sum-of-digits function. This conjecture is\nconcerned with the \\emph{central tendency} of the corresponding probability\ndistributions, and is still unsolved.",
        "split": "arXiv"
    },
    {
        "text": "Existing guarantees for algorithms sampling from nonlogconcave measures on\n$\\mathbb{R}^d$ are generally inexplicit or unscalable. Even for the class of\nmeasures with logdensities that have bounded Hessians and are strongly concave\noutside a Euclidean ball of radius $R$, no available theory is comprehensively\nsatisfactory with respect to both $R$ and $d$. In this paper, it is shown that\ncomplete polynomial complexity can in fact be achieved if $R\\leq c\\sqrt{d}$,\nwhilst an exponential number of point evaluations is generally necessary for\nany algorithm as soon as $R\\geq C\\sqrt{d}$ for constants $C>c>0$. A simple\nimportance sampler with tail-matching proposal achieves the former, owing to a\nblessing of dimensionality. On the other hand, if strong concavity outside a\nball is replaced by a distant dissipativity condition, then sampling guarantees\nmust generally scale exponentially with $d$ in all parameter regimes.",
        "split": "arXiv"
    },
    {
        "text": "Energy considerations can significantly affect the behavior of a population\nof energy-consuming agents with limited energy budgets, for instance, in the\nmovement process of people in a city. We consider a population of interacting\nagents with an initial energy budget walking on a graph according to an\nexploration and return (to home) strategy that is based on the current energy\nof the person. Each move reduces the available energy depending on the flow of\nmovements and the strength of interactions, and the movement ends when an agent\nreturns home with a negative energy. We observe that a uniform distribution of\ninitial energy budgets results in a larger number of visited sites per consumed\nenergy (efficiency) compared to case that all agents have the same initial\nenergy if return to home is relevant from the beginning of the process. The\nuniform energy distribution also reduces the amount of uncertainties in the\ntotal travel times (entropy production) which is more pronounced when the\nstrength of interactions and exploration play the relevant role in the movement\nprocess. That is variability in the energies can help to increase the\nefficiency and reduce the entropy production specially in presence of strong\ninteractions.",
        "split": "arXiv"
    },
    {
        "text": "Recommender Systems (RSs) are pivotal in diverse domains such as e-commerce,\nmusic streaming, and social media. This paper conducts a comparative analysis\nof prevalent loss functions in RSs: Binary Cross-Entropy (BCE), Categorical\nCross-Entropy (CCE), and Bayesian Personalized Ranking (BPR). Exploring the\nbehaviour of these loss functions across varying negative sampling settings, we\nreveal that BPR and CCE are equivalent when one negative sample is used.\nAdditionally, we demonstrate that all losses share a common global minimum.\nEvaluation of RSs mainly relies on ranking metrics known as Normalized\nDiscounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR). We produce\nbounds of the different losses for negative sampling settings to establish a\nprobabilistic lower bound for NDCG. We show that the BPR bound on NDCG is\nweaker than that of BCE, contradicting the common assumption that BPR is\nsuperior to BCE in RSs training. Experiments on five datasets and four models\nempirically support these theoretical findings. Our code is available at\n\\url{https://anonymous.4open.science/r/recsys_losses} .",
        "split": "arXiv"
    },
    {
        "text": "In recent work, Lusztig's positive root vectors (with respect to a\ndistinguished choice of reduced decomposition of the longest element of the\nWeyl group) were shown to give a quantum tangent space for every $A$-series\nDrinfeld--Jimbo full quantum flag manifold $\\mathcal{O}_q(\\mathrm{F}_n)$.\nMoreover, the associated differential calculus\n$\\Omega^{(0,\\bullet)}_q(\\mathrm{F}_n)$ was shown to have classical dimension,\ngiving a direct $q$-deformation of the classical anti-holomorphic Dolbeault\ncomplex of $\\mathrm{F}_n$. Here we examine in detail the rank two case, namely\nthe full quantum flag manifold of $\\mathcal{O}_q(\\mathrm{SU}_3)$. In\nparticular, we examine the $*$-differential calculus associated to\n$\\Omega^{(0,\\bullet)}_q(\\mathrm{F}_3)$ and its non-commutative complex\ngeometry. We find that the number of almost-complex structures reduces from $8$\n(that is $2$ to the power of the number of positive roots of $\\frak{sl}_3$) to\n$4$ (that is $2$ to the power of the number of simple roots of $\\frak{sl}_3$).\nMoreover, we show that each of these almost-complex structures is integrable,\nwhich is to say, each of them is a complex structure. Finally, we observe that,\ndue to non-centrality of all the non-degenerate coinvariant $2$-forms, none of\nthese complex structures admits a left $\\mathcal{O}_q(\\mathrm{SU}_3)$-covariant\nnoncommutative K\\\"ahler structure.",
        "split": "arXiv"
    },
    {
        "text": "We unveil a new mechanism of nonreciprocal magneto-transport from cooperative\naction of Lorentz force and skew scattering. The significance of this Lorentz\nskew scattering mechanism lies in that it dominates both longitudinal and\ntransverse responses in highly conductive systems, and it exhibits a scaling\nbehavior distinct from all known mechanisms. At low temperature, it shows a\ncubic scaling in linear conductivity, whereas the scaling becomes quartic at\nelevated temperature when phonon scattering kicks in. We develop its\nmicroscopic formulation and reveal its close connection with Berry curvature on\nFermi surface. Applying our theory to surface transport in topological\ncrystalline insulator SnTe and bulk transport in Weyl semimetals leads to\nsignificant results, suggesting a new route to achieve giant transport\nnonreciprocity in high-mobility materials with topological band features.",
        "split": "arXiv"
    },
    {
        "text": "Multi-instance point cloud registration aims to estimate the pose of all\ninstances of a model point cloud in the whole scene. Existing methods all adopt\nthe strategy of first obtaining the global correspondence and then clustering\nto obtain the pose of each instance. However, due to the cluttered and occluded\nobjects in the scene, it is difficult to obtain an accurate correspondence\nbetween the model point cloud and all instances in the scene. To this end, we\npropose a simple yet powerful 3D focusing-and-matching network for\nmulti-instance point cloud registration by learning the multiple pair-wise\npoint cloud registration. Specifically, we first present a 3D multi-object\nfocusing module to locate the center of each object and generate object\nproposals. By using self-attention and cross-attention to associate the model\npoint cloud with structurally similar objects, we can locate potential matching\ninstances by regressing object centers. Then, we propose a 3D dual masking\ninstance matching module to estimate the pose between the model point cloud and\neach object proposal. It performs instance mask and overlap mask masks to\naccurately predict the pair-wise correspondence. Extensive experiments on two\npublic benchmarks, Scan2CAD and ROBI, show that our method achieves a new\nstate-of-the-art performance on the multi-instance point cloud registration\ntask. Code is available at https://github.com/zlynpu/3DFMNet.",
        "split": "arXiv"
    },
    {
        "text": "We discuss several aspects of the loss landscape of regularized neural\nnetworks: the structure of stationary points, connectivity of optimal\nsolutions, path with nonincreasing loss to arbitrary global optimum, and the\nnonuniqueness of optimal solutions, by casting the problem into an equivalent\nconvex problem and considering its dual. Starting from two-layer neural\nnetworks with scalar output, we first characterize the solution set of the\nconvex problem using its dual and further characterize all stationary points.\nWith the characterization, we show that the topology of the global optima goes\nthrough a phase transition as the width of the network changes, and construct\ncounterexamples where the problem may have a continuum of optimal solutions.\nFinally, we show that the solution set characterization and connectivity\nresults can be extended to different architectures, including two-layer\nvector-valued neural networks and parallel three-layer neural networks.",
        "split": "arXiv"
    },
    {
        "text": "Multimodal large language models (MLLMs) have shown impressive capabilities\nin document understanding, a rapidly growing research area with significant\nindustrial demand in recent years. As a multimodal task, document understanding\nrequires models to possess both perceptual and cognitive abilities. However,\ncurrent MLLMs often face conflicts between perception and cognition. Taking a\ndocument VQA task (cognition) as an example, an MLLM might generate answers\nthat do not match the corresponding visual content identified by its OCR\n(perception). This conflict suggests that the MLLM might struggle to establish\nan intrinsic connection between the information it \"sees\" and what it\n\"understands.\" Such conflicts challenge the intuitive notion that cognition is\nconsistent with perception, hindering the performance and explainability of\nMLLMs. In this paper, we define the conflicts between cognition and perception\nas Cognition and Perception (C&P) knowledge conflicts, a form of multimodal\nknowledge conflicts, and systematically assess them with a focus on document\nunderstanding. Our analysis reveals that even GPT-4o, a leading MLLM, achieves\nonly 68.6% C&P consistency. To mitigate the C&P knowledge conflicts, we propose\na novel method called Multimodal Knowledge Consistency Fine-tuning. This method\nfirst ensures task-specific consistency and then connects the cognitive and\nperceptual knowledge. Our method significantly reduces C&P knowledge conflicts\nacross all tested MLLMs and enhances their performance in both cognitive and\nperceptual tasks in most scenarios.",
        "split": "arXiv"
    },
    {
        "text": "The numerical approximation of low-regularity solutions to the nonlinear\nSchr\\\"odinger equation is notoriously difficult and even more so if\nstructure-preserving schemes are sought. Recent works have been successful in\nestablishing symmetric low-regularity integrators for this equation. However,\nso far, all prior symmetric low-regularity algorithms are fully implicit, and\ntherefore require the solution of a nonlinear equation at each time step,\nleading to significant numerical cost in the iteration. In this work, we\nintroduce the first fully explicit (multi-step) symmetric low-regularity\nintegrators for the nonlinear Schr\\\"odinger equation. We demonstrate the\nconstruction of an entire class of such schemes which notably can be used to\nsymmetrise (in explicit form) a large amount of existing low-regularity\nintegrators. We provide rigorous convergence analysis of our schemes and\nnumerical examples demonstrating both the favourable structure preservation\nproperties obtained with our novel schemes, and the significant reduction in\ncomputational cost over implicit methods.",
        "split": "arXiv"
    },
    {
        "text": "To handle the complexities of real-world traffic, learning planners for\nself-driving from data is a promising direction. While recent approaches have\nshown great progress, they typically assume a setting in which the ground-truth\nworld state is available as input. However, when deployed, planning needs to be\nrobust to the long-tail of errors incurred by a noisy perception system, which\nis often neglected in evaluation. To address this, previous work has proposed\ndrawing adversarial samples from a perception error model (PEM) mimicking the\nnoise characteristics of a target object detector. However, these methods use\nsimple PEMs that fail to accurately capture all failure modes of detection. In\nthis paper, we present EMPERROR, a novel transformer-based generative PEM,\napply it to stress-test an imitation learning (IL)-based planner and show that\nit imitates modern detectors more faithfully than previous work. Furthermore,\nit is able to produce realistic noisy inputs that increase the planner's\ncollision rate by up to 85%, demonstrating its utility as a valuable tool for a\nmore complete evaluation of self-driving planners.",
        "split": "arXiv"
    },
    {
        "text": "We prove that $\\alpha$-dissipative solutions to the Cauchy problem of the\nHunter-Saxton equation, where $\\alpha \\in W^{1, \\infty}(\\mathbb{R}, [0, 1))$,\ncan be computed numerically with order $\\mathcal{O}(\\Delta x^{{1}/{8}}+\\Delta\nx^{{\\beta}/{4}})$ in $L^{\\infty}(\\mathbb{R})$, provided there exist constants\n$C > 0$ and $\\beta \\in (0, 1]$ such that the initial spatial derivative\n$\\bar{u}_{x}$ satisfies $\\|\\bar{u}_x(\\cdot + h) - \\bar{u}_x(\\cdot)\\|_2 \\leq\nCh^{\\beta}$ for all $h \\in (0, 2]$. The derived convergence rate is exemplified\nby a number of numerical experiments.",
        "split": "arXiv"
    },
    {
        "text": "Dynamic programming (DP) is a fundamental and powerful algorithmic paradigm\ntaught in most undergraduate (and many graduate) algorithms classes. DP\nproblems are challenging for many computer science students because they\nrequire identifying unique problem structures and a refined understanding of\nrecursion. In this paper, we present dpvis, a Python library that helps\nstudents understand DP through a frame-by-frame animation of dynamic programs.\ndpvis can easily generate animations of dynamic programs with as little as two\nlines of modifications compared to a standard Python implementation. For each\nframe, dpvis highlight the cells that have been read from and written to during\nan iteration. Moreover, dpvis allows users to test their understanding by\nprompting them with questions about the next operation performed by the\nalgorithm.\n  We deployed dpvis as a learning tool in an undergraduate algorithms class,\nand report on the results of a survey. The survey results suggest that dpvis is\nespecially helpful for visualizing the recursive structure of DP. Although some\nstudents struggled with the installation of the tool (which has been simplified\nsince the reported deployment), essentially all other students found the tool\nto be useful for understanding dynamic programs. dpvis is available at\nhttps://github.com/itsdawei/dpvis.",
        "split": "arXiv"
    },
    {
        "text": "In many Deep Reinforcement Learning (RL) problems, decisions in a trained\npolicy vary in significance for the expected safety and performance of the\npolicy. Since RL policies are very complex, testing efforts should concentrate\non states in which the agent's decisions have the highest impact on the\nexpected outcome. In this paper, we propose a novel model-based method to\nrigorously compute a ranking of state importance across the entire state space.\nWe then focus our testing efforts on the highest-ranked states. In this paper,\nwe focus on testing for safety. However, the proposed methods can be easily\nadapted to test for performance. In each iteration, our testing framework\ncomputes optimistic and pessimistic safety estimates. These estimates provide\nlower and upper bounds on the expected outcomes of the policy execution across\nall modeled states in the state space. Our approach divides the state space\ninto safe and unsafe regions upon convergence, providing clear insights into\nthe policy's weaknesses. Two important properties characterize our approach.\n(1) Optimal Test-Case Selection: At any time in the testing process, our\napproach evaluates the policy in the states that are most critical for safety.\n(2) Guaranteed Safety: Our approach can provide formal verification guarantees\nover the entire state space by sampling only a fraction of the policy. Any\nsafety properties assured by the pessimistic estimate are formally proven to\nhold for the policy. We provide a detailed evaluation of our framework on\nseveral examples, showing that our method discovers unsafe policy behavior with\nlow testing effort.",
        "split": "arXiv"
    },
    {
        "text": "We present deep JWST/NIRSpec integral-field spectroscopy (IFS) and ALMA\n[CII]$\\lambda$158$\\mu$m observations of COS-3018, a star-forming galaxy at\nz$\\sim$6.85, as part of the GA-NIFS programme. Both G395H (R$\\sim$ 2700) and\nPRISM (R$\\sim$ 100) NIRSpec observations revealed that COS-3018 is comprised of\nthree separate components detected in [OIII]$\\lambda$5008, which we dub as\nMain, North and East, with stellar masses of 10$^{9.4 \\pm 0.1}$, 10$^{9.2 \\pm\n0.07}$, 10$^{7.7 \\pm 0.15}$ M$_{\\odot}$. We detect [OIII]$\\lambda$5008,\n[OIII]$\\lambda\\lambda$3727,29 and multiple Balmer lines in all three components\ntogether with [OIII]$\\lambda$4363 in the Main and North components. This allows\nus to measure an ISM temperature of T$_{e}$= 1.27$\\pm0.07\\times 10^4$ and\nT$_{e}$= 1.6$\\pm0.14\\times 10^4$ K with densities of $n_{e}$ = 1250$\\pm$250 and\n$n_{e}$ = 700$\\pm$200 cm$^{-3}$, respectively. These deep observations allow us\nto measure an average metallicity of 12+log(O/H)=7.9--8.2 for the three\ncomponents with the T$_{e}$-method. We do not find any significant evidence of\nmetallicity gradients between the components. Furthermore, we also detect\n[NII]$\\lambda$6585, one of the highest redshift detections of this emission\nline. We find that in a small, metal-poor clump 0.2 arcsec west of the North\ncomponent, N/O is elevated compared to other regions, indicating that nitrogen\nenrichment originates from smaller substructures, possibly proto-globular\nclusters. [OIII]$\\lambda$5008 kinematics show that this system is merging,\nwhich is probably driving the ongoing, luminous starburst.",
        "split": "arXiv"
    },
    {
        "text": "Given a measure equivalence coupling between two finitely generated groups,\nDelabie, Koivisto, Le Ma\\^itre and Tessera have found explicit upper bounds on\nhow integrable the associated cocycles can be. These bounds are optimal in many\ncases but the integrability of the cocycles with respect to these critical\nthresholds remained unclear. For instance, a cocycle from $\\mathbb{Z}^{k+\\ell}$\nto $\\mathbb{Z}^{k}$ can be $\\mathrm{L}^p$ for all $p<\\frac{k}{k+\\ell}$ but not\nfor $p>\\frac{k}{k+\\ell}$, and the case $p=\\frac{k}{k+\\ell}$ was an open\nquestion which we answer by the negative. Our main result actually yields much\nmore examples where the integrability threshold given by Delabie-Koivisto-Le\nMa\\^itre-Tessera Theorems cannot be reached.",
        "split": "arXiv"
    },
    {
        "text": "This paper proposes an automated framework for efficient application\nprofiling and training of Machine Learning (ML) performance models, composed of\ntwo parts: OSCAR-P and aMLLibrary. OSCAR-P is an auto-profiling tool designed\nto automatically test serverless application workflows running on multiple\nhardware and node combinations in cloud and edge environments. OSCAR-P obtains\nrelevant profiling information on the execution time of the individual\napplication components. These data are later used by aMLLibrary to train\nML-based performance models. This makes it possible to predict the performance\nof applications on unseen configurations. We test our framework on clusters\nwith different architectures (x86 and arm64) and workloads, considering\nmulti-component use-case applications. This extensive experimental campaign\nproves the efficiency of OSCAR-P and aMLLibrary, significantly reducing the\ntime needed for the application profiling, data collection, and data\nprocessing. The preliminary results obtained on the ML performance models\naccuracy show a Mean Absolute Percentage Error lower than 30% in all the\nconsidered scenarios.",
        "split": "arXiv"
    },
    {
        "text": "This work aims at investigating the impact of DNA geometry, compaction and\ncalculation chain on DNA break and chromosome aberration predictions for high\ncharge and energy (HZE) ions, using the Monte Carlo codes Geant4-DNA, RITRACKS\nand RITCARD. To ensure consistency of ion transport of both codes, we first\ncompared microdosimetry and nanodosimetry spectra for different ions of\ninterest in hadrontherapy and space research. The Rudd model was used for the\ntransport of ions in both models. Developments were made in Geant4 (v11.2) to\ninclude periodic boundary conditions (PBC) to account for electron equilibrium\nin small targets. Excellent agreements were found for both microdosimetric and\nnanodosimetric spectra for all ion types, with and without PBC. Some\ndiscrepancies remain for low-energy deposition events, likely due to\ndifferences in electron interaction models. The latest results obtained using\nthe newly available Geant4 example ``dsbandrepair'' will be presented and\ncompared to DNA break predictions obtained with RITCARD.",
        "split": "arXiv"
    },
    {
        "text": "We completely classify the asymptotic behavior of the number of alternating\nsign matrices classically avoiding a single permutation pattern, in the sense\nof [Johansson and Linusson 2007]. In particular, we give a uniform proof of an\nexponential upper bound for the number of alternating sign matrices classically\navoiding one of twelve particular patterns, and a super-exponential lower bound\nfor all other single-pattern avoidance classes. We also show that for any fixed\ninteger $k$, there is an exponential upper bound for the number of alternating\nsign matrices that classically avoid any single permutation pattern and contain\nprecisely $k$ negative ones. Finally, we prove that there must be at most $3$\nnegative ones in an alternating sign matrix which classically avoids both\n$2143$ and $3412$, and we exactly enumerate the number of them with precisely\n$3$ negative ones.",
        "split": "arXiv"
    },
    {
        "text": "In the paper we prove criteria for convexity and concavity of $f$-potentials\n($f$-means, or Kolvogorov means), which particular cases are the arithmetic,\ngeometric, harmonic means, the thermodynamic potential (exponential mean), and\nthe $L^{p}$-norm. Then we compute in quadratures all functions $f$ satisfying\nthese criteria.",
        "split": "arXiv"
    },
    {
        "text": "Large Language Models (LLMs) often perpetuate biases in pronoun usage,\nleading to misrepresentation or exclusion of queer individuals. This paper\naddresses the specific problem of biased pronoun usage in LLM outputs,\nparticularly the inappropriate use of traditionally gendered pronouns (\"he,\"\n\"she\") when inclusive language is needed to accurately represent all\nidentities. We introduce a collaborative agent pipeline designed to mitigate\nthese biases by analyzing and optimizing pronoun usage for inclusivity. Our\nmulti-agent framework includes specialized agents for both bias detection and\ncorrection. Experimental evaluations using the Tango dataset-a benchmark\nfocused on gender pronoun usage-demonstrate that our approach significantly\nimproves inclusive pronoun classification, achieving a 32.6 percentage point\nincrease over GPT-4o in correctly disagreeing with inappropriate traditionally\ngendered pronouns $(\\chi^2 = 38.57, p < 0.0001)$. These results accentuate the\npotential of agent-driven frameworks in enhancing fairness and inclusivity in\nAI-generated content, demonstrating their efficacy in reducing biases and\npromoting socially responsible AI.",
        "split": "arXiv"
    },
    {
        "text": "Emerging distributed generation demands highly reliable and resilient\ncoordinating control in microgrids. To improve on these aspects, spiking neural\nnetwork is leveraged, as a grid-edge intelligence tool to establish a talkative\ninfrastructure, Spike Talk, expediting coordination in next-generation\nmicrogrids without the need of communication at all. This paper unravels the\nphysics behind Spike Talk from the perspective of its distributed\ninfrastructure, which aims to address the Von Neumann Bottleneck. Relying on\ninferring information via power flows in tie lines, Spike Talk allows adaptive\nand flexible control and coordination itself, and features in synaptic\nplasticity facilitating online and local training functionality. Preliminary\ncase studies are demonstrated with results, while more extensive validations\nare to be included as future scopes of work.",
        "split": "arXiv"
    },
    {
        "text": "Large language models (LLMs) typically employ greedy decoding or\nlow-temperature sampling for reasoning tasks, reflecting a perceived trade-off\nbetween diversity and accuracy. We challenge this convention by introducing\ntop-$n\\sigma$, a novel sampling method that operates directly on pre-softmax\nlogits by leveraging a statistical threshold. Our key insight is that logits\nnaturally separate into a Gaussian-distributed noisy region and a distinct\ninformative region, enabling efficient token filtering without complex\nprobability manipulations. Unlike existing methods (e.g., top-$p$, min-$p$)\nthat inadvertently include more noise tokens at higher temperatures,\ntop-$n\\sigma$ maintains a stable sampling space regardless of temperature\nscaling. We also provide a theoretical analysis of top-$n\\sigma$ to better\nunderstand its behavior. The extensive experimental results across four\nreasoning-focused datasets demonstrate that our method not only outperforms\nexisting sampling approaches but also surpasses greedy decoding, while\nmaintaining consistent performance even at high temperatures.",
        "split": "arXiv"
    },
    {
        "text": "One of fascinating phenomena of nature is quantum nonlocality, which is\nobserved upon measurements on spacelike entangled systems. However, there are\nsets of post-quantum models which have stronger correlations than quantum\nmechanics, wherein instantaneous communication remains impossible. The set of\nalmost quantum correlations is one of post-quantum models which satisfies all\nkinematic axioms of standard quantum correlations except one, meanwhile they\ncontain correlations slightly stronger than quantum correlations. There arises\nthe natural question whether there is some fundamental principle of nature\nwhich can genuinely characterizes quantum correlations. Here, we provide an\nanswer and close this gap by invoking the isotropy and homogeneity principles\nof the flat space as a conclusive and distinguishing criterion to rule out the\nalmost-quantum correlations model. In particular, to characterize quantum\ncorrelations we impose the isotropy and homogeneity symmetry group structure on\nthe almost quantum correlations model and request that the joint probability\ndistributions corresponding to the Born rule remain invariant. We prove that\nthis condition is sufficient (and necessary) to reduce the almost quantum\ncorrelations model to quantum mechanics in both bipartite and multipartite\nsystems.",
        "split": "arXiv"
    },
    {
        "text": "The Activated Random Walk (ARW) model is a promising candidate for\ndemonstrating self-organized criticality due to its potential for universality.\nRecent studies have shown that the ARW model exhibits a well-defined critical\ndensity in one dimension, supporting its universality. In this paper, we extend\nthese results by demonstrating that the ARW model on $\\mathbb{Z}$, with a\nsingle initially active particle and all other particles sleeping, maintains\nthe same critical density. Our findings relax the previous assumption that\nrequired all particles to be initially active. This provides further evidence\nof the ARW model's robustness and universality in depicting self-organized\ncriticality.",
        "split": "arXiv"
    },
    {
        "text": "Air-to-air missiles are used on many modern military combat aircraft for\nself-defence. It is imperative for the pilots using the weapons that the\nmissiles hit their target first time. The important goals for a missile control\nsystem to achieve are minimising the time constant, overshoot, and settling\ntime of the missile dynamics. The combination of high angles of attack,\ntime-varying mass, thrust, and centre of gravity, actuator delay, and signal\nnoise create a highly non-linear dynamic system with many uncertainties that is\nextremely challenging to control. A robust control system based on saturated\nsliding mode control is proposed to overcome the time-varying parameters and\nnon-linearities. A lag compensator is designed to overcome actuator delay. A\nsecond-order filter is selected to reduce high-frequency measurement noise.\nWhen combined, the proposed solutions can make the system stable despite the\nexistence of changing mass, centre of gravity, thrust, and sensor noise. The\nsystem was evaluated for desired pitch angles of 0{\\deg} to 90{\\deg}. The time\nconstant for the system stayed below 0.27s for all conditions, with\nsatisfactory performance for both settling time and overshoot.",
        "split": "arXiv"
    },
    {
        "text": "Tilt rotor aircraft combine the benefits of both helicopters and fixed wing\naircraft, this makes them popular for a variety of applications, including\nSearch and Rescue and VVIP transport. However, due to the multiple flight\nmodes, significant challenges with regards to the control system design are\nexperienced. The main challenges with VTOL aircraft, comes during the dynamic\nphase (mode transition), where the aircraft transitions from a hover state to\nfull forwards flight. In this transition phase the aerodynamic lift and torque\ngenerated by the wing/control surfaces increases and as such, the rotor thrust,\nand the tilt rate must be carefully considered, such that the height and\nattitude remain invariant during the mode transition. In this paper, a digital\nPID controller with the applicable digital filter and data hold functions is\ndesigned so that a successful mode transition between hover and forwards flight\ncan be ascertained. Finally, the presented control system for the tilt-rotor\nUAV is demonstrated through simulations by using the MATLAB software suite. The\nperformance obtained from the simulations confirm the success of the\nimplemented methods, with full stability in all three degrees of freedom being\ndemonstrated.",
        "split": "arXiv"
    },
    {
        "text": "From prehistoric encirclement for hunting to GPS orbiting the earth for\npositioning, target encirclement has numerous real world applications. However,\nencircling multiple non-cooperative targets in GPS-denied environments remains\nchallenging. In this work, multiple targets encirclement by using a minimum of\ntwo tasking agents, is considered where the relative distance measurements\nbetween the agents and the targets can be obtained by using onboard sensors.\nBased on the measurements, the center of all the targets is estimated directly\nby a fuzzy wavelet neural network (FWNN) and the least squares fit method.\nThen, a new distributed anti-synchronization controller (DASC) is designed so\nthat the two tasking agents are able to encircle all targets while staying\nopposite to each other. In particular, the radius of the desired encirclement\ntrajectory can be dynamically determined to avoid potential collisions between\nthe two agents and all targets. Based on the Lyapunov stability analysis\nmethod, the convergence proofs of the neural network prediction error, the\ntarget-center position estimation error, and the controller error are addressed\nrespectively. Finally, both numerical simulations and UAV flight experiments\nare conducted to demonstrate the validity of the encirclement algorithms. The\nflight tests recorded video and other simulation results can be found in\nhttps://youtu.be/B8uTorBNrl4.",
        "split": "arXiv"
    },
    {
        "text": "The inherent volatility and dynamic fluctuations within the financial stock\nmarket underscore the necessity for investors to employ a comprehensive and\nreliable approach that integrates risk management strategies, market trends,\nand the movement trends of individual securities. By evaluating specific data,\ninvestors can make more informed decisions. However, the current body of\nliterature lacks substantial evidence supporting the practical efficacy of\nreinforcement learning (RL) agents, as many models have only demonstrated\nsuccess in back testing using historical data. This highlights the urgent need\nfor a more advanced methodology capable of addressing these challenges. There\nis a significant disconnect in the effective utilization of financial\nindicators to better understand the potential market trends of individual\nsecurities. The disclosure of successful trading strategies is often restricted\nwithin financial markets, resulting in a scarcity of widely documented and\npublished strategies leveraging RL. Furthermore, current research frequently\noverlooks the identification of financial indicators correlated with various\nmarket trends and their potential advantages.\n  This research endeavors to address these complexities by enhancing the\nability of RL agents to effectively differentiate between positive and negative\nbuy/sell actions using financial indicators. While we do not address all\nconcerns, this paper provides deeper insights and commentary on the utilization\nof technical indicators and their benefits within reinforcement learning. This\nwork establishes a foundational framework for further exploration and\ninvestigation of more complex scenarios.",
        "split": "arXiv"
    },
    {
        "text": "A solid electrolyte having the ionic conductivity comparable to that of the\nconventional liquid electrolyte can be used in All Solid State Batteries. The\nseries Li6.75+xLa3-xSrxZr1.75Ta0.25O12 (x = 0 to 0.20) was synthesized to\nimprove the ionic conductivity of garnet Li7La3Zr2O12 (LLZO). The structural,\nphysical and morphological investigations have been carried out for all the\nsynthesized samples using X ray diffraction, density measurement and scanning\nelectron microscopy respectively. The results of electrochemical analysis\nshowed that the maximum room temperature ionic conductivity of 3.5 x 10-4 S/Cm\nand minimum activation energy of 0.29 eV is achieved by the 0.05 Sr ceramic\nsample. The DC conductivity measurement confirmed the dominance of ionic\nconduction in the prepared ceramic samples. The highest ionic conductivity with\nthe minimum activation energy makes the 0.05 Sr ceramic sample a suitable\nchoice as solid electrolyte for All Solid State Lithium Ion Batteries.",
        "split": "arXiv"
    },
    {
        "text": "We investigates the massless scalar perturbations of the\nPleba\\'nski-Demia\\'nski black hole considering the general case that admits all\nnonzero parameters. This case is the most generic black hole spacetime in\ngeneral relativity, characterized by mass, spin, acceleration, electric and\nmagnetic charges, NUT parameter, and cosmological constant. Employing conformal\ntransformations, we can separate the massless scalar field equation and reduce\nthe effective potential in the radial perturbation equation into the\nP\\\"oschl--Teller potential in the near-Nariai limit where the event and\ncosmo-acceleration horizons are close. This allows us to obtain an exact\nanalytical solution of the quasinormal frequency, implying that the decay rate\nof the field is quantized depending only on the surface gravity of the black\nhole.",
        "split": "arXiv"
    },
    {
        "text": "Moist thermodynamics is a fundamental driver of atmospheric dynamics across\nall scales, making accurate modeling of these processes essential for reliable\nweather forecasts and climate change projections. However, atmospheric models\noften make a variety of inconsistent approximations in representing moist\nthermodynamics. These inconsistencies can introduce spurious sources and sinks\nof energy, potentially compromising the integrity of the models.\n  Here, we present a thermodynamically consistent and structure preserving\nformulation of the moist compressible Euler equations. When discretised with a\nsummation by parts method, our spatial discretisation conserves: mass, water,\nentropy, and energy. These properties are achieved by discretising a skew\nsymmetric form of the moist compressible Euler equations, using entropy as a\nprognostic variable, and the summation-by-parts property of discrete derivative\noperators. Additionally, we derive a discontinuous Galerkin spectral element\nmethod with energy and tracer variance stable numerical fluxes, and\nexperimentally verify our theoretical results through numerical simulations.",
        "split": "arXiv"
    },
    {
        "text": "We give an algorithm for the fully-dynamic carpooling problem with recourse:\nEdges arrive and depart online from a graph $G$ with $n$ nodes according to an\nadaptive adversary. Our goal is to maintain an orientation $H$ of $G$ that\nkeeps the discrepancy, defined as $\\max_{v \\in V} |\\text{deg}_H^+(v) -\n\\text{deg}_H^-(v)|$, small at all times. We present a simple algorithm and\nanalysis for this problem with recourse based on cycles that simplifies and\nimproves on a result of Gupta et al. [SODA '22].",
        "split": "arXiv"
    },
    {
        "text": "We develop a variant of the hypergraph container lemma with non-uniform\nconditions on the co-degrees. In particular, an upper bound on the co-degree of\nsome subset of vertices $T$ is allowed to depend on where the vertices in $T$\nlive in the hypergraph, rather than having one condition which holds for all\nsubsets. We use this to extend recent results on nearly-orthogonal sets in\n$\\mathbb{F}_p^d$. For a field $\\mathbb{F}$ and integers $d, k$ and $\\ell$, a\nset $A \\subseteq \\mathbb{F}^d$ is called $(k,\\ell)$-nearly orthogonal if all\nvectors in $A$ are non-self-orthogonal and every $k+1$ vectors in $A$ contain\n$\\ell + 1$ pairwise orthogonal vectors. Recently, Haviv, Mattheus,\nMilojevi\\'{c} and Wigderson have improved the lower bound on nearly orthogonal\nsets over finite fields, using counting arguments and a hypergraph container\nlemma. They showed that for every prime $p$ and an integer $\\ell$, there is a\nconstant $\\delta(p,\\ell)$ such that for every field $\\mathbb{F}$ of\ncharacteristic $p$ and for all integers $d \\geq k \\geq \\ell + 1$,\n$\\mathbb{F}^d$ contains a $(k,\\ell)$-nearly orthogonal set of size $d^{\\delta k\n/ \\log k}$. This nearly matches an upper bound $\\binom{d+k}{k}$ coming from\nRamsey theory. Moreover, they proved the same lower bound for the size of a\nlargest set $A$ where for any two subsets of $A$ of size $k+1$ each, there is a\nvector in one of the subsets orthogonal to a vector in the other one. We prove\nthat essentially the same lower bound holds for the size of a largest set $A\n\\subseteq \\mathbb{F}^d$ with the stronger property that given any family of\nsubsets $A_1, \\ldots, A_{\\ell+1} \\subset A$, each of size $k+1$, we can find a\nvector in each $A_i$ such that they are all pairwise orthogonal.",
        "split": "arXiv"
    },
    {
        "text": "Auscultation of internal body sounds is essential for diagnosing a range of\nhealth conditions, yet its effectiveness is often limited by clinicians'\nexpertise and the acoustic constraints of human hearing, restricting its use\nacross various clinical scenarios. To address these challenges, we introduce\nAuscultaBase, a foundational framework aimed at advancing body sound\ndiagnostics through innovative data integration and contrastive learning\ntechniques. Our contributions include the following: First, we compile\nAuscultaBase-Corpus, a large-scale, multi-source body sound database\nencompassing 11 datasets with 40,317 audio recordings and totaling 322.4 hours\nof heart, lung, and bowel sounds. Second, we develop AuscultaBase-Model, a\nfoundational diagnostic model for body sounds, utilizing contrastive learning\non the compiled corpus. Third, we establish AuscultaBase-Bench, a comprehensive\nbenchmark containing 16 sub-tasks, assessing the performance of various\nopen-source acoustic pre-trained models. Evaluation results indicate that our\nmodel outperforms all other open-source models in 12 out of 16 tasks,\ndemonstrating the efficacy of our approach in advancing diagnostic capabilities\nfor body sound analysis.",
        "split": "arXiv"
    },
    {
        "text": "DNSSEC, a DNS security extension, is essential to accurately translating\ndomain names to IP addresses. Digital signatures provide the foundation for\nthis reliable translation, however, the evolution of 'Quantum Computers' has\nmade traditional digital signatures vulnerable. In light of this, NIST has\nrecently selected potential post-quantum digital signatures that can operate on\nconventional computers and resist attacks made with Quantum Computers. Since\nthese post-quantum digital signatures are still in their early stages of\ndevelopment, replacing pre-quantum digital signature schemes in DNSSEC with\npost-quantum candidates is risky until the post-quantum candidates have\nundergone a thorough security analysis. Given this, herein, we investigate the\nviability of employing 'Double-Signatures' in DNSSEC, combining a post-quantum\ndigital signature and a classic one. The rationale is that double-signatures\nwill offer protection against quantum threats on conventional signature schemes\nas well as unknown non-quantum attacks on post-quantum signature schemes, hence\neven if one fails the other provides security guarantees. However, the\ninclusion of two signatures in the DNSSEC response message doesn't bode well\nwith the maximum allowed size of DNSSEC responses (i.e., 1232B, a limitation\nenforced by MTU of physical links). To counter this issue, we leverage a way to\ndo application-layer fragmentation of DNSSEC responses with two signatures. We\nimplement our solution on top of OQS-BIND and through experiments show that the\naddition of two signatures in DNSSEC and application-layer fragmentation of all\nrelevant resource records and their reassembly does not have any substantial\nimpact on the efficiency of the resolution process and thus is suitable for the\ninterim period at least until the quantum computers are fully realized.",
        "split": "arXiv"
    },
    {
        "text": "ChatGPT and other large language models (LLMs) promise to revolutionize\nsoftware development by automatically generating code from program\nspecifications. We assess the performance of ChatGPT's GPT-3.5-turbo model on\nLeetCode, a popular platform with algorithmic coding challenges for technical\ninterview practice, across three difficulty levels: easy, medium, and hard. We\ntest three main hypotheses. First, ChatGPT solves fewer problems as difficulty\nrises (Hypothesis 1). Second, prompt engineering improves ChatGPT's\nperformance, with greater gains on easier problems and diminishing returns on\nharder ones (Hypothesis 2). Third, ChatGPT performs better in popular languages\nlike Python, Java, and C++ than in less common ones like Elixir, Erlang, and\nRacket (Hypothesis 3). To investigate these hypotheses, we conduct automated\nexperiments using Python scripts to generate prompts that instruct ChatGPT to\ncreate Python solutions. These solutions are stored and manually submitted on\nLeetCode to check their correctness. For Hypothesis 1, results show the\nGPT-3.5-turbo model successfully solves 92% of easy, 79% of medium, and 51% of\nhard problems. For Hypothesis 2, prompt engineering yields improvements: 14-29%\nfor Chain of Thought Prompting, 38-60% by providing failed test cases in a\nsecond feedback prompt, and 33-58% by switching to GPT-4. From a random subset\nof problems ChatGPT solved in Python, it also solved 78% in Java, 50% in C++,\nand none in Elixir, Erlang, or Racket. These findings generally validate all\nthree hypotheses.",
        "split": "arXiv"
    },
    {
        "text": "Large and Small Language Models (LMs) are typically pretrained using\nextensive volumes of text, which are sourced from publicly accessible platforms\nsuch as Wikipedia, Book Corpus, or through web scraping. These models, due to\ntheir exposure to a wide range of language data, exhibit impressive\ngeneralization capabilities and can perform a multitude of tasks\nsimultaneously. However, they often fall short when it comes to domain-specific\ntasks due to their broad training data. This paper introduces SecEncoder, a\nspecialized small language model that is pretrained using security logs.\nSecEncoder is designed to address the domain-specific limitations of general\nLMs by focusing on the unique language and patterns found in security logs.\nExperimental results indicate that SecEncoder outperforms other LMs, such as\nBERTlarge, DeBERTa-v3-large and OpenAI's Embedding (textembedding-ada-002)\nmodels, which are pretrained mainly on natural language, across various tasks.\nFurthermore, although SecEncoder is primarily pretrained on log data, it\noutperforms models pretrained on natural language for a range of tasks beyond\nlog analysis, such as incident prioritization and threat intelligence document\nretrieval. This suggests that domain specific pretraining with logs can\nsignificantly enhance the performance of LMs in security. These findings pave\nthe way for future research into security-specific LMs and their potential\napplications.",
        "split": "arXiv"
    },
    {
        "text": "Distributionally robust offline reinforcement learning (RL) aims to find a\npolicy that performs the best under the worst environment within an uncertainty\nset using an offline dataset collected from a nominal model. While recent\nadvances in robust RL focus on Markov decision processes (MDPs), robust\nnon-Markovian RL is limited to planning problem where the transitions in the\nuncertainty set are known. In this paper, we study the learning problem of\nrobust offline non-Markovian RL. Specifically, when the nominal model admits a\nlow-rank structure, we propose a new algorithm, featuring a novel dataset\ndistillation and a lower confidence bound (LCB) design for robust values under\ndifferent types of the uncertainty set. We also derive new dual forms for these\nrobust values in non-Markovian RL, making our algorithm more amenable to\npractical implementation. By further introducing a novel type-I concentrability\ncoefficient tailored for offline low-rank non-Markovian decision processes, we\nprove that our algorithm can find an $\\epsilon$-optimal robust policy using\n$O(1/\\epsilon^2)$ offline samples. Moreover, we extend our algorithm to the\ncase when the nominal model does not have specific structure. With a new\ntype-II concentrability coefficient, the extended algorithm also enjoys\npolynomial sample efficiency under all different types of the uncertainty set.",
        "split": "arXiv"
    },
    {
        "text": "Deep Learning Recommendation Model(DLRM)s utilize the embedding layer to\nrepresent various categorical features. Traditional DLRMs adopt unified\nembedding size for all features, leading to suboptimal performance and\nredundant parameters. Thus, lots of Automatic Embedding size Search (AES) works\nfocus on obtaining mixed embedding sizes with strong model performance.\nHowever, previous AES works can hardly address several challenges together: (1)\nThe search results of embedding sizes are unstable; (2) Recommendation effect\nwith AES results is unsatisfactory; (3) Memory cost of embeddings is\nuncontrollable. To address these challenges, we propose a novel one-shot AES\nframework called AdaS&S, in which a supernet encompassing various candidate\nembeddings is built and AES is performed as searching network architectures\nwithin it. Our framework contains two main stages: In the first stage, we\ndecouple training parameters from searching embedding sizes, and propose the\nAdaptive Sampling method to yield a well-trained supernet, which further helps\nto produce stable AES results. In the second stage, to obtain embedding sizes\nthat benefits the model effect, we design a reinforcement learning search\nprocess which utilizes the supernet trained previously. Meanwhile, to adapt\nsearching to specific resource constraint, we introduce the resource\ncompetition penalty to balance the model effectiveness and memory cost of\nembeddings. We conduct extensive experiments on public datasets to show the\nsuperiority of AdaS&S. Our method could improve AUC by about 0.3% while saving\nabout 20% of model parameters. Empirical analysis also shows that the stability\nof searching results in AdaS&S significantly exceeds other methods.",
        "split": "arXiv"
    },
    {
        "text": "Objective: Ensuring the precision in motion tracking for MRI-guided\nRadiotherapy (MRIgRT) is crucial for the delivery of effective treatments. This\nstudy refined the motion tracking accuracy in MRIgRT through the innovation of\nan automatic real-time tracking method, leveraging an enhanced\nTracking-Learning-Detection (ETLD) framework coupled with automatic\nsegmentation. Methods: We developed a novel MRIgRT motion tracking method by\nintegrating two primary methods: the ETLD framework and an improved Chan-Vese\nmodel (ICV), named ETLD+ICV. The TLD framework was upgraded to suit real-time\ncine MRI, including advanced image preprocessing, no-reference image quality\nassessment, an enhanced median-flow tracker, and a refined detector with\ndynamic search region adjustments. Additionally, ICV was combined for precise\ncoverage of the target volume, which refined the segmented region frame by\nframe using tracking results, with key parameters optimized. Tested on 3.5D MRI\nscans from 10 patients with liver metastases, our method ensures precise\ntracking and accurate segmentation vital for MRIgRT. Results: An evaluation of\n106,000 frames across 77 treatment fractions revealed sub-millimeter tracking\nerrors of less than 0.8mm, with over 99% precision and 98% recall for all\nsubjects, underscoring the robustness and efficacy of the ETLD. Moreover, the\nETLD+ICV yielded a dice global score of more than 82% for all subjects,\ndemonstrating the proposed method's extensibility and precise target volume\ncoverage. Conclusions: This study successfully developed an automatic real-time\nmotion tracking method for MRIgRT that markedly surpasses current methods. The\nnovel method not only delivers exceptional precision in tracking and\nsegmentation but also demonstrates enhanced adaptability to clinical demands,\npositioning it as an indispensable asset in the quest to augment the efficacy\nof radiotherapy treatments.",
        "split": "arXiv"
    },
    {
        "text": "In the past decade, an asymmetry in the large-scale distribution of galaxy\nspin directions has been observed in data from all relevant digital sky\nsurveys, all showing a higher number of galaxies rotating in the opposite\ndirection relative to the Milky Way as observed from Earth. Additionally, JWST\ndeep fields have shown that the asymmetry is clear and obvious, and can be\nsensed even by the naked human eye. These experiments were performed using two\nseparate statistical methods: standard binomial distribution and simple\n$\\chi^2$ statistics. Stiskalek \\& Desmond (2024) suggested that the asymmetry\nin the distribution of galaxy spin directions is due to the use of binomial or\n$\\chi^2$ statistics. Instead, they developed a new complex ad-hoc statistical\nmethod that shows random distribution in galaxy spin directions, and\nspecifically in data from HSC. Source code for the method was also made\navailable. The primary downside of the new method is that it is not able to\nidentify asymmetry in the distribution of galaxy spin directions. Even when the\nnew method is provided with synthetic data with extreme and obvious asymmetry,\nit still reports a null-hypothesis Universe with random distribution. That\nshows empirically that the method cannot sense asymmetry in the distribution of\nthe directions of rotation of galaxies. While this further concludes that the\ndistribution of galaxy spin direction as observed from Earth is not symmetric,\nit is not necessarily an indication of an anomaly in the large-scale structure.\nThe excessive number of galaxies that rotate in the opposite direction relative\nto the Milky Way can also be driven by the internal structure of galaxies and\nthe physics of galaxy rotation. The phenomenon can be related to other puzzling\nanomalies such the Ho tension. Data are publicly available, and no code is\nneeded to reproduce the results since only conventional statistics is used.",
        "split": "arXiv"
    },
    {
        "text": "As large language models (LLMs) grow more powerful, ensuring their safety\nagainst misuse becomes crucial. While researchers have focused on developing\nrobust defenses, no method has yet achieved complete invulnerability to\nattacks. We propose an alternative approach: instead of seeking perfect\nadversarial robustness, we develop rapid response techniques to look to block\nwhole classes of jailbreaks after observing only a handful of attacks. To study\nthis setting, we develop RapidResponseBench, a benchmark that measures a\ndefense's robustness against various jailbreak strategies after adapting to a\nfew observed examples. We evaluate five rapid response methods, all of which\nuse jailbreak proliferation, where we automatically generate additional\njailbreaks similar to the examples observed. Our strongest method, which\nfine-tunes an input classifier to block proliferated jailbreaks, reduces attack\nsuccess rate by a factor greater than 240 on an in-distribution set of\njailbreaks and a factor greater than 15 on an out-of-distribution set, having\nobserved just one example of each jailbreaking strategy. Moreover, further\nstudies suggest that the quality of proliferation model and number of\nproliferated examples play an key role in the effectiveness of this defense.\nOverall, our results highlight the potential of responding rapidly to novel\njailbreaks to limit LLM misuse.",
        "split": "arXiv"
    },
    {
        "text": "Large Language Models (LLMs) excel in diverse applications including\ngeneration of code snippets, but often struggle with generating code for\ncomplex Machine Learning (ML) tasks. Although existing LLM single-agent based\nsystems give varying performance depending on the task complexity, they purely\nrely on larger and expensive models such as GPT-4. Our investigation reveals\nthat no-cost and low-cost models such as Gemini-Pro, Mixtral and CodeLlama\nperform far worse than GPT-4 in a single-agent setting. With the motivation of\ndeveloping a cost-efficient LLM based solution for solving ML tasks, we propose\nan LLM Multi-Agent based system which leverages combination of experts using\nprofiling, efficient retrieval of past observations, LLM cascades, and\nask-the-expert calls. Through empirical analysis on ML engineering tasks in the\nMLAgentBench benchmark, we demonstrate the effectiveness of our system, using\nno-cost models, namely Gemini as the base LLM, paired with GPT-4 in cascade and\nexpert to serve occasional ask-the-expert calls for planning. With 94.2\\%\nreduction in the cost (from \\$0.931 per run cost averaged over all tasks for\nGPT-4 single agent system to \\$0.054), our system is able to yield better\naverage success rate of 32.95\\% as compared to GPT-4 single-agent system\nyielding 22.72\\% success rate averaged over all the tasks of MLAgentBench.",
        "split": "arXiv"
    },
    {
        "text": "We present preliminary results of a Chandra Large Program to monitor the\nultraluminous X-ray source (ULX) populations of three nearby, ULX-rich galaxies\nover the course of a year, finding the ULX population to show a variety of\nlong-term variability behaviours. Of a sample of 36 ULXs, some show persistent\nor moderately variable flux, often with a significant relationship between\nhardness and luminosity, consistent with a supercritically accreting source\nwith varying accretion rates. Six show very high-amplitude variability with no\nstrong relationship between luminosity and hardness, though not all of them\nshow evidence of any long-term periodicity, nor of the bimodal distribution\nindicative of the propeller effect. We find evidence of additional eclipses for\ntwo previously-identified eclipsing ULXs. Additionally, many sources that were\npreviously identified as ULXs in previous studies were not detected at ULX\nluminosities during our monitoring campaign, indicating a large number of\ntransient ULXs.",
        "split": "arXiv"
    },
    {
        "text": "We investigate critical points of eigencurves of bivariate matrix pencils\n$A+\\lambda B +\\mu C$. Points $(\\lambda,\\mu)$ for which $\\det(A+\\lambda B+\\mu\nC)=0$ form algebraic curves in $\\mathbb C^2$ and we focus on points where\n$\\mu'(\\lambda)=0$. Such points are referred to as zero-group-velocity (ZGV)\npoints, following terminology from engineering applications. We provide a\ngeneral theory for the ZGV points and show that they form a subset (with\nequality in the generic case) of the 2D points $(\\lambda_0,\\mu_0)$, where\n$\\lambda_0$ is a multiple eigenvalue of the pencil $(A+\\mu_0 C)+\\lambda B$, or,\nequivalently, there exist nonzero $x$ and $y$ such that $(A+\\lambda_0 B+\\mu_0\nC)x=0$, $y^H(A+\\lambda_0 B+\\mu_0 C)=0$, and $y^HBx=0$.\n  We introduce three numerical methods for computing 2D and ZGV points. The\nfirst method calculates all 2D (ZGV) points from the eigenvalues of a related\nsingular two-parameter eigenvalue problem. The second method employs a\nprojected regular two-parameter eigenvalue problem to compute either all\neigenvalues or only a subset of eigenvalues close to a given target. The third\napproach is a locally convergent Gauss--Newton-type method that computes a\nsingle 2D point from an inital approximation, the later can be provided for all\n2D points via the method of fixed relative distance by Jarlebring, Kvaal, and\nMichiels. In our numerical examples we use these methods to compute\n2D-eigenvalues, solve double eigenvalue problems, determine ZGV points of a\nparameter-dependent quadratic eigenvalue problem, evaluate the distance to\ninstability of a stable matrix, and find critical points of eigencurves of a\ntwo-parameter Sturm-Liouville problem.",
        "split": "arXiv"
    },
    {
        "text": "The growing usage of Large Language Models (LLMs) highlights the demands and\nchallenges in scalable LLM inference systems, affecting deployment and\ndevelopment processes. On the deployment side, there is a lack of comprehensive\nanalysis on the conditions under which a particular scheduler performs better\nor worse, with performance varying substantially across different schedulers,\nhardware, models, and workloads. Manually testing each configuration on GPUs\ncan be prohibitively expensive. On the development side, unpredictable\nperformance and unknown upper limits can lead to inconclusive trial-and-error\nprocesses, consuming resources on ideas that end up ineffective. To address\nthese challenges, we introduce INFERMAX, an analytical framework that uses\ninference cost models to compare various schedulers, including an optimal\nscheduler formulated as a constraint satisfaction problem (CSP) to establish an\nupper bound on performance. Our framework offers in-depth analysis and raises\nessential questions, challenging assumptions and exploring opportunities for\nmore efficient scheduling. Notably, our findings indicate that preempting\nrequests can reduce GPU costs by 30% compared to avoiding preemptions at all.\nWe believe our methods and insights will facilitate the cost-effective\ndeployment and development of scalable, efficient inference systems and pave\nthe way for cost-based scheduling.",
        "split": "arXiv"
    },
    {
        "text": "Existing approaches for all-in-one weather-degraded image restoration suffer\nfrom inefficiencies in leveraging degradation-aware priors, resulting in\nsub-optimal performance in adapting to different weather conditions. To this\nend, we develop an adaptive degradation-aware self-prompting model (ADSM) for\nall-in-one weather-degraded image restoration. Specifically, our model employs\nthe contrastive language-image pre-training model (CLIP) to facilitate the\ntraining of our proposed latent prompt generators (LPGs), which represent three\ntypes of latent prompts to characterize the degradation type, degradation\nproperty and image caption. Moreover, we integrate the acquired\ndegradation-aware prompts into the time embedding of diffusion model to improve\ndegradation perception. Meanwhile, we employ the latent caption prompt to guide\nthe reverse sampling process using the cross-attention mechanism, thereby\nguiding the accurate image reconstruction. Furthermore, to accelerate the\nreverse sampling procedure of diffusion model and address the limitations of\nfrequency perception, we introduce a wavelet-oriented noise estimating network\n(WNE-Net). Extensive experiments conducted on eight publicly available datasets\ndemonstrate the effectiveness of our proposed approach in both task-specific\nand all-in-one applications.",
        "split": "arXiv"
    },
    {
        "text": "Traditional compilers, designed for optimizing low-level code, fall short\nwhen dealing with modern, computation-heavy applications like image processing,\nmachine learning, or numerical simulations. Optimizations should understand the\nprimitive operations of the specific application domain and thus happen on that\nlevel.\n  Domain-specific languages (DSLs) fulfill these requirements. However, DSL\ncompilers reinvent the wheel over and over again as standard optimizations,\ncode generators, and general infrastructure & boilerplate code must be\nreimplemented for each DSL compiler.\n  This paper presents MimIR, an extensible, higher-order intermediate\nrepresentation. At its core, MimIR is a pure type system and, hence, a form of\na typed lambda calculus. Developers can declare the signatures of new\n(domain-specific) operations, called \"axioms\". An axiom can be the declaration\nof a function, a type operator, or any other entity with a possibly\npolymorphic, polytypic, and/or dependent type. This way, developers can extend\nMimIR at any low or high level and bundle them in a plugin. Plugins extend the\ncompiler and take care of optimizing and lowering the plugins' axioms.\n  We show the expressiveness and effectiveness of MimIR in three case studies:\nLow-level plugins that operate at the same level of abstraction as LLVM, a\nregular-expression matching plugin, and plugins for linear algebra and\nautomatic differentiation. We show that in all three studies, MimIR produces\ncode that has state-of-the-art performance.",
        "split": "arXiv"
    },
    {
        "text": "Deceptive patterns (DPs) in digital interfaces manipulate users into making\nunintended decisions, exploiting cognitive biases and psychological\nvulnerabilities. These patterns have become ubiquitous across various digital\nplatforms. While efforts to mitigate DPs have emerged from legal and technical\nperspectives, a significant gap in usable solutions that empower users to\nidentify and make informed decisions about DPs in real-time remains. In this\nwork, we introduce AutoBot, an automated, deceptive pattern detector that\nanalyzes websites' visual appearances using machine learning techniques to\nidentify and notify users of DPs in real-time. AutoBot employs a two-staged\npipeline that processes website screenshots, identifying interactable elements\nand extracting textual features without relying on HTML structure. By\nleveraging a custom language model, AutoBot understands the context surrounding\nthese elements to determine the presence of deceptive patterns. We implement\nAutoBot as a lightweight Chrome browser extension that performs all analyses\nlocally, minimizing latency and preserving user privacy. Through extensive\nevaluation, we demonstrate AutoBot's effectiveness in enhancing users' ability\nto navigate digital environments safely while providing a valuable tool for\nregulators to assess and enforce compliance with DP regulations.",
        "split": "arXiv"
    },
    {
        "text": "Current wireless communication technologies are insufficient in the face of\never-increasing demands. Therefore, novel and high-performance communication\nsystems are needed. In this paper, a novel high data rate and high-performance\nindex modulation scheme called double media-based modulation (DMBM) is\nproposed. The DMBM system doubles the number of mirror activation patterns\n(MAPs) and the number of transmitted symbols compared to the traditional MBM\nsystem during the same symbol period. In this way, the spectral efficiency of\nthe DMBM is doubled and the error performance improves as the number of bits\ncarried in the indices increases. Performance analysis of the DMBM scheme is\nevaluated for $M$-ary quadrature amplitude modulation ($M$-QAM) on Rayleigh\nfading channels. The error performance of the proposed DMBM system is compared\nwith spatial modulation (SM), quadrature SM (QSM), MBM, and double SM (DSM)\ntechniques. Also, the throughput, complexity, energy efficiency, spectral\nefficiency, and capacity analyses for the proposed DMBM system and SM, QSM,\nMBM, and DSM systems are presented. All analysis results show that the proposed\nDMBM system is superior to the compared systems.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we introduce a new technique to study the distribution in\nresidue classes of sets of integers with digit and sum-of-digits restrictions.\nFrom our main theorem, we derive a necessary and sufficient condition for\nintegers with missing digits to be uniformly distributed in arithmetic\nprogressions, extending previous results going back to work of Erd\\H{o}s,\nMauduit and S\\'ark\\\"ozy. Our approach utilizes Markov chains and does not rely\non Fourier analysis as many results of this nature do. Our results apply more\ngenerally to the class of multiplicatively invariant sets of integers. This\nclass, defined by Glasscock, Moreira and Richter using symbolic dynamics, is an\ninteger analogue to fractal sets and includes all missing digits sets. We\naddress uniform distribution in this setting, partially answering an open\nquestion posed by the same authors.",
        "split": "arXiv"
    },
    {
        "text": "Given a simple finite graph $G=(V(G),E(G))$, a vertex subset $D\\subseteq\nV(G)$ is said to be a dominating set if every vertex $v\\in V(G)-D$ is adjacent\nto a vertex in $D$. The independent domination number $\\gamma_i(G)$ is the\nminimum cardinality among all independent dominating sets of $G$. As the\nproblem of finding the domination number for general graphs is NP-complete, we\nfocus on the class of $k$-trees. In particular, we determine a tight upper\nbound for the independent domination number of $k$-trees for all $k\\in\n\\mathbb{N}$.",
        "split": "arXiv"
    },
    {
        "text": "DDO68 is a star-forming (SF) dwarf galaxy residing in a nearby void. Its gas\nmetallicity is among the lowest known in the local Universe, with parameter\n12+log(O/H) in the range of 6.96-7.3 dex. Six of its SF regions are located in\nor near the so-called 'Northern Ring', in which the Hubble Space Telescope\n(HST) images reveal many luminous young stars. We present for these SF regions\n(Knots) the results of optical monitoring in 35 epochs during the years\n2016--2023. The data was acquired with the 6m (BTA) and the 1m telescopes of\nthe Special Astrophysical Observatory and the 2.5m telescope of the MSU\nCaucasian Mountain Observatory. We complement the above results with the\narchive data from 10 other telescopes for 11 epochs during the years 1988-2013\nand with 3 our BTA observations between 2005 and 2015. Our goal is to search\nfor variability of these Knots and to relate it to the probable light\nvariations of their brightest stars. One of them, DDO68-V1 (in Knot 3), was\nidentified in 2008 with a luminous blue variable (LBV) star, born in the lowest\nmetallicity environments. For Knot 3, variations of its integrated light in the\nprevious epochs reached ~0.8 mag. In the period since 2016, the amplitude of\nvariations of Knot 3 reached ~0.3 mag. For the rest Knots, due to the lower\namplitudes, the manifestation of variability is less pronounced. We examine the\npresence of variability via the criterion chi^{2} and the Robust Median\nStatistics and discuss the robustness of the detected variations. The\nvariability is detected according to the both criteria in the lightcurves of\nall Knots with the chi^{2} confidence level of alpha = 0.0005. The peak-to-peak\namplitudes of variations are ~0.09, ~0.13, ~0.11, ~0.08 and ~0.16 mag for Knots\n1, 2, 4, 5 and 6, respectively. The amplitudes of the related variations of the\nbrightest supergiants in these regions can reach of ~3.0 mag.",
        "split": "arXiv"
    },
    {
        "text": "We show that the CNF satisfiability problem (SAT) can be solved in time\n$O^*(1.1199^{(d-2)n})$, where $d$ is either the maximum number of occurrences\nof any variable or the average number of occurrences of all variables if no\nvariable occurs only once. This improves upon the known upper bound of\n$O^*(1.1279^{(d-2)n})$ by Wahlstr$\\ddot{\\text{o}}$m (SAT 2005) and\n$O^*(1.1238^{(d-2)n})$ by Peng and Xiao (IJCAI 2023). For $d\\leq 4$, our\nalgorithm is better than previous results. Our main technical result is an\nalgorithm that runs in $O^*(1.1199^n)$ for 3-occur-SAT, a restricted instance\nof SAT where all variables have at most 3 occurrences. Through deeper case\nanalysis and a reduction rule that allows us to resolve many variables under a\nrelatively broad criteria, we are able to circumvent the bottlenecks in\nprevious algorithms.",
        "split": "arXiv"
    },
    {
        "text": "We use new measurements of the M31 proper motion to examine the Milky Way\n(MW) - M31 orbit and angular momentum. For Local Group (LG) mass consistent\nwith measured values, and assuming the system evolves in isolation, we show a\nwide range of orbits is possible. We compare to a sample of LG-like systems in\nthe Illustris simulation and find that $\\sim 13\\%$ of these pairs have\nundergone a pericentric passage. Using the simulated sample, we examine how\naccurately an isolated, two-body model describes the MW-M31 orbit, and show\nthat $\\sim 10\\%$ of the analogues in the simulation are well-modeled by such an\norbit. Systems that evolve in isolation by this definition are found to have a\nlower rate of major mergers and, in particular, have no major mergers since $z\n\\approx 0.3$. For all systems, we find an increase in the orbital angular\nmomentum, which is fairly independent of the merger rate and is possibly\nexplained by the influence of tidal torques on the LG. Given the likely quiet\nrecent major merger history of the MW, it is plausible that the isolated\ntwo-body model appropriately describes the orbit, though recent evidence for a\nmajor merger in M31 may complicate this interpretation.",
        "split": "arXiv"
    },
    {
        "text": "Audio super-resolution aims to enhance low-resolution signals by creating\nhigh-frequency content. In this work, we modify the architecture of AERO (a\nstate-of-the-art system for this task) for music super-resolution.\nSPecifically, we replace its original Attention and LSTM layers with Mamba, a\nState Space Model (SSM), across all network layers. Mamba is capable of\neffectively substituting the mentioned modules, as it offers a mechanism\nsimilar to that of Attention while also functioning as a recurrent network.\nWith the proposed AEROMamba, training requires 2-4x less GPU memory, since\nMamba exploits the convolutional formulation and leverages GPU memory\nhierarchy. Additionally, during inference, Mamba operates in constant memory\ndue to recurrence, avoiding memory growth associated with Attention. This\nresults in a 14x speed improvement using 5x less GPU. Subjective listening\ntests (0 to 100 scale) show that the proposed model surpasses the AERO model.\nIn the MUSDB dataset, degraded signals scored 38.22, while AERO and AEROMamba\nscored 60.03 and 66.74, respectively. For the PianoEval dataset, scores were\n72.92 for degraded signals, 76.89 for AERO, and 84.41 for AEROMamba.",
        "split": "arXiv"
    },
    {
        "text": "We show that any infinite ring has an infinite nonunital compressed commuting\ngraph. We classify all infinite unital rings with finite unital compressed\ncommuting graph, using semidirect product of rings as our main tool. As a\nconsequence we also classify infinite unital rings with only finitely many\nunital subrings.",
        "split": "arXiv"
    },
    {
        "text": "In Special Relativity, massless objects are characterized as either vacuum\nstates or as radiation propagating at the speed of light. This distinction\nextends to General Relativity for asymptotically flat initial data sets (IDS)\n\\((M^n, g, k)\\), where vacuum is represented by slices of Minkowski space, and\nradiation is modeled by slices of \\(pp\\)-wave spacetimes. In contrast, we\ndemonstrate that asymptotically hyperboloidal IDS with zero mass must embed\nisometrically into Minkowski space, with no possible IDS configurations\nmodeling radiation in this setting. Our result holds under the most general\nassumptions. The proof relies on precise decay estimates for spinors on level\nsets of spacetime harmonic functions and works in all dimensions.",
        "split": "arXiv"
    },
    {
        "text": "Advanced simulations of the mechanical behavior of soft tissues frequently\nrely on structure-based constitutive models, including smeared descriptions of\ncollagen fibers. Among them, the so-called Discrete Fiber Dispersion (DFD)\nmodel is based on a discrete integration of the fiber-strain energy over all\nthe fiber directions. In this paper, we recall the theoretical framework of the\nDFD model, including a derivation of the stress and stiffness tensors required\nfor the finite element implementation. Specifically, their expressions for\nincompressible plane stress problems are obtained. The use of a Lebedev\nquadrature, built exploiting the octahedral symmetry, is then proposed,\nillustrating the particular choice adopted for the orientation of the\nintegration points. Next, the convergence of this quadrature scheme is assessed\nby means of three numerical benchmark tests, highlighting the advantages with\nrespect to other angular integration methods available in the literature.\nFinally, we propose as applicative example a simulation of Z-plasty, a\ntechnique commonly used in reconstructive skin surgery, considering multiple\ngeometrical configurations and orientations of the fibers. Results are provided\nin terms of key mechanical quantities relevant for the surgical practice.",
        "split": "arXiv"
    },
    {
        "text": "We derive a complete set of Feynman rules in the general two-Higgs doublet\nmodel effective field theory where the effects of additional new physics are\nparametrized by operators up to mass dimension-six. We calculate the physical\nHiggs spectrum, contributions to the couplings and masses of electroweak gauge\nbosons and fermions, and all contact interactions arising from dimension-six\noperators. We also present results in specific limits and types, which include\nthe $CP$-conserving limit, alignment limit, and the four types of two-Higgs\ndoublet models: type-I, -II, -X, and -Y. We discuss the differences between the\ntwo-Higgs doublet model effective field theory and the renormalizable two-Higgs\ndoublet model or the standard model effective field theory. We create a\nFeynRules model package for calculating all Feynman rules in the general\neffective field theory and its specific limits and types.",
        "split": "arXiv"
    },
    {
        "text": "Multimodal learning can complete the picture of information extraction by\nuncovering key dependencies between data sources. However, current systems fail\nto fully leverage multiple modalities for optimal performance. This has been\nattributed to modality competition, where modalities strive for training\nresources, leaving some underoptimized. We show that current balancing methods\nstruggle to train multimodal models that surpass even simple baselines, such as\nensembles. This raises the question: how can we ensure that all modalities in\nmultimodal training are sufficiently trained, and that learning from new\nmodalities consistently improves performance? This paper proposes the\nMultimodal Competition Regularizer (MCR), a new loss component inspired by\nmutual information (MI) decomposition designed to prevent the adverse effects\nof competition in multimodal training. Our key contributions are: 1)\nIntroducing game-theoretic principles in multimodal learning, where each\nmodality acts as a player competing to maximize its influence on the final\noutcome, enabling automatic balancing of the MI terms. 2) Refining lower and\nupper bounds for each MI term to enhance the extraction of task-relevant unique\nand shared information across modalities. 3) Suggesting latent space\npermutations for conditional MI estimation, significantly improving\ncomputational efficiency. MCR outperforms all previously suggested training\nstrategies and is the first to consistently improve multimodal learning beyond\nthe ensemble baseline, clearly demonstrating that combining modalities leads to\nsignificant performance gains on both synthetic and large real-world datasets.",
        "split": "arXiv"
    },
    {
        "text": "In this paper we study the partition function of the mass deformed ABJM\ntheory on a squashed three sphere. In particular, we focus on the case with the\nChern-Simons levels being $\\pm 1$ and apply a duality between this theory and\nthe $\\mathcal{N}=4$ $\\mathrm{U}\\left(N\\right)$ super Yang-Mills theory with an\nadjoint hypermultiplet and a fundamental hypermultiplet. For a special mass\nparameter depending on the squashing parameter, we find that the partition\nfunction can be written as that of an ideal Fermi gas with a non-trivial\ndensity matrix. By studying this density matrix, we analytically derive the all\norder perturbative expansion of the partition function in $1/N$, which turns\nout to take the form of the Airy function. Our results not only align with\nprevious findings and conjectures but also lead to a new formula for the\noverall constant factor of the partition function. We also study the exact\nvalues of the partition function for small but finite values of $N$.",
        "split": "arXiv"
    },
    {
        "text": "Quantum mechanics started out as a theory to describe the smallest scales of\nenergy in Nature. After hundred years of development it is now routinely\nemployed to describe, for example, quantum computers with thousands of qubits.\nThis tremendous progress turns the debate of foundational questions into a\ntechnological imperative. In what follows we introduce a model of a quantum\nmeasurement process that consistently includes the impact of having access only\nto finite resources when describing a macroscopic system, like a measurement\napparatus. Leveraging modern tools from equilibration of closed systems and\ntypicality, we show how the collapse can be seen as an effective description of\na closed dynamics, of which we do not know all its details. Our model is then\nexploited to address the ``Wigner Friend Scenario'', and we observe that an\nagreement is reached when both Wigner and his friend acknowledge their finite\nresources perspective and describe the measurement process accordingly.",
        "split": "arXiv"
    },
    {
        "text": "We give very simple proofs of the classical results of Magnus and Hill on the\nspectral properties of the Hilbert matrix $$ H = \\left ( {1 \\over i+j+ 1 }\n\\right )_{i,j\\geq 0} $$ which defines a bounded linear operator on the sequence\nspace $\\ell^2$. In particular, we use the Mehler-Fock transform to find the\nspectrum and the latent eigenfunctions of the Hilbert matrix, that is, we show\nthat the spectrum of $H$ is $[0,\\pi]$ with no eigenvalues (Magnus' result) and\ndescribe all complex sequences $x$ such that $Hx=\\mu x$ for some complex number\n$\\mu$ (Hill's result).",
        "split": "arXiv"
    },
    {
        "text": "Background. Breast cancer screening programs using mammography have led to\nsignificant mortality reduction in high-income countries. However, many low-\nand middle-income countries lack resources for mammographic screening. Handheld\nbreast ultrasound (BUS) is a low-cost alternative but requires substantial\ntraining. Artificial intelligence (AI) enabled BUS may aid in both the\ndetection (perception) and classification (interpretation) of breast cancer.\nMaterials and Methods. This review (CRD42023493053) is reported in accordance\nwith the PRISMA (Preferred Reporting Items for Systematic Reviews and\nMeta-Analysis) and SWiM (Synthesis Without Meta-analysis) guidelines. PubMed\nand Google Scholar were searched from January 1, 2016 to December 12, 2023. A\nmeta-analysis was not attempted. Studies are grouped according to their AI task\ntype, application time, and AI task. Study quality is assessed using the\nQUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results.\nOf 763 candidate studies, 314 total full texts were reviewed. 34 studies are\nincluded. The AI tasks of included studies are as follows: 1 frame selection, 6\ndetection, 11 segmentation, and 16 classification. In total, 5.7 million BUS\nimages from over 185,000 patients were used for AI training or validation. A\nsingle study included a prospective testing set. 79% of studies were at high or\nunclear risk of bias. Conclusion. There has been encouraging development of AI\nfor BUS. Despite studies demonstrating high performance across all identified\ntasks, the evidence supporting AI-enhanced BUS generally lacks robustness.\nHigh-quality model validation will be key to realizing the potential for\nAI-enhanced BUS in increasing access to screening in resource-limited\nenvironments.",
        "split": "arXiv"
    },
    {
        "text": "Spatially-resolved emission line kinematics are invaluable to investigating\nfundamental galaxy properties and have become increasingly accessible for\ngalaxies at $z\\gtrsim0.5$ through sensitive near-infrared imaging spectroscopy\nand millimeter interferometry. Kinematic modeling is at the core of the\nanalysis and interpretation of such data sets, which at high-z present\nchallenges due to lower signal-to-noise ratio (S/N) and resolution compared to\ndata of local galaxies. We present and test the 3D fitting functionality of\nDysmalPy, examining how well it recovers intrinsic disk rotation velocity and\nvelocity dispersion, using a large suite of axisymmetric models, covering a\nrange of galaxy properties and observational parameters typical of $z\\sim1$-$3$\nstar-forming galaxies. We also compare DysmalPy's recovery performance to that\nof two other commonly used codes, GalPak3D and 3DBarolo, which we use in turn\nto create additional sets of models to benchmark DysmalPy. Over the ranges of\nS/N, resolution, mass, and velocity dispersion explored, the rotation velocity\nis accurately recovered by all tools. The velocity dispersion is recovered well\nat high S/N, but the impact of methodology differences is more apparent. In\nparticular, template differences for parametric tools and S/N sensitivity for\nthe non-parametric tool can lead to differences up to a factor of 2. Our tests\nhighlight and the importance of deep, high-resolution data and the need for\ncareful consideration of: (1) the choice of priors (parametric approaches), (2)\nthe masking (all approaches) and, more generally, evaluating the suitability of\neach approach to the specific data at hand. This paper accompanies the public\nrelease of DysmalPy.",
        "split": "arXiv"
    },
    {
        "text": "Design and manufacturing of integrated circuits predominantly use a globally\ndistributed semiconductor supply chain involving diverse entities. The modern\nsemiconductor supply chain has been designed to boost production efficiency,\nbut is filled with major security concerns such as malicious modifications\n(hardware Trojans), reverse engineering (RE), and cloning. While being\ndeployed, digital systems are also subject to a plethora of threats such as\npower, timing, and electromagnetic (EM) side channel attacks. Many\nDesign-for-Security (DFS) solutions have been proposed to deal with these\nvulnerabilities, and such solutions (DFS) relays on strategic modifications\n(e.g., logic locking, side channel resilient masking, and dummy logic\ninsertion) of the digital designs for ensuring a higher level of security.\nHowever, most of these DFS strategies lack robust formalism, are often not\nhuman-understandable, and require an extensive amount of human expert effort\nduring their development/use. All of these factors make it difficult to keep up\nwith the ever growing number of microelectronic vulnerabilities. In this work,\nwe propose X-DFS, an explainable Artificial Intelligence (AI) guided DFS\nsolution-space exploration approach that can dramatically cut down the\nmitigation strategy development/use time while enriching our understanding of\nthe vulnerability by providing human-understandable decision rationale. We\nimplement X-DFS and comprehensively evaluate it for reverse engineering threats\n(SAIL, SWEEP, and OMLA) and formalize a generalized mechanism for applying\nX-DFS to defend against other threats such as hardware Trojans, fault attacks,\nand side channel attacks for seamless future extensions.",
        "split": "arXiv"
    },
    {
        "text": "Computing photo-z for AGN is challenging, primarily due to the interplay of\nrelative emissions associated with the SMBH and its host galaxy. SED fitting\nmethods, effective in pencil-beam surveys, face limitations in all-sky surveys\nwith fewer bands available, lacking the ability to capture the AGN contribution\nto the SED accurately. This limitation affects the many 10s of millions of AGN\nclearly singled out and identified by SRG/eROSITA. Our goal is to significantly\nenhance photometric redshift performance for AGN in all-sky surveys while\navoiding the need to merge multiple data sets. Instead, we employ readily\navailable data products from the 10th Data Release of the Imaging Legacy Survey\nfor DESI, covering > 20,000 deg$^{2}$ with deep images and catalog-based\nphotometry in the grizW1-W4 bands. We introduce PICZL, a machine-learning\nalgorithm leveraging an ensemble of CNNs. Utilizing a cross-channel approach,\nthe algorithm integrates distinct SED features from images with those obtained\nfrom catalog-level data. Full probability distributions are achieved via the\nintegration of Gaussian mixture models. On a validation sample of 8098 AGN,\nPICZL achieves a variance $\\sigma_{\\textrm{NMAD}}$ of 4.5% with an outlier\nfraction $\\eta$ of 5.6%, outperforming previous attempts to compute accurate\nphoto-z for AGN using ML. We highlight that the model's performance depends on\nmany variables, predominantly the depth of the data. A thorough evaluation of\nthese dependencies is presented in the paper. Our streamlined methodology\nmaintains consistent performance across the entire survey area when accounting\nfor differing data quality. The same approach can be adopted for future deep\nphotometric surveys such as LSST and Euclid, showcasing its potential for\nwide-scale realisation. With this paper, we release updated photo-z (including\nerrors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.",
        "split": "arXiv"
    },
    {
        "text": "Gravitational Wave (GW) sources offer a valuable window to the physical\nprocesses that govern the formation of binary compact objects (BCOs). However,\ndeciphering such information from GW data is substantially challenging due to\nthe difficulty in mapping from the space of observation to the space of\nnumerous theoretical models. We introduce the concept of BCO Phase-Space that\nconnects the observable space to the evolution trajectories of the BCO\nformation channels with cosmic time and apply it to the third GW transient\ncatalog (GWTC-3) that brings new insights into probable astrophysical formation\nscenarios of nearly $90$ events. Our study reveals that two events, GW190425\nand GW230529, show an overlap with a $\\texttt{BCO Phase Space}$ trajectory of\nthe same formation channel arising from a sub-solar mass black hole scenario\nthat has grown into a higher mass by accretion, hinting towards the common\nprimordial origin of both these sources. Though the actual formation channel is\nyet to be confirmed, with the availability of more GW events, the $\\texttt{BCO\nPhase Space}$ can delve into distinguishing features of different formation\nchannels for both astrophysical and primordial origin and opens the possibility\nof bringing new and deeper insights on the formation and evolution of BCOs\nacross all observable masses over most of the cosmic time.",
        "split": "arXiv"
    },
    {
        "text": "Recent developments in particle physics have revealed deep connections\nbetween scattering amplitudes and tropical geometry. At the heart of this\nrelationship are the chirotropical Grassmannian $\\text{Trop}^\\chi\n\\text{G}(k,n)$ and chirotropical Dressian $\\text{Dr}^\\chi(k,n)$, polyhedral\nfans built from uniform realizable chirotopes that encode the combinatorial\nstructure of generalized Feynman diagrams. We prove that $\\text{Trop}^\\chi\n\\text{G}(3,n) = \\text{Dr}^\\chi(3,n)$ for $n = 6,7,8$, and develop algorithms to\ncompute these objects from their rays modulo lineality. Using these algorithms,\nwe compute all chirotropical Grassmannians $\\text{Trop}^\\chi \\text{G}(3,n)$ for\n$n = 6,7,8$ across all isomorphism classes of chirotopes. We prove that each\nchirotopal configuration space $X^\\chi(3,6)$ is diffeomorphic to a polytope\nwith an associated canonical logarithmic differential form. Finally, we show\nthat the equality between chirotropical Grassmannian and Dressian fails for\n$(k,n) = (4,8)$.",
        "split": "arXiv"
    },
    {
        "text": "Systems where multiple sources at different redshifts are strongly lensed by\nthe same deflector allow one to directly investigate the evolution of the\nangular diameter distances with redshift, and thus to learn about the geometry\nof the Universe. We present measurements of the values of the total matter\ndensity, $\\Omega_m$, and of the dark energy equation of state parameter, $w$,\nthrough a strong lensing analysis of SDSSJ0100+1818, a group-scale system at\n$z=0.581$ with five lensed sources, from $z=1.698$ to $4.95$. We use new MUSE\ndata to securely measure the redshift of 65 sources, including the five\nmultiply imaged background sources (lensed into a total of 18 multiple images)\nand 19 galaxies on the deflector plane (the brightest group galaxy, BGG, and 18\nfainter members), all employed to build robust strong lensing models with the\nsoftware GLEE. We measure $\\Omega_m = 0.14^{+0.16}_{-0.09}$ in a flat $\\Lambda$\ncold dark matter (CDM) model, and $\\Omega_m = 0.19^{+0.17}_{-0.10}$ and\n$w=-1.27_{-0.48}^{+0.43}$ in a flat $w$CDM model. We quantify, through a\nmulti-plane approach, the impact of different sources angularly close in\nprojection on the inferred values of the cosmological parameters. We obtain\nconsistent median values, with uncertainties for only $\\Omega_m$ increasing by\na factor of 1.5. We accurately measure a total mass of $(1.55 \\pm 0.01) \\times\n10^{13}$ M$_\\odot$ within 50 kpc and a stellar over total mass profile\ndecreasing from $45.6^{+8.7}_{-8.3}\\%$ at the BGG effective radius to $(6.6\\pm\n1.1)\\%$ at $R\\approx 77$ kpc. Our results confirm that SDSSJ0100+1818 is one of\nthe most massive (lens) galaxies known at intermediate redshift and that\ngroup-scale systems that act as lenses for $\\geq 3$ background sources at\ndifferent redshifts enable to estimate the values of the cosmological\nparameters with an accuracy that is competitive with that obtained from lens\ngalaxy clusters.",
        "split": "arXiv"
    },
    {
        "text": "Gauged boson stars are exotic compact objects that can potentially mimic\nblack holes or magnetized neutron stars in both their gravitational and\nelectromagnetic signatures, offering a compelling new description or even an\nalternative explanation for various multimessenger phenomena. As a crucial step\ntoward establishing boson stars as viable multimessenger sources, we perform 3D\nnumerical simulations of the fully nonlinear Einstein-Maxwell-Klein-Gordon\nsystem, focusing on both spherical and axisymmetric boson star configurations\nthat vary in their electromagnetic coupling between the neutral case up to\nvalues close to the critical case, and so their magnetic field content. For\nspherical configurations, we consistently find stable solutions. In contrast,\nfor axially symmetric, electrically neutral, magnetized configurations, the\ndynamics are highly sensitive to the electromagnetic coupling. Configurations\nwith stronger coupling develop a one-armed mode instability, which leads to\ncollapse into black holes. Configurations with weaker coupling undergo a\ntwo-stage process: an initial bar-mode instability that triggers a one-armed\nspiral deformation. This eventually also results in black hole formation,\naccompanied by emissions of both gravitational and electromagnetic radiation. A\nsimilar instability and two-stage pattern is observed in all charged rotating\nboson stars analyzed. However, all of these configurations become stable when\nself-interactions are introduced.",
        "split": "arXiv"
    },
    {
        "text": "The datasets used for Deep Neural Network training (e.g., ImageNet, MSCOCO,\netc.) are often manually balanced across categories (classes) to facilitate\nlearning of all the categories. This curation process is often expensive and\nrequires throwing away precious annotated data to balance the frequency across\nclasses. This is because the distribution of data in the world (e.g., internet,\netc.) significantly differs from the well-curated datasets and is often\nover-populated with samples from common categories. The algorithms designed for\nwell-curated datasets perform suboptimally when used to learn from imperfect\ndatasets with long-tailed imbalances and distribution shifts. For deep models\nto be widely used, getting away with the costly curation process by developing\nrobust algorithms that can learn from real-world data distribution is\nnecessary. Toward this goal, we develop practical algorithms for Deep Neural\nNetworks that can learn from limited and imperfect data present in the real\nworld. These works are divided into four segments, each covering a scenario of\nlearning from limited or imperfect data. The first part of the works focuses on\nLearning Generative Models for Long-Tail Data, where we mitigate the\nmode-collapse for tail (minority) classes and enable diverse aesthetic image\ngenerations as head (majority) classes. In the second part, we enable effective\ngeneralization on tail classes through Inductive Regularization schemes, which\nallow tail classes to generalize as the head classes without enforcing explicit\ngeneration of images. In the third part, we develop algorithms for Optimizing\nRelevant Metrics compared to the average accuracy for learning from long-tailed\ndata with limited annotation (semi-supervised), followed by the fourth part,\nwhich focuses on the effective domain adaptation of the model to various\ndomains with zero to very few labeled samples.",
        "split": "arXiv"
    },
    {
        "text": "The Fermi-edge singularity in x-ray absorption spectra of metals is a\nparadigmatic case of a logarithmically divergent perturbation series. Prior\nwork has thoroughly analyzed the leading logarithmic terms. Here, we\ninvestigate the perturbation theory beyond leading logarithms and formulate\nself-consistent equations to incorporate all leading and next-to-leading\nlogarithmic terms. This parquet solution of the Fermi-edge singularity goes\nbeyond the previous first-order parquet solution and sheds new light on the\nparquet formalism regarding logarithmic behavior. We present numerical results\nin the Matsubara formalism and discuss the characteristic power laws. We also\nshow that, within the single-boson exchange framework, multi-boson exchange\ndiagrams are needed already at the leading logarithmic level.",
        "split": "arXiv"
    },
    {
        "text": "Entanglement can improve the measurement precision of quantum sensors beyond\nthe shot noise limit. Neutral atoms, the basis of some of the most precise and\naccurate optical clocks and interferometers, do not naturally exhibit\nall-to-all interactions that are traditionally used to generate such entangled\nstates. Instead, we take advantage of the magnetic dipole-dipole interaction\nnative to most neutral atoms to realize spin-squeezed states. We achieve 7.1 dB\nof metrologically useful squeezing using the finite-range spin exchange\ninteractions in an erbium quantum gas microscope. We further propose and\ndemonstrate that introducing atomic motion protects the spin sector coherence\nat low fillings, significantly improving the achievable spin squeezing in a 2D\ndipolar system. This work's protocol can be implemented with most neutral\natoms, opening the door to quantum-enhanced metrology in other itinerant\ndipolar systems, such as molecules or optical lattice clocks, and serves as a\nnovel method for studying itinerant quantum magnetism with long-range\ninteractions.",
        "split": "arXiv"
    },
    {
        "text": "User-schedulable languages (USLs) help programmers productively optimize\nprograms by providing safe means of transforming them. Current USLs are\ndesigned to give programmers exactly the control they want, while automating\nall other concerns. However, there is no universal answer for what\nperformance-conscious programmers want to control, how they want to control it,\nand what they want to automate, even in relatively narrow domains.\n  We claim that USLs should, instead, be designed to grow. We present Exo 2, a\nscheduling language that enables users to define new scheduling operations\nexternally to the compiler. By composing a set of trusted, fine-grained\nprimitives, users can safely write their own scheduling library to build up\ndesired automation. We identify actions (ways of modifying code), inspection\n(ways of interrogating code), and references (ways of pointing to code) as\nessential for any user-extensible USL.\n  We fuse these ideas into a new mechanism called Cursors that enables the\ncreation of scheduling libraries in user code. We demonstrate libraries that\namortize scheduling effort across more than 80 high-performance kernels,\nreducing total scheduling code by an order of magnitude and delivering\nperformance competitive with state-of-the-art implementations on three\ndifferent platforms.",
        "split": "arXiv"
    },
    {
        "text": "Supporting the health and well-being of dynamic populations around the world\nrequires governmental agencies, organizations and researchers to understand and\nreason over complex relationships between human behavior and local contexts in\norder to identify high-risk groups and strategically allocate limited\nresources. Traditional approaches to these classes of problems often entail\ndeveloping manually curated, task-specific features and models to represent\nhuman behavior and the natural and built environment, which can be challenging\nto adapt to new, or even, related tasks. To address this, we introduce a\nPopulation Dynamics Foundation Model (PDFM) that aims to capture the\nrelationships between diverse data modalities and is applicable to a broad\nrange of geospatial tasks. We first construct a geo-indexed dataset for postal\ncodes and counties across the United States, capturing rich aggregated\ninformation on human behavior from maps, busyness, and aggregated search\ntrends, and environmental factors such as weather and air quality. We then\nmodel this data and the complex relationships between locations using a graph\nneural network, producing embeddings that can be adapted to a wide range of\ndownstream tasks using relatively simple models. We evaluate the effectiveness\nof our approach by benchmarking it on 27 downstream tasks spanning three\ndistinct domains: health indicators, socioeconomic factors, and environmental\nmeasurements. The approach achieves state-of-the-art performance on all 27\ngeospatial interpolation tasks, and on 25 out of the 27 extrapolation and\nsuper-resolution tasks. We combined the PDFM with a state-of-the-art\nforecasting foundation model, TimesFM, to predict unemployment and poverty,\nachieving performance that surpasses fully supervised forecasting. The full set\nof embeddings and sample code are publicly available for researchers.",
        "split": "arXiv"
    },
    {
        "text": "Instruction-guided image editing methods have demonstrated significant\npotential by training diffusion models on automatically synthesized or manually\nannotated image editing pairs. However, these methods remain far from\npractical, real-life applications. We identify three primary challenges\ncontributing to this gap. Firstly, existing models have limited editing skills\ndue to the biased synthesis process. Secondly, these methods are trained with\ndatasets with a high volume of noise and artifacts. This is due to the\napplication of simple filtering methods like CLIP-score. Thirdly, all these\ndatasets are restricted to a single low resolution and fixed aspect ratio,\nlimiting the versatility to handle real-world use cases. In this paper, we\npresent \\omniedit, which is an omnipotent editor to handle seven different\nimage editing tasks with any aspect ratio seamlessly. Our contribution is in\nfour folds: (1) \\omniedit is trained by utilizing the supervision from seven\ndifferent specialist models to ensure task coverage. (2) we utilize importance\nsampling based on the scores provided by large multimodal models (like GPT-4o)\ninstead of CLIP-score to improve the data quality. (3) we propose a new editing\narchitecture called EditNet to greatly boost the editing success rate, (4) we\nprovide images with different aspect ratios to ensure that our model can handle\nany image in the wild. We have curated a test set containing images of\ndifferent aspect ratios, accompanied by diverse instructions to cover different\ntasks. Both automatic evaluation and human evaluations demonstrate that\n\\omniedit can significantly outperform all the existing models. Our code,\ndataset and model will be available at\n\\url{https://tiger-ai-lab.github.io/OmniEdit/}",
        "split": "arXiv"
    },
    {
        "text": "The negatively charged nickel vacancy center (NiV$^-$) in diamond is a\npromising spin qubit candidate with predicted inversion symmetry, large ground\nstate spin orbit splitting to limit phonon-induced decoherence, and emission in\nthe near-infrared. Here, we experimentally confirm the proposed geometric and\nelectronic structure of the NiV defect via magneto-optical spectroscopy. We\ncharacterize the optical properties and find a Debye-Waller factor of 0.62.\nAdditionally, we engineer charge state stabilized defects using electrical bias\nin all-diamond p-i-p junctions. We measure a vanishing static dipole moment and\nno spectral diffusion, characteristic of inversion symmetry. Under bias, we\nobserve stable transitions with lifetime limited linewidths as narrow as\n16\\,MHz and convenient frequency tuning of the emission via a second order\nStark shift. Overall, this work provides a pathway towards coherent control of\nthe NiV$^-$ and its use as a spin qubit and contributes to a more general\nunderstanding of charge dynamics experienced by defects in diamond.",
        "split": "arXiv"
    },
    {
        "text": "We generate different orders of quantum coherence in a three-qubit NMR system\nand study their dynamics in the presence of inherent noise. Robust dynamical\ndecoupling (DD) sequences are applied to preserve the different coherence\norders. Initially, DD sequences are implemented simultaneously on all three\nspins, which effectively protects third-order coherence; however, other\ncoherence orders decay rapidly instead of being preserved. The robust DD\nsequences were suitably modified in order to preserve other coherence orders.\nThese sequences are applied to the two participating qubits that generate each\nzero and second order coherence, ensuring their effective preservation. In\ncontrast, first-order coherence is preserved more efficiently when DD sequences\nare applied exclusively on the qubit responsible for generating it. Instead of\nperforming full state tomography, coherence orders are measured directly using\nsingle pulses. The robust DD protection schemes are finally applied to\nsuccessfully protect two-qubit entanglement in three-qubit star states.",
        "split": "arXiv"
    },
    {
        "text": "Age of incorrect information (AoII) is a recently proposed freshness and\nmismatch metric that penalizes an incorrect estimation along with its duration.\nTherefore, keeping track of AoII requires the knowledge of both the source and\nestimation processes. In this paper, we consider a time-slotted pull-based\nremote estimation system under a sampling rate constraint where the information\nsource is a general discrete-time Markov chain (DTMC) process. Moreover, packet\ntransmission times from the source to the monitor are non-zero which disallows\nthe monitor to have perfect information on the actual AoII process at any time.\nHence, for this pull-based system, we propose the monitor to maintain a\nsufficient statistic called {\\em belief} which stands for the joint\ndistribution of the age and source processes to be obtained from the history of\nall observations. Using belief, we first propose a maximum a posteriori (MAP)\nestimator to be used at the monitor as opposed to existing martingale\nestimators in the literature. Second, we obtain the optimality equations from\nthe belief-MDP (Markov decision process) formulation. Finally, we propose two\nbelief-dependent policies one of which is based on deep reinforcement learning,\nand the other one is a threshold-based policy based on the instantaneous\nexpected AoII.",
        "split": "arXiv"
    },
    {
        "text": "Let C[0, 1] be the Banach space of all continuous real valued functions on\n[0, 1]. For an arbitrarily given nonnegative integer n, let Pn denote the set\nof all polynomials with degree less than or equal to n. Pn is a closed subspace\nof C[0, 1]. In this paper, we first prove (in details) that the metric\nprojection operator from C[0, 1] to Pn is a single-valued mapping and it is\n(norm to norm) continuous. Then, we use the continuity of Pn to investigate the\nGateaux directional derivatives, some properties and fixed-point properties of\nthe Mordukhovich derivatives of the metric projection operator.",
        "split": "arXiv"
    },
    {
        "text": "The nature of gravity can be tested by how gravitational waves (GWs) are\nemitted, detected, and propagate through the universe. Propagation tests are\npowerful, as small deviations compound over cosmological distances. However, GW\npropagation tests of theories beyond Einstein's general relativity (GR) are\nlimited by the high degree of symmetry of the average cosmological spacetime.\nDeviations from homogeneity, i.e. gravitational lenses, allow for new\ninteractions, e.g., between standard GW polarization and new scalar or vector\nfields, with different spin. Therefore, GW lensing beyond GR offers novel tests\nof cosmological gravity. Here we present the theory of GW propagation beyond GR\nin the short-wave expansion, including corrections to the leading-order\namplitude and phase for the first time. As an example, we compute the\ndispersive (frequency-dependent) corrections to all metric and scalar field\nperturbations in Brans-Dicke, the simplest modified theory exhibiting GW\ndispersion. GW lensing effects are too small to observe in Brans-Dicke theories\ncompatible with solar system and binary pulsar limits. Nevertheless, our\nformalism opens the possibility of novel tests of gravity, including\ndark-energy theories and screening mechanisms.",
        "split": "arXiv"
    },
    {
        "text": "A search for pair production of vector-like leptons coupling to first- and\nsecond-generation Standard Model leptons is presented. The search is based on a\ndataset of proton-proton collisions at $\\sqrt{s}=13$ TeV recorded with the\nATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an\nintegrated luminosity of 140 fb$^{-1}$. Events are categorised depending on the\nflavour and multiplicity of leptons (electrons or muons), as well as on the\nscores of a deep neural network targeting particular signal topologies\naccording to the decay modes of the vector-like leptons. In each of the signal\nregions, the scalar sum of the transverse momentum of the leptons and the\nmissing transverse momentum is analysed. The main background processes are\nestimated using dedicated control regions in a simultaneous fit with the signal\nregions to data. No significant excess above the Standard Model background\nexpectation is observed and limits are set at 95% confidence level on the\nproduction cross-sections of vector-like electrons and muons as a function of\nthe vector-like lepton mass, separately for SU(2) doublet and singlet\nscenarios. The resulting mass lower limits are 1220 GeV (1270 GeV) and 320 GeV\n(400 GeV) for vector-like electrons (muons) in the doublet and singlet\nscenarios, respectively.",
        "split": "arXiv"
    },
    {
        "text": "Instruction tuning has been widely adopted to ensure large language models\n(LLMs) follow user instructions effectively. The resulting\ninstruction-following capabilities of LLMs heavily rely on the instruction\ndatasets used for tuning. Recently, synthetic instruction datasets have emerged\nas an economically viable solution to provide LLMs diverse and high-quality\ninstructions. However, existing approaches typically assume that larger or\nstronger models are stronger teachers for instruction tuning, and hence simply\nadopt these models as response generators to the synthetic instructions. In\nthis paper, we challenge this commonly-adopted assumption. Our extensive\nexperiments across five base models and twenty response generators reveal that\nlarger and stronger models are not necessarily stronger teachers of smaller\nmodels. We refer to this phenomenon as the Larger Models' Paradox. We observe\nthat existing metrics cannot precisely predict the effectiveness of response\ngenerators since they ignore the compatibility between teachers and base models\nbeing fine-tuned. We thus develop a novel metric, named as\nCompatibility-Adjusted Reward (CAR) to measure the effectiveness of response\ngenerators. Our experiments across five base models demonstrate that CAR\noutperforms almost all baselines.",
        "split": "arXiv"
    },
    {
        "text": "Although text-to-image (T2I) models exhibit remarkable generation\ncapabilities, they frequently fail to accurately bind semantically related\nobjects or attributes in the input prompts; a challenge termed semantic\nbinding. Previous approaches either involve intensive fine-tuning of the entire\nT2I model or require users or large language models to specify generation\nlayouts, adding complexity. In this paper, we define semantic binding as the\ntask of associating a given object with its attribute, termed attribute\nbinding, or linking it to other related sub-objects, referred to as object\nbinding. We introduce a novel method called Token Merging (ToMe), which\nenhances semantic binding by aggregating relevant tokens into a single\ncomposite token. This ensures that the object, its attributes and sub-objects\nall share the same cross-attention map. Additionally, to address potential\nconfusion among main objects with complex textual prompts, we propose end token\nsubstitution as a complementary strategy. To further refine our approach in the\ninitial stages of T2I generation, where layouts are determined, we incorporate\ntwo auxiliary losses, an entropy loss and a semantic binding loss, to\niteratively update the composite token to improve the generation integrity. We\nconducted extensive experiments to validate the effectiveness of ToMe,\ncomparing it against various existing methods on the T2I-CompBench and our\nproposed GPT-4o object binding benchmark. Our method is particularly effective\nin complex scenarios that involve multiple objects and attributes, which\nprevious methods often fail to address. The code will be publicly available at\n\\url{https://github.com/hutaihang/ToMe}.",
        "split": "arXiv"
    },
    {
        "text": "Language models (LMs) have demonstrated an improved capacity to handle\nlong-context information, yet existing long-context benchmarks primarily\nmeasure LMs' retrieval abilities with extended inputs, e.g., pinpointing a\nshort phrase from long-form text. Therefore, they may fall short when\nevaluating models' global context understanding capacity, such as synthesizing\nand reasoning over content across input to generate the response. In this\npaper, we study long-context language model (LCLM) evaluation through many-shot\nin-context learning (ICL). Concretely, we identify the skills each ICL task\nrequires, and examine models' long-context capabilities on them. We first ask:\nWhat types of ICL tasks benefit from additional demonstrations, and are these\ntasks effective at evaluating LCLMs? We find that classification and\nsummarization tasks show notable performance improvements with additional\ndemonstrations, while translation and reasoning tasks do not exhibit clear\ntrends. This suggests the classification tasks predominantly test models'\nretrieval skills. Next, we ask: To what extent does each task require retrieval\nskills versus global context understanding from LCLMs? We develop metrics to\ncategorize ICL tasks into two groups: (i) retrieval tasks that require strong\nretrieval ability to pinpoint relevant examples, and (ii) global context\nunderstanding tasks that necessitate a deeper comprehension of the full input.\nWe find that not all datasets can effectively evaluate these long-context\ncapabilities. To address this gap, we introduce a new many-shot ICL benchmark,\nMANYICLBENCH, designed to characterize LCLMs' retrieval and global context\nunderstanding capabilities separately. Benchmarking 11 open-weight LCLMs with\nMANYICLBENCH, we find that while state-of-the-art models perform well in\nretrieval tasks up to 64k tokens, many show significant drops in global context\ntasks at just 16k tokens.",
        "split": "arXiv"
    },
    {
        "text": "We introduce the GEM (Generative Estimator for Mutual Information), an\nevaluation metric for assessing language generation by Large Language Models\n(LLMs), particularly in generating informative judgments, without the need for\na gold standard reference. GEM broadens the scenarios where we can benchmark\nLLM generation performance-from traditional ones, like machine translation and\nsummarization, where gold standard references are readily available, to\nsubjective tasks without clear gold standards, such as academic peer review.\n  GEM uses a generative model to estimate mutual information between candidate\nand reference responses, without requiring the reference to be a gold standard.\nIn experiments on a human-annotated dataset, GEM demonstrates competitive\ncorrelations with human scores compared to the state-of-the-art GPT-4o\nExaminer, and outperforms all other baselines. Additionally, GEM is more robust\nagainst strategic manipulations, such as rephrasing or elongation, which can\nartificially inflate scores under a GPT-4o Examiner.\n  We also present GRE-bench (Generating Review Evaluation Benchmark) which\nevaluates LLMs based on how well they can generate high-quality peer reviews\nfor academic research papers. Because GRE-bench is based upon GEM, it inherits\nits robustness properties. Additionally, GRE-bench circumvents data\ncontamination problems (or data leakage) by using the continuous influx of new\nopen-access research papers and peer reviews each year. We show GRE-bench\nresults of various popular LLMs on their peer review capabilities using the\nICLR2023 dataset.",
        "split": "arXiv"
    },
    {
        "text": "We provide a complete characterization of the solvability/impossibility of\ndeterministic stabilizing consensus in any computing model with benign process\nand communication faults using point-set topology. Relying on the topologies\nfor infinite executions introduced by Nowak, Schmid and Winkler (JACM, 2024)\nfor terminating consensus, we prove that semi-open decision sets and\nsemi-continuous decision functions as introduced by Levin (AMM, 1963) are the\nappropriate means for this characterization: Unlike the decision functions for\nterminating consensus, which are continuous, semi-continuous functions do not\nrequire the inverse image of an open set to be open and hence allow to map a\nconnected space to a disconnected one. We also show that multi-valued\nstabilizing consensus with weak and strong validity are equivalent, as is the\ncase for terminating consensus. By applying our results to (variants of) all\nthe known possibilities/impossibilities for stabilizing consensus, we easily\nprovide a topological explanation of these results.",
        "split": "arXiv"
    },
    {
        "text": "Let $F(z)=\\prod\\limits_{k=1}^n(z-z_k)$ be a monic complex polynomial of\ndegree $n$. In 1998, Pawlowski [Trans. Amer. Math. Soc. 350 (1998)] studied the\nradius $\\gamma_n$ of the smallest concentric disk with center at\n$\\tfrac{\\sum\\limits_{k=1}^nz_k}{n}$ contained at least one critical point of\n$F(z)$. He showed that $\\gamma_n\\le\n\\tfrac{2n^\\frac{1}{n-1}}{n^\\frac{2}{n-1}+1}$. In this paper, we refine\nPawlowski's result in the spirit of Borcea variance conjectures and classic\nSchoenberg inequality, specifically, we show that\n$\\gamma_n\\le\\sqrt{\\tfrac{n-2}{n-1}}$ in a very concise manner. Moreover, we\nobtain various generalizations of Schoenberg inequalities based on classic\nSchoenberg inequality including refining Lin-Xie-Zhang's result [J. Math. Anal.\nAppl. 502 (2021)], which is inspired by the author's recent work on\nClarkson-McCarthy inequalities [arXiv:2410.21961] . Finally, we make\n$D$-companion matrix introduced by Cheung-Ng [Linear Algebra Appl. 432 (2010)]\nand operator inequalities involved Schatten $p$-norm react so that we provide\nan additional relationship between the zeros of $F(z)$ and its critical points\nin the case where all $z_k\\ge 0$, which can be regarded as complements of\nSchmeisser's result [Comput. Methods Funct. Theory 3 (2003)]. By an operator\n2-norm identity, we also prove Sendov conjecture with a condition that depends\nonly on $\\left(\n\\tfrac{1}{n-1}\\sum\\limits_{k=1}^{n-1}\\left|z_k-z_n\\right|^2\\right)^\\frac{1}{2}$.",
        "split": "arXiv"
    },
    {
        "text": "Uncertainty Quantification (UQ) is crucial for reliable image segmentation.\nYet, while the field sees continual development of novel methods, a lack of\nagreed-upon benchmarks limits their systematic comparison and evaluation:\nCurrent UQ methods are typically tested either on overly simplistic toy\ndatasets or on complex real-world datasets that do not allow to discern true\nuncertainty. To unify both controllability and complexity, we introduce\nArctique, a procedurally generated dataset modeled after histopathological\ncolon images. We chose histopathological images for two reasons: 1) their\ncomplexity in terms of intricate object structures and highly variable\nappearance, which yields challenging segmentation problems, and 2) their broad\nprevalence for medical diagnosis and respective relevance of high-quality UQ.\nTo generate Arctique, we established a Blender-based framework for 3D scene\ncreation with intrinsic noise manipulation. Arctique contains 50,000 rendered\nimages with precise masks as well as noisy label simulations. We show that by\nindependently controlling the uncertainty in both images and labels, we can\neffectively study the performance of several commonly used UQ methods. Hence,\nArctique serves as a critical resource for benchmarking and advancing UQ\ntechniques and other methodologies in complex, multi-object environments,\nbridging the gap between realism and controllability. All code is publicly\navailable, allowing re-creation and controlled manipulations of our shipped\nimages as well as creation and rendering of new scenes.",
        "split": "arXiv"
    },
    {
        "text": "We consider the problem of collaborative personalized mean estimation under a\nprivacy constraint in an environment of several agents continuously receiving\ndata according to arbitrary unknown agent-specific distributions. In\nparticular, we provide a method based on hypothesis testing coupled with\ndifferential privacy and data variance estimation. Two privacy mechanisms and\ntwo data variance estimation schemes are proposed, and we provide a theoretical\nconvergence analysis of the proposed algorithm for any bounded unknown\ndistributions on the agents' data, showing that collaboration provides faster\nconvergence than a fully local approach where agents do not share data.\nMoreover, we provide analytical performance curves for the case with an oracle\nclass estimator, i.e., the class structure of the agents, where agents\nreceiving data from distributions with the same mean are considered to be in\nthe same class, is known. The theoretical faster-than-local convergence\nguarantee is backed up by extensive numerical results showing that for a\nconsidered scenario the proposed approach indeed converges much faster than a\nfully local approach, and performs comparably to ideal performance where all\ndata is public. This illustrates the benefit of private collaboration in an\nonline setting.",
        "split": "arXiv"
    },
    {
        "text": "The celebrated Andr\\'{a}sfai--Erd\\H{o}s--S\\'{o}s Theorem from 1974 shows that\nevery $n$-vertex triangle-free graph with minimum degree greater than $2n/5$\nmust be bipartite.\n  We establish a positive codegree extension of this result for the $r$-uniform\ngeneralized triangle $\\mathrm{T}_{r} = \\left\\{\\{1,\\ldots, r-1,r\\}, \\{1,\\ldots,\nr-1,r+1\\},\\{r,r+1, \\ldots, 2r-1\\}\\right\\}$$\\colon$ For every $n \\ge\n(r-1)(2r+1)/2$, if $\\mathcal{H}$ is an $n$-vertex $\\mathrm{T}_{r}$-free\n$r$-uniform hypergraph in which each $(r-1)$-tuple of vertices is contained in\neither zero edges or more than $2n/(2r+1)$ edges of $\\mathcal{H}$, then\n$\\mathcal{H}$ is $r$-partite. This result provides the first tight positive\ncodegree Andr{\\'a}sfai--Erd\\H{o}s--S\\'{o}s type theorem for hypergraphs. It\nalso immediately implies that the positive codegree Tur\\'{a}n number of\n$\\mathrm{T}_{r}$ is $\\lfloor n/r \\rfloor$ for all $r$. Additionally, for $r=3$,\nour result answers one of the questions posed by Hou et al.~\\cite{HLYZZ22} in a\nstrong form.",
        "split": "arXiv"
    },
    {
        "text": "We conducted the MeerKAT Vela Supercluster survey, named Vela$-$HI, to bridge\nthe gap between the Vela SARAO MeerKAT Galactic Plane Survey (Vela$-$SMGPS,\n$-2^{\\circ} \\leq b \\leq 1^{\\circ}$), and optical and near-infrared\nspectroscopic observations of the Vela Supercluster (hereafter Vela$-$OPT/NIR)\nat $|b| \\gtrsim 7^{\\circ}$. Covering coordinates from $263^{\\circ} \\leq \\ell\n\\leq 284^{\\circ}$ and $1^{\\circ} \\leq b \\leq 6.2^{\\circ}$ above, and\n$-6.7^{\\circ} \\leq b \\leq -2^{\\circ}$ below the Galactic Plane (GP), we sampled\n667 fields spread across an area of ${\\sim} \\rm 242 ~deg^2$. With a beam size\nof ${\\sim} 38'' \\times 31''$, Vela$-$HI achieved a sensitivity of $\\langle \\rm\nrms \\rangle = 0.74$ mJy beam$^{-1}$ at 44.3 km s$^{-1}$ velocity resolution\nover ${\\sim}$67 hours of observations. We cataloged 719 galaxies, with only 211\n(29%) previously documented in the literature, primarily through the HIZOA,\n2MASX, and WISE databases. Among these known galaxies, only 66 had optical\nspectroscopic redshift information. We found marginal differences of less than\none channel resolution for all galaxies in common between HIZOA and\nVela$-$SMGPS, and a mean difference of $70 \\pm 15$ km s$^{-1}$ between optical\nand HI velocities. Combining data from Vela$-$SMGPS, Vela$-$HI, and\nVela$-$OPT/NIR confirmed the connection of the Hydra/Antlia filament across the\nGP and revealed a previously unknown diagonal wall at a heliocentric velocity\nrange of $6500-8000$ km s$^{-1}$. Vela$-$HI reinforces the connection between\nthe first wall at $18500-20000$ km s$^{-1}$ and the inner ZOA. The second wall\nseems to traverse the GP at $270^{\\circ} \\leq \\ell \\leq 279^{\\circ}$, where it\nappears that both walls intersect, jointly covering the velocity range\n$18500-21500$ km s$^{-1}$.",
        "split": "arXiv"
    },
    {
        "text": "Existing large vision-language models (LVLMs) are largely limited to\nprocessing short, seconds-long videos and struggle with generating coherent\ndescriptions for extended video spanning minutes or more. Long video\ndescription introduces new challenges, such as plot-level consistency across\ndescriptions. To address these, we figure out audio-visual character\nidentification, matching character names to each dialogue, as a key factor. We\npropose StoryTeller, a system for generating dense descriptions of long videos,\nincorporating both low-level visual concepts and high-level plot information.\nStoryTeller uses a multimodal large language model that integrates visual,\naudio, and text modalities to perform audio-visual character identification on\nminute-long video clips. The results are then fed into a LVLM to enhance\nconsistency of video description. We validate our approach on movie description\ntasks and introduce MovieStory101, a dataset with dense descriptions for\nthree-minute movie clips. To evaluate long video descriptions, we create\nMovieQA, a large set of multiple-choice questions for the MovieStory101 test\nset. We assess descriptions by inputting them into GPT-4 to answer these\nquestions, using accuracy as an automatic evaluation metric. Experiments show\nthat StoryTeller outperforms all open and closed-source baselines on MovieQA,\nachieving 9.5% higher accuracy than the strongest baseline, Gemini-1.5-pro, and\ndemonstrating a +15.56% advantage in human side-by-side evaluations.\nAdditionally, incorporating audio-visual character identification from\nStoryTeller improves the performance of all video description models, with\nGemini-1.5-pro and GPT-4o showing relative improvement of 5.5% and 13.0%,\nrespectively, in accuracy on MovieQA.",
        "split": "arXiv"
    },
    {
        "text": "To predict upcoming text, language models must in some cases retrieve\nin-context information verbatim. In this report, we investigated how the\nability of language models to retrieve arbitrary in-context nouns developed\nduring training (across time) and as language models trained on the same\ndataset increase in size (across scale). We then asked whether learning of\nin-context retrieval correlates with learning of more challenging zero-shot\nbenchmarks. Furthermore, inspired by semantic effects in human short-term\nmemory, we evaluated the retrieval with respect to a major semantic component\nof target nouns, namely whether they denote a concrete or abstract entity, as\nrated by humans. We show that verbatim in-context retrieval developed in a\nsudden transition early in the training process, after about 1% of the training\ntokens. This was observed across model sizes (from 14M and up to 12B\nparameters), and the transition occurred slightly later for the two smallest\nmodels. We further found that the development of verbatim in-context retrieval\nis positively correlated with the learning of zero-shot benchmarks. Around the\ntransition point, all models showed the advantage of retrieving concrete nouns\nas opposed to abstract nouns. In all but two smallest models, the advantage\ndissipated away toward the end of training.",
        "split": "arXiv"
    },
    {
        "text": "While induction is considered a key mechanism for in-context learning in\nLLMs, understanding its precise circuit decomposition beyond toy models remains\nelusive. Here, we study the emergence of induction behavior within LLMs by\nprobing their response to weak single-token perturbations of the residual\nstream. We find that LLMs exhibit a robust, universal regime in which their\nresponse remains scale-invariant under changes in perturbation strength,\nthereby allowing us to quantify the build-up of token correlations throughout\nthe model. By applying our method, we observe signatures of induction behavior\nwithin the residual stream of Gemma-2-2B, Llama-3.2-3B, and GPT-2-XL. Across\nall models, we find that these induction signatures gradually emerge within\nintermediate layers and identify the relevant model sections composing this\nbehavior. Our results provide insights into the collective interplay of\ncomponents within LLMs and serve as a benchmark for large-scale circuit\nanalysis.",
        "split": "arXiv"
    },
    {
        "text": "We study $p$-Wasserstein spaces over the branching spaces $\\mathbb{R}^2$ and\n$[0,1]^2$ equipped with the maximum norm metric. We show that these spaces are\nisometrically rigid for all $p\\geq1,$ meaning that all isometries of these\nspaces are induced by isometries of the underlying space via the push-forward\noperation. This is in contrast to the case of the Euclidean metric since with\nthat distance the $2$-Wasserstein space over $\\mathbb{R}^2$ is not rigid. Also,\nwe highlight that $1$-Wasserstein space is not rigid over the interval $[0,1]$,\nwhile according to our result, its two-dimensional analog with the more\ncomplicated geodesic structure is rigid. To the best of our knowledge, our\nresults are the first ones concerning the structure of isometries of\nWasserstein spaces over branching spaces. Accordingly, our proof techniques\nsubstantially deviate from those used for non-branching underlying spaces.",
        "split": "arXiv"
    },
    {
        "text": "Partially Observable Markov Decision Processes (POMDPs) are a general and\nprincipled framework for motion planning under uncertainty. Despite tremendous\nimprovement in the scalability of POMDP solvers, long-horizon POMDPs (e.g.,\n$\\geq15$ steps) remain difficult to solve. This paper proposes a new\napproximate online POMDP solver, called Reference-Based Online POMDP Planning\nvia Rapid State Space Sampling (ROP-RaS3). ROP-RaS3 uses novel extremely fast\nsampling-based motion planning techniques to sample the state space and\ngenerate a diverse set of macro actions online which are then used to bias\nbelief-space sampling and infer high-quality policies without requiring\nexhaustive enumeration of the action space -- a fundamental constraint for\nmodern online POMDP solvers. ROP-RaS3 is evaluated on various long-horizon\nPOMDPs, including on a problem with a planning horizon of more than 100 steps\nand a problem with a 15-dimensional state space that requires more than 20 look\nahead steps. In all of these problems, ROP-RaS3 substantially outperforms other\nstate-of-the-art methods by up to multiple folds.",
        "split": "arXiv"
    },
    {
        "text": "An intelligent tutoring system (ITS) aims to provide instructions and\nexercises tailored to the ability of a student. To do this, the ITS needs to\nestimate the ability based on student input. Rather than including frequent\nfull-scale tests to update our ability estimate, we want to base estimates on\nthe outcomes of practice exercises that are part of the learning process. A\nchallenge with this approach is that the ability changes as the student learns,\nwhich makes traditional item response theory (IRT) models inappropriate. Most\nIRT models estimate an ability based on a test result, and assume that the\nability is constant throughout a test.\n  We review some existing methods for measuring abilities that change\nthroughout the measurement period, and propose a new method which we call the\nElo-informed growth model. This method assumes that the abilities for a group\nof respondents who are all in the same stage of the learning process follow a\ndistribution that can be estimated. The method does not assume a particular\nshape of the growth curve. It performs better than the standard Elo algorithm\nwhen the measured outcomes are far apart in time, or when the ability change is\nrapid.",
        "split": "arXiv"
    },
    {
        "text": "We present the design, rationale, properties and catalogs of the MusE Gas\nFLOw and Wind survey (MEGAFLOW), a survey of the cool gaseous halos of $z\\sim1$\ngalaxies using low-ionization MgII absorption systems. The survey consists of\n22 quasar fields selected from the Sloan Digital Sky Survey (SDSS) having\nmultiple ($\\geq3$) strong MgII absorption lines over the redshift range\n$0.3<z<1.5$. Each quasar was observed with the Multi-Unit Spectroscopic\nExplorer (MUSE) and the Ultraviolet and Visual Echelle Spectrograph (UVES), for\na total of 85~hr and 63~hr, respectively. The UVES data resulted in 127 MgII\nabsorption lines over $0.25<z<1.6$, with a median rest-frame equivalent width\n(REW) $3\\sigma$ limit of $\\approx 0.05$~\\AA. The MUSE data resulted in\n$\\sim$2400 galaxies of which 1403 with redshift confidence ZCONF$>1$, i.e. more\nthan 60 galaxies per arcmin$^{2}$. They were identified using a dual detection\nalgorithm based on both continuum and emission line objects. The achieved [OII]\n50\\%\\ completeness is 3.7$\\times 10^{-18}$ erg/s/cm$^2$ (corresponding to\nSFR$>0.01$ M$_\\odot$ yr$^{-1}$ at $z=1$) using realistic mock [OII] emitters\nand the 50\\%\\ completeness is $m_{F775W}\\approx26$ AB magnitudes for continuum\nsources. We find that (i) the fraction of [OII] emitting galaxies which have no\ncontinuum is $\\sim15$\\%; (ii) the success rate in identifying at least one\ngalaxy within 500 km/s and 100 kpc is $\\approx90$\\%\\ for MgII absorptions with\n$W_r^{2796}\\gtrsim0.5$~\\AA; (iii) the mean number of galaxies per MgII\nabsorption is $2.9\\pm1.6$ within the MUSE field-of-view; (iv) of the 80 MgII\nsystems at $0.3<z<1.5$, 40 (20) have 1 (2) galaxies within 100 kpc,\nrespectively; (v) all but two host galaxies have stellar masses $M_\\star>10^9$\nM$_\\odot$, and star-formation rates $>1$ M$_\\odot$ yr$^{-1}$.",
        "split": "arXiv"
    },
    {
        "text": "A core-corona decomposition of compact (neutron) star models is compared to\nrecent NICER data of masses and radii. It is in particular interesting to\ncapture the outlier XTE~J1814-338. Instead of integrating the TOV equations\nfrom the center to surface, we follow here another pathway by accommodating all\nuncertainties of the equation(s) of state (EoS) at supra-nuclear density or/and\nan unknown dark matter admixture in a parameterization of the core by its\nradius $r_x$, the included mass $m_x$ and the pressure $p_x$ at $r_x$. The\ncorona, which may be dubbed also envelope or halo or outer crust, is assumed to\nbe of standard-model matter where the EoS is supposed to be faithfully known.",
        "split": "arXiv"
    },
    {
        "text": "This thesis addresses the use of Cooperative Intelligent Transport Systems\n(CITS) to improve road safety and efficiency by enabling vehicle-to-vehicle\ncommunication, highlighting the importance of secure and accurate data\nexchange. To ensure safety, the thesis proposes a Machine Learning-based\nMisbehavior Detection System (MDS) using Long Short-Term Memory (LSTM) networks\nto detect and mitigate incorrect or misleading messages within vehicular\nnetworks. Trained offline on the VeReMi dataset, the detection model is tested\nin real-time within a platooning scenario, demonstrating that it can prevent\nnearly all accidents caused by misbehavior by triggering a defense protocol\nthat dissolves the platoon if anomalies are detected. The results show that\nwhile the system can accurately detect general misbehavior, it struggles to\nlabel specific types due to varying traffic conditions, implying the difficulty\nof creating a universally adaptive protocol. However, the thesis suggests that\nwith more data and further refinement, this MDS could be implemented in\nreal-world CITS, enhancing driving safety by mitigating risks from misbehavior\nin cooperative driving networks.",
        "split": "arXiv"
    },
    {
        "text": "It has been shown that one can design distributed algorithms that are\n(nearly) singularly optimal, meaning they simultaneously achieve optimal time\nand message complexity (within polylogarithmic factors), for several\nfundamental global problems such as broadcast, leader election, and spanning\ntree construction, under the $\\text{KT}_0$ assumption. With this assumption,\nnodes have initial knowledge only of themselves, not their neighbors. In this\ncase the time and message lower bounds are $\\Omega(D)$ and $\\Omega(m)$,\nrespectively, where $D$ is the diameter of the network and $m$ is the number of\nedges, and there exist (even) deterministic algorithms that simultaneously\nmatch these bounds.\n  On the other hand, under the $\\text{KT}_1$ assumption, whereby each node has\ninitial knowledge of itself and the identifiers of its neighbors, the situation\nis not clear. For the $\\text{KT}_1$ CONGEST model (where messages are of small\nsize), King, Kutten, and Thorup (KKT) showed that one can solve several\nfundamental global problems (with the notable exception of BFS tree\nconstruction) such as broadcast, leader election, and spanning tree\nconstruction with $\\tilde{O}(n)$ message complexity ($n$ is the network size),\nwhich can be significantly smaller than $m$. Randomization is crucial in\nobtaining this result. While the message complexity of the KKT result is\nnear-optimal, its time complexity is $\\tilde{O}(n)$ rounds, which is far from\nthe standard lower bound of $\\Omega(D)$.\n  In this paper, we show that in the $\\text{KT}_1$ LOCAL model (where message\nsizes are not restricted), singular optimality is achievable. Our main result\nis that all global problems, including BFS tree construction, can be solved in\n$\\tilde{O}(D)$ rounds and $\\tilde{O}(n)$ messages, where both bounds are\noptimal up to polylogarithmic factors. Moreover, we show that this can be\nachieved deterministically.",
        "split": "arXiv"
    },
    {
        "text": "For a uniformly locally finite metric space $(X, d)$, we investigate\n\\emph{coarse} flows on its uniform Roe algebra $\\mathrm{C}^*_u(X)$, defined as\none-parameter groups of automorphisms whose differentiable elements include all\npartial isometries arising from partial translations on $X$. We first show that\nany flow $\\sigma$ on $\\mathrm{C}^*_u(X)$ corresponds to a (possibly unbounded)\nself-adjoint operator $h$ on $\\ell_2(X)$ such that $\\sigma_t(a) = e^{ith} a\ne^{-ith}$ for all $t \\in \\mathbb{R}$, allowing us to focus on operators $h$\nthat generate flows on $ \\mathrm{C}^*_u (X)$.\n  Assuming Yu's property A, we prove that a self-adjoint operator $h$ on\n$\\ell_2(X)$ induces a coarse flow on $\\mathrm{C}^*_u(X)$ if and only if $h$ can\nbe expressed as $h = a + d$, where $a \\in \\mathrm{C}^*_u(X)$ and $d$ is a\ndiagonal operator with entries forming a coarse function on $X$. We further\nstudy cocycle equivalence and cocycle perturbations of coarse flows, showing\nthat, under property A, any coarse flow is a cocycle perturbation of a diagonal\nflow. Finally, for self-adjoint operators $h$ and $k$ that induce coarse flows\non $\\mathrm{C}^*_u(X)$, we characterize conditions under which the associated\nflows are either cocycle perturbations of each other or cocycle conjugate. In\nparticular, if $h - k$ is bounded, then the flow induced by $h$ is a cocycle\nperturbation of the flow induced by $k$.",
        "split": "arXiv"
    },
    {
        "text": "Fast radio bursts (FRBs) are bright milliseconds-duration radio bursts from\ncosmological distances. Despite intense observational and theoretical studies,\ntheir physical origin is still mysterious. One major obstacle is the lack of\nidentification of multi-wavelength counterparts for FRBs at cosmological\ndistances. So far, all the searches other than in the radio wavelength,\nincluding those in the gamma-ray energies, have only left upper limits. Here we\nreport a gigaelectronvolt (GeV) gamma-ray flare lasting 15.6 seconds as well as\nadditional evidence of variable gamma-ray emission in temporal and spatial\nassociation with the hyper-active, newly discovered repeating FRB 20240114A,\nwhich has been localized to a dwarf galaxy at a redshift of 0.13. The\nenergetic, short GeV gamma-ray flare reached a prompt isotropic luminosity of\nthe order of ${10}^{48}~{\\rm ergs~{s}^{-1}}$. The additional less-significant\ngamma-ray flares, if true, also have similar luminosities; such flares could\ncontribute to a 5-day average luminosity of the order of ${10}^{45}~{\\rm\nergs~{s}^{-1}}$. These high-luminosity flares challenge the traditional FRB\nengine scenario involving a seconds-period magnetar. Rather, it suggests a\npowerful, long-lived, but newborn energy source at the location of this active\nrepeater, either directly powering the bursts or indirectly triggering bursts\nin the vicinity of the FRB engine.",
        "split": "arXiv"
    },
    {
        "text": "We present the first set of galaxy scaling relations derived from kinematic\nmodels of the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY)\npilot phase observations. Combining the results of the first and second pilot\ndata releases, there are 236 available kinematic models. We develop a framework\nfor robustly measuring HI disk structural properties from these kinematic\nmodels; applicable to the full WALLABY survey. Utilizing this framework, we\nobtained the HI size, a measure of the rotational velocity, and angular\nmomentum for 148 galaxies. These comprise the largest sample of galaxy\nproperties from an untargetted, uniformly observed and modelled HI survey to\ndate. We study the neutral atomic Hydrogen (HI) size-mass, size-velocity,\nmass-velocity, and angular momentum-mass scaling relations. We calculate the\nslope, intercept, and scatter for these scaling relations and find that they\nare similar to those obtained from other HI surveys. We also obtain stellar\nmasses for 92 of the 148 robustly measured galaxies using multiband photometry\nthrough the Dark Energy Sky Instrument Legacy Imaging Survey Data Release-10\nimages. We use a subset of 61 of these galaxies that have consistent optical\nand kinematic inclinations to examine the stellar and baryonic Tully Fisher\nrelations, the gas fraction-disk stability and gas fraction-baryonic mass\nrelations. These measurements and relations demonstrate the unprecedented\nresource that WALLABY will represent for resolved galaxy scaling relations in\nHI.",
        "split": "arXiv"
    },
    {
        "text": "A demonstrator for each slice of the ATLAS pixel detector was built to\nreplicate the real detector and provide early solutions for operating and\nmaintaining its components. This system-level testing of the all-silicon Inner\nTracker (ITk) pixel detector for the ATLAS experiment at CERN's HL LHC\nencompasses a wide array of system components, which is essential for managing\nthe increased luminosity and radiation levels expected at HL LHC, thereby\nenhancing tracking performance. Utilizing advanced silicon sensor technologies,\nserial powering, and lightweight carbon fiber structures, the demonstrator and\nassembled components on the support structure will undergo several studies for\nverification and commissioning. Extensive tests on serial powering, monitoring,\nand data acquisition were conducted, ensuring the system's robustness and\nreliability for future high-energy physics experiments. Additionally, three\ndifferent sub-components will be introduced for the novel ITk pixel detector,\nspecifically designed for the outer barrel (OB), outer end caps (OEC), and\ninner system (IS) sections.",
        "split": "arXiv"
    },
    {
        "text": "The $k$-cover of a point cloud $X$ of $\\mathbb{R}^{d}$ at radius $r$ is the\nset of all those points within distance $r$ of at least $k$ points of $X$. By\nvarying the order $k$ and radius $r$ we obtain a two-parameter filtration known\nas the multicover bifiltration. This bifiltration has received attention\nrecently due to being parameter-free and its robustness to outliers. However,\nit is hard to compute: the smallest known equivalent simplicial bifiltration\nhas $O(|X|^{d+1})$ simplices, where $d$ is the dimension. In this paper we\nintroduce a $(1+\\epsilon)$-approximation of the multicover that has linear size\n$O(|X|)$, for a fixed $d$ and $\\epsilon$. The methods also apply to the\nsubdivision Rips bifiltration on metric spaces of bounded doubling dimension to\nobtain analogous results.",
        "split": "arXiv"
    },
    {
        "text": "We consider the problem of decomposing the edges of a digraph into as few\npaths as possible. A natural lower bound for the number of paths in any path\ndecomposition of a digraph $D$ is $\\frac{1}{2}\\sum_{v\\in V(D)}|d^+(v)-d^-(v)|$;\nany digraph that achieves this bound is called consistent. Alspach, Mason, and\nPullman conjectured in 1976 that every tournament of even order is consistent\nand this was recently verified for large tournaments by Gir\\~ao, Granet,\nK\\\"uhn, Lo, and Osthus. A more general conjecture of Pullman states that for\nodd $d$, every orientation of a $d$-regular graph is consistent. We prove that\nthe conjecture holds for random $d$-regular graphs with high probability i.e.\nfor fixed odd $d$ and as $n \\to \\infty$ the conjecture holds for almost all\n$d$-regular graphs. Along the way, we verify Pullman's conjecture for graphs\nwhose girth is sufficiently large (as a function of the degree).",
        "split": "arXiv"
    },
    {
        "text": "This study seeks to identify the potential role of AI-driven assistive\ntechnologies in enhancing access to libraries for persons with varying degrees\nof challenges. Traditional libraries pose a problem to many users with vision\nand mobility, among other conditions related to physical and infirmities. This\nmixed-methods research approach will examine ways in which AI-powered assistive\ntools and applications associated with text-to-speech, navigation systems, and\npersonalized assistants are revolutionizing library services through a\nliterature review, survey methods, interviews, and case studies. Our findings\nsuggest that these technologies greatly increase the autonomy and participation\nof people with physical disabilities, providing personalized support and access\nto a wide range of resources. From this, some key findings have been deduced\nfrom the research, showing a strong impact on user experience and efficiency in\nservices, while at the same time bringing out important considerations related\nto privacy and ethical implementation. This study highlights the central role\nof AI in making library settings more inclusive, thereby allowing equal access\nto knowledge and participation in the community. Such insight thus serves\nprofessionals working in libraries, policymakers, and technology developers for\ninnovations to occur uninterruptedly, with future research directions proposed\nthat would refine such technologies, especially toward the special needs of\ndiverse populations. By adopting AI, libraries could uphold their mission of\nproviding equal access to knowledge through full and equal participation of all\npersons, regardless of any type of physical ability, in the learning and\ncommunity activities carried out by the library. This study paves the way for\nfuture innovations in creating more accessible and inclusive library spaces.",
        "split": "arXiv"
    },
    {
        "text": "Patterns are words with terminals and variables. The language of a pattern is\nthe set of words obtained by uniformly substituting all variables with words\nthat contain only terminals. Regular constraints restrict valid substitutions\nof variables by associating with each variable a regular language representable\nby, e.g., finite automata. Pattern languages with regular constraints contain\nonly words in which each variable is substituted according to a set of regular\nconstraints. We consider the membership, inclusion, and equivalence problems\nfor erasing and non-erasing pattern languages with regular constraints. Our\nmain result shows that the erasing equivalence problem, one of the most\nprominent open problems in the realm of patterns, becomes undecidable if\nregular constraints are allowed in addition to variable equality.",
        "split": "arXiv"
    },
    {
        "text": "As new manufacturing methods enable manufacturing of microstructured surfaces\nwith varying structure geometries, questions remain on some effects on the\nwetting behavior and the resulting apparent contact angles. In this study, we\nreport the manufacturing process using 3D Direct Laser Writing (3D-DLW) and hot\nembossing for Poly-methylmethacrylate (PMMA) surfaces with micrometric\npyramids, cubes on a staggered grid and two types of ramped structures. We\nmeasure the azimuthal variation of the apparent contact angle of sessile\ndroplets on the surfaces. Using plasma polymerization or no treatment of the\nsurfaces, two different inherent wettabilities are studied. We find that while\nall structure types cause an azimuthal variation of the apparent contact angle,\npinning at the ramp tops increases the contact angle more strongly on one side.\nOn pyramid structures, pinning lines can occur on the structure axes and\ndiagonals similarly. For cubes on a hexagonal grid, the strongest contact angle\nincreases are observed along the primary structure axes, where pinning is\npreferred while smaller peaks are seen on the secondary axes at 60{\\deg} and\n120{\\deg} to the primary axis.",
        "split": "arXiv"
    },
    {
        "text": "Counting logics with a bounded number of variables form one of the central\nconcepts in descriptive complexity theory. Although they restrict the number of\nvariables that a formula can contain, the variables can be nested within scopes\nof quantified occurrences of themselves. In other words, the variables can be\nrequantified. We study the fragments obtained from counting logics by\nrestricting requantification for some but not necessarily all the variables.\nSimilar to the logics without limitation on requantification, we develop tools\nto investigate the restricted variants. Specifically, we introduce a bijective\npebble game in which certain pebbles can only be placed once and for all, and a\ncorresponding two-parametric family of Weisfeiler-Leman algorithms. We show\nclose correspondences between the three concepts. By using a suitable\ncops-and-robber game and adaptations of the Cai-F\\\"urer-Immerman construction,\nwe completely clarify the relative expressive power of the new logics. We show\nthat the restriction of requantification has beneficial algorithmic\nimplications in terms of graph identification. Indeed, we argue that with\nregard to space complexity, non-requantifiable variables only incur an additive\npolynomial factor when testing for equivalence. In contrast, for all we know,\nrequantifiable variables incur a multiplicative linear factor. Finally, we\nobserve that graphs of bounded tree-depth and 3-connected planar graphs can be\nidentified using no, respectively, only a very limited number of requantifiable\nvariables.",
        "split": "arXiv"
    },
    {
        "text": "In the recent breakthrough work \\cite{xu2023lack}, a rigorous numerical\nanalysis was conducted on the numerical solution of a scalar ODE containing a\ncubic polynomial derived from the Allen-Cahn equation. It was found that only\nthe implicit Euler method converge to the correct steady state for any given\ninitial value $u_0$ under the unique solvability and energy stability. But all\nthe other commonly used second-order numerical schemes exhibit sensitivity to\ninitial conditions and may converge to an incorrect equilibrium state as\n$t_n\\to\\infty$. This indicates that energy stability may not be decisive for\nthe long-term qualitative correctness of numerical solutions. We found that\nusing another fundamental property of the solution, namely monotonicity instead\nof energy stability, is sufficient to ensure that many common numerical schemes\nconverge to the correct equilibrium state. This leads us to introduce the\ncritical step size constant $h^*=h^*(u_0,\\epsilon)$ that ensures the\nmonotonicity and unique solvability of the numerical solutions, where the\nscaling parameter $\\epsilon \\in(0,1)$. We prove that the implicit Euler scheme\n$h^*=h^*(\\epsilon)$, which is independent of $u_0$ and only depends on\n$\\epsilon$. Hence regardless of the initial value taken, the simulation can be\nguaranteed to be correct when $h<h^*$. But for various other numerical methods,\nno mater how small the step size $h$ is in advance, there will always be\ninitial values that cause simulation errors. In fact, for these numerical\nmethods, we prove that $\\inf_{u_0\\in \\mathbb{R}}h^*(u_0,\\epsilon)=0$. Various\nnumerical experiments are used to confirm the theoretical analysis.",
        "split": "arXiv"
    },
    {
        "text": "In this work the evolution of a fluid droplet in vacuum is considered. This\nmeans that the surface tension and the fluid forces are in equilibrium at the\nfree boundary. The fluid is governed by the incompressible quasi-steady Stokes\nequation.\n  We present higher order energy estimates for this setting in the planar case.\nIn particular bounds of the curvature and its tangential derivative combined\nwith the second and third spacial derivatives of the fluid velocity as\nrespective dissipation. These estimates are shown to hold until the point of a\ntopological degeneracy. They provide quantitative bounds, that depend on\nspecific properties of the initial geometry only.\n  The work contrasts previous approaches, which are based on the use of local\ncoordinates and instead performs all estimates in an Eulerian setting. Indeed,\nthe estimates provided here are geometrically intrinsic and collapse only once\nthese intrinsic qualities break.",
        "split": "arXiv"
    },
    {
        "text": "We present polarization images with the Karl G. Jansky Very Large Array (VLA)\nin A and B-array configurations at 6 GHz of 7 radio-loud (RL) quasars and 8 BL\nLac objects belonging to the Palomar-Green (PG) `blazar' sample. This completes\nour arcsecond-scale polarization study of an optically-selected volume-limited\nblazar sample comprising 16 radio-loud quasars and 8 BL Lac objects. Using the\nVLA, we identify kpc-scale polarization in the cores and jets/lobes of all the\nblazars, with fractional polarization varying from around $0.8 \\pm 0.3$% to $37\n\\pm 6$%. The kpc-scale jets in PG RL quasars are typically aligned along their\nparsec-scale jets and show apparent magnetic fields parallel to jet directions\nin their jets/cores and magnetic field compression in their hotspots. The\nquasars show evidence of interaction with their environment as well as\nrestarted AGN activity through morphology, polarization and spectral indices.\nThese quasi-periodic jet modulations and restarted activity may be indicative\nof an unstable accretion disk undergoing transition. We find that the\npolarization characteristics of the BL Lacs are consistent with their jets\nbeing reoriented multiple times, with no correlation between their core\napparent magnetic field orientations and pc-scale jet directions. We find that\nthe low synchrotron peaked BL Lacs show polarization and radio morphology\nfeatures typical of `strong' jet sources as defined by Meyer et al. (2011) for\nthe `blazar envelope scenario', which posits a division based on jet profiles\nand velocity gradients rather than total jet power.",
        "split": "arXiv"
    },
    {
        "text": "The Lagrange and Markov spectra $L$ and $M$ describe the best constants of\nDiophantine approximations for irrational numbers and binary quadratic forms.\nIn 1880, A. Markov showed that the initial portions of these spectra coincide:\nindeed, $L\\cap (0,3) = M\\cap (0,3)$ is a discrete set of explicit quadratic\nirrationals accumulating only at $3$.\n  In this article, we show that the statement above ceases to be true\nimmediately after $3$: in particular, $L\\cap (3,3+\\varepsilon)\\neq M\\cap\n(3,3+\\varepsilon)$ for all $\\varepsilon>0$, and thus $\\inf(M\\setminus L)=3$. In\nfact, we derive this result as a by-product of lower bounds on the Hausdorff\ndimension of $(M\\setminus L)\\cap (3,3+\\varepsilon)$ implying that\n$\\liminf\\limits_{\\varepsilon\\to 0} \\frac{\\dim_H((M\\setminus\nL)\\cap(3,3+\\varepsilon))}{\\dim_H(M\\cap (3,3+\\varepsilon))}\\geq \\frac{1}{2}$\nand, as it turns out, these bounds are obtained from the study of projections\nof Cartesian products of almost affine dynamical Cantor sets via an argument of\nprobabilistic flavor based on Baker--W\\\"ustholz theorem on linear forms in\nlogarithms of algebraic numbers.",
        "split": "arXiv"
    },
    {
        "text": "A common challenge in continual learning (CL) is catastrophic forgetting,\nwhere the performance on old tasks drops after new, additional tasks are\nlearned. In this paper, we propose a novel framework called ReCL to slow down\nforgetting in CL. Our framework exploits an implicit bias of gradient-based\nneural networks due to which these converge to margin maximization points. Such\nconvergence points allow us to reconstruct old data from previous tasks, which\nwe then combine with the current training data. Our framework is flexible and\ncan be applied on top of existing, state-of-the-art CL methods to slow down\nforgetting. We further demonstrate the performance gain from our framework\nacross a large series of experiments, including different CL scenarios (class\nincremental, domain incremental, task incremental learning) different datasets\n(MNIST, CIFAR10), and different network architectures. Across all experiments,\nwe find large performance gains through ReCL. To the best of our knowledge, our\nframework is the first to address catastrophic forgetting by leveraging models\nin CL as their own memory buffers.",
        "split": "arXiv"
    },
    {
        "text": "Instrumental variables (IVs) are widely used to estimate causal effects in\nthe presence of unobserved confounding between exposure and outcome. An IV must\naffect the outcome exclusively through the exposure and be unconfounded with\nthe outcome. We present a framework for relaxing either or both of these strong\nassumptions with tuneable and interpretable budget constraints. Our algorithm\nreturns a feasible set of causal effects that can be identified exactly given\nrelevant covariance parameters. The feasible set may be disconnected but is a\nfinite union of convex subsets. We discuss conditions under which this set is\nsharp, i.e., contains all and only effects consistent with the background\nassumptions and the joint distribution of observable variables. Our method\napplies to a wide class of semiparametric models, and we demonstrate how its\nability to select specific subsets of instruments confers an advantage over\nconvex relaxations in both linear and nonlinear settings. We also adapt our\nalgorithm to form confidence sets that are asymptotically valid under a common\nstatistical assumption from the Mendelian randomization literature.",
        "split": "arXiv"
    },
    {
        "text": "We consider the problem of detecting deviations from a white noise assumption\nin time series. Our approach differs from the numerous methods proposed for\nthis purpose with respect to two aspects. First, we allow for non-stationary\ntime series. Second, we address the problem that a white noise test, for\nexample checking the residuals of a model fit, is usually not performed because\none believes in this hypothesis, but thinks that the white noise hypothesis may\nbe approximately true, because a postulated models describes the unknown\nrelation well. This reflects a meanwhile classical paradigm of Box(1976) that\n\"all models are wrong but some are useful\". We address this point of view by\ninvestigating if the maximum deviation of the local autocovariance functions\nfrom 0 exceeds a given threshold $\\Delta$ that can either be specified by the\nuser or chosen in a data dependent way.\n  The formulation of the problem in this form raises several mathematical\nchallenges, which do not appear when one is testing the classical white noise\nhypothesis. We use high dimensional Gaussian approximations for dependent data\nto furnish a bootstrap test, prove its validity and showcase its performance on\nboth synthetic and real data, in particular we inspect log returns of stock\nprices and show that our approach reflects some observations of Fama(1970)\nregarding the efficient market hypothesis.",
        "split": "arXiv"
    },
    {
        "text": "Patterns are words with terminals and variables. The language of a pattern is\nthe set of words obtained by uniformly substituting all variables with words\nthat contain only terminals. Length constraints restrict valid substitutions of\nvariables by associating the variables of a pattern with a system (or\ndisjunction of systems) of linear diophantine inequalities. Pattern languages\nwith length constraints contain only words in which all variables are\nsubstituted to words with lengths that fulfill such a given set of length\nconstraints. We consider membership, inclusion, and equivalence problems for\nerasing and non-erasing pattern languages with length constraints. Our main\nresult shows that the erasing equivalence problem, one of the most prominent\nopen problems in the realm of patterns-becomes undecidable if length\nconstraints are allowed in addition to variable equality. Additionally, it is\nshown that the terminal-free inclusion problem-another prominent open problem\nin the realm of patterns-is also undecidable in this setting. It is also shown\nthat considering regular constraints, i.e., associating variables also with\nregular languages as additional restrictions together with length constraints\nfor valid substitutions, results in undecidability of the non-erasing\nequivalence problem. This sets a first upper bound on constraints to obtain\nundecidability in this case, as this problem is trivially decidable in the case\nof no constraints and as it has unknown decidability if only regular- or only\nlength-constraints are considered.",
        "split": "arXiv"
    },
    {
        "text": "Simple mechanical exfoliation of layered materials is the most frequently\nemployed method for producing high-quality monolayers of two-dimensional\nsemiconducting materials. However, the mechanical exfoliation by human hands is\na microscopically sophisticated process with a large number of microscopic\nparameters, which requires significant operator efforts and limits the\nreproducibility in achieving high-quality and large-area monolayer\nsemiconducting materials. Herein, we have proposed a new strategy for the\nmechanical exfoliation by combining a developed robotic system and Bayesian\noptimization. We have demonstrated that it is possible to explore the optimized\nexperimental conditions among a large number of parameter combinations for\nmechanical exfoliation in a relatively small number of experimental trials.\nMoreover, the entire mechanical exfoliation process from preparation to\ndetection of monolayer semiconductors was performed by the developed autonomous\nrobotic system. The optimized experimental condition was determined through\nonly 30 trials of mechanical exfoliation experiments, representing 2.5% of all\nexperimental parameter conditions. As a result, the critical parameters for the\nefficient fabrication of large-area monolayer WSe$_2$ were elucidated.",
        "split": "arXiv"
    },
    {
        "text": "The action of the subgroup $\\operatorname{G}_2$ of $\\operatorname{SO}(7)$\n(resp.\\ $\\operatorname{Spin}(7)$ of $\\operatorname{SO}(8)$) on the Grassmannian\nspace\n$M=\\frac{\\operatorname{SO}(7)}{\\operatorname{SO}(5)\\times\\operatorname{SO}(2)}$\n(resp.\\\n$M=\\frac{\\operatorname{SO}(8)}{\\operatorname{SO}(5)\\times\\operatorname{SO}(3)}$)\nis still transitive. We prove that the spectrum (i.e.\\ the collection of\neigenvalues of its Laplace-Beltrami operator) of a symmetric metric $g_0$ on\n$M$ coincides with the spectrum of a $\\operatorname{G}_2$-invariant (resp.\\\n$\\operatorname{Spin}(7)$-invariant) metric $g$ on $M$ only if $g_0$ and $g$ are\nisometric.\n  As a consequence, each non-flat compact irreducible symmetric space of\nnon-group type is spectrally unique among the family of all currently known\nhomogeneous metrics on its underlying differentiable manifold.",
        "split": "arXiv"
    },
    {
        "text": "Federated Learning (FL) employs a training approach to address scenarios\nwhere users' data cannot be shared across clients. Achieving fairness in FL is\nimperative since training data in FL is inherently geographically distributed\namong diverse user groups. Existing research on fairness predominantly assumes\naccess to the entire training data, making direct transfer to FL challenging.\nHowever, the limited existing research on fairness in FL does not effectively\naddress two key challenges, i.e., (CH1) Current methods fail to deal with the\ninconsistency between fair optimization results obtained with surrogate\nfunctions and fair classification results. (CH2) Directly aggregating local\nfair models does not always yield a globally fair model due to non Identical\nand Independent data Distributions (non-IID) among clients. To address these\nchallenges, we propose a Wasserstein Fair Federated Learning framework, namely\nWassFFed. To tackle CH1, we ensure that the outputs of local models, rather\nthan the loss calculated with surrogate functions or classification results\nwith a threshold, remain independent of various user groups. To resolve CH2, we\nemploy a Wasserstein barycenter calculation of all local models' outputs for\neach user group, bringing local model outputs closer to the global output\ndistribution to ensure consistency between the global model and local models.\nWe conduct extensive experiments on three real-world datasets, demonstrating\nthat WassFFed outperforms existing approaches in striking a balance between\naccuracy and fairness.",
        "split": "arXiv"
    },
    {
        "text": "Commonsense question answering is a crucial task that requires machines to\nemploy reasoning according to commonsense. Previous studies predominantly\nemploy an extracting-and-modeling paradigm to harness the information in KG,\nwhich first extracts relevant subgraphs based on pre-defined rules and then\nproceeds to design various strategies aiming to improve the representations and\nfusion of the extracted structural knowledge. Despite their effectiveness,\nthere are still two challenges. On one hand, subgraphs extracted by rule-based\nmethods may have the potential to overlook critical nodes and result in\nuncontrollable subgraph size. On the other hand, the misalignment between graph\nand text modalities undermines the effectiveness of knowledge fusion,\nultimately impacting the task performance. To deal with the problems above, we\npropose a novel framework: \\textbf{S}ubgraph R\\textbf{E}trieval Enhanced by\nGra\\textbf{P}h-\\textbf{T}ext \\textbf{A}lignment, named \\textbf{SEPTA}. Firstly,\nwe transform the knowledge graph into a database of subgraph vectors and\npropose a BFS-style subgraph sampling strategy to avoid information loss,\nleveraging the analogy between BFS and the message-passing mechanism. In\naddition, we propose a bidirectional contrastive learning approach for\ngraph-text alignment, which effectively enhances both subgraph retrieval and\nknowledge fusion. Finally, all the retrieved information is combined for\nreasoning in the prediction module. Extensive experiments on five datasets\ndemonstrate the effectiveness and robustness of our framework.",
        "split": "arXiv"
    },
    {
        "text": "This paper presents a detailed system description of our entry for the\nCHiPSAL 2025 shared task, focusing on language detection, hate speech\nidentification, and target detection in Devanagari script languages. We\nexperimented with a combination of large language models and their ensembles,\nincluding MuRIL, IndicBERT, and Gemma-2, and leveraged unique techniques like\nfocal loss to address challenges in the natural understanding of Devanagari\nlanguages, such as multilingual processing and class imbalance. Our approach\nachieved competitive results across all tasks: F1 of 0.9980, 0.7652, and 0.6804\nfor Sub-tasks A, B, and C respectively. This work provides insights into the\neffectiveness of transformer models in tasks with domain-specific and\nlinguistic challenges, as well as areas for potential improvement in future\niterations.",
        "split": "arXiv"
    },
    {
        "text": "Given a finite group $G$ and a commutative ring $G$-spectrum $R$, we study\nthe separable commutative algebras in the category of compact $R$-modules. We\nisolate three conditions on the geometric fixed points of $R$ which ensure that\nevery separable commutative algebra is standard, i.e. arises from a finite\n$G$-set. In particular we show that all separable commutative algebras in the\ncategories of compact objects in $G$-spectra and in derived $G$-Mackey functors\nare standard provided that $G$ is a $p$-group. In these categories we also show\nthat for a general finite group $G$, we cannot expect all separable commutative\nalgebras to be standard.",
        "split": "arXiv"
    },
    {
        "text": "Fibre optic communication system is expected to increase exponentially in\nterms of application due to the numerous advantages over copper wires. The\noptical network evolution presents several advantages such as over\nlong-distance, low-power requirement, higher carrying capacity and high\nbandwidth among others Such network bandwidth surpasses methods of transmission\nthat include copper cables and microwaves. Despite these benefits, free-space\noptical communications are severely impacted by harsh weather situations like\nmist, precipitation, blizzard, fume, soil, and drizzle debris in the\natmosphere, all of which have an impact on the Quality of Service (QoS)\nrendered by the systems. The primary goal of this article is to optimize the\nQoS using the ensemble learning models Random Forest, ADaBoost Regression,\nStacking Regression, Gradient Boost Regression, and Multilayer Neural Network.\nTo accomplish the stated goal, meteorological data, visibility, wind speed, and\naltitude were obtained from the South Africa Weather Services archive during a\nten-year period (2010 to 2019) at four different locations: Polokwane,\nKimberley, Bloemfontein, and George. We estimated the data rate, power\nreceived, fog-induced attenuation, bit error rate and power penalty using the\ncollected and processed data. The RMSE and R-squared values of the model across\nall the study locations, Polokwane, Kimberley, Bloemfontein, and George, are\n0.0073 and 0.9951, 0.0065 and 0.9998, 0.0060 and 0.9941, and 0.0032 and 0.9906,\nrespectively. The result showed that using ensemble learning techniques in\ntransmission modeling can significantly enhance service quality and meet\ncustomer service level agreements and ensemble method was successful in\nefficiently optimizing the signal to noise ratio, which in turn enhanced the\nQoS at the point of reception.",
        "split": "arXiv"
    },
    {
        "text": "Mixture-of-Experts (MOE) has recently become the de facto standard in\nMulti-domain recommendation (MDR) due to its powerful expressive ability.\nHowever, such MOE-based method typically employs all experts for each instance,\nleading to scalability issue and low-discriminability between domains and\nexperts. Furthermore, the design of commonly used domain-specific networks\nexacerbates the scalability issues. To tackle the problems, We propose a novel\nmethod named CESAA consists of Conditional Expert Selection (CES) Module and\nAdaptive Expert Aggregation (AEA) Module to tackle these challenges.\nSpecifically, CES first combines a sparse gating strategy with domain-shared\nexperts. Then AEA utilizes mutual information loss to strengthen the\ncorrelations between experts and specific domains, and significantly improve\nthe distinction between experts. As a result, only domain-shared experts and\nselected domain-specific experts are activated for each instance, striking a\nbalance between computational efficiency and model performance. Experimental\nresults on both public ranking and industrial retrieval datasets verify the\neffectiveness of our method in MDR tasks.",
        "split": "arXiv"
    },
    {
        "text": "Time-varying media break the temporal translation symmetry of wave\npropagation in materials, enabling advanced wave manipulations. However, this\nnovel phenomenon has been rarely explored in magnonic systems due to the\nsignificant challenge of achieving a sudden and prominent change in magnon\ndispersion within materials. Here, we drive a ferrimagnet with periodic pump\npulses to construct time-varying strong coupling between two magnon modes. We\nobserve a change in the beats of Rabi-like oscillations near the pulse edges,\nindicating the time-varying strong magnon coupling and the formation of time\ninterfaces. Using a frequency comb spectroscopy technique developed in this\nwork, we characterize the frequency conversion of magnon modes induced by the\ntime-varying strong coupling effect. Moreover, we construct time slits with\nadjacent time interfaces and demonstrate, for the first time, the double-slit\ntime diffraction of magnon modes. The frequency spacing of the multiplied\nmagnon modes inversely correlates with the separation between two time slits,\nanalogous to the well-known Yang's double-slit experiment. These findings rely\nsolely on the time-varying strong magnon coupling, independent of device\nreconfiguration. Our results open avenues for applications such as all-magnetic\nmixers or on-chip GHz sources.",
        "split": "arXiv"
    },
    {
        "text": "With the rising demand for code quality assurance, developers are not only\nutilizing existing static code checkers but also seeking custom checkers to\nsatisfy their specific needs. Nowadays, various code-checking frameworks\nprovide extensive checker customization interfaces to meet this need. However,\nboth the abstract checking logic as well as the complex API usage of\nlarge-scale frameworks make this task challenging. To this end, automated code\nchecker generation is anticipated to ease the burden of checker development. In\nthis paper, we explore the feasibility of automated checker generation and\npropose AutoChecker, an innovative LLM-powered approach that can write code\ncheckers automatically based on only a rule description and a test suite.\nInstead of generating the checker at once, AutoChecker incrementally updates\nthe checker with the rule and one single test case each time, i.e., it\niteratively generates the checker case by case. During each iteration,\nAutoChecker first decomposes the whole logic into a series of sub-operations\nand then uses the logic-guided API-context retrieval strategy to search related\nAPI-contexts from all the framework APIs. To evaluate the effectiveness of\nAutoChecker, we apply AutoChecker and two LLM-based baseline approaches to\nautomatically generate checkers for 20 built-in PMD rules, including easy rules\nand hard rules. Experimental results demonstrate that AutoChecker significantly\noutperforms baseline approaches across all effectiveness metrics, where its\naverage test pass rate improved over 4.2 times. Moreover, the checkers\ngenerated by AutoChecker are successfully applied to real-world projects,\nmatching the performance of official checkers.",
        "split": "arXiv"
    },
    {
        "text": "A recent study of the distribution of dwarf galaxies in the MATLAS sample in\ngalaxy groups revealed an excess of flattened satellite structures, reminiscent\nof the co-rotating planes of dwarf galaxies discovered in the local Universe.\nIf confirmed, this lends credence to the plane-of-satellite problem and further\nchallenges the standard model of hierarchical structure formation. However,\nwith only photometric data and no confirmation of the satellite membership, the\nstudy could not address the plane-of-satellite problem in full detail. Here we\npresent spectroscopic follow-up observations of one of the most promising\nplanes-of-satellites candidates in the MATLAS survey, the satellite system of\nNGC 474. Employing MUSE at the VLT and full spectrum fitting, we studied 13\ndwarf galaxy candidates and confirmed nine to be members of the field around\nNGC 474. Measuring the stellar populations of all observed galaxies, we find\nthat the MATLAS dwarfs have lower metallicities than the Local Group dwarfs at\ngiven luminosity. Two dwarf galaxies may form a pair of satellites based on\ntheir close projection and common velocity. Within the virial radius, we do not\nfind a significant plane-of-satellites, however, there is a sub-population of\nsix dwarf galaxies which seem to be anti-correlated in phase-space. Due to the\nlow number of dwarf galaxies, this signal may arise by chance. With over 2000\ndwarf galaxy candidates found in the MATLAS survey, this remains an intriguing\ndata set to study the plane-of-satellites problem in a statistical fashion once\nmore follow-up observations have been conducted.",
        "split": "arXiv"
    },
    {
        "text": "In camera-based 3D multi-object tracking (MOT), the prevailing methods follow\nthe tracking-by-query-propagation paradigm, which employs track queries to\nmanage the lifecycle of identity-consistent tracklets while object queries\nhandle the detection of new-born tracklets. However, this intertwined paradigm\nleads the inter-temporal tracking task and the single-frame detection task\nutilize the same model parameters, complicating training optimization. Drawing\ninspiration from studies on the roles of attention components in\ntransformer-based decoders, we identify that the dispersing effect of\nself-attention necessitates object queries to match with new-born tracklets.\nThis matching strategy diverges from the detection pre-training phase, where\nobject queries align with all ground-truth targets, resulting in insufficient\nsupervision signals. To address these issues, we present HSTrack, a novel\nplug-and-play method designed to co-facilitate multi-task learning for\ndetection and tracking. HSTrack constructs a parallel weight-share decoder\ndevoid of self-attention layers, circumventing competition between different\ntypes of queries. Considering the characteristics of cross-attention layer and\ndistinct query types, our parallel decoder adopt one-to-one and one-to-many\nlabel assignment strategies for track queries and object queries, respectively.\nLeveraging the shared architecture, HSTrack further improve trackers for\nspatio-temporal modeling and quality candidates generation. Extensive\nexperiments demonstrate that HSTrack consistently delivers improvements when\nintegrated with various query-based 3D MOT trackers. For example, HSTrack\nimproves the state-of-the-art PF-Track method by $+2.3\\%$ AMOTA and $+1.7\\%$\nmAP on the nuScenes dataset.",
        "split": "arXiv"
    },
    {
        "text": "Front-line police officers often categorize all police call reported cases of\nTelecom Fraud into 14 subcategories to facilitate targeted prevention measures,\nsuch as precise public education. However, the associated data is characterized\nby its large volume, diverse information content, and variations in expression.\nCurrently, there is a lack of efficient and accurate intelligent models to\nreplace manual classification, which, while precise, is relatively inefficient.\nTo address these challenges, this paper proposes a text classification model\nthat combines adversarial training with Pre-trained Language Model and neural\nnetworks. The Linguistically-motivated Pre-trained Language Model model\nextracts three types of language features and then utilizes the Fast Gradient\nMethod algorithm to perturb the generated embedding layer. Subsequently, the\nBi-directional Long Short-Term Memory and Convolutional Neural Networks\nnetworks extract contextual syntactic information and local semantic\ninformation, respectively. The model achieved an 83.9% classification accuracy\nwhen trained on a portion of telecom fraud case data provided by the\noperational department. The model established in this paper has been deployed\nin the operational department, freeing up a significant amount of manpower and\nimproving the department's efficiency in combating Telecom Fraud crimes.\nFurthermore, considering the universality of the model established in this\npaper, other application scenarios await further exploration.",
        "split": "arXiv"
    },
    {
        "text": "Let $U_S$ be the localization of $U(\\mathfrak{sp}_{2n})$ with respect to the\nOre subset $S$ generated by the root vectors\n$X_{\\epsilon_1-\\epsilon_2},\\dots,X_{\\epsilon_1-\\epsilon_n}, X_{2\\epsilon_1}$.\nWe show that the minimal nilpotent finite $W$-algebra $W(\\mathfrak{sp}_{2n},\ne)$ is isomorphic to the centralizer $C_{U_S}(B)$ of some subalgebra $B$ in\n$U_S$, and it can be identified with a tensor product factor of $U_S$. As an\napplication, we show that the category of weight $\\mathfrak{sp}_{2n}$-modules\nwith injective actions of all root vectors and finite-dimensional weight spaces\nis equivalent to the category of finite-dimensional modules over\n$W(\\mathfrak{sp}_{2n}, e)$, explaining the coincidence that both of them are\nsemi-simple.",
        "split": "arXiv"
    },
    {
        "text": "Code Large Language Models (Code LLMs), such as Code llama and\nDeepSeek-Coder, have demonstrated exceptional performance in the code\ngeneration tasks. However, most existing models focus on the abilities of\ngenerating correct code, but often struggle with bug repair. We introduce a\nsuit of methods to enhance LLM's SQL bug-fixing abilities. The methods are\nmainly consisted of two parts: A Progressive Dataset Construction (PDC) from\nscratch and Dynamic Mask Supervised Fine-tuning (DM-SFT). PDC proposes two data\nexpansion methods from the perspectives of breadth first and depth first\nrespectively. DM-SFT introduces an efficient bug-fixing supervised learning\napproach, which effectively reduce the total training steps and mitigate the\n\"disorientation\" in SQL code bug-fixing training. In our evaluation, the code\nLLM models trained with two methods have exceeds all current best performing\nmodel which size is much larger.",
        "split": "arXiv"
    },
    {
        "text": "Utilizing fault diagnosis methods is crucial for nuclear power professionals\nto achieve efficient and accurate fault diagnosis for nuclear power plants\n(NPPs). The performance of traditional methods is limited by their dependence\non complex feature extraction and skilled expert knowledge, which can be\ntime-consuming and subjective. This paper proposes a novel intelligent fault\ndiagnosis method for NPPs that combines enhanced temporal convolutional network\n(ETCN) with sparrow search algorithm (SSA). ETCN utilizes temporal\nconvolutional network (TCN), self-attention (SA) mechanism and residual block\nfor enhancing performance. ETCN excels at extracting local features and\ncapturing time series information, while SSA adaptively optimizes its\nhyperparameters for superior performance. The proposed method's performance is\nexperimentally verified on a CPR1000 simulation dataset. Compared to other\nadvanced intelligent fault diagnosis methods, the proposed one demonstrates\nsuperior performance across all evaluation metrics. This makes it a promising\ntool for NPP intelligent fault diagnosis, ultimately enhancing operational\nreliability.",
        "split": "arXiv"
    },
    {
        "text": "For any compact Lie group $G$, we introduce a novel notion of average\nsignature $\\mathbb A(G)$ valued in its tensor Lie algebra, by taking the\naverage value of the signature of the unique length-minimizing geodesics\nbetween all pairs of generic points in $G$. We prove that the trace spectrum of\n$\\mathbb A(G)$ recovers certain geometric quantities of $G$, including the\ndimension, the diameter, the volume and the scalar curvature.",
        "split": "arXiv"
    },
    {
        "text": "Bitumen extraction for the production of synthetic crude oil in Canada's\nAthabasca Oil Sands industry has recently come under spotlight for being a\nsignificant source of greenhouse gas emission. A major cause of concern is\nmethane, a greenhouse gas produced by the anaerobic biodegradation of\nhydrocarbons in oil sands residues, or tailings, stored in settle basins\ncommonly known as oil sands tailing ponds. In order to determine the methane\nemitting potential of these tailing ponds and have future methane projections,\nwe use real-time weather data, mechanistic models developed from laboratory\ncontrolled experiments, and industrial reports to train a physics constrained\nmachine learning model. Our trained model can successfully identify the\ndirections of active ponds and estimate their emission levels, which are\ngenerally hard to obtain due to data sampling restrictions. We found that each\nactive oil sands tailing pond could emit between 950 to 1500 tonnes of methane\nper year, whose environmental impact is equivalent to carbon dioxide emissions\nfrom at least 6000 gasoline powered vehicles. Although abandoned ponds are\noften presumed to have insignificant emissions, our findings indicate that\nthese ponds could become active over time and potentially emit up to 1000\ntonnes of methane each year. Taking an average over all datasets that was used\nin model training, we estimate that emissions around major oil sands regions\nwould need to be reduced by approximately 12% over a year, to reduce the\naverage methane concentrations to 2005 levels.",
        "split": "arXiv"
    },
    {
        "text": "Molecular docking enables virtual screening of compound libraries to identify\npotential ligands that target proteins of interest, a crucial step in drug\ndevelopment; however, as the size of the compound library increases, the\ncomputational complexity of traditional docking models increases. Deep learning\nalgorithms can provide data-driven research and development models to increase\nthe speed of the docking process. Unfortunately, few models can achieve\nsuperior screening performance compared to that of traditional models.\nTherefore, a novel deep learning-based docking approach named Dockformer is\nintroduced in this study. Dockformer leverages multimodal information to\ncapture the geometric topology and structural knowledge of molecules and can\ndirectly generate binding conformations with the corresponding confidence\nmeasures in an end-to-end manner. The experimental results show that Dockformer\nachieves success rates of 90.53\\% and 82.71\\% on the PDBbind core set and\nPoseBusters benchmarks, respectively, and more than a 100-fold increase in the\ninference process speed, outperforming almost all state-of-the-art docking\nmethods. In addition, the ability of Dockformer to identify the main protease\ninhibitors of coronaviruses is demonstrated in a real-world virtual screening\nscenario. Considering its high docking accuracy and screening efficiency,\nDockformer can be regarded as a powerful and robust tool in the field of drug\ndesign.",
        "split": "arXiv"
    },
    {
        "text": "Generalization in Deep Reinforcement Learning (DRL) across unseen environment\nvariations often requires training over a diverse set of scenarios. Many\nexisting DRL algorithms struggle with efficiency when handling numerous\nvariations. The Generalist-Specialist Learning (GSL) framework addresses this\nby first training a generalist model on all variations, then creating\nspecialists from the generalist's weights, each focusing on a subset of\nvariations. The generalist then refines its learning with assistance from the\nspecialists. However, random task partitioning in GSL can impede performance by\nassigning vastly different variations to the same specialist, often resulting\nin each specialist focusing on only one variation, which raises computational\ncosts. To improve this, we propose Generalist-Specialist Learning with Point\nCloud Feature-based Task Partitioning (GSL-PCD). Our approach clusters\nenvironment variations based on features extracted from object point clouds and\nuses balanced clustering with a greedy algorithm to assign similar variations\nto the same specialist. Evaluations on robotic manipulation tasks from the\nManiSkill benchmark demonstrate that point cloud feature-based partitioning\noutperforms vanilla partitioning by 9.4%, with a fixed number of specialists,\nand reduces computational and sample requirements by 50% to achieve comparable\nperformance.",
        "split": "arXiv"
    },
    {
        "text": "This study compares Sporo Health's AI Scribe, a proprietary model fine-tuned\nfor medical scribing, with various LLMs (GPT-4o, GPT-3.5, Gemma-9B, and\nLlama-3.2-3B) in clinical documentation. We analyzed de-identified patient\ntranscripts from partner clinics, using clinician-provided SOAP notes as the\nground truth. Each model generated SOAP summaries using zero-shot prompting,\nwith performance assessed via recall, precision, and F1 scores. Sporo\noutperformed all models, achieving the highest recall (73.3%), precision\n(78.6%), and F1 score (75.3%) with the lowest performance variance.\nStatistically significant differences (p < 0.05) were found between Sporo and\nthe other models, with post-hoc tests showing significant improvements over\nGPT-3.5, Gemma-9B, and Llama 3.2-3B. While Sporo outperformed GPT-4o by up to\n10%, the difference was not statistically significant (p = 0.25). Clinical user\nsatisfaction, measured with a modified PDQI-9 inventory, favored Sporo.\nEvaluations indicated Sporo's outputs were more accurate and relevant. This\nhighlights the potential of Sporo's multi-agentic architecture to improve\nclinical workflows.",
        "split": "arXiv"
    },
    {
        "text": "Direct detection of gravitons in gravitational experiments, including\ngravitational wave observatories, has been all but ruled out given the weak\ncoupling between the gravitational field and matter. Here we propose an\nalternative: looking not for the presence but for the absence of graviton shot\nnoise in gravitational wave data. Gravitational wave experiments detect very\nweak signals that correspond to a surprisingly small number of gravitons even\nat the relatively low frequencies that characterize signals from gravitational\nwave events. A detailed calculation, which also yields results that are\nconsistent with the existing literature, demonstrates that graviton shot noise\nmay be present at detectable levels in gravitational wave observations. The\nabsence of elevated noise levels due to graviton shot noise, in turn, would\nindicate that gravity is not a quantum field theory with a conventional\nperturbative expansion at low energies.",
        "split": "arXiv"
    },
    {
        "text": "Recent papers in the graph machine learning literature have introduced a\nnumber of approaches for hyperbolic representation learning. The asserted\nbenefits are improved performance on a variety of graph tasks, node\nclassification and link prediction included. Claims have also been made about\nthe geometric suitability of particular hierarchical graph datasets to\nrepresentation in hyperbolic space. Despite these claims, our work makes a\nsurprising discovery: when simple Euclidean models with comparable numbers of\nparameters are properly trained in the same environment, in most cases, they\nperform as well, if not better, than all introduced hyperbolic graph\nrepresentation learning models, even on graph datasets previously claimed to be\nthe most hyperbolic as measured by Gromov $\\delta$-hyperbolicity (i.e., perfect\ntrees). This observation gives rise to a simple question: how can this be? We\nanswer this question by taking a careful look at the field of hyperbolic graph\nrepresentation learning as it stands today, and find that a number of papers\nfail to diligently present baselines, make faulty modelling assumptions when\nconstructing algorithms, and use misleading metrics to quantify geometry of\ngraph datasets. We take a closer look at each of these three problems,\nelucidate the issues, perform an analysis of methods, and introduce a\nparametric family of benchmark datasets to ascertain the applicability of\n(hyperbolic) graph neural networks.",
        "split": "arXiv"
    },
    {
        "text": "We explore the impact of pre-training data composition on the performance of\nsmall language models in a sample-efficient setting. Using datasets limited to\n10 million words, we evaluate several dataset sources, including child-directed\nspeech (CHILDES), classic books (Gutenberg), synthetic data (TinyStories), and\na mix of these (Mix) across different model sizes ranging from 18 million to\n705 million parameters. Our experiments show that smaller models (e.g.,\nGPT2-97M, GPT2-705M, Llama-360M) perform better when trained on more complex\nand rich datasets like Gutenberg. Models trained on the CHILDES and TinyStories\ndatasets underperformed across all model sizes. These findings suggest that the\noptimal dataset for sample efficient training depends on the model size, and\nthat neither child-directed speech nor simplified stories are optimal for\nlanguage models of all sizes. We highlight the importance of considering both\ndataset composition and model capacity for effective sample efficient language\nmodel training.",
        "split": "arXiv"
    },
    {
        "text": "Structured light carrying angular momentum, such as spin angular momentum\n(SAM) and orbital angular momentum (OAM), has been at the core of new science\nand applications, driving the need for compact on-chip sources. While many\nstatic on-chip solutions have been demonstrated, as well as on-chip sources of\nfree-space modes, no architecture that is fully reconfigurable in all angular\nmomentum states and all on-chip has so far been possible. Here we report the\nfirst all-on-chip structured light generator for the creation of both scalar\nand vectorial angular momentum beams, facilitated through a\nsilicon-on-insulator (SOI) chip with a silica mode multiplexer (silica chip).\nWe selectively stimulate six linearly-polarized (LP) modes of the silica\nmultimode bus waveguide, precisely controlling the modal powers and phases with\nthe SOI chip. This allows us to tailor arbitrary superpositions of the mode set\nthus synthesizing common cylindrical vector vortex beams as well as OAM beams\nof controlled spin and topological charge. Our compact structured light\ngenerator exhibits high switching speed and operates across the telecom band,\npaving the way for applications such as optical communication and integrated\nquantum technologies.",
        "split": "arXiv"
    },
    {
        "text": "In the past several years there has been an explosion of available models for\nvision-language tasks. Unfortunately, the literature still leaves open a number\nof questions related to best practices in designing and training such models.\nIn this paper we seek to answer several questions related to the pretraining of\nvision-language encoders through meta-analysis. In our first set of\nexperiments, we show that we can save significant compute at no cost to\ndownstream performance, by freezing large parts of vision-language models\nduring pretraining. In our second set of experiments we examine the effect of\nbasing a VL transformer on a vision model versus a text model. Additionally, we\nintroduce a VL modeling platform called Renaissance that we use to conduct all\nof the experiments. This program offers a great deal of flexibility in\ncreating, training and evaluating transformer encoders for VL modeling. The\nsource code for Renaissance can be found at\nhttps://github.com/bsu-slim/renaissance.",
        "split": "arXiv"
    },
    {
        "text": "A light field camera can reconstruct 3D scenes using captured multi-focus\nimages that contain rich spatial geometric information, enhancing applications\nin stereoscopic photography, virtual reality, and robotic vision. In this work,\na state-of-the-art salient object detection model for multi-focus light field\nimages, called LFSamba, is introduced to emphasize four main insights: (a)\nEfficient feature extraction, where SAM is used to extract modality-aware\ndiscriminative features; (b) Inter-slice relation modeling, leveraging Mamba to\ncapture long-range dependencies across multiple focal slices, thus extracting\nimplicit depth cues; (c) Inter-modal relation modeling, utilizing Mamba to\nintegrate all-focus and multi-focus images, enabling mutual enhancement; (d)\nWeakly supervised learning capability, developing a scribble annotation dataset\nfrom an existing pixel-level mask dataset, establishing the first\nscribble-supervised baseline for light field salient object\ndetection.https://github.com/liuzywen/LFScribble",
        "split": "arXiv"
    },
    {
        "text": "The two-way flow of information and energy is an important feature of the\nEnergy Internet. Data analytics is a powerful tool in the information flow that\naims to solve practical problems using data mining techniques. As the problem\nof electricity thefts via tampering with smart meters continues to increase,\nthe abnormal behaviors of thefts become more diversified and more difficult to\ndetect. Thus, a data analytics method for detecting various types of\nelectricity thefts is required. However, the existing methods either require a\nlabeled dataset or additional system information which is difficult to obtain\nin reality or have poor detection accuracy. In this paper, we combine two novel\ndata mining techniques to solve the problem. One technique is the Maximum\nInformation Coefficient (MIC), which can find the correlations between the\nnon-technical loss (NTL) and a certain electricity behavior of the consumer.\nMIC can be used to precisely detect thefts that appear normal in shapes. The\nother technique is the clustering technique by fast search and find of density\npeaks (CFSFDP). CFSFDP finds the abnormal users among thousands of load\nprofiles, making it quite suitable for detecting electricity thefts with\narbitrary shapes. Next, a framework for combining the advantages of the two\ntechniques is proposed. Numerical experiments on the Irish smart meter dataset\nare conducted to show the good performance of the combined method.",
        "split": "arXiv"
    },
    {
        "text": "The optimal execution problem has always been a continuously focused research\nissue, and many reinforcement learning (RL) algorithms have been studied. In\nthis article, we consider the execution problem of targeting the volume\nweighted average price (VWAP) and propose a relaxed stochastic optimization\nproblem with an entropy regularizer to encourage more exploration. We derive\nthe explicit formula of the optimal policy, which is Gaussian distributed, with\nits mean value being the solution to the original problem. Extending the\nframework of continuous RL to processes with jumps, we provide some theoretical\nproofs for RL algorithms. First, minimizing the martingale loss function leads\nto the optimal parameter estimates in the mean-square sense, and the second\nalgorithm is to use the martingale orthogonality condition. In addition to the\nRL algorithm, we also propose another learning algorithm: adaptive dynamic\nprogramming (ADP) algorithm, and verify the performance of both in two\ndifferent environments across different random seeds. Convergence of all\nalgorithms has been verified in different environments, and shows a larger\nadvantage in the environment with stronger price impact. ADP is a good choice\nwhen the agent fully understands the environment and can estimate the\nparameters well. On the other hand, RL algorithms do not require any model\nassumptions or parameter estimation, and are able to learn directly from\ninteractions with the environment.",
        "split": "arXiv"
    },
    {
        "text": "Country instability is a global issue, with unpredictably high levels of\ninstability thwarting socio-economic growth and possibly causing a slew of\nnegative consequences. As a result, uncertainty prediction models for a country\nare becoming increasingly important in the real world, and they are expanding\nto provide more input from 'big data' collections, as well as the\ninterconnectedness of global economies and social networks. This has culminated\nin massive volumes of qualitative data from outlets like television, print,\ndigital, and social media, necessitating the use of artificial intelligence\n(AI) tools like machine learning to make sense of it all and promote predictive\nprecision [1]. The Global Database of Activities, Voice, and Tone (GDELT\nProject) records broadcast, print, and web news in over 100 languages every\nsecond of every day, identifying the people, locations, organisations, counts,\nthemes, outlets, and events that propel our global community and offering a\nfree open platform for computation on the entire world. The main goal of our\nresearch is to investigate how, when our data grows more voluminous and\nfine-grained, we can conduct a more complex methodological analysis of\npolitical conflict. The GDELT dataset, which was released in 2012, is the first\nand potentially the most technologically sophisticated publicly accessible\ndataset on political conflict.",
        "split": "arXiv"
    },
    {
        "text": "The stock market provides a rich well of information that can be split across\nmodalities, making it an ideal candidate for multimodal evaluation. Multimodal\ndata plays an increasingly important role in the development of machine\nlearning and has shown to positively impact performance. But information can do\nmore than exist across modes -- it can exist across time. How should we attend\nto temporal data that consists of multiple information types? This work\nintroduces (i) the MEANT model, a Multimodal Encoder for Antecedent information\nand (ii) a new dataset called TempStock, which consists of price, Tweets, and\ngraphical data with over a million Tweets from all of the companies in the S&P\n500 Index. We find that MEANT improves performance on existing baselines by\nover 15%, and that the textual information affects performance far more than\nthe visual information on our time-dependent task from our ablation study.",
        "split": "arXiv"
    },
    {
        "text": "Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned\nfrom labeled source domains to improve performance on the unlabeled target\ndomains. While Convolutional Neural Networks (CNNs) have been dominant in\nprevious UDA methods, recent research has shown promise in applying Vision\nTransformers (ViTs) to this task. In this study, we propose a novel Feature\nFusion Transferability Aware Transformer (FFTAT) to enhance ViT performance in\nUDA tasks. Our method introduces two key innovations: First, we introduce a\npatch discriminator to evaluate the transferability of patches, generating a\ntransferability matrix. We integrate this matrix into self-attention, directing\nthe model to focus on transferable patches. Second, we propose a feature fusion\ntechnique to fuse embeddings in the latent space, enabling each embedding to\nincorporate information from all others, thereby improving generalization.\nThese two components work in synergy to enhance feature representation\nlearning. Extensive experiments on widely used benchmarks demonstrate that our\nmethod significantly improves UDA performance, achieving state-of-the-art\n(SOTA) results.",
        "split": "arXiv"
    },
    {
        "text": "As fine-tuning large language models (LLMs) becomes increasingly prevalent,\nusers often rely on third-party services with limited visibility into their\nfine-tuning processes. This lack of transparency raises the question: how do\nconsumers verify that fine-tuning services are performed correctly? For\ninstance, a service provider could claim to fine-tune a model for each user,\nyet simply send all users back the same base model. To address this issue, we\npropose vTune, a simple method that uses a small number of backdoor data points\nadded to the training data to provide a statistical test for verifying that a\nprovider fine-tuned a custom model on a particular user's dataset. Unlike\nexisting works, vTune is able to scale to verification of fine-tuning on\nstate-of-the-art LLMs, and can be used both with open-source and closed-source\nmodels. We test our approach across several model families and sizes as well as\nacross multiple instruction-tuning datasets, and find that the statistical test\nis satisfied with p-values on the order of $\\sim 10^{-40}$, with no negative\nimpact on downstream task performance. Further, we explore several attacks that\nattempt to subvert vTune and demonstrate the method's robustness to these\nattacks.",
        "split": "arXiv"
    },
    {
        "text": "Mossinghoff, Trudgian, and the first author~\\cite{MMT23} recently introduced\na family of arithmetic functions called ``fake $\\mu$'s'', which are\nmultiplicative functions for which there is a $\\{-1,0,1\\}$-valued sequence\n$(\\varepsilon_j)_{j=1}^{\\infty}$ such that $f(p^j) = \\varepsilon_j$ for all\nprimes $p$. They investigated comparative number-theoretic results for fake\n$\\mu$'s and in particular proved oscillation results at scale $\\sqrt{x}$ for\nthe summatory functions of fake $\\mu$'s with $\\varepsilon_1=-1$ and\n$\\varepsilon_2=1$. In this paper, we establish new oscillation results for the\nsummatory functions of all nontrivial fake $\\mu$'s at scales $x^{1/2\\ell}$\nwhere $\\ell$ is a positive integer (the ``critical index'') depending on $f$;\nfor $\\ell=1$ this recovers the oscillation results in~\\cite{MMT23}. Our work\nalso recovers results on the indicator functions of powerfree and powerfull\nnumbers; we generalize techniques applied to each of these examples to extend\nto all fake $\\mu$'s.",
        "split": "arXiv"
    },
    {
        "text": "Stimulated Brillouin scattering (SBS) is revolutionizing low-noise lasers and\nmicrowave photonic systems. However, despite extensive explorations of a\nlow-loss and versatile integrated platform for Brillouin photonic circuits,\ncurrent options fall short due to limited technological scalability or\ninadequate SBS gain. Here we introduce the thin-film lithium niobate (TFLN)\nplatform as the go-to choice for integrated Brillouin photonics applications.\nWe report the angle-dependent strong SBS gain in this platform, which can\novercome the intrinsic propagation loss. Furthermore, we demonstrate the first\nstimulated Brillouin laser in TFLN with a tuning range > 20 nm and utilize it\nto achieve high-purity RF signal generation with an intrinsic linewidth of 9\nHz. Finally, we devise a high-rejection Brillouin-based microwave photonic\nnotch filter, for the first time, integrating an SBS spiral, an on-chip\nmodulator, and a tunable ring all within the same platform. This TFLN-based\nBrillouin photonics engine uniquely combines the scalability of this platform\nand the versatility of SBS. Moreover, it bridges SBS with other functionalities\nin the TFLN platform, unlocking new possibilities for Brillouin-based\napplications with unparalleled performances.",
        "split": "arXiv"
    },
    {
        "text": "Single-layer semiconducting transition-metal dichalcogenides, lacking point\ninversion symmetry, provide an efficient platform for valleytronics, where the\nelectronic, magnetic, valley and lattice degrees of freedom can be selectively\nmanipulated by using polarized light. This task is however thought to be\nlimited in parent bulk compounds where the point inversion symmetry is\nrestored. Exploiting the underlying quantum physics in bulk materials is thus\none of the biggest paradigmatic challenges. Here we show that a sizable optical\nKerr rotation can be efficiently generated in a wide energy range on ultrafast\ntimescales in bulk WSe$_2$, by means of circularly-polarized light. We\nrationalize these findings as a result of the hidden spin/layer/valley quantum\nentanglement. The spectral analysis reveals clear features at the three\ncharacteristic frequencies corresponding to the A-, B- and C-exciton edges. The\norigin and the relative sign of all these features is shown to stem from the\nselective Pauli blocking of intralayer and interlayer optical transitions. The\nlong lifetime of the broadband Kerr response ($\\tau \\sim 500$ fs) provides a\nstrong indication that coupled photo-induced electron and hole densities\nsurvive in bulk compounds longer than previously expected. The present report\ndemonstrates that a hidden quantum entanglement is operative also in bulk\ncentrosymmetric layered materials, opening the way for an effective\nexploitation of bulk WSe$_2$ in optoelectronic applications.",
        "split": "arXiv"
    },
    {
        "text": "Regional prompting, or compositional generation, which enables fine-grained\nspatial control, has gained increasing attention for its practicality in\nreal-world applications. However, previous methods either introduce additional\ntrainable modules, thus only applicable to specific models, or manipulate on\nscore maps within cross-attention layers using attention masks, resulting in\nlimited control strength when the number of regions increases. To handle these\nlimitations, we present RAG, a Regional-Aware text-to-image Generation method\nconditioned on regional descriptions for precise layout composition. RAG\ndecouple the multi-region generation into two sub-tasks, the construction of\nindividual region (Regional Hard Binding) that ensures the regional prompt is\nproperly executed, and the overall detail refinement (Regional Soft Refinement)\nover regions that dismiss the visual boundaries and enhance adjacent\ninteractions. Furthermore, RAG novelly makes repainting feasible, where users\ncan modify specific unsatisfied regions in the last generation while keeping\nall other regions unchanged, without relying on additional inpainting models.\nOur approach is tuning-free and applicable to other frameworks as an\nenhancement to the prompt following property. Quantitative and qualitative\nexperiments demonstrate that RAG achieves superior performance over attribute\nbinding and object relationship than previous tuning-free methods.",
        "split": "arXiv"
    },
    {
        "text": "Robotic platforms provide repeatable and precise tool positioning that\nsignificantly enhances retinal microsurgery. Integration of such systems with\nintraoperative optical coherence tomography (iOCT) enables image-guided robotic\ninterventions, allowing to autonomously perform advanced treatment\npossibilities, such as injecting therapeutic agents into the subretinal space.\nYet, tissue deformations due to tool-tissue interactions are a major challenge\nin autonomous iOCT-guided robotic subretinal injection, impacting correct\nneedle positioning and, thus, the outcome of the procedure. This paper presents\na novel method for autonomous subretinal injection under iOCT guidance that\nconsiders tissue deformations during the insertion procedure. This is achieved\nthrough real-time segmentation and 3D reconstruction of the surgical scene from\ndensely sampled iOCT B-scans, which we refer to as B5-scans, to monitor the\npositioning of the instrument regarding a virtual target layer defined at a\nrelative position between the ILM and RPE. Our experiments on ex-vivo porcine\neyes demonstrate dynamic adjustment of the insertion depth and overall improved\naccuracy in needle positioning compared to previous autonomous insertion\napproaches. Compared to a 35% success rate in subretinal bleb generation with\nprevious approaches, our proposed method reliably and robustly created\nsubretinal blebs in all our experiments.",
        "split": "arXiv"
    },
    {
        "text": "Graph convolutional networks (GCNs) are an effective skeleton-based human\naction recognition (HAR) technique. GCNs enable the specification of CNNs to a\nnon-Euclidean frame that is more flexible. The previous GCN-based models still\nhave a lot of issues: (I) The graph structure is the same for all model layers\nand input data.",
        "split": "arXiv"
    },
    {
        "text": "Since the COVID-19 pandemic, clinicians have seen a large and sustained\ninflux in patient portal messages, significantly contributing to clinician\nburnout. To the best of our knowledge, there are no large-scale public patient\nportal messages corpora researchers can use to build tools to optimize\nclinician portal workflows. Informed by our ongoing work with a regional\nhospital, this study introduces an LLM-powered framework for configurable and\nrealistic patient portal message generation. Our approach leverages few-shot\ngrounded text generation, requiring only a small number of de-identified\npatient portal messages to help LLMs better match the true style and tone of\nreal data. Clinical experts in our team deem this framework as HIPAA-friendly,\nunlike existing privacy-preserving approaches to synthetic text generation\nwhich cannot guarantee all sensitive attributes will be protected. Through\nextensive quantitative and human evaluation, we show that our framework\nproduces data of higher quality than comparable generation methods as well as\nall related datasets. We believe this work provides a path forward for (i) the\nrelease of large-scale synthetic patient message datasets that are\nstylistically similar to ground-truth samples and (ii) HIPAA-friendly data\ngeneration which requires minimal human de-identification efforts.",
        "split": "arXiv"
    },
    {
        "text": "In recent years, YouTube has become the leading platform for Bangla movies\nand dramas, where viewers express their opinions in comments that convey their\nsentiments about the content. However, not all comments are relevant for\nsentiment analysis, necessitating a filtering mechanism. We propose a system\nthat first assesses the relevance of comments and then analyzes the sentiment\nof those deemed relevant. We introduce a dataset of 14,000 manually collected\nand preprocessed comments, annotated for relevance (relevant or irrelevant) and\nsentiment (positive or negative). Eight transformer models, including\nBanglaBERT, were used for classification tasks, with BanglaBERT achieving the\nhighest accuracy (83.99% for relevance detection and 93.3% for sentiment\nanalysis). The study also integrates LIME to interpret model decisions,\nenhancing transparency.",
        "split": "arXiv"
    },
    {
        "text": "We study the problem of multilateral collaboration among agents with\ntransferable utilities. Any group of agents can sign a contract consisting of a\nprimitive contract and monetary transfers among the signatories. We propose a\ndynamic auction that finds a stable outcome when primitive contracts are gross\ncomplements for all participants.",
        "split": "arXiv"
    },
    {
        "text": "This research proposes the development of a next generation airline\nreservation system that incorporates the Cloud microservices, distributed\nartificial intelligence modules and the blockchain technology to improve on the\nefficiency, safety and customer satisfaction. The traditional reservation\nsystems encounter issues related to the expansion of the systems, the integrity\nof the data provided and the level of service offered to the customers, which\nis the main focus of this architecture through the modular and data centric\ndesign approaches. This will allow different operations such as reservations,\npayments, and customer data management among others to be performed separately\nthereby facilitating high availability of the system by 30% and enhancing\nperformance of the system by 40% on its scalability. Such systems contain AI\ndriven modules that utilize the past booking patterns along with the profile of\nthe customer to estimate the demand and make recommendations, which increases\nto 25 % of customer engagement. Moreover, blockchain is effective in engaging\nan incorruptible ledger system for the all transactions therefore mitigating\nfraud incidences and increasing the clarity by 20%. The system was subjected to\nanalysis using a simulator and using machine learning evaluations that rated it\nagainst other conventional systems. The results show that there were clear\nenhancements in the speed of transactions where the rates of secure data\nprocessing rose by 35%, and the system response time by 15 %. The system can\nalso be used for other high transaction industries like logistics and\nhospitality. This structural design is indicative of how the use of advanced\ntechnologies will revolutionize the airline reservation sector. The\nimplications are growing effectiveness, improvement in security and greater\ncustomer contentment.",
        "split": "arXiv"
    },
    {
        "text": "Spin fluctuations have been proposed as a key mechanism for mediating\nsuperconductivity, particularly in high-temperature superconducting cuprates,\nwhere conventional electron-phonon interactions alone cannot account for the\nobserved critical temperatures. Traditionally, their role has been analyzed\nthrough tight-binding based model Hamiltonians. In this work we present a\nmethod that combines density functional theory with a momentum- and\nfrequency-dependent pairing interaction derived from the Fluctuation Exchange\n(FLEX) type Random Phase Approximation (FLEX-RPA) to compute Eliashberg\nspectral functions $\\alpha ^{2}F(\\omega )$ which are central to spin\nfluctuation theory of superconductivity. We apply our numerical procedure to\nstudy a series of cuprates where our extracted material specific $\\alpha\n^{2}F(\\omega )$ are found to exhibit remarkable similarities characterized by a\nsharp peak in the vicinity of 40-60 meV and their rapid decay at higher\nfrequencies. Our exact diagonalization of a linearized BCS gap equation\nextracts superconducting energy gap functions for realistic Fermi surfaces of\nthe cuprates and predicts their symmetry to be $d_{x^{2}-y^{2}}$ in all studied\nsystems. Via a variation of on-site Coulomb repulsion $U$ for the copper\n$d$-electrons we show that that the range of the experimental values of $T_{c}$\ncan be reproduced in this approach but is extremely sensitive to the proximity\nof the spin density wave instability. These data highlight challenges in\nbuilding first-principle theories of high temperature superconductivity but\noffer new insights beyond previous treatments, such as the confirmation of the\nusability of approximate BCS-like $T_{c}$ equations, together with the\nevaluations of the material specific coupling constant $\\lambda $ without\nreliance on tight-binding approximations of their electronic structures.",
        "split": "arXiv"
    },
    {
        "text": "Deep learning (DL)-based methods have demonstrated remarkable achievements in\naddressing orthogonal frequency division multiplexing (OFDM) channel estimation\nchallenges. However, existing DL-based methods mainly rely on separate real and\nimaginary inputs while ignoring the inherent correlation between the two\nstreams, such as amplitude and phase information that are fundamental in\ncommunication signal processing. This paper proposes AE-DENet, a novel\nautoencoder(AE)-based data enhancement network to improve the performance of\nexisting DL-based channel estimation methods. AE-DENet focuses on enriching the\nclassic least square (LS) estimation input commonly used in DL-based methods by\nemploying a learning-based data enhancement method, which extracts interaction\nfeatures from the real and imaginary components and fuses them with the\noriginal real/imaginary streams to generate an enhanced input for better\nchannel inference. Experimental findings in terms of the mean square error\n(MSE) results demonstrate that the proposed method enhances the performance of\nall state-of-the-art DL-based channel estimators with negligible added\ncomplexity. Furthermore, the proposed approach is shown to be robust to channel\nvariations and high user mobility.",
        "split": "arXiv"
    },
    {
        "text": "We study Friedel oscillations (FOs) in two-dimensional topological materials\nwith Mexican hat band dispersion, which attract great interest due to the\ncomplex of its inherent non-trivial features, including the Van Hove\nsingularity, doubly connected Fermi surface, non-trivial quantum-geometric\nproperties, and the presence of states with negative effective mass. These\nfactors are found to lead to a three-mode structure of the FOs. One of the\nmodes, arising from electron transitions between the Fermi contours, has an\nunexpectedly large amplitude. The evolution of the amplitudes of all modes with\nFermi energy is largely determined by the interplay of three main factors:\nintra-contour and inter-contour electron transitions, the quantum metric of the\nbasis states, and the electron-electron interaction. We traced the role of each\nfactor in the formation of the FO pattern and identified the corresponding\nfeatures of the FO evolution.",
        "split": "arXiv"
    },
    {
        "text": "The tree edit distance (TED) between two rooted ordered trees with $n$ nodes\nlabeled from an alphabet $\\Sigma$ is the minimum cost of transforming one tree\ninto the other by a sequence of valid operations consisting of insertions,\ndeletions and relabeling of nodes. The tree edit distance is a well-known\ngeneralization of string edit distance and has been studied since the 1970s.\nYears of steady improvements have led to an $O(n^3)$ algorithm [DMRW 2010].\nFine-grained complexity casts light onto the hardness of TED showing that a\ntruly subcubic time algorithm for TED implies a truly subcubic time algorithm\nfor All-Pairs Shortest Paths (APSP) [BGMW 2020]. Therefore, under the popular\nAPSP hypothesis, a truly subcubic time algorithm for TED cannot exist. However,\nunlike many problems in fine-grained complexity for which conditional hardness\nbased on APSP also comes with equivalence to APSP, whether TED can be reduced\nto APSP has remained unknown.\n  In this paper, we resolve this. Not only we show that TED is fine-grained\nequivalent to APSP, our reduction is tight enough, so that combined with the\nfastest APSP algorithm to-date [Williams 2018] it gives the first ever subcubic\ntime algorithm for TED running in $n^3/2^{\\Omega(\\sqrt{\\log{n}})}$ time.\n  We also consider the unweighted tree edit distance problem in which the cost\nof each edit is one. For unweighted TED, a truly subcubic algorithm is known\ndue to Mao [Mao 2022], later improved slightly by D\\\"{u}rr [D\\\"{u}rr 2023] to\nrun in $O(n^{2.9148})$. Their algorithm uses bounded monotone min-plus product\nas a crucial subroutine, and the best running time for this product is\n$\\tilde{O}(n^{\\frac{3+\\omega}{2}})\\leq O(n^{2.6857})$ (where $\\omega$ is the\nexponent of fast matrix multiplication). In this work, we close this gap and\ngive an algorithm for unweighted TED that runs in\n$\\tilde{O}(n^{\\frac{3+\\omega}{2}})$ time.",
        "split": "arXiv"
    },
    {
        "text": "During the COVID-19 crisis, mechanistic models have been proven fundamental\nto guide evidence-based decision making. However, time-critical decisions in a\ndynamically changing environment restrict the time available for modelers to\ngather supporting evidence. As infectious disease dynamics are often\nheterogeneous on a spatial or demographic scale, models should be resolved\naccordingly. In addition, with a large number of potential interventions, all\nscenarios can barely be computed on time, even when using supercomputing\nfacilities. We suggest to combine complex mechanistic models with data-driven\nsurrogate models to allow for on-the-fly model adaptations by public health\nexperts. We build upon a spatially and demographically resolved infectious\ndisease model and train a graph neural network for data sets representing early\nphases of the pandemic. The resulting networks reached an execution time of\nless than a second, a significant speedup compared to the metapopulation\napproach. The suggested approach yields potential for on-the-fly execution and,\nthus, integration of disease dynamics models in low-barrier website\napplications. For the approach to be used with decision-making, datasets with\nlarger variance will have to be considered.",
        "split": "arXiv"
    },
    {
        "text": "When training data are distributed across{ time or space,} covariate shift\nacross fragments of training data biases cross-validation, compromising model\nselection and assessment. We present \\textit{Fragmentation-Induced\ncovariate-shift Remediation} ($FIcsR$), which minimizes an $f$-divergence\nbetween a fragment's covariate distribution and that of the standard\ncross-validation baseline. We s{how} an equivalence with popular\nimportance-weighting methods. {The method}'s numerical solution poses a\ncomputational challenge owing to the overparametrized nature of a neural\nnetwork, and we derive a Fisher Information approximation. When accumulated\nover fragments, this provides a global estimate of the amount of shift\nremediation thus far needed, and we incorporate that as a prior via the\nminimization objective. In the paper, we run extensive classification\nexperiments on multiple data classes, over $40$ datasets, and with data batched\nover multiple sequence lengths. We extend the study to the $k$-fold\ncross-validation setting through a similar set of experiments. An ablation\nstudy exposes the method to varying amounts of shift and demonstrates slower\ndegradation with $FIcsR$ in place. The results are promising under all these\nconditions; with improved accuracy against batch and fold state-of-the-art by\nmore than $5\\%$ and $10\\%$, respectively.",
        "split": "arXiv"
    },
    {
        "text": "Theoretically, Josephson junction (JJ) arrays can exhibit either a\nsuperconducting or insulating state, separated by a quantum phase transition\n(QPT). In this work, we analyzed published data on QPTs in three\none-dimensional arrays and two two-dimensional arrays using a recently\ndeveloped phenomenological model of QPTs. The model is based on the insight\nthat the scaled experimental data depend in a universal way on two\ncharacteristic length scales of the system: the microscopic length scale $L_0$\nfrom which the renormalization group flow starts, and the dephasing length,\n$L_{\\varphi}(T)$ as given by the distance travelled by system-specific\nelementary excitations over the Planckian time. Our analysis reveals that the\ndata for all five arrays (both 1D and 2D) can be quantitatively and\nself-consistently explained within the framework of interacting superconducting\nplasmons. In this picture, $L_{\\varphi}=v_p\\hbar/k_B T$, and $L_0 \\approx\n\\Lambda$, where $v_p$ is the speed of the plasmons and $\\Lambda$ is the Coulomb\nscreening length of the Cooper pairs. We also observe that, in 1D arrays, the\ntransition is significantly shifted towards the insulating side compared to the\npredictions of the sine-Gordon model. Finally, we discuss similarities and\ndifferences with recent microwave studies of extremely long JJ chains, as well\nas with the pair-breaking QPT observed in superconducting nanowires and films.",
        "split": "arXiv"
    },
    {
        "text": "Detecting Beyond Standard Model (BSM) signals in high-energy particle\ncollisions presents significant challenges due to complex data and the need to\ndifferentiate rare signal events from Standard Model (SM) backgrounds. This\nstudy investigates the efficacy of deep learning models, specifically Deep\nNeural Networks (DNNs) and Graph Neural Networks (GNNs), in classifying\nparticle collision events as either BSM signal or background. The research\nutilized a dataset comprising 214,000 SM background and 10,755 BSM events. To\naddress class imbalance, an undersampling method was employed, resulting in\nbalanced classes. Three models were developed and compared: a DNN and two GNN\nvariants with different graph construction methods. All models demonstrated\nhigh performance, achieving Area Under the Receiver Operating Characteristic\ncurve (AUC) values exceeding $94\\%$. While the DNN model slightly outperformed\nGNNs across various metrics, both GNN approaches showed comparable results\ndespite different graph structures. The GNNs' ability to explicitly capture\ninter-particle relationships within events highlights their potential for BSM\nsignal detection.",
        "split": "arXiv"
    },
    {
        "text": "Imagine having a system to control and only know that it belongs to a certain\nclass of dynamical systems. Would it not be amazing to simply plug in a\ncontroller and have it work as intended? With the rise of in-context learning\nand powerful architectures like Transformers, this might be possible, and we\nwant to show it. In this work, within the model reference framework, we hence\npropose the first in-context learning-based approach to design a unique\ncontextual controller for an entire class of dynamical systems rather than\nfocusing on just a single instance. Our promising numerical results show the\npossible advantages of the proposed paradigm, paving the way for a shift from\nthe \"one-system-one-controller\" control design paradigm to a new\n\"one-class-one-controller\" logic.",
        "split": "arXiv"
    },
    {
        "text": "Learning natural and diverse behaviors from human motion datasets remains\nchallenging in physics-based character control. Existing conditional\nadversarial models often suffer from tight and biased embedding distributions\nwhere embeddings from the same motion are closely grouped in a small area and\nshorter motions occupy even less space. Our empirical observations indicate\nthis limits the representational capacity and diversity under each skill. An\nideal latent space should be maximally packed by all motion's embedding\nclusters. In this paper, we propose a skill-conditioned controller that learns\ndiverse skills with expressive variations. Our approach leverages the Neural\nCollapse phenomenon, a natural outcome of the classification-based encoder, to\nuniformly distributed cluster centers. We additionally propose a novel\nEmbedding Expansion technique to form stylistic embedding clusters for diverse\nskills that are uniformly distributed on a hypersphere, maximizing the\nrepresentational area occupied by each skill and minimizing unmapped regions.\nThis maximally packed and uniformly distributed embedding space ensures that\nembeddings within the same cluster generate behaviors conforming to the\ncharacteristics of the corresponding motion clips, yet exhibiting noticeable\nvariations within each cluster. Compared to existing methods, our controller\nnot only generates high-quality, diverse motions covering the entire dataset\nbut also achieves superior controllability, motion coverage, and diversity\nunder each skill. Both qualitative and quantitative results confirm these\ntraits, enabling our controller to be applied to a wide range of downstream\ntasks and serving as a cornerstone for diverse applications.",
        "split": "arXiv"
    },
    {
        "text": "Federated Learning (FL) enables clients to train a joint model without\ndisclosing their local data. Instead, they share their local model updates with\na central server that moderates the process and creates a joint model. However,\nFL is susceptible to a series of privacy attacks. Recently, the source\ninference attack (SIA) has been proposed where an honest-but-curious central\nserver tries to identify exactly which client owns a specific data record. n\nthis work, we propose a defense against SIAs by using a trusted shuffler,\nwithout compromising the accuracy of the joint model. We employ a combination\nof unary encoding with shuffling, which can effectively blend all clients'\nmodel updates, preventing the central server from inferring information about\neach client's model update separately. In order to address the increased\ncommunication cost of unary encoding we employ quantization. Our preliminary\nexperiments show promising results; the proposed mechanism notably decreases\nthe accuracy of SIAs without compromising the accuracy of the joint model.",
        "split": "arXiv"
    },
    {
        "text": "Recovering impact parameter variations in multi-planet systems is an\neffective approach for detecting non-transiting planets and refining planetary\nmass estimates. Traditionally, two methodologies have been employed: the\nIndividual Fit, which fits each transit independently to analyze impact\nparameter changes, and the Dynamical Fit, which simulates planetary dynamics to\nmatch transit light curves. We introduce a new fitting method, Simultaneous\nImpact Parameter Variation Analysis (SIPVA), which outperforms the Individual\nFit and is computationally more efficient than the Dynamical Fit. SIPVA\ndirectly integrates a linear time-dependent model for impact parameters into\nthe Monte Carlo Markov Chain (MCMC) algorithm by fitting all transits\nsimultaneously. We evaluate SIPVA and the Individual Fit on artificial systems\nwith varying LLRs and find that SIPVA consistently outperforms the Individual\nFit in recovery rates and accuracy. When applied to selected Kepler planetary\ncandidates exhibiting significant transit duration variations (TDVs), SIPVA\nidentifies significant impact parameter trends in 10 out of 16 planets. In\ncontrast, the Individual Fit does so in only 4. We also employ probabilistic\nmodeling to calculate the theoretical distribution of planets with significant\nimpact parameter variations across all observed Kepler systems and compare the\ndistribution of recovered candidates by the Individual Fit and Dynamical Fit\nfrom previous work with our theoretical distribution. Our findings offer an\nalternative framework for analyzing planetary transits, relying solely on\nBayesian inference without requiring prior assumptions about the planetary\nsystem's dynamical architecture.",
        "split": "arXiv"
    },
    {
        "text": "This textbook introduces the basic concepts of the theory of causal fermion\nsystems, a recent approach to the description of fundamental physics. The\ntheory yields quantum mechanics, general relativity and quantum field theory as\nlimiting cases and is therefore a candidate for a unified physical theory. From\nthe mathematical perspective, causal fermion systems provide a general\nframework for describing and analyzing non-smooth geometries and \"quantum\ngeometries.\" The dynamics is described by a novel variational principle, the\ncausal action principle.\n  The book includes a detailed summary of the mathematical and physical\npreliminaries. It explains the physical concepts behind the causal fermion\nsystem approach from the basics. Moreover, all the mathematical objects and\nstructures are introduced step by step. The mathematical methods used for the\nanalysis of causal fermion systems and the causal action principle are\nintroduced in depth. Many examples and applications are worked out.\n  The textbook is addressed to master and graduate students in mathematics or\nphysics. Furthermore, it serves as a reference work for researchers working in\nthe field.",
        "split": "arXiv"
    },
    {
        "text": "Graph Anomaly Detection (GAD) aims to identify uncommon, deviated, or\nsuspicious objects within graph-structured data. Existing methods generally\nfocus on a single graph object type (node, edge, graph, etc.) and often\noverlook the inherent connections among different object types of graph\nanomalies. For instance, a money laundering transaction might involve an\nabnormal account and the broader community it interacts with. To address this,\nwe present UniGAD, the first unified framework for detecting anomalies at node,\nedge, and graph levels jointly. Specifically, we develop the Maximum Rayleigh\nQuotient Subgraph Sampler (MRQSampler) that unifies multi-level formats by\ntransferring objects at each level into graph-level tasks on subgraphs. We\ntheoretically prove that MRQSampler maximizes the accumulated spectral energy\nof subgraphs (i.e., the Rayleigh quotient) to preserve the most significant\nanomaly information. To further unify multi-level training, we introduce a\nnovel GraphStitch Network to integrate information across different levels,\nadjust the amount of sharing required at each level, and harmonize conflicting\ntraining goals. Comprehensive experiments show that UniGAD outperforms both\nexisting GAD methods specialized for a single task and graph prompt-based\napproaches for multiple tasks, while also providing robust zero-shot task\ntransferability. All codes can be found at\nhttps://github.com/lllyyq1121/UniGAD.",
        "split": "arXiv"
    },
    {
        "text": "As the integration of the Large Language Models (LLMs) into various\napplications increases, so does their susceptibility to misuse, raising\nsignificant security concerns. Numerous jailbreak attacks have been proposed to\nassess the security defense of LLMs. Current jailbreak attacks mainly rely on\nscenario camouflage, prompt obfuscation, prompt optimization, and prompt\niterative optimization to conceal malicious prompts. In particular, sequential\nprompt chains in a single query can lead LLMs to focus on certain prompts while\nignoring others, facilitating context manipulation. This paper introduces\nSequentialBreak, a novel jailbreak attack that exploits this vulnerability. We\ndiscuss several scenarios, not limited to examples like Question Bank, Dialog\nCompletion, and Game Environment, where the harmful prompt is embedded within\nbenign ones that can fool LLMs into generating harmful responses. The distinct\nnarrative structures of these scenarios show that SequentialBreak is flexible\nenough to adapt to various prompt formats beyond those discussed. Extensive\nexperiments demonstrate that SequentialBreak uses only a single query to\nachieve a substantial gain of attack success rate over existing baselines\nagainst both open-source and closed-source models. Through our research, we\nhighlight the urgent need for more robust and resilient safeguards to enhance\nLLM security and prevent potential misuse. All the result files and website\nassociated with this research are available in this GitHub repository:\nhttps://anonymous.4open.science/r/JailBreakAttack-4F3B/.",
        "split": "arXiv"
    },
    {
        "text": "We show that two natural and a priori unrelated structures encapsulate the\nsame data, namely certain commutative and associative product structures and a\nclass of superintegrable Hamiltonian systems. More precisely, consider a\nEuclidean space of dimension at least three, equipped with a commutative and\nassociative product structure that satisfies certain compatibility conditions.\nWe prove that such a product structure encapsulates precisely the conditions of\na so-called abundant structure. Such a structure provides the data needed to\nconstruct a family of second-order (maximally) superintegrable Hamiltonian\nsystems of second order. We prove that all abundant superintegrable Hamiltonian\nsystems on Euclidean space of dimension at least three arise in this way. As an\nexample, we present the Smorodinski-Winternitz I Hamiltonian system.",
        "split": "arXiv"
    },
    {
        "text": "Radio Frequency Fingerprinting (RFF) techniques allow a receiver to\nauthenticate a transmitter by analyzing the physical layer of the radio\nspectrum. Although the vast majority of scientific contributions focus on\nimproving the performance of RFF considering different parameters and\nscenarios, in this work, we consider RFF as an attack vector to identify and\ntrack a target device.\n  We propose, implement, and evaluate HidePrint, a solution to prevent tracking\nthrough RFF without affecting the quality of the communication link between the\ntransmitter and the receiver. HidePrint hides the transmitter's fingerprint\nagainst an illegitimate eavesdropper by injecting controlled noise in the\ntransmitted signal. We evaluate our solution against state-of-the-art\nimage-based RFF techniques considering different adversarial models, different\ncommunication links (wired and wireless), and different configurations. Our\nresults show that the injection of a Gaussian noise pattern with a standard\ndeviation of (at least) 0.02 prevents device fingerprinting in all the\nconsidered scenarios, thus making the performance of the identification process\nindistinguishable from the random guess while affecting the Signal-to-Noise\nRatio (SNR) of the received signal by only 0.1 dB. Moreover, we introduce\nselective radio fingerprint disclosure, a new technique that allows the\ntransmitter to disclose the radio fingerprint to only a subset of intended\nreceivers. This technique allows the transmitter to regain anonymity, thus\npreventing identification and tracking while allowing authorized receivers to\nauthenticate the transmitter without affecting the quality of the transmitted\nsignal.",
        "split": "arXiv"
    },
    {
        "text": "We describe a method for constructing $n$-orthogonal coordinate systems in\nconstant curvature spaces. The construction proposed is a modification of\nKrichever's method for producing orthogonal curvilinear coordinate systems in\nthe $n$-dimensional Euclidean space. To demonstrate how this method works, we\nconstruct examples of orthogonal coordinate systems on the two-dimensional\nsphere and the hyperbolic plane, in the case when the spectral curve is\nreducible and all irreducible components are isomorphic to a complex projective\nline.",
        "split": "arXiv"
    },
    {
        "text": "Compared to rigid hands, underactuated compliant hands offer greater\nadaptability to object shapes, provide stable grasps, and are often more\ncost-effective. However, they introduce uncertainties in hand-object\ninteractions due to their inherent compliance and lack of precise finger\nproprioception as in rigid hands. These limitations become particularly\nsignificant when performing contact-rich tasks like insertion. To address these\nchallenges, additional sensing modalities are required to enable robust\ninsertion capabilities. This letter explores the essential sensing requirements\nfor successful insertion tasks with compliant hands, focusing on the role of\nvisuotactile perception. We propose a simulation-based multimodal policy\nlearning framework that leverages all-around tactile sensing and an extrinsic\ndepth camera. A transformer-based policy, trained through a teacher-student\ndistillation process, is successfully transferred to a real-world robotic\nsystem without further training. Our results emphasize the crucial role of\ntactile sensing in conjunction with visual perception for accurate\nobject-socket pose estimation, successful sim-to-real transfer and robust task\nexecution.",
        "split": "arXiv"
    },
    {
        "text": "Sound event localization and detection (SELD) has seen substantial\nadvancements through learning-based methods. These systems, typically trained\nfrom scratch on specific datasets, have shown considerable generalization\ncapabilities. Recently, deep neural networks trained on large-scale datasets\nhave achieved remarkable success in the sound event classification (SEC) field,\nprompting an open question of whether these advancements can be extended to\ndevelop general-purpose SELD models. In this paper, leveraging the power of\npre-trained SEC models, we propose pre-trained SELD networks (PSELDNets) on\nlarge-scale synthetic datasets. These synthetic datasets, generated by\nconvolving sound events with simulated spatial room impulse responses (SRIRs),\ncontain 1,167 hours of audio clips with an ontology of 170 sound classes. These\nPSELDNets are transferred to downstream SELD tasks. When we adapt PSELDNets to\nspecific scenarios, particularly in low-resource data cases, we introduce a\ndata-efficient fine-tuning method, AdapterBit. PSELDNets are evaluated on a\nsynthetic-test-set using collected SRIRs from TAU Spatial Room Impulse Response\nDatabase (TAU-SRIR DB) and achieve satisfactory performance. We also conduct\nour experiments to validate the transferability of PSELDNets to three publicly\navailable datasets and our own collected audio recordings. Results demonstrate\nthat PSELDNets surpass state-of-the-art systems across all publicly available\ndatasets. Given the need for direction-of-arrival estimation, SELD generally\nrelies on sufficient multi-channel audio clips. However, incorporating the\nAdapterBit, PSELDNets show more efficient adaptability to various tasks using\nminimal multi-channel or even just monophonic audio clips, outperforming the\ntraditional fine-tuning approaches.",
        "split": "arXiv"
    },
    {
        "text": "COVID-19 is extremely contagious and its rapid growth has drawn attention\ntowards its early diagnosis. Early diagnosis of COVID-19 enables healthcare\nprofessionals and government authorities to break the chain of transition and\nflatten the epidemic curve. With the number of cases accelerating across the\ndeveloped world, COVID-19 induced Viral Pneumonia cases is a big challenge.\nOverlapping of COVID-19 cases with Viral Pneumonia and other lung infections\nwith limited dataset and long training hours is a serious problem to cater.\nLimited amount of data often results in over-fitting models and due to this\nreason, model does not predict generalized results. To fill this gap, we\nproposed GAN-based approach to synthesize images which later fed into the deep\nlearning models to classify images of COVID-19, Normal, and Viral Pneumonia.\nSpecifically, customized Wasserstein GAN is proposed to generate 19% more Chest\nX-ray images as compare to the real images. This expanded dataset is then used\nto train four proposed deep learning models: VGG-16, ResNet-50, GoogLeNet and\nMNAST. The result showed that expanded dataset utilized deep learning models to\ndeliver high classification accuracies. In particular, VGG-16 achieved highest\naccuracy of 99.17% among all four proposed schemes. Rest of the models like\nResNet-50, GoogLeNet and MNAST delivered 93.9%, 94.49% and 97.75% testing\naccuracies respectively. Later, the efficiency of these models is compared with\nthe state of art models on the basis of accuracy. Further, our proposed models\ncan be applied to address the issue of scant datasets for any problem of image\nanalysis.",
        "split": "arXiv"
    },
    {
        "text": "On a connected finite graph, we propose an evolution of weights including\nOllivier's Ricci flow as a special case. During the evolution process, on each\nedge, the speed of change of weight is exactly the difference between the\nWasserstein distance related to two probability measures and certain graph\ndistance. Here the probability measure may be chosen as an $\\alpha$-lazy\none-step random walk, an $\\alpha$-lazy two-step random walk, or a general\nprobability measure. Based on the ODE theory, we show that the initial value\nproblem has a unique global solution.\n  A discrete version of the above evolution is applied to the problem of\ncommunity detection. Our algorithm is based on such a discrete evolution, where\nprobability measures are chosen as $\\alpha$-lazy one-step random walk and\n$\\alpha$-lazy two-step random walk respectively. Note that the later measure\nhas not been used in previous works [2, 16, 20, 23]. Here, as in [20], only one\nsurgery needs to be performed after the last iteration. Moreover, our algorithm\nis much easier than those of [2, 16, 20], which were all based on Lin-Lu-Yau's\nRicci curvature. The code is available at\nhttps://github.com/mjc191812/Evolution-of-weights-on-a-connected-finite-graph.",
        "split": "arXiv"
    },
    {
        "text": "Purpose: This study aims to assess the accuracy of degree adaptive strategies\nin the context of incompressible Navier-Stokes flows using the high order\nhybridisable discontinuous Galerkin (HDG) method.\n  Design/methodology/approach: The work presents a series of numerical examples\nto show the inability of standard degree adaptive processes to accurate capture\naerodynamic quantities of interest, in particular the drag. A new conservative\nprojection is proposed and the results between a standard degree adaptive\nprocedure and the adaptive process enhanced with this correction are compared.\nThe examples involve two transient problems where flow vortices or a gust needs\nto be accurately propagated over long distances.\n  \\noindent \\textbf{}Findings:polynomials with a lower degree. Due to the\ncoupling of velocity-pressure in incompressible flows, the violation of the\nincompressibility constraint leads to inaccurate pressure fields in the wake\nthat have a sizeable effect on the drag. The new conservative projection\nproposed is found to remove all the numerical artefacts shown by the standard\nadaptive process.\n  Originality/value: This work proposes a new conservative projection for the\ndegree adaptive process. The projection does not introduce a significant\noverhead because it requires to solve an element-by-element problem and only\nfor those elements where the adaptive process lowers the degree of\napproximation. Numerical results show that with the proposed projection\nnon-physical oscillations in the drag disappear and the results are in good\nagreement with reference solutions.",
        "split": "arXiv"
    },
    {
        "text": "Let $Z$ be an abelian group, $ x \\in Z$, and $[x] = \\{ y : \\langle x \\rangle\n= \\langle y \\rangle \\}$. A graph is called integral if all its eigenvalues are\nintegers. It is known that a Cayley graph is integral if and only if its\nconnection set can be express as union of the sets $[x] $. In this paper, we\ndetermine an algebraic formula for eigenvalues of the integral Cayley graph\nwhen the connection set is $ [x]$. This formula involves an analogue of\nM$\\ddot{\\text{o}}$bius function.",
        "split": "arXiv"
    },
    {
        "text": "Context-free language (CFL) reachability is a standard approach in static\nanalyses, where the analysis question is phrased as a language reachability\nproblem on a graph $G$ wrt a CFL L. While CFLs lack the expressiveness needed\nfor high precision, common formalisms for context-sensitive languages are such\nthat the corresponding reachability problem is undecidable. Are there useful\ncontext-sensitive language-reachability models for static analysis?\n  In this paper, we introduce Multiple Context-Free Language (MCFL)\nreachability as an expressive yet tractable model for static program analysis.\nMCFLs form an infinite hierarchy of mildly context sensitive languages\nparameterized by a dimension $d$ and a rank $r$. We show the utility of MCFL\nreachability by developing a family of MCFLs that approximate interleaved Dyck\nreachability, a common but undecidable static analysis problem.\n  We show that MCFL reachability be computed in $O(n^{2d+1})$ time on a graph\nof $n$ nodes when $r=1$, and $O(n^{d(r+1)})$ time when $r>1$. Moreover, we show\nthat when $r=1$, the membership problem has a lower bound of $n^{2d}$ based on\nthe Strong Exponential Time Hypothesis, while reachability for $d=1$ has a\nlower bound of $n^{3}$ based on the combinatorial Boolean Matrix Multiplication\nHypothesis. Thus, for $r=1$, our algorithm is optimal within a factor $n$ for\nall levels of the hierarchy based on $d$.\n  We implement our MCFL reachability algorithm and evaluate it by\nunderapproximating interleaved Dyck reachability for a standard taint analysis\nfor Android. Used alongside existing overapproximate methods, MCFL reachability\ndiscovers all tainted information on 8 out of 11 benchmarks, and confirms\n$94.3\\%$ of the reachable pairs reported by the overapproximation on the\nremaining 3. To our knowledge, this is the first report of high and provable\ncoverage for this challenging benchmark set.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we derive a new Kalman filter with probabilistic data\nassociation between measurements and states. We formulate a variational\ninference problem to approximate the posterior density of the state conditioned\non the measurement data. We view the unknown data association as a latent\nvariable and apply Expectation Maximization (EM) to obtain a filter with update\nstep in the same form as the Kalman filter but with expanded measurement vector\nof all potential associations. We show that the association probabilities can\nbe computed as permanents of matrices with measurement likelihood entries. We\nalso propose an ambiguity check that associates only a subset of ambiguous\nmeasurements and states probabilistically, thus reducing the association time\nand preventing low-probability measurements from harming the estimation\naccuracy. Experiments in simulation show that our filter achieves lower\ntracking errors than the well-established joint probabilistic data association\nfilter (JPDAF), while running at comparable rate. We also demonstrate the\neffectiveness of our filter in multi-object tracking (MOT) on multiple\nreal-world datasets, including MOT17, MOT20, and DanceTrack. We achieve better\nhigher order tracking accuracy (HOTA) than previous Kalman-filter methods and\nremain real-time. Associating only bounding boxes without deep features or\nvelocities, our method ranks top-10 on both MOT17 and MOT20 in terms of HOTA.\nGiven offline detections, our algorithm tracks at 250+ fps on a single laptop\nCPU. Code is available at https://github.com/hwcao17/pkf.",
        "split": "arXiv"
    },
    {
        "text": "We define and study geometric versions of the Benoist limit cone and matrix\njoint spectrum, which we call the translation cone and the joint translation\nspectrum, respectively. These new notions allow us to generalize the study of\nembeddings into products of rank-one simple Lie groups and to compare group\nactions on different metric spaces, quasi-morphisms, Anosov representations and\nmany other natural objects of study.\n  We identify the joint translation spectrum with the image of the gradient\nfunction of a corresponding Manhattan manifold: a higher dimensional version of\nthe well known and studied Manhattan curve. As a consequence we deduce many\nproperties of the spectrum. For example we show that it is given by the closure\nof the set of all possible drift vectors associated to finitely supported,\nsymmetric, admissible random walks on the associated group.",
        "split": "arXiv"
    },
    {
        "text": "Cardinality sketches are compact data structures for representing sets or\nvectors, enabling efficient approximation of their cardinality (or the number\nof nonzero entries). These sketches are space-efficient, typically requiring\nonly logarithmic storage relative to input size, and support incremental\nupdates, allowing for dynamic modifications. A critical property of many\ncardinality sketches is composability, meaning that the sketch of a union of\nsets can be computed from individual sketches. Existing designs typically\nprovide strong statistical guarantees, accurately answering an exponential\nnumber of queries in terms of sketch size $k$. However, these guarantees\ndegrade to quadratic in $k$ when queries are adaptive and may depend on\nprevious responses.\n  Prior works on statistical queries (Steinke and Ullman, 2015) and specific\nMinHash cardinality sketches (Ahmadian and Cohen, 2024) established that the\nquadratic bound on the number of adaptive queries is, in fact, unavoidable. In\nthis work, we develop a unified framework that generalizes these results across\nbroad classes of cardinality sketches. We show that any union-composable\nsketching map is vulnerable to attack with $\\tilde{O}(k^4)$ queries and, if the\nsketching map is also monotone (as for MinHash and statistical queries), we\nobtain a tight bound of $\\tilde{O}(k^2)$ queries. Additionally, we demonstrate\nthat linear sketches over the reals $\\mathbb{R}$ and fields $\\mathbb{F}_p$ can\nbe attacked using $\\tilde{O}(k^2)$ adaptive queries, which is optimal and\nstrengthens some of the recent results by Gribelyuk et al. (2024), which\nrequired a larger polynomial number of rounds for such matrices.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we study the partial data inverse problem for nonlinear\nmagnetic Schr\\\"odinger equations. We show that the knowledge of the\nDirichlet-to-Neumann map, measured on an arbitrary part of the boundary,\ndetermines the time-dependent linear coefficients, electric and magnetic\npotentials, and nonlinear coefficients, provided that the divergence of the\nmagnetic potential is given. Additionally, we also investigate both the forward\nand inverse problems for the linear magnetic Schr\\\"odinger equation with a\ntime-dependent leading term. In particular, all coefficients are uniquely\nrecovered from boundary data.",
        "split": "arXiv"
    },
    {
        "text": "Transcranial direct current stimulation (tDCS) has emerged as a promising\nnon-invasive therapeutic intervention for major depressive disorder (MDD), yet\nits effects on neural mechanisms remain incompletely understood. This study\ninvestigates the impact of tDCS in individuals with MDD using resting-state EEG\ndata and network neuroscience to analyze functional connectivity. We examined\npower spectral density (PSD) changes and functional connectivity (FC) patterns\nacross theta, alpha, and beta bands before and after tDCS intervention. A\nnotable aspect of this research involves the modification of the binarizing\nthreshold algorithm to assess functional connectivity networks, facilitating a\nmeaningful comparison at the group level. Our analysis using optimal threshold\nbinarization techniques revealed significant modifications in network topology,\nparticularly evident in the beta band, indicative of reduced randomization or\nenhanced small-worldness after tDCS. Furthermore, the hubness analysis\nidentified specific brain regions, notably the dorsolateral prefrontal cortex\n(DLPFC) regions across all frequency bands, exhibiting increased functional\nconnectivity, suggesting their involvement in the antidepressant effects of\ntDCS. Notably, tDCS intervention transformed the dispersed high connectivity\ninto localized connectivity and increased left-sided asymmetry across all\nfrequency bands. Overall, this study provides valuable insights into the\neffects of tDCS on neural mechanisms in MDD, offering a potential direction for\nfurther research and therapeutic development in the field of neuromodulation\nfor mental health disorders.",
        "split": "arXiv"
    },
    {
        "text": "Collision-resistant, cryptographic hash (CRH) functions have long been an\nintegral part of providing security and privacy in modern systems. Certain\nconstructions of zero-knowledge proof (ZKP) protocols aim to utilize CRH\nfunctions to perform cryptographic hashing. Standard CRH functions, such as\nSHA2, are inefficient when employed in the ZKP domain, thus calling for\nZK-friendly hashes, which are CRH functions built with ZKP efficiency in mind.\nThe most mature ZK-friendly hash, MiMC, presents a block cipher and hash\nfunction with a simple algebraic structure that is well-suited, due to its\nachieved security and low complexity, for ZKP applications. Although\nZK-friendly hashes have improved the performance of ZKP generation in software,\nthe underlying computation of ZKPs, including CRH functions, must be optimized\non hardware to enable practical applications. The challenge we address in this\nwork is determining how to efficiently incorporate ZK-friendly hash functions,\nsuch as MiMC, into hardware accelerators, thus enabling more practical\napplications. In this work, we introduce AMAZE, a highly hardware-optimized\nopen-source framework for computing the MiMC block cipher and hash function.\nOur solution has been primarily directed at resource-constrained edge devices;\nconsequently, we provide several implementations of MiMC with varying power,\nresource, and latency profiles. Our extensive evaluations show that the\nAMAZE-powered implementation of MiMC outperforms standard CPU implementations\nby more than 13$\\times$. In all settings, AMAZE enables efficient ZK-friendly\nhashing on resource-constrained devices. Finally, we highlight AMAZE's\nunderlying open-source arithmetic backend as part of our end-to-end design,\nthus allowing developers to utilize the AMAZE framework for custom ZKP\napplications.",
        "split": "arXiv"
    },
    {
        "text": "The efficient scheduling of multi-task jobs across multiprocessor systems has\nbecome increasingly critical with the rapid expansion of computational systems.\nThis challenge, known as Multiprocessor Multitask Scheduling (MPMS), is\nessential for optimizing the performance and scalability of applications in\nfields such as cloud computing and deep learning. In this paper, we study the\nMPMS problem under both deterministic and stochastic models, where each job is\ncomposed of multiple tasks and can only be completed when all its tasks are\nfinished. We introduce $\\mathsf{NP}$-$\\mathsf{SRPT}$, a non-preemptive variant\nof the Shortest Remaining Processing Time (SRPT) algorithm, designed to\naccommodate scenarios with non-preemptive tasks. Our algorithm achieves a\ncompetitive ratio of $\\ln \\alpha + \\beta + 1$ for minimizing response time,\nwhere $\\alpha$ represents the ratio of the largest to the smallest job\nworkload, and $\\beta$ captures the ratio of the largest non-preemptive task\nworkload to the smallest job workload. We further establish that this\ncompetitive ratio is order-optimal when the number of processors is fixed. For\nstochastic systems modeled as M/G/N queues, where job arrivals follow a Poisson\nprocess and task workloads are drawn from a general distribution, we prove that\n$\\mathsf{NP}$-$\\mathsf{SRPT}$ achieves asymptotically optimal mean response\ntime as the traffic intensity $\\rho$ approaches $1$, assuming the task size\ndistribution has finite support. Moreover, the asymptotic optimality extends to\ncases with infinite task size distributions under mild probabilistic\nassumptions, including the standard M/M/N model. Experimental results validate\nthe effectiveness of $\\mathsf{NP}$-$\\mathsf{SRPT}$, demonstrating its\nasymptotic optimality in both theoretical and practical settings.",
        "split": "arXiv"
    },
    {
        "text": "Video geolocalization is a crucial problem in current times. Given just a\nvideo, ascertaining where it was captured from can have a plethora of\nadvantages. The problem of worldwide geolocalization has been tackled before,\nbut only using the image modality. Its video counterpart remains relatively\nunexplored. Meanwhile, video geolocalization has also garnered some attention\nin the recent past, but the existing methods are all restricted to specific\nregions. This motivates us to explore the problem of video geolocalization at a\nglobal scale. Hence, we propose a novel problem of worldwide video\ngeolocalization with the objective of hierarchically predicting the correct\ncity, state/province, country, and continent, given a video. However, no large\nscale video datasets that have extensive worldwide coverage exist, to train\nmodels for solving this problem. To this end, we introduce a new dataset,\nCityGuessr68k comprising of 68,269 videos from 166 cities all over the world.\nWe also propose a novel baseline approach to this problem, by designing a\ntransformer-based architecture comprising of an elegant Self-Cross Attention\nmodule for incorporating scenes as well as a TextLabel Alignment strategy for\ndistilling knowledge from textlabels in feature space. To further enhance our\nlocation prediction, we also utilize soft-scene labels. Finally we demonstrate\nthe performance of our method on our new dataset as well as Mapillary(MSLS).\nOur code and datasets are available at: https://github.com/ParthPK/CityGuessr",
        "split": "arXiv"
    },
    {
        "text": "We use multi-regional input-output analysis to calculate the paid labour,\nenergy, emissions, and material use required to provide basic needs for all\npeople. We calculate two different low-consumption scenarios, using the UK as a\ncase study: (1) a \"decent living\" scenario, which includes only the bare\nnecessities, and (2) a \"good life\" scenario, which is based on the minimum\nliving standards demanded by UK residents. We compare the resulting footprints\nto the current footprint of the UK, and to the footprints of the US, China,\nIndia, and a global average. Labour footprints are disaggregated by sector,\nskill level, and region of origin.\n  We find that both low-consumption scenarios would still require an\nunsustainable amount of labour and resources at the global scale. The decent\nliving scenario would require a 26-hour working week, and on a per capita\nbasis, 89 GJ of energy use, 5.9 tonnes of emissions, and 5.7 tonnes of used\nmaterials per year. The more socially sustainable good life scenario would\nrequire a 53-hour working week, 165 GJ of energy use, 9.9 tonnes of emissions,\nand 11.5 tonnes of used materials per capita. Both scenarios represent\nsubstantial reductions from the UK's current labour footprint of 68 hours per\nweek, which the UK is only able to sustain by importing a substantial portion\nof its labour from other countries. We conclude that reducing consumption to\nthe level of basic needs is not enough to achieve either social or\nenvironmental sustainability. Dramatic improvements in provisioning systems are\nalso required.",
        "split": "arXiv"
    },
    {
        "text": "We consider non-Hermitian tight-binding one-dimensional Hamiltonians and show\nthat imposing a certain symmetry causes all eigenvalues to pair up and the\ncorresponding eigenstates to coalesce in pairs. This Pairwise Coalescence (PC)\nis an enhanced version of an exceptional point -- the complete spectrum pairs\nup, not just one pair of eigenstates. The symmetry is that of reflection\nexcluding the central two sites, and allowing flipping of non-reciprocal\nhoppings (``generalized off-center reflection symmetry''). Two simple examples\nof PC exist in the literature -- our construction encompasses these examples\nand extends them to a vast class of Hamiltonians. We study several families of\nsuch Hamiltonians, extend to cases of full-spectrum higher-order coalescences,\nand show how the PC point corresponds to amplified non-orthogonality of the\neigenstates and enhanced loss of norm in time evolution.",
        "split": "arXiv"
    },
    {
        "text": "This study focuses on Intelligent Fault Diagnosis (IFD) in rotating machinery\nutilizing a single microphone and a data-driven methodology, effectively\ndiagnosing 42 classes of fault types and severities. The research leverages\nsound data from the imbalanced MaFaulDa dataset, aiming to strike a balance\nbetween high performance and low resource consumption. The testing phase\nencompassed a variety of configurations, including sampling, quantization,\nsignal normalization, silence removal, Wiener filtering, data scaling,\nwindowing, augmentation, and classifier tuning using XGBoost. Through the\nanalysis of time, frequency, mel-frequency, and statistical features, we\nachieved an impressive accuracy of 99.54% and an F-Beta score of 99.52% with\njust 6 boosting trees at an 8 kHz, 8-bit configuration. Moreover, when\nutilizing only MFCCs along with their first- and second-order deltas, we\nrecorded an accuracy of 97.83% and an F-Beta score of 97.67%. Lastly, by\nimplementing a greedy wrapper approach, we obtained a remarkable accuracy of\n96.82% and an F-Beta score of 98.86% using 50 selected features, nearly all of\nwhich were first- and second-order deltas of the MFCCs.",
        "split": "arXiv"
    },
    {
        "text": "This paper introduces the Smooth Zone Barrier Lyapunov Function (s-ZBLF) for\noutput and full-state constrained nonlinear control systems. Unlike traditional\nBLF methods, where control effort continuously increases as the state moves\ntoward the constraint boundaries, the s-ZBLF method keeps the control effort\nnearly zero near the origin, with a more aggressive increase as the system\napproaches the boundary. However, unlike previous works where control effort\nwas zero within a predefined safe region around the origin, the s-ZBLF\novercomes the disadvantage of discontinuous control activation by providing a\nsmooth, gradual increase in control effort as the state nears the constraints.\nThis smooth transition improves continuity in the control response and enhances\nstability by reducing chattering. Additionally, the s-ZBLF provides the\nadvantage of minimal control effort in regions far from the constraints,\nreducing energy consumption and actuator wear. Two forms of the s-ZBLF,\nlogarithmic-based and rational-based, are presented. Theoretical analysis\nguarantees that all system states remain within the defined constraints,\nensuring boundedness and stability of the closed-loop system. Simulation\nresults validate the effectiveness of the proposed method in handling\nconstrained nonlinear systems.",
        "split": "arXiv"
    },
    {
        "text": "Coronal Mass Ejections (CMEs) erupting from the host star are expected to\nhave effects on the atmospheric erosion processes of the orbiting planets. For\nplanets with a magnetosphere, the embedded magnetic field in the CMEs is\nthought to be the most important parameter to affect planetary mass loss. In\nthis work, we investigate the effect of different magnetic field structures of\nstellar CMEs on the atmosphere of a hot Jupiter with a dipolar magnetosphere.\nWe use a time-dependent 3D radiative magnetohydrodynamics (MHD) atmospheric\nescape model that self-consistently models the outflow from hot Jupiters\nmagnetosphere and its interaction with stellar CMEs. For our study, we consider\nthree configurations of magnetic field embedded in stellar CMEs -- (a)\nnorthward $B_z$ component, (b) southward $B_z$ component, and (c) radial\ncomponent. {We find that both the CMEs with northward $B_z$ component and\nsouthward $B_z$ component increase the planetary mass-loss rate when the CME\narrives from the stellar side, with the mass-loss rate remaining higher for the\nCME with northward $B_z$ component until it arrives at the opposite side.} The\nlargest magnetopause is found for the CME with a southward $B_z$ component when\nthe dipole and the CME magnetic field have the same direction. We also find\nthat during the passage of a CME, the planetary magnetosphere goes through\nthree distinct changes - (1) compressed magnetosphere, (2) enlarged\nmagnetosphere, and (3) relaxed magnetosphere for all three considered CME\nconfigurations. We compute synthetic Ly-$\\alpha$ transits at different times\nduring the passage of the CMEs. The synthetic Ly-$\\alpha$ transit absorption\ngenerally increases when the CME is in interaction with the planet for all\nthree magnetic configurations. The maximum Ly-$\\alpha$ absorption is found for\nthe radial CME case when the magnetosphere is the most compressed.",
        "split": "arXiv"
    },
    {
        "text": "We introduce stationary generalized Bratteli diagrams $B$ which are\nrepresented as the union of countably many classical Pascal-Bratteli diagrams.\nWe describe all ergodic invariant measures on $B$. We show that there exist\norders which produce no infinite minimal or maximal paths and the corresponding\nVershik map is a homeomorphism. We also describe orders that generate\nuncountably many infinite minimal and uncountably many infinite maximal paths\nboth on $B$ and on the classical Pascal-Bratteli diagram.",
        "split": "arXiv"
    },
    {
        "text": "We investigate the locality properties of $T \\overline T$-deformed CFTs\nwithin perturbation theory. Up to third order in the deformation parameter, we\nfind a Hamiltonian operator which solves the flow equation, reproduces the\nZamolodchikov energy spectrum, and is consistent with quasi-locality of the\ntheory. This Hamiltonian includes terms proportional to the central charge\nwhich have not appeared before and which are necessary to reproduce the correct\nspectrum. We show that the Hamiltonian is not uniquely defined since it\ncontains free parameters, starting at second order, which do not spoil the\nabove properties. We then use it to determine the full conserved stress tensor.\nIn our approach, the KdV charges are automatically conserved to all orders but\nare not a priori local. Nevertheless, we show that they can be made local to\nfirst order. Our techniques allow us to further comment on the space of\nHamiltonians constructed from products of KdV charges which also flow to local\ncharges in the deformed theory in the IR.",
        "split": "arXiv"
    },
    {
        "text": "This work delves into the study of flavor invariants and, in special,\ninvariants capable of detecting CP (Charge-Parity) violation. Through the\nmathematical tool of the Hilbert series, we systematically enumerate and\nexplore flavor invariants that are unchanged under weak basis transformations.\nAfter reviewing the Hilbert series and the flavor invariants of the SM quark\nsector, we apply the tool of Hilbert series to the SM extended by a singlet\nvector-like quark (VLQ) of down-type. The introduction of these hypothetical\nparticles leads to a simple extension of the SM that can be motivated by many\nproblems, including the need for new sources of CP violation to explain the\nobserved matter-antimatter asymmetry in the universe. We were successful in\ncalculating the Hilbert series for the VLQ extension in the mass basis of the\nVLQ, where the spurion transformations are simpler. Based on the Hilbert\nseries, we build and enumerate the basic flavor invariants with which all\ninvariants can be constructed. For a generic basis, where the spurion\ntransformations involve a larger group, we could only get the first few terms\nof the Hilbert series.",
        "split": "arXiv"
    },
    {
        "text": "We used the Condor Array Telescope to obtain deep imaging observations\nthrough luminance broad-band and He II, [O III], He I, H$\\alpha$, [N II], and\n[S II] narrow-band filters of an extended region of the M81 Group spanning\n$\\approx 8 \\times 8$ deg$^2$ on the sky centered near M81 and M82. Here we\nreport aspects of these observations that are specifically related to (1) a\nremarkable filament known as the \"Ursa Major Arc\" that stretches $\\approx 30$\ndeg on the sky roughly in the direction of Ursa Major, (2) a \"Giant Shell of\nIonized Gas\" that stretches $\\approx 0.8$ deg on the sky located $\\approx 0.6$\ndeg NW of M82, and (3) a remarkable network of ionized gaseous filaments\nrevealed by the new Condor observations that appear to connect the arc, the\nshell, and various of the galaxies of the M81 Group and, by extension, the\ngroup itself. We measure flux ratios between the various ions to help to\ndistinguish photoionized from shock-ionized gas, and we find that the flux\nratios of the arc and shell are not indicative of shock ionization. This\nprovides strong evidence against a previous interpretation of the arc as an\ninterstellar shock produced by an unrecognized supernova. We suggest that all\nof these objects, including the arc, are associated with the M81 Group and are\nlocated at roughly the distance $\\approx 3.6$ Mpc of M81, that the arc is an\nintergalactic filament, and that the objects are associated with the\nlow-redshift cosmic web.",
        "split": "arXiv"
    },
    {
        "text": "We used the Condor Array Telescope to obtain deep imaging observations\nthrough the luminance broad-band and He II 468.6 nm, [O III] 500.7 nm, He I\n587.5 nm, H$\\alpha$, [N II] 658.4 nm, and [S II] 671.6 nm narrow-band filters\nof an extended region comprising 13 \"Condor fields\" spanning $\\approx 8 \\times\n8$ deg$^2$ on the sky centered near M81 and M82. Here we describe the\nacquisition and processing of these observations, which together constitute\nunique very deep imaging observations of a large portion of the M81 Group\nthrough a complement of broad- and narrow-band filters. The images are\ncharacterized by an intricate web of faint, diffuse, continuum produced by\nstarlight scattered from Galactic cirrus, and all prominent cirrus features\nidentified in the broad-band image can also be identified in the narrow-band\nimages. We subtracted the luminance image from the narrow-band images to leave\nmore or less only line emission in the difference images, and we masked regions\nof the resulting images around stars at an isophotal limit. The difference\nimages exhibit extensive extended structures of ionized gas in the direction of\nthe M81 Group, from known galaxies of the M81 Group, clouds of gas, filamentary\nstructures, and apparent or possible bubbles or shells. Specifically, the\ndifference images show a remarkable filament known as the \"Ursa Major Arc;\" a\nremarkable network of criss-crossed filaments between M81 and NGC 2976, some of\nwhich intersect and overlap the Ursa Major Arc; and details of a \"giant shell\nof ionized gas.\"",
        "split": "arXiv"
    },
    {
        "text": "The ability of large language models to generate complex texts allows them to\nbe widely integrated into many aspects of life, and their output can quickly\nfill all network resources. As the impact of LLMs grows, it becomes\nincreasingly important to develop powerful detectors for the generated text.\nThis detector is essential to prevent the potential misuse of these\ntechnologies and to protect areas such as social media from the negative\neffects of false content generated by LLMS. The main goal of LLM-generated text\ndetection is to determine whether text is generated by an LLM, which is a basic\nbinary classification task. In our work, we mainly use three different\nclassification methods based on open source datasets: traditional machine\nlearning techniques such as logistic regression, k-means clustering, Gaussian\nNaive Bayes, support vector machines, and methods based on converters such as\nBERT, and finally algorithms that use LLMs to detect LLM-generated text. We\nfocus on model generalization, potential adversarial attacks, and accuracy of\nmodel evaluation. Finally, the possible research direction in the future is\nproposed, and the current experimental results are summarized.",
        "split": "arXiv"
    },
    {
        "text": "Machine learning models have demonstrated substantial performance\nenhancements over non-learned alternatives in various fundamental data\nmanagement operations, including indexing (locating items in an array),\ncardinality estimation (estimating the number of matching records in a\ndatabase), and range-sum estimation (estimating aggregate attribute values for\nquery-matched records). However, real-world systems frequently favor less\nefficient non-learned methods due to their ability to offer (worst-case) error\nguarantees - an aspect where learned approaches often fall short. The primary\nobjective of these guarantees is to ensure system reliability, ensuring that\nthe chosen approach consistently delivers the desired level of accuracy across\nall databases. In this paper, we embark on the first theoretical study of such\nguarantees for learned methods, presenting the necessary conditions for such\nguarantees to hold when using machine learning to perform indexing, cardinality\nestimation and range-sum estimation. Specifically, we present the first known\nlower bounds on the model size required to achieve the desired accuracy for\nthese three key database operations. Our results bound the required model size\nfor given average and worst-case errors in performing database operations,\nserving as the first theoretical guidelines governing how model size must\nchange based on data size to be able to guarantee an accuracy level. More\nbroadly, our established guarantees pave the way for the broader adoption and\nintegration of learned models into real-world systems.",
        "split": "arXiv"
    },
    {
        "text": "Together with David Schlang we computed the discriminants of the invariant\nHermitian forms for all indicator $o$ even degree absolutely irreducible\ncharacters of the ATLAS groups supplementing the tables of orthogonal\ndeterminants computed in collaboration with Richard Parker, Tobias Braun and\nThomas Breuer. The methods that are used in the unitary case are described in\nthis paper. A character has a well defined unitary discriminant, if and only if\nit is unitary stable, i.e. all irreducible unitary constituents have even\ndegree. Computations for large degree characters are only possible because of a\nnew method called {\\em unitary condensation}. A suitable automorphism helps to\nsingle out a square class of the real subfield of the character field\nconsisting of representatives of the discriminant of the invariant Hermitian\nforms. This square class can then be determined modulo enough primes.",
        "split": "arXiv"
    },
    {
        "text": "Voice user interfaces (VUIs) have facilitated the efficient interactions\nbetween humans and machines through spoken commands. Since real-word acoustic\nscenes are complex, speech enhancement plays a critical role for robust VUI.\nTransformer and its variants, such as Conformer, have demonstrated cutting-edge\nresults in speech enhancement. However, both of them suffers from the quadratic\ncomputational complexity with respect to the sequence length, which hampers\ntheir ability to handle long sequences. Recently a novel State Space Model\ncalled Mamba, which shows strong capability to handle long sequences with\nlinear complexity, offers a solution to address this challenge. In this paper,\nwe propose a novel hybrid convolution-Mamba backbone, denoted as MambaDC, for\nspeech enhancement. Our MambaDC marries the benefits of convolutional networks\nto model the local interactions and Mamba's ability for modeling long-range\nglobal dependencies. We conduct comprehensive experiments within both basic and\nstate-of-the-art (SoTA) speech enhancement frameworks, on two commonly used\ntraining targets. The results demonstrate that MambaDC outperforms Transformer,\nConformer, and the standard Mamba across all training targets. Built upon the\ncurrent advanced framework, the use of MambaDC backbone showcases superior\nresults compared to existing \\textcolor{black}{SoTA} systems. This sets the\nstage for efficient long-range global modeling in speech enhancement.",
        "split": "arXiv"
    },
    {
        "text": "Advances in artificial intelligence (AI) have great potential to help address\nsocietal challenges that are both collective in nature and present at national\nor trans-national scale. Pressing challenges in healthcare, finance,\ninfrastructure and sustainability, for instance, might all be productively\naddressed by leveraging and amplifying AI for national-scale collective\nintelligence. The development and deployment of this kind of AI faces\ndistinctive challenges, both technical and socio-technical. Here, a research\nstrategy for mobilising inter-disciplinary research to address these challenges\nis detailed and some of the key issues that must be faced are outlined.",
        "split": "arXiv"
    },
    {
        "text": "We revisit particle creation in strong fields, and backreaction on those\nfields, from an amplitudes perspective. We describe the strong field by an\ninitial coherent state of photons which we explicitly evolve in time, thus\ngoing beyond the background field approximation, and then consider observables\nwhich quantify the effects of backreaction. We present expressions for the\nwaveform, vacuum persistence probability, and number of produced photons at\nnext-to-leading order, all of which are impacted by backreaction, along with\nthe number and statistics of produced pairs. We find that converting between\nin-out (amplitude) and in-in (expectation value) expressions requires explicit\nresummation of an infinite number of disconnected loop diagrams.",
        "split": "arXiv"
    },
    {
        "text": "We study a class of dynamical systems generated by random substitutions,\nwhich contains both intrinsically ergodic systems and instances with several\nmeasures of maximal entropy. In this class, we show that the measures of\nmaximal entropy are classified by invariance under an appropriate symmetry\nrelation. All measures of maximal entropy are fully supported and they are\ngenerally not Gibbs measures. We prove that there is a unique measure of\nmaximal entropy if and only if an associated Markov chain is ergodic in inverse\ntime. This Markov chain has finitely many states and all transition matrices\nare explicitly computable. Thereby, we obtain several sufficient conditions for\nintrinsic ergodicity that are easy to verify. A practical way to compute the\ntopological entropy in terms of inflation words is extended from previous work\nto a more general geometric setting.",
        "split": "arXiv"
    },
    {
        "text": "Let $G=(V,E)$ be a simple graph of order $n$. A Majority Roman Dominating\nFunction (MRDF) on a graph G is a function $f: V\\rightarrow\\{-1, +1, 2\\}$ if\nthe sum of its function values over at least half the closed neighborhoods is\nat least one , this is , for at least half of the vertices $v\\in V$,\n$f(N[v])\\geq 1$. Moreover, every vertex u with $f(u)=-1$ is adjacent to at\nleast one vertex $w$ with $f(w)=2$. The Majority Roman Domination number of a\ngraph $G$, denoted by $\\gamma_{MR}(G)$ , is the minimum value of\n$\\sum_{v\\in{V(G)}}f(v)$ over all Majority Roman Dominating Function $f$ of $G$.\nIn this paper we study properties of the Majority Roman Domination in graphs\nand obtain lower and upper bounds the Majority Roman Domination number of some\ngraphs.",
        "split": "arXiv"
    },
    {
        "text": "Multi-object tracking is advancing through two dominant paradigms:\ntraditional tracking by detection and newly emerging tracking by query. In this\nwork, we fuse them together and propose the tracking-by-detection-and-query\nparadigm, which is achieved by a Learnable Associator. Specifically, the basic\ninformation interaction module and the content-position alignment module are\nproposed for thorough information Interaction among object queries. Tracking\nresults are directly Decoded from these queries. Hence, we name the method as\nLAID. Compared to tracking-by-query models, LAID achieves competitive tracking\naccuracy with notably higher training efficiency. With regard to\ntracking-by-detection methods, experimental results on DanceTrack show that\nLAID significantly surpasses the state-of-the-art heuristic method by 3.9% on\nHOTA metric and 6.1% on IDF1 metric. On SportsMOT, LAID also achieves the best\nscore on HOTA metric. By holding low training cost, strong tracking\ncapabilities, and an elegant end-to-end approach all at once, LAID presents a\nforward-looking direction for the field.",
        "split": "arXiv"
    },
    {
        "text": "Recently, the successful synthesis of the pentagonal form of PdTe$_{2}$\nmonolayer (\\emph{p}-PdTe$_{2}$) was reported [Liu~\\emph{et al.}, Nature\nMaterials \\textbf{23}, 1339 (2024)]. In this work, we present an extensive\nfirst-principles density-functional theory (DFT) based computational study of\nvacancies in this material. Our study covers the evolution of the electronic,\noptical, and magnetic properties of various defect configurations and compares\nthose to the pristine monolayer (\\emph{p}-PdTe$_{2}$). We find that V$_{Pd}$\n(V$_{Te}$) is the most stable defect in the~\\emph{p}-PdTe$_{2}$ monolayer in\nthe Te-rich (Pd-rich) limit. The defects alter the electronic properties of the\nmonolayer significantly, leading to changes in their magnetic and optical\nproperties due to the emergence of midgap impurity states. The defect complex\nV$_{Pd+4Te}$ is found to induce spin-polarization in the system with a total\nmagnetic moment of 1.87 $\\mu_{B}$. The obtained low diffusion energy barriers\nof 1.13 eV (in-plane) and 0.063 eV (top-bottom) corresponding to V$_{Te}$\nindicates its facile migration probability is higher in the top-bottom\ndirection at room temperature, as revealed by AIMD simulations as well. In\norder to guide the experimentalists, we also simulated the scanning-tunneling\nmicroscope (STM) images corresponding to all the defect configurations.\nMoreover, we also computed the electron-beam energies required for creating\nmono-vacancies. In the optical absorption spectra of the defective\nconfigurations, finite peaks appear below the band edge that are unique to the\nrespective defective configuration. We have also computed the excess\npolarizability of the defective configurations with respect to the pristine one\nand found that maximum changes occur in the infrared and visible regions,\nproviding insights into the change in their optical response as compared to the\npristine monolayer.",
        "split": "arXiv"
    },
    {
        "text": "This paper presents ViTOC (Vision Transformer and Object-aware Captioner), a\nnovel vision-language model for image captioning that addresses the challenges\nof accuracy and diversity in generated descriptions. Unlike conventional\napproaches, ViTOC employs a dual-path architecture based on Vision Transformer\nand object detector, effectively fusing global visual features and local object\ninformation through learnable vectors. The model introduces an innovative\nobject-aware prompting strategy that significantly enhances its capability in\nhandling long-tail data. Experiments on the standard COCO dataset demonstrate\nthat ViTOC outperforms baseline models across all evaluation metrics.\nAdditionally, we propose a reference-free evaluation method based on CLIP to\nfurther validate the model's effectiveness. By utilizing pretrained visual\nmodel parameters, ViTOC achieves efficient end-to-end training.",
        "split": "arXiv"
    },
    {
        "text": "The ground state properties of strongly rotating bosons confined in an\nasymmetric anharmonic potential exhibit a split density distribution. However,\nthe out-of-equilibrium dynamics of this split structure remain largely\nunexplored. Given that rotation is responsible for the breakup of the bosonic\ncloud, we investigate the out-of-equilibrium dynamics by abruptly changing the\nrotation frequency. Our study offers insights into the dynamics of trapped\nBose-Einstein condensates in both symmetric and asymmetric anharmonic\npotentials under different rotation quench scenarios. In the rotationally\nsymmetric trap, angular momentum is a good quantum number. This makes it\nchallenging to exchange angular momentum within the system; hence, a rotation\nquench does practically not impact the density distribution. In contrast, the\nabsence of angular momentum conservation in asymmetric traps results in more\ncomplex dynamics. This allows rotation quenches to either inject into or\nextract angular momentum from the system. We observe and analyze these\nintricate dynamics both for the mean-field condensed and the many-body\nfragmented systems. The dynamical evolution of the condensed system and the\nfragmented system exhibits similarities in several observables during small\nrotation quenches. However, these similarities diverge notably for larger\nquenches. Additionally, we investigate the formation and the impact of the\nvortices on the angular momentum dynamics of the evolving split density. All in\nall, our findings offer valuable insights into the dynamics of trapped\ninteracting bosons under different rotation quenches.",
        "split": "arXiv"
    },
    {
        "text": "Text emotion detection constitutes a crucial foundation for advancing\nartificial intelligence from basic comprehension to the exploration of\nemotional reasoning. Most existing emotion detection datasets rely on manual\nannotations, which are associated with high costs, substantial subjectivity,\nand severe label imbalances. This is particularly evident in the inadequate\nannotation of micro-emotions and the absence of emotional intensity\nrepresentation, which fail to capture the rich emotions embedded in sentences\nand adversely affect the quality of downstream task completion. By proposing an\nall-labels and training-set label regression method, we map label values to\nenergy intensity levels, thereby fully leveraging the learning capabilities of\nmachine models and the interdependencies among labels to uncover multiple\nemotions within samples. This led to the establishment of the Emotion\nQuantization Network (EQN) framework for micro-emotion detection and\nannotation. Using five commonly employed sentiment datasets, we conducted\ncomparative experiments with various models, validating the broad applicability\nof our framework within NLP machine learning models. Based on the EQN\nframework, emotion detection and annotation are conducted on the GoEmotions\ndataset. A comprehensive comparison with the results from Google literature\ndemonstrates that the EQN framework possesses a high capability for automatic\ndetection and annotation of micro-emotions. The EQN framework is the first to\nachieve automatic micro-emotion annotation with energy-level scores, providing\nstrong support for further emotion detection analysis and the quantitative\nresearch of emotion computing.",
        "split": "arXiv"
    },
    {
        "text": "Online controlled experiments, or A/B tests, are large-scale randomized\ntrials in digital environments. This paper investigates the estimands of the\ndifference-in-means estimator in these experiments, focusing on scenarios with\nrepeated measurements on users. We compare cumulative metrics that use all\npost-exposure data for each user to windowed metrics that measure each user\nover a fixed time window. We analyze the estimands and highlight trade-offs\nbetween the two types of metrics. Our findings reveal that while cumulative\nmetrics eliminate the need for pre-defined measurement windows, they result in\nestimands that are more intricately tied to the experiment intake and runtime.\nThis complexity can lead to counter-intuitive practical consequences, such as\ndecreased statistical power with more observations. However, cumulative metrics\noffer earlier results and can quickly detect strong initial signals. We\nconclude that neither metric type is universally superior. The optimal choice\ndepends on the temporal profile of the treatment effect, the distribution of\nexposure, and the stopping time of the experiment. This research provides\ninsights for experimenters to make informed decisions about how to define\nmetrics based on their specific experimental contexts and objectives.",
        "split": "arXiv"
    },
    {
        "text": "This paper investigates the semantic communication and cooperative tracking\ncontrol for an UAV swarm comprising a leader UAV and a group of follower UAVs,\nall interconnected via unreliable wireless multiple-input-multiple-output\n(MIMO) channels. Initially, we develop a dynamic model for the UAV swarm that\naccounts for both the internal interactions among the cooperative follower UAVs\nand the imperfections inherent in the MIMO channels that interlink the leader\nand follower UAVs. Building on this model, we incorporate the power costs of\nthe UAVs and formulate the communication and cooperative tracking control\nchallenge as a drift-plus-penalty optimization problem. We then derive a\nclosed-form optimal solution that maintains a decentralized semantic\narchitecture, dynamically adjusting to the tracking error costs and local\nchannel conditions within the swarm. Employing Lyapunov drift analysis, we\nestablish closed-form sufficient conditions for the stabilization of the UAV\nswarm's tracking performance. Numerical results demonstrate the significant\nenhancements in our proposed scheme over various state-of-the-art methods.",
        "split": "arXiv"
    },
    {
        "text": "Generative Artificial Intelligence offers a powerful tool for adversaries who\nwish to engage in influence operations, such as the Chinese Spamouflage\noperation and the Russian Internet Research Agency effort that both sought to\ninterfere with recent US election cycles. Therefore, this study seeks to\ninvestigate the propensity of current Generative AI models for producing\nharmful disinformation during an election cycle. The probability that different\nGenerative AI models produced disinformation when given adversarial prompts was\nevaluated, in addition the associated harm. This allows for the expected harm\nfor each model to be computed and it was discovered that Copilot and Gemini\ntied for the overall safest performance by realizing the lowest expected harm,\nwhile GPT-4o produced the greatest rates of harmful disinformation, resulting\nin much higher expected harm scores. The impact of disinformation category was\nalso investigated and Gemini was safest within the political category of\ndisinformation, while Copilot was safest for topics related to health.\nMoreover, characteristics of adversarial roles were discovered that led to\ngreater expected harm across all models. Finally, classification models were\ndeveloped that predicted disinformation production based on the conditions\nconsidered in this study, which offers insight into factors important for\npredicting disinformation production. Based on all of these insights,\nrecommendations are provided that seek to mitigate factors that lead to harmful\ndisinformation being produced by Generative AI models. It is hoped that\ndevelopers will use these insights to improve future models.",
        "split": "arXiv"
    },
    {
        "text": "Depth measures quantify central tendency in the analysis of statistical and\ngeometric data. Selecting a depth measure that is simple and efficiently\ncomputable is often important, e.g., when calculating depth for multiple query\npoints or when applied to large sets of data. In this work, we introduce\n\\emph{Hyperplane Distance Depth (HDD)}, which measures the centrality of a\nquery point $q$ relative to a given set $P$ of $n$ points in $\\mathbb{R}^d$,\ndefined as the sum of the distances from $q$ to all $\\binom{n}{d}$ hyperplanes\ndetermined by points in $P$. We present algorithms for calculating the HDD of\nan arbitrary query point $q$ relative to $P$ in $O(d \\log n)$ time after\npreprocessing $P$, and for finding a median point of $P$ in $O(d n^{d^2} \\log\nn)$ time. We study various properties of hyperplane distance depth and show\nthat it is convex, symmetric, and vanishing at infinity.",
        "split": "arXiv"
    },
    {
        "text": "Determining the atomic-level structure of crystalline solids is critically\nimportant across a wide array of scientific disciplines. The challenges\nassociated with obtaining samples suitable for single-crystal diffraction,\ncoupled with the limitations inherent in classical structure determination\nmethods that primarily utilize powder diffraction for most polycrystalline\nmaterials, underscore an urgent need to develop alternative approaches for\nelucidating the structures of commonly encountered crystalline compounds. In\nthis work, we present an artificial intelligence-directed leapfrog model\ncapable of accurately determining the structures of both organic and\ninorganic-organic hybrid crystalline solids through direct analysis of powder\nX-ray diffraction data. This model not only offers a comprehensive solution\nthat effectively circumvents issues related to insoluble challenges in\nconventional structure solution methodologies but also demonstrates\napplicability to crystal structures across all conceivable space groups.\nFurthermore, it exhibits notable compatibility with routine powder diffraction\ndata typically generated by standard instruments, featuring rapid data\ncollection and normal resolution levels.",
        "split": "arXiv"
    },
    {
        "text": "A charge qubit couples to environmental electric field fluctuations through\nits dipole moment, resulting in fast decoherence. We propose the p orbital (pO)\nqubit, formed by the single electron, p-like valence states of a five-electron\nSi quantum dot, which couples to charge noise through the quadrupole moment. We\ndemonstrate that the pO qubit offers distinct advantages in quality factor,\ngate speed, readout and size. We use a phenomenological, dipole\ntwo-level-fluctuator charge noise model to estimate a $T_2^* \\sim 80$ ns. In\nconjunction with Rabi frequencies of order 10 GHz, an order of magnitude\nimprovement in qubit quality factor is expected relative to state-of-the-art\nsemiconductor spin qubits. The pO qubit features all-electrical control via\nmodulating the dot's eccentricity. We also show how to perform two-qubit gates\nvia the $1/r^5$ quadrupole-quadrupole interaction. We find a universal gate set\nusing gradient ascent based control pulse optimization, subject to 10 GHz\nmaximum allowable bandwidth and 1 ns pulse times.",
        "split": "arXiv"
    },
    {
        "text": "In the past two years, large language models (LLMs) have achieved rapid\ndevelopment and demonstrated remarkable emerging capabilities. Concurrently,\nwith powerful semantic understanding and reasoning capabilities, LLMs have\nsignificantly empowered the rapid advancement of the recommendation system\nfield. Specifically, in news recommendation (NR), systems must comprehend and\nprocess a vast amount of clicked news text to infer the probability of\ncandidate news clicks. This requirement exceeds the capabilities of traditional\nNR models but aligns well with the strengths of LLMs. In this paper, we propose\na novel NR algorithm to reshape the news model via LLM Embedding and\nCo-Occurrence Pattern (LECOP). On one hand, we fintuned LLM by contrastive\nlearning using large-scale datasets to encode news, which can fully explore the\nsemantic information of news to thoroughly identify user preferences. On the\nother hand, we explored multiple co-occurrence patterns to mine collaborative\ninformation. Those patterns include news ID co-occurrence, Item-Item keywords\nco-occurrence and Intra-Item keywords co-occurrence. The keywords mentioned\nabove are all generated by LLM. As far as we know, this is the first time that\nconstructing such detailed Co-Occurrence Patterns via LLM to capture\ncollaboration. Extensive experiments demonstrate the superior performance of\nour proposed novel method",
        "split": "arXiv"
    },
    {
        "text": "In this article, we explore the lifetime of localized excitations in\nnonlinear lattices, called breathers, when a thermalized lattice is perturbed\nwith localized energy delivered to a single site. We develop a method to\nmeasure the time it takes for the system to approach equilibrium based on a\nsingle scalar quantity, the participation number, and deduce the value\ncorresponding to thermal equilibrium. We observe the time to achieve\nthermalization as a function of the energy of the excited site. We explore a\nvariety of different physical system models. The result is that the lifetime of\nbreathers increases exponentially with the breather energy for all the systems.\nThese results may provide a method to detect the existence of breathers in real\nsystems.",
        "split": "arXiv"
    },
    {
        "text": "Exploring the optimal management strategy for nitrogen and irrigation has a\nsignificant impact on crop yield, economic profit, and the environment. To\ntackle this optimization challenge, this paper introduces a deployable\n\\textbf{CR}op Management system \\textbf{O}ver all \\textbf{P}ossible\n\\textbf{S}tate availabilities (CROPS). CROPS employs a language model (LM) as a\nreinforcement learning (RL) agent to explore optimal management strategies\nwithin the Decision Support System for Agrotechnology Transfer (DSSAT) crop\nsimulations. A distinguishing feature of this system is that the states used\nfor decision-making are partially observed through random masking.\nConsequently, the RL agent is tasked with two primary objectives: optimizing\nmanagement policies and inferring masked states. This approach significantly\nenhances the RL agent's robustness and adaptability across various real-world\nagricultural scenarios. Extensive experiments on maize crops in Florida, USA,\nand Zaragoza, Spain, validate the effectiveness of CROPS. Not only did CROPS\nachieve State-of-the-Art (SOTA) results across various evaluation metrics such\nas production, profit, and sustainability, but the trained management policies\nare also immediately deployable in over of ten millions of real-world contexts.\nFurthermore, the pre-trained policies possess a noise resilience property,\nwhich enables them to minimize potential sensor biases, ensuring robustness and\ngeneralizability. Finally, unlike previous methods, the strength of CROPS lies\nin its unified and elegant structure, which eliminates the need for pre-defined\nstates or multi-stage training. These advancements highlight the potential of\nCROPS in revolutionizing agricultural practices.",
        "split": "arXiv"
    },
    {
        "text": "Trapped ions provide a highly controlled platform for quantum sensors,\nclocks, simulators, and computers, all of which depend on cooling ions close to\ntheir motional ground state. Existing methods like Doppler, resolved sideband,\nand dark resonance cooling balance trade-offs between the final temperature and\ncooling rate. A traveling polarization gradient has been shown to cool multiple\nmodes quickly and in parallel, but utilizing a stable polarization gradient can\nachieve lower ion energies, while also allowing more tailorable light-matter\ninteractions in general. In this paper, we demonstrate cooling of a trapped ion\nbelow the Doppler limit using a phase-stable polarization gradient created\nusing trap-integrated photonic devices. At an axial frequency of\n$2\\pi\\cdot1.45~ \\rm MHz$ we achieve $\\langle n \\rangle = 1.3 \\pm 1.1$ in\n$500~\\mu \\rm s$ and cooling rates of ${\\sim}0.3 \\, \\rm quanta/\\mu s$. We\nexamine ion dynamics under different polarization gradient phases, detunings,\nand intensities, showing reasonable agreement between experimental results and\na simple model. Cooling is fast and power-efficient, with improved performance\ncompared to simulated operation under the corresponding running wave\nconfiguration.",
        "split": "arXiv"
    },
    {
        "text": "Grey-body factors and quasinormal modes are two distinct characteristics of\nradiation near black holes, each associated with different boundary conditions.\nNevertheless, a correspondence exists between them, which we use to calculate\nthe grey-body factors of three recently constructed quantum-corrected black\nhole models. Our findings demonstrate that the grey-body factors are\nsignificantly influenced by the quantum corrections for some of the models\nunder consideration, and the correspondence holds with reasonable accuracy\nacross all three models. We confirm that the grey-body factors are less\nsensitive to the near-horizon corrections of the spacetime, because the\ngrey-body factors are reproduced via the correspondence using only the\nfundamental mode and the first overtone.",
        "split": "arXiv"
    },
    {
        "text": "Brown dwarfs with measured dynamical masses and spectra from direct imaging\nare benchmarks that anchor substellar atmosphere cooling and evolution models.\nWe present Subaru SCExAO/CHARIS infrared spectroscopy of HIP 93398 B, a brown\ndwarf companion recently discovered by Li et al. 2023 as part of an informed\nsurvey using the Hipparcos-Gaia Catalog of Accelerations. This object was\npreviously classified as a T6 dwarf based on its luminosity, with its\nindependently-derived age and dynamical mass in tension with existing models of\nbrown dwarf evolution. Spectral typing via empirical standard spectra,\ntemperatures derived by fitting substellar atmosphere models, and J-H, J-K and\nH-L' colors all suggest that this object has a substantially higher temperature\nand luminosity, consistent with classification as a late-L dwarf near the L/T\ntransition (T = 1200$^{+140}_{-119}$ K) with moderate to thick clouds possibly\npresent in its atmosphere. When compared with the latest generation of\nevolution models that account for clouds with our revised luminosity and\ntemperature for the object, the tension between the model-independent mass/age\nand model predictions is resolved.",
        "split": "arXiv"
    },
    {
        "text": "Picture countably many logicians all wearing a hat in one of $\\kappa$-many\ncolours. They each get to look at finitely many other hats and afterwards make\nfinitely many guesses for their own hat's colour. For which $\\kappa$ can the\nlogicians guarantee that at least one of them guesses correctly? This will be\nthe archetypical hat problem we analyse and solve here. We generalise this by\nvarying the amount of logicians as well as the number of allowed guesses and\ndescribe exactly for which combinations the logicians have a winning strategy.\n  We also solve these hat problems under the additional restriction that their\nvision is restrained in terms of a partial order. Picture e.g.~countably many\nlogicians standing on the real number line and each logician is only allowed to\nlook at finitely many others in front of them.\n  In many cases, the least $\\kappa$ for which the logicians start losing can be\ndescribed by an instance of the free subset property which in turn is connected\nto large cardinals. In particular, $\\mathrm{ZFC}$ can sometimes not decide\nwhether or not the logicians can win for every possible set of colours.",
        "split": "arXiv"
    },
    {
        "text": "We study eigenfunction localization for higher dimensional cat maps, a\npopular model of quantum chaos. These maps are given by linear symplectic maps\nin ${\\mathrm Sp}(2g,\\mathbb Z)$, which we take to be ergodic. Under some\nnatural assumptions, we show that there is a density one sequence of integers\n$N$ so that as $N$ tends to infinity along this sequence, all eigenfunctions of\nthe quantized map at the inverse Planck constant $N$ are uniformly distributed.\nFor the two-dimensional case ($g=1$), this was proved by P. Kurlberg and Z.\nRudnick (2001). The higher dimensional case offers several new features and\nrequires a completely different set of tools, including from additive\ncombinatorics, in particular Bourgain's bound (2005) for Mordell sums, and a\nstudy of tensor product structures for the cat map.",
        "split": "arXiv"
    },
    {
        "text": "All-dielectric metasurfaces can produce structural colors, but the most\nadvantageous design criteria are still being investigated. This work\nnumerically studies how the two-dimensional shape of nanoparticles affects the\ncolorimetric response under circularly polarized light (CPL) to develop a\nsensor distinguishing CPL orientations. Using lossless dielectric materials\n(silicon nitride on silicon dioxide), we achieve far-field dichroism by\nmodifying oblong nanoparticles into L-shaped structures through corner cuts.\nThis design suppresses one electric dipole under CPL illumination, leading to\ndifferential colorimetric responses. We link these responses to a decoupling\neffect in the near-field net electric flux. Our findings provide design\nguidelines for all-dielectric, lossless colorimetric sensors of chiral light.",
        "split": "arXiv"
    },
    {
        "text": "We present high pressure electrical transport, magnetization, and single\ncrystal X-ray diffraction data on SrCo2P2 single crystals. X-ray diffraction\ndata show that there is a transition to a collapsed tetragonal structure for p\n~> 10 GPa and measurements of resistance show that above ~ 10 GPa, a clear\ntransition-like feature can be observed at temperatures up to 260 K. Further\nmagnetization, magnetoresistance and Hall measurements made under pressure all\nindicate that this transition is to a ferromagnetic ground state. First\nprinciples-based density functional theory (DFT) calculations also show that\nthere is a first-order transition between tetragonal and collapsed tetragonal\n(cT) phases, with an onset near ~ 10 GPa as well as the appearance of the\nferromagnetic (FM) ordering in the cT phase. Above ~ 30 GPa, the experimental\nsignatures of the magnetic ordering vanish in a first-order-like manner,\nconsistent with the theoretical calculation results, indicating that SrCo2P2 is\nanother example of the avoidance of quantum criticality in ferromagnetic\nintermetallic compounds. SrCo2P2 provides clear evidence that the structural,\nelectronic and magnetic properties associated with the cT transition are\nstrongly entangled and are not only qualitatively captured by our first\nprinciples-based calculations but are quantitatively reproduced as well.",
        "split": "arXiv"
    },
    {
        "text": "Consider $E$ a vector bundle over a smooth curve $C$. We compute the\n$\\delta$-invariant of all ample ($\\mathbb{Q}$-) line bundles on $\\mathbb{P}(E)$\nwhen $E$ is strictly Mumford semistable. We also investigate the case when one\nassumes that the Harder-Narasimhan filtration of $E$ has only one step.",
        "split": "arXiv"
    },
    {
        "text": "This paper considers real-time control and learning problems for\nfinite-dimensional linear systems under binary-valued and randomly disturbed\noutput observations. This has long been regarded as an open problem because the\nexact values of the traditional regression vectors used in the construction of\nadaptive algorithms are unavailable, as one only has binary-valued output\ninformation. To overcome this difficulty, we consider the adaptive estimation\nproblem of the corresponding infinite-impulse-response (IIR) dynamical systems,\nand apply the double array martingale theory that has not been previously used\nin adaptive control. This enables us to establish global convergence results\nfor both the adaptive prediction regret and the parameter estimation error,\nwithout resorting to such stringent data conditions as persistent excitation\nand bounded system signals that have been used in almost all existing related\nliterature. Based on this, an adaptive control law will be designed that can\neffectively combine adaptive learning and feedback control. Finally, we are\nable to show that the closed-loop adaptive control system is optimal in the\nsense that the long-run average tracking error is minimized almost surely for\nany given bounded reference signals. To the best of the authors' knowledge,\nthis appears to be the first adaptive control result for general linear systems\nwith general binary sensors and arbitrarily given bounded reference signals.",
        "split": "arXiv"
    },
    {
        "text": "Let M be a transitive model of set theory and X be a space in the sense of M.\nIs there a reasonable way to interpret X as a space in V? A general theory due\nto Zapletal provides a natural candidate which behaves well on sufficiently\ncomplete spaces (for instance \\v{C}ech complete spaces) but behaves poorly on\nmore general spaces - for instance, the Zapletal interpretation does not\ncommute with products. We extend Zapletal's framework to instead interpret\nlocales, a generalization of topological spaces which focuses on the structure\nof open sets. Our extension has a number of desirable properties; for instance,\nlocalic products always interpret as spatial products. We show that a number of\nlocalic notions coincide exactly with properties of their interpretations; for\ninstance, we show a locale is $T_U$ if and only if all its interpretations are\n$T_1$, a locale is $I$-Hausdorff if and only if all its interpretations are\n$T_2$, a locale is regular if and only if all its interpretations are $T_3$,\nand a locale is compact if and only if all its interpretations are compact.",
        "split": "arXiv"
    },
    {
        "text": "Seismic data inevitably suffers from random noise and missing traces in field\nacquisition. This limits the utilization of seismic data for subsequent imaging\nor inversion applications. Recently, dictionary learning has gained remarkable\nsuccess in seismic data denoising and interpolation. Variants of the\npatch-based learning technique, such as the K-SVD algorithm, have been shown to\nimprove denoising and interpolation performance compared to the analytic\ntransform-based methods. However, patch-based learning algorithms work on\noverlapping patches of data and do not take the full data into account during\nreconstruction. By contrast, the data patches (CSC) model treats signals\nglobally and, therefore, has shown superior performance over patch-based\nmethods in several image processing applications. In consequence, we test the\nuse of CSC model for seismic data denoising and interpolation. In particular,\nwe use the local block coordinate descent (LoBCoD) algorithm to reconstruct\nmissing traces and clean seismic data from noisy input. The denoising and\ninterpolation performance of the LoBCoD algorithm has been compared with that\nof K-SVD and orthogonal matching pursuit (OMP) algorithms using synthetic and\nfield data examples. We use three quality measures to test the denoising\naccuracy: the peak signal-to-noise ratio (PSNR), the relative L2-norm of the\nerror (RLNE), and the structural similarity index (SSIM). We find that LoBCoD\nperforms better than K-SVD and OMP for all test cases in improving PSNR and\nSSIM, and in reducing RLNE. These observations suggest a huge potential of the\nCSC model in seismic data denoising and interpolation applications.",
        "split": "arXiv"
    },
    {
        "text": "Multifractality is a concept that helps compactly grasping the most essential\nfeatures of the financial dynamics. In its fully developed form, this concept\napplies to essentially all mature financial markets and even to more liquid\ncryptocurrencies traded on the centralized exchanges. A new element that adds\ncomplexity to cryptocurrency markets is the possibility of decentralized\ntrading. Based on the extracted tick-by-tick transaction data from the\nUniversal Router contract of the Uniswap decentralized exchange, from June 6,\n2023, to June 30, 2024, the present study using Multifractal Detrended\nFluctuation Analysis (MFDFA) shows that even though liquidity on these new\nexchanges is still much lower compared to centralized exchanges convincing\ntraces of multifractality are already emerging on this new trading as well. The\nresulting multifractal spectra are however strongly left-side asymmetric which\nindicates that this multifractality comes primarily from large fluctuations and\nsmall ones are more of the uncorrelated noise type. What is particularly\ninteresting here is the fact that multifractality is more developed for time\nseries representing transaction volumes than rates of return. On the level of\nthese larger events a trace of multifractal cross-correlations between the two\ncharacteristics is also observed.",
        "split": "arXiv"
    },
    {
        "text": "Active learning aims to train accurate classifiers while minimizing labeling\ncosts by strategically selecting informative samples for annotation. This study\nfocuses on image classification tasks, comparing AL methods on CIFAR10,\nCIFAR100, Food101, and the Chest X-ray datasets under varying label noise\nrates. We investigate the impact of model architecture by comparing\nConvolutional Neural Networks (CNNs) and Vision Transformer (ViT)-based models.\nAdditionally, we propose a novel deep active learning algorithm, GCI-ViTAL,\ndesigned to be robust to label noise. GCI-ViTAL utilizes prediction entropy and\nthe Frobenius norm of last-layer attention vectors compared to class-centric\nclean set attention vectors. Our method identifies samples that are both\nuncertain and semantically divergent from typical images in their assigned\nclass. This allows GCI-ViTAL to select informative data points even in the\npresence of label noise while flagging potentially mislabeled candidates. Label\nsmoothing is applied to train a model that is not overly confident about\npotentially noisy labels. We evaluate GCI-ViTAL under varying levels of\nsymmetric label noise and compare it to five other AL strategies. Our results\ndemonstrate that using ViTs leads to significant performance improvements over\nCNNs across all AL strategies, particularly in noisy label settings. We also\nfind that using the semantic information of images as label grounding helps in\ntraining a more robust model under label noise. Notably, we do not perform\nextensive hyperparameter tuning, providing an out-of-the-box comparison that\naddresses the common challenge practitioners face in selecting models and\nactive learning strategies without an exhaustive literature review on training\nand fine-tuning vision models on real-world application data.",
        "split": "arXiv"
    },
    {
        "text": "Silicon photonics is a leading platform for realizing the vast numbers of\nphysical qubits needed for useful quantum information processing because it\nleverages mature complementary metal-oxide-semiconductor (CMOS) manufacturing\nto integrate on-chip thousands of optical devices for generating and\nmanipulating quantum states of light. A challenge to the practical operation\nand scale-up of silicon quantum-photonic integrated circuits, however, is the\nneed to control their extreme sensitivity to process and temperature\nvariations, free-carrier and self-heating nonlinearities, and thermal\ncrosstalk. To date these challenges have been partially addressed using bulky\noff-chip electronics, sacrificing many benefits of a chip-scale platform. Here,\nwe demonstrate the first electronic-photonic quantum system-on-chip (EPQSoC)\nconsisting of quantum-correlated photon-pair sources stabilized via on-chip\nfeedback control circuits, all fabricated in a high-volume 45nm CMOS\nmicroelectronics foundry. We use non-invasive photocurrent sensing in a tunable\nmicroring cavity photon-pair source to actively lock it to a fixed pump laser\nwhile operating in the quantum regime, enabling large scale microring-based\nquantum systems. In this first demonstration of such a capability, we achieve a\nhigh CAR of 134 with an ultra-low g(2)(0) of 0.021 at 2.2 kHz off-chip detected\npair rate and 3.3 MHz/mW2 on-chip pair generation efficiency, and over 100 kHz\noff-chip detected pair rate at higher pump powers (1.5 MHz on-chip). These\nsources maintain stable quantum properties in the presence of temperature\nvariations, operating reliably in practical settings with many adjacent devices\ncreating thermal disturbances on the same chip. Such dense electronic-photonic\nintegration enables implementation and control of quantum-photonic systems at\nthe scale required for useful quantum information processing with\nCMOS-fabricated chips.",
        "split": "arXiv"
    },
    {
        "text": "Direct imaging observations are biased towards wide-separation, massive\ncompanions that have degenerate formation histories. Although the majority of\nexoplanets are expected to form via core accretion, most directly imaged\nexoplanets have not been convincingly demonstrated to follow this formation\npathway. We obtained new interferometric observations of the directly imaged\ngiant planet AF Lep b with the VLTI/GRAVITY instrument. We present three epochs\nof 50$\\mu$as relative astrometry and the K-band spectrum of the planet for the\nfirst time at a resolution of R=500. Using only these measurements, spanning\nless than two months, and the Hipparcos-Gaia Catalogue of Accelerations, we are\nable to significantly constrain the planet's orbit; this bodes well for\ninterferometric observations of planets discovered by Gaia DR4. Including all\navailable measurements of the planet, we infer an effectively circular orbit\n($e<0.02, 0.07, 0.13$ at $1, 2, 3 \\sigma$) in spin-orbit alignment with the\nhost, and a measure a dynamical mass of\n$M_\\mathrm{p}=3.75\\pm0.5\\,M_\\mathrm{Jup}$. Models of the spectrum of the planet\nshow that it is metal rich ([M/H]$=0.75\\pm0.25$), with a C/O ratio encompassing\nthe solar value. This ensemble of results show that the planet is consistent\nwith core accretion formation.",
        "split": "arXiv"
    },
    {
        "text": "In this work we illustrate a general framework to describe the LHC\nphenomenology of extended scalar (and fermion) sectors, with focus on dark\nmatter (DM) physics, based on an effective field theory (EFT) with non-linearly\nrealized electroweak symmetry. Generalizing Higgs EFT (HEFT), the setup allows\nto include a generic set of new scalar resonances, without the need to specify\ntheir UV origin, that could for example be at the interface of the Standard\nModel (SM) and the DM world. In particular, we study the case of fermionic DM\ninteracting with the SM via two mediators, each of which can possess either CP\nproperty and originate from various electroweak representations in the UV\ntheory. Besides trilinear interactions between the mediators and DM or SM pairs\n(including pairs of gauge field-strength tensors), the EFT contains all further\ngauge-invariant operators up to mass dimension $D=5$. While remaining\ntheoretically consistent, this setup offers enough flexibility to capture the\nphenomenology of many benchmark models used to interpret the results of\nexperimental DM and BSM searches, such as two-Higgs doublet extensions of the\nSM or singlet extensions. Furthermore, the presence of two mediators with\npotentially sizable couplings allows to account for a broad variety of\ninteresting collider signatures, as for example detectable mono-$h$ and\nmono-$Z$ signals. Correlations can be employed to diagnose the nature of the\nnew particles.",
        "split": "arXiv"
    },
    {
        "text": "We introduce \"Method Actors\" as a mental model for guiding LLM prompt\nengineering and prompt architecture. Under this mental model, LLMs should be\nthought of as actors; prompts as scripts and cues; and LLM responses as\nperformances. We apply this mental model to the task of improving LLM\nperformance at playing Connections, a New York Times word puzzle game that\nprior research identified as a challenging benchmark for evaluating LLM\nreasoning. Our experiments with GPT-4o show that a \"Method Actors\" approach can\nsignificantly improve LLM performance over both a vanilla and \"Chain of\nThoughts\" approach. A vanilla approach solves 27% of Connections puzzles in our\ndataset and a \"Chain of Thoughts\" approach solves 41% of puzzles, whereas our\nstrongest \"Method Actor\" approach solves 86% of puzzles. We also test OpenAI's\nnewest model designed specifically for complex reasoning tasks, o1-preview.\nWhen asked to solve a puzzle all at once, o1-preview solves 79% of Connections\npuzzles in our dataset, and when allowed to build puzzle solutions one guess at\na time over multiple API calls, o1-preview solves 100% of the puzzles.\nIncorporating a \"Method Actor\" prompt architecture increases the percentage of\npuzzles that o1-preview solves perfectly from 76% to 87%.",
        "split": "arXiv"
    },
    {
        "text": "Time-dependent Thermoremanent Magnetization (TRM) studies have been\ninstrumental in probing energy dynamics within the spin glass phase. In this\npaper, we will review the evolution of the TRM experiment over the last half\ncentury and discuss some aspects related to how it has been employed in the\nunderstanding of spin glasses. We will also report on recent experiments using\nhigh resolution DC SQUID magnetometry to probe the TRM at temperatures less\nthan but near to the transition temperature Tc. These experiments have been\nperformed as a function of waiting time, temperature, and five different\nmagnetic fields. We find that as the transition temperature is approached from\nbelow, the characteristic time scale of the TRM is suppressed up to several\norders of magnitude in time. In the highest temperature region, we find that\nthe waiting time effect goes away, and a waiting time independent crossover\nline is reached. We also find that increasing the magnetic field, further\nsuppresses the crossover line. Using a first principles energy argument across\nthe crossover line, we derive an equation that is an excellent fit to the\ncrossover lines for all magnetic fields probed. The data show strong evidence\nfor an H = 0 Oe phase transition.",
        "split": "arXiv"
    },
    {
        "text": "This article presents a data-driven equation-free modeling of the dynamics of\na hexafloat floating offshore wind turbine based on the Dynamic Mode\nDecomposition (DMD). The DMD is here used to provide a modal analysis and\nextract knowledge from the dynamic system. A forecasting algorithm for the\nmotions, accelerations, and forces acting on the floating system, as well as\nthe height of the incoming waves, the wind speed, and the power extracted by\nthe wind turbine, is developed by using a methodological extension called\nHankel-DMD, that includes time-delayed copies of the states in an augmented\nstate vector. All the analyses are performed on experimental data collected\nfrom an operating prototype. The quality of the forecasts obtained varying two\nmain hyperparameters of the algorithm, namely the number of delayed copies and\nthe length of the observation time, is assessed using three different error\nmetrics, each analyzing complementary aspects of the prediction. A statistical\nanalysis exposed the existence of optimal values for the algorithm\nhyperparameters. Results show the approach's capability for short-term future\nestimates of the system's state, which can be used for real-time prediction and\ncontrol. Furthermore, a novel Stochastic Hankel-DMD formulation is introduced\nby considering hyperparameters as stochastic variables. The stochastic version\nof the method not only enriches the prediction with its related uncertainty but\nis also found to improve the normalized root mean square error up to 10% on a\nstatistical basis compared to the deterministic counterpart.",
        "split": "arXiv"
    },
    {
        "text": "Weakly collisional plasmas contain a wealth of information about the dynamics\nof the plasma in the particle velocity distribution functions, yet our ability\nto exploit fully that information remains relatively primitive. Here we aim to\npresent the fundamentals of a new technique denoted Plasma Seismology that aims\nto invert the information from measurements of the particle velocity\ndistribution functions at a single point in space over time to enable the\ndetermination of the electric field variation over an extended spatial region.\nThe fundamental mathematical tool at the heart of this technique is the\nMorrison $G$ Transform. Using kinetic numerical simulations of Langmuir waves\nin a Vlasov-Poisson plasma, we demonstrate the application of the standard\nMorrison $G$ Transform, which uses measurements of the particle velocity\ndistribution function over all space at one time to predict the evolution of\nthe electric field in time. Next, we introduce a modified Morrison $G$\nTransform which uses measurements of the particle velocity distribution\nfunction at one point in space over time to determine the spatial variation of\nthe electric field over an extended spatial region. We discuss the limitations\nof this approach, particularly for the numerically challenging case of Langmuir\nwaves. The application of this technique to Alfven waves in a magnetized plasma\nholds the promise to apply the technique to existing spacecraft particle\nmeasurement instrumentation to determine the electric fields over an extended\nspatial region away from the spacecraft.",
        "split": "arXiv"
    },
    {
        "text": "We study higher uniformity properties of the von Mangoldt function $\\Lambda$,\nthe M\\\"obius function $\\mu$, and the divisor functions $d_k$ on short intervals\n$(x,x+H]$ for almost all $x \\in [X, 2X]$.\n  Let $\\Lambda^\\sharp$ and $d_k^\\sharp$ be suitable approximants of $\\Lambda$\nand $d_k$, $G/\\Gamma$ a filtered nilmanifold, and $F\\colon G/\\Gamma \\to\n\\mathbb{C}$ a Lipschitz function. Then our results imply for instance that when\n$X^{1/3+\\varepsilon} \\leq H \\leq X$ we have, for almost all $x \\in [X, 2X]$, \\[\n\\sup_{g \\in \\text{Poly}(\\mathbb{Z} \\to G)} \\left| \\sum_{x < n \\leq x+H}\n(\\Lambda(n)-\\Lambda^\\sharp(n)) \\overline{F}(g(n)\\Gamma) \\right| \\ll H\\log^{-A}\nX \\] for any fixed $A>0$, and that when $X^{\\varepsilon} \\leq H \\leq X$ we\nhave, for almost all $x \\in [X, 2X]$, \\[ \\sup_{g \\in \\text{Poly}(\\mathbb{Z} \\to\nG)} \\left| \\sum_{x < n \\leq x+H} (d_k(n)-d_k^\\sharp(n))\n\\overline{F}(g(n)\\Gamma) \\right| = o(H \\log^{k-1} X). \\]\n  As a consequence, we show that the short interval Gowers norms\n$\\|\\Lambda-\\Lambda^\\sharp\\|_{U^s(X,X+H]}$ and $\\|d_k-d_k^\\sharp\\|_{U^s(X,X+H]}$\nare also asymptotically small for any fixed $s$ in the same ranges of $H$. This\nin turn allows us to establish the Hardy-Littlewood conjecture and the divisor\ncorrelation conjecture with a short average over one variable.\n  Our main new ingredients are type $II$ estimates obtained by developing a\n\"contagion lemma\" for nilsequences and then using this to \"scale up\" an\napproximate functional equation for the nilsequence to a larger scale. This\nextends an approach developed by Walsh for Fourier uniformity.",
        "split": "arXiv"
    },
    {
        "text": "Separating disinformation from fact on the web has long challenged both the\nsearch and the reasoning powers of humans. We show that the reasoning power of\nlarge language models (LLMs) and the retrieval power of modern search engines\ncan be combined to automate this process and explainably verify claims. We\nintegrate LLMs and search under a multi-hop evidence pursuit strategy. This\nstrategy generates an initial question based on an input claim using a sequence\nto sequence model, searches and formulates an answer to the question, and\niteratively generates follow-up questions to pursue the evidence that is\nmissing using an LLM. We demonstrate our system on the FEVER 2024 (AVeriTeC)\nshared task. Compared to a strategy of generating all the questions at once,\nour method obtains .045 higher label accuracy and .155 higher AVeriTeC score\n(evaluating the adequacy of the evidence). Through ablations, we show the\nimportance of various design choices, such as the question generation method,\nmedium-sized context, reasoning with one document at a time, adding metadata,\nparaphrasing, reducing the problem to two classes, and reconsidering the final\nverdict. Our submitted system achieves .510 AVeriTeC score on the dev set and\n.477 AVeriTeC score on the test set.",
        "split": "arXiv"
    },
    {
        "text": "We study the performances of a world-wide network made by a European\nthird-generation gravitational-wave (GW) detector, together with a 40-km Cosmic\nExplorer detector in the US, considering three scenarios for the European\ndetector: (1) Einstein Telescope (ET) in its 10-km triangle configuration; (2)\nET in its configuration featuring two 15-km L-shaped detectors in different\nsites, still taken to have all other ET characteristics (underground, and with\neach detector made of a high-frequency interferometer and a cryogenic\nlow-frequency interferometer); (3) A single L-shaped underground interferometer\nwith the ET amplitude spectral density, either with 15~km or with 20~km arm\nlength. Overall, we find that, if a 2L configuration should be retained for ET,\nthe network made by a single-L European underground detector together with\nCE-40km could already provide a very interesting intermediate step toward the\nconstruction of a full 2L+CE network, and is in any case superior to a 10-km\ntriangle not inserted in an international network.",
        "split": "arXiv"
    },
    {
        "text": "Given a database of bit strings $A_1,\\ldots,A_m\\in \\{0,1\\}^n$, a fundamental\ndata structure task is to estimate the distances between a given query $B\\in\n\\{0,1\\}^n$ with all the strings in the database. In addition, one might further\nwant to ensure the integrity of the database by releasing these distance\nstatistics in a secure manner. In this work, we propose differentially private\n(DP) data structures for this type of tasks, with a focus on Hamming and edit\ndistance. On top of the strong privacy guarantees, our data structures are also\ntime- and space-efficient. In particular, our data structure is $\\epsilon$-DP\nagainst any sequence of queries of arbitrary length, and for any query $B$ such\nthat the maximum distance to any string in the database is at most $k$, we\noutput $m$ distance estimates. Moreover,\n  - For Hamming distance, our data structure answers any query in $\\widetilde\nO(mk+n)$ time and each estimate deviates from the true distance by at most\n$\\widetilde O(k/e^{\\epsilon/\\log k})$;\n  - For edit distance, our data structure answers any query in $\\widetilde\nO(mk^2+n)$ time and each estimate deviates from the true distance by at most\n$\\widetilde O(k/e^{\\epsilon/(\\log k \\log n)})$.\n  For moderate $k$, both data structures support sublinear query operations. We\nobtain these results via a novel adaptation of the randomized response\ntechnique as a bit flipping procedure, applied to the sketched strings.",
        "split": "arXiv"
    },
    {
        "text": "Language model performance depends on identifying the optimal mixture of data\ngroups to train on (e.g., law, code, math). Prior work has proposed a diverse\nset of methods to efficiently learn mixture proportions, ranging from fitting\nregression models over training runs to dynamically updating proportions\nthroughout training. Surprisingly, we find that no existing method consistently\noutperforms a simple stratified sampling baseline in terms of average test\nperplexity per group. In this paper, we study the cause of this inconsistency\nby unifying existing methods into a standard optimization framework. We show\nthat all methods set proportions to minimize total loss, subject to a\nmethod-specific mixing law -- an assumption on how loss is a function of\nmixture proportions. We find that existing parameterizations of mixing laws can\nexpress the true loss-proportion relationship empirically, but the methods\nthemselves often set the mixing law parameters inaccurately, resulting in poor\nand inconsistent performance. Finally, we leverage the insights from our\nframework to derive a new online method named Aioli, which directly estimates\nthe mixing law parameters throughout training and uses them to dynamically\nadjust proportions. Empirically, Aioli outperforms stratified sampling on 6 out\nof 6 datasets by an average of 0.28 test perplexity points, whereas existing\nmethods fail to consistently beat stratified sampling, doing up to 6.9 points\nworse. Moreover, in a practical setting where proportions are learned on\nshorter runs due to computational constraints, Aioli can dynamically adjust\nthese proportions over the full training run, consistently improving\nperformance over existing methods by up to 12.01 test perplexity points.",
        "split": "arXiv"
    },
    {
        "text": "When the period of an incommensurate charge density wave (ICDW) approaches an\ninteger multiple of a lattice vector, the energy gain obtained from locking the\nperiod to the lattice can lead to a fascinating transition into a commensurate\nstate. This transition actually occurs through an intermediate\nnear-commensurate (NC) phase, with locally commensurate regions separated by an\nordered array of phase slips of a complex CDW order parameter. TiSe2 is a\nparadigmatic CDW system where incommensuration is believed to be induced by\ncarrier doping, yet its putative NC state has never been imaged or its nature\nestablished. Here we report the observation of a striking NC state in\nultraclean, slightly doped monolayers of TiSe2, displaying an intricate network\nof coherent, unidirectional CDW domain walls over hundreds of nanometers.\nDetailed analysis reveals these are not phase slips of a complex CDW, but\nrather sign-changing Ising-type domain walls of two coupled real CDWs of\npreviously known symmetry, consistent with the period doubling nature of the\nparent commensurate state. In addition, we observe an unexpected nematic\nmodulation at the original lattice Bragg peaks which couples to the CDW order\nparameters. A Ginzburg-Landau analysis naturally explains the couplings and\nrelative modulations of all order parameters, unveiling TiSe2 as a rare example\nof an NC-CDW of two intertwined real modulations and emergent nematicity.",
        "split": "arXiv"
    },
    {
        "text": "One of the fundamental open problems in the field of tensors is the border\nComon's conjecture: given a symmetric tensor $F\\in(\\mathbb{C}^n)^{\\otimes d}$\nfor $d\\geq 3$, its border and symmetric border ranks are equal. In this paper,\nwe prove the conjecture for large classes of concise tensors in\n$(\\mathbb{C}^n)^{\\otimes d}$ of border rank $n$, i.e., tensors of minimal\nborder rank. These families include all tame tensors and all tensors whenever\n$n\\leq d+1$. Our technical tools are border apolarity and border varieties of\nsums of powers.",
        "split": "arXiv"
    },
    {
        "text": "We introduce and study a generalized form of derivations for dendriform\nalgebras, specifying all admissible parameter values that define these\nderivations. Additionally, we present a complete classification of generalized\nderivations for two-dimensional left-symmetric dialgebras over the field\n$\\mathbb{K}$.",
        "split": "arXiv"
    },
    {
        "text": "Humans are able to fuse information from both auditory and visual modalities\nto help with understanding speech. This is frequently demonstrated through an\nphenomenon known as the McGurk Effect, during which a listener is presented\nwith incongruent auditory and visual speech that fuse together into the percept\nof an illusory intermediate phoneme. Building on a recent framework that\nproposes how to address developmental 'why' questions using artificial neural\nnetworks, we evaluated a set of recent artificial neural networks trained on\naudiovisual speech by testing them with audiovisually incongruent words\ndesigned to elicit the McGurk effect. We compared networks trained on clean\nspeech to those trained on noisy speech, and discovered that training with\nnoisy speech led to an increase in both visual responses and McGurk responses\nacross all models. Furthermore, we observed that systematically increasing the\nlevel of auditory noise during ANN training also increased the amount of\naudiovisual integration up to a point, but at extreme noise levels, this\nintegration failed to develop. These results suggest that excessive noise\nexposure during critical periods of audiovisual learning may negatively\ninfluence the development of audiovisual speech integration. This work also\ndemonstrates that the McGurk effect reliably emerges untrained from the\nbehaviour of both supervised and unsupervised networks. This supports the\nnotion that artificial neural networks might be useful models for certain\naspects of perception and cognition.",
        "split": "arXiv"
    },
    {
        "text": "We advance the study of pure de Sitter supergravity by introducing a finite\nformulation of unimodular supergravity via the super-St\\\"uckelberg mechanism.\nBuilding on previous works, we construct a complete four-dimensional action of\nspontaneously broken ${\\cal N}\\!\\!=\\!\\!1$ supergravity to all orders, which\nallows for de Sitter solutions. The introduction of finite supergravity\ntransformations extends the super-St\\\"uckelberg procedure beyond the second\norder, offering a recursive solution to all orders in the goldstino sector.\nThis work bridges the earlier perturbative approaches and the complete finite\ntheory, opening new possibilities for de Sitter vacua in supergravity models\nand eventually string theory.",
        "split": "arXiv"
    },
    {
        "text": "Let $p$ be a prime number. We consider diagonal $p$-permutation functors over\na (commutative, unital) ring $\\mathsf{R}$ in which all prime numbers different\nfrom $p$ are invertible. We first determine the finite groups $G$ for which the\nassociated essential algebra $\\mathcal{E}_\\mathsf{R}(G)$ is non zero: These are\ngroups of the form $G=L\\rtimes \\langle u\\rangle$, where $(L,u)$ is a\n$D^\\Delta$-pair. When $\\mathsf{R}$ is an algebraically closed field\n$\\mathbb{F}$ of characteristic 0 or $p$, this yields a parametrization of the\nsimple diagonal $p$-permutation functors over $\\mathbb{F}$ by triples\n$(L,u,W)$, where $(L,u)$ is a $D^\\Delta$-pair, and $W$ is a simple\n$\\mathbb{F}\\mathrm{Out}(L,u)$-module. Finally, we describe the evaluations of\nthe simple functor $\\mathsf{S}_{L,u,W}$ parametrized by the triple $(L,u,W)$.\nWe show in particular that if $G$ is a finite group and $\\mathbb{F}$ has\ncharacteristic $p$, the dimension of $\\mathsf{S}_{L,1,\\mathbb{F}}(G)$ is equal\nto the number of conjugacy classes of $p$-regular elements of $G$ with defect\nisomorphic to $L$.",
        "split": "arXiv"
    },
    {
        "text": "Complex systems, such as economic, social, biological, and ecological\nsystems, usually feature interactions not only between pairwise entities but\nalso among three or more entities. These multi-entity interactions are known as\nhigher-order interactions. Hypergraph, as a mathematical tool, can effectively\ncharacterize higher-order interactions, where nodes denote entities and\nhyperedges represent interactions among multiple entities. Meanwhile, all\nhigher-order interactions can also be projected into a number of lower-order\ninteractions or even some pairwise interactions. Whether it is necessary to\nconsider all higher-order interactions, and whether it is with little loss to\nreplace them by lower-order or even pairwise interactions, remain a\ncontroversial issue. If the role of higher-order interactions is insignificant,\nthe complexity of computation and the difficulty of analysis can be drastically\nreduced by projecting higher-order interactions into lower-order or pairwise\ninteractions. We use link prediction, a fundamental problem in network science,\nas the entry point. Specifically, we evaluate the impact of higher-order\ninteractions on link predictive accuracy to explore the necessity of these\nstructures. We propose a method to decompose the higher-order structures in a\nstepwise way, thereby allowing to systematically explore the impacts of\nstructures at different orders on link prediction. The results indicate that in\nsome networks, incorporating higher-order interactions significantly enhances\nthe accuracy of link prediction, while in others, the effect is insignificant.\nTherefore, we think that the role of higher-order interactions varies in\ndifferent types of networks. Overall, since the improvement in predictive\naccuracy provided by higher-order interactions is significant in some networks,\nwe believe that the study of higher-order interactions is both necessary and\nvaluable.",
        "split": "arXiv"
    },
    {
        "text": "The integration of unmanned platforms equipped with advanced sensors promises\nto enhance situational awareness and mitigate the \"fog of war\" in military\noperations. However, managing the vast influx of data from these platforms\nposes a significant challenge for Command and Control (C2) systems. This study\npresents a novel multi-agent learning framework to address this challenge. Our\nmethod enables autonomous and secure communication between agents and humans,\nwhich in turn enables real-time formation of an interpretable Common\nOperational Picture (COP). Each agent encodes its perceptions and actions into\ncompact vectors, which are then transmitted, received and decoded to form a COP\nencompassing the current state of all agents (friendly and enemy) on the\nbattlefield. Using Deep Reinforcement Learning (DRL), we jointly train COP\nmodels and agent's action selection policies. We demonstrate resilience to\ndegraded conditions such as denied GPS and disrupted communications.\nExperimental validation is performed in the Starcraft-2 simulation environment\nto evaluate the precision of the COPs and robustness of policies. We report\nless than 5% error in COPs and policies resilient to various adversarial\nconditions. In summary, our contributions include a method for autonomous COP\nformation, increased resilience through distributed prediction, and joint\ntraining of COP models and multi-agent RL policies. This research advances\nadaptive and resilient C2, facilitating effective control of heterogeneous\nunmanned platforms.",
        "split": "arXiv"
    },
    {
        "text": "Mobility-as-a-Service (MaaS) integrates different transport modalities and\ncan support more personalisation of travellers' journey planning based on their\nindividual preferences, behaviours and wishes. To fully achieve the potential\nof MaaS, a range of AI (including machine learning and data mining) algorithms\nare needed to learn personal requirements and needs, to optimise journey\nplanning of each traveller and all travellers as a whole, to help transport\nservice operators and relevant governmental bodies to operate and plan their\nservices, and to detect and prevent cyber attacks from various threat actors\nincluding dishonest and malicious travellers and transport operators. The\nincreasing use of different AI and data processing algorithms in both\ncentralised and distributed settings opens the MaaS ecosystem up to diverse\ncyber and privacy attacks at both the AI algorithm level and the connectivity\nsurfaces. In this paper, we present the first comprehensive review on the\ncoupling between AI-driven MaaS design and the diverse cyber security\nchallenges related to cyber attacks and countermeasures. In particular, we\nfocus on how current and emerging AI-facilitated privacy risks (profiling,\ninference, and third-party threats) and adversarial AI attacks (evasion,\nextraction, and gamification) may impact the MaaS ecosystem. These risks often\ncombine novel attacks (e.g., inverse learning) with traditional attack vectors\n(e.g., man-in-the-middle attacks), exacerbating the risks for the wider\nparticipation actors and the emergence of new business models.",
        "split": "arXiv"
    },
    {
        "text": "NGC 5139 ($\\omega$ Cen), is the closest candidate of a Nuclear Star Cluster\nthat has been stripped of its host galaxy in the Milky Way. Despite extensive\nstudies through the last decades, many open questions about the cluster remain,\nincluding the properties of the binary population. In this study we use MUSE\nmulti-epoch spectroscopy to identify binary systems in $\\omega$ Cen. The\nobservations span 8 years, with a total of 312 248 radial velocity measurements\nfor 37 225 stars. Following the removal of known photometric variables, we\nidentify 275 stars that show RV variations, corresponding to a discovery\nfraction of 1.4$\\pm$0.1%. Using dedicated simulations, we find that our data is\nsensitive to 70$\\pm$10% of the binaries expected in the sample, resulting in a\ncompleteness-corrected binary fraction of 2.1$\\pm$0.4% in the central region of\n$\\omega$ Cen. We find similar binary fractions for all stellar evolutionary\nstages covered by our data, the only notable exception being the blue straggler\nstars, which show an enhanced binary fraction. We also find no distinct\ncorrelation with distance from the cluster centre, indicating a limited amount\nof mass segregation within the half-light radius of $\\omega$ Cen.",
        "split": "arXiv"
    },
    {
        "text": "The dissociation of quarkonium states with different binding energies\nproduced in heavy-ion collisions is a powerful probe for investigating the\nformation and properties of the quark-gluon plasma. The ratio of production\ncross-sections of $\\psi(2S)$ and $J/\\psi$ mesons times the ratio of their\nbranching fractions into the dimuon final state is measured as a function of\ncentrality using data collected by the LHCb detector in PbPb collisions at\n$\\sqrt{s_{\\text{NN}}}$ = 5.02 TeV. The measured ratio shows no dependence on\nthe collision centrality, and is compared to the latest theory predictions and\nto the recent measurements in literature.",
        "split": "arXiv"
    },
    {
        "text": "We define the Super-Unique-Tarski problem, which is a Tarski instance in\nwhich all slices are required to have a unique fixed point. We show that\nSuper-Unique-Tarski lies in UEOPL under promise-preserving reductions.",
        "split": "arXiv"
    },
    {
        "text": "We assess the prospects for detecting gravitational wave echoes arising due\nto the quantum nature of black hole horizons with LISA. In a recent proposal,\nBekenstein's black hole area quantization is connected to a discrete absorption\nspectrum for black holes in the context of gravitational radiation.\nConsequently, for incoming radiation at the black hole horizon, not all\nfrequencies are absorbed, raising the possibility that the unabsorbed radiation\nis reflected, producing an echo-like signal closely following the binary\ncoalescence waveform. In this work, we further develop this proposal by\nintroducing a robust, phenomenologically motivated model for black hole\nreflectivity. Using this model, we calculate the resulting echoes for an\nensemble of Numerical Relativity waveforms and examine their detectability with\nthe LISA space-based interferometer. Our analysis demonstrates promising\ndetection prospects and shows that, upon detection, LISA provides a direct\nprobe of the Bekenstein-Hawking entropy. In addition, we find that the\ninformation extractable from LISA data offers valuable constraints on a wide\nrange of quantum gravity theories.",
        "split": "arXiv"
    },
    {
        "text": "There is a persistent $\\sim 5 \\sigma$ tension between the value of the Hubble\nconstant, as derived from either the local distance ladder or the cosmic\nmicrowave background, signaling either unaccounted for systematics in the\nmeasurements or `new physics'. Determining the Hubble constant using Type Ia\nsupernovae requires non-trivial and accurate corrections for dust extinction.\nTo circumvent this obstacle, we here determine the Hubble constant from blue,\nand hence presumably unextinguished, supernovae, only. For two different\ncompilations of Type Ia supernova data and lightcurve fitting methods we find\nthat the derived Hubble constant is consistently lower by $\\sim$ 3 km s$^{-1}$\nMpc$^{-1}$ ($\\sim 70$ km s$^{-1}$ Mpc$^{-1}$), and within 1 $\\sigma$ of the\nCosmic Microwave Background measurement, when using only blue supernovae as\nopposed to using all supernovae. Although the number of blue calibrating Type\nIa supernovae is small, this indicates potential systematic effects in dust\ncorrections in standard supernova cosmology. Upcoming major transient surveys\nwill discover numerous unextinguished SNe~Ia, and thus be able to increase\nprecision of the Hubble constant measured from blue SNe~Ia, heralding a\npromising path toward resolving the Hubble constant tension.",
        "split": "arXiv"
    },
    {
        "text": "To address the challenges of high computational costs and long-distance\ndependencies in exist ing video understanding methods, such as CNNs and\nTransformers, this work introduces RWKV to the video domain in a novel way. We\npropose a LSTM CrossRWKV (LCR) framework, designed for spatiotemporal\nrepresentation learning to tackle the video understanding task. Specifically,\nthe proposed linear complexity LCR incorporates a novel Cross RWKV gate to\nfacilitate interaction be tween current frame edge information and past\nfeatures, enhancing the focus on the subject through edge features and globally\naggregating inter-frame features over time. LCR stores long-term mem ory for\nvideo processing through an enhanced LSTM recurrent execution mechanism. By\nleveraging the Cross RWKV gate and recurrent execution, LCR effectively\ncaptures both spatial and temporal features. Additionally, the edge information\nserves as a forgetting gate for LSTM, guiding long-term memory management.Tube\nmasking strategy reduces redundant information in food and reduces\noverfitting.These advantages enable LSTM CrossRWKV to set a new benchmark in\nvideo under standing, offering a scalable and efficient solution for\ncomprehensive video analysis. All code and models are publicly available.",
        "split": "arXiv"
    },
    {
        "text": "Extended gravitational models have gained large attention in the last couple\nof decades. In this work, we examine the solution space of vacuum, static, and\nspherically symmetric spacetimes within $F(R)$ theories, introducing novel\nmethods that reduce the vacuum equations to a single second-order equation. For\nthe first time, we derive analytic expressions for the metric functions in\nterms of the arbitrary functional $F(R)$, providing detailed insight into how\nthe gravitational action impacts the structure of spacetime. We analyze\nconditions under which solutions are asymptotically flat, regular at the core,\nand contain an event horizon, obtaining explicit expressions for entropy,\ntemperature, and specific heat in terms of $F(R)$. By using a single metric\ndegree of freedom, we identify the most general solution and examine its\n(un)physical properties, showing that resolving singularities is not possible\nwithin this restricted framework in vacuum. For the general case involving two\nmetric functions, we use several approximation schemes to explore corrections\nto Schwarzschild-(anti)de Sitter spacetimes, finding that $F(R)$ extensions to\nGeneral Relativity induce instabilities that are not negligible. Finally,\nthrough an analysis of axial perturbations, we derived a general expression for\nthe potential of quasinormal modes of a black hole as a function of the\narbitrary Lagrangian.",
        "split": "arXiv"
    },
    {
        "text": "This paper presents a data-driven min-max model predictive control (MPC)\nscheme for linear parameter-varying (LPV) systems. Contrary to existing\ndata-driven LPV control approaches, we assume that the scheduling signal is\nunknown during offline data collection and online system operation. Assuming a\nquadratic matrix inequality (QMI) description for the scheduling signal, we\ndevelop a novel data-driven characterization of the consistent system matrices\nusing only input-state data. The proposed data-driven min-max MPC minimizes a\ntractable upper bound on the worst-case cost over the consistent system\nmatrices set and over all scheduling signals satisfying the QMI. The proposed\napproach guarantees recursive feasibility, closed-loop exponential stability\nand constraint satisfaction if it is feasible at the initial time. We\ndemonstrate the effectiveness of the proposed method in simulation.",
        "split": "arXiv"
    },
    {
        "text": "The butterfly diagram of the solar cycle exhibits a poleward migration of the\ndiffuse magnetic field resulting from the decay of trailing sunspots. It is one\ncomponent of what is sometimes referred to as the \"rush to the poles\". We\ninvestigate under which conditions the rush to the poles can be reproduced in\nflux-transport Babcock-Leighton dynamo models. We identify three main ways to\nreproduce it: a flux emergence probability that decreases rapidly with\nlatitude; a threshold in subsurface toroidal field strength between slow and\nfast emergence; and an emergence rate based on magnetic buoyancy. We find that\nall three mechanisms lead to solar-like butterfly diagrams, but which present\nnotable differences between them. The shape of the butterfly diagram is very\nsensitive to model parameters for the threshold prescription, while most models\nincorporating magnetic buoyancy converge to very similar butterfly diagrams,\nwith butterfly wings widths of $\\lesssim\\pm 30^\\circ$, in very good agreement\nwith observations. With turbulent diffusivities above $35~\\text{km}^2/\\text{s}$\nbut below about $40~\\text{km}^2/\\text{s}$, buoyancy models are strikingly\nsolar-like. The threshold and magnetic buoyancy prescriptions make the models\nnon-linear and as such can saturate the dynamo through latitudinal quenching.\nThe period of the models involving buoyancy is independent of the source term\namplitude, but emergence loss increases it by $\\simeq 60\\%$. For the rush to\nthe poles to be visible, a mechanism suppressing (enhancing) emergences at high\n(low) latitudes must operate. It is not sufficient that the toroidal field be\nstored at low latitudes for emergences to be limited to low latitudes. From\nthese models we infer that the Sun is not in the advection-dominated regime,\nbut also not in the diffusion-dominated regime. The cycle period is set through\na balance between advection, diffusion and flux emergence.",
        "split": "arXiv"
    },
    {
        "text": "The main challenges hindering the adoption of deep learning-based systems in\nclinical settings are the scarcity of annotated data and the lack of\ninterpretability and trust in these systems. Concept Bottleneck Models (CBMs)\noffer inherent interpretability by constraining the final disease prediction on\na set of human-understandable concepts. However, this inherent interpretability\ncomes at the cost of greater annotation burden. Additionally, adding new\nconcepts requires retraining the entire system. In this work, we introduce a\nnovel two-step methodology that addresses both of these challenges. By\nsimulating the two stages of a CBM, we utilize a pretrained Vision Language\nModel (VLM) to automatically predict clinical concepts, and a Large Language\nModel (LLM) to generate disease diagnoses based on the predicted concepts. We\nvalidate our approach on three skin lesion datasets, demonstrating that it\noutperforms traditional CBMs and state-of-the-art explainable methods, all\nwithout requiring any training and utilizing only a few annotated examples. The\ncode is available at\nhttps://github.com/CristianoPatricio/2-step-concept-based-skin-diagnosis.",
        "split": "arXiv"
    },
    {
        "text": "We study all the ways that a given convex body in $d$ dimensions can break\ninto countably many pieces that move away from each other rigidly at constant\nvelocity, with no rotation or shearing. The initial velocity field is locally\nconstant, but may be continuous and/or fail to be integrable. For any choice of\nmass-velocity pairs for the pieces, such a motion can be generated by the\ngradient of a convex potential that is affine on each piece. We classify such\npotentials in terms of a countable version of a theorem of Alexandrov for\nconvex polytopes, and prove a stability theorem. For bounded velocities, there\nis a bijection between the mass-velocity data and optimal transport flows\n(Wasserstein geodesics) that are locally incompressible.\n  Given any rigidly breaking velocity field that is the gradient of a\ncontinuous potential, the convexity of the potential is established under any\nof several conditions, such as the velocity field being continuous, the\npotential being semi-convex, the mass measure generated by a convexified\ntransport potential being absolutely continuous, or there being a finite number\nof pieces. Also we describe a number of curious and paradoxical examples having\nfractal structure.",
        "split": "arXiv"
    },
    {
        "text": "Document-level Event Argument Extraction (EAE) faces two challenges due to\nincreased input length: 1) difficulty in distinguishing semantic boundaries\nbetween events, and 2) interference from redundant information. To address\nthese issues, we propose two methods. The first method introduces the Co and\nStructure Event Argument Extraction model (CsEAE) based on Small Language\nModels (SLMs). CsEAE includes a co-occurrences-aware module, which integrates\ninformation about all events present in the current input through context\nlabeling and co-occurrences event prompts extraction. Additionally, CsEAE\nincludes a structure-aware module that reduces interference from redundant\ninformation by establishing structural relationships between the sentence\ncontaining the trigger and other sentences in the document. The second method\nintroduces new prompts to transform the extraction task into a generative task\nsuitable for Large Language Models (LLMs), addressing gaps in EAE performance\nusing LLMs under Supervised Fine-Tuning (SFT) conditions. We also fine-tuned\nmultiple datasets to develop an LLM that performs better across most datasets.\nFinally, we applied insights from CsEAE to LLMs, achieving further performance\nimprovements. This suggests that reliable insights validated on SLMs are also\napplicable to LLMs. We tested our models on the Rams, WikiEvents, and MLEE\ndatasets. The CsEAE model achieved improvements of 2.1\\%, 2.3\\%, and 3.2\\% in\nthe Arg-C F1 metric compared to the baseline, PAIE~\\cite{PAIE}. For LLMs, we\ndemonstrated that their performance on document-level datasets is comparable to\nthat of SLMs~\\footnote{All code is available at\nhttps://github.com/simon-p-j-r/CsEAE}.",
        "split": "arXiv"
    },
    {
        "text": "Let $M$ be a compact complex $n$-manifold. A Gauduchon metric is a Hermitian\nmetric whose fundamental 2-form $\\omega$ satisfies the equation\n$dd^c(\\omega^{n-1})=0$. Paul Gauduchon has proven that any Hermitian metric is\nconformally equivalent to a Gauduchon metric, which is unique (up to a constant\nmultiplier) in its conformal class. Then $d^c(\\omega^{n-1})$ is a closed\n$(2n-1)$-form; the set of cohomology classes of all such forms, called the\nLee-Gauduchon cone, is a convex cone, superficially similar to the Kahler cone.\nWe prove that the Lee-Gauduchon cone is a bimeromorphic invariant, and compute\nit for several classes of non-Kahler manifolds.",
        "split": "arXiv"
    },
    {
        "text": "Background: Screening trials require large sample sizes and long\ntime-horizons to demonstrate mortality reductions. We recently proposed\nincreasing statistical power by testing stored control-arm specimens, called\nthe Intended Effect (IE) design. To evaluate feasibility of the IE design, the\nUS National Cancer Institute (NCI) is collecting blood specimens in the\ncontrol-arm of the NCI Vanguard Multicancer Detection pilot feasibility trial.\nHowever, key assumptions of the IE design require more investigation and\nrelaxation. Methods: We relax the IE design to (1) reduce costs by testing only\na stratified sample of control-arm specimens by incorporating\ninverse-probability sampling weights, (2) correct for potential loss-of-signal\nin stored control-arm specimens, and (3) correct for non-compliance with\ncontrol-arm specimen collections. We also examine sensitivity to unintended\neffects of screening. Results: In simulations, testing all primary-outcome\ncontrol-arm specimens and a 50% sample of the rest maintains nearly all the\npower of the IE while only testing half the control-arm specimens. Power\nremains increased from the IE analysis (versus the standard analysis) even if\nunintended effects exist. The IE design is robust to some loss-of-signal\nscenarios, but otherwise requires retest-positive fractions that correct bias\nat a small loss of power. The IE can be biased and lose power under control-arm\nnon-compliance scenarios, but corrections correct bias and can increase power.\nConclusions: The IE design can be made more cost-efficient and robust to\nloss-of-signal. Unintended effects will not typically reduce the power gain\nover the standard trial design. Non-compliance with control-arm specimen\ncollections can cause bias and loss of power that can be mitigated by\ncorrections. Although promising, practical experience with the IE design in\nscreening trials is necessary.",
        "split": "arXiv"
    },
    {
        "text": "Game solving is the process of finding the theoretical outcome for a game,\nassuming that all player choices are optimal. This paper focuses on a technique\nthat can reduce the heuristic search space significantly for 7x7 Killall-Go. In\nGo and Killall-Go, live patterns are stones that are protected from opponent\ncapture. Mutual life, also referred to as seki, is when both players' stones\nachieve life by sharing liberties with their opponent. Whichever player\nattempts to capture the opponent first will leave their own stones vulnerable.\nTherefore, it is critical to recognize seki patterns to avoid putting oneself\nin jeopardy. Recognizing seki can reduce the search depth significantly. In\nthis paper, we enumerate all seki patterns up to a predetermined area size,\nthen store these patterns into a seki table. This allows us to recognize seki\nduring search, which significantly improves solving efficiency for the game of\nKillall-Go. Experiments show that a day-long, unsolvable position can be solved\nin 482 seconds with the addition of a seki table. For general positions, a 10%\nto 20% improvement in wall clock time and node count is observed.",
        "split": "arXiv"
    },
    {
        "text": "In this work, we present a novel Koopman spectrum-based reachability\nverification method for nonlinear systems. Contrary to conventional methods\nthat focus on characterizing all potential states of a dynamical system over a\npresupposed time span, our approach seeks to verify the reachability by\nassessing the non-emptiness of estimated time-to-reach intervals without\nengaging in the explicit computation of reachable set. Based on the spectral\nanalysis of the Koopman operator, we reformulate the problem of verifying\nexistence of reachable trajectories into the problem of determining feasible\ntime-to-reach bounds required for system reachability. By solving linear\nprogramming (LP) problems, our algorithm can effectively estimate all potential\ntime intervals during which a dynamical system can enter (and exit) target sets\nfrom given initial sets over an unbounded time horizon. Finally, we demonstrate\nour method in challenging settings, such as verifying the reachability between\nnon-convex or even disconnected sets, as well as backward reachability and\nmultiple entries into target sets. Additionally, we validate its applicability\nin addressing real-world challenges and scalability to high-dimensional systems\nthrough case studies in verifying the reachability of the cart-pole and\nmulti-agent consensus systems.",
        "split": "arXiv"
    },
    {
        "text": "This study explores the effectiveness of Large Language Models in meal\nplanning, focusing on their ability to identify and decompose compound\ningredients. We evaluated three models-GPT-4o, Llama-3 (70b), and Mixtral\n(8x7b)-to assess their proficiency in recognizing and breaking down complex\ningredient combinations. Preliminary results indicate that while Llama-3 (70b)\nand GPT-4o excels in accurate decomposition, all models encounter difficulties\nwith identifying essential elements like seasonings and oils. Despite strong\noverall performance, variations in accuracy and completeness were observed\nacross models. These findings underscore LLMs' potential to enhance\npersonalized nutrition but highlight the need for further refinement in\ningredient decomposition. Future research should address these limitations to\nimprove nutritional recommendations and health outcomes.",
        "split": "arXiv"
    },
    {
        "text": "Rotation is an important, yet poorly-modelled phenomenon of stellar structure\nand evolution. Accurate estimates of internal rotation rates are therefore\nvaluable for constraining stellar evolution models. We aim to assess the\naccuracy of asteroseismic estimates of internal rotation rates and how these\ndepend on the fundamental stellar parameters. We apply the recently-developed\nmethod called extended-MOLA inversions to infer localised estimates of internal\nrotation rates of synthetic observations of red giants. We search for suitable\nreference stellar models following a grid-based approach, and assess the\nrobustness of the resulting inferences to the choice of reference model. We\nfind that matching the mixed mode pattern between the observation and the\nreference model is an important criterion to select suitable reference models.\nWe propose to i) select a set of reference models based on the correlation\nbetween the observed rotational splittings and the mode-trapping parameter ii)\ncompute rotation rates for all these models iii) use the mean value obtained\nacross the whole set as the estimate of the internal rotation rates. We find\nthat the effect of a near surface perturbation in the synthetic observations on\nthe rotation rates estimated based on the correlation between the observed\nrotational splittings and the mode-trapping parameter is negligible. We\nconclude that when using an ensemble of reference models, constructed based on\nmatching the mixed mode pattern, the input rotation rates can be recovered\nacross a range of fundamental stellar parameters like mass, mixing-length\nparameter and composition. Further, red-giant rotation rates determined in this\nway are also independent of a near surface perturbation of stellar structure.",
        "split": "arXiv"
    },
    {
        "text": "The design and optimization of optical components, such as Bragg gratings,\nare critical for applications in telecommunications, sensing, and photonic\ncircuits. To overcome the limitations of traditional design methods that rely\nheavily on computationally intensive simulations and large datasets, we propose\nan integrated methodology that significantly reduces these burdens while\nmaintaining high accuracy in predicting optical response. First, we employ a\nBayesian optimization technique to strategically select a limited yet\ninformative number of simulation points from the design space, ensuring that\neach contributes maximally to the model's performance. Second, we utilize\nsingular value decomposition to effectively parametrize the entire reflectance\nspectra into a reduced set of coefficients, allowing us to capture all\nsignificant spectral features without losing crucial information. Finally, we\napply XGBoost, a robust machine learning algorithm, to predict the entire\nreflectance spectra from the reduced dataset. The combination of Bayesian\noptimization for data selection, SVD for full-spectrum fitting, and XGBoost for\npredictive modeling provides a powerful, generalizable framework for the design\nof optical components.",
        "split": "arXiv"
    },
    {
        "text": "We review the multivariate holomorphic functional calculus for tuples in a\ncommutative Banach algebra and establish a simple \"na\\\"ive\" extension to\ncommuting tuples in a general Banach algebra. The approach is na\\\"ive in the\nsense that the na\\\"ively defined joint spectrum maybe too big. The advantage of\nthe approach is that the functional calculus then is given by a simple concrete\nformula from which all its continuity properties can easily be derived.\n  We apply this framework to multivariate functions arising as divided\ndifferences of a univariate function. This provides a rich set of examples to\nwhich our na\\\"ive calculus applies. Foremost, we offer a natural and\nstraightforward proof of the Connes-Moscovici Rearrangement Lemma in the\ncontext of the multivariate holomorphic functional calculus. Secondly, we show\nthat the Daletski-Krein type noncommutative Taylor expansion is a natural\nconsequence of our calculus. Also Magnus' Theorem which gives a nonlinear\ndifferential equation for the $\\log$ of the solutions to a linear matrix ODE\nfollows naturally and easily from our calculus. Finally, we collect various\ncombinatorial related formulas.",
        "split": "arXiv"
    },
    {
        "text": "Differential privacy (DP) is a formal notion that restricts the privacy\nleakage of an algorithm when running on sensitive data, in which\nprivacy-utility trade-off is one of the central problems in private data\nanalysis. In this work, we investigate the fundamental limits of differential\nprivacy in online learning algorithms and present evidence that separates three\ntypes of constraints: no DP, pure DP, and approximate DP. We first describe a\nhypothesis class that is online learnable under approximate DP but not online\nlearnable under pure DP under the adaptive adversarial setting. This indicates\nthat approximate DP must be adopted when dealing with adaptive adversaries. We\nthen prove that any private online learner must make an infinite number of\nmistakes for almost all hypothesis classes. This essentially generalizes\nprevious results and shows a strong separation between private and non-private\nsettings since a finite mistake bound is always attainable (as long as the\nclass is online learnable) when there is no privacy requirement.",
        "split": "arXiv"
    },
    {
        "text": "We explore the phenomenology of Weinberg's $Z_2\\times Z_2$ symmetric\nthree-Higgs-doublet potential, allowing for spontaneous violation of CP due to\ncomplex vacuum expectation values. An overview of all possible ways of\nsatisfying the stationary-point conditions is given, with one, two or three\nnon-vanishing vacuum expectation values, together with conditions for CP\nconservation in terms of basis invariants. All possible ways of satisfying the\nconditions for CP conservation are given. Scans of allowed parameter regions\nare given, together with measures of CP violation, in terms of the invariants.\nThe light states identified in an earlier paper are further explored in terms\nof their CP-violating couplings. Loop-induced CP violation in $WWZ$ couplings,\nas well as charge-asymmetric scattering are also commented on.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we revisit the concept of noncommuting common causes; refute\ntwo objections raised against them, the triviality objection and the lack of\ncausal explanatory force; and explore how their existence modifies the EPR\nargument. More specifically, we show that 1) product states screening off all\nquantum correlations do not compromise noncommuting common causal explanations;\n2) noncommuting common causes can satisfy the law of total probability; 3)\nperfect correlations can have indeterministic noncommuting common causes; and,\nas a combination of the above claims, 4) perfect correlations can have\nnoncommuting common causes which are both nontrivial and satisfy the law of\ntotal probability.",
        "split": "arXiv"
    },
    {
        "text": "Given a symmetric monoidal stable $\\infty$-category $\\mathcal{C}$ which is\nrigidly-compactly generated and a set of compact objects $\\mathcal{K}$ of\n$\\mathcal{C}$, one can form the subcategories of $\\mathcal{K}$-complete and\n$\\mathcal{K}$-local objects. The goal of this paper is to explain how to\nrecover $\\mathcal{C}$ from its $\\mathcal{K}$-local and $\\mathcal{K}$-complete\nsubcategories while retaining the symmetric monoidal structure. Specializing to\nthe case where $\\mathcal{C}$ is the $\\infty$-category of $G$-spectra for a\nfinite group $G$, our result can be viewed as a symmetric monoidal variant of\nthe isotropy separation decomposition, a version of which appeared previously\nin work of Krause.",
        "split": "arXiv"
    },
    {
        "text": "We analyze the universality and generalization of graph neural networks\n(GNNs) on attributed graphs, i.e., with node attributes. To this end, we\npropose pseudometrics over the space of all attributed graphs that describe the\nfine-grained expressivity of GNNs. Namely, GNNs are both Lipschitz continuous\nwith respect to our pseudometrics and can separate attributed graphs that are\ndistant in the metric. Moreover, we prove that the space of all attributed\ngraphs is relatively compact with respect to our metrics. Based on these\nproperties, we prove a universal approximation theorem for GNNs and\ngeneralization bounds for GNNs on any data distribution of attributed graphs.\nThe proposed metrics compute the similarity between the structures of\nattributed graphs via a hierarchical optimal transport between computation\ntrees. Our work extends and unites previous approaches which either derived\ntheory only for graphs with no attributes, derived compact metrics under which\nGNNs are continuous but without separation power, or derived metrics under\nwhich GNNs are continuous and separate points but the space of graphs is not\nrelatively compact, which prevents universal approximation and generalization\nanalysis.",
        "split": "arXiv"
    },
    {
        "text": "In the acquisition of Magnetic Resonance (MR) images shorter scan times lead\nto higher image noise. Therefore, automatic image denoising using deep learning\nmethods is of high interest. MR images containing line-like structures such as\nroots or vessels yield special characteristics as they display connected\nstructures and yield sparse information. For this kind of data, it is important\nto consider voxel neighborhoods when training a denoising network. In this\npaper, we translate the Perceptual Loss to 3D data by comparing feature maps of\nuntrained networks in the loss function as done previously for 2D data. We\ntested the performance of untrained Perceptual Loss (uPL) on 3D image denoising\nof MR images displaying brain vessels (MR angiograms - MRA) and images of plant\nroots in soil. We investigate the impact of various uPL characteristics such as\nweight initialization, network depth, kernel size, and pooling operations on\nthe results. We tested the performance of the uPL loss on four Rician noise\nlevels using evaluation metrics such as the Structural Similarity Index Metric\n(SSIM). We observe, that our uPL outperforms conventional loss functions such\nas the L1 loss or a loss based on the Structural Similarity Index Metric\n(SSIM). The uPL network's initialization is not important, while network depth\nand pooling operations impact denoising performance. E.g. for both datasets a\nnetwork with five convolutional layers led to the best performance while a\nnetwork with more layers led to a performance drop. We also find that small uPL\nnetworks led to better or comparable results than using large networks such as\nVGG. We observe superior performance of our loss for both datasets, all noise\nlevels, and three network architectures. In conclusion, for images containing\nline-like structures, uPL is an alternative to other loss functions for 3D\nimage denoising.",
        "split": "arXiv"
    },
    {
        "text": "In the rapidly evolving landscape of cyber security, intelligent chatbots are\ngaining prominence. Artificial Intelligence, Machine Learning, and Natural\nLanguage Processing empower these chatbots to handle user inquiries and deliver\nthreat intelligence. This helps cyber security knowledge readily available to\nboth professionals and the public. Traditional rule-based chatbots often lack\nflexibility and struggle to adapt to user interactions. In contrast, Large\nLanguage Model-based chatbots offer contextually relevant information across\nmultiple domains and adapt to evolving conversational contexts. In this work,\nwe develop IntellBot, an advanced cyber security Chatbot built on top of\ncutting-edge technologies like Large Language Models and Langchain alongside a\nRetrieval-Augmented Generation model to deliver superior capabilities. This\nchatbot gathers information from diverse data sources to create a comprehensive\nknowledge base covering known vulnerabilities, recent cyber attacks, and\nemerging threats. It delivers tailored responses, serving as a primary hub for\ncyber security insights. By providing instant access to relevant information\nand resources, this IntellBot enhances threat intelligence, incident response,\nand overall security posture, saving time and empowering users with knowledge\nof cyber security best practices. Moreover, we analyzed the performance of our\ncopilot using a two-stage evaluation strategy. We achieved BERT score above 0.8\nby indirect approach and a cosine similarity score ranging from 0.8 to 1, which\naffirms the accuracy of our copilot. Additionally, we utilized RAGAS to\nevaluate the RAG model, and all evaluation metrics consistently produced scores\nabove 0.77, highlighting the efficacy of our system.",
        "split": "arXiv"
    },
    {
        "text": "Twists are defects that are used to encode and process quantum information in\ntopological codes like surface and color codes. Color codes can host three\nbasic types of twists viz., charge-permuting, color-permuting and domino\ntwists. In this paper, we study domino twists from the viewpoint of\ncomputation. Specifically, we give a systematic construction for domino twists\nin qubit color codes. We also present protocols for measurement of logical\nqubits. Finally, we show that all Clifford gates can be implemented by braiding\ntwists.",
        "split": "arXiv"
    },
    {
        "text": "In this study, we report a detailed calculation of the static dipole\npolarizabilities for group 12 elements using the finite-field approach combined\nwith the relativistic coupled-cluster method, including single, double, and\nperturbative triple excitations. We examine three types of relativistic effects\non dipole polarizabilities: scalar-relativistic, spin-orbit coupling (SOC), and\nfully relativistic Dirac-Coulomb contributions. The final recommended\npolarizability values, with their associated uncertainties, are $37.95 \\pm\n0.72$ for Zn, $45.68 \\pm 1.16$ for Cd, $34.04 \\pm 0.67$ for Hg, and $27.92 \\pm\n0.24$ for Cn. Our results align closely with the recommended values in the 2018\nTable of static dipole polarizabilities for neutral atoms [Mol. Phys.\n\\textbf{117}, 1200 (2019)], while providing reduced uncertainties for Cd and\nCn. The analysis indicates that scalar-relativistic effects are the dominant\nrelativistic contributions to atomic dipole polarizabilities for these atoms,\nwith SOC effects found to be negligible. Furthermore, we evaluate the influence\nof electron correlation across all relativistic regimes, underscoring its\ncritical role in the precise determination of dipole polarizabilities.",
        "split": "arXiv"
    },
    {
        "text": "A partition is finitary if all its blocks are finite. For a cardinal\n$\\mathfrak{a}$ and a natural number $n$, let $\\mathrm{fin}(\\mathfrak{a})$ and\n$\\mathscr{B}_{n}(\\mathfrak{a})$ be the cardinalities of the set of finite\nsubsets and the set of finitary partitions with exactly $n$ non-singleton\nblocks of a set which is of cardinality $\\mathfrak{a}$, respectively. In this\npaper, we prove in $\\mathsf{ZF}$ (without the axiom of choice) that for all\ninfinite cardinals $\\mathfrak{a}$ and all non-zero natural numbers $n$, \\[\n(2^{\\mathscr{B}_{n}(\\mathfrak{a})})^{\\aleph_0}=2^{\\mathscr{B}_{n}(\\mathfrak{a})}\n\\] and \\[\n2^{\\mathrm{fin}(\\mathfrak{a})^n}=2^{\\mathscr{B}_{2^n-1}(\\mathfrak{a})}. \\] It\nis also proved consistent with $\\mathsf{ZF}$ that there exists an infinite\ncardinal $\\mathfrak{a}$ such that \\[\n2^{\\mathscr{B}_{1}(\\mathfrak{a})}<2^{\\mathscr{B}_{2}(\\mathfrak{a})}<2^{\\mathscr{B}_{3}(\\mathfrak{a})}<\\cdots<2^{\\mathrm{fin}(\\mathrm{fin}(\\mathfrak{a}))}.\n\\]",
        "split": "arXiv"
    },
    {
        "text": "By cross-matching the eclipsing binary catalog from TESS with that from\nLAMOST MRS, semi-detached eclipsing binaries with radial velocities coverage\nspanning more than 0.3 phases were authenticated. The absolute parameters for\nthese systems were determined by simultaneous modeling of light curves and\nradial velocities using the Wilson-Devinney program. Additionally, the secular\norbital variations were further analyzed using O-C curves. Eight semi-detached\neclipsing binaries have been identified. Among them, seven feature primary\nstars situated within the main-sequence band, while their secondaries are all\nin evolved stages. This suggests that these systems likely originated as\ndetached binaries and have undergone a reversal of the mass ratio. However, TIC\n428257299 is an exception where the primary is Roche lobe-filling, and its\nsecondary has experienced mass loss events. Additionally, TIC 8677671 and TIC\n318217844 demonstrate secular cyclical changes of orbital periods.\nSpecifically, for TIC 8677671, the cyclical change could result from magnetic\nactivity or a third body which is likely to be compact, with a mass of at least\n2.97 M$_{\\odot}$.",
        "split": "arXiv"
    },
    {
        "text": "Practical superconducting nanowire single photon detectors (SNSPDs)\ndemonstrate a strong trade-off between detection sensitivity and the reset\ntime. Often, there are wide variations in sensitivity and response times from\nSNSPDs of the same superconducting material. Here, using detailed physical\nmodels, we show that the dirtiness in a superconductor enforces a sensitivity\nand bandwidth trade-off in all practical devices. More importantly, a certain\ndegree of dirtiness is a necessary requirement for achieving single photon\ndetection. Under typical bias conditions close to the transition setpoints, the\nminimum number of photons required to register a voltage pulse decreases by the\ndirtiness parameter (Ioffe-Regel parameter) and the reset time of SNSPD\nincreases by the same dirtiness parameter, thereby giving a constant value for\nthe sensitivity-bandwidth product. The constant is weakly modified by biasing\ncurrent and the temperature. Since dirtiness in the superconducting nanowire is\na physically controllable parameter with an important bearing on the final\nresponse of an SNSPD, this work opens new opportunities to develop SNSPD\ndevices with engineered sensitivity-bandwidth setpoint as dictated by an\napplication.",
        "split": "arXiv"
    },
    {
        "text": "Facial landmark detection is a fundamental problem in computer vision for\nmany downstream applications. This paper introduces a new facial landmark\ndetector based on vision transformers, which consists of two unique designs:\nDual Vision Transformer (D-ViT) and Long Skip Connections (LSC). Based on the\nobservation that the channel dimension of feature maps essentially represents\nthe linear bases of the heatmap space, we propose learning the interconnections\nbetween these linear bases to model the inherent geometric relations among\nlandmarks via Channel-split ViT. We integrate such channel-split ViT into the\nstandard vision transformer (i.e., spatial-split ViT), forming our Dual Vision\nTransformer to constitute the prediction blocks. We also suggest using long\nskip connections to deliver low-level image features to all prediction blocks,\nthereby preventing useful information from being discarded by intermediate\nsupervision. Extensive experiments are conducted to evaluate the performance of\nour proposal on the widely used benchmarks, i.e., WFLW, COFW, and 300W,\ndemonstrating that our model outperforms the previous SOTAs across all three\nbenchmarks.",
        "split": "arXiv"
    },
    {
        "text": "Contemporary machine learning models, such as language models, are powerful,\nbut come with immense resource requirements both at training and inference\ntime. It has been shown that decoder-only language models can be trained to a\ncompetitive state with ternary weights (1.58 bits per weight), facilitating\nefficient inference. Here, we start our exploration with non-transformer model\narchitectures, investigating 1.58-bit training for multi-layer perceptrons and\ngraph neural networks. Then, we explore 1.58-bit training in other\ntransformer-based language models, namely encoder-only and encoder-decoder\nmodels. Our results show that in all of these settings, 1.58-bit training is on\npar with or sometimes even better than the standard 32/16-bit models.",
        "split": "arXiv"
    },
    {
        "text": "Let $E$ be a subset in $\\mathbb{F}_p^2$ and $S$ be a subset in the special\nlinear group $SL_2(\\mathbb{F}_p)$ or the $1$-dimensional Heisenberg linear\ngroup $\\mathbb{H}_1(\\mathbb{F}_p)$. We define $S(E):= \\bigcup_{\\theta \\in S}\n\\theta (E)$. In this paper, we provide optimal conditions on $S$ and $E$ such\nthat the set $S(E)$ covers a positive proportion of all elements in the plane\n$\\mathbb{F}_p$. When the sizes of $S$ and $E$ are small, we prove structural\ntheorems that guarantee that $|S(E)|\\gg |E|^{1+\\epsilon}$ for some\n$\\epsilon>0$. The main ingredients in our proofs are novel results on algebraic\nincidence-type structures associated with the groups, in which energy estimates\nplay a crucial role. The higher-dimensional version will also be discussed in\nthis paper.",
        "split": "arXiv"
    },
    {
        "text": "Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized\nhuman-machine interactions by seamlessly integrating various forms of data.\nDeveloping a universal spoken language model that comprehends a wide range of\nnatural language instructions is critical for bridging communication gaps and\nfacilitating more intuitive interactions. However, the absence of a\ncomprehensive evaluation benchmark poses a significant challenge. We present\nDynamic-SUPERB Phase-2, an open and evolving benchmark for the comprehensive\nevaluation of instruction-based universal speech models. Building upon the\nfirst generation, this second version incorporates 125 new tasks contributed\ncollaboratively by the global research community, expanding the benchmark to a\ntotal of 180 tasks, making it the largest benchmark for speech and audio\nevaluation. While the first generation of Dynamic-SUPERB was limited to\nclassification tasks, Dynamic-SUPERB Phase-2 broadens its evaluation\ncapabilities by introducing a wide array of novel and diverse tasks, including\nregression and sequence generation, across speech, music, and environmental\naudio. Evaluation results indicate that none of the models performed well\nuniversally. SALMONN-13B excelled in English ASR, while WavLLM demonstrated\nhigh accuracy in emotion recognition, but current models still require further\ninnovations to handle a broader range of tasks. We will soon open-source all\ntask data and the evaluation pipeline.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we define double almost-Riordan arrays and find that the set\nof all double almost-Riordan arrays forms a group, called the double\nalmost-Riordan group. We also obtain the sequence characteristics of double\nalmost-Riordan arrays and give the production matrices for double\nalmost-Riordan arrays. We define the compression of double almost-Riordan\narrays and present their sequence characterization. Finally we give a\ncharacteristic for the total positivity of double Riordan arrays, by using\nwhich we discuss the total positivity for several double almost-Riordan arrays.",
        "split": "arXiv"
    },
    {
        "text": "We establish global well-posedness for both the defocusing and focusing\ncomplex-valued modified Korteweg--de Vries equations on the real line in\nmodulation spaces $M_p^{s,2}(\\mathbb{R})$, for all $1\\leq p<\\infty$ and $0\\leq\ns<3/2-1/p$. We will also show that such solutions admit global-in-time bounds\nin these spaces and that equicontinuous sets of initial data lead to\nequicontinuous ensembles of orbits. Indeed, such information forms a crucial\npart of our well-posedness argument.",
        "split": "arXiv"
    },
    {
        "text": "In streaming media services, video transcoding is a common practice to\nalleviate bandwidth demands. Unfortunately, traditional methods employing a\nuniform rate factor (RF) across all videos often result in significant\ninefficiencies. Content-adaptive encoding (CAE) techniques address this by\ndynamically adjusting encoding parameters based on video content\ncharacteristics. However, existing CAE methods are often tightly coupled with\nspecific encoding strategies, leading to inflexibility. In this paper, we\npropose a model that predicts both RF-quality and RF-bitrate curves, which can\nbe utilized to derive a comprehensive bitrate-quality curve. This approach\nfacilitates flexible adjustments to the encoding strategy without necessitating\nmodel retraining. The model leverages codec features, content features, and\nanchor features to predict the bitrate-quality curve accurately. Additionally,\nwe introduce an anchor suspension method to enhance prediction accuracy.\nExperiments confirm that the actual quality metric (VMAF) of the compressed\nvideo stays within 1 of the target, achieving an accuracy of 99.14%. By\nincorporating our quality improvement strategy with the rate-quality curve\nprediction model, we conducted online A/B tests, obtaining both +0.107%\nimprovements in video views and video completions and +0.064% app duration\ntime. Our model has been deployed on the Xiaohongshu App.",
        "split": "arXiv"
    },
    {
        "text": "During intracranial aneurysm (IA) treatment with Diverters (FDs), the\ndevice/parent artery diameters ratio may influence the ability of the device to\ninduce aneurysm healing response. Oversized FDs are safer to deploy but may not\ninduce enough hemodynamic resistance to ensure aneurysm occlusion. Methods\nbased on Computational Fluid Dynamics (CFD) could allow optimal device\nselection but are time-consuming and inadequate for intra-operative guidance.\nTo address this limitation, we propose to investigate a method for optimal FD\nselection using Angiographic Parametric Imaging (API) and machine learning\n(ML). We selected 128 pre-treatment angiographic sequences of IAs which\ndemonstrated full occlusion at six months follow-up. For each IA, we extracted\nfive API parameters from the aneurysm dome and normalized them to the feeding\nartery corresponding parameters. We dichotomized the dataset based on the FD/\nproximal artery diameter ratio as undersized, if the ratio<1 or if multiple FDs\nwere used and oversized otherwise. Single API parameter and ML analysis were\nused to determine whether API parameters could be used to determine the need\nfor FD under-sizing (i.e., increased flow resistance). Classification accuracy\nwas assessed using area under the receiver operator characteristic (AUROC). In\ntotal we identified 51 and 77 cases for the undersized and oversized cohorts\nrespectively. Single API parameter analysis yielded an inadequate AUROC ~0.5\nwhile machine learning using all five API parameters yielded and AUROC of 0.72.",
        "split": "arXiv"
    },
    {
        "text": "We present Fox-1, a series of small language models (SLMs) consisting of\nFox-1-1.6B and Fox-1-1.6B-Instruct-v0.1. These models are pre-trained on 3\ntrillion tokens of web-scraped document data and fine-tuned with 5 billion\ntokens of instruction-following and multi-turn conversation data. Aiming to\nimprove the pre-training efficiency, Fox-1-1.6B model introduces a novel\n3-stage data curriculum across all the training data with 2K-8K sequence\nlength. In architecture design, Fox-1 features a deeper layer structure, an\nexpanded vocabulary, and utilizes Grouped Query Attention (GQA), offering a\nperformant and efficient architecture compared to other SLMs. Fox-1 achieves\nbetter or on-par performance in various benchmarks compared to StableLM-2-1.6B,\nGemma-2B, Qwen1.5-1.8B, and OpenELM1.1B, with competitive inference speed and\nthroughput. The model weights have been released under the Apache 2.0 license,\nwhere we aim to promote the democratization of LLMs and make them fully\naccessible to the whole open-source community.",
        "split": "arXiv"
    },
    {
        "text": "Distractions in mixed reality (MR) environments can significantly influence\nuser experience, affecting key factors such as presence, reaction time,\ncognitive load, and Break in Presence (BIP). Presence measures immersion,\nreaction time captures user responsiveness, cognitive load reflects mental\neffort, and BIP represents moments when attention shifts from the virtual to\nthe real world, breaking immersion. However, the effects of distractions on\nthese elements remain insufficiently explored. To address this gap, we have\npresented a theoretical model to understand how congruent and incongruent\ndistractions affect all these constructs. We conducted a within-subject study\n(N=54) where participants performed image-sorting tasks under different\ndistraction conditions. Our findings show that incongruent distractions\nsignificantly increase cognitive load, slow reaction times, and elevate BIP\nfrequency, with presence mediating these effects.",
        "split": "arXiv"
    },
    {
        "text": "We study the eigenfunctions of the classical Liouville operator and\ninvestigate the conditions they must obey to be separable as a product state.\nWe point out that the conditions for separability are equivalent to\nrequirements of Liouville's integrability theorem, this is, the eigenfunctions\nare separable if and only if the system is integrable. On the other hand, if\nthe classical system is not integrable, then the eigenfunctions are entangled\nin all canonical coordinates. This results in a link between the classical\nnotions of chaos and integrability with mathematical concepts that are usually\nrestricted to quantum mechanics.",
        "split": "arXiv"
    },
    {
        "text": "We associate a sequence of positive integers, termed the type sequence, with\na cochordal graph. Using this type sequence, we compute all graded Betti\nnumbers of its edge ideal. We then classify all positive integer $n$ such that\nthe zero divisor graph of $\\mathbb{Z}/n \\mathbb{Z}$ is cochordal and determine\nall the graded Betti numbers of its edge ideal.",
        "split": "arXiv"
    },
    {
        "text": "The Pettitt test has been widely used in climate change and hydrological\nanalyzes. However, studies evidence difficulties of this test in detecting\nchange points, especially in small samples. This study presents a bootstrap\napplication of the Pettitt test, which is numerically compared with the\nclassical Pettitt test by an extensive Monte Carlo simulation study. The\nproposed test outperforms the classical test in all simulated scenarios. An\napplication of the tests is conducted in the historical series of naturalized\nflows of the Itaipu Hydroelectric plant in Brazil, where several studies have\nshown a change point in the 70s. When the series is split into shorter series,\nto simulate small sample actual situations, the proposed test is more powerful\nthan the classical Pettitt test to detect the change point. The proposed test\ncan be an important tool to detect abrupt changes in water availability,\nsupporting hydroclimatological resources decision making.",
        "split": "arXiv"
    },
    {
        "text": "Out-of-Time-Order Correlation function measures transport properties of\ndynamical systems. They are ubiquitously used to measure quantum mechanical\nquantities, such as scrambling times, criticality in phase transitions, and\ndetect onset of thermalisation. We characterise the computational complexity of\nestimating OTOCs over all eigenstates and show it is Complete for the One Clean\nQubit model (DQC1). We then generalise our setup to establish DQC1-Completeness\nof N-time Correlation functions over all eigenstates. Building on previous\nresults, the DQC1-Completeness of OTOCs and N-time Correlation functions then\nallows us to highlight a dichotomy between query complexity and circuit\ncomplexity of estimating correlation functions.",
        "split": "arXiv"
    },
    {
        "text": "Context. Star-forming regions are gaining considerable interest in the\nhigh-energy astrophysics community as possible Galactic particle accelerators.\nIn general, the role of electrons has not been fully considered in this kind of\ncosmic-ray source. However, the intense radiation fields inside these regions\nmight make electrons significant gamma-ray contributors. Aims. We study the\nyoung and compact star-forming region NGC 3603, a well known gamma-ray emitter.\nOur intention is to test whether its gamma-ray emission can be produced by\ncosmic-ray electrons. Methods. We build a novel model by creating realistic 3D\ndistributions of the gas and the radiation field in the region. We introduce\nthese models into PICARD to perform cosmic-ray transport simulations and\nproduce gamma-ray emission maps. The results are compared with a dedicated\nFermi Large Area Telescope data analysis at high energies. We also explore the\nradio and neutrino emissions of the system. Results. We improve the existing\nupper limits of the NGC 3603 gamma-ray source extension. Although the gamma-ray\nspectrum is well reproduced with the injection of CR protons, it requires\nnearly 30\\% acceleration efficiency. In addition, the resulting extension of\nthe simulated hadronic source is in mild tension with the extension data upper\nlimit. The radio data disfavours the lepton-only scenario. Finally, combining\nboth populations, the results are consistent with all observables, although the\nexact contributions are ambiguous.",
        "split": "arXiv"
    },
    {
        "text": "Recent research suggests that the failures of Vision-Language Models (VLMs)\nat visual reasoning often stem from erroneous agreements -- when semantically\ndistinct images are ambiguously encoded by the CLIP image encoder into\nembeddings with high cosine similarity. In this paper, we show that erroneous\nagreements are not always the main culprit, as Multimodal Large Language Models\n(MLLMs) can still extract distinct information from them. For instance, when\ndistinguishing objects on the left vs right in the What'sUp benchmark, the CLIP\nimage embeddings of the left/right pairs have an average cosine similarity\n$>0.99$, and CLIP performs at random chance; but LLaVA-1.5-7B, which uses the\nsame CLIP image encoder, achieves nearly $100\\%$ accuracy. We find that the\nextractable information in CLIP image embeddings is likely obscured by CLIP's\ninadequate vision-language alignment: Its matching score learned by the\ncontrastive objective might not capture all diverse image-text correspondences.\nWe also study the MMVP benchmark, on which prior work has shown that LLaVA-1.5\ncannot distinguish image pairs with high cosine similarity. We observe a\nperformance gain brought by attending more to visual input through an\nalternative decoding algorithm. Further, the accuracy significantly increases\nif the model can take both images as input to emphasize their nuanced\ndifferences. Both findings indicate that LLaVA-1.5 did not utilize extracted\nvisual information sufficiently. In conclusion, our findings suggest that while\nimproving image encoders could benefit VLMs, there is still room to enhance\nmodels with a fixed image encoder by applying better strategies for extracting\nand utilizing visual information.",
        "split": "arXiv"
    },
    {
        "text": "Value-based reinforcement learning (RL) can in principle learn effective\npolicies for a wide range of multi-turn problems, from games to dialogue to\nrobotic control, including via offline RL from static previously collected\ndatasets. However, despite the widespread use of policy gradient methods to\ntrain large language models for single turn tasks (e.g., question answering),\nvalue-based methods for multi-turn RL in an off-policy or offline setting have\nproven particularly challenging to scale to the setting of large language\nmodels. This setting requires effectively leveraging pretraining, scaling to\nlarge architectures with billions of parameters, and training on large\ndatasets, all of which represent major challenges for current value-based RL\nmethods. In this work, we propose a novel offline RL algorithm that addresses\nthese drawbacks, casting Q-learning as a modified supervised fine-tuning (SFT)\nproblem where the probabilities of tokens directly translate to Q-values. In\nthis way we obtain an algorithm that smoothly transitions from maximizing the\nlikelihood of the data during pretraining to learning a near-optimal Q-function\nduring finetuning. Our algorithm has strong theoretical foundations, enjoying\nperformance bounds similar to state-of-the-art Q-learning methods, while in\npractice utilizing an objective that closely resembles SFT. Because of this,\nour approach can enjoy the full benefits of the pretraining of language models,\nwithout the need to reinitialize any weights before RL finetuning, and without\nthe need to initialize new heads for predicting values or advantages.\nEmpirically, we evaluate our method on both pretrained LLMs and VLMs, on a\nvariety of tasks including both natural language dialogue and robotic\nmanipulation and navigation from images.",
        "split": "arXiv"
    },
    {
        "text": "Data assimilation (DA) combines partial observations with a dynamical model\nto improve state estimation. Filter-based DA uses only past and present data\nand is the prerequisite for real-time forecasts. Smoother-based DA exploits\nboth past and future observations. It aims to fill in missing data, provide\nmore accurate estimations, and develop high-quality datasets. However, the\nstandard smoothing procedure requires using all historical state estimations,\nwhich is storage-demanding, especially for high-dimensional systems. This paper\ndevelops an adaptive-lag online smoother for a large class of complex dynamical\nsystems with strong nonlinear and non-Gaussian features, which has important\napplications to many real-world problems. The adaptive lag allows the DA to\nutilize only observations within a nearby window, significantly reducing\ncomputational storage. Online lag adjustment is essential for tackling\nturbulent systems, where temporal autocorrelation varies significantly over\ntime due to intermittency, extreme events, and nonlinearity. Based on the\nuncertainty reduction in the estimated state, an information criterion is\ndeveloped to systematically determine the adaptive lag. Notably, the\nmathematical structure of these systems facilitates the use of closed analytic\nformulae to calculate the online smoother and the adaptive lag, avoiding\nempirical tunings as in ensemble-based DA methods. The adaptive online smoother\nis applied to studying three important scientific problems. First, it helps\ndetect online causal relationships between state variables. Second, its\nadvantage of computational storage is illustrated via Lagrangian DA, a\nhigh-dimensional nonlinear problem. Finally, the adaptive smoother advances\nonline parameter estimation with partial observations, emphasizing the role of\nthe observed extreme events in accelerating convergence.",
        "split": "arXiv"
    },
    {
        "text": "Quantum state change can not occurs instantly, but the speed of quantum\nevolution is limited to an upper bound value, called quantum speed limit (QSL).\nEngineering QSL is an important task for quantum information and computation\nscience and technologies. This paper devotes to engineering QSL and quantum\ncorrelation in simple two-qubit system suffering dephasing via Periodic\nDynamical Decoupling (PDD) method in both Markovian and non-Markovian dynamical\nregimes. The results show that when decoupling pulses are applied to both\nqubits this method removes all undesirable effects of the dephasing process,\ncompletely. Applying the PDD on only one of the qubits also works but with\nlower efficiency. Additionally, ultra-high speedup of the quantum processes\nbecome possible during the pulse application period, for enough large number of\npulses. The results is useful for high speed quantum gate implementation\napplication.",
        "split": "arXiv"
    },
    {
        "text": "Is it possible to comprehensively destroy a piece of quantum information, so\nthat nothing is left behind except the memory of whether one had it at one\npoint? For example, various works, most recently Morimae, Poremba, and Yamakawa\n(TQC 2024), show how to construct a signature scheme with certified deletion\nwhere a user who deletes a signature on m cannot later produce a signature for\nm. However, in all of the existing schemes, even after deletion the user is\nstill able keep irrefutable evidence that m was signed, and thus they do not\nfully capture the spirit of deletion.\n  In this work, we initiate the study of certified deniability in order to\nobtain a more comprehensive notion of deletion. Certified deniability uses a\nsimulation-based security definition, ensuring that any information the user\nhas kept after deletion could have been learned without being given the\ndeleteable object to begin with; meaning that deletion leaves no trace behind!\nWe define and construct two non-interactive primitives that satisfy certified\ndeniability in the quantum random oracle model: signatures and non-interactive\nzero-knowledge arguments (NIZKs). As a consequence, for example, it is not\npossible to delete a signature/NIZK and later provide convincing evidence that\nit used to exist. Notably, our results utilize uniquely quantum phenomena to\nbypass the celebrated result of Pass (CRYPTO, 2003) showing that deniable NIZKs\nare impossible even in the random oracle model.",
        "split": "arXiv"
    },
    {
        "text": "The Gaussian process (GP) is a widely used probabilistic machine learning\nmethod for stochastic function approximation, stochastic modeling, and\nanalyzing real-world measurements of nonlinear processes. Unlike many other\nmachine learning methods, GPs include an implicit characterization of\nuncertainty, making them extremely useful across many areas of science,\ntechnology, and engineering. Traditional implementations of GPs involve\nstationary kernels (also termed covariance functions) that limit their\nflexibility and exact methods for inference that prevent application to data\nsets with more than about ten thousand points. Modern approaches to address\nstationarity assumptions generally fail to accommodate large data sets, while\nall attempts to address scalability focus on approximating the Gaussian\nlikelihood, which can involve subjectivity and lead to inaccuracies. In this\nwork, we explicitly derive an alternative kernel that can discover and encode\nboth sparsity and nonstationarity. We embed the kernel within a fully Bayesian\nGP model and leverage high-performance computing resources to enable the\nanalysis of massive data sets. We demonstrate the favorable performance of our\nnovel kernel relative to existing exact and approximate GP methods across a\nvariety of synthetic data examples. Furthermore, we conduct space-time\nprediction based on more than one million measurements of daily maximum\ntemperature and verify that our results outperform state-of-the-art methods in\nthe Earth sciences. More broadly, having access to exact GPs that use\nultra-scalable, sparsity-discovering, nonstationary kernels allows GP methods\nto truly compete with a wide variety of machine learning methods.",
        "split": "arXiv"
    },
    {
        "text": "We introduce average-distortion sketching for metric spaces. As in\n(worst-case) sketching, these algorithms compress points in a metric space\nwhile approximately recovering pairwise distances. The novelty is studying\naverage-distortion: for any fixed (yet, arbitrary) distribution $\\mu$ over the\nmetric, the sketch should not over-estimate distances, and it should\n(approximately) preserve the average distance with respect to draws from $\\mu$.\nThe notion generalizes average-distortion embeddings into $\\ell_1$ [Rabinovich\n'03, Kush-Nikolov-Tang '21] as well as data-dependent locality-sensitive\nhashing [Andoni-Razenshteyn '15, Andoni-Naor-Nikolov-et-al. '18], which have\nbeen recently studied in the context of nearest neighbor search.\n  $\\bullet$ For all $p \\in [1, \\infty)$ and any $c$ larger than a fixed\nconstant, we give an average-distortion sketch for $([\\Delta]^d, \\ell_p)$ with\napproximation $c$ and bit-complexity $\\text{poly}(cp \\cdot 2^{p/c} \\cdot\n\\log(d\\Delta))$, which is provably impossible in (worst-case) sketching.\n  $\\bullet$ As an application, we improve on the approximation of\nsublinear-time data structures for nearest neighbor search over $\\ell_p$ (for\nlarge $p > 2$). The prior best approximation was $O(p)$\n[Andoni-Naor-Nikolov-et-al. '18, Kush-Nikolov-Tang '21], and we show it can be\nany $c$ larger than a fixed constant (irrespective of $p$) by using\n$n^{\\text{poly}(cp \\cdot 2^{p/c})}$ space.\n  We give some evidence that $2^{\\Omega(p/c)}$ space may be necessary by giving\na lower bound on average-distortion sketches which produce a certain\nprobabilistic certificate of farness (which our sketches crucially rely on).",
        "split": "arXiv"
    },
    {
        "text": "Radiology report generation (RRG) aims to create free-text radiology reports\nfrom clinical imaging. Grounded radiology report generation (GRRG) extends RRG\nby including the localisation of individual findings on the image. Currently,\nthere are no manually annotated chest X-ray (CXR) datasets to train GRRG\nmodels. In this work, we present a dataset called PadChest-GR\n(Grounded-Reporting) derived from PadChest aimed at training GRRG models for\nCXR images. We curate a public bi-lingual dataset of 4,555 CXR studies with\ngrounded reports (3,099 abnormal and 1,456 normal), each containing complete\nlists of sentences describing individual present (positive) and absent\n(negative) findings in English and Spanish. In total, PadChest-GR contains\n7,037 positive and 3,422 negative finding sentences. Every positive finding\nsentence is associated with up to two independent sets of bounding boxes\nlabelled by different readers and has categorical labels for finding type,\nlocations, and progression. To the best of our knowledge, PadChest-GR is the\nfirst manually curated dataset designed to train GRRG models for understanding\nand interpreting radiological images and generated text. By including detailed\nlocalization and comprehensive annotations of all clinically relevant findings,\nit provides a valuable resource for developing and evaluating GRRG models from\nCXR images. PadChest-GR can be downloaded under request from\nhttps://bimcv.cipf.es/bimcv-projects/padchest-gr/",
        "split": "arXiv"
    },
    {
        "text": "In $(2+1)d$ topological quantum field theory, topological entanglement\nentropy (TEE) can be computed using the replica and surgery methods. We\nclassify all bipartitions on a torus and propose a general method for\ncalculating their corresponding TEEs. For each bipartition, the TEEs for\ndifferent ground states are bounded by a topological quantity, termed the\nintrinsic TEE, which depends solely on the number of entanglement interfaces $\n\\pi_{\\partial A}$, $S_{\\text{iTEE}}(A) = - \\pi_{\\partial A} \\ln \\mathcal{D}$\nwith $\\mathcal{D}$ being the total quantum dimension. We derive a modified form\nof strong subadditivity (SSA) for the intrinsic TEE, with the modification\ndepending on the genus $g_X$ of the subregions $X$, $S_{\\text{iTEE}}(A) +\nS_{\\text{iTEE}}(B) - S_{\\text{iTEE}}(A\\cup B) - S_{\\text{iTEE}}(A\\cap B) \\geq\n-2\\ln \\mathcal{D} (g_A + g_B - g_{A\\cup B} - g_{A\\cap B})$. Additionally, we\nshow that SSA for the full TEE holds when the intersection number between torus\nknots of the subregions is not equal to one. When the intersection number is\none, the SSA condition is satisfied if and only if $\\sum_a |\\psi_a|^2 (\\ln\nS_{0a} - \\ln |\\psi_a|) + |S\\psi_a|^2 (\\ln S_{0a} - \\ln |S\\psi_a|) \\geq 2 \\ln\n\\mathcal{D}$, with $S$ being the modular $S$-matrix and $\\psi_a$ being the\nprobability amplitudes. This condition has been verified for unitary modular\ncategories up to rank $11$, while counterexamples have been found in\nnon-pseudo-unitary modular categories, such as the Yang-Lee anyon.",
        "split": "arXiv"
    },
    {
        "text": "The LIGO, Virgo and KAGRA gravitational wave observatories are currently\nundertaking their O4 observing run offering the opportunity to discover new\nelectromagnetic counterparts to gravitational wave events. We examine the\ncapability of the Neil Gehrels Swift Observatory (Swift) to respond to these\ntriggers, primarily binary neutron star mergers, with both the UV/Optical\nTelescope (UVOT) and the X-ray Telescope (XRT). We simulate Swift's response to\na trigger under different strategies using model skymaps, convolving these with\nthe 2MPZ catalogue to produce an ordered list of observing fields, deriving the\ntime taken for Swift to reach the correct field and simulating the instrumental\nresponses to modelled kilonovae and short gamma-ray burst afterglows. We find\nthat UVOT using the $u$ filter with an exposure time of order 120 s is optimal\nfor most follow-up observations and that we are likely to detect counterparts\nin $\\sim6$% of all binary neutron star triggers. We find that the gravitational\nwave 90% error area and measured distance to the trigger allow us to select\noptimal triggers to follow-up. Focussing on sources less than 300 Mpc away or\n500 Mpc if the error area is less than a few hundred square degrees, distances\ngreater than previously assumed, offer the best opportunity for discovery by\nSwift with $\\sim5 - 30$% of triggers having detection probabilities $\\geq 0.5$.\nAt even greater distances, we can further optimise our follow-up by adopting a\nlonger 250 s or 500 s exposure time.",
        "split": "arXiv"
    },
    {
        "text": "The origin of the binary black hole mergers observed by LIGO-Virgo-KAGRA\n(LVK) remains an open question. We calculate the merger rate from primordial\nblack holes (PBHs) within the density spike around supermassive black holes\n(SMBHs) at the center of galaxies. We show that the merger rate within the\nspike is comparable to that within the wider dark matter halo. We also\ncalculate the extreme mass ratio inspiral (EMRI) signal from PBHs hosted within\nthe density spike spiralling into their host SMBHs due to GW emission. We\npredict that LISA may detect $\\sim10^4$ of these EMRIs with signal-to-noise\nratio of 5 within a 4-year observation run, if all dark matter is made up of\nPBHs. Uncertainties in our rates come from the uncertain mass fraction of PBHs\nwithin the dark matter spike, relative to the host central SMBHs, which defines\nthe parameter space LISA can constrain.",
        "split": "arXiv"
    },
    {
        "text": "The large eccentricities of cold Jupiters and the existence of hot Jupiters\nhave long challenged theories of planet formation. A proposed solution to both\nof these puzzles is high-eccentricity migration, in which an initially cold\nJupiter is excited to high eccentricities before being tidally circularized.\nSecular perturbations from an inclined stellar companion are a potential source\nof eccentricity oscillations, a phenomenon known as the Eccentric Kozai-Lidov\n(EKL) mechanism. Previous studies have found that the cold Jupiter eccentricity\ndistribution produced by EKL is inconsistent with observations. However, these\nstudies assumed all planets start on circular orbits. Here, we revisit this\nquestion, considering that an initial period of planet-planet scattering on\n$\\sim$Myr timescales likely places planets on slightly eccentric orbits before\nbeing modulated by EKL on $\\sim$Myr-Gyr timescales. Small initial\neccentricities can have a dramatic effect by enabling EKL to act at lower\ninclinations. We numerically integrate the secular hierarchical three-body\nequations of motion, including general relativity and tides, for populations of\ncold giant planets in stellar binaries with varied initial eccentricity\ndistributions. For populations with modest initial mean eccentricities, the\nsimulated eccentricity distribution produced by EKL is statistically consistent\nwith the observed eccentricities of cold single-planet systems. The lower\neccentricities in a multi-planet control sample suggest that planetary\ncompanions quench stellar EKL. We show that scattering alone is unlikely to\nreproduce the present-day eccentricity distribution. We also show that the\nanisotropic inclination distribution produced by EKL may lead radial velocity\nmeasurements to underestimate giant planet masses.",
        "split": "arXiv"
    },
    {
        "text": "This paper proposes ProEdit - a simple yet effective framework for\nhigh-quality 3D scene editing guided by diffusion distillation in a novel\nprogressive manner. Inspired by the crucial observation that multi-view\ninconsistency in scene editing is rooted in the diffusion model's large\nfeasible output space (FOS), our framework controls the size of FOS and reduces\ninconsistency by decomposing the overall editing task into several subtasks,\nwhich are then executed progressively on the scene. Within this framework, we\ndesign a difficulty-aware subtask decomposition scheduler and an adaptive 3D\nGaussian splatting (3DGS) training strategy, ensuring high quality and\nefficiency in performing each subtask. Extensive evaluation shows that our\nProEdit achieves state-of-the-art results in various scenes and challenging\nediting tasks, all through a simple framework without any expensive or\nsophisticated add-ons like distillation losses, components, or training\nprocedures. Notably, ProEdit also provides a new way to control, preview, and\nselect the \"aggressivity\" of editing operation during the editing process.",
        "split": "arXiv"
    },
    {
        "text": "Recently, breakthroughs in video modeling have allowed for controllable\ncamera trajectories in generated videos. However, these methods cannot be\ndirectly applied to user-provided videos that are not generated by a video\nmodel. In this paper, we present ReCapture, a method for generating new videos\nwith novel camera trajectories from a single user-provided video. Our method\nallows us to re-generate the reference video, with all its existing scene\nmotion, from vastly different angles and with cinematic camera motion. Notably,\nusing our method we can also plausibly hallucinate parts of the scene that were\nnot observable in the reference video. Our method works by (1) generating a\nnoisy anchor video with a new camera trajectory using multiview diffusion\nmodels or depth-based point cloud rendering and then (2) regenerating the\nanchor video into a clean and temporally consistent reangled video using our\nproposed masked video fine-tuning technique.",
        "split": "arXiv"
    },
    {
        "text": "This work presents a modification of the self-attention dynamics proposed by\nGeshkovski et al. (arXiv:2312.10794) to better reflect the practically\nrelevant, causally masked attention used in transformer architectures for\ngenerative AI. This modification translates into an interacting particle system\nthat cannot be interpreted as a mean-field gradient flow. Despite this loss of\nstructure, we significantly strengthen the results of Geshkovski et al.\n(arXiv:2312.10794) in this context: While previous rigorous results focused on\ncases where all three matrices (Key, Query, and Value) were scaled identities,\nwe prove asymptotic convergence to a single cluster for arbitrary key-query\nmatrices and a value matrix equal to the identity. Additionally, we establish a\nconnection to the classical R\\'enyi parking problem from combinatorial geometry\nto make initial theoretical steps towards demonstrating the existence of\nmeta-stable states.",
        "split": "arXiv"
    },
    {
        "text": "The classification of topological phases of matter is a fundamental challenge\nin quantum many-body physics, with applications to quantum technology.\nRecently, this classification has been extended to the setting of Adaptive\nFinite-Depth Local Unitary (AFDLU) circuits which allow global classical\ncommunication. In this setting, the trivial phase is the collection of all\ntopological states that can be prepared via AFDLU. Here, we propose a complete\nclassification of the trivial phase by showing how to prepare all solvable\nanyon theories that admit a gapped boundary via AFDLU, extending recent results\non solvable groups. Our construction includes non-Abelian anyons with\nirrational quantum dimensions, such as Ising anyons, and more general acyclic\nanyons. Specifically, we introduce a sequential gauging procedure, with an\nAFDLU implementation, to produce a string-net ground state in any topological\nphase described by a solvable anyon theory with gapped boundary. In addition,\nwe introduce a sequential ungauging and regauging procedure, with an AFDLU\nimplementation, to apply string operators of arbitrary length for anyons and\nsymmetry twist defects in solvable anyon theories. We apply our procedure to\nthe quantum double of the group $S_3$ and to several examples that are beyond\nsolvable groups, including the doubled Ising theory, the $\\mathbb{Z}_3$\nTambara-Yamagami string-net, and doubled $SU(2)_4$ anyons.",
        "split": "arXiv"
    },
    {
        "text": "We present new advances in achieving exponential quantum speedups for solving\noptimization problems by low-depth quantum algorithms. Specifically, we focus\non families of combinatorial optimization problems that exhibit symmetry and\ncontain planted solutions. We rigorously prove that the 1-step Quantum\nApproximate Optimization Algorithm (QAOA) can achieve a success probability of\n$\\Omega(1/\\sqrt{n})$, and sometimes $\\Omega(1)$, for finding the exact solution\nin many cases. Furthermore, we construct near-symmetric optimization problems\nby randomly sampling the individual clauses of symmetric problems, and prove\nthat the QAOA maintains a strong success probability in this setting even when\nthe symmetry is broken. Finally, we construct various families of\nnear-symmetric Max-SAT problems and benchmark state-of-the-art classical\nsolvers, discovering instances where all known classical algorithms require\nexponential time. Therefore, our results indicate that low-depth QAOA could\nachieve an exponential quantum speedup for optimization problems.",
        "split": "arXiv"
    },
    {
        "text": "Zero-shot coordination (ZSC) is a popular setting for studying the ability of\nreinforcement learning (RL) agents to coordinate with novel partners. Prior ZSC\nformulations assume the $\\textit{problem setting}$ is common knowledge: each\nagent knows the underlying Dec-POMDP, knows others have this knowledge, and so\non ad infinitum. However, this assumption rarely holds in complex real-world\nsettings, which are often difficult to fully and correctly specify. Hence, in\nsettings where this common knowledge assumption is invalid, agents trained\nusing ZSC methods may not be able to coordinate well. To address this\nlimitation, we formulate the $\\textit{noisy zero-shot coordination}$ (NZSC)\nproblem. In NZSC, agents observe different noisy versions of the ground truth\nDec-POMDP, which are assumed to be distributed according to a fixed noise\nmodel. Only the distribution of ground truth Dec-POMDPs and the noise model are\ncommon knowledge. We show that a NZSC problem can be reduced to a ZSC problem\nby designing a meta-Dec-POMDP with an augmented state space consisting of all\nthe ground-truth Dec-POMDPs. For solving NZSC problems, we propose a simple and\nflexible meta-learning method called NZSC training, in which the agents are\ntrained across a distribution of coordination problems - which they only get to\nobserve noisy versions of. We show that with NZSC training, RL agents can be\ntrained to coordinate well with novel partners even when the (exact) problem\nsetting of the coordination is not common knowledge.",
        "split": "arXiv"
    },
    {
        "text": "Let $F$ be a non-archimedean local field of characteristic zero. If $F$ has\neven residual characteristic, we assume $F/\\mathbb{Q}_2$ is unramified. Let $V$\nbe a depth zero, irreducible, nongeneric supercuspidal representation of\n$GSp(4, F)$. We calculate the dimensions of the spaces of Siegel-invariant\nvectors in $V$ of level $\\mathfrak{p}^n$ for all $n\\geq0$.",
        "split": "arXiv"
    },
    {
        "text": "Misinformation is a complex societal issue, and mitigating solutions are\ndifficult to create due to data deficiencies. To address this problem, we have\ncurated the largest collection of (mis)information datasets in the literature,\ntotaling 75. From these, we evaluated the quality of all of the 36 datasets\nthat consist of statements or claims. We assess these datasets to identify\nthose with solid foundations for empirical work and those with flaws that could\nresult in misleading and non-generalizable results, such as insufficient label\nquality, spurious correlations, or political bias. We further provide\nstate-of-the-art baselines on all these datasets, but show that regardless of\nlabel quality, categorical labels may no longer give an accurate evaluation of\ndetection model performance. We discuss alternatives to mitigate this problem.\nOverall, this guide aims to provide a roadmap for obtaining higher quality data\nand conducting more effective evaluations, ultimately improving research in\nmisinformation detection. All datasets and other artifacts are available at\nhttps://misinfo-datasets.complexdatalab.com/.",
        "split": "arXiv"
    },
    {
        "text": "Time-varying inhomogeneities on stellar surfaces constitute one of the\nlargest sources of radial velocity (RV) error for planet detection and\ncharacterization. We show that stellar variations, because they manifest on\ncoherent, rotating surfaces, give rise to changes that are complex but useably\ncompact and coherent in the spectral domain. Methods for disentangling stellar\nsignals in RV measurements benefit from modeling the full domain of spectral\npixels. We simulate spectra of spotted stars using starry and construct a\nsimple spectrum projection space that is sensitive to the orientation and size\nof stellar surface features. Regressing measured RVs in this projection space\nreduces RV scatter by 60-80% while preserving planet shifts. We note that\nstellar surface variability signals do not manifest in spectral changes that\nare purely orthogonal to a Doppler shift or exclusively asymmetric in line\nprofiles; enforcing orthogonality or focusing exclusively on asymmetric\nfeatures will not make use of all the information present in the spectra. We\nconclude with a discussion of existing and possible implementations on real\ndata based on the presented compact, coherent framework for stellar signal\nmitigation.",
        "split": "arXiv"
    },
    {
        "text": "We provide a complete classification of the integrability and\nnonintegrability of the spin-1 bilinear-biquadratic model with a uniaxial\nanisotropic field, which includes the Heisenberg model and the\nAffleck-Kennedy-Lieb-Tasaki model. It is rigorously shown that all systems,\nexcept for the known integrable systems, are nonintegrable, meaning that they\ndo not have nontrivial local conserved quantities. In particular, this result\nguarantees the nonintegrability of the Affleck-Kennedy-Lieb-Tasaki model, which\nis a fundamental assumption for quantum many-body scarring. Furthermore, we\ngive simple necessary conditions for integrability in an extended model of the\nbilinear-biquadratic model with anisotropic interactions. Our result has\naccomplished a breakthrough in nonintegrability proofs by expanding their scope\nto spin-1 systems.",
        "split": "arXiv"
    },
    {
        "text": "There is great interest in fine-tuning frontier large language models (LLMs)\nto inject new information and update existing knowledge. While commercial LLM\nfine-tuning APIs from providers such as OpenAI and Google promise flexible\nadaptation for various applications, the efficacy of fine-tuning remains\nunclear. In this study, we introduce FineTuneBench, an evaluation framework and\ndataset for understanding how well commercial fine-tuning APIs can successfully\nlearn new and updated knowledge. We analyze five frontier LLMs with\ncommercially available fine-tuning APIs, including GPT-4o and Gemini 1.5 Pro,\non their effectiveness in two settings: (1) ingesting novel information, such\nas recent news events and new people profiles, and (2) updating existing\nknowledge, such as updated medical guidelines and code frameworks. Our results\nreveal substantial shortcomings in all the models' abilities to effectively\nlearn new information through fine-tuning, with an average generalization\naccuracy of 37% across all models. When updating existing knowledge, such as\nincorporating medical guideline updates, commercial fine-tuning APIs show even\nmore limited capability (average generalization accuracy of 19%). Overall,\nfine-tuning GPT-4o mini is the most effective for infusing new knowledge and\nupdating knowledge, followed by GPT-3.5 Turbo and GPT-4o. The fine-tuning APIs\nfor Gemini 1.5 Flesh and Gemini 1.5 Pro are unable to learn new knowledge or\nupdate existing knowledge. These findings underscore a major shortcoming in\nusing current commercial fine-tuning services to achieve reliable knowledge\ninfusion in common scenarios. We open source the FineTuneBench dataset at\nhttps://github.com/kevinwu23/StanfordFineTuneBench.",
        "split": "arXiv"
    },
    {
        "text": "Galaxy mergers are a key driver of galaxy formation and evolution, including\nthe triggering of AGN and star formation to a still unknown degree. We thus\ninvestigate the impact of galaxy mergers on star formation and AGN activity\nusing a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields\n(CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, and\nMACS-0416), collectively covering an unmasked area of 189 arcmin$^2$. We\nfocuses on star formation rate (SFR) enhancement, AGN fraction, and AGN excess\nin major merger ($\\mu > 1/4$) close-pair samples, defined by $\\Delta z < 0.3$\nand projected separations $r_p < 100$ kpc, compared to non-merger samples. We\nfind that SFR enhancement occurs only at $r_p < 20$ kpc, with values of $0.25\n\\pm 0.10$ dex and $0.26 \\pm 0.11$ dex above the non-merger medians for $z =\n[4.5, 6.5]$ and $z = [6.5, 8.5]$, respectively. No other statistically\nsignificant enhancements in galaxy sSFR or stellar mass are observed at any\nprojected separation or redshift bin. We also compare our observational results\nwith predictions from the SC-SAM simulation and find no evidence of star\nformation enhancement in the simulations at any separation range. Finally, we\nexamine the AGN fraction and AGN excess, finding that the fraction of AGNs in\nAGN-galaxy pairs, relative to the total AGN population, is\n$3.25^{+1.50}_{-1.06}$ times greater than the fraction of galaxy pairs relative\nto the overall galaxy population at the same redshift. We find that nearly all\nAGNs have a companion within 100 kpc and observe an excess AGN fraction in\nclose-pair samples compared to non-merger samples. This excess is found to be\n$1.26 \\pm 0.06$ and $1.34 \\pm 0.06$ for AGNs identified via the inferred BPT\ndiagram and photometric SED selection, respectively.",
        "split": "arXiv"
    },
    {
        "text": "Using optical technology for current injection and electromagnetic emission\nsimplifies the comparison between materials. Here, we inject current into\nmonolayer graphene and bulk gallium arsenide (GaAs) using two-color quantum\ninterference and detect the emitted electric field by electro-optic sampling.\nWe find the amplitude of emitted terahertz (THz) radiation scales in the same\nway for both materials even though they differ in dimension, band gap, atomic\ncomposition, symmetry and lattice structure. In addition, we observe the same\nmapping of the current direction to the light characteristics. With no\nelectrodes for injection or detection, our approach will allow electron\nscattering timescales to be directly measured. We envisage that it will enable\nexploration of new materials suitable for generating terahertz magnetic fields.",
        "split": "arXiv"
    },
    {
        "text": "This paper investigates the impact of noise in the quantum query model, a\nfundamental framework for quantum algorithms. We focus on the scenario where\nthe oracle is subject to non-unitary (or irreversible) noise, specifically\nunder the \\textit{faulty oracle} model, where the oracle fails with a constant\nprobability and acts as identity. Regev and Schiff (ICALP'08) showed that\nquantum advantage is lost for the search problem under this noise model. Our\nmain result shows that every quantum query algorithm can be made robust in this\nnoise model with a roughly quadratic blow-up in query complexity, thereby\npreserving quantum speedup for all problems where the quantum advantage is\nsuper-cubic. This is the first non-trivial robustification of quantum query\nalgorithms against an oracle that is noisy.",
        "split": "arXiv"
    },
    {
        "text": "Fine-grained alignment between videos and text is challenging due to complex\nspatial and temporal dynamics in videos. Existing video-based Large Multimodal\nModels (LMMs) handle basic conversations but struggle with precise pixel-level\ngrounding in videos. To address this, we introduce VideoGLaMM, a LMM designed\nfor fine-grained pixel-level grounding in videos based on user-provided textual\ninputs. Our design seamlessly connects three key components: a Large Language\nModel, a dual vision encoder that emphasizes both spatial and temporal details,\nand a spatio-temporal decoder for accurate mask generation. This connection is\nfacilitated via tunable V-L and L-V adapters that enable close Vision-Language\n(VL) alignment. The architecture is trained to synchronize both spatial and\ntemporal elements of video content with textual instructions. To enable\nfine-grained grounding, we curate a multimodal dataset featuring detailed\nvisually-grounded conversations using a semiautomatic annotation pipeline,\nresulting in a diverse set of 38k video-QA triplets along with 83k objects and\n671k masks. We evaluate VideoGLaMM on three challenging tasks: Grounded\nConversation Generation, Visual Grounding, and Referring Video Segmentation.\nExperimental results show that our model consistently outperforms existing\napproaches across all three tasks.",
        "split": "arXiv"
    },
    {
        "text": "The generalized hydrodynamics (GHD) equation is the equivalent of the Euler\nequations of hydrodynamics for integrable models. Systems of hyperbolic\nequations such as the Euler equations usually develop shocks and are plagued by\nproblems of uniqueness. We establish for the first time the existence and\nuniqueness of solutions to the full GHD equation and the absence of shocks,\nfrom a large class of initial conditions with bounded occupation function. We\nassume only absolute integrability of the two-body scattering shift. In\napplications to quantum models of fermionic type, this includes all commonly\nused physical initial states, such as locally thermal states and zero-entropy\nstates. We show in particular that differentiable initial conditions give\ndifferentiable solutions at all times and that weak initial conditions such as\nthe Riemann problem have unique weak solutions which preserve entropy. For this\npurpose, we write the GHD equation as a new fixed-point problem (announced in a\ncompanion paper). We show that the fixed point exists, is unique, and is\napproached, under an iterative solution procedure, in the Banach topology on\nfunctions of momenta.",
        "split": "arXiv"
    },
    {
        "text": "In this paper, we study the optimal control for an SEIR model adapted to the\nvaccination strategy of susceptible individuals. There are factors associated\nwith a vaccination campaign that make this strategy not only a public health\nissue but also an economic one. In this case, optimal control is important as\nit minimizes implementation costs. We consider the availability of two vaccines\nwith different efficacy levels, and the control indicates when each vaccine\nshould be used. The optimal strategy specifies in all cases how vaccine\npurchases should be distributed. For similar efficacy values, we perform a\nsensitivity analysis on parameters that depend on the intrinsic characteristics\nof the vaccines. Additionally, we investigate the behavior of the number of\ninfections under the optimal vaccination strategy.",
        "split": "arXiv"
    },
    {
        "text": "In this paper we study flow problems on temporal networks, where edge\ncapacities and travel times change over time. We consider a network with $n$\nnodes and $m$ edges where the capacity and length of each edge is a piecewise\nconstant function, and use $\\mu=\\Omega(m)$ to denote the total number of pieces\nin all of the $2m$ functions. Our goal is to design exact algorithms for\nvarious flow problems that run in time polynomial in the parameter $\\mu$.\nImportantly, the algorithms we design are strongly polynomial, i.e. have no\ndependence on the capacities, flow value, or the time horizon of the flow\nprocess, all of which can be exponentially large relative to the other\nparameters; and return an integral flow when all input parameters are integral.\n  Our main result is an algorithm for checking feasibility of a dynamic\ntransshipment problem on temporal networks -- given multiple sources and sinks\nwith supply and demand values, is it possible to satisfy the desired supplies\nand demands within a given time horizon? We develop a fast ($O(\\mu^3)$ time)\nalgorithm for this feasibility problem when the input network has a certain\ncanonical form, by exploiting the cut structure of the associated time expanded\nnetwork. We then adapt an approach of \\cite{hoppe2000} to show how other flow\nproblems on temporal networks can be reduced to the canonical format.\n  For computing dynamic transshipments on temporal networks, this results in a\n$O(\\mu^7)$ time algorithm, whereas the previous best integral exact algorithm\nruns in time $\\tilde O(\\mu^{19})$. We achieve similar improvements for other\nflow problems on temporal networks.",
        "split": "arXiv"
    },
    {
        "text": "Large language models (LLMs) for code have become indispensable in various\ndomains, including code generation, reasoning tasks and agent systems. While\nopen-access code LLMs are increasingly approaching the performance levels of\nproprietary models, high-quality code LLMs suitable for rigorous scientific\ninvestigation, particularly those with reproducible data processing pipelines\nand transparent training protocols, remain limited. The scarcity is due to\nvarious challenges, including resource constraints, ethical considerations, and\nthe competitive advantages of keeping models advanced. To address the gap, we\nintroduce OpenCoder, a top-tier code LLM that not only achieves performance\ncomparable to leading models but also serves as an \"open cookbook\" for the\nresearch community. Unlike most prior efforts, we release not only model\nweights and inference code, but also the reproducible training data, complete\ndata processing pipeline, rigorous experimental ablation results, and detailed\ntraining protocols for open scientific research. Through this comprehensive\nrelease, we identify the key ingredients for building a top-tier code LLM: (1)\ncode optimized heuristic rules for data cleaning and methods for data\ndeduplication, (2) recall of text corpus related to code and (3) high-quality\nsynthetic data in both annealing and supervised fine-tuning stages. By offering\nthis level of openness, we aim to broaden access to all aspects of a top-tier\ncode LLM, with OpenCoder serving as both a powerful model and an open\nfoundation to accelerate research, and enable reproducible advancements in code\nAI.",
        "split": "arXiv"
    },
    {
        "text": "We study $\\varepsilon$-stability in continuous logic. We first consider\nstability in a model, where we obtain a definability of types result with a\nbetter approximation than that in the literature. We also prove forking\nsymmetry for $\\varepsilon$-stability and briefly discuss finitely satisfiable\ntypes. We then do a short survey of $\\varepsilon$-stability in a theory.\nFinally, we consider the map that takes each formula to its \"degree\" of\nstability in a given theory and show that it is a seminorm. All of this is done\nin the context of a first-order formalism that allows predicates to take values\nin arbitrary compact metric spaces.",
        "split": "arXiv"
    },
    {
        "text": "Cosmological parameters and dark energy (DE) behavior are generally\nconstrained assuming \\textit{a priori} models. We work out a model-independent\nreconstruction to bound the key cosmological quantities and the DE evolution.\nThrough the model-independent \\textit{B\\'ezier interpolation} method, we\nreconstruct the Hubble rate from the observational Hubble data and derive\nanalytic expressions for the distances of galaxy clusters, type Ia supernovae,\nand uncorrelated baryonic acoustic oscillation (BAO) data. In view of the\ndiscrepancy between Sloan Digital Sky Survey (SDSS) and Dark Energy\nSpectroscopic Instrument (DESI) BAO data, they are kept separate in two\ndistinct analyses. Correlated BAO data are employed to break the baryonic--dark\nmatter degeneracy. All these interpolations enable us to single out and\nreconstruct the DE behavior with the redshift $z$ in a totally\nmodel-independent way. In both analyses, with SDSS-BAO or DESI-BAO data sets,\nthe constraints agree at $1$--$\\sigma$ confidence level (CL) with the flat\n$\\Lambda$CDM model. The Hubble constant tension appears solved in favor of the\nPlanck satellite value. The reconstructed DE behavior exhibits deviations at\nsmall $z$ ($>1$--$\\sigma$ CL), but agrees ($<1$--$\\sigma$ CL) with the\ncosmological constant paradigm at larger $z$. Our method hints for a slowly\nevolving DE, consistent with a cosmological constant at early times.",
        "split": "arXiv"
    },
    {
        "text": "We consider quantum circuit models where the gates are drawn from arbitrary\ngate ensembles given by probabilistic distributions over certain gate sets and\ncircuit architectures, which we call stochastic quantum circuits. Of main\ninterest in this work is the speed of convergence of stochastic circuits with\ndifferent gate ensembles and circuit architectures to unitary t-designs. A key\nmotivation for this theory is the varying preference for different gates and\ncircuit architectures in different practical scenarios. In particular, it\nprovides a versatile framework for devising efficient circuits for implementing\n$t$-designs and relevant applications including random circuit and scrambling\nexperiments, as well as benchmarking the performance of gates and circuit\narchitectures. We examine various important settings in depth. A key aspect of\nour study is an \"ironed gadget\" model, which allows us to systematically\nevaluate and compare the convergence efficiency of entangling gates and circuit\narchitectures. Particularly notable results include i) gadgets of two-qubit\ngates with KAK coefficients\n$\\left(\\frac{\\pi}{4}-\\frac{1}{8}\\arccos(\\frac{1}{5}),\\frac{\\pi}{8},\\frac{1}{8}\\arccos(\\frac{1}{5})\\right)$\n(which we call $\\chi$ gates) directly form exact 2- and 3-designs; ii) the\niSWAP gate family achieves the best efficiency for convergence to 2-designs\nunder mild conjectures with numerical evidence, even outperforming the\nHaar-random gate, for generic many-body circuits; iii) iSWAP + complete graph\nachieve the best efficiency for convergence to 2-designs among all graph\ncircuits. A variety of numerical results are provided to complement our\nanalysis. We also derive robustness guarantees for our analysis against gate\nperturbations. Additionally, we provide cursory analysis on gates with higher\nlocality and found that the Margolus gate outperforms various other well-known\ngates.",
        "split": "arXiv"
    },
    {
        "text": "New metal-organic frameworks (MOFs) are periodically synthesized all over the\nworld due to the wide range of societally and environmentally relevant\napplications they possess. However, the mechanisms and thermodynamics\nassociated to MOF self-assembly are poorly understood because of the\ndifficulties in studying such a multi-scale process with molecular-level\nresolution. In this work, we performed well-tempered metadynamics simulations\nof the early nucleation and late growth steps of the self-assembly of ZIF-4\nusing a reactive force field. We found that the formation of building blocks is\na complex, multi-step process that involves changes in the coordination of the\nmetal ion. Saturating the ligand coordination of a metal ion is more\nenergetically favorable during growth than during early nucleation. The\naddition of a fourth ligand is less exergonic than it is for the first three\nand the associated free energy is highly dependent on the local environment of\nthe undercoordinated metal ion. The stability of this bond depends on the\nstrength of the solvent--metal ion interaction. Incorporating a ligand to a\nZIF-1 crystal is less favorable compared to the more stable ZIF-4 polymorph.\nMilder differences were found when comparing the growth of (100), (010) and\n(001) ZIF-4 surfaces.",
        "split": "arXiv"
    },
    {
        "text": "The $\\sigma_{t}$-irregularity (or sigma total index) is a graph invariant\nwhich is defined as $\\sigma_{t}(G)=\\sum_{\\{u,v\\}\\subseteq\nV(G)}(d(u)-d(v))^{2},$ where $d(z)$ denotes the degree of $z$. This\nirregularity measure was proposed by R\\' {e}ti [Appl. Math. Comput. 344-345\n(2019) 107-115], and recently rediscovered by Dimitrov and Stevanovi\\'c [Appl.\nMath. Comput. 441 (2023) 127709]. In this paper we remark that\n$\\sigma_{t}(G)=n^{2}\\cdot Var(G)$, where $Var(G)$ is the degree variance of the\ngraph. Based on this observation, we characterize irregular graphs with maximum\n$\\sigma_{t}$-irregularity. We show that among all connected graphs on $n$\nvertices, the split graphs $S_{\\lceil\\frac{n}{4}\\rceil,\n\\lfloor\\frac{3n}{4}\\rfloor }$ and $S_{\\lfloor\\frac{n}{4}\\rfloor,\n\\lceil\\frac{3n}{4}\\rceil }$ have the maximum $\\sigma_{t}$-irregularity, and\namong all complete bipartite graphs on $n$ vertices, either the complete\nbipartite graph $K_{\\lfloor\\frac{n}{4}(2-\\sqrt{2})\\rfloor,\n\\lceil\\frac{n}{4}(2+\\sqrt{2})\\rceil }$ or\n$K_{\\lceil\\frac{n}{4}(2-\\sqrt{2})\\rceil, \\lfloor\\frac{n}{4}(2+\\sqrt{2})\\rfloor\n}$ has the maximum sigma total index. Moreover, various upper and lower bounds\nfor $\\sigma_{t}$-irregularity are provided; in this direction we give a\nrelation between the graph energy $\\mathcal{E}(G)$ and sigma total index\n$\\sigma_{t}(G)$ and give another proof of two results by Dimitrov and\nStevanovi\\'c. Applying Fiedler's characterization of the largest and the second\nsmallest Laplacian eigenvalue of the graph, we also establish new relationships\nbetween $\\sigma_{t}$ and $\\sigma$. We conclude the paper with two conjectures.",
        "split": "arXiv"
    },
    {
        "text": "This paper combines a techno-economic energy system model with an econometric\nmodel to maximise electricity price forecasting accuracy. The proposed\ncombination model is tested on the German day-ahead wholesale electricity\nmarket. Our paper also benchmarks the results against several econometric\nalternatives. Lastly, we demonstrate the economic value of improved price\nestimators maximising the revenue from an electric storage resource. The\nresults demonstrate that our integrated model improves overall forecasting\naccuracy by 18 %, compared to available literature benchmarks. Furthermore, our\nrobustness checks reveal that a) the Ensemble Deep Neural Network model\nperforms best in our dataset and b) adding output from the techno-economic\nenergy systems model as econometric model input improves the performance of all\neconometric models. The empirical relevance of the forecast improvement is\nconfirmed by the results of the exemplary storage optimisation, in which the\nintegration of the techno-economic energy system model leads to a revenue\nincrease of up to 10 %.",
        "split": "arXiv"
    },
    {
        "text": "Title: Sentiment Analysis of Spanish Political Party Communications on\nTwitter Using Pre-trained Language Models\n  Authors: Chuqiao Song, Shunzhang Chen, Xinyi Cai, Hao Chen\n  Comments: 21 pages, 6 figures\n  Abstract: This study investigates sentiment patterns within Spanish political\nparty communications on Twitter by leveraging BETO and RoBERTuito, two\npre-trained language models optimized for Spanish text. Using a dataset of\ntweets from major Spanish political parties: PSOE, PP, Vox, Podemos, and\nCiudadanos, spanning 2019 to 2024, this research analyzes sentiment\ndistributions and explores the relationship between sentiment expression and\nparty ideology. The findings indicate that both models consistently identify a\npredominant Neutral sentiment across all parties, with significant variations\nin Negative and Positive sentiments that align with ideological distinctions.\nSpecifically, Vox exhibits higher levels of Negative sentiment, while PSOE\ndemonstrates relatively high Positive sentiment, supporting the hypothesis that\nemotional appeals in political messaging reflect ideological stances. This\nstudy underscores the potential of pre-trained language models for non-English\nsentiment analysis on social media, providing insights into sentiment dynamics\nthat shape public discourse within Spain's multi-party political system.\n  Keywords: Spanish politics, sentiment analysis, pre-trained language models,\nTwitter, BETO, RoBERTuito, political ideology, multi-party system",
        "split": "arXiv"
    },
    {
        "text": "We show that every Born Lie algebra can be obtained by the bicross product\nconstruction starting from two pseudo-Riemannian Lie algebras. We then obtain a\nclassification of all Lie algebras up to dimension four and all six-dimensional\nnilpotent Lie algebras admitting an integrable Born structure. Finally, we\nstudy the curvature properties of the pseudo-Riemannian metrics of the\nintegrable Born structures obtained in our classification results.",
        "split": "arXiv"
    },
    {
        "text": "In many repeated auction settings, participants care not only about how\nfrequently they win but also how their winnings are distributed over time. This\nproblem arises in various practical domains where avoiding congested demand is\ncrucial, such as online retail sales and compute services, as well as in\nadvertising campaigns that require sustained visibility over time. We introduce\na simple model of this phenomenon, modeling it as a budgeted auction where the\nvalue of a win is a concave function of the time since the last win. This\nimplies that for a given number of wins, even spacing over time is optimal. We\nalso extend our model and results to the case when not all wins result in\n\"conversions\" (realization of actual gains), and the probability of conversion\ndepends on a context. The goal is to maximize and evenly space conversions\nrather than just wins.\n  We study the optimal policies for this setting in second-price auctions and\noffer learning algorithms for the bidders that achieve low regret against the\noptimal bidding policy in a Bayesian online setting. Our main result is a\ncomputationally efficient online learning algorithm that achieves $\\tilde\nO(\\sqrt T)$ regret. We achieve this by showing that an infinite-horizon Markov\ndecision process (MDP) with the budget constraint in expectation is essentially\nequivalent to our problem, even when limiting that MDP to a very small number\nof states. The algorithm achieves low regret by learning a bidding policy that\nchooses bids as a function of the context and the system's state, which will be\nthe time elapsed since the last win (or conversion). We show that\nstate-independent strategies incur linear regret even without uncertainty of\nconversions. We complement this by showing that there are state-independent\nstrategies that, while still having linear regret, achieve a $(1-\\frac 1 e)$\napproximation to the optimal reward.",
        "split": "arXiv"
    },
    {
        "text": "We propose the use of the Extended Kalman Filter (EKF) for online data\nassimilation and update of a dynamic model, preliminary identified through the\nSparse Identification of Nonlinear Dynamics (SINDy). This data-driven technique\nmay avoid biases due to incorrect modelling assumptions and exploits SINDy to\napproximate the system dynamics leveraging a predefined library of functions,\nwhere active terms are selected and weighted by a sparse set of coefficients.\nThis results in a physically-sound and interpretable dynamic model allowing to\nreduce epistemic uncertainty often affecting machine learning approaches.\nTreating the SINDy model coefficients as random variables, we propose to update\nthem while acquiring (possibly noisy) system measurements, thus enabling the\nonline identification of time-varying systems. These changes can stem from,\ne.g., varying operational conditions or unforeseen events. The EKF performs\nmodel adaptation through joint state-parameters estimation, with the Jacobian\nmatrices required to computed the model sensitivity inexpensively evaluated\nfrom the SINDy model formulation. The effectiveness of this approach is\ndemonstrated through three case studies: (i) a Lokta-Volterra model in which\nall parameters simultaneously evolve during the observation period; (ii) a\nSelkov model where the system undergoes a bifurcation not seen during the SINDy\ntraining; (iii) a MEMS arch exhibiting a 1:2 internal resonance. The ability of\nEKF of recovering inactivated functional terms from the SINDy library, or\ndiscarding unnecessary contribution, is also highlighted. Based on the\npresented applications, this method shows strong promise for handling\ntime-varying nonlinear dynamic systems possibly experiencing bifurcating\nbehaviours.",
        "split": "arXiv"
    },
    {
        "text": "We study the robust regulation of labour contracts in moral hazard problems.\nA firm offers a contract to incentivise production by an agent protected by\nlimited liability. A regulator chooses the set of permissible contracts to (i)\nimprove efficiency and (ii) protect the worker. The regulator ignores the\nagent's productive actions and the firm's costs and evaluates regulation by its\nworst-case regret. The regret-minimising regulation imposes a linear minimum\nwage, allowing all contracts above this linear threshold. The slope of the\nminimum contract balances the worker's protection - by ensuring they receive a\nminimal share of the production - and the necessary flexibility for incentive\nprovision.",
        "split": "arXiv"
    },
    {
        "text": "The present article is concerned with the nonlinear approximation of\nfunctions in the Sobolev space H^q with respect to a tensor-product, or\nhyperbolic wavelet basis on the unit n-cube. Here, q is a real number, which is\nnot necessarily positive. We derive Jackson and Bernstein inequalities to\nobtain that the approximation classes contain Besov spaces of hybrid\nregularity. Especially, we show that all functions that can be approximated by\nclassical wavelets are also approximable by tensor-product wavelets at least at\nthe same rate. In particular, this implies that for nonnegative regularity, the\nclassical Besov spaces of regularity q+sn, integrability and weak index t, with\n1/t = s + 1/2, are included in the Besov spaces of hybrid regularity with\nisotropic regularity q and additional mixed regularity s.",
        "split": "arXiv"
    },
    {
        "text": "As implemented in the commercialized device modeling software, the four-state\nnonradiative multi-phonon model has attracted intensive attention in the past\ndecade for describing the physics in negative bias temperature instability\n(NBTI) and other reliability issues of Si/SiO$_\\text{2}$ MOSFET devices. It was\nproposed initially based on the assumption that the oxygen vacancy defects\n(V$_\\text{O}$) in SiO$_\\text{2}$ dielectric layer are bistable in the Si-dimer\nand back-projected structures during carrier capture and emission. Through\nhigh-throughput first-principles structural search, we found V$_\\text{O}$ on\nnon-equivalent O sites in amorphous SiO$_\\text{2}$ can take 4 types of\nstructural configurations in neutral state and 7 types of configurations in +1\ncharged state after capturing holes, which produce a wide range of charge-state\ntransition levels for trapping holes. The finding contrasts the\nstructural-bistability assumption and makes the four-state model invalid for\nmost of O sites. To describe the reliability physics accurately, we propose an\nall-state model to consider all these structural configurations as well as all\nthe carrier capture/emission transitions and thermal transitions between them.\nWith the all-state model, we show that the V$_\\text{O}$ defects play important\nroles in causing NBTI, which challenges the recent studies that discarded\nV$_\\text{O}$ as a possible hole trap in NBTI. Our systematical calculations on\nthe diversified V$_\\text{O}$ properties and the all-state model provide the\nmicroscopic foundation for describing the reliability physics of MOSFETs and\nother transistors accurately.",
        "split": "arXiv"
    },
    {
        "text": "The detection of cosmic antideuterons ($\\overline{\\rm D}$) at kinetic\nenergies below a few GeV/n could provide a smoking gun signature for dark\nmatter (DM). However, the theoretical uncertainties of coalescence models have\nrepresented so far one of the main limiting factors for precise predictions of\nthe $\\overline{\\rm D}$ flux. In this Letter we present a novel calculation of\nthe $\\overline{\\rm D}$ source spectra, based on the Wigner formalism, for which\nwe implement the Argonne $v_{18}$ antideuteron wavefunction that does not have\nany free parameters related to the coalescence process. We show that the\nArgonne Wigner model excellently reproduces the $\\overline{\\rm D}$ multiplicity\nmeasured by ALEPH at the $Z$-boson pole, which is usually adopted to tune the\ncoalescence models based on different approaches. Our analysis is based on\nPythia~8 Monte Carlo event generator and the state-of-the-art Vincia shower\nalgorithm. We succeed, with our model, to reduce the current theoretical\nuncertainty on the prediction of the $\\overline{\\rm D}$ source spectra to a few\npercent, for $\\overline{\\rm D}$ kinetic energies relevant to DM searches with\nGAPS and AMS, and for DM masses above a few tens of GeV. This result implies\nthat the theoretical uncertainties due to the coalescence process are no longer\nthe main limiting factor in the predictions. We provide the tabulated source\nspectra for all the relevant DM annihilation/decay channels and DM masses\nbetween 5 GeV and 100 TeV, on the CosmiXs github repository\n(https://github.com/ajueid/CosmiXs.git).",
        "split": "arXiv"
    },
    {
        "text": "Deep regression models are used in a wide variety of safety-critical\napplications, but are vulnerable to backdoor attacks. Although many defenses\nhave been proposed for classification models, they are ineffective as they do\nnot consider the uniqueness of regression models. First, the outputs of\nregression models are continuous values instead of discretized labels. Thus,\nthe potential infected target of a backdoored regression model has infinite\npossibilities, which makes it impossible to be determined by existing defenses.\nSecond, the backdoor behavior of backdoored deep regression models is triggered\nby the activation values of all the neurons in the feature space, which makes\nit difficult to be detected and mitigated using existing defenses. To resolve\nthese problems, we propose DRMGuard, the first defense to identify if a deep\nregression model in the image domain is backdoored or not. DRMGuard formulates\nthe optimization problem for reverse engineering based on the unique\noutput-space and feature-space characteristics of backdoored deep regression\nmodels. We conduct extensive evaluations on two regression tasks and four\ndatasets. The results show that DRMGuard can consistently defend against\nvarious backdoor attacks. We also generalize four state-of-the-art defenses\ndesigned for classifiers to regression models, and compare DRMGuard with them.\nThe results show that DRMGuard significantly outperforms all those defenses.",
        "split": "arXiv"
    },
    {
        "text": "We consider the space of all configurations of finitely many (potentially\nnested) circles in the plane. We prove that this space is aspherical, and\ncompute the fundamental group of each of its connected components. It turns out\nthese fundamental groups are obtained as iterated semdirect products of braid\ngroups, with the structure for each component dictated by a finite rooted tree.\nThese groups can be viewed as \"braided\" versions of the automorphism groups of\nsuch trees. We also discuss connections to statistical mechanics, topological\ndata analysis, and geometric group theory.",
        "split": "arXiv"
    },
    {
        "text": "We present a framework for learning a single policy capable of producing all\nquadruped gaits and transitions. The framework consists of a policy trained\nwith deep reinforcement learning (DRL) to modulate the parameters of a system\nof abstract oscillators (i.e. Central Pattern Generator), whose output is\nmapped to joint commands through a pattern formation layer that sets the gait\nstyle, i.e. body height, swing foot ground clearance height, and foot offset.\nDifferent gaits are formed by changing the coupling between different\noscillators, which can be instantaneously selected at any velocity by a user.\nWith this framework, we systematically investigate which gait should be used at\nwhich velocity, and when gait transitions should occur from a Cost of Transport\n(COT), i.e. energy-efficiency, point of view. Additionally, we note how gait\nstyle changes as a function of locomotion speed for each gait to keep the most\nenergy-efficient locomotion. While the currently most popular gait (trot) does\nnot result in the lowest COT, we find that considering different co-dependent\nmetrics such as mean base velocity and joint acceleration result in different\n`optimal' gaits than those that minimize COT. We deploy our controller in\nvarious hardware experiments, showing all 9 typical quadruped animal gaits, and\ndemonstrate generalizability to unseen gaits during training, and robustness to\nleg failures. Video results can be found at https://youtu.be/OLoWSX_R868.",
        "split": "arXiv"
    },
    {
        "text": "We present a comprehensive first-principles analysis of the thermoelectric\ntransport properties of hole-doped pyrite FeS$_2$ that includes electron-phonon\ninteractions. This work was motivated by the observed variations in the\nmagnitude of thermopower reported in previous experimental and theoretical\nstudies of hole-doped FeS$_2$ systems. Our calculations reveal that hole-doped\nFeS$_2$ exhibits large positive room-temperature thermopower across all doping\nlevels, with a room-temperature thermopower of 608 $\\mu$V/K at a low\nhole-doping concentration of 10$^{19}$ cm$^{-3}$. This promising thermopower\nfinding prompted a comprehensive investigation of other key thermoelectric\nparameters governing the thermoelectric figure of merit $ZT$. The calculated\nelectrical conductivity is modest and remains below 10$^5$ S/m at\nroom-temperature for all doping levels, limiting the achievable power factor.\nFurthermore, the thermal conductivity is found to be phonon driven, with a high\nroom-temperature lattice thermal conductivity of 40.5 W/mK. Consequently, the\ncalculated $ZT$ remains below 0.1, suggesting that hole-doped FeS$_2$ may not a\nviable candidate for effective thermoelectric applications despite its\npromising thermopower.",
        "split": "arXiv"
    },
    {
        "text": "The advantage of quantum protocols lies in the inherent properties of the\nshared quantum states. These states are sometimes provided by sources that are\nnot trusted, and therefore need to be verified. Finding secure and efficient\nquantum state verification protocols remains a big challenge, and recent works\nillustrate trade-offs between efficiency and security for different groups of\nstates in restricted settings. However, whether a universal trade-off exists\nfor all quantum states and all verification strategies remains unknown. In this\nwork, we instantiate the categorical composable cryptography framework to show\na fundamental limit for quantum state verification for all cut-and-choose\napproaches used to verify arbitrary quantum states. Our findings show that the\nprevailing cut-and-choose techniques cannot lead to quantum state verification\nprotocols that are both efficient and secure.",
        "split": "arXiv"
    },
    {
        "text": "Let $n$ be a positive integer. The Diophantine equation $n(x_1+x_2+\\dots\n+x_n)=x_1x_2\\dots x_n$, $1 \\le x_1\\le x_2\\le \\dots \\le x_n$ is called\nErd\\H{o}s's last equation. We prove that $x_n\\to \\infty $ as $n\\to \\infty$ and\ndetermine all tuples $(n,x_1,\\dots ,x_n)$ with $x_n\\le 10$.",
        "split": "arXiv"
    },
    {
        "text": "Surveys are an indispensable source of data for applied economic research;\nhowever, their reliance on self-reported information can introduce bias,\nespecially if core variables such as personal income are misreported. To assess\nthe extent and impact of this misreporting bias, we compare self-reported wages\nfrom the German Socio-Economic Panel (SOEP) with administrative wages from\nsocial security records (IEB) for the same individuals. Using a novel and\nunique data linkage (SOEP-ADIAB), we identify a modest but economically\nsignificant reporting bias, with SOEP respondents underreporting their\nadministrative wages by about 7.3%. This misreporting varies systematically\nwith individual, household, and especially job and firm characteristics. In\nreplicating common empirical analyses in which wages serve as either dependent\nor independent variables, we find that misreporting is consequential for some,\nbut not all estimated relationships. It turns out to be inconsequential for\nexamining the returns to education, but relevant for analyzing the gender wage\ngap. In addition we find that misreporting bias can significantly affect the\nresults when wage is used as the independent variable. Specifically, estimates\nof the wage-satisfaction relationship are substantially overestimated when\nbased on survey data, although this bias is mitigated when focusing on\ninterpersonal changes. Our findings underscore that survey-based measures of\nindividual wages can significantly bias commonly estimated empirical\nrelationships. They also demonstrate the enormous research potential of linked\nadministrative-survey data.",
        "split": "arXiv"
    },
    {
        "text": "Cancer is the second leading cause of death, with chemotherapy as one of the\nprimary forms of treatment. As a result, researchers are turning to drug\ncombination therapy to decrease drug resistance and increase efficacy. Current\nmethods of drug combination screening, such as in vivo and in vitro, are\ninefficient due to stark time and monetary costs. In silico methods have become\nincreasingly important for screening drugs, but current methods are inaccurate\nand generalize poorly to unseen anticancer drugs. In this paper, I employ a\ngeometric deep-learning model utilizing a graph attention network that is\nequivariant to 3D rotations, translations, and reflections with structural\nmotifs. Additionally, the gene expression of cancer cell lines is utilized to\nclassify synergistic drug combinations specific to each cell line. I compared\nthe proposed geometric deep learning framework to current state-of-the-art\n(SOTA) methods, and the proposed model architecture achieved greater\nperformance on all 12 benchmark tasks performed on the DrugComb dataset.\nSpecifically, the proposed framework outperformed other SOTA methods by an\naccuracy difference greater than 28%. Based on these results, I believe that\nthe equivariant graph attention network's capability of learning geometric data\naccounts for the large performance improvements. The model's ability to\ngeneralize to foreign drugs is thought to be due to the structural motifs\nproviding a better representation of the molecule. Overall, I believe that the\nproposed equivariant geometric deep learning framework serves as an effective\ntool for virtually screening anticancer drug combinations for further\nvalidation in a wet lab environment. The code for this work is made available\nonline at: https://github.com/WeToTheMoon/EGAT_DrugSynergy.",
        "split": "arXiv"
    },
    {
        "text": "We consider the problem of counting the copies of a length-$k$ pattern\n$\\sigma$ in a sequence $f \\colon [n] \\to \\mathbb{R}$, where a copy is a subset\nof indices $i_1 < \\ldots < i_k \\in [n]$ such that $f(i_j) < f(i_\\ell)$ if and\nonly if $\\sigma(j) < \\sigma(\\ell)$. This problem is motivated by a range of\nconnections and applications in ranking, nonparametric statistics,\ncombinatorics, and fine-grained complexity, especially when $k$ is a small\nfixed constant.\n  Recent advances have significantly improved our understanding of counting and\ndetecting patterns. Guillemot and Marx [2014] demonstrated that the detection\nvariant is solvable in $O(n)$ time for any fixed $k$. Their proof has laid the\nfoundations for the discovery of the twin-width, a concept that has notably\nadvanced parameterized complexity in recent years. Counting, in contrast, is\nharder: it has a conditional lower bound of $n^{\\Omega(k / \\log k)}$\n[Berendsohn, Kozma, and Marx 2019] and is expected to be polynomially harder\nthan detection as early as $k = 4$, given its equivalence to counting\n$4$-cycles in graphs [Dudek and Gawrychowski, 2020].\n  In this work, we design a deterministic near-linear time\n$(1+\\varepsilon)$-approximation algorithm for counting $\\sigma$-copies in $f$\nfor all $k \\leq 5$. Combined with the conditional lower bound for $k=4$, this\nestablishes the first known separation between approximate and exact algorithms\nfor pattern counting. Interestingly, our algorithm leverages the Birg\\'e\ndecomposition -- a sublinear tool for monotone distributions widely used in\ndistribution testing -- which, to our knowledge, has not been applied in a\npattern counting context before.",
        "split": "arXiv"
    },
    {
        "text": "Following the milestones in large language models (LLMs) and multimodal\nmodels, we have seen a surge in applying LLMs to biochemical tasks. Leveraging\ngraph features and molecular text representations, LLMs can tackle various\ntasks, such as predicting chemical reaction outcomes and describing molecular\nproperties. However, most current work overlooks the multi-level nature of\ngraph features. The impact of different feature levels on LLMs and the\nimportance of each level remain unexplored, and it is possible that different\nchemistry tasks require different feature levels. In this work, we first\ninvestigate the effect of feature granularity by fusing GNN-generated feature\ntokens, discovering that even reducing all tokens to a single token does not\nsignificantly impact performance. We then explore the effect of various feature\nlevels on performance, finding that both the quality of LLM-generated molecules\nand performance on different tasks benefit from different feature levels. We\nconclude with two key insights: (1) current molecular Multimodal LLMs(MLLMs)\nlack a comprehensive understanding of graph features, and (2) static processing\nis not sufficient for hierarchical graph feature. Our code will be publicly\navailable soon.",
        "split": "arXiv"
    },
    {
        "text": "Large language models (LLMs), such as ChatGPT released by OpenAI, have\nattracted significant attention from both industry and academia due to their\ndemonstrated ability to generate high-quality content for various tasks.\nDespite the impressive capabilities of LLMs, there are growing concerns\nregarding their potential risks in various fields, such as news, education, and\nsoftware engineering. Recently, several commercial and open-source\nLLM-generated content detectors have been proposed, which, however, are\nprimarily designed for detecting natural language content without considering\nthe specific characteristics of program code. This paper aims to fill this gap\nby proposing a novel ChatGPT-generated code detector, CodeGPTSensor, based on a\ncontrastive learning framework and a semantic encoder built with UniXcoder. To\nassess the effectiveness of CodeGPTSensor on differentiating ChatGPT-generated\ncode from human-written code, we first curate a large-scale Human and Machine\ncomparison Corpus (HMCorp), which includes 550K pairs of human-written and\nChatGPT-generated code (i.e., 288K Python code pairs and 222K Java code pairs).\nBased on the HMCorp dataset, our qualitative and quantitative analysis of the\ncharacteristics of ChatGPT-generated code reveals the challenge and opportunity\nof distinguishing ChatGPT-generated code from human-written code with their\nrepresentative features. Our experimental results indicate that CodeGPTSensor\ncan effectively identify ChatGPT-generated code, outperforming all selected\nbaselines.",
        "split": "arXiv"
    },
    {
        "text": "In this work, we address the cooperation problem among large language model\n(LLM) based embodied agents, where agents must cooperate to achieve a common\ngoal. Previous methods often execute actions extemporaneously and incoherently,\nwithout long-term strategic and cooperative planning, leading to redundant\nsteps, failures, and even serious repercussions in complex tasks like\nsearch-and-rescue missions where discussion and cooperative plan are crucial.\nTo solve this issue, we propose Cooperative Plan Optimization (CaPo) to enhance\nthe cooperation efficiency of LLM-based embodied agents. Inspired by human\ncooperation schemes, CaPo improves cooperation efficiency with two phases: 1)\nmeta-plan generation, and 2) progress-adaptive meta-plan and execution. In the\nfirst phase, all agents analyze the task, discuss, and cooperatively create a\nmeta-plan that decomposes the task into subtasks with detailed steps, ensuring\na long-term strategic and coherent plan for efficient coordination. In the\nsecond phase, agents execute tasks according to the meta-plan and dynamically\nadjust it based on their latest progress (e.g., discovering a target object)\nthrough multi-turn discussions. This progress-based adaptation eliminates\nredundant actions, improving the overall cooperation efficiency of agents.\nExperimental results on the ThreeDworld Multi-Agent Transport and Communicative\nWatch-And-Help tasks demonstrate that CaPo achieves much higher task completion\nrate and efficiency compared with state-of-the-arts.",
        "split": "arXiv"
    },
    {
        "text": "An improved bilinear fuzzy genetic algorithm (BFGA) is introduced in this\nchapter for the design optimization of steel structures with semi-rigid\nconnections. Semi-rigid connections provide a compromise between the stiffness\nof fully rigid connections and the flexibility of fully pinned connections.\nHowever, designing such structures is challenging due to the nonlinear behavior\nof semi-rigid connections. The BFGA is a robust optimization method that\ncombines the strengths of fuzzy logic and genetic algorithm to handle the\ncomplexity and uncertainties of structural design problems. The BFGA, compared\nto standard GA, demonstrated to generate high-quality solutions in a reasonable\ntime. The application of the BFGA is demonstrated through the optimization of\nsteel structures with semirigid connections, considering the weight and\nperformance criteria. The results show that the proposed BFGA is capable of\nfinding optimal designs that satisfy all the design requirements and\nconstraints. The proposed approach provides a promising solution for the\noptimization of complex structures with nonlinear behavior.",
        "split": "arXiv"
    },
    {
        "text": "A wide range of transformer-based language models have been proposed for\ninformation retrieval tasks. However, fine-tuning and inference of these models\nis often complex and requires substantial engineering effort. This paper\nintroduces Lightning IR, a PyTorch Lightning-based framework for fine-tuning\nand inference of transformer-based language models for information retrieval.\nLightning IR provides a modular and extensible architecture that supports all\nstages of an information retrieval pipeline: from fine-tuning and indexing to\nsearching and re-ranking. It is designed to be straightforward to use,\nscalable, and reproducible. Lightning IR is available as open-source:\nhttps://github.com/webis-de/lightning-ir.",
        "split": "arXiv"
    },
    {
        "text": "We study the birational geometry of hypersurfaces in products of weighted\nprojective spaces, extending results previously established by J. C. Ottem. For\nmost cases where these hypersurfaces are Mori dream spaces, we determine all\nrelevant cones and characterise their birational models, along with the small\n$\\mathbf{Q}$-factorial modifications to them. We also provide a presentation of\ntheir Cox ring. Finally, we establish the birational form of the\nKawamata-Morrison cone conjecture for terminal Calabi-Yau hypersurfaces in\nGorenstein products of weighted projective spaces.",
        "split": "arXiv"
    },
    {
        "text": "Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is\ncharacterized by a high incidence of disability. Accurate segmentation of the\nICH region and prognosis prediction are critically important for developing and\nrefining treatment plans for post-ICH patients. However, existing approaches\naddress these two tasks independently and predominantly focus on imaging data\nalone, thereby neglecting the intrinsic correlation between the tasks and\nmodalities. This paper introduces a multi-task network, ICH-SCNet, designed for\nboth ICH segmentation and prognosis classification. Specifically, we integrate\na SAM-CLIP cross-modal interaction mechanism that combines medical text and\nsegmentation auxiliary information with neuroimaging data to enhance\ncross-modal feature recognition. Additionally, we develop an effective feature\nfusion module and a multi-task loss function to improve performance further.\nExtensive experiments on an ICH dataset reveal that our approach surpasses\nother state-of-the-art methods. It excels in the overall performance of\nclassification tasks and outperforms competing models in all segmentation task\nmetrics.",
        "split": "arXiv"
    },
    {
        "text": "With the rapid advancement of neural language models, the deployment of\nover-parameterized models has surged, increasing the need for interpretable\nexplanations comprehensible to human inspectors. Existing post-hoc\ninterpretability methods, which often focus on unigram features of single input\ntextual instances, fail to capture the models' decision-making process fully.\nAdditionally, many methods do not differentiate between decisions based on\nspurious correlations and those based on a holistic understanding of the input.\nOur paper introduces DISCO, a novel method for discovering global, rule-based\nexplanations by identifying causal n-gram associations with model predictions.\nThis method employs a scalable sequence mining technique to extract relevant\ntext spans from training data, associate them with model predictions, and\nconduct causality checks to distill robust rules that elucidate model behavior.\nThese rules expose potential overfitting and provide insights into misleading\nfeature combinations. We validate DISCO through extensive testing,\ndemonstrating its superiority over existing methods in offering comprehensive\ninsights into complex model behaviors. Our approach successfully identifies all\nshortcuts manually introduced into the training data (100% detection rate on\nthe MultiRC dataset), resulting in an 18.8% regression in model performance --\na capability unmatched by any other method. Furthermore, DISCO supports\ninteractive explanations, enabling human inspectors to distinguish spurious\ncauses in the rule-based output. This alleviates the burden of abundant\ninstance-wise explanations and helps assess the model's risk when encountering\nout-of-distribution (OOD) data.",
        "split": "arXiv"
    },
    {
        "text": "This paper presents the second-placed solution for task 8 and the\nparticipation solution for task 7 of BraTS 2024. The adoption of automated\nbrain analysis algorithms to support clinical practice is increasing. However,\nmany of these algorithms struggle with the presence of brain lesions or the\nabsence of certain MRI modalities. The alterations in the brain's morphology\nleads to high variability and thus poor performance of predictive models that\nwere trained only on healthy brains. The lack of information that is usually\nprovided by some of the missing MRI modalities also reduces the reliability of\nthe prediction models trained with all modalities. In order to improve the\nperformance of these models, we propose the use of conditional 3D wavelet\ndiffusion models. The wavelet transform enabled full-resolution image training\nand prediction on a GPU with 48 GB VRAM, without patching or downsampling,\npreserving all information for prediction. For the inpainting task of BraTS\n2024, the use of a large and variable number of healthy masks and the stability\nand efficiency of the 3D wavelet diffusion model resulted in 0.007, 22.61 and\n0.842 in the validation set and 0.07 , 22.8 and 0.91 in the testing set (MSE,\nPSNR and SSIM respectively). The code for these tasks is available at\nhttps://github.com/ShadowTwin41/BraTS_2023_2024_solutions.",
        "split": "arXiv"
    },
    {
        "text": "The hydrodynamic instabilities of propagating interfaces in Hele-Shaw\nchannels or porous media under the influence of an imposed flow and\ngravitational acceleration are investigated within the framework of Darcy's\nlaw. The stability analysis pertains to an interface between two fluids with\ndifferent densities, viscosities, and permeabilities, which can be susceptible\nto Darrieus-Landau, Saffman-Taylor, and Rayleigh-Taylor instabilities. A\ntheoretical analysis, treating the interface as a hydrodynamic discontinuity,\nyields a simple dispersion relation between the perturbation growth rate $s$\nand its wavenumber $k$ in the form $s=(ak - bk^2)/(1+ck)$, where $a$, $b$ and\n$c$ are constants determined by problem parameters. The constant $a$\ncharacterises all three hydrodynamic instabilities, which are long-wave in\nnature. In contrast, $b$ and $c$, which characterize the influences of local\ncurvature and flow strain on interface propagation speed, typically provide\nstabilisation at short wavelengths comparable to interface's diffusive\nthickness. The theoretical findings for Darcy's law are compared with a\ngeneralisation of the classical work by Joulin & Sivashinsky, which is based on\nan Euler-Darcy model. The comparison provides a conceptual bridge between\npredictions based on Darcy's law and those on Euler's equation and offers\nvaluable insights into the role of confinement on interface instabilities in\nHele-Shaw channels. Numerical analyses of the instabilities are carried out for\npremixed flames using a simplified chemistry model and Darcy's law. The\nnumerical results corroborate with the explicit formula with a reasonable\naccuracy. Time-dependent numerical simulations of unstable premixed flames are\ncarried out to gain insights into the nonlinear development of these\ninstabilities.",
        "split": "arXiv"
    },
    {
        "text": "Distributed traces contain valuable information but are often massive in\nvolume, posing a core challenge in tracing framework design: balancing the\ntradeoff between preserving essential trace information and reducing trace\nvolume. To address this tradeoff, previous approaches typically used a '1 or 0'\nsampling strategy: retaining sampled traces while completely discarding\nunsampled ones. However, based on an empirical study on real-world production\ntraces, we discover that the '1 or 0' strategy actually fails to effectively\nbalance this tradeoff.\n  To achieve a more balanced outcome, we shift the strategy from the '1 or 0'\nparadigm to the 'commonality + variability' paradigm. The core of 'commonality\n+ variability' paradigm is to first parse traces into common patterns and\nvariable parameters, then aggregate the patterns and filter the parameters. We\npropose a cost-efficient tracing framework, Mint, which implements the\n'commonality + variability' paradigm on the agent side to enable all requests\ncapturing. Our experiments show that Mint can capture all traces and retain\nmore trace information while optimizing trace storage (reduced to an average of\n2.7%) and network overhead (reduced to an average of 4.2%). Moreover,\nexperiments also demonstrate that Mint is lightweight enough for production\nuse.",
        "split": "arXiv"
    },
    {
        "text": "Large language models (LLMs), with advanced linguistic capabilities, have\nbeen employed in reranking tasks through a sequence-to-sequence approach. In\nthis paradigm, multiple passages are reranked in a listwise manner and a\ntextual reranked permutation is generated. However, due to the limited context\nwindow of LLMs, this reranking paradigm requires a sliding window strategy to\niteratively handle larger candidate sets. This not only increases computational\ncosts but also restricts the LLM from fully capturing all the comparison\ninformation for all candidates. To address these challenges, we propose a novel\nself-calibrated listwise reranking method, which aims to leverage LLMs to\nproduce global relevance scores for ranking. To achieve it, we first propose\nthe relevance-aware listwise reranking framework, which incorporates explicit\nlist-view relevance scores to improve reranking efficiency and enable global\ncomparison across the entire candidate set. Second, to ensure the comparability\nof the computed scores, we propose self-calibrated training that uses\npoint-view relevance assessments generated internally by the LLM itself to\ncalibrate the list-view relevance assessments. Extensive experiments and\ncomprehensive analysis on the BEIR benchmark and TREC Deep Learning Tracks\ndemonstrate the effectiveness and efficiency of our proposed method.",
        "split": "arXiv"
    },
    {
        "text": "An outerplanar graph is a planar graph that has a planar drawing with all\nvertices on the unbounded face. The matching complex of a graph is the\nsimplicial complex whose faces are subsets of disjoint edges of the graph. In\nthis paper we prove that the matching complexes of outerplanar graphs are\ncontractible or homotopy equivalent to a wedge of spheres. This extends known\nresults about trees and polygonal line tilings.",
        "split": "arXiv"
    },
    {
        "text": "Robustness is a fundamental aspect for developing safe and trustworthy\nmodels, particularly when they are deployed in the open world. In this work we\nanalyze the inherent capability of one-stage object detectors to robustly\noperate in the presence of out-of-distribution (OoD) data. Specifically, we\npropose a novel detection algorithm for detecting unknown objects in image\ndata, which leverages the features extracted by the model from each sample.\nDifferently from other recent approaches in the literature, our proposal does\nnot require retraining the object detector, thereby allowing for the use of\npretrained models. Our proposed OoD detector exploits the application of\nsupervised dimensionality reduction techniques to mitigate the effects of the\ncurse of dimensionality on the features extracted by the model. Furthermore, it\nutilizes high-resolution feature maps to identify potential unknown objects in\nan unsupervised fashion. Our experiments analyze the Pareto trade-off between\nthe performance detecting known and unknown objects resulting from different\nalgorithmic configurations and inference confidence thresholds. We also compare\nthe performance of our proposed algorithm to that of logits-based post-hoc OoD\nmethods, as well as possible fusion strategies. Finally, we discuss on the\ncompetitiveness of all tested methods against state-of-the-art OoD approaches\nfor object detection models over the recently published Unknown Object\nDetection benchmark. The obtained results verify that the performance of\navant-garde post-hoc OoD detectors can be further improved when combined with\nour proposed algorithm.",
        "split": "arXiv"
    },
    {
        "text": "The challenges in dense ultra-reliable low-latency communication networks to\ndeliver the required service to multiple devices are addressed by three main\ntechnologies: multiple antennas at the base station (MISO), rate splitting\nmultiple access (RSMA) with private and common message encoding, and\nsimultaneously transmitting and reflecting reconfigurable intelligent surfaces\n(STAR-RIS). Careful resource allocation, encompassing beamforming and RIS\noptimization, is required to exploit the synergy between the three. We propose\nan alternating optimization-based algorithm, relying on\nminorization-maximization. Numerical results show that the achievable\nsecond-order max-min rates of the proposed scheme outperform the baselines\nsignificantly. MISO, RSMA, and STAR-RIS all contribute to enabling\nultra-reliable low-latency communication (URLLC).",
        "split": "arXiv"
    },
    {
        "text": "Statistical heterogeneity is a measure of how skewed the samples of a dataset\nare. It is a common problem in the study of differential privacy that the usage\nof a statistically heterogeneous dataset results in a significant loss of\naccuracy. In federated scenarios, statistical heterogeneity is more likely to\nhappen, and so the above problem is even more pressing. We explore the three\nmost promising ways to measure statistical heterogeneity and give formulae for\ntheir accuracy, while simultaneously incorporating differential privacy. We\nfind the optimum privacy parameters via an analytic mechanism, which\nincorporates root finding methods. We validate the main theorems and related\nhypotheses experimentally, and test the robustness of the analytic mechanism to\ndifferent heterogeneity levels. The analytic mechanism in a distributed setting\ndelivers superior accuracy to all combinations involving the classic mechanism\nand/or the centralized setting. All measures of statistical heterogeneity do\nnot lose significant accuracy when a heterogeneous sample is used.",
        "split": "arXiv"
    },
    {
        "text": "BosonSampling is a popular candidate for near-term quantum advantage, which\nhas now been experimentally implemented several times. The original proposal of\nAaronson and Arkhipov from 2011 showed that classical hardness of BosonSampling\nis implied by a proof of the \"Gaussian Permanent Estimation\" conjecture. This\nconjecture states that $e^{-n\\log{n}-n-O(\\log n)}$ additive error estimates to\nthe output probability of most random BosonSampling experiments are $\\#P$-hard.\nProving this conjecture has since become the central question in the theory of\nquantum advantage.\n  In this work we make progress by proving that $e^{-n\\log n -n - O(n^\\delta)}$\nadditive error estimates to output probabilities of most random BosonSampling\nexperiments are $\\#P$-hard, for any $\\delta>0$. In the process, we circumvent\nall known barrier results for proving the hardness of BosonSampling\nexperiments. This is nearly the robustness needed to prove hardness of\nBosonSampling -- the remaining hurdle is now \"merely\" to show that the\n$n^\\delta$ in the exponent can be improved to $O(\\log n).$ We also obtain an\nanalogous result for Random Circuit Sampling.\n  Our result allows us to show, for the first time, a hardness of classical\nsampling result for random BosonSampling experiments, under an\nanticoncentration conjecture. Specifically, we prove the impossibility of\nmultiplicative-error sampling from random BosonSampling experiments with\nprobability $1-e^{-O(n)}$, unless the Polynomial Hierarchy collapses.",
        "split": "arXiv"
    },
    {
        "text": "We explore the sensitivity of future muon colliders to CP-violating\ninteractions in the Higgs sector, specifically focusing on the process $\\mu^-\n\\mu^+ \\to h \\bar{\\nu_{l}} \\nu_{l}$. Using a model-independent approach within\nthe framework of the Standard Model Effective Field Theory (SMEFT), we analyze\nthe contribution of dimension-six operators to Higgs-gauge boson couplings,\nemphasizing CP-violating effects. To simulate the process, all signal and\nbackground events are generated through MadGraph. The analysis provides 95\\%\nconfidence level limits on the relevant Wilson coefficients $\\tilde{c}_{HB}$,\n$\\tilde{c}_{HW}$, $\\tilde{c}_{\\gamma}$, with a comparative discussion of\nexisting experimental and phenomenological constraints. Our best constraints on\nthe $\\tilde{c}_{HB}$, $\\tilde{c}_{HW}$, $\\tilde{c}_{\\gamma}$ with an integrated\nluminosity of 10 ab$^{-1}$ are $[-0.017148;0.018711]$, $[-0.002545;0.002837]$\nand $[-0.010613;0.011210]$, respectively. In this context, this study\nhighlights the capability of future muon collider experiments to probe new\nphysics in the Higgs sector, potentially offering tighter constraints on\nCP-violating Higgs-gauge boson interactions than those provided by current\ncolliders.",
        "split": "arXiv"
    },
    {
        "text": "Consider an undirected graph G, representing a social network, where each\nnode is blue or red, corresponding to positive or negative opinion on a topic.\nIn the voter model, in discrete time rounds, each node picks a neighbour\nuniformly at random and adopts its colour. Despite its significant popularity,\nthis model does not capture some fundamental real-world characteristics such as\nthe difference in the strengths of individuals connections, individuals with\nneutral opinion on a topic, and individuals who are reluctant to update their\nopinion. To address these issues, we introduce and study a generalisation of\nthe voter model. Motivating by campaigning strategies, we study the problem of\nselecting a set of seeds blue nodes to maximise the expected number of blue\nnodes after some rounds. We prove that the problem is NP- hard and provide a\npolynomial time approximation algorithm with the best possible approximation\nguarantee. Our experiments on real-world and synthetic graph data demonstrate\nthat the proposed algorithm outperforms other algorithms. We also investigate\nthe convergence properties of the model. We prove that the process could take\nan exponential number of rounds to converge. However, if we limit ourselves to\nstrongly connected graphs, the convergence time is polynomial and the period\n(the number of states in convergence) divides the length of all cycles in the\ngraph.",
        "split": "arXiv"
    },
    {
        "text": "In offline reinforcement learning, a policy is learned using a static dataset\nin the absence of costly feedback from the environment. In contrast to the\nonline setting, only using static datasets poses additional challenges, such as\npolicies generating out-of-distribution samples. Model-based offline\nreinforcement learning methods try to overcome these by learning a model of the\nunderlying dynamics of the environment and using it to guide policy search. It\nis beneficial but, with limited datasets, errors in the model and the issue of\nvalue overestimation among out-of-distribution states can worsen performance.\nCurrent model-based methods apply some notion of conservatism to the Bellman\nupdate, often implemented using uncertainty estimation derived from model\nensembles. In this paper, we propose Constrained Latent Action Policies (C-LAP)\nwhich learns a generative model of the joint distribution of observations and\nactions. We cast policy learning as a constrained objective to always stay\nwithin the support of the latent action distribution, and use the generative\ncapabilities of the model to impose an implicit constraint on the generated\nactions. Thereby eliminating the need to use additional uncertainty penalties\non the Bellman update and significantly decreasing the number of gradient steps\nrequired to learn a policy. We empirically evaluate C-LAP on the D4RL and\nV-D4RL benchmark, and show that C-LAP is competitive to state-of-the-art\nmethods, especially outperforming on datasets with visual observations.",
        "split": "arXiv"
    },
    {
        "text": "In a directed graph $D$, a vertex subset $S\\subseteq V$ is a total dominating\nset if every vertex of $D$ has an in-neighbor from $S$. A total dominating set\nexists if and only if every vertex has at least one in-neighbor. We call the\norientation of such directed graphs valid. The total domination number of $D$,\ndenoted by $\\gamma_t(D)$, is the size of the smallest total dominating set of\n$D$. For an undirected graph $G$, we investigate the upper (or lower)\norientable total domination number of $G$, denoted by $\\mathrm{DOM}_t(G)$ (or\n$\\mathrm{dom}_t(G)$), that is the maximum (or minimum) of the total domination\nnumbers over all valid orientations of $G$. We characterize those graphs for\nwhich $\\mathrm{DOM}_t(G)=|V(G)|-1$, and consequently we show that there exists\na family of graphs for which $\\mathrm{DOM}_t(G)$ and $\\mathrm{dom}_t(G)$ can be\nas far as possible, namely $\\mathrm{DOM}_t(G)=|V(G)|-1$ and\n$\\mathrm{dom}_t(G)=3$.",
        "split": "arXiv"
    },
    {
        "text": "Predicting temporal progress from visual trajectories is important for\nintelligent robots that can learn, adapt, and improve. However, learning such\nprogress estimator, or temporal value function, across different tasks and\ndomains requires both a large amount of diverse data and methods which can\nscale and generalize. To address these challenges, we present Generative Value\nLearning (\\GVL), a universal value function estimator that leverages the world\nknowledge embedded in vision-language models (VLMs) to predict task progress.\nNaively asking a VLM to predict values for a video sequence performs poorly due\nto the strong temporal correlation between successive frames. Instead, GVL\nposes value estimation as a temporal ordering problem over shuffled video\nframes; this seemingly more challenging task encourages VLMs to more fully\nexploit their underlying semantic and temporal grounding capabilities to\ndifferentiate frames based on their perceived task progress, consequently\nproducing significantly better value predictions. Without any robot or task\nspecific training, GVL can in-context zero-shot and few-shot predict effective\nvalues for more than 300 distinct real-world tasks across diverse robot\nplatforms, including challenging bimanual manipulation tasks. Furthermore, we\ndemonstrate that GVL permits flexible multi-modal in-context learning via\nexamples from heterogeneous tasks and embodiments, such as human videos. The\ngenerality of GVL enables various downstream applications pertinent to\nvisuomotor policy learning, including dataset filtering, success detection, and\nadvantage-weighted regression -- all without any model training or finetuning.",
        "split": "arXiv"
    },
    {
        "text": "Evolutionary Multi-Objective Optimization Algorithms (EMOAs) are widely\nemployed to tackle problems with multiple conflicting objectives. Recent\nresearch indicates that not all objectives are equally important to the\ndecision-maker (DM). In the context of interactive EMOAs, preference\ninformation elicited from the DM during the optimization process can be\nleveraged to identify and discard irrelevant objectives, a crucial step when\nobjective evaluations are computationally expensive. However, much of the\nexisting literature fails to account for the dynamic nature of DM preferences,\nwhich can evolve throughout the decision-making process and affect the\nrelevance of objectives. This study addresses this limitation by simulating\ndynamic shifts in DM preferences within a ranking-based interactive algorithm.\nAdditionally, we propose methods to discard outdated or conflicting preferences\nwhen such shifts occur. Building on prior research, we also introduce a\nmechanism to safeguard relevant objectives that may become trapped in local or\nglobal optima due to the diminished correlation with the DM-provided rankings.\nOur experimental results demonstrate that the proposed methods effectively\nmanage evolving preferences and significantly enhance the quality and\ndesirability of the solutions produced by the algorithm.",
        "split": "arXiv"
    },
    {
        "text": "Exact solutions are presented which describe, either the evolution of fluid\ndistributions corresponding to a ghost star (vanishing total mass), or\ndescribing the evolution of fluid distributions which attain the ghost star\nstatus at some point of their lives. The first two solutions correspond to the\nformer case, they admit a conformal Killing vector (CKV) and describe the\nadiabatic evolution of a ghost star. Other two solutions corresponding to the\nlatter case are found, which describe evolving fluid spheres absorbing energy\nfrom the outside, leading to a vanishing total mass at some point of their\nevolution. In this case the fluid is assumed to be expansion-free. In all four\nsolutions the condition of vanishing complexity factor was imposed. The\nphysical implications of the results, are discussed",
        "split": "arXiv"
    },
    {
        "text": "It is well known that multiple Galactic thermal dust emission components may\nexist along the line of sight, but a single-component approximation is still\nwidely used, since a full multi-component estimation requires a large number of\nfrequency bands that are only available with future experiments. In light of\nthis, we present a reliable, quantitative, and sensitive criterion to test the\ngoodness of all kinds of dust emission estimations. This can not only give a\ndefinite answer to the quality of current single-component approximations; but\nalso help determine preconditions of future multi-component estimations. Upon\nthe former, previous works usually depend on a more complicated model to\nimprove the single-component dust emission; however, our method is free from\nany additional model, and is sensitive enough to directly discover a\nsubstantial discrepancy between the Planck HFI data (100-857 GHz) and\nassociated single-component dust emission estimations. This is the first time\nthat the single-component estimation is ruled out by the data itself. For the\nlatter, a similar procedure will be able to answer two important questions for\nestimating the complicated Galactic emissions: the number of necessary\nforeground components and their types.",
        "split": "arXiv"
    },
    {
        "text": "The increasing growth of social media provides us with an instant opportunity\nto be informed of the opinions of a large number of politically active\nindividuals in real-time. We can get an overall idea of the ideologies of these\nindividuals on governmental issues by analyzing the social media texts.\nNowadays, different kinds of news websites and popular social media such as\nFacebook, YouTube, Instagram, etc. are the most popular means of communication\nfor the mass population. So the political perception of the users toward\ndifferent parties in the country is reflected in the data collected from these\nsocial sites. In this work, we have extracted three types of features, such as\nthe stylometric feature, the word-embedding feature, and the TF-IDF feature.\nTraditional machine learning classifiers and deep learning models are employed\nto identify political ideology from the text. We have compared our methodology\nwith the research work in different languages. Among them, the word embedding\nfeature with LSTM outperforms all other models with 88.28% accuracy.",
        "split": "arXiv"
    },
    {
        "text": "Recent studies have highlighted the significant potential of Large Language\nModels (LLMs) as zero-shot relevance rankers. These methods predominantly\nutilize prompt learning to assess the relevance between queries and documents\nby generating a ranked list of potential documents. Despite their promise, the\nsubstantial costs associated with LLMs pose a significant challenge for their\ndirect implementation in commercial search systems. To overcome this barrier\nand fully exploit the capabilities of LLMs for text ranking, we explore\ntechniques to transfer the ranking expertise of LLMs to a more compact model\nsimilar to BERT, using a ranking loss to enable the deployment of less\nresource-intensive models. Specifically, we enhance the training of LLMs\nthrough Continued Pre-Training, taking the query as input and the clicked title\nand summary as output. We then proceed with supervised fine-tuning of the LLM\nusing a rank loss, assigning the final token as a representative of the entire\nsentence. Given the inherent characteristics of autoregressive language models,\nonly the final token </s> can encapsulate all preceding tokens. Additionally,\nwe introduce a hybrid point-wise and margin MSE loss to transfer the ranking\nknowledge from LLMs to smaller models like BERT. This method creates a viable\nsolution for environments with strict resource constraints. Both offline and\nonline evaluations have confirmed the efficacy of our approach, and our model\nhas been successfully integrated into a commercial web search engine as of\nFebruary 2024.",
        "split": "arXiv"
    },
    {
        "text": "Human understanding of language is robust to different word choices as far as\nthey represent similar semantic concepts. To what extent does our human\nintuition transfer to language models, which represent all subwords as distinct\nembeddings? In this work, we take an initial step on measuring the role of\nshared semantics among subwords in the encoder-only multilingual language\nmodels (mLMs). To this end, we form \"semantic tokens\" by merging the\nsemantically similar subwords and their embeddings, and evaluate the updated\nmLMs on 5 heterogeneous multilingual downstream tasks. Results show that the\ngeneral shared semantics could get the models a long way in making the\npredictions on mLMs with different tokenizers and model sizes. Inspections on\nthe grouped subwords show that they exhibit a wide range of semantic\nsimilarities, including synonyms and translations across many languages and\nscripts. Lastly, we found the zero-shot results with semantic tokens are on par\nor even better than the original models on certain classification tasks,\nsuggesting that the shared subword-level semantics may serve as the anchors for\ncross-lingual transferring.",
        "split": "arXiv"
    },
    {
        "text": "Video-sharing platforms (VSPs) have been increasingly embracing social\nfeatures such as likes, comments, and Danmaku to boost user engagement.\nHowever, viewers may post inappropriate content through video commentary to\ngain attention or express themselves anonymously and even toxically. For\nexample, on VSPs that support Danmaku, users may even intentionally create a\n\"flood\" of Danmaku with inappropriate content shown overlain on videos,\ndisrupting the overall user experience. Despite of the prevalence of\ninappropriate Danmaku on these VSPs, there is a lack of understanding about the\nchallenges and limitations of Danmaku content moderation on video-sharing\nplatforms. To explore how users perceive the challenges and limitations of\ncurrent Danmaku moderation methods on VSPs, we conducted probe-based interviews\nand co-design activities with 21 active end-users. Our findings reveal that the\none-size-fits-all rules set by users or customizaibility moderation cannot\naccurately match the continuous Danmaku. Additionally, the moderation\nrequirements of the Danmaku and the definition of offensive content must\ndynamically adjust to the video content. Non-intrusive methods should be used\nto maintain the coherence of the video browsing experience. Our findings inform\nthe design of future Danmaku moderation tools on video-sharing platforms.",
        "split": "arXiv"
    },
    {
        "text": "Query optimization has become a research area where classical algorithms are\nbeing challenged by machine learning algorithms. At the same time, recent\ntrends in learned query optimizers have shown that it is prudent to take\nadvantage of decades of database research and augment classical query\noptimizers by shrinking the plan search space through different types of hints\n(e.g. by specifying the join type, scan type or the order of joins) rather than\ncompletely replacing the classical query optimizer with machine learning\nmodels. It is especially relevant for cases when classical optimizers cannot\nfully enumerate all logical and physical plans and, as an alternative, need to\nrely on less robust approaches like genetic algorithms. However, even\nsymbiotically learned query optimizers are hampered by the need for vast\namounts of training data, slow plan generation during inference and unstable\nresults across various workload conditions. In this paper, we present GenJoin -\na novel learned query optimizer that considers the query optimization problem\nas a generative task and is capable of learning from a random set of subplan\nhints to produce query plans that outperform the classical optimizer. GenJoin\nis the first learned query optimizer that significantly and consistently\noutperforms PostgreSQL as well as state-of-the-art methods on two well-known\nreal-world benchmarks across a variety of workloads using rigorous machine\nlearning evaluations.",
        "split": "arXiv"
    },
    {
        "text": "Answering a question raised by V. V. Tkachuk, we present several examples of\n$\\sigma$-compact spaces, some only consistent and some in ZFC, that are not\ncountably tight but in which the closure of any discrete subset is countably\ntight. In fact, in some of our examples the closures of all discrete subsets\nare even first countable.",
        "split": "arXiv"
    },
    {
        "text": "For a commutative noetherian ring $R$, we classify all the hereditary\ncotorsion pairs cogenerated by pure-injective modules of finite injective\ndimension. The classification is done in terms of integer-valued functions on\nthe spectrum of the ring. Each such function gives rise to a system of local\ndepth conditions which describes the left-hand class in the corresponding\ncotorsion pair. Furthermore, we show that these cotorsion pairs correspond by\nexplicit duality to hereditary Tor-pairs generated by modules of finite flat\ndimension.",
        "split": "arXiv"
    },
    {
        "text": "Forward modeling the galaxy density within the Effective Field Theory of\nLarge Scale Structure (EFT of LSS) enables field-level analyses that are robust\nto theoretical uncertainties. At the same time, they can maximize the\nconstraining power from galaxy clustering on the scales amenable to\nperturbation theory. In order to apply the method to galaxy surveys, the\nforward model must account for the full observational complexity of the data.\nIn this context, a major challenge is the inclusion of redshift space\ndistortions (RSDs) from the peculiar motion of galaxies. Here, we present\nimprovements in the efficiency and accuracy of the RSD modeling in the\nperturbative LEFTfield forward model. We perform a detailed quantification of\nthe perturbative and numerical error for the prediction of momentum, velocity\nand the redshift-space matter density. Further, we test the recovery of\ncosmological parameters at the field level, namely the growth rate $f$, from\nsimulated halos in redshift space. For a rigorous test and to scan through a\nwide range of analysis choices, we fix the linear (initial) density field to\nthe known ground truth but marginalize over all unknown bias coefficients and\nnoise amplitudes. With a third-order model for gravity and bias, our results\nyield $<1\\,\\%$ statistical and $<1.5\\,\\%$ systematic error. The computational\ncost of the redshift-space forward model is only $\\sim 1.5$ times of the rest\nframe equivalent, enabling future field-level inference that simultaneously\ntargets cosmological parameters and the initial matter distribution.",
        "split": "arXiv"
    },
    {
        "text": "We have calculated the fission fragments' mass distributions for several\nisotopes of heavy and super-heavy nuclei from uranium to flerovium within an\nimproved scission point model. For all considered nuclei, in addition to the\nstandard mass-asymmetric fission mode we have found the mass super-asymmetric\nmode with the mass of heavy fragments equal 190. For the actinide nuclei, the\nprobability of super-asymmetric fission is by 6 orders of magnitude smaller\nthan for standard asymmetric fission. For the superheavy nuclei this\nprobability is only by 2 orders of magnitude smaller. In all cases, the\nsuper-asymmetric scission shapes are dumbbells with the heavy fragment close to\na sphere. We have estimated the stability of the light fragment concerning the\nvariation of the neck and found out that sequential ternary fission is not\nfavored energetically. The calculations were carried out with nuclear shape\ndescribed by generalized Cassinian ovals with 6 deformation parameters,\n$\\alpha, \\alpha_1, \\alpha_2, \\alpha_3, \\alpha_4$ and $\\alpha_5$. The\nconfiguration at the moment of the neck rupture was defined by fixing\n$\\alpha=0.98$. This value corresponds to a neck radius $r_{neck}\\approx$ 1.5\nfm.",
        "split": "arXiv"
    },
    {
        "text": "As a generalisation of the periodic orbit structure often seen in reflection\nor mirror symmetric MHD equilibria, we consider equilibria with other\norientation-reversing symmetries. An example of such a symmetry, which is a not\na reflection, is $(x,y,z) \\mapsto (-x,-y,-z)$ in $\\mathbb{R}^3$. It is shown\nunder any orientation-reversing isometry, that if the pressure function is\nassumed to have a nested toroidal structure, then all orbits on the tori are\nnecessarily periodic. The techniques involved are almost entirely topological\nin nature and give rise to a handy index describing how a diffeomorphism of\n$\\mathbb{R}^3$ alters the poloidal and toroidal curves of an invariant embedded\n2-torus.",
        "split": "arXiv"
    },
    {
        "text": "In this communication, we propose a tentative to set the fundamental problem\nof measuring process done by a large structure on a microsopic one. We consider\nthe example of voting when an entire society tries to measure globally opinions\nof all social actors in order to elect a delegate. We present a quantum model\nto interpret an operational voting system and propose a quantum approach for\ngrading step of Range Voting, developed by M. Balinski and R. Laraki in 2007.",
        "split": "arXiv"
    },
    {
        "text": "The commuting graph ${\\Gamma(G)}$ of a group $G$ is the simple undirected\ngraph with group elements as a vertex set and two elements $x$ and $y$ are\nadjacent if and only if $xy=yx$ in $G$. By eliminating the identity element of\n$G$ and all the dominant vertices of $\\Gamma(G)$, the resulting subgraphs of\n$\\Gamma(G)$ are $\\Gamma^*(G)$ and $\\Gamma^{**}(G)$, respectively. In this\npaper, we classify all the finite groups $G$ such that the graph $\\Delta(G) \\in\n\\{\\Gamma(G), \\Gamma^*(G), \\Gamma^{**}(G)\\}$ is the line graph of some graph. We\nalso classify all the finite groups $G$ whose graph $\\Delta(G) \\in \\{\\Gamma(G),\n\\Gamma^*(G), \\Gamma^{**}(G)\\}$ is the complement of line graph.",
        "split": "arXiv"
    },
    {
        "text": "Currently artificial intelligence (AI)-enabled chatbots are capturing the\nhearts and imaginations of the public at large. Chatbots that users can build\nand personalize, as well as pre-designed avatars ready for users' selection,\nall of these are on offer in applications to provide social companionship,\nfriends and even love. These systems, however, have demonstrated challenges on\nthe privacy and ethics front. This paper takes a critical approach towards\nexamining the intricacies of these issues within AI companion services. We\nchose Replika as a case and employed close reading to examine the service's\nprivacy policy. We additionally analyze articles from public media about the\ncompany and its practices to gain insight into the trustworthiness and\nintegrity of the information provided in the policy. The aim is to ascertain\nwhether seeming General Data Protection Regulation (GDPR) compliance equals\nreliability of required information, or whether the area of GDPR compliance in\nitself is one riddled with ethical challenges. The paper contributes to a\ngrowing body of scholarship on ethics and privacy related matters in the sphere\nof social chatbots. The results reveal that despite privacy notices, data\ncollection practices might harvest personal data without users' full awareness.\nCross-textual comparison reveals that privacy notice information does not fully\ncorrespond with other information sources.",
        "split": "arXiv"
    },
    {
        "text": "Interpolatory filters are of great interest in subdivision schemes and\nwavelet analysis. Due to the high-order linear-phase moment property,\ninterpolatory refinement filters are often used to construct wavelets and\nframelets with high-order vanishing moments. In this paper, given a general\ndilation matrix $\\mathsf{M}$, we propose a method that allows us to construct a\ndual $\\mathsf{M}$-framelet from an arbitrary pair of $\\mathsf{M}$-interpolatory\nfilters such that all framelet generators/high-pass filters (1) have the\ninterpolatory properties; (2) have high-order vanishing moments. Our method is\neasy to implement, as the high-pass filters are either given in explicit\nformulas or can be obtained by solving specific linear systems. Motivated by\nconstructing interpolatory dual framelets, we can further deduce a method to\nconstruct an interpolatory quasi-tight framelet from an arbitrary interpolatory\nfilter. If, in addition, the refinement filters have symmetry, we will perform\na detailed analysis of the symmetry properties that the high-pass filters can\nachieve. We will present several examples to demonstrate our theoretical\nresults.",
        "split": "arXiv"
    },
    {
        "text": "For a set $A$ of positive integers with $\\gcd(A)=1$, let $\\langle A \\rangle$\ndenote the set of all finite linear combinations of elements of $A$ over the\nnon-negative integers. The it is well known that only finitely many positive\nintegers do not belong to $\\langle A \\rangle$. The Frobenius number and the\ngenus associated with the set $A$ is the largest number and the cardinality of\nthe set of integers non-representable by $A$. By a generalized Fibonacci\nsequence $\\{V_n\\}_{n \\ge 1}$ we mean any sequence of positive integers\nsatisfying the recurrence $V_n=V_{n-1}+V_{n-2}$ for $n \\ge 3$. We study the\nproblem of determining the Frobenius number and genus for sets\n$A=\\{V_n,V_{n+d},V_{n+2d},\\ldots\\}$ for arbitrary $n$, where $d$ odd or $d=2$.",
        "split": "arXiv"
    },
    {
        "text": "The process of reconstructing quantum states from experimental measurements,\naccomplished through quantum state tomography (QST), plays a crucial role in\nverifying and benchmarking quantum devices. A key challenge of QST is to find\nout how the accuracy of the reconstruction depends on the number of state\ncopies used in the measurements. When multiple measurement settings are used,\nthe total number of state copies is determined by multiplying the number of\nmeasurement settings with the number of repeated measurements for each setting.\nDue to statistical noise intrinsic to quantum measurements, a large number of\nrepeated measurements is often used in practice. However, recent studies have\nshown that even with single-sample measurements--where only one measurement\nsample is obtained for each measurement setting--high accuracy QST can still be\nachieved with a sufficiently large number of different measurement settings. In\nthis paper, we establish a theoretical understanding of the trade-off between\nthe number of measurement settings and the number of repeated measurements per\nsetting in QST. Our focus is primarily on low-rank density matrix recovery\nusing Pauli measurements. We delve into the global landscape underlying the\nlow-rank QST problem and demonstrate that the joint consideration of\nmeasurement settings and repeated measurements ensures a bounded recovery error\nfor all second-order critical points, to which optimization algorithms tend to\nconverge. This finding suggests the advantage of minimizing the number of\nrepeated measurements per setting when the total number of state copies is held\nfixed. Additionally, we prove that the Wirtinger gradient descent algorithm can\nconverge to the region of second-order critical points with a linear\nconvergence rate. We have also performed numerical experiments to support our\ntheoretical findings.",
        "split": "arXiv"
    },
    {
        "text": "Perfect complementary sequence sets (PCSSs) are widely used in multi-carrier\ncode-division multiple-access (MC-CDMA) communication system. However, the set\nsize of a PCSS is upper bounded by the number of row sequences of each\ntwo-dimensional matrix in PCSS. Then quasi-complementary sequence set (QCSS)\nwas proposed to support more users in MC-CDMA communications. For practical\napplications, it is desirable to construct an $(M,K,N,\\vartheta_{max})$-QCSS\nwith $M$ as large as possible and $\\vartheta_{max}$ as small as possible, where\n$M$ is the number of matrices with $K$ rows and $N$ columns in the set and\n$\\vartheta_{max}$ denotes its periodic tolerance. There exists a tradoff among\nthese parameters and constructing QCSSs achieving or nearly achieving the known\ncorrelation lower bound has been an interesting research topic. Up to now, only\na few constructions of asymptotically optimal or near-optimal periodic QCSSs\nwere reported in the literature. In this paper, we construct five families of\nasymptotically optimal or near-optimal periodic QCSSs with large set sizes and\nlow periodic tolerances. These families of QCSSs have set size $\\Theta(q^2)$ or\n$\\Theta(q^3)$ and flock size $\\Theta(q)$, where $q$ is a power of a prime. To\nthe best of our knowledge, only three known families of periodic QCSSs with set\nsize $\\Theta(q^2)$ and flock size $\\Theta(q)$ were constructed and all other\nknown periodic QCSSs have set sizes much smaller than $\\Theta(q^2)$. Our new\nconstructed periodic QCSSs with set size $\\Theta(q^2)$ and flock size\n$\\Theta(q)$ have better parameters than known ones. They have larger set sizes\nor lower periodic tolerances.The periodic QCSSs with set size $\\Theta(q^3)$ and\nflock size $\\Theta(q)$ constructed in this paper have the largest set size\namong all known families of asymptotically optimal or near-optimal periodic\nQCSSs.",
        "split": "arXiv"
    },
    {
        "text": "Probing and manipulating the spatiotemporal dynamics of hot carriers in\nnanoscale metals is crucial to a plethora of applications ranging from\nnonlinear nanophotonics to single molecule photochemistry. The direct\ninvestigation of these highly non-equilibrium carriers requires the\nexperimental capability of high energy resolution (~ meV) broadband femtosecond\nspectroscopy. When considering the ultimate limits of atomic scale structures,\nthis capability has remained out of reach until date. Using a two color\nfemtosecond pump-probe spectroscopy, we present here the real-time tracking of\nhot carrier dynamics in a well-defined plasmonic picocavity, formed in the\ntunnel junction of a scanning tunneling microscope (STM). The excitation of hot\ncarriers in the picocavity enables ultrafast all optical control over the\nbroadband (~ eV) anti Stokes electronic resonance Raman scattering (ERRS) and\nthe four-wave mixing (FWM) signals generated at the atomic length scale. By\nmapping the ERRS and FWM signals from a single graphene nanoribbon (GNR), we\ndemonstrate that both signals are more efficiently generated along the edges of\nthe GNR: a manifestation of atomic-scale nonlinear optical microscopy. This\ndemonstration paves the way to the development of novel ultrafast nonlinear\npicophotonic platforms, affording unique opportunities in a variety of\ncontexts, from the direct investigation of non equilibrium light matter\ninteractions in complex quantum materials, to the development of robust\nstrategies for hot carriers harvesting in single molecules and the next\ngeneration of active metasurfaces with deep-sub-wavelength meta-atoms.",
        "split": "arXiv"
    },
    {
        "text": "We study timelike supersymmetric solutions of a $D=3, N=4$ gauged\nsupergravity using Killing spinor bilinears method and prove that AdS$_3$ is\nthe only solution within this class. We then consider the ungauged version of\nthis model. It is found that for this type of solutions, the ungauged theory\neffectively truncates to a supergravity coupled to a sigma model with a\n2-dimensional hyperbolic target space $\\mathbb{H}^2$, and all solutions can be\nexpressed in terms of two arbitrary holomorphic functions. The spacetime metric\nis a warped product of the time direction with a 2-dimensional space, and the\nwarp factor is given in terms of the K\\\"ahler potential of $\\mathbb{H}^2$. We\nshow that when the holomorphic function that determines the sigma model scalar\nfields is not constant, the metric on the sigma model target manifold becomes\npart of the spacetime metric. We then look at some special choices for these\nholomorphic functions for which the spacetime metric and the Killing spinors\nare only radial dependent. We also derive supersymmetric null solutions of the\nungauged model which are pp-waves on the Minkowski spacetime.",
        "split": "arXiv"
    },
    {
        "text": "We prove that the Kazhdan-Lusztig basis of Specht modules is upper triangular\nwith respect to all generalized Gelfand-Tsetlin bases constructed from any\nmultiplicity-free tower of standard parabolic subgroups.",
        "split": "arXiv"
    },
    {
        "text": "Fine-tuning large language models (LLMs) is essential for enhancing their\nperformance on specific tasks but is often resource-intensive due to redundant\nor uninformative data. To address this inefficiency, we introduce DELIFT (Data\nEfficient Language model Instruction Fine-Tuning), a novel algorithm that\nsystematically optimizes data selection across the three key stages of\nfine-tuning: (1) instruction tuning, (2) task-specific fine-tuning (e.g.,\nreasoning, question-answering), and (3) continual fine-tuning (e.g.,\nincorporating new data versions). Unlike existing methods that focus on\nsingle-stage optimization or rely on computationally intensive gradient\ncalculations, DELIFT operates efficiently across all stages. Central to our\napproach is a pairwise utility metric that quantifies how beneficial a data\nsample is for improving the model's responses to other samples, effectively\nmeasuring the informational value relative to the model's current capabilities.\nBy leveraging different submodular functions applied to this metric, DELIFT\nselects diverse and optimal subsets that are useful across all stages of\nfine-tuning. Experiments across various tasks and model scales demonstrate that\nDELIFT can reduce the fine-tuning data size by up to 70% without compromising\nperformance, offering significant computational savings and outperforming\nexisting methods in both efficiency and efficacy.",
        "split": "arXiv"
    },
    {
        "text": "Vision-language model (VLM) embeddings have been shown to encode biases\npresent in their training data, such as societal biases that prescribe negative\ncharacteristics to members of various racial and gender identities. VLMs are\nbeing quickly adopted for a variety of tasks ranging from few-shot\nclassification to text-guided image generation, making debiasing VLM embeddings\ncrucial. Debiasing approaches that fine-tune the VLM often suffer from\ncatastrophic forgetting. On the other hand, fine-tuning-free methods typically\nutilize a \"one-size-fits-all\" approach that assumes that correlation with the\nspurious attribute can be explained using a single linear direction across all\npossible inputs. In this work, we propose Bend-VLM, a nonlinear,\nfine-tuning-free approach for VLM embedding debiasing that tailors the\ndebiasing operation to each unique input. This allows for a more flexible\ndebiasing approach. Additionally, we do not require knowledge of the set of\ninputs a priori to inference time, making our method more appropriate for\nonline, open-set tasks such as retrieval and text guided image generation.",
        "split": "arXiv"
    },
    {
        "text": "Over the years, there has been extensive work on fully dynamic algorithms for\nclassic graph problems that admit greedy solutions. Examples include\n$(\\Delta+1)$ vertex coloring, maximal independent set, and maximal matching.\nFor all three problems, there are randomized algorithms that maintain a valid\nsolution after each edge insertion or deletion to the $n$-vertex graph by\nspending $\\polylog n$ time, provided that the adversary is oblivious. However,\nnone of these algorithms work against adaptive adversaries whose updates may\ndepend on the output of the algorithm. In fact, even breaking the trivial bound\nof $O(n)$ against adaptive adversaries remains open for all three problems. For\ninstance, in the case of $(\\Delta+1)$ vertex coloring, the main challenge is\nthat an adaptive adversary can keep inserting edges between vertices of the\nsame color, necessitating a recoloring of one of the endpoints. The trivial\nalgorithm would simply scan all neighbors of one endpoint to find a new\navailable color (which always exists) in $O(n)$ time.\n  In this paper, we break this linear barrier for the $(\\Delta+1)$ vertex\ncoloring problem. Our algorithm is randomized, and maintains a valid\n$(\\Delta+1)$ vertex coloring after each edge update by spending\n$\\widetilde{O}(n^{8/9})$ time with high probability.",
        "split": "arXiv"
    },
    {
        "text": "Reconfigurable holographic surfaces (RHSs) have been suggested as an\nenergy-efficient solution for extremely large-scale arrays. By controlling the\namplitude of RHS elements, high-gain directional holographic patterns can be\nachieved. However, the complexity of acquiring real-time channel state\ninformation (CSI) for beamforming is exceedingly high, particularly in\nlarge-scale RHS-assisted communications, where users may distribute in the\nnear-field region of RHS. This paper proposes a one-shot multi-user beam\ntraining scheme in large-scale RHS-assisted systems applicable to both near and\nfar fields. The proposed beam training scheme comprises two phases: angle\nsearch and distance search, both conducted simultaneously for all users. For\nthe angle search, an RHS angular codebook is designed based on holographic\nprinciples so that each codeword covers multiple angles in both near-field and\nfar-field regions, enabling simultaneous angular search for all users. For the\ndistance search, we construct the distance-adaptive codewords covering all\ncandidate angles of users in a real-time way by leveraging the additivity of\nholographic patterns, which is different from the traditional phase array case.\nSimulation results demonstrate that the proposed scheme achieves higher system\nthroughput compared to traditional beam training schemes. The beam training\naccuracy approaches the upper bound of exhaustive search at a significantly\nreduced overhead.",
        "split": "arXiv"
    },
    {
        "text": "Let us consider the Schr\\\"{o}dinger operator $\\mathcal{L}=-\\Delta+V$ on\n$\\mathbb R^d$ with $d\\geq3$, where $\\Delta$ is the Laplacian operator on\n$\\mathbb R^d$ and the nonnegative potential $V$ belongs to certain reverse\nH\\\"{o}lder class $RH_s$ with $s\\geq d/2$. In this paper, the authors first\nintroduce two kinds of function spaces related to the Schr\\\"{o}dinger operator\n$\\mathcal{L}$. A real-valued function $f\\in L^1_{\\mathrm{loc}}(\\mathbb R^d)$\nbelongs to the (BLO) space $\\mathrm{BLO}_{\\rho,\\theta}(\\mathbb R^d)$ with\n$0\\leq\\theta<\\infty$ if \\begin{equation*} \\|f\\|_{\\mathrm{BLO}_{\\rho,\\theta}}\n:=\\sup_{\\mathcal{Q}}\\bigg(1+\\frac{r}{\\rho(x_0)}\\bigg)^{-\\theta}\\bigg(\\frac{1}{|Q(x_0,r)|}\n\\int_{Q(x_0,r)}\\Big[f(x)-\\underset{y\\in\\mathcal{Q}}{\\mathrm{ess\\,inf}}\\,f(y)\\Big]\\,dx\\bigg),\n\\end{equation*} where the supremum is taken over all cubes\n$\\mathcal{Q}=Q(x_0,r)$ in $\\mathbb R^d$, $\\rho(\\cdot)$ is the critical radius\nfunction in the Schr\\\"{o}dinger context. For $0<\\beta<1$, a real-valued\nfunction $f\\in L^1_{\\mathrm{loc}}(\\mathbb R^d)$ belongs to the (Campanato)\nspace $\\mathcal{C}^{\\beta,\\ast}_{\\rho,\\theta}(\\mathbb R^d)$ with\n$0\\leq\\theta<\\infty$ if \\begin{equation*}\n\\|f\\|_{\\mathcal{C}^{\\beta,\\ast}_{\\rho,\\theta}}\n:=\\sup_{\\mathcal{B}}\\bigg(1+\\frac{r}{\\rho(x_0)}\\bigg)^{-\\theta}\n\\bigg(\\frac{1}{|B(x_0,r)|^{1+\\beta/d}}\\int_{B(x_0,r)}\\Big[f(x)-\\underset{y\\in\\mathcal{B}}{\\mathrm{ess\\,inf}}\\,f(y)\\Big]\\,dx\\bigg),\n\\end{equation*} where the supremum is taken over all balls\n$\\mathcal{B}=B(x_0,r)$ in $\\mathbb R^d$. Then we establish the corresponding\nJohn--Nirenberg inequality suitable for the space\n$\\mathrm{BLO}_{\\rho,\\theta}(\\mathbb R^d)$ with $0\\leq\\theta<\\infty$ and\n$d\\geq3$. Moreover, we give some new characterizations of the BLO and Campanato\nspaces related to $\\mathcal{L}$ on weighted Lebesgue spaces, which is the\nextension of some earlier results.",
        "split": "arXiv"
    },
    {
        "text": "Adversarial attacks pose significant threats to the reliability and safety of\ndeep learning models, especially in critical domains such as medical imaging.\nThis paper introduces a novel framework that integrates conformal prediction\nwith game-theoretic defensive strategies to enhance model robustness against\nboth known and unknown adversarial perturbations. We address three primary\nresearch questions: constructing valid and efficient conformal prediction sets\nunder known attacks (RQ1), ensuring coverage under unknown attacks through\nconservative thresholding (RQ2), and determining optimal defensive strategies\nwithin a zero-sum game framework (RQ3). Our methodology involves training\nspecialized defensive models against specific attack types and employing\nmaximum and minimum classifiers to aggregate defenses effectively. Extensive\nexperiments conducted on the MedMNIST datasets, including PathMNIST,\nOrganAMNIST, and TissueMNIST, demonstrate that our approach maintains high\ncoverage guarantees while minimizing prediction set sizes. The game-theoretic\nanalysis reveals that the optimal defensive strategy often converges to a\nsingular robust model, outperforming uniform and simple strategies across all\nevaluated datasets. This work advances the state-of-the-art in uncertainty\nquantification and adversarial robustness, providing a reliable mechanism for\ndeploying deep learning models in adversarial environments.",
        "split": "arXiv"
    },
    {
        "text": "The interest in developing small language models (SLM) for on-device\ndeployment is fast growing. However, the existing SLM design hardly considers\nthe device hardware characteristics. Instead, this work presents a simple yet\neffective principle for SLM design: architecture searching for (near-)optimal\nruntime efficiency before pre-training. Guided by this principle, we develop\nPhoneLM SLM family (currently with 0.5B and 1.5B versions), that acheive the\nstate-of-the-art capability-efficiency tradeoff among those with similar\nparameter size. We fully open-source the code, weights, and training datasets\nof PhoneLM for reproducibility and transparency, including both base and\ninstructed versions. We also release a finetuned version of PhoneLM capable of\naccurate Android Intent invocation, and an end-to-end Android demo. All\nmaterials are available at https://github.com/UbiquitousLearning/PhoneLM.",
        "split": "arXiv"
    },
    {
        "text": "We present GazeGen, a user interaction system that generates visual content\n(images and videos) for locations indicated by the user's eye gaze. GazeGen\nallows intuitive manipulation of visual content by targeting regions of\ninterest with gaze. Using advanced techniques in object detection and\ngenerative AI, GazeGen performs gaze-controlled image adding/deleting,\nrepositioning, and surface style changes of image objects, and converts static\nimages into videos. Central to GazeGen is the DFT Gaze (Distilled and\nFine-Tuned Gaze) agent, an ultra-lightweight model with only 281K parameters,\nperforming accurate real-time gaze predictions tailored to individual users'\neyes on small edge devices. GazeGen is the first system to combine visual\ncontent generation with real-time gaze estimation, made possible exclusively by\nDFT Gaze. This real-time gaze estimation enables various visual content\ngeneration tasks, all controlled by the user's gaze. The input for DFT Gaze is\nthe user's eye images, while the inputs for visual content generation are the\nuser's view and the predicted gaze point from DFT Gaze. To achieve efficient\ngaze predictions, we derive the small model from a large model (10x larger) via\nnovel knowledge distillation and personal adaptation techniques. We integrate\nknowledge distillation with a masked autoencoder, developing a compact yet\npowerful gaze estimation model. This model is further fine-tuned with Adapters,\nenabling highly accurate and personalized gaze predictions with minimal user\ninput. DFT Gaze ensures low-latency and precise gaze tracking, supporting a\nwide range of gaze-driven tasks. We validate the performance of DFT Gaze on AEA\nand OpenEDS2020 benchmarks, demonstrating low angular gaze error and low\nlatency on the edge device (Raspberry Pi 4). Furthermore, we describe\napplications of GazeGen, illustrating its versatility and effectiveness in\nvarious usage scenarios.",
        "split": "arXiv"
    },
    {
        "text": "Sample selection problems arise when treatment affects both the outcome and\nthe researcher's ability to observe it. This paper generalizes Lee (2009)\nbounds for the average treatment effect of a binary treatment to a\ncontinuous/multivalued treatment. We evaluate the Job Crops program to study\nthe causal effect of training hours on wages. To identify the average treatment\neffect of always-takers who are selected regardless of the treatment values, we\nassume that if a subject is selected at some sufficient treatment values, then\nit remains selected at all treatment values. For example, if program\nparticipants are employed with one month of training, then they remain employed\nwith any training hours. This sufficient treatment values assumption includes\nthe monotone assumption on the treatment effect on selection as a special case.\nWe further allow the conditional independence assumption and subjects with\ndifferent pretreatment covariates to have different sufficient treatment\nvalues. The estimation and inference theory utilize the orthogonal moment\nfunction and cross-fitting for double debiased machine learning.",
        "split": "arXiv"
    },
    {
        "text": "Large language models (LLMs) offer promise in generating educational content,\nproviding instructor feedback, and reducing teacher workload on assessments.\nWhile prior studies have focused on studying LLM-powered learning analytics,\nlimited research has examined how effective LLMs are in a bilingual context. In\nthis paper, we study the effectiveness of multilingual large language models\n(MLLMs) across monolingual (English-only, Spanish-only) and bilingual\n(Spanglish) student writing. We present a learning analytics use case that\ndetails LLM performance in assessing acceptable and unacceptable explanations\nof Science and Social Science concepts. Our findings reveal a significant bias\nin the grading performance of pre-trained models for bilingual writing compared\nto English-only and Spanish-only writing. Following this, we fine-tune\nopen-source MLLMs including Llama 3.1 and Mistral NeMo using synthetic datasets\ngenerated in English, Spanish, and Spanglish. Our experiments indicate that the\nmodels perform significantly better for all three languages after fine-tuning\nwith bilingual data. This study highlights the potential of enhancing MLLM\neffectiveness to support authentic language practices amongst bilingual\nlearners. It also aims to illustrate the value of incorporating non-English\nlanguages into the design and implementation of language models in education.",
        "split": "arXiv"
    },
    {
        "text": "Amazon is the world number one online retailer and has nearly every product a\nperson could need along with a treasure trove of product reviews to help\nconsumers make educated purchases. Companies want to find a way to increase\ntheir sales in a very crowded market, and using this data is key. A very good\nindicator of how a product is selling is its sales rank; which is calculated\nbased on all-time sales of a product where recent sales are weighted more than\nolder sales. Using the data from the Amazon products and reviews we determined\nthat the most influential factors in determining the sales rank of a product\nwere the number of products Amazon showed that other customers also bought, the\nnumber of products Amazon showed that customers also viewed, and the price of\nthe product. These results were consistent for the Digital Music category, the\nOffice Products category, and the subcategory Holsters under Cell Phones and\nAccessories.",
        "split": "arXiv"
    },
    {
        "text": "We revisit integrity checking in relational and deductive databases with an\napproach that tolerates erroneous, inconsistent data. In particular, we relax\nthe fundamental prerequisite that, in order to apply any method for simplified\nintegrity checking, all data must initially have integrity. As opposed to a\nlong-standing belief, integrity in the old state before the update is not\nneeded for a correct application of simplification methods. Rather, we show\nthat correct simplifications preserve what was consistent across updates. We\nformally characterize this property, that we call inconsistency tolerance, and\nstate its validity for some well-known methods for integrity checking.",
        "split": "arXiv"
    },
    {
        "text": "Drought has been perceived as a persistent threat globally and the complex\nmechanism of various factors contributing to its emergence makes it more\ntroublesome to understand. Droughts and their severity trends have been a point\nof concern in the USA as well, since the economic impact of droughts has been\nsubstantial, especially in parts that contribute majorly to US agriculture.\nCalifornia is the biggest agricultural contributor to the United States with\nits share amounting up to 12% approximately for all of US agricultural produce.\nAlthough, according to a 20-year average, California ranks fifth on the list of\nthe highest average percentage of drought-hit regions. Therefore, drought\nanalysis and drought prediction are of crucial importance for California in\norder to mitigate the associated risks. However, the design of a consistent\ndrought prediction model based on the dynamic relationship of the drought index\nremains a challenging task. In the present study, we trained a Voting Ensemble\nclassifier utilizing a soft voting system and three different Random Forest\nmodels, to predict the presence of drought and also its intensity. In this\npaper, initially, we have discussed the trends of droughts and their\nintensities in various California counties reviewed the correlation of\nmeteorological indicators with drought intensities and used these\nmeteorological indicators for drought prediction so as to evaluate their\neffectiveness as well as significance.",
        "split": "arXiv"
    },
    {
        "text": "Artificial Intelligence (AI) techniques, especially Large Language Models\n(LLMs), have started gaining popularity among researchers and software\ndevelopers for generating source code. However, LLMs have been shown to\ngenerate code with quality issues and also incurred copyright/licensing\ninfringements. Therefore, detecting whether a piece of source code is written\nby humans or AI has become necessary. This study first presents an empirical\nanalysis to investigate the effectiveness of the existing AI detection tools in\ndetecting AI-generated code. The results show that they all perform poorly and\nlack sufficient generalizability to be practically deployed. Then, to improve\nthe performance of AI-generated code detection, we propose a range of\napproaches, including fine-tuning the LLMs and machine learning-based\nclassification with static code metrics or code embedding generated from\nAbstract Syntax Tree (AST). Our best model outperforms state-of-the-art\nAI-generated code detector (GPTSniffer) and achieves an F1 score of 82.55. We\nalso conduct an ablation study on our best-performing model to investigate the\nimpact of different source code features on its performance.",
        "split": "arXiv"
    },
    {
        "text": "We present new ALMA observations of a starburst galaxy at cosmic noon hosting\na radio-loud active galactic nucleus: PKS 0529-549 at $z=2.57$. To investigate\nthe conditions of its cold interstellar medium, we use ALMA observations which\nspatially resolve the [CI] fine-structure lines, [CI] (2-1) and [CI] (1-0), CO\nrotational lines, CO (7-6) and CO (4-3), and the rest-frame continuum emission\nat 461 and 809 GHz. The four emission lines display different morphologies,\nsuggesting spatial variation in the gas excitation conditions. The radio jets\nhave just broken out of the molecular gas but not through the more extended\nionized gas halo. The [CI] (2-1) emission is more extended ($\\approx8\\,{\\rm\nkpc}\\times5\\,{\\rm kpc}$) than detected in previous shallower ALMA observations.\nThe [CI] luminosity ratio implies an excitation temperature of $44\\pm16$ K,\nsimilar to the dust temperature. Using the [CI] lines, CO (4-3), and 227 GHz\ndust continuum, we infer the mass of molecular gas $M_{\\mathrm{mol}}$ using\nthree independent approaches and typical assumptions in the literature. All\napproaches point to a massive molecular gas reservoir of about $10^{11}$\n$M_{\\odot}$, but the exact values differ by up to a factor of 4. Deep\nobservations are critical in correctly characterizing the distribution of cold\ngas in high-redshift galaxies, and highlight the need to improve systematic\nuncertainties in inferring accurate molecular gas masses.",
        "split": "arXiv"
    },
    {
        "text": "Background: The rate of energy production in the hot-CNO cycle and breakout\nto the rapid-proton capture process in Type I X-ray bursts is strongly related\nto the $^{14}$O($\\alpha,p$)$^{17}$F reaction rate. The properties of states in\n$^{18}$Ne near $E_x=6.1-6.3$ MeV are important for understanding this reaction\nrate.\n  Experiment: The RESOLUT radioactive-ion beam facility at Florida State\nUniversity was used to study $^{18}$Ne resonances around this energy region\nusing $^{17}$F(p,p)$^{17}$F elastic scattering on a polypropylene target under\ninverse kinematics. Scattered protons were detected in a silicon-strip detector\narray while recoiling $^{17}$F ions were detected in coincidence in a gas\nionization detector.\n  Analysis: An $R$-matrix analysis of measured cross sections was conducted\nalong with a reanalysis of data from previous measurements.\n  Results: All the data analyzed are well described by a consistent set of\nparameters with with a $1^-$ assignment for a state at 6.14(1) MeV. A second\ncomparable solution is also found with a $3^-$ assignment for the 6.14(1) MeV\nstate. The rate of the $^{14}$O($\\alpha$,p)$^{17}$F reaction that is determined\nfrom the two solutions differs by up to an order of magnitude.",
        "split": "arXiv"
    },
    {
        "text": "We conduct a scoping review of existing approaches for synthetic EHR data\ngeneration, and benchmark major methods with proposed open-source software to\noffer recommendations for practitioners. We search three academic databases for\nour scoping review. Methods are benchmarked on open-source EHR datasets,\nMIMIC-III/IV. Seven existing methods covering major categories and two baseline\nmethods are implemented and compared. Evaluation metrics concern data fidelity,\ndownstream utility, privacy protection, and computational cost. 42 studies are\nidentified and classified into five categories. Seven open-source methods\ncovering all categories are selected, trained on MIMIC-III, and evaluated on\nMIMIC-III or MIMIC-IV for transportability considerations. Among them,\nGAN-based methods demonstrate competitive performance in fidelity and utility\non MIMIC-III; rule-based methods excel in privacy protection. Similar findings\nare observed on MIMIC-IV, except that GAN-based methods further outperform the\nbaseline methods in preserving fidelity. A Python package, ``SynthEHRella'', is\nprovided to integrate various choices of approaches and evaluation metrics,\nenabling more streamlined exploration and evaluation of multiple methods. We\nfound that method choice is governed by the relative importance of the\nevaluation metrics in downstream use cases. We provide a decision tree to guide\nthe choice among the benchmarked methods. Based on the decision tree, GAN-based\nmethods excel when distributional shifts exist between the training and testing\npopulations. Otherwise, CorGAN and MedGAN are most suitable for association\nmodeling and predictive modeling, respectively. Future research should\nprioritize enhancing fidelity of the synthetic data while controlling privacy\nexposure, and comprehensive benchmarking of longitudinal or conditional\ngeneration methods.",
        "split": "arXiv"
    },
    {
        "text": "We propose a novel technique for optimizing a modular fault-tolerant quantum\ncomputing architecture, taking into account any desired space-time trade--offs\nbetween the number of physical qubits and the fault-tolerant execution time of\na quantum algorithm. We consider a concept architecture comprising a dedicated\nzone as a multi-level magic state factory and a core processor for efficient\nlogical operations, forming a supply chain network for production and\nconsumption of magic states. Using a heuristic algorithm, we solve the\nmulti-objective optimization problem of minimizing space and time subject to a\nuser-defined error budget for the success of the computation, taking the\nperformance of various fault-tolerant protocols such as quantum memory, state\npreparation, magic state distillation, code growth, and logical operations into\naccount. As an application, we show that physical quantum resource estimation\nreduces to a simple model involving a small number of key parameters, namely,\nthe circuit volume, the error prefactors ($\\mu$) and error suppression rates\n($\\Lambda$) of the fault-tolerant protocols, and an allowed slowdown factor\n($\\beta$). We show that, in the proposed architecture, $10^5$--$10^8$ physical\nqubits are required for quantum algorithms with $T$-counts in the range\n$10^6$--$10^{15}$ and logical qubit counts in the range $10^2$--$10^4$, when\nrun on quantum computers with quantum memory $\\Lambda$ in the range 3--10, for\nall slowdown factors $\\beta \\geq 0.2$.",
        "split": "arXiv"
    },
    {
        "text": "The Ramsey number $R(s,t)$ is the smallest integer $n$ such that all graphs\nof size $n$ contain a clique of size $s$ or an independent set of size $t$.\n$\\mathcal{R}(s,t,n)$ is the set of all counterexample graphs without this\nproperty for a given $n$. We prove that if a graph $G_{n+1}$ of size $n+1$ has\n$\\max\\{s,t\\}+1$ subgraphs in $\\mathcal{R}(s,t,n)$, then $G_{n+1}$ is in\n$\\mathcal{R}(s,t,n+1)$. Based on this, we introduce algorithms for one-vertex\nextension and counterexample checking with runtime linearly bound by $s$ and\n$t$. We prove the utility of these algorithms by verifying\n$\\mathcal{R}(4,6,36)$ and $\\mathcal{R}(5,5,43)$ are empty given current sets\n$\\mathcal{R}(4,6,35)$ and $\\mathcal{R}(5,5,42)$.",
        "split": "arXiv"
    },
    {
        "text": "Graph neural networks (GNNs) provide state-of-the-art results in a wide\nvariety of tasks which typically involve predicting features at the vertices of\na graph. They are built from layers of graph convolutions which serve as a\npowerful inductive bias for describing the flow of information among the\nvertices. Often, more than one data modality is available. This work considers\na setting in which several graphs have the same vertex set and a common\nvertex-level learning task. This generalizes standard GNN models to GNNs with\nseveral graph operators that do not commute. We may call this model graph-tuple\nneural networks (GtNN).\n  In this work, we develop the mathematical theory to address the stability and\ntransferability of GtNNs using properties of non-commuting non-expansive\noperators. We develop a limit theory of graphon-tuple neural networks and use\nit to prove a universal transferability theorem that guarantees that all\ngraph-tuple neural networks are transferable on convergent graph-tuple\nsequences. In particular, there is no non-transferable energy under the\nconvergence we consider here. Our theoretical results extend well-known\ntransferability theorems for GNNs to the case of several simultaneous graphs\n(GtNNs) and provide a strict improvement on what is currently known even in the\nGNN case.\n  We illustrate our theoretical results with simple experiments on synthetic\nand real-world data. To this end, we derive a training procedure that provably\nenforces the stability of the resulting model.",
        "split": "arXiv"
    },
    {
        "text": "Proxima Cen (GJ 551; dM5.5e) is one of only about a dozen fully convective\nstars known to have a stellar cycle, and the only one to have long-term X-ray\nmonitoring. A previous analysis found that X-ray and mid-UV observations,\nparticularly two epochs of data from Swift, were consistent with a well sampled\n7 yr optical cycle seen in ASAS data, but not convincing by themselves. The\npresent work incorporates several years of new ASAS-SN optical data and an\nadditional five years of Swift XRT and UVOT observations, with Swift\nobservations now spanning 2009 to 2021 and optical coverage from late 2000.\nX-ray observations by XMM-Newton and Chandra are also included. Analysis of the\ncombined data, which includes modeling and adjustments for stellar\ncontamination in the optical and UV, now reveals clear cyclic behavior in all\nthree wavebands with a period of 8.0 yr. We also show that UV and X-ray\nintensities are anti-correlated with optical brightness variations caused by\nthe cycle and by rotational modulation, discuss possible indications of two\ncoronal mass ejections, and provide updated results for the previous finding of\na simple correlation between X-ray cycle amplitude and Rossby number over a\nwide range of stellar types and ages.",
        "split": "arXiv"
    },
    {
        "text": "We investigate the time evolution generated by the two-sided chord\nHamiltonian in the double-scaled SYK model, which produces a probability\ndistribution over operators in the double-scaled algebra. Via the\nbulk-to-boundary map, this distribution translates into dynamic profiles of\nbulk states within the chord Hilbert space. We derive analytic expressions for\nthese states, valid across a wide parameter range and at all time scales.\nAdditionally, we show how distinct semi-classical behaviors emerge by\nlocalizing within specific regions of the energy spectrum in the semi-classical\nlimit.\n  We reformulate the doubled Hilbert space formalism as an isometric map\nbetween the one-particle sector of the chord Hilbert space and the doubled\nzero-particle sector. Using this map, we obtain analytic results for\ncorrelation functions and examine the dynamical properties of operator Krylov\ncomplexity for chords, establishing an equivalence between the chord number\ngenerating function and the crossed four-point correlation function. We also\nconsider finite-temperature effects, showing how operator spreading slows as\ntemperature decreases.\n  In the semi-classical limit, we apply a saddle point analysis and include the\none-loop determinant to derive the normalized time-ordered four-point\ncorrelation function. The leading correction mirrors the \\(1/N\\) connected\ncontribution observed in the large-\\(p\\) SYK model at infinite temperature.\nFinally, we analyze the time evolution of operator Krylov complexity for a\nmatter chord in the triple-scaled regime, linking it to the renormalized\ntwo-sided length in JT gravity with matter.",
        "split": "arXiv"
    },
    {
        "text": "In many causal learning problems, variables of interest are often not all\nmeasured over the same observations, but are instead distributed across\nmultiple datasets with overlapping variables. Tillman et al. (2008) presented\nthe first algorithm for enumerating the minimal equivalence class of\nground-truth DAGs consistent with all input graphs by exploiting local\nindependence relations, called ION. In this paper, this problem is formulated\nas a more computationally efficient answer set programming (ASP) problem, which\nwe call ION-C, and solved with the ASP system clingo. The ION-C algorithm was\nrun on random synthetic graphs with varying sizes, densities, and degrees of\noverlap between subgraphs, with overlap having the largest impact on runtime,\nnumber of solution graphs, and agreement within the output set. To validate\nION-C on real-world data, we ran the algorithm on overlapping graphs learned\nfrom data from two successive iterations of the European Social Survey (ESS),\nusing a procedure for conducting joint independence tests to prevent\ninconsistencies in the input.",
        "split": "arXiv"
    },
    {
        "text": "Let $\\mathcal{H}$ be the space of all functions that are analytic in\n$\\mathbb{D}$. Let $\\mathcal{A}$ denote the family of all functions\n$f\\in\\mathcal{H}$ and normalized by the conditions $f(0)=0=f'(0)-1$. In 2011,\nObradovi\\'{c} and Ponnusamy introduced the class $\\mathcal{M}(\\lambda)$ of all\nfunctions $f\\in\\mathcal{A}$ satisfying the condition\n$\\left|z^2\\left(z/f(z)\\right)''+f'(z)\\left(z/f(z)\\right)^2-1\\right|\\leq\n\\lambda$ for $z\\in\\mathbb{D}$ with $\\lambda>0$. We show that the class\n$\\mathcal{M}(\\lambda)$ is preserved under omitted-value transformation, but\nthis class is not preserved under dilation. In this paper, we investigate the\nlargest disk in which the property of preservation under dilation of the class\n$\\mathcal{M}:=\\mathcal{M}(1)$ holds. We also address a radius property of the\nclass $\\mathcal{M}(\\lambda)$ and a number of associated results pertaining to\n$\\mathcal{M}$. Furthermore, we examine the largest disks with sharp radius for\nwhich the functions $F$ defined by the relations $g(z)h(z)/z$, $z^2/g(z)$, and\n$z^2/\\int_0^z (t/g(t))dt$ belong to the class $\\mathcal{M}$, where $g$ and $h$\nbelong to some suitable subclasses of $\\mathcal{S}$, the class of univalent\nfunctions from $\\mathcal{A}$. In the final analysis, we obtain the sharp Bohr\nradius, Bohr-Rogosinski radius and improved Bohr radius for a certain subclass\nof starlike functions.",
        "split": "arXiv"
    },
    {
        "text": "A hypersurface $M^n$ in a real space form ${\\bf R}^{n+1}$, $S^{n+1}$, or\n$H^{n+1}$ is isoparametric if it has constant principal curvatures. This paper\nis a survey of the fundamental work of Cartan and M\\\"{u}nzner on the theory of\nisoparametric hypersurfaces in real space forms, in particular, spheres. This\nwork is contained in four papers of Cartan published during the period\n1938--1940, and two papers of M\\\"{u}nzner that were published in preprint form\nin the early 1970's, and as journal articles in 1980--1981. These papers of\nCartan and M\\\"{u}nzner have been the foundation of the extensive field of\nisoparametric hypersurfaces, and they have all been recently translated into\nEnglish by the author. The paper concludes with a brief survey of the recently\ncompleted classification of isoparametric hypersurfaces in spheres.",
        "split": "arXiv"
    },
    {
        "text": "We explicitly compute the effective action from Open Superstring Field Theory\nin the hybrid formalism to quartic order in the $\\alpha'\\rightarrow 0$ limit,\nand show that it reproduces ten-dimensional Super Yang-Mills in terms of\nfour-dimensional superfields. We also show that in this limit the gauge\ntransformations coincide with SYM to all orders, which means that the effective\naction should reproduce SYM to all orders.",
        "split": "arXiv"
    },
    {
        "text": "We consider the quantum magic in systems of dense neutrinos undergoing\ncoherent flavor transformations, relevant for supernova and neutron-star binary\nmergers. Mapping the three-flavor-neutrino system to qutrits, the evolution of\nquantum magic is explored in the single scattering angle limit for a selection\nof initial tensor-product pure states for $N_\\nu \\le 8$ neutrinos. For\n$|\\nu_e\\rangle^{\\otimes N_\\nu}$ initial states, the magic, as measured by the\n$\\alpha=2$ stabilizer Renyi entropy $M_2$, is found to decrease with radial\ndistance from the neutrino sphere, reaching a value that lies below the maximum\nfor tensor-product qutrit states. Further, the asymptotic magic per neutrino,\n$M_2/N_\\nu$, decreases with increasing $N_\\nu$. In contrast, the magic evolving\nfrom states containing all three flavors reaches values only possible with\nentanglement, with the asymptotic $M_2/N_\\nu$ increasing with $N_\\nu$. These\nresults highlight the connection between the complexity in simulating quantum\nphysical systems and the parameters of the Standard Model.",
        "split": "arXiv"
    },
    {
        "text": "In this study, we aim to enhance radiology reporting by improving both the\nconciseness and structured organization of findings (also referred to as\ntemplating), specifically by organizing information according to anatomical\nregions. This structured approach allows physicians to locate relevant\ninformation quickly, increasing the report's utility. We utilize Large Language\nModels (LLMs) such as Mixtral, Mistral, and Llama to generate concise,\nwell-structured reports. Among these, we primarily focus on the Mixtral model\ndue to its superior adherence to specific formatting requirements compared to\nother models. To maintain data security and privacy, we run these LLMs locally\nbehind our institution's firewall. We leverage the LangChain framework and\napply five distinct prompting strategies to enforce a consistent structure in\nradiology reports, aiming to eliminate extraneous language and achieve a high\nlevel of conciseness. We also introduce a novel metric, the Conciseness\nPercentage (CP) score, to evaluate report brevity. Our dataset comprises 814\nradiology reports authored by seven board-certified body radiologists at our\ncancer center. In evaluating the different prompting methods, we discovered\nthat the most effective approach for generating concise, well-structured\nreports involves first instructing the LLM to condense the report, followed by\na prompt to structure the content according to specific guidelines. We assessed\nall prompting strategies based on their ability to handle formatting issues,\nreduce report length, and adhere to formatting instructions. Our findings\ndemonstrate that open-source, locally deployed LLMs can significantly improve\nradiology report conciseness and structure while conforming to specified\nformatting standards.",
        "split": "arXiv"
    },
    {
        "text": "Coherent Elastic Neutrino-Nucleus (CE$\\nu$NS) and Elastic Neutrino-Electron\nScattering (E$\\nu$ES) data are exploited to constrain \"chiral\" $U(1)_{X}$\ngauged models with light vector mediator mass. These models fall under a\ndistinct class of new symmetries called Dark Hypercharge Symmetries. A key\nfeature is the fact that the $Z'$ boson can couple to all Standard Model\nfermions at tree level, with the $U(1)_X$ charges determined by the requirement\nof anomaly cancellation. Notably, the charges of leptons and quarks can differ\nsignificantly depending on the specific anomaly cancellation solution. As a\nresult, different models exhibit distinct phenomenological signatures and can\nbe constrained through various experiments. In this work, we analyze the recent\ndata from the COHERENT experiment, along with results from Dark Matter (DM)\ndirect detection experiments such as XENONnT, LUX-ZEPLIN, and PandaX-4T, and\nplace new constraints on three benchmark models. Additionally, we set\nconstraints from a performed analysis of TEXONO data and discuss the prospects\nof improvement in view of the next-generation DM direct detection DARWIN\nexperiment.",
        "split": "arXiv"
    },
    {
        "text": "We report the discovery of the first example of an Einstein zig-zag lens, an\nextremely rare lensing configuration. In this system, J1721+8842, six images of\nthe same background quasar are formed by two intervening galaxies, one at\nredshift $z_1 = 0.184$ and a second one at $z_2 = 1.885$. Two out of the six\nmultiple images are deflected in opposite directions as they pass the first\nlens galaxy on one side, and the second on the other side -- the optical paths\nforming zig-zags between the two deflectors. In this letter, we demonstrate\nthat J1721+8842, previously thought to be a lensed dual quasar, is in fact a\ncompound lens with the more distant lens galaxy also being distorted as an arc\nby the foreground galaxy. Evidence supporting this unusual lensing scenario\nincludes: 1- identical light curves in all six lensed quasar images obtained\nfrom two years of monitoring at the Nordic Optical Telescope; 2- detection of\nthe additional deflector at redshift $z_2 = 1.885$ in JWST/NIRSpec IFU data;\nand 3- a multiple-plane lens model reproducing the observed image positions.\nThis unique configuration offers the opportunity to combine two major lensing\ncosmological probes: time-delay cosmography and dual source-plane lensing since\nJ1721+8842 features multiple lensed sources forming two distinct Einstein radii\nof different sizes, one of which being a variable quasar. We expect tight\nconstraints on the Hubble constant and the equation of state of dark energy by\ncombining these two probes on the same system. The $z_2 = 1.885$ deflector, a\nquiescent galaxy, is also the highest-redshift strong galaxy-scale lens with a\nspectroscopic redshift measurement.",
        "split": "arXiv"
    },
    {
        "text": "In this letter, we present a new formulation of loss cone theory as a\nreaction-diffusion system, which is orbit averaged and accounts for loss cone\nevents through a sink term. This formulation can recover the standard approach\nbased on boundary conditions, and is derived from a simple physical model that\novercomes many of the classical theoretical constraints. The relaxed\ndistribution of disruptive orbits in phase space has a simple analytic form,\nand it predicts accurately the pericentre of tidal disruption events at\ndisruption, better than other available formulas. This formulation of the\nproblem is particularly suitable for including more physics in tidal\ndisruptions and the analogous problem of gravitational captures, e.g. strong\nscatterings, gravitational waves emission, physical stellar collisions, and\nrepeating partial disruptions - that can all act on timescale shorter than\ntwo-body relaxation. This allows to explore in a simple way dynamical effects\nthat might affect tidal disruption events rates, tackling the expected vs\nobserved rate tension and the over-representation of E+A galaxies.",
        "split": "arXiv"
    },
    {
        "text": "Quantum materials governed by emergent topological fermions have become a\ncornerstone of physics. Dirac fermions in graphene form the basis for moir\\'e\nquantum matter, and Dirac fermions in magnetic topological insulators enabled\nthe discovery of the quantum anomalous Hall effect. In contrast, there are few\nmaterials whose electromagnetic response is dominated by emergent Weyl\nfermions. Nearly all known Weyl materials are overwhelmingly metallic, and are\nlargely governed by irrelevant, conventional electrons. Here we theoretically\npredict and experimentally observe a semimetallic Weyl ferromagnet in van der\nWaals (Cr,Bi)$_2$Te$_3$. In transport, we find a record bulk anomalous Hall\nangle $> 0.5$ along with non-metallic conductivity, a regime sharply distinct\nfrom conventional ferromagnets. Together with symmetry analysis, our data\nsuggest a semimetallic Fermi surface composed of two Weyl points, with a giant\nseparation $> 75\\%$ of the linear dimension of the bulk Brillouin zone, and no\nother electronic states. Using state-of-the-art crystal synthesis techniques,\nwe widely tune the electronic structure, allowing us to annihilate the Weyl\nstate and visualize a unique topological phase diagram exhibiting broad Chern\ninsulating, Weyl semimetallic and magnetic semiconducting regions. Our\nobservation of a semimetallic Weyl ferromagnet offers an avenue toward novel\ncorrelated states and non-linear phenomena, as well as zero-magnetic-field Weyl\nspintronic and optical devices.",
        "split": "arXiv"
    },
    {
        "text": "While many statistical properties of deep random quantum circuits can be\ndeduced, often rigorously and other times heuristically, by an approximation to\nglobal Haar-random unitaries, the statistics of constant-depth random quantum\ncircuits are generally less well-understood due to a lack of amenable tools and\ntechniques. We circumvent this barrier by considering a related constant-time\nBrownian circuit model which shares many similarities with constant-depth\nrandom quantum circuits but crucially allows for direct calculations of higher\norder moments of its output distribution. Using mean-field (large-n)\ntechniques, we fully characterize the output distributions of Brownian circuits\nat shallow depths and show that they follow a Porter-Thomas distribution, just\nlike in the case of deep circuits, but with a truncated Hilbert space. The\naccess to higher order moments allows for studying the expected and typical\nLinear Cross-entropy (XEB) benchmark scores achieved by an ideal quantum\ncomputer versus the state-of-the-art classical spoofers for shallow Brownian\ncircuits. We discover that for these circuits, while the quantum computer\ntypically scores within a constant factor of the expected value, the classical\nspoofer suffers from an exponentially larger variance. Numerical evidence\nsuggests that the same phenomenon also occurs in constant-depth discrete random\nquantum circuits, like those defined over the all-to-all architecture. We\nconjecture that the same phenomenon is also true for random brickwork circuits\nin high enough spatial dimension.",
        "split": "arXiv"
    },
    {
        "text": "The recent experimental observation of quantum anomalous Hall (QAH) effects\nin the rhombohedrally stacked pentalayer graphene has motivated theoretical\ndiscussions on the possibility of quantum anomalous Hall crystal (QAHC), a\ntopological version of Wigner crystal. Conventionally Wigner crystal was\nassumed to have a period $a_{\\text{crystal}}=1/\\sqrt{n}$ locked to the density\n$n$. In this work we propose new types of topological Wigner crystals labeled\nas QAHC-$z$ with period $a_{\\text{crystal}}=\\sqrt{z/n}$. In rhombohedrally\nstacked graphene aligned with hexagon boron nitride~(hBN), we find parameter\nregimes where QAHC-2 and QAHC-3 have lower energy than the conventional QAHC-1\nat total filling $\\nu=1$ per moir\\'e unit cell. These states all have total\nChern number $C_\\mathrm{tot}=1$ and are consistent with the QAH effect observed\nin the experiments. The larger period QAHC states have better kinetic energy\ndue to the unique Mexican-hat dispersion of the pentalayer graphene, which can\ncompensate for the loss in the interaction energy. Unlike QAHC-1, QAHC-2 and\nQAHC-3 also break the moir\\'e translation symmetry and are sharply distinct\nfrom a moir\\'e band insulator. We also briefly discuss the competition between\ninteger QAHC and fractional QAHC states at filling $\\nu=2/3$. Besides, we\nnotice the importance of the moir\\'e potential. A larger moir\\'e potential can\ngreatly change the phase diagram and even favors a QAHC-1 ansatz with $C=2$\nChern band.",
        "split": "arXiv"
    },
    {
        "text": "We show that all totally positive formal power series with integer\ncoefficients and constant term $1$ are precisely the rank-generating functions\nof Schur-positive upho posets, thereby resolving the main conjecture proposed\nby Gao, Guo, Seetharaman, and Seidel. To achieve this, we construct a bijection\nbetween finitary colored upho posets and atomic, left-cancellative,\ninvertible-free monoids, which restricts to a correspondence between\n$\\mathbb{N}$-graded colored upho posets and left-cancellative homogeneous\nmonoids. Furthermore, we introduce semi-upho posets and develop a convolution\noperation on colored upho posets with colored semi-upho posets within this\nmonoid-theoretic framework.",
        "split": "arXiv"
    },
    {
        "text": "Several recent works seek to develop foundation models specifically for\nmedical applications, adapting general-purpose large language models (LLMs) and\nvision-language models (VLMs) via continued pretraining on publicly available\nbiomedical corpora. These works typically claim that such domain-adaptive\npretraining (DAPT) improves performance on downstream medical tasks, such as\nanswering medical licensing exam questions. In this paper, we compare seven\npublic \"medical\" LLMs and two VLMs against their corresponding base models,\narriving at a different conclusion: all medical VLMs and nearly all medical\nLLMs fail to consistently improve over their base models in the zero-/few-shot\nprompting regime for medical question-answering (QA) tasks. For instance,\nacross the tasks and model pairs we consider in the 3-shot setting, medical\nLLMs only outperform their base models in 12.1% of cases, reach a (statistical)\ntie in 49.8% of cases, and are significantly worse than their base models in\nthe remaining 38.2% of cases. Our conclusions are based on (i) comparing each\nmedical model head-to-head, directly against the corresponding base model; (ii)\noptimizing the prompts for each model separately; and (iii) accounting for\nstatistical uncertainty in comparisons. While these basic practices are not\nconsistently adopted in the literature, our ablations show that they\nsubstantially impact conclusions. Our findings suggest that state-of-the-art\ngeneral-domain models may already exhibit strong medical knowledge and\nreasoning capabilities, and offer recommendations to strengthen the conclusions\nof future studies.",
        "split": "arXiv"
    },
    {
        "text": "We define Poisson genericity for infinite sequences in any finite or\ncountable alphabet with an invariant exponentially-mixing probability measure.\nA sequence is Poisson generic if the number of occurrences of blocks of symbols\nasymptotically follows a Poisson law as the block length increases. We prove\nthat almost all sequences are Poisson generic. Our result generalizes Peres and\nWeiss' theorem about Poisson genericity of integral bases numeration systems.\nIn particular, we obtain that their continued fraction expansions for almost\nall real numbers are Poisson generic.",
        "split": "arXiv"
    },
    {
        "text": "We investigate the task of deterministically condensing randomness from\nOnline Non-Oblivious Symbol Fixing (oNOSF) sources, a natural model for which\nextraction is impossible [AORSV, EUROCRYPT'20]. A $(g,\\ell)$-oNOSF source is a\nsequence of $\\ell$ blocks where at least $g$ of the blocks are good\n(independent and have some min-entropy) and the remaining bad blocks are\ncontrolled by an online adversary where each bad block can be arbitrarily\ncorrelated with any block that appears before it.\n  The existence of condensers was studied in [CGR, FOCS'24]. They proved\ncondensing impossibility results for various values of $g, \\ell$ and showed the\nexistence of condensers matching the impossibility results in the case when $n$\nis extremely large compared to $\\ell$.\n  In this work, we make significant progress on proving the existence of\ncondensers with strong parameters in almost all parameter regimes, even when\n$n$ is a large enough constant and $\\ell$ is growing. This almost resolves the\nquestion of the existence of condensers for oNOSF sources, except when $n$ is a\nsmall constant.\n  We construct the first explicit condensers for oNOSF sources, achieve\nparameters that match the existential results of [CGR, FOCS'24], and obtain an\nimproved construction for transforming low-entropy oNOSF sources into uniform\nones.\n  We find applications of our results to collective coin flipping and sampling,\nwell-studied problems in fault-tolerant distributed computing. We use our\ncondensers to provide simple protocols for these problems.\n  To understand the case of small $n$, we focus on $n=1$ which corresponds to\nonline non-oblivious bit-fixing (oNOBF) sources. We initiate a study of a new,\nnatural notion of influence of Boolean functions which we call online\ninfluence. We establish tight bounds on the total online influence of Boolean\nfunctions, implying extraction lower bounds.",
        "split": "arXiv"
    },
    {
        "text": "Centralized learning requires data to be aggregated at a central server,\nwhich poses significant challenges in terms of data privacy and bandwidth\nconsumption. Federated learning presents a compelling alternative, however,\nvanilla federated learning methods deployed in robotics aim to learn a single\nglobal model across robots that works ideally for all. But in practice one\nmodel may not be well suited for robots deployed in various environments. This\npaper proposes Federated-EmbedCluster (Fed-EC), a clustering-based federated\nlearning framework that is deployed with vision based autonomous robot\nnavigation in diverse outdoor environments. The framework addresses the key\nfederated learning challenge of deteriorating model performance of a single\nglobal model due to the presence of non-IID data across real-world robots.\nExtensive real-world experiments validate that Fed-EC reduces the communication\nsize by 23x for each robot while matching the performance of centralized\nlearning for goal-oriented navigation and outperforms local learning. Fed-EC\ncan transfer previously learnt models to new robots that join the cluster.",
        "split": "arXiv"
    },
    {
        "text": "Background: Fluorescent Timer proteins, which display time-dependent changes\nin their emission spectra, are invaluable for analyzing the temporal dynamics\nof cellular events at the single-cell level. We previously developed the\nTimer-of-cell-kinetics-and-activity (Tocky) tools, utilizing a specific Timer\nprotein, Fast-FT, to monitor temporal changes in cellular activities. Despite\ntheir potential, the analysis of Timer fluorescence in flow cytometry is\nfrequently compromised by variability in instrument settings and the absence of\nstandardized preprocessing methods. The development and implementation of\neffective data preprocessing methods remain to be achieved.\n  Results: In this study, we introduce the R package that automates the data\npreprocessing of Timer fluorescence data from flow cytometry experiments for\nquantitative analysis at single-cell level. Our aim is to standardize Timer\ndata analysis to enhance reproducibility and accuracy across different\nexperimental setups. The package includes a trigonometric transformation method\nto elucidate the dynamics of Fluorescent Timer proteins. We have identified the\nnormalization of immature and mature Timer fluorescence data as essential for\nrobust analysis, clarifying how this normalization affects the analysis of\nTimer maturation. These preprocessing methods are all encapsulated within the\nTockyPrep R package.\n  Conclusions: TockyPrep is available for distribution via GitHub at\nhttps://github.com/MonoTockyLab/TockyPrep, providing tools for data\npreprocessing and basic visualization of Timer fluorescence data. This toolkit\nis expected to enhance the utility of experimental systems utilizing\nFluorescent Timer proteins, including the Tocky tools.",
        "split": "arXiv"
    },
    {
        "text": "The classification of multipartite entanglement is essential as it serves as\na resource for various quantum information processing tasks. This study\nconcerns a particular class of highly entangled multipartite states, the\nso-called absolutely maximally entangled (AME) states. These are characterized\nby maximal entanglement across all possible bipartitions. In particular we\nanalyze the local unitary equivalence among AME states using invariants. One of\nour main findings is that the existence of special irredundant orthogonal\narrays implies the existence of an infinite number of equivalence classes of\nAME states constructed from these. In particular, we show that there are\ninfinitely many local unitary inequivalent three-party AME states for local\ndimension $d > 2$ and five-party AME states for $d \\geq 2$.",
        "split": "arXiv"
    },
    {
        "text": "Global partisan hostility and polarization has increased, and this\npolarization is heightened around presidential elections. Models capable of\ngenerating accurate summaries of diverse perspectives can help reduce such\npolarization by exposing users to alternative perspectives. In this work, we\nintroduce a novel dataset and task for independently summarizing each political\nperspective in a set of passages from opinionated news articles. For this task,\nwe propose a framework for evaluating different dimensions of perspective\nsummary performance. We benchmark 10 models of varying sizes and architectures\nthrough both automatic and human evaluation. While recent models like GPT-4o\nperform well on this task, we find that all models struggle to generate\nsummaries faithful to the intended perspective. Our analysis of summaries\nfocuses on how extraction behavior depends on the features of the input\ndocuments.",
        "split": "arXiv"
    },
    {
        "text": "We explore synergies between the Nancy Grace Roman Space Telescope High\nLatitude Wide Area Survey (HLWAS) and CMB experiments, specifically Simons\nObservatory (SO) and CMB-Stage4 (S4). Our simulated analyses include weak\nlensing, photometric galaxy clustering, CMB lensing, thermal SZ, and\ncross-correlations between these probes. While we assume the nominal 16,500\nsquare degree area for SO and S4, we consider multiple survey designs for Roman\nthat overlap with Rubin Observatory's Legacy Survey of Space and Time (LSST):\nthe 2000 square degree reference survey using four photometric bands, and two\nshallower single-band surveys that cover 10,000 and 18,000 square degree,\nrespectively. We find a ~2x increase in the dark energy figure of merit when\nincluding CMB-S4 data for all Roman survey designs. We further find a strong\nincrease in constraining power for the Roman wide survey scenario cases,\ndespite the reduction in galaxy number density, and the increased systematic\nuncertainties assumed due to the single band coverage. Even when tripling the\nalready worse systematic uncertainties in the Roman wide scenarios, which\nreduces the 10,000 square degree FoM from 269 to 178, we find that the larger\nsurvey area is still significantly preferred over the reference survey (FoM\n64). We conclude that for the specific analysis choices and metrics of this\npaper, a Roman wide survey is unlikely to be systematics-limited (in the sense\nthat one saturates the improvement that can be obtained by increasing survey\narea). We outline several specific implementations of a two-tier Roman survey\n(1000 square degree with 4 bands, and a second wide tier in one band) that can\nfurther mitigate the risk of systematics for Roman wide concepts.",
        "split": "arXiv"
    },
    {
        "text": "Existing benchmarks for evaluating foundation models mainly focus on\nsingle-document, text-only tasks. However, they often fail to fully capture the\ncomplexity of research workflows, which typically involve interpreting\nnon-textual data and gathering information across multiple documents. To\naddress this gap, we introduce M3SciQA, a multi-modal, multi-document\nscientific question answering benchmark designed for a more comprehensive\nevaluation of foundation models. M3SciQA consists of 1,452 expert-annotated\nquestions spanning 70 natural language processing paper clusters, where each\ncluster represents a primary paper along with all its cited documents,\nmirroring the workflow of comprehending a single paper by requiring multi-modal\nand multi-document data. With M3SciQA, we conduct a comprehensive evaluation of\n18 foundation models. Our results indicate that current foundation models still\nsignificantly underperform compared to human experts in multi-modal information\nretrieval and in reasoning across multiple scientific documents. Additionally,\nwe explore the implications of these findings for the future advancement of\napplying foundation models in multi-modal scientific literature analysis.",
        "split": "arXiv"
    },
    {
        "text": "Among all materials, mono-crystalline diamond has one of the highest measured\nthermal conductivities, with values above 2000 W/m/K at room temperature. This\nstems from momentum-conserving `normal' phonon-phonon scattering processes\ndominating over momentum-dissipating `Umklapp' processes, a feature that also\nsuggests diamond as an ideal platform to experimentally investigate phonon heat\ntransport phenomena that violate Fourier's law. Here, we introduce dilute\nnitrogen-vacancy color centers as in-situ, highly precise spin defect\nthermometers to image temperature inhomogeneities in single-crystal diamond\nmicrostructures heated from ambient conditions. We analyze cantilevers with\ncross-sections in the range from about 0.2 to 2.6 $\\mathrm{\\mu m}^2$, observing\na relation between cross-section and heat flux that departs from Fourier's law\npredictions. We rationalize such behavior relying on first-principles\nsimulations based on the linearized phonon Boltzmann transport equation, also\ndiscussing how fabrication-induced impurities influence conduction. Our\ntemperature-imaging method can be applied to diamond devices of arbitrary\ngeometry, paving the way for the exploration of unconventional, non-diffusive\nheat transport phenomena.",
        "split": "arXiv"
    },
    {
        "text": "We determine parameters of the renormalization group-consistent\n(RG-consistent) three-flavor color-superconducting Nambu-Jona-Lasinio (NJL)\nmodel that are suited to investigate possible compact-star configurations. Our\ngoal is to provide viable quark-matter equation of state (EoS) that can\ngenerally be used for hybrid-star constructions. To that end, we mainly focus\non quark-star properties in this work. By varying the vector and diquark\ncoupling constants, we analyze their impact on the EoS, speed of sound (SoS),\nthe maximum diquark gap, and the mass-radius relation. In almost all\nconfigurations, a stable color-flavor-locked (CFL) phase appears in the core of\nthe maximum-mass configurations, typically spanning several kms in radius. In\nother cases, the star's two-flavor color-superconducting (2SC) branch of the\nEoS becomes unstable before reaching the CFL transition density. At\nneutron-star densities, the SoS squared reaches up to 0.6 and the CFL diquark\ngap up to 250 MeV. We argue that adding a hadronic EoS at lower densities by\nperforming a Maxwell construction, does not increase the maximum mass\nsubstantially, thus we use the 2 solar mass constraint to constrain the NJL\nmodel parameters that are suited for the construction of hybrid-star EoS. We\nconstruct three examples of the hybrid star model, demonstrating that there is\nroom for different CSC compositions. The hybrid EoS obtained in this way can\nhave no 2SC matter or different ratios of 2SC and CFL quark matter in the core.\nWe show that early hadron-quark transitions are possible that can modify the\ntidal deformability at 1.4 solar mass. We will provide tabulated EoS of the\nRG-consistent NJL model for these three parameter sets. We find that these EoS\nare consistent with the imposed constraints from astrophysics and perturbative\nQCD. They allow for different hybrid-star scenarios with a hadronic EoS that is\nsoft at low densities.",
        "split": "arXiv"
    },
    {
        "text": "We investigate whether neural networks (NNs) can accurately differentiate\nbetween growth-rate data of the large-scale structure (LSS) of the Universe\nsimulated via two models: a cosmological constant and $\\Lambda$ cold dark\nmatter (CDM) model and a tomographic coupled dark energy (CDE) model. We built\nan NN classifier and tested its accuracy in distinguishing between cosmological\nmodels. For our dataset, we generated $f\\sigma_8(z)$ growth-rate observables\nthat simulate a realistic Stage IV galaxy survey-like setup for both\n$\\Lambda$CDM and a tomographic CDE model for various values of the model\nparameters. We then optimised and trained our NN with \\texttt{Optuna}, aiming\nto avoid overfitting and to maximise the accuracy of the trained model. We\nconducted our analysis for both a binary classification, comparing between\n$\\Lambda$CDM and a CDE model where only one tomographic coupling bin is\nactivated, and a multi-class classification scenario where all the models are\ncombined. For the case of binary classification, we find that our NN can\nconfidently (with $>86\\%$ accuracy) detect non-zero values of the tomographic\ncoupling regardless of the redshift range at which coupling is activated and,\nat a $100\\%$ confidence level, detect the $\\Lambda$CDM model. For the\nmulti-class classification task, we find that the NN performs adequately well\nat distinguishing $\\Lambda$CDM, a CDE model with low-redshift coupling, and a\nmodel with high-redshift coupling, with 99\\%, 79\\%, and 84\\% accuracy,\nrespectively. By leveraging the power of machine learning, our pipeline can be\na useful tool for analysing growth-rate data and maximising the potential of\ncurrent surveys to probe for deviations from general relativity.",
        "split": "arXiv"
    },
    {
        "text": "The sum-of-squares hierarchy of semidefinite programs has become a common\ntool for algorithm design in theoretical computer science, including problems\nin quantum information. In this work we study a connection between a Hermitian\nversion of the SoS hierarchy, related to the quantum de Finetti theorem, and\ngeometric quantization of compact K\\\"ahler manifolds (such as complex\nprojective space $\\mathbb{C}P^{d}$, the set of all pure states in a $(d +\n1)$-dimensional Hilbert space). We show that previously known HSoS rounding\nalgorithms can be recast as quantizing an objective function to obtain a\nfinite-dimensional matrix, finding its top eigenvector, and then (possibly\nnonconstructively) rounding it by using a version of the Husimi\nquasiprobability distribution. Dually, we recover most known quantum de Finetti\ntheorems by doing the same steps in the reverse order: a quantum state is first\napproximated by its Husimi distribution, and then quantized to obtain a\nseparable state approximating the original one. In cases when there is a\ntransitive group action on the manifold we give some new proofs of existing de\nFinetti theorems, as well as some applications including a new version of\nRenner's exponential de Finetti theorem proven using the Borel--Weil--Bott\ntheorem, and hardness of approximation results and optimal degree-2 integrality\ngaps for the basic SDP relaxation of \\textsc{Quantum Max-$d$-Cut} (for\narbitrary $d$). We also describe how versions of these results can be proven\nwhen there is no transitive group action. In these cases we can deduce some\nerror bounds for the HSoS hierarchy on complex projective varieties which are\nsmooth.",
        "split": "arXiv"
    },
    {
        "text": "Causal knowledge about the relationships among decision variables and a\nreward variable in a bandit setting can accelerate the learning of an optimal\ndecision. Current works often assume the causal graph is known, which may not\nalways be available a priori. Motivated by this challenge, we focus on the\ncausal bandit problem in scenarios where the underlying causal graph is unknown\nand may include latent confounders. While intervention on the parents of the\nreward node is optimal in the absence of latent confounders, this is not\nnecessarily the case in general. Instead, one must consider a set of possibly\noptimal arms/interventions, each being a special subset of the ancestors of the\nreward node, making causal discovery beyond the parents of the reward node\nessential. For regret minimization, we identify that discovering the full\ncausal structure is unnecessary; however, no existing work provides the\nnecessary and sufficient components of the causal graph. We formally\ncharacterize the set of necessary and sufficient latent confounders one needs\nto detect or learn to ensure that all possibly optimal arms are identified\ncorrectly. We also propose a randomized algorithm for learning the causal graph\nwith a limited number of samples, providing a sample complexity guarantee for\nany desired confidence level. In the causal bandit setup, we propose a\ntwo-stage approach. In the first stage, we learn the induced subgraph on\nancestors of the reward, along with a necessary and sufficient subset of latent\nconfounders, to construct the set of possibly optimal arms. The regret incurred\nduring this phase scales polynomially with respect to the number of nodes in\nthe causal graph. The second phase involves the application of a standard\nbandit algorithm, such as the UCB algorithm. We also establish a regret bound\nfor our two-phase approach, which is sublinear in the number of rounds.",
        "split": "arXiv"
    },
    {
        "text": "Optical sensing technologies are emerging technologies used in cancer\nsurgeries to ensure the complete removal of cancerous tissue. While point-wise\nassessment has many potential applications, incorporating automated large area\nscanning would enable holistic tissue sampling. However, such scanning tasks\nare challenging due to their long-horizon dependency and the requirement for\nfine-grained motion. To address these issues, we introduce Memorized Action\nChunking with Transformers (MACT), an intuitive yet efficient imitation\nlearning method for tissue surface scanning tasks. It utilizes a sequence of\npast images as historical information to predict near-future action sequences.\nIn addition, hybrid temporal-spatial positional embeddings were employed to\nfacilitate learning. In various simulation settings, MACT demonstrated\nsignificant improvements in contour scanning and area scanning over the\nbaseline model. In real-world testing, with only 50 demonstration trajectories,\nMACT surpassed the baseline model by achieving a 60-80% success rate on all\nscanning tasks. Our findings suggest that MACT is a promising model for\nadaptive scanning in surgical settings.",
        "split": "arXiv"
    },
    {
        "text": "Minimal unitary representation of $SO(d,2)$ and its deformations describe all\nthe conformally massless fields in $d$ dimensional Minkowskian spacetimes. In\ncritical dimensions these spacetimes admit extensions with twistorial\ncoordinates plus a dilatonic coordinate to causal spacetimes coordinatized by\nJordan algebras $J_3^{A}$ of degree three over the four division algebras $A= R\n, C , H , O $. We study the minimal unitary representation (minrep) of the\nconformal group $E_{7(-25)}$ of the spacetime coordinatized by the exceptional\nJordan algebra $J_3^{O}$. We show that the minrep of $E_{7(-25)}$ decomposes\ninto infinitely many massless representations of the conformal group\n$SO(10,2)$. Corresponding conformal fields transform as symmetric tensors in\nspinor indices of $SO(9,1)$ subject to certain constraints. Even and odd\ntensorial fields describe bosonic and fermionic conformal fields, respectively.\nEach irrep of $SO(10,2)$ falls into a unitary representation of an $SU(1,1)$\nsubgroup that commutes with $SO(10,2)$. The noncompact generators in spinor\nrepresentation $16 $ of $SO(10)$ interpolate between the bosonic and fermionic\nrepresentations and hence act like \"bosonic supersymmetry\" generators. We also\ngive the decomposition of the minrep of $E_{7(-25)}$ with respect to the\nsubgroup $SO^*(12)\\times SU(2)$ with $SO^*(12) $ acting as the conformal group\nof the spacetime coordinatized by $J_3^{H}$. Group $E_{7(-25)}$ is also the\nU-duality group of the exceptional $N=2$ Maxwell-Einstein supergravity in four\ndimensions. We discuss the relevance of our results to the composite scenario\nthat was proposed for the exceptional supergravity so as to accommodate the\nfamilies of quarks and leptons of the standard model as well as to the proposal\nthat $E_{7(-25)}$ acts as spectrum generating symmetry group of the $5d$\nexceptional supergravity",
        "split": "arXiv"
    },
    {
        "text": "We introduce a string-based parametrization for nucleon quark and gluon\ngeneralized parton distributions (GPDs) that is valid for all skewness. Our\napproach leverages conformal moments, representing them as the sum of spin-j\nnucleon A-form factor and skewness-dependent spin-j nucleon D-form factor,\nderived from t-channel string exchange in AdS spaces consistent with Lorentz\ninvariance and unitarity. This model-independent framework, satisfying the\npolynomiality condition due to Lorentz invariance, uses Mellin moments from\nempirical data to estimate these form factors. With just five Regge slope\nparameters, our method accurately produces various nucleon quark GPD types and\nsymmetric nucleon gluon GPDs through pertinent Mellin-Barnes integrals. Our\nisovector nucleon quark GPD is in agreement with existing lattice data,\npromising to improve the empirical extraction and global analysis of nucleon\nGPDs in exclusive processes, by avoiding the deconvolution problem at any\nskewness, for the first time.",
        "split": "arXiv"
    },
    {
        "text": "We establish a generalized quantum asymptotic equipartition property (AEP)\nbeyond the i.i.d. framework where the random samples are drawn from two sets of\nquantum states. In particular, under suitable assumptions on the sets, we prove\nthat all operationally relevant divergences converge to the quantum relative\nentropy between the sets. More specifically, both the smoothed min- and\nmax-relative entropy approach the regularized relative entropy between the\nsets. Notably, the asymptotic limit has explicit convergence guarantees and can\nbe efficiently estimated through convex optimization programs, despite the\nregularization, provided that the sets have efficient descriptions.\n  We give four applications of this result: (i) The generalized AEP directly\nimplies a new generalized quantum Stein's lemma for conducting quantum\nhypothesis testing between two sets of quantum states. (ii) We introduce a\nquantum version of adversarial hypothesis testing where the tester plays\nagainst an adversary who possesses internal quantum memory and controls the\nquantum device and show that the optimal error exponent is precisely\ncharacterized by a new notion of quantum channel divergence, named the minimum\noutput channel divergence. (iii) We derive a relative entropy accumulation\ntheorem stating that the smoothed min-relative entropy between two sequential\nprocesses of quantum channels can be lower bounded by the sum of the\nregularized minimum output channel divergences. (iv) We apply our generalized\nAEP to quantum resource theories and provide improved and efficient bounds for\nentanglement distillation, magic state distillation, and the entanglement cost\nof quantum states and channels.\n  At a technical level, we establish new additivity and chain rule properties\nfor the measured relative entropy which we expect will have more applications.",
        "split": "arXiv"
    }
]