  We discuss various realizations of the Weyl group in the background field
expansion of quantum gravity, in the presence of a cutoff, as required in
applications of the functional renormalization group. In order to study the
background--dependence, special attention is given to split gauge
transformations, which act on the background field and fluctuation keeping the
total metric unchanged. The results generalize previous works on global and
local scale transformations.


  Optimizing the performance of many objectives (instantiated by tasks or
clients) jointly with a few Pareto stationary solutions (models) is critical in
machine learning. However, previous multi-objective optimization methods often
focus on a few number of objectives and cannot scale to many objectives that
outnumber the solutions, leading to either subpar performance or ignored
objectives. We introduce Many-objective multi-solution Transport (MosT), a
framework that finds multiple diverse solutions in the Pareto front of many
objectives. Our insight is to seek multiple solutions, each performing as a
domain expert and focusing on a specific subset of objectives while
collectively covering all of them. MosT formulates the problem as a bi-level
optimization of weighted objectives for each solution, where the weights are
defined by an optimal transport between the objectives and solutions. Our
algorithm ensures convergence to Pareto stationary solutions for complementary
subsets of objectives. On a range of applications in federated learning,
multi-task learning, and mixture-of-prompt learning for LLMs, MosT distinctly
outperforms strong baselines, delivering high-quality, diverse solutions that
profile the entire Pareto frontier, thus ensuring balanced trade-offs across
many objectives.


  Saturn is an innovative platform that assists Foundation Model (FM) building
and its integration with IT operations (Ops). It is custom-made to meet the
requirements of data scientists, enabling them to effectively create and
implement FMs while enhancing collaboration within their technical domain. By
offering a wide range of tools and features, Saturn streamlines and automates
different stages of FM development, making it an invaluable asset for data
science teams. This white paper introduces prospective applications of
generative AI models derived from FMs in the financial sector.


  We introduce a robust variant of the Kelly portfolio optimization model,
called the Wasserstein-Kelly portfolio optimization. Our model, taking a
Wasserstein distributionally robust optimization (DRO) formulation, addresses
the fundamental issue of estimation error in Kelly portfolio optimization by
defining a ``ball" of distributions close to the empirical return distribution
using the Wasserstein metric and seeking a robust log-optimal portfolio against
the worst-case distribution from the Wasserstein ball. Enhancing the Kelly
portfolio using Wasserstein DRO is a natural step to take, given many
successful applications of the latter in areas such as machine learning for
generating robust data-driven solutions. However, naive application of
Wasserstein DRO to the growth-optimal portfolio problem can lead to several
issues, which we resolve through careful modelling. Our proposed model is both
practically motivated and efficiently solvable as a convex program. Using
empirical financial data, our numerical study demonstrates that the
Wasserstein-Kelly portfolio can outperform the Kelly portfolio in out-of-sample
testing across multiple performance metrics and exhibits greater stability.


  We present spectra of charged pions and protons in 0-10% central Au+Au
collisions at $\sqrt{s_{NN}}=200$ GeV at mid-rapidity ($y=0$) and forward
pseudorapidity ($\eta=2.2$) measured with the BRAHMS experiment at RHIC. The
spectra are compared to spectra from p+p collisions at the same energy scaled
by the number of binary collisions. The resulting nuclear modification factors
for central Au+Au collisions at both $y=0$ and $\eta=2.2$ exhibit suppression
for charged pions but not for (anti-)protons at intermediate $p_T$. The
$\bar{p}/\pi^-$ ratios have been measured up to $p_T\sim 3$ GeV/$c$ at the two
rapidities and the results indicate that a significant fraction of the charged
hadrons produced at intermediate $p_T$ range are (anti-)protons at both
mid-rapidity and $\eta = 2.2$.


  Information theoretic criteria (ITC) have been widely adopted in engineering
and statistics for selecting, among an ordered set of candidate models, the one
that better fits the observed sample data. The selected model minimizes a
penalized likelihood metric, where the penalty is determined by the criterion
adopted. While rules for choosing a penalty that guarantees a consistent
estimate of the model order are known, theoretical tools for its design with
finite samples have never been provided in a general setting. In this paper, we
study model order selection for finite samples under a design perspective,
focusing on the generalized information criterion (GIC), which embraces the
most common ITC. The theory is general, and as case studies we consider: a) the
problem of estimating the number of signals embedded in additive white Gaussian
noise (AWGN) by using multiple sensors; b) model selection for the general
linear model (GLM), which includes e.g. the problem of estimating the number of
sinusoids in AWGN. The analysis reveals a trade-off between the probabilities
of overestimating and underestimating the order of the model. We then propose
to design the GIC penalty to minimize underestimation while keeping the
overestimation probability below a specified level. For the considered
problems, this method leads to analytical derivation of the optimal penalty for
a given sample size. A performance comparison between the penalty optimized GIC
and common AIC and BIC is provided, demonstrating the effectiveness of the
proposed design strategy.


  Building upon an analytical technique introduced by Chung and Peschel [M.
Chung and I. Peschel, Phys. Rev. B 64, art. 064412 (2001)], we calculated the
density matrix rho_B of a finite block of B sites within an infinite system of
free spinless fermions. In terms of the block Green function matrix G (whose
elements are G_ij = < c_i^+ c_j>, where c_i^+ and c_j are fermion creation and
annihilation operators acting on sites i and j within the block respectively),
the density matrix can be written as rho_B = det(1 - G) exp[ sum_ij (log G(1 -
G^{-1})_ij c_i^+ c_j]. Implications of such a result to Hilbert space
truncation for real-space renormalization schemes is discussed.


  The Hubble Space Telescope (HST) has proven to be uniquely suited for the
measurement of proper motions (PMs) of stars and galaxies in the nearby
Universe. Here we summarize the main results and ongoing studies of the
HSTPROMO collaboration, which over the past decade has executed some two dozen
observational and theoretical HST projects on this topic. This is continuing to
revolutionize our dynamical understanding of many objects, including: globular
clusters; young star clusters; stars and stellar streams in the Milky Way halo;
Local Group galaxies, including dwarf satellite galaxies, the Magellanic
Clouds, and the Andromeda galaxy; and AGN Black Hole Jets.


  The Soft Gamma Repeater 1806-20 produced patterns of bursts during its 1983
outburst that indicate multiple independent energy accumulation sites, each
driven by a continuous power source, with sudden, incomplete releases of the
accumulated energy. The strengths of the power sources and their durations of
activity vary over several orders of magnitude.


  Massive stars are powerful sources of radiation, stellar winds, and supernova
explosions. The radiative and mechanical energies injected by massive stars
into the interstellar medium (ISM) profoundly alter the structure and evolution
of the ISM, which subsequently influences the star formation and chemical
evolution of the host galaxy. In this review, we will use the Large Magellanic
Cloud (LMC) as a laboratory to showcase effects of energy feedback from massive
young stellar objects (YSOs) and mature stars. We will also use the Carina
Nebula in the Galaxy to illustrate a multi-wavelength study of feedback from
massive star.


  We study Dehn surgeries on null-homotopic knots that yield fibred
$3$--manifolds when an additional (but natural) homological restriction is
imposed. The major tool used is Gabai's theory of sutured manifold
decomposition. Such surgeries are negative examples to a question of Michel
Boileau. Another result we will prove is about surgeries which reduce the
Thurston norm of a fibred manifold.


  We develop a theory in order to describe the effect of relaxation in a
condensed medium upon the quantum decay of a metastable liquid near the
spinodal at low temperatures. We find that both the regime and the rate of
quantum nucleation strongly depend on the relaxation time and its temperature
behavior. The quantum nucleation rate slows down with the decrease of the
relaxation time. We also discuss the low temperature experiments on cavitation
in normal $^3$He and superfluid $^4$He at negative pressures. It is the sharp
distinctions in the high frequency sound mode and in the temperature behavior
of the relaxation time that make the quantum cavitation kinetics in $^3$He and
$^4$He completely different in kind.


  The impact of liquid drops on a rigid surface is central in cleaning, cooling
and coating processes in both nature and industrial applications. However, it
is not clear how details of pores, roughness and texture on the solid surface
influence the initial stages of the impact dynamics. Here, we experimentally
study drop impacting at low velocities onto surfaces textured with asymmetric
(tilted) ridges. We define the line-friction capillary number $Ca_f={\mu_f
V_0}/{\sigma}$ (where $\mu_f$, $V_0$ and $\sigma$ are the line friction, impact
velocity and surface tension, respectively) as a measure of the importance of
the topology of surface textures for the dynamics of droplet impact. We show
that when $Ca_f \ll 1$, the contact line speed in the direction against the
inclination of the ridges is set by line-friction, whereas in the direction
with inclination the contact line is pinned at acute corners of the ridge. When
$Ca_f \sim 1$, the pinning is only temporary until the liquid-vapor interface
reaches to the next ridge where a new contact line is formed. Finally, when
$Ca_f\gg 1$, the geometric details of non-smooth surfaces play little role.


  Graph clustering is a fundamental task in graph analysis, and recent advances
in utilizing graph neural networks (GNNs) have shown impressive results.
Despite the success of existing GNN-based graph clustering methods, they often
overlook the quality of graph structure, which is inherent in real-world graphs
due to their sparse and multifarious nature, leading to subpar performance.
Graph structure learning allows refining the input graph by adding missing
links and removing spurious connections. However, previous endeavors in graph
structure learning have predominantly centered around supervised settings, and
cannot be directly applied to our specific clustering tasks due to the absence
of ground-truth labels. To bridge the gap, we propose a novel method called
\textbf{ho}mophily-enhanced structure \textbf{le}arning for graph clustering
(HoLe). Our motivation stems from the observation that subtly enhancing the
degree of homophily within the graph structure can significantly improve GNNs
and clustering outcomes. To realize this objective, we develop two
clustering-oriented structure learning modules, i.e., hierarchical correlation
estimation and cluster-aware sparsification. The former module enables a more
accurate estimation of pairwise node relationships by leveraging guidance from
latent and clustering spaces, while the latter one generates a sparsified
structure based on the similarity matrix and clustering assignments.
Additionally, we devise a joint optimization approach alternating between
training the homophily-enhanced structure learning and GNN-based clustering,
thereby enforcing their reciprocal effects. Extensive experiments on seven
benchmark datasets of various types and scales, across a range of clustering
metrics, demonstrate the superiority of HoLe against state-of-the-art
baselines.


  Carbon nanotubes are nano-objects with quite anisotropic properties, for
example the mechanical properties in longitudinal and radial directions differ
significantly. This feature of the carbon nanotubes yields many interesting
phenomena investigated in last decades. One of them is the ability to form both
hollow and collapsed states if the radius of the nanotube is large enough. The
transitions between the two states have been also reported. In our study we
present single-walled carbon nanotube interacting with a plane substrate and
characterize the energy of interaction with the substrate using effective
Lennard-Jones-type potential. We show energy of the homogeneous open and
collapsed states depending on the radius of the carbon nanotube and report on
the bi-stability in some range of the nanotube diameters. Using the
molecular-dynamical simulations we look at the evolution of the initial
half-opened, half-collapsed state and demonstrate that the transition area from
one state to another is spatially localized having features of topological
soliton (kink or anti-kink). We show that the value and the direction of the
kink propagation speed depend significantly on the nanotube diameter as well as
on the temperature of the system. We also discuss the mechanism of the process
using a simplified model with asymmetric double-well potential and show the
entropic nature of the transition.


  In the directed detour problem one is given a digraph $G$ and a pair of
vertices $s$ and~$t$, and the task is to decide whether there is a directed
simple path from $s$ to $t$ in $G$ whose length is larger than
$\mathsf{dist}_{G}(s,t)$. The more general parameterized variant, directed long
detour, asks for a simple $s$-to-$t$ path of length at least
$\mathsf{dist}_{G}(s,t)+k$, for a given parameter $k$. Surprisingly, it is
still unknown whether directed detour is polynomial-time solvable on general
digraphs. However, for planar digraphs, Wu and Wang~[Networks, '15] proposed an
$\mathcal{O}(n^3)$-time algorithm for directed detour, while Fomin et
al.~[STACS 2022] gave a $2^{\mathcal{O}(k)}\cdot n^{\mathcal{O}(1)}$-time fpt
algorithm for directed long detour. The algorithm of Wu and Wang relies on a
nontrivial analysis of how short detours may look like in a plane embedding,
while the algorithm of Fomin et al.~is based on a reduction to the
${\S}$-disjoint paths problem on planar digraphs. This latter problem is
solvable in polynomial time using the algebraic machinery of
Schrijver~[SIAM~J.~Comp.,~'94], but the degree of the obtained polynomial
factor is huge.
  In this paper we propose two simple algorithms: we show how to solve, in
planar digraphs, directed detour in time $\mathcal{O}(n^2)$ and directed long
detour in time $2^{\mathcal{O}(k)}\cdot n^4 \log n$. In both cases, the idea is
to reduce to the $2$-disjoint paths problem in a planar digraph, and to observe
that the obtained instances of this problem have a certain topological
structure that makes them amenable to a direct greedy strategy.


  Observations for seven infrared luminous starburst galaxies are reported in
the mid-infrared from 8 - 18 microns using the Keck Telescopes with spatial
resolution approaching the diffraction limit. All of the galaxies observed show
evidence of strong interactions based on optical morphologies. For these
galaxies, a substantial fraction, usually more than 50%, of the infrared
luminosity is generated in regions ranging in sizes from 100pc -- 1 Kpc.
Nuclear starbursts often dominate the infrared luminosity, but this is not
always true. In some galaxies, most notably NGC 6090, substantial infrared
luminosity greatly in excess of the nuclear luminosity is generated in regions
associated with the physical interaction between two galaxies. The radio
emission is a good tracer of the location of high luminosity young stars. The
visual/ultraviolet radiation output of the nearby star forming galaxies is
dominated by emission from regions that are generally not producing the copious
infrared luminosity of the systems. The regions of high infrared luminosity in
local starburst galaxies are significantly smaller than the galaxies as a
whole. The integrated spectral energy distributions (SEDs) of these galaxies
are very different from the SEDs of the regions of star formation. If the SEDs
of star-forming regions in these galaxies reflect the SEDs found in forming
galaxies at high redshift, the distant galaxies should be dominated by the mid
and far-infrared luminosity output far more than the integrated luminous output
of nearby starburst galaxies would suggest.


  We consider the escaping parameters in the family $\beta\wp_\Lambda$, i.e.
these parameters for which the orbits of critical values of $\beta\wp_\Lambda$
approach infinity, where $\wp_\Lambda$ is the Weierstrass function. Unlike to
the exponential map the considered functions are ergodic. They admit a
non-atomic, $\sigma$-finite, ergodic, conservative and invariant measure $\mu$
absolutely continuous with respect to the Lebesgue measure. Under additional
assumptions on the $\wp_\Lambda$-function we estimate from below the Hausdorff
dimension of the set of escaping parameters in the family $\beta\wp_\Lambda$,
and compare it with the Hausdorff dimension of escaping set in dynamical space,
proving a similarity between parameter plane and dynamical space.


  We report the first identification of forbidden line components from an
element heavier than helium in the spectrum of astrophysical plasmas. As yet,
these components were identified only in laboratory plasmas and not in
astrophysical objects. Forbidden components are well known for neutral helium
lines in hot stars, particularly in helium-rich post-AGB stars and white
dwarfs. We discovered that two hitherto unidentified lines in the ultraviolet
spectra of hot hydrogen-deficient (pre-) white dwarfs can be identified as
forbidden line components of triply ionized carbon (C IV). The forbidden
components (3p-4f and 3d-4d) appear in the blue and red wings of the strong,
Stark broadened 3p-4d and 3d-4f lines at 1108 Angstroem and 1169 Angstroem,
respectively. They are visible over a wide effective temperature range
(60,000-200,000 K) in helium-rich (DO) white dwarfs and PG1159 stars that have
strongly oversolar carbon abundances.


  RESTful services on the Web expose information through retrievable resource
representations that represent self-describing descriptions of resources, and
through the way how these resources are interlinked through the hyperlinks that
can be found in those representations. This basic design of RESTful services
means that for extracting the most useful information from a service, it is
necessary to understand a service's representations, which means both the
semantics in terms of describing a resource, and also its semantics in terms of
describing its linkage with other resources. Based on the Resource Linking
Language (ReLL), this paper describes a framework for how RESTful services can
be described, and how these descriptions can then be used to harvest
information from these services. Building on this framework, a layered model of
RESTful service semantics allows to represent a service's information in
RDF/OWL. Because REST is based on the linkage between resources, the same model
can be used for aggregating and interlinking multiple services for extracting
RDF data from sets of RESTful services.


  3D object detection plays a crucial role in numerous intelligent vision
systems. Detection in the open world inevitably encounters various adverse
scenes, such as dense fog, heavy rain, and low light conditions. Although
existing efforts primarily focus on diversifying network architecture or
training schemes, resulting in significant progress in 3D object detection,
most of these learnable modules fail in adverse scenes, thereby hindering
detection performance. To address this issue, this paper proposes a monocular
3D detection model designed to perceive twin depth in adverse scenes, termed
MonoTDP, which effectively mitigates the degradation of detection performance
in various harsh environments. Specifically, we first introduce an adaptive
learning strategy to aid the model in handling uncontrollable weather
conditions, significantly resisting degradation caused by various degrading
factors. Then, to address the depth/content loss in adverse regions, we propose
a novel twin depth perception module that simultaneously estimates scene and
object depth, enabling the integration of scene-level features and object-level
features. Additionally, we assemble a new adverse 3D object detection dataset
encompassing a wide range of challenging scenes, including rainy, foggy, and
low light weather conditions, with each type of scene containing 7,481 images.
Experimental results demonstrate that our proposed method outperforms current
state-of-the-art approaches by an average of 3.12% in terms of AP_R40 for car
category across various adverse environments.


  Graphs are a common model for complex relational data such as social networks
and protein interactions, and such data can evolve over time (e.g., new
friendships) and be noisy (e.g., unmeasured interactions). Link prediction aims
to predict future edges or infer missing edges in the graph, and has diverse
applications in recommender systems, experimental design, and complex systems.
Even though link prediction algorithms strongly depend on the set of edges in
the graph, existing approaches typically do not modify the graph topology to
improve performance. Here, we demonstrate how simply adding a set of edges,
which we call a \emph{proposal set}, to the graph as a pre-processing step can
improve the performance of several link prediction algorithms. The underlying
idea is that if the edges in the proposal set generally align with the
structure of the graph, link prediction algorithms are further guided towards
predicting the right edges; in other words, adding a proposal set of edges is a
signal-boosting pre-processing step. We show how to use existing link
prediction algorithms to generate effective proposal sets and evaluate this
approach on various synthetic and empirical datasets. We find that proposal
sets meaningfully improve the accuracy of link prediction algorithms based on
both neighborhood heuristics and graph neural networks. Code is available at
\url{https://github.com/CUAI/Edge-Proposal-Sets}.


  Jamming attacks on wireless networked control systems are investigated for
the scenarios where the system dynamics face exogenous disturbance. In
particular, the control input packets are assumed to be transmitted from a
controller to a remotely located linear plant over an insecure wireless
communication channel that is subject to jamming attacks. The time-varying
likelihood of transmission failures on this channel depends on the power of the
jamming interference signal emitted by an attacker. We show that jamming
attacks can prevent stability when the system faces disturbance, even if the
attacked system without disturbance is stable. We also show that stability
under jamming and disturbance can be achieved if the average jamming
interference power is restricted in a certain way that we characterize in the
paper. We illustrate our results on an example networked control system with a
fading wireless channel, where the outage probability is affected by jamming
attacks.


  Deep learning models used in medical image analysis are prone to raising
reliability concerns due to their black-box nature. To shed light on these
black-box models, previous works predominantly focus on identifying the
contribution of input features to the diagnosis, i.e., feature attribution. In
this work, we explore counterfactual explanations to identify what patterns the
models rely on for diagnosis. Specifically, we investigate the effect of
changing features within chest X-rays on the classifier's output to understand
its decision mechanism. We leverage a StyleGAN-based approach (StyleEx) to
create counterfactual explanations for chest X-rays by manipulating specific
latent directions in their latent space. In addition, we propose EigenFind to
significantly reduce the computation time of generated explanations. We
clinically evaluate the relevancy of our counterfactual explanations with the
help of radiologists. Our code is publicly available.


  Datasets with hundreds to tens of thousands features is the new norm. Feature
selection constitutes a central problem in machine learning, where the aim is
to derive a representative set of features from which to construct a
classification (or prediction) model for a specific task. Our experimental
study involves microarray gene expression datasets, these are high-dimensional
and noisy datasets that contain genetic data typically used for distinguishing
between benign or malicious tissues or classifying different types of cancer.
In this paper, we formulate feature selection as a multiagent coordination
problem and propose a novel feature selection method using multiagent
reinforcement learning. The central idea of the proposed approach is to
"assign" a reinforcement learning agent to each feature where each agent learns
to control a single feature, we refer to this approach as MARL. Applying this
to microarray datasets creates an enormous multiagent coordination problem
between thousands of learning agents. To address the scalability challenge we
apply a form of reward shaping called CLEAN rewards. We compare in total nine
feature selection methods, including state-of-the-art methods, and show that
the proposed method using CLEAN rewards can significantly scale-up, thus
outperforming the rest of learning-based methods. We further show that a hybrid
variant of MARL achieves the best overall performance.


  We describe all possible bimodal over-twist patterns. In particular, we give
an algorithm allowing one to determine what the left endpoint of the
over-rotation interval of a given bimodal map is. We then define a new class of
polymodal interval maps called well behaved, and generalize the above results
onto well behaved maps.


  Calabi-Yau four-folds may be constructed as hypersurfaces in weighted
projective spaces of complex dimension 5 defined via weight systems of 6
weights. In this work, neural networks were implemented to learn the Calabi-Yau
Hodge numbers from the weight systems, where gradient saliency and symbolic
regression then inspired a truncation of the Landau-Ginzburg model formula for
the Hodge numbers of any dimensional Calabi-Yau constructed in this way. The
approximation always provides a tight lower bound, is shown to be dramatically
quicker to compute (with compute times reduced by up to four orders of
magnitude), and gives remarkably accurate results for systems with large
weights. Additionally, complementary datasets of weight systems satisfying the
necessary but insufficient conditions for transversality were constructed,
including considerations of the interior point, reflexivity, and
intradivisibility properties. Overall producing a classification of this weight
system landscape, further confirmed with machine learning methods. Using the
knowledge of this classification, and the properties of the presented
approximation, a novel dataset of transverse weight systems consisting of 7
weights was generated for a sum of weights $\leq 200$; producing a new database
of Calabi-Yau five-folds, with their respective topological properties
computed. Further to this an equivalent database of candidate Calabi-Yau
six-folds was generated with approximated Hodge numbers.


  Adversarial robustness has attracted extensive studies recently by revealing
the vulnerability and intrinsic characteristics of deep networks. However,
existing works on adversarial robustness mainly focus on balanced datasets,
while real-world data usually exhibits a long-tailed distribution. To push
adversarial robustness towards more realistic scenarios, in this work we
investigate the adversarial vulnerability as well as defense under long-tailed
distributions. In particular, we first reveal the negative impacts induced by
imbalanced data on both recognition performance and adversarial robustness,
uncovering the intrinsic challenges of this problem. We then perform a
systematic study on existing long-tailed recognition methods in conjunction
with the adversarial training framework. Several valuable observations are
obtained: 1) natural accuracy is relatively easy to improve, 2) fake gain of
robust accuracy exists under unreliable evaluation, and 3) boundary error
limits the promotion of robustness. Inspired by these observations, we propose
a clean yet effective framework, RoBal, which consists of two dedicated
modules, a scale-invariant classifier and data re-balancing via both margin
engineering at training stage and boundary adjustment during inference.
Extensive experiments demonstrate the superiority of our approach over other
state-of-the-art defense methods. To our best knowledge, we are the first to
tackle adversarial robustness under long-tailed distributions, which we believe
would be a significant step towards real-world robustness. Our code is
available at: https://github.com/wutong16/Adversarial_Long-Tail .


  We study a sample of 207 nearby galaxy groups and clusters observed with
XMM-Newton. Key aspects of this sample include the large size, the high data
quality, and the large diversity of cluster dynamical states. We determine the
overall metallicity within 0.3R$_{500}$ and the radial distribution of the
metals. On average, we find a mild dependence of the core metallicity with the
average temperature of the system in agreement with previous results. However,
we identify the cause of this mild dependence to be due to relaxed systems
only; disturbed systems do not show this trend, on average. The large scatter
observed in this relation is strongly associated with the dynamical state of
the systems: relaxed systems have on average a higher metallicity in the core
than disturbed objects. The radial profiles of relaxed systems are centrally
peaked and show a steep decrease with radius, flattening beyond
0.3-0.4R$_{500}$. The metallicity of disturbed systems is also higher in the
center but at much lower values than what is observed for relaxed objects. This
finding is consistent with the picture that cluster mergers mix the abundance
distribution by inducing large scale motions. The scatter of the radial
profiles is quite large, but while for relaxed systems it decreases almost
monotonically as function of the radius, for disturbed systems it shows a
significant boost at large radii. Systems with a central radio source have a
flatter profile indicating that central AGNs are an efficient mechanism to
uplift and redistribute the metals in the ICM.


  This paper derives an objective Bayesian "prior" based on considerations of
entropy/information. By this means, it produces a quantitative measure of
goodness of fit (the "H-statistic") that balances higher likelihood against the
number of fitting parameters employed. The method is intended for
phenomenological applications where the underlying theory is uncertain or
unknown.
  For example, it can help decide whether the large angle anomalies in the CMB
data should be taken seriously.
  I am therefore posting it now, even though it was published before the arxiv
existed.


  Homophily, the tendency of individuals who are alike to form ties with one
another, is an important concept in the study of social networks. Yet
accounting for homophily effects is complicated in the context of bipartite
networks where ties connect individuals not with one another but rather with a
separate set of nodes, which might also be individuals but which are often an
entirely different type of objects. As a result, much work on the effect of
homophily in a bipartite network proceeds by first eliminating the bipartite
structure, collapsing a two-mode network to a one-mode network and thereby
ignoring potentially meaningful structure in the data. We introduce a set of
methods to model homophily on bipartite networks without losing information in
this way, then we demonstrate that these methods allow for substantively
interesting findings in management science not possible using standard
techniques. These methods are implemented in the widely-used ergm package for
R.


  Multivalent batteries are an energy storage technology with the potential to
surpass lithium-ion batteries, however their performance has been limited by
the low voltages and poor solid-state ionic mobility of available cathodes. A
computational screening approach to identify high-performance multivalent
intercalation cathodes among materials that do not contain the working ion of
interest has been developed which greatly expands the search space that can be
considered for materials discovery. This approach has been applied to magnesium
cathodes as a proof of concept and four resulting candidate materials (NASICON
V$_2$(PO$_4$)$_3$, birnessite NaMn$_4$O$_8$, tavorite MnPO$_4$F, and spinel
MnO$_2$) are discussed in further detail. In examining the ion migration
environment and associated Mg$^{2+}$ migration energy in these materials, local
energy maxima are found to correspond with pathway positions where Mg$^{2+}$
passes through a plane of anion atoms. While previous works have established
the influence of local coordination on multivalent ion mobility, these results
suggest that considering both the type of local bonding environment as well as
available free volume for the mobile ion along its migration pathway can be
significant for improving solid-state mobility.


  We re-examine the issue of the pQCD factorization scale in the exclusive rho
production in diffractive DIS from the k_t-factorization point of view. We find
that this scale differs significantly from, and possesses much flatter Q^2
behavior than widely used value (Q^2 + m_\rho^2)/4. With these results in mind,
we discuss the Q^2 shape of the rho meson production cross section. We
introduce rescaled cross sections, which might provide further insight into the
dynamics of rho production. We also comment on the recent ZEUS observation of
energy-independent ratio sigma(gamma* p --> rho p) / sigma_{tot}(gamma*p).


  Spanning sets for vertex operator algebras satisfying difference-zero and
difference-one conditions have been extensively studied in the recent years. In
this paper, we extend these results. More specifically, we show that for a
suitably chosen generating set, any N-graded Moebius vertex algebra is spanned
by monomials satisfying a difference-N ordering condition.


  We investigate the Cherenkov radiation triggered by qubit acceleration
simulated by superconducting circuit. By analyzing the radiation probability,
we confirm the existence of Cherenkov speed threshold, implying that simulating
superluminal qubit motion is possible for such a scenario. A question
immediately arises: Is such motion compatible with the causality principle? To
address the question, we perform a causality test on the simulating system
based on the recently developed notion of temporal quantum correlations,
pseudo-density matrix and temporal quantum steering. The results suggest that
single-mode approximation breaks down even when the system is restricted in
weak coupling regime.


  Observations of ionized AGN outflows have provided compelling evidence that
the radiation field transfers both momentum and energy to the plasma. At parsec
scale distances in AGN, energy transfer can dominate, in which case the only
force needed to launch an outflow is that from gas pressure. Much closer to the
black hole, gravity dominates thermal energy due to insufficient heating by the
radiation and the gas is in the so-called `cold' regime. Only magnetic or
radiation forces can then lead to outflow, but it is unclear at what
temperature and ionization state the radiation force weakens, as these
properties depend on the spectral energy distribution (SED). In this work, we
survey the parameter space of radiation forces due to spectral lines resulting
from blackbody SEDs with varying temperatures in the range $\sim 10^4 - 10^6$~K
to identify the radiation temperature at which line-driving begins to lose
efficiency. We find that the temperature $\lesssim4\times10^5$~K marks the
transition to inefficient line driving. We also self-consistently compute the
heating and cooling balance to estimate the gas temperature, so that our
parameter survey covers the transition where thermal driving goes from
negligible to comparable to line driving. We summarize a large set of
hydrodynamical simulations of radial flows to illustrate how the wind
properties change during the transition and the dependence of these properties
on the assumed SED and governing flow parameters.


  The central path problem is a variation on the single facility location
problem. The aim is to find, in a given connected graph $G$, a path $P$
minimizing its eccentricity, which is the maximal distance from $P$ to any
vertex of the graph $G$. The path eccentricity of $G$ is the minimal
eccentricity achievable over all paths in $G$. In this article we consider the
path eccentricity of the class of the $k$-AT-free graphs. They are graphs in
which any set of three vertices contains a pair for which every path between
them uses at least one vertex of the closed neighborhood at distance $k$ of the
third. We prove that they have path eccentricity bounded by $k$.
  Moreover, we answer a question of G\'omez and Guti\'errez asking if there is
a relation between path eccentricity and the consecutive ones property. The
latter is the property for a binary matrix to admit a permutation of the rows
placing the 1's consecutively on the columns. It was already known that graphs
whose adjacency matrices have the consecutive ones property have path
eccentricity at most 1, and that the same remains true when the augmented
adjacency matrices (with ones on the diagonal) has the consecutive ones
property. We generalize these results as follow. We study graphs whose
adjacency matrices can be made to satisfy the consecutive ones property after
changing some values on the diagonal, and show that those graphs have path
eccentricity at most 2, by showing that they are 2-AT-free.


  In current Medium Voltage DC (MVDC) Shipboard Power Systems (SPSs), multiple
sources exist to supply power to a common dc bus. Conventionally, the power
management of such systems is performed by controlling Power Generation Modules
(PGMs) which include fuel operated generators and underlying converters.
Moreover, energy management is performed by the emerging single or hybrid
Energy Storage Systems (ESSs). This paper presents a model and load predictive
control framework for power and energy management of SPSs. Here, MPC with load
prediction is used for three main objectives: (1) to request power and energy
from generators and energy storage elements according to their individual State
of Power (SOP) and ramp-rate limitations, (2) to consider and integrate the
generator cost and degradation, and (3) to reach a specific parking (final)
State of Charge (SOC) for the ESSs at the end of the prediction horizon. The
solution of the optimization problem is demonstrated using MATLAB and the
functionality of the control framework is validated in real-time simulation
environment.


  GEM are widely used as amplification stages in gaseous detectors exposed to
high rates. The GEM consists of a polyimide foil which is coated by two thin
copper layers. Charges collected into the holes or created during the
amplification process may adhere to the polyimide part inside the holes. This
is often accompanied by a change of the effective gain. The effect is commonly
known as the charging-up effect.
  This work presents a systematic investigation of the effect under
well-controlled and monitored conditions for a single standard GEM foil in
Ar/CO$_2$ (90/10) gas for varying rates of X-ray interactions in the detector
and for different gains of the foil. In order to cover a wide range of
different rates, we apply two different methods. The first one is based on a
current measurement while the second one relies on the analysis of $^{55}$Fe
spectra over time. Both methods give consistent results, showing an initial
increase of the effective gain with time and an asymptotic saturation, which
can be well described by a single-exponential function. We find that the
characteristic time constants extracted from our measurements scale inversely
proportional to the rate of incoming electrons for a given GEM voltage.
Introducing characteristic quantities, which describe either the number of
incoming electrons per hole or the total number of electrons in a hole per
characteristic time, we find consistent numbers for measurements taken at the
same GEM voltages. For measurements taken at different GEM voltages, however,
also the characteristic total number of electrons inside the hole, which is
supposed to take into account the different effective gains, is found to be
higher by a factor of about 3.5 for $U_{\rm GEM}=$ 350V ($n_{\rm tot}=8.8\times
10^8$) compared to 400V ($n_{\rm tot}=2.4\times 10^8$). This hints at a
residual dependence of the charging-up characteristics on the GEM voltage.


  We present HOMERUN (Highly Optimized Multi-cloud Emission-line Ratios Using
photo-ionizatioN), a new approach to modelling emission lines from photoionized
gas that can simultaneously reproduce all observed line intensities from a wide
range of ionization levels and with high accuracy. Our approach is based on the
weighted combination of multiple single-cloud photoionization models and,
contrary to previous works, the novelty of our approach consists in using the
weights as free parameters of the fit and constraining them with the observed
data. One of the main applications of HOMERUN is the accurate determination of
gas-phase metallicities and we show that a critical point is to allow for a
variation of the N/O and S/O abundance ratios which can significantly improve
the quality of the fit and the accuracy of the results. Moreover, our approach
provides a major improvement compared to the single-cloud, constant-pressure
models commonly used in the literature. By using high-quality literature
spectra of H ii regions where 10 to 20 emission lines (including several
auroral lines) are detected with high signal-to-noise ratio, we show that all
lines are reproduced by the model with an accuracy better than 10%. In
particular, the model is able to simultaneously reproduce [O i]6300, 6363, [O
ii]3726, 3729, [O iii]4959, 5007, [S ii]6717, 6731, and [S iii]9069, 9532
emission lines which, to our knowledge, is an unprecedented result. Finally, we
show that the gas metallicities estimated with our models for HII regions in
the Milky Way are in agreement with the stellar metallicities than the
estimates based on the Te-method. Overall, our method provides a new accurate
tool to estimate the metallicity and the physical conditions of the ionized
gas. It can be applied to many different science cases from HII regions to AGN
and wherever there are emission lines from photoionized gas.


  The aim of this short article is to investigate the possibility of existence
of totally umbilical isometric immersions in R^3 with isothermal
parametrization and harmonic metric.


  Financial stock returns correlations have been studied in the prism of random
matrix theory, to distinguish the signal from the "noise". Eigenvalues of the
matrix that are above the rescaled Marchenko Pastur distribution can be
interpreted as collective modes behavior while the modes under are usually
considered as noise. In this analysis we use complex network analysis to
simulate the "noise" and the "market" component of the return correlations, by
introducing some meaningful correlations in simulated geometric Brownian motion
for the stocks. We find that the returns correlation matrix is dominated by
stocks with high eigenvector centrality and clustering found in the network. We
then use simulated "market" random walks to build an optimal portfolio and find
that the overall return performs better than using the historical mean-variance
data, up to 50% on short time scale.


  Fountain codes are erasure correcting codes realizing reliable communication
systems for the multicast on the Internet. The zigzag decodable fountain (ZDF)
code is one of generalization of the Raptor code, i.e, applying shift operation
to generate the output packets. The ZDF code is decoded by a two-stage
iterative decoding algorithm, which combines the packet-wise peeling algorithm
and the bit-wise peeling algorithm. By the bit-wise peeling algorithm and shift
operation, ZDF codes outperform Raptor codes under iterative decoding in terms
of decoding erasure rate and overhead. However, the bit-wise peeling algorithm
spends long decoding time. This paper proposes a fast bit-wise decoding
algorithm for the ZDF codes. Simulation results show that the proposed
algorithm drastically reduces the decoding time compared with the existing
algorithm.


  In epidemiological research, it is common to investigate the interaction
between risk factors for an outcome such as a disease and hence to estimate the
risk associated with being exposed for either or both of two risk factors under
investigation. Interactions can be estimated both on the additive and
multiplicative scale using the same regression model. We here present a review
for calculating interaction and estimating the risk and confidence interval of
two exposures using a single regression model and the relationship between
measures, particularly the standard error for the combined exposure risk group.


  While large language models have made strides in natural language processing,
their proficiency in complex reasoning tasks requiring formal language
comprehension, such as chess, remains less investigated. This paper probes the
performance of ChatGPT, a sophisticated language model by OpenAI in tackling
such complex reasoning tasks, using chess as a case study. Through robust
metrics examining both the legality and quality of moves, we assess ChatGPT's
understanding of the chessboard, adherence to chess rules, and strategic
decision-making abilities. Our evaluation identifies limitations within
ChatGPT's attention mechanism that affect its formal language comprehension and
uncovers the model's underdeveloped self-regulation abilities. Our study also
reveals ChatGPT's propensity for a coherent strategy in its gameplay and a
noticeable uptick in decision-making assertiveness when the model is presented
with a greater volume of natural language or possesses a more lucid
understanding of the state of the chessboard. These findings contribute to the
growing exploration of language models' abilities beyond natural language
processing, providing valuable information for future research towards models
demonstrating human-like cognitive abilities.


  The Lie conformal algebra of loop Virasoro algebra, denoted by
$\mathscr{CW}$, is introduced in this paper. Explicitly, $\mathscr{CW}$ is a
Lie conformal algebra with $\mathbb{C}[\partial]$-basis
$\{L_i\,|\,i\in\mathbb{C}\}$ and $\lambda$-brackets $[L_i\, {}_\lambda \,
L_j]=(-\partial-2\lambda) L_{i+j}$. Then conformal derivations of
$\mathscr{CW}$ are determined. Finally, rank one conformal modules and
$\mathbb{Z}$-graded free intermediate series modules over $\mathscr{CW}$ are
classified.


  Ghost imaging (GI) reconstructs images using a single-pixel or bucket
detector, which has the advantages of scattering robustness, wide spectrum and
beyond-visual-field imaging. However, this technique needs large amount of
measurements to obtain a sharp image. There have been a lot of methods proposed
to overcome this disadvantage. Retina-like patterns, as one of the compressive
sensing approaches, enhance the imaging quality of region of interest (ROI)
while not increase measurements. The design of the retina-like patterns
determines the performance of the ROI in the reconstructed image. Unlike the
conventional method to fill in ROI with random patterns, we propose to optimize
retina-like patterns by filling in the ROI with the patterns containing the
sparsity prior of objects. This proposed method is verified by simulations and
experiments compared with conventional GI, retina-like GI and GI using patterns
optimized by principal component analysis. The method using optimized
retina-like patterns obtain the best imaging quality in ROI than other methods.
Meanwhile, the good generalization ability of the optimized retina-like pattern
is also verified. While designing the size and position of the ROI of
retina-like pattern, the feature information of the target can be obtained to
optimize the pattern of ROI. This proposed method paves the way for realizing
high-quality GI.


  In 2009, Etzion and Siberstein proposed a conjecture on the largest dimension
of a linear space of matrices over a finite field in which all nonzero matrices
are supported on a Ferrers diagram and have rank bounded below by a given
integer. Although several cases of the conjecture have been established in the
past decade, proving or disproving it remains to date a wide open problem. In
this paper, we take a new look at the Etzion-Siberstein Conjecture,
investigating its connection with rook theory. Our results show that the
combinatorics behind this open problem is closely linked to the theory of
$q$-rook polynomials associated with Ferrers diagrams, as defined by Garsia and
Remmel. In passing, we give a closed formula for the trailing degree of the
$q$-rook polynomial associated with a Ferrers diagram in terms of the
cardinalities of its diagonals. The combinatorial approach taken in this paper
allows us to establish some new instances of the Etzion-Silberstein Conjecture
using a non-constructive argument. We also solve the asymptotic version of the
conjecture over large finite fields, answering a current open question.


  We formulate and solve a two-state model for the elasticity of nicked,
double-stranded DNA that borrows features from both the Worm Like Chain and the
Bragg--Zimm model. Our model is computationally simple, and gives an excellent
fit to recent experimental data through the entire overstretching transition.
The fit gives the first value for the bending stiffness of the overstretched
state as about 10 nm*kbt, a value quite different from either B-form or
single-stranded DNA.


  The study of temporal trends in altmetrics is under-developed, and this
multi-year observation study addresses some of the deficits in our
understanding of altmetric behaviour over time. The attention surrounding
research outputs, as partially captured by altmetrics, or alternative metrics,
constitutes many varied forms of data. Over the years 2008-2013, a set of 7739
papers were sampled on six occasions. Five altmetric data sources were recorded
(Twitter, Mendeley, News, Blogs and Policy) and analysed for temporal trends,
with particular attention being paid to their Open Access status and
discipline. Twitter attention both starts and ends quickly. Mendeley readers
accumulate quickly, and continue to grow over the following years. News and
blog attention is quick to start, although news attention persists over a
longer timeframe. Citations in policy documents are slow to start, and are
observed to be growing over a decade after publication. Over time, growth in
Twitter activity is confirmed, alongside an apparent decline in blogging
attention. Mendeley usage is observed to grow, but shows signs of recent
decline. Policy attention is identified as the slowest form of impact studied
by altmetrics, and one that strongly favours the Humanities and Social
Sciences. The Open Access Altmetrics Advantage is seen to emerge and evolve
over time, with each attention source showing different trends. The existence
of late-emergent attention in all attention sources is confirmed.


  A construction supporting a conjecture that different ground state pairs
exist in the 2-dimensional Edwards-Anderson Ising spin glass is presented.


  We consider the problem of allocating indivisible goods fairly among n agents
who have additive and submodular valuations for the goods. Our fairness
guarantees are in terms of the maximin share, that is defined to be the maximum
value that an agent can ensure for herself, if she were to partition the goods
into n bundles, and then receive a minimum valued bundle. Since maximin fair
allocations (i.e., allocations in which each agent gets at least her maximin
share) do not always exist, prior work has focused on approximation results
that aim to find allocations in which the value of the bundle allocated to each
agent is (multiplicatively) as close to her maximin share as possible. In
particular, Procaccia and Wang (2014) along with Amanatidis et al. (2015) have
shown that under additive valuations a 2/3-approximate maximin fair allocation
always exists and can be found in polynomial time. We complement these results
by developing a simple and efficient algorithm that achieves the same
approximation guarantee.
  Furthermore, we initiate the study of approximate maximin fair division under
submodular valuations. Specifically, we show that when the valuations of the
agents are nonnegative, monotone, and submodular, then a 0.21-approximate
maximin fair allocation is guaranteed to exist. In fact, we show that such an
allocation can be efficiently found by using a simple round-robin algorithm. A
technical contribution of the paper is to analyze the performance of this
combinatorial algorithm by employing the concept of multilinear extensions.


  Orphan afterglows from short $\gamma$-ray bursts (GRB) are potential
candidates for electromagnetic (EM) counterpart searches to gravitational wave
(GW) detected neutron star or neutron star black hole mergers. Various jet
dynamical and structure models have been proposed that can be tested by the
detection of a large sample of GW-EM counterparts. We make predictions for the
expected rate of optical transients from these jet models for future survey
telescopes, without a GW or GRB trigger. A sample of merger jets is generated
in the redshift limits $0\leq z\leq 3.0$, and the expected peak $r$-band flux
and timescale above the LSST or ZTF detection threshold, $m_r=24.5$ and $20.4$
respectively, is calculated. General all-sky rates are shown for $m_r\leq26.0$
and $m_r\leq21.0$. The detected orphan and GRB afterglow rate depends on jet
model, typically $16\lesssim R\lesssim 76$ yr$^{-1}$ for the LSST, and
$2\lesssim R \lesssim 8$ yr$^{-1}$ for ZTF. An excess in the rate of orphan
afterglows for a survey to a depth of $m_r\leq26$ would indicate that merger
jets have a dominant low-Lorentz factor population, or the jets exhibit
intrinsic jet structure. Careful filtering of transients is required to
successfully identify orphan afterglows from either short or long GRB
progenitors.


  It is well-known that the Fourier coefficients of Siegel-Eisenstein series
can be expressed in terms of the Siegel series. The functional equation of the
Siegel series of a quadratic form over $\mathbb{Q}_p$ was first proved by
Katsurada. In this paper, we prove the functional equation of the Siegel series
over a non-archimedean local field by using the representation theoretic
argument by Kudla and Sweet.


  Given a graph $G$ and an integer $\ell\ge 2$, we denote by $\alpha_{\ell}(G)$
the maximum size of a $K_{\ell}$-free subset of vertices in $V(G)$. A recent
question of Nenadov and Pehova asks for determining the best possible minimum
degree conditions forcing clique-factors in $n$-vertex graphs $G$ with
$\alpha_{\ell}(G) = o(n)$, which can be seen as a Ramsey--Tur\'an variant of
the celebrated Hajnal--Szemer\'edi theorem. In this paper we find the
asymptotical sharp minimum degree threshold for $K_r$-factors in $n$-vertex
graphs $G$ with $\alpha_\ell(G)=n^{1-o(1)}$ for all $r\ge \ell\ge 2$.


  Recent X-ray observations by Jiang et al. have identified an active galactic
nucleus (AGN) in the bulgeless spiral galaxy NGC 3319, located just
$14.3\pm1.1\,$Mpc away, and suggest the presence of an intermediate-mass black
hole (IMBH; $10^2\leq M_\bullet/\mathrm{M_{\odot}}\leq10^5$) if the Eddington
ratios are as high as 3 to $3\times10^{-3}$. In an effort to refine the black
hole mass for this (currently) rare class of object, we have explored multiple
black hole mass scaling relations, such as those involving the (not previously
used) velocity dispersion, logarithmic spiral-arm pitch angle, total galaxy
stellar mass, nuclear star cluster mass, rotational velocity, and colour of NGC
3319, to obtain ten mass estimates, of differing accuracy. We have calculated a
mass of $3.14_{-2.20}^{+7.02}\times10^4\,\mathrm{M_\odot}$, with a confidence
of 84% that it is $\leq$$10^5\,\mathrm{M_\odot}$, based on the combined
probability density function from seven of these individual estimates. Our
conservative approach excluded two black hole mass estimates (via the nuclear
star cluster mass, and the fundamental plane of black hole activity
$\unicode{x2014}$ which only applies to black holes with low accretion rates)
that were upper limits of $\sim$$10^5\,{\rm M}_{\odot}$, and it did not use the
$M_\bullet\unicode{x2013}L_{\rm 2-10\,keV}$ relation's prediction of
$\sim$$10^5\,{\rm M}_{\odot}$. This target provides an exceptional opportunity
to study an IMBH in AGN mode and advance our demographic knowledge of black
holes. Furthermore, we introduce our novel method of meta-analysis as a
beneficial technique for identifying new IMBH candidates by quantifying the
probability that a galaxy possesses an IMBH.


  Neural networks trained on distilled data often produce over-confident output
and require correction by calibration methods. Existing calibration methods
such as temperature scaling and mixup work well for networks trained on
original large-scale data. However, we find that these methods fail to
calibrate networks trained on data distilled from large source datasets. In
this paper, we show that distilled data lead to networks that are not
calibratable due to (i) a more concentrated distribution of the maximum logits
and (ii) the loss of information that is semantically meaningful but unrelated
to classification tasks. To address this problem, we propose Masked Temperature
Scaling (MTS) and Masked Distillation Training (MDT) which mitigate the
limitations of distilled data and achieve better calibration results while
maintaining the efficiency of dataset distillation.


  We investigate the altermagnetic properties of strongly-correlated transition
metal oxides considering the family of the quasi two-dimensional A2BO4 and
three-dimensional ABO3. As a test study, we analyze the Mott insulators Ca2RuO4
and YVO3. In both cases, the orbital physics is extremely relevant in the t2g
subsector with the presence of an orbital-selective Mott physics in the first
case and of a robust orbital-order in the second case. Using first-principles
calculations, we show the presence of an orbital-selective altermagnetism in
the case of Ca2RuO4. In the case of YVO3, we study the altermagnetism as a
function of the magnetic ordering and of the Coulomb repulsion U. We find that
the altermagnetism is present in all magnetic orders with the symmetries of the
Brillouin zone depending on the magnetic order. Finally, the Coulomb repulsion
enhances the non-relativistic spin-splitting making the strongly-correlated
systems an exciting playground for the study of the altermagnetism.


  We show that the explosion of the first supernovae can trigger low-mass star
formation via gravitational fragmentation of the supernova-driven gas shell. If
the shell mass does not exceed the host galaxy gas mass, all explosions with
energies E_SN > 10^{51} erg can lead to shell fragmentation. However, the
minimum ambient density required to induce such fragmentation is much larger,
n_0 > 300 cm^{-3}, for Type II supernovae than for pair-instability ones, which
can induce star formation even at lower ambient densities. The typical mass of
the unstable fragments is 10^{4-7} Msun; their density is in the range
110-6x10^7 cm{-3}. Fragments have a metallicity strictly lower than 10^{-2.6}
Zsun and large values of the gravitational-to-pressure force ratio f ~ 8. Based
on these findings, we conclude that the second generation of stars produced by
such self-propagating star formation is predominantly constituted by low-mass,
long-living, extremely metal-poor (or even metal-free, if mixing is suppressed)
stars. We discuss the implications of such results for Pop III star formation
scenarios and for the most iron-poor halo star HE0107-5240.


  In this paper, we present LiRaNet, a novel end-to-end trajectory prediction
method which utilizes radar sensor information along with widely used lidar and
high definition (HD) maps. Automotive radar provides rich, complementary
information, allowing for longer range vehicle detection as well as
instantaneous radial velocity measurements. However, there are factors that
make the fusion of lidar and radar information challenging, such as the
relatively low angular resolution of radar measurements, their sparsity and the
lack of exact time synchronization with lidar. To overcome these challenges, we
propose an efficient spatio-temporal radar feature extraction scheme which
achieves state-of-the-art performance on multiple large-scale datasets.Further,
by incorporating radar information, we show a 52% reduction in prediction error
for objects with high acceleration and a 16% reduction in prediction error for
objects at longer range.


  How much dark matter is there in the universe and where is it located? These
are two of the most fundamental questions in cosmology. We use in this paper
optical and x-ray mass determinations of galaxies, groups, and clusters of
galaxies to suggest that most of the dark matter may reside in very large halos
around galaxies, typically extending to ~200 kpc for bright galaxies. We show
that the mass-to-light ratio of galaxy systems does not increase significantly
with linear scale beyond the very large halos suggested for individual
galaxies. Rather, the total mass of large scale systems such as groups and rich
clusters of galaxies, even superclusters, can on average be accounted for by
the total mass of their member galaxies, including their large halos (which may
be stripped-off in the dense cluster environment but still remain in the
clusters) plus the mass of the hot intracluster gas. This conclusion also
suggests that we may live in a low-density universe with Omega ~ 0.2 - 0.3.


  Previous studies concerning the interaction of branes and black holes
suggested that a small black hole intersecting a brane may escape via a
mechanism of reconnection. Here we consider this problem by studying the
interaction of a small black hole and a domain wall composed of a scalar field
and simulate the evolution of this system when the black hole acquires an
initial recoil velocity. We test and confirm previous results, however, unlike
the cases previously studied, in the more general set-up considered here, we
are able to follow the evolution of the system also during the separation, and
completely illustrate how the escape of the black hole takes place.


  The OpenAI Gym provides researchers and enthusiasts with simple to use
environments for reinforcement learning. Even the simplest environment have a
level of complexity that can obfuscate the inner workings of RL approaches and
make debugging difficult. This whitepaper describes a Python framework that
makes it very easy to create simple Markov-Decision-Process environments
programmatically by specifying state transitions and rewards of deterministic
and non-deterministic MDPs in a domain-specific language in Python. It then
presents results and visualizations created with this MDP framework.


  We review some results on the equivalence of quantum states under local
unitary transformations (LUT). In particular, the classification of two-qubit
Schmidt correlated (SC) states under LUT is investigated. By presenting the
standard form of quantum states under LUT, the sufficient and necessary
conditions of whether two different SC states are local unitary equivalent are
provided. The correlations of SC states are also discussed.


  A recently developed semiclassical approximation to exchange in one dimension
is shown to be almost exact, with essentially no computational cost. The
variational stability of this approximation is tested, and its far greater
accuracy relative to local density functional calculations demonstrated. Even a
fully orbital-free potential-functional calculation (no orbitals of any kind)
yields little error relative to exact exchange, for more than one orbital.


  A conformal field theory (CFT) is a quantum field theory which is invariant
under conformal transformations; a group action that preserve angles but not
necessarily lengths. There are two traditional approaches to the construction
of CFTs: analyzing a statistical system near a critical point as a euclidean
field theory, and in holographic duality within the context of string theory.
This pedagogical paper presents a construction of CFTs using purely group
theoretic techniques. Starting with the basic definition of a Lie algebra and
quantum field theory, we generalize to affine Lie algebras and form a energy
momentum tensor via the Sugawara construction.


  A challenging problem is to find an algorithm to decide whether a morphism is
k-power-free. We provide such an algorithm when k >= 3 for uniform morphisms
showing that in such a case, contrarily to the general case, there exist finite
test-sets for k-power-freeness.


  A well-known result of Nosal states that a graph $G$ with $m$ edges and
$\lambda(G) > \sqrt{m}$ contains a triangle. Nikiforov [Combin. Probab. Comput.
11 (2002)] extended this result to cliques by showing that if $\lambda (G) >
\sqrt{2m(1-1/r)}$, then $G$ contains a copy of $K_{r+1}$. Let $C_k^+$ be the
graph obtained from a cycle $C_k$ by adding an edge to two vertices with
distance two, and let $F_k$ be the friendship graph consisting of $k$ triangles
that share a common vertex. Recently, Zhai, Lin and Shu [European J. Combin. 95
(2021)], Sun, Li and Wei [Discrete Math. 346 (2023)], and Li, Lu and Peng
[Discrete Math. 346 (2023)] proved that if $ m\ge 8$ and $\lambda (G) \ge
\frac{1}{2} (1+\sqrt{4m-3})$, then $G$ contains a copy of $C_5,C_5^+$ and
$F_2$, respectively, unless $G=K_2\vee \frac{m-1}{2}K_1$. In this paper, we
give a unified extension by showing that such a graph contains a copy of $V_5$,
where $V_5=K_1\vee P_4$ is the join of a vertex and a path on four vertices.
Our result extends the aforementioned results since $C_5,C_5^+$ and $F_2$ are
proper subgraphs of $V_5$. In addition, we prove that if $m\ge 33$ and $\lambda
(G) \ge 1+ \sqrt{m-2}$, then $G$ contains a copy of $F_3$, unless $G=K_3\vee
\frac{m-3}{3}K_1$. This confirms a conjecture on the friendship graph $F_k$ in
the case $k=3$. Finally, we conclude some spectral extremal graph problems
concerning the large fan graphs and wheel graphs.


  Text classification is usually studied by labeling natural language texts
with relevant categories from a predefined set. In the real world, new classes
might keep challenging the existing system with limited labeled data. The
system should be intelligent enough to recognize upcoming new classes with a
few examples. In this work, we define a new task in the NLP domain, incremental
few-shot text classification, where the system incrementally handles multiple
rounds of new classes. For each round, there is a batch of new classes with a
few labeled examples per class. Two major challenges exist in this new task:
(i) For the learning process, the system should incrementally learn new classes
round by round without re-training on the examples of preceding classes; (ii)
For the performance, the system should perform well on new classes without much
loss on preceding classes. In addition to formulating the new task, we also
release two benchmark datasets in the incremental few-shot setting: intent
classification and relation classification. Moreover, we propose two entailment
approaches, ENTAILMENT and HYBRID, which show promise for solving this novel
problem.


  Emerging smart home platforms, which interface with a variety of physical
devices and support third-party application development, currently use
permission models inspired by smartphone operating systems-they group
functionally similar device operations into separate units, and require users
to grant apps access to devices at that granularity. Unfortunately, this leads
to two issues: (1) apps that do not require access to all of the granted device
operations have overprivileged access to them, (2) apps might pose a higher
risk to users than needed because physical device operations are fundamentally
risk-asymmetric-"door.unlock" provides access to burglars, and "door.lock" can
potentially lead to getting locked out. Overprivileged apps with access to
mixed-risk operations only increase the potential for damage. We present Tyche,
a system that leverages the risk-asymmetry in physical device operations to
limit the risk that apps pose to smart home users, without increasing the
user's decision overhead. Tyche introduces the notion of risk-based
permissions. When using risk-based permissions, device operations are grouped
into units of similar risk, and users grant apps access to devices at that
risk-based granularity. Starting from a set of permissions derived from the
popular Samsung SmartThings platform, we conduct a user study involving
domain-experts and Mechanical Turk users to compute a relative ranking of risks
associated with device operations. We find that user assessment of risk closely
matches that of domain experts. Using this ranking, we define risk-based
groupings of device operations, and apply it to existing SmartThings apps,
showing that risk-based permissions indeed limit risk if apps are malicious or
exploitable.


  We propose the Malceiver, a hierarchical Perceiver model for Android malware
detection that makes use of multi-modal features. The primary inputs are the
opcode sequence and the requested permissions of a given Android APK file. To
reach a malware classification decision the model combines hierarchical
features extracted from the opcode sequence together with the requested
permissions. The model's architecture is based on the Perceiver/PerceiverIO
which allows for very long opcode sequences to be processed efficiently. Our
proposed model can be easily extended to use multi-modal features. We show
experimentally that this model outperforms a conventional CNN architecture for
opcode sequence based malware detection. We then show that using additional
modalities improves performance. Our proposed architecture opens new avenues
for the use of Transformer-style networks in malware research.


  We have investigated the in-plane uniaxial pressure effect on the
antiferromagnetic Mott insulator Ca2RuO4 from resistivity and magnetization
measurements. We succeeded in inducing the ferromagnetic metallic phase at
lower critical pressure than by hydrostatic pressure, indicating that the
flattening distortion of the RuO6 octahedra is more easily released under
in-plane uniaxial pressure. We also found a striking in-plane anisotropy in the
pressure responses of various magnetic phases: Although the magnetization
increases monotonically with pressure diagonal to the orthorhombic principal
axes, the magnetization exhibits peculiar dependence on pressure along the
in-plane orthorhombic principal axes. This peculiar dependence can be explained
by a qualitative difference between the uniaxial pressure effects along the
orthorhombic a and b axes, as well as by the presence of twin domain
structures.


  We investigate a spatially flat Friedmann-Robertson-Walker (FRW) scenario
with two interacting components, dark matter and variable vacuum energy (VVE)
densities, plus two decoupled components, one is a baryon term while the other
behaves as a radiation component. We consider a linear interaction in the
derivative dark component density. We apply the $\chi^2$ method to the
observational Hubble data for constraining the cosmological parameters and
analyze the amount of dark energy in the radiation era for the model. It turns
out that our model fulfills the severe bound of $\Omega_{x}(z\simeq
1100)<0.009$ at $2\sigma$ level, so is consistent with the recent analysis that
includes cosmic microwave background anisotropy measurements from Planck
survey, the future constraints achievable by Euclid and CMBPol experiments,
reported for the behavior of the dark energy at early times, and fulfills the
stringent bound $\Omega_{x}(z\simeq 10^{10})<0.04$ at $2\sigma$ level in the
big-bang nucleosynthesis epoch. We also examine the cosmic age problem at high
redshift associated with the old quasar APM 08279+5255 and estimate the age of
the universe today.


  In the inverse see-saw model the effective neutrino Yukawa couplings can be
sizable due to a large mixing angle between the light $(\nu)$and heavy
neutrinos $(N)$. When the right handed neutrino $(N)$ can be lighter than the
Standard Model (SM) Higgs boson $(h)$. It can be produced via the on-shell
decay of the Higgs, $h\to N\nu$ at a significant branching fraction at the LHC.
In such a process $N$ mass can be reconstructed in its dominant $N\rightarrow W
\ell$ decays. We perform an analysis on this channel and its relevant
backgrounds, among which the $W+$jets background is the largest. Considering
the existing mixing constraints from the Higgs and electroweak precision data,
the best sensitivity of the heavy neutrino search is achieved for benchmark $N$
mass at 100 and 110 GeV for upcoming high luminosity LHC runs.


  We report the feasibility of detecting the gravity-induced entanglement (GIE)
with optomechanical systems, which is the first investigation that clarifies
the feasible experimental parameters to achieve a signal-to-noise ratio of
S/N=1. Our proposal focuses on GIE generation between optomechanical mirrors,
coupled via gravitational interactions, under continuous measurement, feedback
control, and Kalman filtering process, which matured in connection with the
field of gravitational wave observations. We solved the Riccati equation to
evaluate the time evolution of the conditional covariance matrix for
optomechanical mirrors that estimated the minimum variance of the motions. The
results demonstrate that GIE is generated faster than a well-known time scale
without optomechanical coupling. The fast generation of entanglement is
associated with quantum-state squeezing by the Kalman filtering process, which
is an advantage of using optomechanical systems to experimentally detect GIE.


  For a regular noetherian scheme $X$ with a divisor with strict normal
crossings $D$ we prove that coherent sheaves satisfy descent w.r.t. the
'covering' consisting of the open parts in the various completions of $X$ along
the components of $D$ and their intersections.


  The presence of erratic or unstable paths in standard kinetic Monte Carlo
simulations significantly undermines the accurate simulation and sampling of
transition pathways. While typically reliable methods, such as the Gillespie
algorithm, are employed to simulate such paths, they encounter challenges in
efficiently identifying rare events due to their sequential nature and reliance
on exact Monte Carlo sampling. In contrast, the weighted ensemble method
effectively samples rare events and accelerates the exploration of complex
reaction pathways by distributing computational resources among multiple
replicas, where each replica is assigned a weight reflecting its importance,
and evolves independently from the others. Here, we implement the highly
efficient and robust weighted ensemble method to model
susceptible-infected-susceptible (SIS) dynamics on large heterogeneous
population networks. In particular, we explore the interplay between
stochasticity and contact heterogeneity which gives rise to large fluctuations,
leading to extinction (spontaneous clearance of infection). By studying a wide
variety of networks characterized by fat-tailed degree distributions, we are
able to compute the mean time to extinction as function of the various network
and epidemic parameters. Importantly, this method allows exploring
previously-inaccessible parameter regimes.


  Auditors demand financial models be transparent yet no consensus exists on
what that means precisely. Without a clear modeling transparency definition we
cannot know when our models are "transparent". The financial modeling community
debates which methods are more or less transparent as though transparency is a
quantifiable entity yet no measures exist. Without a transparency measure
modelers cannot objectively evaluate methods and know which improves model
transparency.
  This paper proposes a definition for spreadsheet modeling transparency that
is specific enough to create measures and automation tools for auditors to
determine if a model meets transparency requirements. The definition also
provides modelers the ability to objectively compare spreadsheet modeling
methods to select which best meets their goals.


  We study the interior spacetimes of stars in the Palatini formalism of f(R)
gravity and derive a generalized Tolman-Oppenheimer-Volkoff and mass equation
for a static, spherically symmetric star. We show that matching the interior
solution with the exterior Schwarzschild-De Sitter solution in general gives a
relation between the gravitational mass and the density profile of a star,
which is different from the one in General Relativity. These modifications
become neglible in models for which $\delta F(R) \equiv \partial f/\partial R -
1$ is a decreasing function of R however. As a result, both Solar System
constraints and stellar dynamics are perfectly consistent with $f(R) = R -
\mu^4/R$.


  This paper proposes a novel zero-shot composed image retrieval (CIR) method
considering the query-target relationship by masked image-text pairs. The
objective of CIR is to retrieve the target image using a query image and a
query text. Existing methods use a textual inversion network to convert the
query image into a pseudo word to compose the image and text and use a
pre-trained visual-language model to realize the retrieval. However, they do
not consider the query-target relationship to train the textual inversion
network to acquire information for retrieval. In this paper, we propose a novel
zero-shot CIR method that is trained end-to-end using masked image-text pairs.
By exploiting the abundant image-text pairs that are convenient to obtain with
a masking strategy for learning the query-target relationship, it is expected
that accurate zero-shot CIR using a retrieval-focused textual inversion network
can be realized. Experimental results show the effectiveness of the proposed
method.


  We present a representation independent solution to the continuum Schwinger
model in light-cone ($A^+ = 0$) gauge. We then discuss the problem of finding
that solution using various quantization schemes. In particular we shall
consider equal-time quantization and quantization on either characteristic
surface, $x^+ = 0$ or $x^- = 0$.


  We investigate the mass spectra of closed-charm mesons with $ J^{PC} = 1^{--}
$, for hybrid charmonium, molecules, and diquark-antidiquark operators, in
quenched lattice QCD with exact chiral symmetry. For two lattice volumes $ 24^3
\times 48 $ and $ 20^3 \times 40 $, each of 100 gauge configurations generated
with single-plaquette action at $ \beta = 6.1 $, we compute point-to-point
quark propagators and measure the time-correlation functions of these exotic
meson operators. For the molecular operator $
\{(\qbar\gamma_5\gamma_i\c)(\cbar\gamma_5\q)-
  (\cbar\gamma_5\gamma_i\q)(\qbar\gamma_5\c) \} $, it detects a resonance with
mass around $ 4238 \pm 31 $ MeV, which is naturally identified with $ Y(4260)
$. Further, for any molecular and diquark-antidiquark operator, it detects
heavier exotic charmed mesons, with quark content $ (\c\s\cbar\sbar) $ around $
4450 \pm 100 $ MeV, and $ (\c\c\cbar\cbar) $ around $ 6400 \pm 50 $ MeV.


  Karloff? and Shirley recently proposed summary trees as a new way to
visualize large rooted trees (Eurovis 2013) and gave algorithms for generating
a maximum-entropy k-node summary tree of an input n-node rooted tree. However,
the algorithm generating optimal summary trees was only pseudo-polynomial (and
worked only for integral weights); the authors left open existence of a
olynomial-time algorithm. In addition, the authors provided an additive
approximation algorithm and a greedy heuristic, both working on real weights.
This paper shows how to construct maximum entropy k-node summary trees in time
O(k^2 n + n log n) for real weights (indeed, as small as the time bound for the
greedy heuristic given previously); how to speed up the approximation algorithm
so that it runs in time O(n + (k^4/eps?) log(k/eps?)), and how to speed up the
greedy algorithm so as to run in time O(kn + n log n). Altogether, these
results make summary trees a much more practical tool than before.


  In this paper we study the sequence of orthonormal polynomials $\{P_n(\mu;
z)\}$ defined by a probability measure $\mu$ with non-polar compact support
$S(\mu)\subset\mathbb C$. We show that the support of any weak* limit of the
sequence of measures of maximal entropy $\omega_n$ for $P_n$ is contained in
the polynomial-convex hull of $S(\mu)$. And for $n$-th root regular measures
the $\omega_n$ converge weak* to the equilibrium measure on $S(\mu)$.


  In this paper we study the numerical method and the convergence for solving
the time-dependent Maxwell-Schr\"{o}dinger equations under the Lorentz gauge.
An alternating Crank-Nicolson finite element method for solving the problem is
presented and the optimal error estimate for the numerical algorithm is
obtained by a mathematical inductive method. Numerical examples are then
carried out to confirm the theoretical results.


  Wireless communication has become a standard solution to address
ever-increasing demands for information transfer in our modern society.
Conventional systems work in an active way in the sense that an active carrier
signal is mandatorily required to transfer information from Alice to Bob.
Especially in the era of 5G6G and with the advent of the Internet of Things
(IoT), an exponential growth of users and connectivity confronts conventional
systems with important challenges including limited spectrum resources,
information security, energy consumption and cost efficiency. Here, we
introduce the fundamentally different concept of passive wireless communication
(PWC) which has the potential to resolve the above-mentioned issues. PWC
transfers digital information by modulating demodulating already existing
omnipresent ambient stray electromagnetic waves using a programmable
metasurface. We provide a theoretical framework for encoding decoding and
modulating demodulating in PWC and build a proof-of-principle prototype system
leveraging existing commodity 2.4GHz Wi-Fi signals. We demonstrate information
transfer from Alice to Bob with data rates on the order of hundreds of Kbps
using distinguishable information-carrying control coding patterns of
metasurface, neither necessitating an active carrier signal and its associated
radio-frequency chain nor affecting the background active wireless
communication. The presented strategy is particularly appealing for green IoT
connectivity. At the same time, the concept is applicable to all types of wave
phenomena and provides a fundamentally new perspective on the design of future
wireless communication architectures.


  Imitation learning provides a way to automatically construct a controller by
mimicking human behavior from data. For safety-critical systems such as
autonomous vehicles, it can be problematic to use controllers learned from data
because they cannot be guaranteed to be collision-free. Recently, a method has
been proposed for learning a multi-mode hybrid automaton cruise controller
(MOHA). Besides being accurate, the logical nature of this model makes it
suitable for formal verification. In this paper, we demonstrate this capability
using the SpaceEx hybrid model checker as follows. After learning, we translate
the automaton model into constraints and equations required by SpaceEx. We then
verify that a pure MOHA controller is not collision-free. By adding a safety
state based on headway in time, a rule that human drivers should follow anyway,
we do obtain a provably safe cruise control. Moreover, the safe controller
remains more human-like than existing cruise controllers.


  Curriculum learning has shown promising improvements in multiple domains by
training machine learning models from easy samples to hard ones. Previous works
which either design rules or train models for scoring the difficulty highly
rely on task-specific expertise, and cannot generalize. Inspired by the
"easy-to-hard" intuition, we propose to do in-sample curriculum learning for
natural language generation tasks. Our learning strategy starts training the
model to generate the last few words, i.e., do sequence completion, and
gradually extends to generate the whole output sequence. Comprehensive
experiments show that it generalizes well to different tasks and achieves
significant improvements over strong baselines.


  Many artificial intelligence (AI) problems naturally map to NP-hard
optimization problems. This has the interesting consequence that enabling
human-level capability in machines often requires systems that can handle
formally intractable problems. This issue can sometimes (but possibly not
always) be resolved by building special-purpose heuristic algorithms, tailored
to the problem in question. Because of the continued difficulties in automating
certain tasks that are natural for humans, there remains a strong motivation
for AI researchers to investigate and apply new algorithms and techniques to
hard AI problems. Recently a novel class of relevant algorithms that require
quantum mechanical hardware have been proposed. These algorithms, referred to
as quantum adiabatic algorithms, represent a new approach to designing both
complete and heuristic solvers for NP-hard optimization problems. In this work
we describe how to formulate image recognition, which is a canonical NP-hard AI
problem, as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The
QUBO format corresponds to the input format required for D-Wave superconducting
adiabatic quantum computing (AQC) processors.


  Recent work on exploration in reinforcement learning (RL) has led to a series
of increasingly complex solutions to the problem. This increase in complexity
often comes at the expense of generality. Recent empirical studies suggest
that, when applied to a broader set of domains, some sophisticated exploration
methods are outperformed by simpler counterparts, such as {\epsilon}-greedy. In
this paper we propose an exploration algorithm that retains the simplicity of
{\epsilon}-greedy while reducing dithering. We build on a simple hypothesis:
the main limitation of {\epsilon}-greedy exploration is its lack of temporal
persistence, which limits its ability to escape local optima. We propose a
temporally extended form of {\epsilon}-greedy that simply repeats the sampled
action for a random duration. It turns out that, for many duration
distributions, this suffices to improve exploration on a large set of domains.
Interestingly, a class of distributions inspired by ecological models of animal
foraging behaviour yields particularly strong performance.


  Following a purely algebraic procedure, we provide an exhaustive
classification of local Weyl-invariant scalar densities in dimension D=8.


  We show that the same special solution of the focusing nonlinear
Schr\"{o}dinger equation that has been shown to arise in a certain
near-field/large-order limit from soliton and Peregrine-like rogue wave
solutions actually arises universally from an arbitrary background solution
when subjected to a sequence of iterated B\"{a}cklund transformations.


  We consider a scenario where the interior spacetime,described by a heat
conducting fluid sphere is matched to a Vaidya metric in higher
dimensions.Interestingly we get a class of solutions, where following heat
radiation the boundary surface collapses without the appearance of an event
horizon at any stage and this happens with reasonable properties of matter
field.The non-occurrence of a horizon is due to the fact that the rate of mass
loss exactly counterbalanced by the fall of boundary radius.Evidently this
poses a counter example to the so-called cosmic censorship hypothesis.Two
explicit examples of this class of solutions are also given and it is observed
that the rate of collapse is delayed with the introduction of extra
dimensions.The work extends to higher dimensions our previous investigation in
4D.


  Recently, deep learning-based algorithms are widely adopted due to the
advantage of being able to establish anomaly detection models without or with
minimal domain knowledge of the task. Instead, to train the artificial neural
network more stable, it should be better to define the appropriate neural
network structure or the loss function. For the training anomaly detection
model, the mean squared error (MSE) function is adopted widely. On the other
hand, the novel loss function, logarithmic mean squared error (LMSE), is
proposed in this paper to train the neural network more stable. This study
covers a variety of comparisons from mathematical comparisons, visualization in
the differential domain for backpropagation, loss convergence in the training
process, and anomaly detection performance. In an overall view, LMSE is
superior to the existing MSE function in terms of strongness of loss
convergence, anomaly detection performance. The LMSE function is expected to be
applicable for training not only the anomaly detection model but also the
general generative neural network.


  Uncertainty is an essential consideration for time series forecasting tasks.
In this work, we specifically focus on quantifying the uncertainty of traffic
forecasting. To achieve this, we develop Deep Spatio-Temporal Uncertainty
Quantification (DeepSTUQ), which can estimate both aleatoric and epistemic
uncertainty. We first leverage a spatio-temporal model to model the complex
spatio-temporal correlations of traffic data. Subsequently, two independent
sub-neural networks maximizing the heterogeneous log-likelihood are developed
to estimate aleatoric uncertainty. For estimating epistemic uncertainty, we
combine the merits of variational inference and deep ensembling by integrating
the Monte Carlo dropout and the Adaptive Weight Averaging re-training methods,
respectively. Finally, we propose a post-processing calibration approach based
on Temperature Scaling, which improves the model's generalization ability to
estimate uncertainty. Extensive experiments are conducted on four public
datasets, and the empirical results suggest that the proposed method
outperforms state-of-the-art methods in terms of both point prediction and
uncertainty quantification.


  We give a formulation of linearized 11D supergravity in 4D, $N=1$ superspace
keeping all eleven bosonic coordinates. The fields are fluctuations around
$\mathbf M=\mathbf R^{4|4}\times Y$, where $Y$ is a background Riemannian
7-manifold admitting a $G_2$ structure. We embed the 11D fields into superfield
representations of the 4D, $N=1$ superconformal algebra. These consist of the
conformal graviton superfield, seven conformal gravitino superfields, a tensor
hierarchy of superfields describing the 11D 3-form, and a non-abelian
Kaluza-Klein vector multiplet gauging the tensor hierarchy by diffeomorphisms
on $Y$. The quadratic action consists of the linearization of a superspace
volume term and a Chern-Simons action for the gauged hierarchy coupled to the
supergravity and gravitino superfields, and the full structure is fixed by
superconformal and gauge invariance. When this action is projected to
components, we recover the full linearized action of 11D supergravity.


  Tremendous growth in the demand for wireless applications such as streaming
audio/videos, Skype and video games require high data rate irrespective of
user's location in the cellular network. However, the Quality of Service (QoS)
of users degrades at the cell boundary. Relay enhanced multi-hop cellular
network is one of the cost effective solution to improve the performance of
cell edge users. Optimal deployment of Fixed Relay Nodes (FRNs) is essential to
satisfy the QoS requirement of edge users. We propose new schemes for channel
partitioning and FRN placement in cellular networks. Path-loss, Signal to
Interference and Noise Ratio (SINR) experienced by users, and effects of
shadowing have been considered. The analysis gives more emphasis on the
cell-edge users (worst case scenario). The results show that these schemes
achieve higher system performance in terms of spectral efficiency and also
increase the user data rate at the cell edge.


  Greig Cowan and Tim Gershon describe new types of matter called tetraquarks
and pentaquarks, and discuss the outlook for understanding these particles.


  This paper proposes a CNN classification network based on Bagging and
stacking ensemble learning methods for breast cancer classification. The model
was trained and tested on the public dataset of DDSM. The model is capable of
fast and accurate classification of input images. According to our research
results, for binary classification (presence or absence of breast cancer), the
accuracy reached 98.84%, and for five-class classification, the accuracy
reached 98.34%. The model also achieved a micro-average recall rate of 94.80%
and an F1 score of 94.19%. In comparative experiments, we compared the effects
of different values of bagging_ratio and n_models on the model, as well as
several methods for ensemble bagging models. Furthermore, under the same
parameter settings, our BSECNN outperformed VGG16 and ResNet-50 in terms of
accuracy by 8.22% and 6.33% respectively.


  Transferring image-based object detectors to the domain of videos remains a
challenging problem. Previous efforts mostly exploit optical flow to propagate
features across frames, aiming to achieve a good trade-off between accuracy and
efficiency. However, introducing an extra model to estimate optical flow can
significantly increase the overall model size. The gap between optical flow and
high-level features can also hinder it from establishing spatial correspondence
accurately. Instead of relying on optical flow, this paper proposes a novel
module called Progressive Sparse Local Attention (PSLA), which establishes the
spatial correspondence between features across frames in a local region with
progressively sparser stride and uses the correspondence to propagate features.
Based on PSLA, Recursive Feature Updating (RFU) and Dense Feature Transforming
(DenseFT) are proposed to model temporal appearance and enrich feature
representation respectively in a novel video object detection framework.
Experiments on ImageNet VID show that our method achieves the best accuracy
compared to existing methods with smaller model size and acceptable runtime
speed.


  The calibration for deep neural networks is currently receiving widespread
attention and research. Miscalibration usually leads to overconfidence of the
model. While, under the condition of long-tailed distribution of data, the
problem of miscalibration is more prominent due to the different confidence
levels of samples in minority and majority categories, and it will result in
more serious overconfidence. To address this problem, some current research
have designed diverse temperature coefficients for different categories based
on temperature scaling (TS) method. However, in the case of rare samples in
minority classes, the temperature coefficient is not generalizable, and there
is a large difference between the temperature coefficients of the training set
and the validation set. To solve this challenge, this paper proposes a
dual-branch temperature scaling calibration model (Dual-TS), which considers
the diversities in temperature parameters of different categories and the
non-generalizability of temperature parameters for rare samples in minority
classes simultaneously. Moreover, we noticed that the traditional calibration
evaluation metric, Excepted Calibration Error (ECE), gives a higher weight to
low-confidence samples in the minority classes, which leads to inaccurate
evaluation of model calibration. Therefore, we also propose Equal Sample Bin
Excepted Calibration Error (Esbin-ECE) as a new calibration evaluation metric.
Through experiments, we demonstrate that our model yields state-of-the-art in
both traditional ECE and Esbin-ECE metrics.


  Our previous studies revealed a good kinematic model for the jet of Cygnus A,
but the counter-jet speed is still not well constrained. The central engine and
part of the counter-jet of Cyg A are likely to be obscured by free-free
absorbing material, presumably a thick torus. At mm-wavelengths, the absorber
becomes optically thin, which provides a more detailed view into the inner
nuclear region. Knowing the speed of jet and counter-jet and their flux density
ratio allows to determine the jet Lorentz factors and orientation. Therefore we
started to monitor Cyg A with global VLBI at 43GHz in Oct. 2007. Our first
epoch reveals a previously unseen gap between both jets. This could be either a
sign for a new counter-jet component that is slowly separating or we start to
see the very inner acceleration region of the jet which is not efficiently
radiating at radio wavelengths. Further more the image shows transversely
resolved jet structures at distances beyond ~0.5pc which facilitate more
detailed investigations addressing jet stratification. Analysis of the resolved
jet structure shows that the initially wide jet (opening angle ~10deg)
collimates within the first parsec into a edge-brightened jet with an opening
angle of ~3deg.