  Here we present the discovery of 895 s-process-rich candidates from 454,180
giant stars observed by the Large Sky Area Multi-Object Fibre Spectroscopic
Telescope (LAMOST) using a data-driven approach. This sample constitutes the
largest number of s-process enhanced stars ever discovered. Our sample includes
187 s-process-rich candidates that are enhanced in both barium and strontium,
49 stars with significant barium enhancement only and 659 stars that show only
a strontium enhancement. Most of the stars in our sample are in the range of
effective temperature and log g typical of red giant branch (RGB) populations,
which is consistent with our observational selection bias towards finding RGB
stars. We estimate that only a small fraction (0.5 per cent) of binary
configurations are favourable for s-process enriched stars. The majority of our
s-process-rich candidates (95 per cent) show strong carbon enhancements,
whereas only five candidates (less than 3 percent) show evidence of sodium
enhancement. Our kinematic analysis reveals that 97 percent of our sample are
disc stars, with the other 3 percent showing velocities consistent with the
Galactic halo. The scaleheight of the disc is estimated to be zh=0.634kpc,
comparable with values in the literature. A comparison with the yields from
asymptotic giant branch (AGB) models suggests that the main neutron source
responsible for the Ba and Sr enhancements is the 13C(alpha,n)16O reaction. We
conclude that the s-process-rich candidates may have received their
overabundances via mass transfer from a previous ABG companion with an initial
mass in the range 1-3Msun.


  Simultaneous multislice (SMS) imaging is a one of the acceleration technique
of magnetic resonance imaging. SMS requires accurate sensitivity distributions
in the slice plane for each receiving coil. This requirement is difficult to
satisfy in practice, limiting the applications of this imaging technique. Here,
images are reconstructed by applying deep learning and amplitude modulation to
each slice image. Simulation experiments show that image reconstruction can be
achieved for both real- and complex-valued images. Image quality tends to
decrease with increasing number of simultaneously acquired images. It is also
shown that a larger difference in the phase modulation coefficients between
slices tends to increase the quality of the reconstructed images. Simulation
experiments and initial MR imaging experiments show promising results for this
method.


  I present a parallel algorithm for exact probabilistic inference in Bayesian
networks. For polytree networks with n variables, the worst-case time
complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write
parallel random-access machine) with n processors, for any constant number of
evidence variables. For arbitrary networks, the time complexity is O(r^{3w}*log
n) for n processors, or O(w*log n) for r^{3w}*n processors, where r is the
maximum range of any variable, and w is the induced width (the maximum clique
size), after moralizing and triangulating the network.


  We investigate two-dimensional trilayered quantum systems with multi-orbital
conduction bands by focusing on the role played by the layer degree of freedom
in setting the character of nodal line semimetals. The layer index can label
the electronic states where the electrons reside in the unit cell and can
enforce symmetry constraints in the electronic structure by protecting bands
crossing. We demonstrate that both the atomic spin-orbit coupling and the
removal of local orbital degeneracy can lead to different types of electronic
transitions with nodal lines that undergo a changeover from a loop structure
enclosing the center of the Brillouin zone to pockets winding around multiple
high symmetry points. We introduce and employ a criterion to find the nodal
lines transitions. On the basis of a zero-dimensional topological invariant
that, for a selected electronic and energy manifold, counts the number of bands
below the Fermi level with a given layer inversion eigenvalue in high symmetry
points of the Brillouin zone, one can determine the structure of the nodal
loops and the ensuing topological transitions.


  We compute the conjugate system of twisted Araki-Woods von Neumann algebras $
\mathcal{L}_T(H) $ for a compatible braided crossing symmetric twist $T$ on a
finite dimensional Hilbert space $ \mathcal{H} $ with norm $ \|T\| <1$. This
implies that those algebras have finite non-microstates free Fisher information
and therefore are always factors of type $\text{III}_\lambda$ ($0<\lambda\leq
1$) or $ \text{II}_1 $. Moreover, using the nontracial free monotone transport,
we show that $ \mathcal{L}_T(H) $ is isomorphic to the free Araki-Woods algebra
$ \mathcal{L}_0(H) $ when $ \|T\|=q $ is small enough.


  Physics is based on probabilities as fundamental entities of a mathematical
description. Expectation values of observables are computed according to the
classical statistical rule. The overall probability distribution for one world
covers all times. The quantum formalism arises once one focuses on the
transport of the time-local probabilistic information from one hypersurface to
a neighboring one. Wave functions or the density matrix allow the formulation
of a general linear evolution law for classical statistics. The density matrix
for classical statistics is a powerful tool which allows us to implement for
generalized Ising models concepts as basis transformations, the momentum
observable and the associated Fourier representation, or the definition of
subsystems by subtraces of the density matrix. The association of operators to
observables permits the computation of expectation values in terms of the
density matrix by the usual quantum rule. We show that probabilistic cellular
automata are quantum systems in a formulation with discrete time steps and real
wave functions. The evolution operator for automata can be expressed in terms
of fermionic creation and annihilation operators. The time-local probabilistic
information amounts to a subsystem of the overall probabilistic system which is
correlated with its environment consisting of the past and future. Such
subsystems typically involve probabilistic observables for which only a
probability distribution for their possible measurement values is available.
Incomplete statistics does not permit to compute classical correlation
functions for arbitrary subsystem-observables since different overall
observables are mapped to the same subsystem observable. Bell's inequalities
are not generally applicable.


  As new research on Large Language Models (LLMs) continues, it is difficult to
keep up with new research and models. To help researchers synthesize the new
research many have written survey papers, but even those have become numerous.
In this paper, we develop a method to automatically assign survey papers to a
taxonomy. We collect the metadata of 144 LLM survey papers and explore three
paradigms to classify papers within the taxonomy. Our work indicates that
leveraging graph structure information on co-category graphs can significantly
outperform the language models in two paradigms; pre-trained language models'
fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our
model surpasses an average human recognition level and that fine-tuning LLMs
using weak labels generated by a smaller model, such as the GCN in this study,
can be more effective than using ground-truth labels, revealing the potential
of weak-to-strong generalization in the taxonomy classification task.


  We present twelve new simulations of unequal mass neutron star mergers. The
simulations were preformed with the SpEC code, and utilize nuclear-theory based
equations of state and a two-moment gray neutrino transport scheme with an
improved energy estimate based on evolving the number density. We model the
neutron stars with the SFHo, LS220 and DD2 equations of state (EOS) and we
study the neutrino and matter emission of all twelve models to search for
robust trends between binary parameters and emission characteristics. We find
that the total mass of the dynamical ejecta exceeds $0.01M_\odot$ only for SFHo
with weak dependence on the mass-ratio across all models. We find that the
ejecta have a broad electron fraction ($Y_e$) distribution ($\approx
0.06-0.48$), with mean $0.2$. $Y_e$ increases with neutrino irradiation over
time, but decreases with increasing binary asymmetry. We also find that the
models have ejecta with a broad asymptotic velocity distribution ($\approx
0.05-0.7c$). The average velocity lies in the range $0.2c - 0.3c$ and decreases
with binary asymmetry. Furthermore, we find that disk mass increases with
binary asymmetry and stiffness of the EOS. The $Y_e$ of the disk increases with
softness of the EOS. The strongest neutrino emission occurs for the models with
soft EOS. For (anti) electron neutrinos we find no significant dependence of
the magnitude or angular distribution or neutrino luminosity with mass-ratio.
The heavier neutrino species have a luminosity dependence on mass-ratio but an
angular distribution which does not change with mass-ratio.


  By replacing the letters to polynomials in F_2[t], an infinite word, over a
finite alphabet, can be seen as the sequence of partial quotients of a
continued fraction in F_2((1/t)). Here is described a family of such infinite
words, corresponding to continued fractions which are algebraic over F_2(t).
This family includes a classical example already studied in different previous
works by Y. Hu, G-N. Han, Y. Bugeaud and the author of this note.


  Artificially crafted images such as memes, seasonal greetings, etc are
flooding the social media platforms today. These eventually start occupying a
lot of internal memory of smartphones and it gets cumbersome for the user to go
through hundreds of images and delete these synthetic images. To address this,
we propose a novel method based on Convolutional Neural Networks (CNNs) for the
on-device filtering of social media images by classifying these synthetic
images and allowing the user to delete them in one go. The custom model uses
depthwise separable convolution layers to achieve low inference time on
smartphones. We have done an extensive evaluation of our model on various
camera image datasets to cover most aspects of images captured by a camera.
Various sorts of synthetic social media images have also been tested. The
proposed solution achieves an accuracy of 98.25% on the Places-365 dataset and
95.81% on the Synthetic image dataset that we have prepared containing 30K
instances.


  We study the main options for a unitary and renormalizable, local quantum
field theory of the gravitational interactions. The first model is a Lee-Wick
superrenormalizable higher-derivative gravity, formulated as a nonanalytically
Wick rotated Euclidean theory. We show that, under certain conditions, the $S$
matrix is unitary when the cosmological constant vanishes. The model is the
simplest of its class. However, infinitely many similar options are allowed,
which raises the issue of uniqueness. To deal with this problem, we propose a
new quantization prescription, by doubling the unphysical poles of the
higher-derivative propagators and turning them into Lee-Wick poles. The
Lagrangian of the simplest theory of quantum gravity based on this idea is the
linear combination of $R$, $R_{\mu \nu }R^{\mu \nu }$, $R^{2}$ and the
cosmological term. Only the graviton propagates in the cutting equations and,
when the cosmological constant vanishes, the $S$ matrix is unitary. The theory
satisfies the locality of counterterms and is renormalizable by power counting.
It is unique in the sense that it is the only one with a dimensionless gauge
coupling.


  The basic notion of the article is a pair (A,U), where A is a commutative
C*-algebra and U is a partial isometry such that mapping U()U* is an
endomorphism of A and U*U belongs to A. We give a description of the maximal
ideal space of the smallest coefficient C*-algebra of the algebra C*(A,U)
generated by the system (A,U).


  Most decisions require information gathering from a stimulus presented with
different gaps. Indeed, the brain process of this integration is rarely
ambiguous. Recently, it has been claimed that humans can optimally integrate
the information of two discrete pulses independent of the temporal gap between
them. Interestingly, subjects' performance on such a task, with two discrete
pulses, is superior to what a perfect accumulator can predict. Although
numerous neuronal and descriptive models have been proposed to explain the
mechanism of perceptual decision-making, none can explain human behavior in
this two-pulse task. In order to investigate the mechanism of decision-making
on the noted tasks, a set of modified drift-diffusion models based on different
hypotheses were used. Model comparisons clarified that, in a sequence of
information arriving at different times, the accumulated information of earlier
evidence affects the process of information accumulation of later evidence. It
was shown that the rate of information extraction depends on whether the pulse
is the first or the second one. The proposed model can also explain the
stronger effect of the second pulse as shown by Kiani et al. 2013.


  The second and third factorial moments of the multiplicity distributions of
gluon and quark jets are calculated up to the next-to-next-to-next-to leading
order in perturbative QCD, using the equations for generating functions. The
results are confronted with experimental data. A general discussion on high
order corrections revealed by such an approach is given. Other possible
corrections and approaches are discussed as well.


  The soft anomalous dimension governs the infrared divergences of scattering
amplitudes in general kinematics to all orders in perturbation theory. By
comparing the recent Regge-limit results for $2\to2$ scattering (through
Next-to-Next-to-Leading Logarithms) in full colour to a general form for the
soft anomalous dimension at four loops we derive powerful constraints on its
kinematic dependence, opening the way for a bootstrap-based determination.


  A relativistic framework for computing the nuclear transparency extracted
from A(p,2p) scattering processes is presented. The model accounts for the
initial- and final-state interactions (IFSI) within the relativistic
multiple-scattering Glauber approximation (RMSGA). For the description of color
transparency, two existing models are used. The nuclear filtering mechanism is
implemented as a possible explanation for the oscillatory energy dependence of
the transparency. Results are presented for the target nuclei 7Li, 12C, 27Al,
and 63Cu. An approximated, computationally less intensive version of the RMSGA
framework is found to be sufficiently accurate for the calculation of the
nuclear transparency. After including the nuclear filtering and color
transparency mechanisms, our calculations are in acceptable agreement with the
data.


  The existence of hadronic molecular-type hybrids consisting of a baryon and a
meson is argued. Long-range interactions due to one-pion exchange is shown to
be strong enough to produce a loosely bound state. Specific features of a
molecular hybrid are discussed.


  A new set of equations for relativistic viscous hydrodynamics that captures
both weak-coupling and strong-coupling physics to second order in gradients has
been developed recently. We apply this framework to bulk physics at RHIC, both
for standard (Glauber-type) as well as for Color-Glass-Condensate initial
conditions and show that the results do not depend strongly on the values for
the second-order transport coefficients. Results for multiplicity, radial flow
and elliptic flow are presented and we quote the ratio of viscosity over
entropy density for which our hydrodynamic model is consistent with
experimental data. For Color-Glass-Condensate initial conditions, early
thermalization does not seem to be required in order for hydrodynamics to
describe charged hadron elliptic flow.


  This review is devoted to the large-scale rheology of suspensions of rigid
particles in Stokes fluid. After describing recent results on the definition of
the effective viscosity of such systems in the framework of homogenization
theory, we turn to our new results on the asymptotic expansion of the effective
viscosity in the dilute regime. This includes an optimal proof of Einstein's
viscosity formula for the first-order expansion, as well as the continuation of
this expansion to higher orders. The essential difficulty originates in the
long-range nature of hydrodynamic interactions: suitable renormalizations are
needed and are captured by means of diagrammatic expansions.


  We present Keck/MOSFIRE and Keck/LRIS spectroscopy of A1689-217, a lensed
(magnification $\sim7.9$), star-forming (SFR $\sim$ 16 $\rm M_\odot\ yr^{-1}$),
dwarf (log($M_\ast/M_\odot$) = $8.07-8.59$), Ly$\alpha$-emitter ($\rm
EW_0\sim138$ A) at $z$ = 2.5918. Dwarf galaxies similar to A1689-217 are common
at high redshift and likely responsible for reionization, yet few have been
studied with detailed spectroscopy. We report a 4.2$\sigma$ detection of the
electron-temperature-sensitive [O III]$\lambda$4363 emission line and use this
line to directly measure an oxygen abundance of 12+$\log$(O/H) = 8.06 $\pm$
0.12 ($\sim1/4\ \rm Z_\odot$). A1689-217 is the lowest mass galaxy at $z>$ 2
with an [O III]$\lambda$4363 detection. Using the rest-optical emission lines,
we measure A1689-217's other nebular conditions including electron temperature
($T_e$([O III]) $\sim$ 14,000 K), electron density ($n_e\sim$ 220 $\rm
cm^{-3}$) and reddening ($E(B-V)\sim$ 0.39). We study relations between
strong-line ratios and direct metallicities with A1689-217 and other galaxies
with [O III]$\lambda$4363 detections at $z\sim0-3.1$, showing that the
locally-calibrated, oxygen-based, strong-line relations are consistent from
$z\sim0-3.1$. We also show additional evidence that the $\rm O_{32}$ vs. $\rm
R_{23}$ excitation diagram can be utilized as a redshift-invariant,
direct-metallicity-based, oxygen abundance diagnostic out to $z\sim$ 3.1. From
this excitation diagram and the strong-line ratio $-$ metallicity plots, we
observe that the ionization parameter at fixed O/H is consistent with no
redshift evolution. Although A1689-217 is metal-rich for its $M_\ast$ and SFR,
we find it to be consistent within the large scatter of the low-mass end of the
Fundamental Metallicity Relation.


  We analyze the paper "The wave mechanics of $\alpha$-ray tracks" (Mott,
1929), published in 1929 by N.F. Mott. In particular, we discuss the
theoretical context in which the paper appeared and give a detailed account of
the approach used by the author and the main result attained. Moreover, we
comment on the relevance of the work not only as far as foundations of Quantum
Mechanics are concerned but also as the earliest pioneering contribution in
decoherence theory.


  This is an extended version of the note taken by the first author (W.-G.P.)
on a lecture given by the second author (M.R.) as a first part of the series on
"Hadronic Matter Under Extreme Conditions," the principal theme of the
WCU-Hanyang Program. It covers the attempts to go in a framework anchored on
effective field theory of QCD from zero density to the nuclear matter density
and slightly beyond, with the ultimate goal of arriving at the density relevant
to compact stars, including chiral phase transition and quark matter. The focus
is on the conceptual aspects rather than detailed "fitting" of the data on the
kinds of physics that are being addressed to in radioactive-ion-beam machines
in operation as well as in project (such as `KoRIA' in Korea) and will be
explored at such forthcoming accelerators as FAIR/GSI. The approach presented
here is basically different from the standard ones found in the literature in
that the notion of hidden local symmetry -- which underlies the chiral symmetry
of the strong interactions -- and its generalization to dual gravity
description involving infinite tower of hidden gauge fields are closely relied
on.


  Several techniques have been proposed to detect vulnerable Spectre gadgets in
widely deployed commercial software. Unfortunately, detection techniques
proposed so far rely on hand-written rules which fall short in covering subtle
variations of known Spectre gadgets as well as demand a huge amount of time to
analyze each conditional branch in software. Moreover, detection tool
evaluations are based only on a handful of these gadgets, as it requires
arduous effort to craft new gadgets manually.
  In this work, we employ both fuzzing and deep learning techniques to automate
the generation and detection of Spectre gadgets. We first create a diverse set
of Spectre-V1 gadgets by introducing perturbations to the known gadgets. Using
mutational fuzzing, we produce a data set with more than 1 million Spectre-V1
gadgets which is the largest Spectre gadget data set built to date. Next, we
conduct the first empirical usability study of Generative Adversarial Networks
(GANs) in the context of assembly code generation without any human
interaction. We introduce SpectreGAN which leverages masking implementation of
GANs for both learning the gadget structures and generating new gadgets. This
provides the first scalable solution to extend the variety of Spectre gadgets.
  Finally, we propose FastSpec which builds a classifier with the generated
Spectre gadgets based on a novel high dimensional Neural Embeddings technique
(BERT). For the case studies, we demonstrate that FastSpec discovers potential
gadgets with a high success rate in OpenSSL libraries and Phoronix benchmarks.
Further, FastSpec offers much greater flexibility and time-related performance
gain compared to the existing tools and therefore can be used for gadget
detection in large-scale software.


  Several designs of inter-qubit coupling are considered. It is shown that by a
combination of Josephson and capacitive coupling one can realize qubit
interactions of variable spin content. Qubit arrays are discussed as models of
quantum spin chains. In particular, a qubit model of the 1D quantum Ising spin
chain is proposed. A realization of unpaired Majorana fermion states in this
system is considered. It is shown that Majorana states are represented by spin
flip excitations localized on the chain ends. Using unpaired Majorana states in
qubit chains for decoherence protected quantum computing is discussed.


  For the phonon Boltzmann equation with only pair collisions we characterize
the set of all collisional invariants under some mild conditions on the
dispersion relation.


  The opaque nature and unexplained behavior of transformer-based language
models (LMs) have spurred a wide interest in interpreting their predictions.
However, current interpretation methods mostly focus on probing models from
outside, executing behavioral tests, and analyzing salience input features,
while the internal prediction construction process is largely not understood.
In this work, we introduce LM-Debugger, an interactive debugger tool for
transformer-based LMs, which provides a fine-grained interpretation of the
model's internal prediction process, as well as a powerful framework for
intervening in LM behavior. For its backbone, LM-Debugger relies on a recent
method that interprets the inner token representations and their updates by the
feed-forward layers in the vocabulary space. We demonstrate the utility of
LM-Debugger for single-prediction debugging, by inspecting the internal
disambiguation process done by GPT2. Moreover, we show how easily LM-Debugger
allows to shift model behavior in a direction of the user's choice, by
identifying a few vectors in the network and inducing effective interventions
to the prediction process. We release LM-Debugger as an open-source tool and a
demo over GPT2 models.


  Geographic routing is an appealing routing strategy that uses the location
information of the nodes to route the data. This technique uses only local
information of the communication graph topology and does not require
computational effort to build routing table or equivalent data structures. A
particularly efficient implementation of this paradigm is greedy routing, where
along the data path the nodes forward the data to a neighboring node that is
closer to the destination. The decreasing distance to the destination implies
the success of the routing scheme. A related problem is to consider an abstract
graph and decide whether there exists an embedding of the graph in a metric
space, called a greedy embedding, such that greedy routing guarantees the
delivery of the data. In the present paper, we use a metric-free definition of
greedy path and we show that greedy routing is successful on planar
triangulations without considering the existence of greedy embedding. Our
algorithm rely entirely on the combinatorial description of the graph structure
and the coordinate system requires O(log(n)) bits where n is the number of
nodes in the graph. Previous works on greedy routing make use of the embedding
to route the data. In particular, in our framework, it is known that there
exists an embedding of planar triangulations such that greedy routing
guarantees the delivery of data. The result presented in this article leads to
the question whether the success of (any) greedy routing strategy is always
coupled with the existence of a greedy embedding?


  We study the dynamics of gravitationally collapsing massive shells in AdS
spacetime, and show in detail how one can determine extremal surfaces
traversing them. The results are used to solve the time evolution of the
holographic entanglement entropy in strongly coupled dual conformal gauge
theory, which is is seen to exhibit a regime of linear growth independent of
the shape of the boundary entangling region and the equation of state of the
shell. Our exact results are finally compared to those of two commonly used
approximation schemes, the Vaidya metric and the quasistatic limit, whose
respective regions of validity are quantitatively determined.


  Data flow testing creates test requirements as definition-use (DU)
associations, where a definition is a program location that assigns a value to
a variable and a use is a location where that value is accessed. Data flow
testing is expensive, largely because of the number of test requirements.
Luckily, many DU-associations are redundant in the sense that if one test
requirement (e.g., node, edge, DU-association) is covered, other
DU-associations are guaranteed to also be covered. This relationship is called
subsumption. Thus, testers can save resources by only covering DU-associations
that are not subsumed by other testing requirements. In this work, we formally
describe the Data Flow Subsumption Framework (DSF) conceived to tackle the data
flow subsumption problem. We show that DFS is a distributive data flow analysis
framework which allows efficient iterative algorithms to find the
Meet-Over-All-Paths (MOP) solution for DSF transfer functions. The MOP solution
implies that the results at a point $p$ are valid for all paths that reach $p$.
We also present an algorithm, called Subsumption Algorithm (SA), that uses DSF
transfer functions and iterative algorithms to find the local
DU-associations-node subsumption; that is, the set of DU-associations that are
covered whenever a node $n$ is toured by a test. A proof of SA's correctness is
presented and its complexity is analyzed.


  The USB protocol has become ubiquitous, supporting devices from high-powered
computing devices to small embedded devices and control systems. USB's greatest
feature, its openness and expandability, is also its weakness, and attacks such
as BadUSB exploit the unconstrained functionality afforded to these devices as
a vector for compromise. Fundamentally, it is virtually impossible to know
whether a USB device is benign or malicious. This work introduces FirmUSB, a
USB-specific firmware analysis framework that uses domain knowledge of the USB
protocol to examine firmware images and determine the activity that they can
produce. Embedded USB devices use microcontrollers that have not been well
studied by the binary analysis community, and our work demonstrates how lifters
into popular intermediate representations for analysis can be built, as well as
the challenges of doing so. We develop targeting algorithms and use domain
knowledge to speed up these processes by a factor of 7 compared to
unconstrained fully symbolic execution. We also successfully find malicious
activity in embedded 8051 firmwares without the use of source code. Finally, we
provide insights into the challenges of symbolic analysis on embedded
architectures and provide guidance on improving tools to better handle this
important class of devices.


  Recently, a superdiffusion exhibiting the Kardar-Parisi-Zhang (KPZ) scaling
in late-time correlators and autocorrelators of certain interacting many-body
systems has been reported. Inspired by these results, we explore the KPZ
scaling in correlation functions using their realization in the Krylov operator
basis. We focus on the Heisenberg time scale, which approximately corresponds
to the ramp--plateau transition for the Krylov complexity in systems with a
large but finite number degrees of freedom. Two frameworks are under
consideration: i) the system with growing Lanczos coefficients and an
artificial cut-off, and ii) the system with the finite Hilbert space. In both
cases via numerical analysis, we observe the transition from Gaussian to
KPZ-like scaling at the critical Euclidean time $t_{E}^*=c_{cr}K$, for the
Krylov chain of finite length $K$, and $c_{cr}=O(1)$. In particular, we find a
scaling $\sim K^{1/3}$ for fluctuations in the one-point correlation function
and a dynamical scaling $\sim K^{-2/3}$ associated with the return probability
(Loschmidt echo) corresponding to autocorrelators in physical space. In the
first case, the transition is of the 3rd order and can be considered as an
example of dynamical quantum phase transition (DQPT), while in the second, it
is a crossover. For case ii), utilizing the relationship between the spectrum
of tridiagonal matrices at the spectral edge and the spectrum of the stochastic
Airy operator, we demonstrate analytically the origin of the KPZ scaling for
the particular Krylov chain using the results of the probability theory. We
argue that there is some outcome of our study for the double scaling limit of
matrix models. For the case of topological gravity, the white noise
$O(\frac{1}{N})$ term is identified, which should be taken into account in the
controversial issue of ensemble averaging in 2D/1D holography.


  Inserting a magnetic flux into a two-dimensional one-particle Hamiltonian
leads to a spectral flow through a given gap which is equal to the Chern number
of the associated Fermi projection. This paper establishes a generalization to
higher even dimension by inserting non-abelian monopoles of the Wu-Yang type.
The associated spectral flow is then equal to a higher Chern number. For the
study of odd spacial dimensions, a new so-called `chirality flow' is introduced
which, for the insertion of a monopole, is then linked to higher winding
numbers. This latter fact follows from a new index theorem for the spectral
flow between two unitaries which are conjugates of each other by a self-adjoint
unitary.


  In this paper we propose a self-organized particle moving model on scale free
network with the algorithm of the shortest path and preferential walk. The
over-capacity property of the vertices in this particle moving system on
complex network is studied from the holistic point of view. Simulation results
show that the number of over-capacity vertices forms punctuated equilibrium
processes as time elapsing, that the average number of over-capacity vertices
under each local punctuated equilibrium process has power law relationship with
the local punctuated equilibrium value. What's more, the number of
over-capacity vertices has the bell-shaped temporal correlation and $1/f^{2}$
behavior. Finally, the average lifetime $L(t)$ of particles accumulated before
time $t$ is analyzed to find the different roles of the shortest path algorithm
and the preferential walk algorithm in our model.


  The ongoing pandemic has highlighted the importance of reliable and efficient
clinical trials in healthcare. Trial sites, where the trials are conducted, are
chosen mainly based on feasibility in terms of medical expertise and access to
a large group of patients. More recently, the issue of diversity and inclusion
in clinical trials is gaining importance. Different patient groups may
experience the effects of a medical drug/ treatment differently and hence need
to be included in the clinical trials. These groups could be based on
ethnicity, co-morbidities, age, or economic factors. Thus, designing a method
for trial site selection that accounts for both feasibility and diversity is a
crucial and urgent goal. In this paper, we formulate this problem as a ranking
problem with fairness constraints. Using principles of fairness in machine
learning, we learn a model that maps a clinical trial description to a ranked
list of potential trial sites. Unlike existing fairness frameworks, the group
membership of each trial site is non-binary: each trial site may have access to
patients from multiple groups. We propose fairness criteria based on
demographic parity to address such a multi-group membership scenario. We test
our method on 480 real-world clinical trials and show that our model results in
a list of potential trial sites that provides access to a diverse set of
patients while also ensuing a high number of enrolled patients.


  MADNESS (multiresolution adaptive numerical environment for scientific
simulation) is a high-level software environment for solving integral and
differential equations in many dimensions that uses adaptive and fast harmonic
analysis methods with guaranteed precision based on multiresolution analysis
and separated representations. Underpinning the numerical capabilities is a
powerful petascale parallel programming environment that aims to increase both
programmer productivity and code scalability. This paper describes the features
and capabilities of MADNESS and briefly discusses some current applications in
chemistry and several areas of physics.


  Recently, metric learning and similarity learning have attracted a large
amount of interest. Many models and optimisation algorithms have been proposed.
However, there is relatively little work on the generalization analysis of such
methods. In this paper, we derive novel generalization bounds of metric and
similarity learning. In particular, we first show that the generalization
analysis reduces to the estimation of the Rademacher average over
"sums-of-i.i.d." sample-blocks related to the specific matrix norm. Then, we
derive generalization bounds for metric/similarity learning with different
matrix-norm regularisers by estimating their specific Rademacher complexities.
Our analysis indicates that sparse metric/similarity learning with $L^1$-norm
regularisation could lead to significantly better bounds than those with
Frobenius-norm regularisation. Our novel generalization analysis develops and
refines the techniques of U-statistics and Rademacher complexity analysis.


  We have explored the effects of the Li intercalation on the electronic and
magnetic properties of transition-metal (TM) doped anatase TiO$_2$. By Li
intercalation, Mn-doped TiO$_2$ exhibits the insulator to metal transition. On
the other hand, Li-intercalated Fe-doped TiO$_2$ has the insulating ground
state for low concentration of Li/Ti=0.067, but the metallic ground state for
high concentration of Li/Ti=0.133. We discuss the $n$-type carrier induced
ferromagnetism in Li-intercalated TM-doped anatase TiO$_2$. Based on the
Li-intercalated TM-doped anatase TiO$_2$, we propose a potential spintronic and
electrochromic device controlled by the electric-field.


  We present results of QCD corrections to Higgs boson production at the CERN
Large Hadron Collider. Potentially large logarithmic contributions from
multiple soft-gluon emission are summed up to all order in the strong coupling.
Various kinematical distributions, including the Higgs transverse momentum, are
predicted with O(alpha_s^3) precision. Comparison is made to outputs of the
popular Monte Carlo event generator PYTHIA.


  Zonoid depth, as a well-known ordering tool, has been widely used in
multivariate analysis. However, since its depth value vanishes outside the
convex hull of the data cloud, it suffers from the so-called `outside problem',
which consequently hinders its many practical applications, e.g.,
classification. In this note, we propose a new class of \emph{$L_q$-norm zonoid
depths}, which has no such problem. Examples are also provided to illustrate
their contours.


  We show how to learn a map that takes a content code, derived from a face
image, and a randomly chosen style code to an anime image. We derive an
adversarial loss from our simple and effective definitions of style and
content. This adversarial loss guarantees the map is diverse -- a very wide
range of anime can be produced from a single content code. Under plausible
assumptions, the map is not just diverse, but also correctly represents the
probability of an anime, conditioned on an input face. In contrast, current
multimodal generation procedures cannot capture the complex styles that appear
in anime. Extensive quantitative experiments support the idea the map is
correct. Extensive qualitative results show that the method can generate a much
more diverse range of styles than SOTA comparisons. Finally, we show that our
formalization of content and style allows us to perform video to video
translation without ever training on videos.


  In this technical report, we consider an approach that combines the PPO
objective and K-FAC natural gradient optimization, for which we call PPOKFAC.
We perform a range of empirical analysis on various aspects of the algorithm,
such as sample complexity, training speed, and sensitivity to batch size and
training epochs. We observe that PPOKFAC is able to outperform PPO in terms of
sample complexity and speed in a range of MuJoCo environments, while being
scalable in terms of batch size. In spite of this, it seems that adding more
epochs is not necessarily helpful for sample efficiency, and PPOKFAC seems to
be worse than its A2C counterpart, ACKTR.


  This paper investigates a damped stochastic wave equation driven by a
non-Gaussian Levy noise. The weak solution is proved to exist and be unique.
Moreover we show the existence of a unique invariant measure associated with
the transition semigroup under mild conditions.


  We report on an old computation of propagators of massless scalar fields on
an ensemble of configurations in the 4D path integral of Euclidean dynamical
triangulations in the collapsed (crumpled) phase. The resulting quantum average
is used to calculate the scale factor of a rotational invariant metric in a
4-dimensional space. This new scale factor is non-zero at the origin, which we
assume to be caused by the presence of the well-known singular structure in the
collapsed phase. It depends on an overall integration constant. A
transformation to a metric in (1+3)-dimensions reveals Euclidean black hole
features at an instant in time, with a horizon separating interior and exterior
parts. Effective Einstein equations in the presence of a `geometric condensate'
are assumed, and computed with the software OGRe. We find similarities with the
energy density and pressure of gravastars, as well as remnants, and investigate
their dependence on the integration constant.


  Concerning different approaches to automatic PoS tagging: EngCG-2, a
constraint-based morphological tagger, is compared in a double-blind test with
a state-of-the-art statistical tagger on a common disambiguation task using a
common tag set. The experiments show that for the same amount of remaining
ambiguity, the error rate of the statistical tagger is one order of magnitude
greater than that of the rule-based one. The two related issues of priming
effects compromising the results and disagreement between human annotators are
also addressed.


  Using a quantum mechanical three-step model we present numerical calculations
on the high-harmonic generation from four polyatomic molecules. Ethylene
(C$_2$H$_4$) serves as an example where orbital symmetry directly affects the
harmonic yield. We treat the case of methane (CH$_4$) to address the
high-harmonic generation resulting from a molecule with degenerate orbitals. To
this end we illustrate how the single orbital contributions show up in the
total high-harmonic signal. This example illustrates the importance of adding
coherently amplitude contributions from the individual degenerate orbitals.
Finally, we study the high-harmonic generation from propane (C$_3$H$_8$) and
butane (C$_4$H$_{10}$). These two molecules, being extended and far from
spherical in structure, produce harmonics with non-trivial orientational
dependencies. In particular, propane can be oriented so that very
high-frequency harmonics are favorized, and thus the molecule contains
prospects for the generation of UV attosecond pulses.


  Nowadays, web search becomes more and more popular all over the world. Many
researchers and developers have done lots of studies on behaviors of search
users. In practice, the full understanding of these behaviors can not only help
to evaluate the usefulness of newly-developed ranking algorithms and other
changes of search engine, but also to guide the growth direction of search
engine. As far as we know, most of past work are mainly focused on single
search evaluation, which do promote the rapid development of search engine in
early stage. However,these page-level behaviors are so limited that can no
longer give explicit feedbacks on minor changes of the search engine. We think
that it will be more accurate and sensitive when more information on search
session are provided. In this paper, a session level evaluation method is
proposed. The session-level features are retrieved and carefully analyzed. Some
linear and non-linear features which can reflect the final degree of
satisfaction are chosen and adopted in evaluation models. A two-layer hybrid
evaluation model with different granularity, which can achieve good precision
and recall, is designed and trained. Lots of real experiments are evaluated by
the model, the result shows it achieved a higher accuracy performance than
traditional page-level evaluation metrics. Furthermore, for practical
application, it is important to interpret the reason of each session's
satisfaction judgement. In all, a session-level evaluation model with improved
performance and well capability on interpretation is proposed and applied in
real practice in search engine companies.


  We show that a set of K-semistable log Fano cone singularities is bounded if
and only if their local volumes are bounded away from zero, and their minimal
log discrepancies of Koll\'ar components are bounded from above. As
corollaries, we confirm the boundedness conjecture for K-semistable log Fano
cone singularities in dimension three, and show that local volumes of
3-dimensional klt singularities only accumulate at zero.


  In this paper, we discuss the inverse problem of determining a semisimple
group algebra from the knowledge of rings of the type sum_{t=1}^s M_{n_t}(Ft),
where j is an arbitrary integer and F_t is finite field for each t, and show
that it is ill-posed. After then, we define the concept of completeness of the
rings of the type sum_{t=1}^s M_{n_t}(Ft) to pose a well-posed inverse problem
and propose a conjecture in this direction.


  We use particle-in-cell (PIC) simulations of a collisionless, electron-ion
plasma with a decreasing background magnetic field, $B$, to study the effect of
velocity-space instabilities on the viscous heating and thermal conduction of
the plasma. If $B$ decreases, the adiabatic invariance of the magnetic moment
gives rise to pressure anisotropies with $p_{||,j} > p_{\perp,j}$ ($p_{||,j}$
and $p_{\perp,j}$ represent the pressure of species $j$ ($=i$ or $e$) parallel
and perpendicular to the magnetic field). Linear theory indicates that, for
sufficiently large anisotropies, different velocity-space instabilities can be
triggered. These instabilities, which grow on scales comparable to the electron
and ion Larmor radii, in principle have the ability to pitch-angle scatter the
particles, limiting the growth of the anisotropies. Our PIC simulations focus
on the nonlinear, saturated regime of the instabilities. This is done through
the permanent decrease of the magnetic field by an imposed shear in the plasma.
Our results show that, in the regime $2 \lesssim \beta_j \lesssim 20$ ($\beta_j
\equiv 8\pi p_j/B^2$), the saturated ion and electron pressure anisotropies are
controlled by the combined effect of the oblique ion firehose (OIF) and the
fast magnetosonic/whistler (FM/W) instabilities. These instabilities grow
preferentially on the ion Larmor radius scale, and make the ion and electron
pressure anisotropies nearly equal: $\Delta p_e/p_{||,e} \approx \Delta
p_i/p_{||,i}$ (where $\Delta p_j=p_{\perp,j} - p_{||,j}$). We also quantify the
thermal conduction of the plasma by directly calculating the mean free path of
electrons along the mean magnetic field, which we find strongly depends on
whether $B$ decreases or increases. Our results can be applied in studies of
low collisionality plasmas such as the solar wind, the intracluster medium, and
some accretion disks around black holes.


  As the capabilities of Large Language Models (LLMs) in healthcare and
medicine continue to advance, there is a growing need for competitive
open-source models that can safeguard public interest. With the increasing
availability of highly competitive open base models, the impact of continued
pre-training is increasingly uncertain. In this work, we explore the role of
instruct tuning, model merging, alignment, red teaming and advanced inference
schemes, as means to improve current open models. To that end, we introduce the
Aloe family, a set of open medical LLMs highly competitive within its scale
range. Aloe models are trained on the current best base models (Mistral, LLaMA
3), using a new custom dataset which combines public data sources improved with
synthetic Chain of Thought (CoT). Aloe models undergo an alignment phase,
becoming one of the first few policy-aligned open healthcare LLM using Direct
Preference Optimization, setting a new standard for ethical performance in
healthcare LLMs. Model evaluation expands to include various bias and toxicity
datasets, a dedicated red teaming effort, and a much-needed risk assessment for
healthcare LLMs. Finally, to explore the limits of current LLMs in inference,
we study several advanced prompt engineering strategies to boost performance
across benchmarks, yielding state-of-the-art results for open healthcare 7B
LLMs, unprecedented at this scale.


  We design carbyne transistor which is integrable into the existing silicon
technology and can be scaled up in a rather broad range -- starting from that
prepared by us (by 0.5-mkm technology) up to the monomolecular one because the
key mechanism here is the inter-chain charge transfer.


  We consider a Josephson junction where the weak-link is formed by a
non-centrosymmetric ferromagnet. In such a junction, the superconducting
current acts as a direct driving force on the magnetic moment. We show that the
a.c. Josephson effect generates a magnetic precession providing then a feedback
to the current. Magnetic dynamics result in several anomalies of current-phase
relations (second harmonic, dissipative current) which are strongly enhanced
near the ferromagnetic resonance frequency.


  Classically, the Bezout matrix or simply Bezoutian of two polynomials is used
to locate the roots of the polynomial and, in particular, test for stability.
In this paper, we develop the theory of Bezoutians on real Riemann surfaces of
dividing type. The main result connects the signature of the Bezoutian of two
real meromorphic functions to the topological data of their quotient, which can
be seen as the generalization of the classical Cauchy index. As an application,
we propose a method to count the number of zeroes of a polynomial in a
quadrature domain using the inertia of the Bezoutian. We provide examples of
our method in the case of simply connected quadrature domains.


  We study the first-order finite-temperature electroweak phase transition of
the SU(2) gauge-Higgs model defined on a 4-dimensional isotropic lattice with
temporal extension N_t=2. Finite-size scaling study of Lee-Yang zeros yields
the value of the Higgs self coupling of the endpoint at lambda_c=0.00116(16).
An independent analysis of Binder cumulant gives a consistent value for the
endpoint. Combined with our zero-temperature measurement of Higgs and W boson
masses, this leads to M_{H,c}=73.3 +- 6.4 GeV for the critical Higgs boson mass
beyond which the electroweak transition turns into a crossover.


  We investigate a class of operators resulting from a quantization scheme
attributed to Berezin. These so-called Berezin-Toeplitz operators are defined
on a Hilbert space of square-integrable holomorphic sections in a line bundle
over the classical phase space. As a first goal we develop self-adjointness
criteria for Berezin-Toeplitz operators defined via quadratic forms. Then,
following a concept of Daubechies and Klauder, the semigroups generated by
these operators may under certain conditions be represented in the form of
Wiener-regularized path integrals. More explicitly, the integration is taken
over Brownian-motion paths in phase space in the ultra-diffusive limit. All
results are the consequence of a relation between Berezin-Toeplitz operators
and Schrodinger operators defined via certain quadratic forms. The
probabilistic representation is derived in conjunction with a version of the
Feynman-Kac formula.


  We use the Spitzer Wide-area InfraRed Extragalactic Legacy Survey (SWIRE) to
explore the specific star-formation activity of galaxies and their evolution
near the peak of the cosmic far-infrared (FIR) background at 70 and 160um. We
use a stacking analysis to determine the mean FIR properties of well defined
subsets of galaxies at flux levels well below the FIR catalogue detection
limits of SWIRE and other Spitzer surveys. We tabulate the contribution of
different subsets of galaxies to the FIR background at 70um and 160um. These
long wavelengths provide a good constraint on the bolometric, obscured
emission. The large area provides good constraints at low z and in finer
redshift bins than previous work. At all redshifts we find that the specific
FIR Luminosity (sLFIR) decreases with increasing mass, following a trend
L_FIR/M* propto M_* ^beta with beta =-0.38\pm0.14. This is a more continuous
change than expected from the {Delucia2007} semi-analytic model suggesting
modifications to the feedback prescriptions. We see an increase in the sLFIR by
about a factor of ~100 from 0<z<2 and find that the sLFIR evolves as
(1+z)^alpha with alpha=4.4\pm0.3 for galaxies with 10.5 < log M*/Msun < 12.
This is considerably steeper than the {Delucia2007} semi-analytic model (alpha
\sim 2.5). When separating galaxies into early and late types on the basis of
the optical/IR spectral energy distributions we find that the decrease in sLFIR
with stellar mass is stronger in early type galaxies (beta ~ -0.46), while late
type galaxies exhibit a flatter trend (beta \sim -0.15). The evolution is
strong for both classes but stronger for the early type galaxies. The early
types show a trend of decreasing strength of evolution as we move from lower to
higher masses while the evolution of the late type galaxies has little
dependence on stellar mass. We suggest that in late-type galaxies we are seeing
a consistently declining sSFR..


  The ongoing Big Data explosion has created a demand for efficient and
scalable algorithms for similarity search.
  Most recent work has focused on \textit{approximate} $k$-NN search, and while
this may be sufficient for some applications, \textit{exact} $k$-NN search
would be ideal for many applications.
  We present CAKES, a set of three novel, exact algorithms for $k$-NN search.
  CAKES's algorithms are generic over \textit{any} distance function, and they
\textit{do not} scale with the cardinality or embedding dimension of the
dataset, but rather with its metric entropy and fractal dimension.
  We test these claims on datasets from the ANN-Benchmarks suite under
commonly-used distance functions, as well as on a genomic dataset with
Levenshtein distance and a radio-frequency dataset with Dynamic Time Warping
distance.
  We demonstrate that CAKES exhibits near-constant scaling with cardinality on
data conforming to the manifold hypothesis, and has perfect recall on data in
\textit{metric} spaces.
  We also demonstrate that CAKES exhibits significantly higher recall than
state-of-the-art $k$-NN search algorithms when the distance function is not a
metric.
  Additionally, we show that indexing and tuning time for CAKES is an order of
magnitude, or more, faster than state-of-the-art approaches.
  We conclude that CAKES is a highly efficient and scalable algorithm for exact
$k$-NN search on Big Data.
  We provide a Rust implementation of CAKES.


  The theory for time-resolved photoemission spectroscopy as applied to
pump-probe experiments is developed and solved for the generic case of a
strongly correlated material. The formal development incorporates all of the
nonequilibrium effects of the pump pulse and the finite time width of the probe
pulse. While our formal development is completely general, in our numerical
illustration for the Hubbard model, we assume the pump pulse drives the
electrons into a nonequilibrium configuration, which rapidly thermalizes to
create a hot (quasi-equilibrium) electronic system, and we then study the
effects of windowing that arise from the finite width of the probe pulse. We
find sharp features in the spectra are broadened, particularly the
quasiparticle peak of strongly correlated metals at low temperature.


  A class of inflationary scenarios for primordial black hole (PBH) formation
include a small barrier in the slope of the potential. There, the inflaton
slows down, generating an enhancement of primordial perturbations. Moreover,
the background solution overcomes the barrier at a very low speed, and large
backward quantum fluctuations can prevent certain regions from overshooting the
barrier. This leads to localized bubbles where the field remains trapped behind
the barrier. In such models, therefore, we have two distinct channels for PBH
production: the standard adiabatic density perturbation channel and the bubble
channel. Here, we perform numerical simulations of bubble formation, addressing
the issues of initial conditions, critical amplitude and bubble expansion.
Further, we explore the scaling behaviour of the co-moving size of bubbles with
the initial amplitude of the field fluctuation. We find that for small to
moderate non-Gaussianity $f_{\rm NL}\lesssim 2.6$, the threshold for the
formation of vacuum bubbles agrees with previous analytical estimates
arXiv:1908.11357 to $5\%$ accuracy or so. We also show that the mass
distribution for the two channels is different, leading to a slightly broader
range of PBH masses. The bubble channel is subdominant for small $f_{\rm NL}$,
and becomes dominant for $f_{\rm NL}\gtrsim 2.6$. We find that the mass of PBHs
in the bubble channel is determined by an adiabatic overdensity surrounding the
bubble at the end of inflation. Remarkably, the profile of this overdensity
turns out to be of type-II. This represents a first clear example showing that
overdensities of type-II can be dominant relative to the standard type-I. We
also comment on exponential tails and on the fact that in models with local
type non-Gaussianity (such as the one considered here), the occurrence of
alternative channels can easily be inferred from unitarity considerations.


  We study the collective escape dynamics of a chain of coupled, weakly damped
nonlinear oscillators from a metastable state over a barrier when driven by a
thermal heat bath in combination with a weak, globally acting periodic
perturbation. Optimal parameter choices are identified that lead to a drastic
enhancement of escape rates as compared to a pure noise-assisted situation. We
elucidate the speed-up of escape in the driven Langevin dynamics by showing
that the time-periodic external field in combination with the thermal
fluctuations triggers an instability mechanism of the stationary homogeneous
lattice state of the system. Perturbations of the latter provided by incoherent
thermal fluctuations grow because of a parametric resonance, leading to the
formation of spatially localized modes (LMs). Remarkably, the LMs persist in
spite of continuously impacting thermal noise. The average escape time assumes
a distinct minimum by either tuning the coupling strength and/or the driving
frequency. This weak ac-driven assisted escape in turn implies a giant speed of
the activation rate of such thermally driven coupled nonlinear oscillator
chains.


  We study a car-following model of traffic flow which assumes only that a
car's acceleration depends on its own speed, the headway ahead of it, and the
rate of change of headway, with only minimal assumptions about the functional
form of that dependence. The velocity of uniform steady flow is found
implicitly from the acceleration function, and its linear stability criterion
can be expressed simply in terms of it. Crucially, unlike in previously
analyzed car-following models, the threshold of absolute stability does not
generally coincide with an inflection point in the steady velocity function.
The Burgers and KdV equations can be derived under the usual assumptions, but
the mKdV equation arises only when absolute stability does coincide with an
inflection point. Otherwise, the KdV equation applies near absolute stability,
while near the inflection point one obtains the mKdV equation plus an extra,
quadratic term. Corrections to the KdV equation "select" a single member of the
one-parameter set of soliton solutions. In previous models this has always
marked the threshold of a finite- amplitude instability of steady flow, but
here it can alternatively be a stable, small-amplitude jam. That is, there can
be a forward bifurcation from steady flow. The new, augmented mKdV equation
which holds near an inflection point admits a continuous family of kink
solutions, like the mKdV equation, and we derive the selection criterion
arising from the corrections to this equation.


  Previous computation models either have equivalent abilities in representing
all computations but fail to provide primitive operators for programming
complex algorithms or lack generalized expression ability to represent
newly-added computations. This article presents a unified computation model
with generalized expression ability and a concise set of primitive operators
for programming high-level algorithms. We propose a unified data abstraction --
Tensor of List, and offer a unified computation model based on Tensor of List,
which we call the ToL model (in short, ToL). ToL introduces five atomic
computations that can represent any elementary computation by finite
composition, ensured with strict formal proof. Based on ToL, we design a
pure-functional language -- ToLang. ToLang provides a concise set of primitive
operators that can be used to program complex big data and AI algorithms. Our
evaluations show ToL has generalized expression ability and a built-in
performance indicator, born with a strictly defined computation metric --
elementary operation count (EOPs), consistent with FLOPs within a small error
range.


  Surface area and mean width of a cylinder (the convex hull of two parallel
disks) in R^3 are computed. It is more difficult to obtain analogous results
for a cone (the convex hull of a disk D and a point p). Oblique formulas for
mean width, as well as those for mean curvature, are new. Let L denote the
unique diameter of D whose endpoints are equidistant from p. We conclude with a
question involving the plane that bisects the cone and contains {p,L}, as p
varies. What is the minimum ratio of the smaller measure to the larger?


  We construct the space of infinitesimal variations for the Strominger system
and an obstruction space to integrability, using elliptic operator theory. We
initiate the study of the geometry of the moduli space, describing the
infinitesimal structure of a natural foliation on this space. The associated
leaves are related to generalized geometry and correspond to moduli spaces of
solutions of suitable Killing spinor equations on a Courant algebroid. As an
application, we propose a unifying framework for metrics with holonomy $\SU(3)$
and solutions of the Strominger system.


  We introduce the $\textbf{O}$ne-shot $\textbf{P}$runing $\textbf{T}$echnique
for $\textbf{I}$nterchangeable $\textbf{N}$etworks ($\textbf{OPTIN}$) framework
as a tool to increase the efficiency of pre-trained transformer architectures
$\textit{without requiring re-training}$. Recent works have explored improving
transformer efficiency, however often incur computationally expensive
re-training procedures or depend on architecture-specific characteristics, thus
impeding practical wide-scale adoption. To address these shortcomings, the
OPTIN framework leverages intermediate feature distillation, capturing the
long-range dependencies of model parameters (coined $\textit{trajectory}$), to
produce state-of-the-art results on natural language, image classification,
transfer learning, and semantic segmentation tasks $\textit{without
re-training}$. Given a FLOP constraint, the OPTIN framework will compress the
network while maintaining competitive accuracy performance and improved
throughput. Particularly, we show a $\leq 2$% accuracy degradation from NLP
baselines and a $0.5$% improvement from state-of-the-art methods on image
classification at competitive FLOPs reductions. We further demonstrate the
generalization of tasks and architecture with comparative performance using
Mask2Former for semantic segmentation and cnn-style networks. OPTIN presents
one of the first one-shot efficient frameworks for compressing transformer
architectures that generalizes well across different class domains, in
particular: natural language and image-related tasks, without
$\textit{re-training}$.


  Building on the work of the fourth author in math.AG/9904074, we prove the
weak factorization conjecture for birational maps in characteristic zero: a
birational map between complete nonsingular varieties over an algebraically
closed field K of characteristic zero is a composite of blowings up and
blowings down with smooth centers. Such a factorization exists which is
functorial with respect to absolute isomorphisms, and compatible with a normal
crossings divisor. The same holds for algebraic and analytic spaces. Another
proof of the main theorem by the fourth author appeared in math.AG/9904076.


  We analyze the initial value problem for semilinear wave equations on
asymptotically anti-de Sitter spaces using energy methods adapted to the
geometry of the problem at infinity. The key feature is that the coefficients
become strongly singular at infinity, which leads to considering nontrivial
data on the conformal boundary of the manifold. This question arises in Physics
as the holographic prescription problem in string theory.


  Quantum degeneracy in error correction is a feature unique to quantum error
correcting codes, unlike their classical counterpart. It allows a quantum error
correcting code to correct errors even when they can not uniquely pinpoint the
error. The diagonal distance of a quantum code is an important parameter that
characterizes if the quantum code is degenerate or not. If code has a distance
more than the diagonal distance, then it is degenerate, whereas if it is below
the diagonal distance, then it is nondegenerate. We show that most of the CWS
codes without a cycle of length four attain the upper bound of diagonal
distance d+1, where d is the minimum vertex degree of the associated graph.
Addressing the question of degeneracy, we give necessary conditions on CWS
codes to be degenerate. We show that any degenerate CWS code with graph $G$ and
classical code C will either have a short cycle in the graph $G$ or will be
such that the classical code C has one of the coordinates trivially zero for
all codewords.


  We study the interplay between the minimal representations of the orthogonal
Lie algebra $\mathfrak{g}=\mathfrak{so}(n+2,\mathbb{C})$ and the \emph{algebra
of symmetries} $\mathscr{S}(\Box^r)$ of powers of the Laplacian $\Box$ on
$\mathbb{C}^{n}$. The connection is made through the construction of highest
weight representation of $\mathfrak{g}$ via the ring of differential operators
$\mathcal{D}(X)$ on the singular scheme $X=(F^r=0)\subset \mathbb{C}^n$, where
$F$ is the sum of squares.
  In particular we prove that $ \mathscr{S}(\Box^r)\cong \mathcal{D}(X)$ is
isomorphic to a primitive factor ring of $U(\mathfrak{g})$. Interestingly, if
(and only if) $n$ is even with $2r\geq n$ then both $\mathcal{D}(X)$ and its
natural module $\mathcal{O}(X)$ have a finite dimensional factor.
  These results all have real analogues, with $\Box$ replaced by the
d'Alembertian on the pseudo-Euclidean space $\mathbb{R}^{p,q}$ and
$\mathfrak{g}$ replaced by the real Lie algebra $\mathfrak{so}(p+1,q+1)$.


  The aim of this paper is to extend the notion of commutativity of vector
fields to the category of singular foliations, using Nambu structures, i.e.
integrable multi-vector fields. We will classify the relationship between
singular foliations and Nambu structures, and show some basic results about
commuting Nambu structures.


  We consider the bilinear inverse problem of recovering two vectors,
$\boldsymbol{x} \in\mathbb{R}^L$ and $\boldsymbol{w} \in\mathbb{R}^L$, from
their entrywise product. We consider the case where $\boldsymbol{x}$ and
$\boldsymbol{w}$ have known signs and are sparse with respect to known
dictionaries of size $K$ and $N$, respectively. Here, $K$ and $N$ may be larger
than, smaller than, or equal to $L$. We introduce $\ell_1$-BranchHull, which is
a convex program posed in the natural parameter space and does not require an
approximate solution or initialization in order to be stated or solved. Under
the assumptions that $\boldsymbol{x}$ and $\boldsymbol{w}$ satisfy a
comparable-effective-sparsity condition and are $S_1$- and $S_2$-sparse with
respect to a random dictionary, we present a recovery guarantee in a noisy
case. We show that $\ell_1$-BranchHull is robust to small dense noise with high
probability if the number of measurements satisfy
$L\geq\Omega\left((S_1+S_2)\log^{2}(K+N)\right)$. Numerical experiments show
that the scaling constant in the theorem is not too large. We also introduce
variants of $\ell_1$-BranchHull for the purposes of tolerating noise and
outliers, and for the purpose of recovering piecewise constant signals. We
provide an ADMM implementation of these variants and show they can extract
piecewise constant behavior from real images.


  We present the correspondence principle between the T-{\rho} diagram, the
Zeno line and the binodal for a given interaction potential of Lennard-Jones
type. We use this correspondence further to construct a distribution of the
Bose-Einstein type for a classical gas with the help of the new notion of Bose
condensate, making it possible to decrease fractal dimension while
simultaneously preserving the number of particles. In so doing, we use new
global distributions in number theory.


  GN-Vision is a novel dual $\gamma$-ray and neutron imaging system, which aims
at simultaneously obtaining information about the spatial origin of
$\gamma$-ray and neutron sources. The proposed device is based on two position
sensitive detection planes and exploits the Compton imaging technique for the
imaging of $\gamma$-rays. In addition, spatial distributions of slow- and
thermal-neutron sources ($<$100 eV) are reconstructed by using a passive
neutron pin-hole collimator attached to the first detection plane. The proposed
gamma-neutron imaging device could be of prime interest for nuclear safety and
security applications. The two main advantages of this imaging system are its
high efficiency and portability, making it well suited for nuclear applications
were compactness and real-time imaging is important. This work presents the
working principle and conceptual design of the GN-Vision system based on Monte
Carlo simulations and explores its simultaneous $\gamma$-ray and neutron
detection and imaging capabilities for a realistic case of a security
inspection where a $^{252}$Cf source is hidden in a neutron moderating
container.


  Using three-dimensional smoothed particle hydrodynamics simulations, we
investigate the formation of multiple stellar populations (MSPs) in globular
clusters (GCs) within the context of their parent galaxies. In our scenario,
the second generation (2G) of stars originate from both asymptotic giant branch
(AGB) polluters and pristine gas accreted from the host galaxy. Previous
theoretical and numerical studies have demonstrated that this 'AGB with
dilution' model has the potential to alleviate several problems faced by the
classical AGB scenario. However, the accretion of pristine gas on to the GC has
yet to be investigated within the context of the parent galaxy. This paper
presents the preliminary results from our original simulation code which models
GC formation from giant molecular clouds in a host galaxy, and subsequent gas
accretion on to the GC. By simulating the genesis of the 2G over a 370 Myr time
frame, we demonstrate that the fraction of 2G stars are inextricably linked to
the GC's environment. Our simulations rationalize the wide variety of abundance
patterns, kinematics, and 2G concentrations by altering the initial conditions
of both the GC progenitor and the host galaxy itself. Most notably, we
reproduce a positive correlation between the fraction of 2G stars and the
initial mass of the cluster. We discuss the physical implications of our
scenario and compare our simulations with observations of the Galactic GC 47
Tucanae (47 Tuc). Finally, we present scaling relations that encompass the
wider GC population and serve as a reference for future observations.


  A tensegrity tower design to support a given payload for the moon mining
operation is proposed in this paper. A non-linear optimization problem for the
minimal-mass structure design is posed and solved, subject to the yielding
constraints for strings and yielding and buckling constraints for bars in the
presence of lunar gravity. The optimization variables for this non-linear
problem are structural complexity and pre-stress in the strings. Apart from
local failure constraints of yielding and buckling, global buckling is also
considered. The structure designed as a deployable tower is a TnD1 tensegrity
structure. A case study demonstrates the feasibility and advantage of the tower
design. The principles developed in this paper are also applicable for building
other structures on the Earth or other planets.


  In this paper, we first propose a general interpolation algorithm in a free
module of a linearized polynomial ring, and then apply this algorithm to decode
several important families of codes, Gabidulin codes, KK codes and MV codes.
Our decoding algorithm for Gabidulin codes is different from the polynomial
reconstruction algorithm by Loidreau. When applied to decode KK codes, our
interpolation algorithm is equivalent to the Sudan-style list-1 decoding
algorithm proposed by K/"otter and Kschischang for KK codes. The general
interpolation approach is also capable of solving the interpolation problem for
the list decoding of MV codes proposed by Mahdavifar and Vardy, and has a lower
complexity than solving linear equations.


  Sentiment analysis of Twitter data is performed. The researcher has made the
following contributions via this paper: (1) an innovative method for deriving
sentiment score dictionaries using an existing sentiment dictionary as seed
words is explored, and (2) an analysis of clustered tweet sentiment scores
based on tweet length is performed.


  Accelerators for sparse matrix multiplication are important components in
emerging systems. In this paper, we study the main challenges of accelerating
Sparse Matrix Multiplication (SpMM). For the situations that data is not stored
in the desired order (row/column order), we propose a compact high performance
sparse format, which allows for random access to a dataset with low memory
access overhead. We show that using this format results in a 14-49 times
speedup for SpMM. Next, we propose a high performance systolic architecture for
SpMM, which uses a mesh of comparators to locate the useful (non-zero)
computation. This design maximizes data reuse by sharing the input data among a
row/column of the mesh. We also show that, with similar memory access
assumptions, the proposed architecture results in a 9-30 times speedup in
comparison with the state of the art.


  Avoiding collisions between obstacles and vehicles such as cars, robots or
aircraft is essential to the development of automation and autonomy. To
simplify the problem, many collision avoidance algorithms and proofs consider
vehicles to be a point mass, though the actual vehicles are not points. In this
paper, we consider a convex polygonal vehicle with nonzero area traveling along
a 2-dimensional trajectory. We derive an easily-checkable, quantifier-free
formula to check whether a given obstacle will collide with the vehicle moving
on the planned trajectory. We apply our method to two case studies of aircraft
collision avoidance and study its performance.


  The quadrupole interaction at 111Cd impurity nuclei in the
intermediate-valence compound YbAl2 has been measured under pressure up to 80
kbar by the TDPAC spectroscopy. It was found that the quadrupole frequency nQ
measured on the 111Cd located at the Al sites in YbAl2, varies nonlinearly and
increases by almost four times with the pressure increase up to 80 kbar. A
linear correlation between the mean Yb valence, derived from Yb L3 OFY-XAS and
RXES measurements, and the electric field gradient (the quadrupole frequency
nQ=eQVzz/h) has been observed. The quadrupole frequencies on 111Cd in the
GdAl2, YbAl3, TmAl3 and CaAl2 compounds have been measured, also. The
possibility of determining the valence of Yb in the Yb compounds with p-metals
from the relation nQ (u(P)) = n2 + (n3 - n2) u(P) has been considered.


  We show that a stationary asymptotically flat electro-vacuum solution of
Einstein's equations that is everywhere locally "almost isometric" to a
Kerr-Newman solution cannot admit more than one event horizon. Axial symmetry
is not assumed. In particular this implies that the assumption of a single
event horizon in Alexakis-Ionescu-Klainerman's proof of perturbative uniqueness
of Kerr black holes is in fact unnecessary.


  Characterizing the spatial and velocity structure of molecular clouds is a
first step towards a better understanding of interstellar turbulence and its
link to star formation. We present observations and structure analysis results
for a large-scale (~ 7.10 deg^2) 13CO J = 2--1 and 12 CO J = 3--2 survey
towards the nearby Perseus molecular cloud observed with the KOSMA 3m
telescope. We study the spatial structure of line-integrated and velocity
channel maps, measuring the $\Delta$-variance as a function of size scale. We
determine the spectral index $\beta$ of the corresponding power spectrum and
study its variation across the cloud and across the lines. We find that the
spectra of all CO line-integrated maps of the whole complex show the same
index, $\beta$ ~ 3.1, for scales between about 0.2 and 3pc, independent of
isotopomer and rotational transition. A complementary 2MASS map of optical
extinction shows a noticeably smaller index of 2.6. In contrast to the overall
region, the CO maps of individual subregions show a significant variation of
$\beta$. The 12CO 3--2 data provide e.g. a spread of indices between 2.9 in
L1455 and 3.5 in NGC1333. In general, active star forming regions show a larger
power-law exponent. We find that the $\Delta$-variance spectra of individual
velocity channel maps are very sensitive to optical depth effects clearly
indicating self-absorption in the densest regions. When studying the dependence
of the channel-map spectra as a function of the velocity channel width, the
expected systematic increase of the spectral index with channel width is only
detected in the blue line wings.This could be explained by a filamentary,
pillar-like structure which is left at low velocities while the overall
molecular gas is swept up by a supernova shock wave.


  The Extragalactic Background Light (EBL) captures the total integrated
emission from stars and galaxies throughout the cosmic history. The amplitude
of the near-infrared EBL from space absolute photometry observations has been
controversial and depends strongly on the modeling and subtraction of the
Zodiacal light foreground. We report the first measurement of the diffuse
background spectrum at 0.8-1.7 um from the CIBER experiment. The observations
were obtained with an absolute spectrometer over two flights in multiple sky
fields to enable the subtraction of Zodiacal light, stars, terrestrial
emission, and diffuse Galactic light. After subtracting foregrounds and
accounting for systematic errors, we find the nominal EBL brightness, assuming
the Kelsall Zodiacal light model, is 42.7+11.9/-10.6 nW/m2/sr at 1.4 um. We
also analyzed the data using the Wright Zodiacal light model, which results in
a worse statistical fit to the data and an unphysical EBL, falling below the
known background light from galaxies at <1.3 um. Using a model-independent
analysis based on the minimum EBL brightness, we find an EBL brightness of
28.7+5.1/-3.3 nW/m2/sr at 1.4 um. While the derived EBL amplitude strongly
depends on the Zodiacal light model, we find that we cannot fit the spectral
data to Zodiacal light, Galactic emission, and EBL from solely integrated
galactic light from galaxy counts. The results require a new diffuse component,
such as an additional foreground or an excess EBL with a redder spectrum than
that of Zodiacal light.


  We examine a simple model of proton pumping through the inner membrane of
mitochondria in the living cell. We demonstrate that the pumping process can be
described using approaches of condensed matter physics. In the framework of
this model, we show that the resonant F\"orster-type energy exchange due to
electron-proton Coulomb interaction can provide an unidirectional flow of
protons against an electrochemical proton gradient, thereby accomplishing
proton pumping. The dependence of this effect on temperature as well as
electron and proton voltage build-ups are obtained taking into account
electrostatic forces and noise in the environment. We find that the proton pump
works with maximum efficiency in the range of temperatures and transmembrane
electrochemical potentials which correspond to the parameters of living cells.


  GGR News:
  we hear that...
  100 years ago
  GGR program at the APS meeting in Savannah, GA
  Conference reports:
  GR20/Amaldi10 Conference in Warsaw, Poland
  School and workshop on quantum gravity in Brazil


  We report on the observation of a new isomeric state in $^{68}$Ni. We suggest
that the newly observed state at 168(1) keV above the first 2$^+$ state is a
$\pi(2p-2h)$ 0$^{+}$ state across the major Z=28 shell gap. Comparison with
theoretical calculations indicates a pure proton intruder configuration and the
deduced low-lying structure of this key nucleus suggests a possible shape
coexistence scenario involving a highly deformed state.


  The magnetoresistance of a low carrier density, disordered GaAs based
two-dimensional (2D) electron gas has been measured in parallel magnetic fields
up to 32 T. The feature in the resistance associated with the complete spin
polarization of the carriers shifts down by more than 20 T as the electron
density is reduced, consistent with recent theories taking into account the
enhancement of the electron-electron interactions at low densities.
Nevertheless, the magnetic field for complete polarization, Bp, remains 2-3
times smaller than predicted for a disorder free system. We show, in particular
by studying the temperature dependance of Bp to probe the effective size of the
Fermi sea, that localization plays an important role in determining the spin
polarization of a 2D electron gas.


  We prove a matrix discrepancy bound that strengthens the famous
Kadison-Singer result of Marcus, Spielman, and Srivastava. Consider any
independent scalar random variables $\xi_1, \ldots, \xi_n$ with finite support,
e.g.
  $\{ \pm 1 \}$ or $\{ 0,1 \}$-valued random variables, or some combination
thereof. Let $u_1, \dots, u_n \in \mathbb{C}^m$ and $$ \sigma^2 = \left\|
\sum_{i=1}^n \text{Var}[ \xi_i ] (u_i u_i^{*})^2 \right\|. $$ Then there exists
a choice of outcomes $\varepsilon_1,\ldots,\varepsilon_n$ in the support of
$\xi_1, \ldots, \xi_n$ s.t. $$ \left \|\sum_{i=1}^n \mathbb{E} [ \xi_i] u_i
u_i^* - \sum_{i=1}^n \varepsilon_i u_i u_i^* \right \| \leq 4 \sigma. $$ A
simple consequence of our result is an improvement of a Lyapunov-type theorem
of Akemann and Weaver.


  This paper investigates a problem of defending safety-critical infrastructure
from an adversarial aerial attacker in an urban environment. A circular arc
formation of defenders is formed around the attacker, and vector-field based
guidance laws herd the attacker to a predefined safe area in the presence of
rectangular obstacles. The defenders' formation is defined based on a novel
vector field that imposes super-elliptic contours around the obstacles, to
closely resemble their rectangular shape. A novel finite-time stabilizing
controller is proposed to guide the defenders to their desired formation while
avoiding obstacles and inter-agent collisions. The efficiency of the approach
is demonstrated via simulation results.


  The aim of this review-style paper is to provide a concise, self-contained
and unified presentation of the construction and main properties of Gaussian
multiplicative chaos (GMC) measures for log-correlated fields in 2D in the
subcritical regime. By considering the case of the 2D Gaussian free field, we
review convergence, uniqueness and characterisations of the measures; revisit
Kahane's convexity inequalities and existence and scaling of moments; discuss
the measurability of the underlying field with respect to the GMC measure and
present a KPZ relation for scaling exponents.


  In nanometer scaled CMOS devices significant increase in the subthreshold,
the gate and the reverse biased junction band-to-band-tunneling (BTBT) leakage,
results in the large increase of total leakage power in a logic circuit.
Leakage components interact with each other in device level (through device
geometry, doping profile) and also in the circuit level (through node
voltages). Due to the circuit level interaction of the different leakage
components, the leakage of a logic gate strongly depends on the circuit
topology i.e. number and nature of the other logic gates connected to its input
and output. In this paper, for the first time, we have analyzed loading effect
on leakage and proposed a method to accurately estimate the total leakage in a
logic circuit, from its logic level description considering the impact of
loading and transistor stacking.


  We perform Monte Carlo simulations of Ising spin-glass models in three and
four dimensions, as well as of Migdal-Kadanoff spin glasses on a hierarchical
lattice. Our results show strong evidence for universal scaling in the
spin-glass phase in all three models. Not only does this allow for a clean way
to compare results obtained from different coupling distributions, it also
suggests that a so far elusive renormalization group approach within the
spin-glass phase may actually be feasible.


  The Seyfert 1.9 galaxy MCG-05-23-016 has been shown to exhibit a complex
X-ray spectrum. This source has moderate X-ray luminosity, hosts a comparably
low-mass black hole, but accretes at a high Eddington rate, and allows us to
study a super massive black hole in an early stage. Three observations of the
INTEGRAL satellite simultaneous with pointed Swift/XRT observations performed
from December 2006 to June 2007 are used in combination with public data from
the INTEGRAL archive to study the variability of the hard X-ray components and
to generate a high-quality spectrum from 1 to 150 keV. The AGN shows little
variability in the hard X-ray spectrum, with some indication of a variation in
the high-energy cut-off energy ranging from 50 keV to >>100 keV, with an
electron plasma temperature in the 10 - 90 keV range. The reflection component
is not evident and, if present, the reflected fraction can be constrained to R
< 0.3 for the combined data set. Comparison to previous observations shows that
the reflection component has to be variable. No variability in the UV and
optical range is observed on a time scale of 1.5 years. The hard X-ray spectrum
of MCG-05-23-016 appears to be stable with the luminosity and underlying power
law varying moderately and the optical/UV flux staying constant. The spectral
energy distribution appears to be similar to that of Galactic black hole
systems, e.g. XTE 1118+480 in the low state. The AGN exhibits a remarkably high
Eddington ratio of L(bol)/L(Edd)> 0.8 (or L(bol)/L(Edd) > 0.1, if we consider a
higher mass of the central engine) and, at the same time, a low cut-off energy
around 70 keV. Objects like MCG-05-23-016 might indicate the early stages of
super massive black holes, in which a strong accretion flow feeds the central
engine.


  The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function
$R$ from a policy $\pi$. To do this, we need a model of how $\pi$ relates to
$R$. In the current literature, the most common models are optimality,
Boltzmann rationality, and causal entropy maximisation. One of the primary
motivations behind IRL is to infer human preferences from human behaviour.
However, the true relationship between human preferences and human behaviour is
much more complex than any of the models currently used in IRL. This means that
they are misspecified, which raises the worry that they might lead to unsound
inferences if applied to real-world data. In this paper, we provide a
mathematical analysis of how robust different IRL models are to
misspecification, and answer precisely how the demonstrator policy may differ
from each of the standard models before that model leads to faulty inferences
about the reward function $R$. We also introduce a framework for reasoning
about misspecification in IRL, together with formal tools that can be used to
easily derive the misspecification robustness of new IRL models.


  Federated learning is a distributed learning framework that takes full
advantage of private data samples kept on edge devices. In real-world federated
learning systems, these data samples are often decentralized and
Non-Independently Identically Distributed (Non-IID), causing divergence and
performance degradation in the federated learning process. As a new solution,
clustered federated learning groups federated clients with similar data
distributions to impair the Non-IID effects and train a better model for every
cluster. This paper proposes StoCFL, a novel clustered federated learning
approach for generic Non-IID issues. In detail, StoCFL implements a flexible
CFL framework that supports an arbitrary proportion of client participation and
newly joined clients for a varying FL system, while maintaining a great
improvement in model performance. The intensive experiments are conducted by
using four basic Non-IID settings and a real-world dataset. The results show
that StoCFL could obtain promising cluster results even when the number of
clusters is unknown. Based on the client clustering results, models trained
with StoCFL outperform baseline approaches in a variety of contexts.


  Constraining the dark energy equation of state, $w_x(z)$, is one of the main
issues of current and future cosmological surveys. In practice, this requires
making assumptions about the evolution of $w_x$ with redshift $z$, which can be
manifested in a choice of a specific parametric form where the number of
cosmological parameters play an important role in the observed cosmic
acceleration. Since any attempt to constrain the EoS requires fixing some prior
in one form or the other, settling a method to constrain cosmological
parameters is of great importance. In this paper, we provide a straightforward
approach to show how cosmological tests can be improved via a parametric
methodology based on cosmography. Using Supernovae Type IA samplers we show how
by performing a statistical analysis of a specific dark energy parameterisation
can give directly the cosmographic parameters values.


  Large Language Models (LLMs) have become the go-to solution for many Natural
Language Processing (NLP) tasks due to their ability to tackle various problems
and produce high-quality results. Specifically, they are increasingly used to
automatically generate code, easing the burden on developers by handling
repetitive tasks. However, this improvement in quality has led to high
computational and memory demands, making LLMs inaccessible to users with
limited resources. In this paper, we focus on Central Processing Unit
(CPU)-compatible models and conduct a thorough semi-manual evaluation of their
strengths and weaknesses in generating Python code. We enhance their
performance by introducing a Chain-of-Thought prompt that guides the model in
problem-solving. Additionally, we propose a dataset of 60 programming problems
with varying difficulty levels for evaluation purposes. Our assessment also
includes testing these models on two state-of-the-art datasets: HumanEval and
EvalPlus. We commit to sharing our dataset and experimental results publicly to
ensure transparency.


  This paper presents a completely analytic treatment of cosmological
fluctuations whose wavelength is small enough to come within the horizon well
before the energy densities of matter and radiation become equal. This analysis
yields a simple formula for the conventional transfer function T(k) at large
wave number k, which agrees very well with computer calculations of T(k). It
also yields an explicit formula for the microwave background multipole
coefficient C_ell at very large ell.


  Quantum dots and single-molecule transistors may exhibit level crossings
induced by tuning external parameters such as magnetic field or gate voltage.
For Coulomb blockaded devices, this shows up as an inelastic cotunneling
threshold in the differential conductance, which can be tuned to zero at the
crossing. Here we show that, in addition, level crossings can give rise to a
nearly vertical step-edge, ridge or even a Fano-like ridge-valley feature in
the differential conductance inside the relevant Coulomb diamond. We study a
gate-tunable quasidegeneracy between singlet and triplet ground states, and
demonstrate how these different shapes may result from a competition between
nonequilibrium occupations and weak (spin-orbit) mixing of the states. Our
results are shown to be in qualitative agreement with recent transport
measurements on a Mn complex [E. A. Osorio, et al., Nano Lett. 10, 105 (2010)].
The effect remains entirely general and should be observable in a wide range of
Coulomb blockaded devices.


  In this paper, we discuss the implementation of a cell based smoothed finite
element method (CSFEM) within the commercial finite element software Abaqus.
The salient feature of the CSFEM is that it does not require an explicit form
of the derivative of the shape functions and there is no isoparametric mapping.
This implementation is accomplished by employing the user element subroutine
(UEL) feature of the software. The details on the input data format together
with the proposed user element subroutine, which forms the core of the finite
element analysis are given. A few benchmark problems from linear elastostatics
in both two and three dimensions are solved to validate the proposed
implementation. The developed UELs and the associated input files can be
downloaded from Github repository link:
https://github.com/nsundar/SFEM\_in\_Abaqus.


  We study the photonic spectrum of a one-dimensional optical lattice
possessing a double primitive cell, when the atoms are well localized at the
lattice minima. While a one-dimensional lattice with a simple Wigner-Seitz cell
always possesses a photonic bandgap at the atomic resonance, in this
configuration the photonic transmission spectrum may exhibit none, double or
multiple photonic bandgaps depending on the ratio between the interparticle
distance $\varrho$ inside the cell and the cell size $a$. The transmission
spectra of a weak incident probe are evaluated when the atoms are trapped in
free space and inside an optical resonator for realistic experimental
parameters.


  Task and Motion Planning (TAMP) integrates high-level task planning and
low-level motion planning to equip robots with the autonomy to effectively
reason over long-horizon, dynamic tasks. Optimization-based TAMP focuses on
hybrid optimization approaches that define goal conditions via objective
functions and are capable of handling open-ended goals, robotic dynamics, and
physical interaction between the robot and the environment. Therefore,
optimization-based TAMP is particularly suited to solve highly complex,
contact-rich locomotion and manipulation problems. This survey provides a
comprehensive review on optimization-based TAMP, covering (i) planning domain
representations, including action description languages and temporal logic,
(ii) individual solution strategies for components of TAMP, including AI
planning and trajectory optimization (TO), and (iii) the dynamic interplay
between logic-based task planning and model-based TO. A particular focus of
this survey is to highlight the algorithm structures to efficiently solve TAMP,
especially hierarchical and distributed approaches. Additionally, the survey
emphasizes the synergy between the classical methods and contemporary
learning-based innovations such as large language models. Furthermore, the
future research directions for TAMP is discussed in this survey, highlighting
both algorithmic and application-specific challenges.


  Context. Theoretical arguments and numerical simulations of radiative shocks
produced by the impact of the accreting gas onto young stars predict
quasi-periodic oscillations in the emitted radiation. However, observational
data do not show evidence of such periodicity. Aims. We investigate whether
physically plausible perturbations in the accretion column or in the
chromosphere could disrupt the shock structure influencing the observability of
the oscillatory behavior. Methods. We performed local 2D magneto-hydrodynamical
simulations of an accretion shock impacting a chromosphere, taking optically
thin radiation losses and thermal conduction into account. We investigated the
effects of several perturbation types, such as clumps in the accretion stream
or chromospheric fluctuations, and also explored a wide range of plasma-\beta
values. Results. In the case of a weak magnetic field, the post-shock region
shows chaotic motion and mixing, smoothing out the perturbations and retaining
a global periodic signature. On the other hand, a strong magnetic field
confines the plasma in flux tubes, which leads to the formation of fibrils that
oscillate independently. Realistic values for the amplitude, length, and time
scales of the perturbation are capable of bringing the fibril oscillations out
of phase, suppressing the periodicity of the emission. Conclusions. The
strength of a locally uniform magnetic field in YSO accretion shocks determines
the structure of the post-shock region, namely, whether it will be somewhat
homogeneous or if it will split up to form a collection of fibrils. In the
second case, the size and shape of the fibrils is found to depend strongly on
the plasma-\beta value but not on the perturbation type. Therefore, the actual
value of the protostellar magnetic field is expected to play a critical role in
the time dependence of the observable emission.


  Using the quantum statistical method, the difficulty of solving the wave
equation on the background of the black hole is avoided.We directly solve the
partition functions of Bose and Fermi field on the background of an
axisymmetric Kerr-Newman black hole using the new equation of state density
motivated by the generalized uncertainty principle in the quantum gravity. Then
near the black hole horizon, we calculate entropies of Bose and Fermi field
between the black hole horizon surface and the hypersurface with the same
inherent radiation temperature measured by an observer at an infinite distance.
In our results there are not cutoffs and little mass approximation introduced
in the conventional brick-wall method. The series expansion of the black hole
entropy is obtained. And this series is convergent. It provides a way for
studying the quantum statistical entropy of a black hole in a non-spherical
symmetric spacetime.


  We study the behavior of the critical price of an American put option near
maturity in the exponential L\'evy model when the underlying stock pays
dividends at a continuous rate. In particular, we prove that, in situations
where the limit of the critical price is equal to the stock price, the rate of
convergence to the limit is linear if and only if the underlying L\'evy process
has finite variation. In the case of infinite variation, a variety of rates of
convergence can be observed: we prove that, when the negative part of the
L\'evy measure exhibits an $\alpha$-stable density near the origin, with
$1<\alpha<2$, the convergence rate is ruled by $\theta^{1/\alpha}|\ln
\theta|^{1-\frac{1}{\alpha}}$, where $\theta$ is time until maturity.


  An otherwise free classical particle moving through an extended spatially
homogeneous medium with which it may exchange energy and momentum will undergo
a frictional drag force in the direction opposite to its velocity with a
magnitude which is typically proportional to a power of its speed. We study
here the quantum equivalent of a classical Hamiltonian model for this friction
phenomenon that was proposed in [11]. More precisely, we study the spectral
properties of the quantum Hamiltonian and compare the quantum and classical
situations. Under suitable conditions on the infrared behaviour of the model,
we prove that the Hamiltonian at fixed total momentum has no ground state
except when the total momentum vanishes, and that its spectrum is otherwise
absolutely continuous.