  We show how quantum coherence governs the quasiprobability statistics of
outcome pairs, consecutively recorded at two distinct times, using weak
measurements. In doing this, we have realised weak-sequential measurement with
photonic qubits, where the first measurement is carried out by a positive
operator-valued measure, whereas the second one is a projective operation. We
determine the quasiprobability distributions associated to this procedure,
based on both the commensurate and the Margenau-Hill approach, by establishing
a link between these descriptions. Our results find application to quantum
monitoring aimed at implementing or stabilising task without completely loosing
the initial quantum coherence.


  We propose a new model selection method, the posterior averaging information
criterion, for Bayesian model assessment from a predictive perspective. The
theoretical foundation is built on the Kullback-Leibler divergence to quantify
the similarity between the proposed candidate model and the underlying true
model. From a Bayesian perspective, our method evaluates the candidate models
over the entire posterior distribution in terms of predicting a future
independent observation. Without assuming that the true distribution is
contained in the candidate models, the new criterion is developed by correcting
the asymptotic bias of the posterior mean of the log-likelihood against its
expected log-likelihood. It can be generally applied even for Bayesian models
with degenerate non-informative prior. The simulation in both normal and
binomial settings demonstrates decent small sample performance.


  We review interactions between certain 7-planes which are composed out of
mutually non-local (p,q) 7-branes and which correspond to specific Kodaira
singularities. We discuss in particular how to compute certain moduli-dependent
terms in the effective action. These do not only probe the local Chern-Simons
terms on the world-volumina, but also certain global aspects of plane
interactions that can be attributed to ``torsion'' (or \ZZ_N-valued) D-brane
charges.


  Most of the seismic inversion techniques currently proposed focus on
robustness with respect to the background model choice or inaccurate physical
modeling assumptions, but are not apt to large-scale 3D applications. On the
other hand, methods that are computationally feasible for industrial problems,
such as full waveform inversion, are notoriously bogged down by local minima
and require adequate starting models. We propose a novel solution that is both
scalable and less sensitive to starting model or inaccurate physics when
compared to full waveform inversion. The method is based on a dual (Lagrangian)
reformulation of the classical wavefield reconstruction inversion, whose
robustness with respect to local minima is well documented in the literature.
However, it is not suited to 3D, as it leverages expensive frequency-domain
solvers for the wave equation. The proposed reformulation allows the deployment
of state-of-the-art time-domain finite-difference methods, and is
computationally mature for industrial scale problems.


  We present the Radiation Oncology NLP Database (ROND), the first dedicated
Natural Language Processing (NLP) dataset for radiation oncology, an important
medical specialty that has received limited attention from the NLP community in
the past. With the advent of Artificial General Intelligence (AGI), there is an
increasing need for specialized datasets and benchmarks to facilitate research
and development. ROND is specifically designed to address this gap in the
domain of radiation oncology, a field that offers many opportunities for NLP
exploration. It encompasses various NLP tasks including Logic Reasoning, Text
Classification, Named Entity Recognition (NER), Question Answering (QA), Text
Summarization, and Patient-Clinician Conversations, each with a distinct focus
on radiation oncology concepts and application cases. In addition, we have
developed an instruction-tuning dataset consisting of over 20k instruction
pairs (based on ROND) and trained a large language model, CancerChat. This
serves to demonstrate the potential of instruction-tuning large language models
within a highly-specialized medical domain. The evaluation results in this
study could serve as baseline results for future research. ROND aims to
stimulate advancements in radiation oncology and clinical NLP by offering a
platform for testing and improving algorithms and models in a domain-specific
context. The ROND dataset is a joint effort of multiple U.S. health
institutions. The data is available at
https://github.com/zl-liu/Radiation-Oncology-NLP-Database.


  This paper proposes a novel hybrid downlinkuplink cooperative NOMA
(HDU-CNOMA) scheme to achieve a better tradeoff between spectral efficiency and
signal reception reliability than the conventional cooperative NOMA schemes. In
particular, the proposed scheme enables the strong user to perform a
cooperative transmission and an interference-free uplink transmission
simultaneously during the cooperative phase, at the expense of a slightly
decrease in signal reception reliability at the weak user. We analyze the
outage probability, diversity order, and outage throughput of the proposed
scheme. Simulation results not only confirm the accuracy of the developed
analytical results, but also unveil the spectral efficiency gains achieved by
the proposed scheme over a baseline cooperative NOMA scheme and a
non-cooperative NOMA scheme.


  We introduce collapsed compilation, a novel approximate inference algorithm
for discrete probabilistic graphical models. It is a collapsed sampling
algorithm that incrementally selects which variable to sample next based on the
partial sample obtained so far. This online collapsing, together with knowledge
compilation inference on the remaining variables, naturally exploits local
structure and context- specific independence in the distribution. These
properties are naturally exploited in exact inference, but are difficult to
harness for approximate inference. More- over, by having a partially compiled
circuit available during sampling, collapsed compilation has access to a highly
effective proposal distribution for importance sampling. Our experimental
evaluation shows that collapsed compilation performs well on standard
benchmarks. In particular, when the amount of exact inference is equally
limited, collapsed compilation is competitive with the state of the art, and
outperforms it on several benchmarks.


  Differentially private stochastic gradient descent (DP-SGD) is the canonical
approach to private deep learning. While the current privacy analysis of DP-SGD
is known to be tight in some settings, several empirical results suggest that
models trained on common benchmark datasets leak significantly less privacy for
many datapoints. Yet, despite past attempts, a rigorous explanation for why
this is the case has not been reached. Is it because there exist tighter
privacy upper bounds when restricted to these dataset settings, or are our
attacks not strong enough for certain datapoints? In this paper, we provide the
first per-instance (i.e., ``data-dependent") DP analysis of DP-SGD. Our
analysis captures the intuition that points with similar neighbors in the
dataset enjoy better data-dependent privacy than outliers. Formally, this is
done by modifying the per-step privacy analysis of DP-SGD to introduce a
dependence on the distribution of model updates computed from a training
dataset. We further develop a new composition theorem to effectively use this
new per-step analysis to reason about an entire training run. Put all together,
our evaluation shows that this novel DP-SGD analysis allows us to now formally
show that DP-SGD leaks significantly less privacy for many datapoints (when
trained on common benchmarks) than the current data-independent guarantee. This
implies privacy attacks will necessarily fail against many datapoints if the
adversary does not have sufficient control over the possible training datasets.


  Recently Flaminio and Montagna, \cite{FlMo}, extended the language of
MV-algebras by adding a unary operation, called a state-operator. This notion
is introduced here also for effect algebras. Having it, we generalize the
Loomis--Sikorski Theorem for monotone $\sigma$-complete effect algebras with
internal state. In addition, we show that the category of divisible
state-morphism effect algebras satisfying (RDP) and countable interpolation
with an order determining system of states is dual to the category of Bauer
simplices $\Omega$ such that $\partial_e \Omega$ is an F-space.


  Faddeev and Niemi (FN) have introduced an abelian gauge theory which
simulates dynamical abelianization in Yang-Mills theory (YM). It contains both
YM instantons and Wu-Yang monopoles and appears to be able to describe the
confining phase. Motivated by the meson degeneracy problem in dynamical
abelianization models, in this note we present a generalization of the FN
theory. We first generalize the Cho connection to dynamical symmetry breaking
pattern SU(N+1) -> U(N), and subsequently try to complete the Faddeev-Niemi
decomposition by keeping the missing degrees of freedom. While it is not
possible to write an on-shell complete FN decomposition, in the case of SU(3)
theory of physical interest we find an off-shell complete decomposition for
SU(3) -> U(2) which amounts to partial gauge fixing, generalizing naturally the
result found by Faddeev and Niemi for the abelian scenario SU(N+1) -> U(1)^N.
We discuss general topological aspects of these breakings, demonstrating for
example that the FN knot solitons never exist when the unbroken gauge symmetry
is nonabelian, and recovering the usual no-go theorems for colored dyons.


  Teleportation and storage of continuous variable states of light and atoms
are essential building blocks for the realization of large scale quantum
networks. Rigorous validation of these implementations require identifying, and
surpassing, benchmarks set by the most effective strategies attainable without
the use of quantum resources. Such benchmarks have been established for special
families of input states, like coherent states and particular subclasses of
squeezed states. Here we solve the longstanding problem of defining quantum
benchmarks for general pure Gaussian single-mode states with arbitrary phase,
displacement, and squeezing, randomly sampled according to a realistic prior
distribution. As a special case, we show that the fidelity benchmark for
teleporting squeezed states with totally random phase and squeezing degree is
1/2, equal to the corresponding one for coherent states. We discuss the use of
entangled resources to beat the benchmarks in experiments.


  This paper explores anomaly detection through temporal network analysis.
Unlike many conventional methods, relying on rule-based algorithms or general
machine learning approaches, our methodology leverages the evolving structure
and relationships within temporal networks, that can be used to model financial
transactions. Focusing on minimal changes in stable ecosystems, such as those
found in large international financial institutions, our approach utilizes
network centrality measures to gain insights into individual nodes. By
monitoring the temporal evolution of centrality-based node rankings, our method
effectively identifies abrupt shifts in the roles of specific nodes, prompting
further investigation by domain experts.
  To demonstrate its efficacy, our methodology is applied in the Anti-Financial
Crime (AFC) domain, analyzing a substantial financial dataset comprising over
80 million cross-country wire transfers. The goal is to pinpoint outliers
potentially involved in malicious activities, aligning with financial
regulations. This approach serves as an initial stride towards automating AFC
and Anti-Money Laundering (AML) processes, providing AFC officers with a
comprehensive top-down view to enhance their efforts. It overcomes many
limitations of current prevalent paradigms, offering a holistic interpretation
of the financial data landscape and addressing potential blindness to phenomena
that cannot be effectively estimated through single-node or narrowly focused
transactional approaches.


  The observation of interstellar objects 1I/'Oumuamua and 2I/Borisov suggests
the existence of a larger population of smaller projectiles that impact our
planet with unbound orbits. We analyze an asteroidal grazing meteor (FH1)
recorded by the Finnish Fireball Network on October 23, 2022. FH1 displayed a
likely hyperbolic orbit lying on the ecliptic plane with an estimated velocity
excess of $\sim$0.7 km$\,$s$^{-1}$ at impact. FH1 may either be an interstellar
object, indicating a high-strength bias in this population, or an Oort cloud
object, which would reinforce migration-based solar system models. Furthermore,
under the calculated uncertainties, FH1 could potentially be associated with
the passage of Scholz's binary star system. Statistical evaluation of
uncertainties in the CNEOS database and study of its hyperbolic fireballs
reveals an anisotropic geocentric radiant distribution and low orbital
inclinations, challenging the assumption of a randomly incoming interstellar
population. Orbital integrations suggest that the event on March 9, 2017 (IM2)
from CNEOS may have experienced gravitational perturbation during the Scholz
fly-by, contingent upon velocity overestimation within the expected range.
These findings suggest that apparent interstellar meteors may, in fact, be the
result of accelerated meteoroid impacts caused by close encounters with massive
objects within or passing through our solar system.


  Modelising the translation errors by suitable mathematical operators in the
crystal basis model of the genetic code and requiring that codons prone to be
misread encode the same amino-acid, the main features of the organisation in
multiplets of the genetic code are described.


  D.R. Woodall's definition of a 'musquash' is extended to that of a
'bi-musquash' and the existence/uniqueness of these is discussed.


  This paper addresses the problem of estimating the shape of objects that
exhibit spatially-varying reflectance. We assume that multiple images of the
object are obtained under a fixed view-point and varying illumination, i.e.,
the setting of photometric stereo. At the core of our techniques is the
assumption that the BRDF at each pixel lies in the non-negative span of a known
BRDF dictionary.This assumption enables a per-pixel surface normal and BRDF
estimation framework that is computationally tractable and requires no
initialization in spite of the underlying problem being non-convex. Our
estimation framework first solves for the surface normal at each pixel using a
variant of example-based photometric stereo. We design an efficient multi-scale
search strategy for estimating the surface normal and subsequently, refine this
estimate using a gradient descent procedure. Given the surface normal estimate,
we solve for the spatially-varying BRDF by constraining the BRDF at each pixel
to be in the span of the BRDF dictionary, here, we use additional priors to
further regularize the solution. A hallmark of our approach is that it does not
require iterative optimization techniques nor the need for careful
initialization, both of which are endemic to most state-of-the-art techniques.
We showcase the performance of our technique on a wide range of simulated and
real scenes where we outperform competing methods.


  We consider the eigenvalues of sample covariance matrices of the form
$\mathcal{Q}=(\Sigma^{1/2}X)(\Sigma^{1/2}X)^*$. The sample $X$ is an $M\times
N$ rectangular random matrix with real independent entries and the population
covariance matrix $\Sigma$ is a positive definite diagonal matrix independent
of $X$. Assuming that the limiting spectral density of $\Sigma$ exhibits convex
decay at the right edge of the spectrum, in the limit $M, N \to \infty$ with
$N/M \to d\in(0,\infty)$, we find a certain threshold $d_+$ such that for
$d>d_+$ the limiting spectral distribution of $\mathcal{Q}$ also exhibits
convex decay at the right edge of the spectrum. In this case, the largest
eigenvalues of $\mathcal{Q}$ are determined by the order statistics of the
eigenvalues of $\Sigma$, and in particular, the limiting distribution of the
largest eigenvalue of $\mathcal{Q}$ is given by a Weibull distribution. In case
$d<d_+$, we also prove that the limiting distribution of the largest eigenvalue
of $\caQ$ is Gaussian if the entries of $\Sigma$ are i.i.d. random variables.
While $\Sigma$ is considered to be random mostly, the results also hold for
deterministic $\Sigma$ with some additional assumptions.


  The 3D fundamental diagrams and phase portraits for tunnel traffic is
constructed based on the empirical data collected during the last years in the
deep long branch of the Lefortovo tunnel located on the 3rd circular highway in
Moscow. This tunnel of length 3 km is equipped with a dense system of
stationary ra-diodetetors distributed uniformly along it chequerwise at spacing
of 60 m. The data were averaged over 30 s. Each detector measures three
characteristics of the vehicle ensemble; the flow rate, the car velocity, and
the occupancy for three lanes individually. The conducted analysis reveals
complexity of phase states of tunnel traffic. In particular, we show the
presence of cooperative traffic dynamics in this tunnel and the variety of
phase states different in properties. Besides, the regions of regular and
stochastic dynamics are found and the presence of dynamical traps is
demonstrated.


  We test a method of estimating the power spectrum of turbulence in molecular
clouds based on the comparison of power spectra of integrated intensity maps
and single-velocity-channel maps, suggested by Lazarian and Pogosyan. We use
synthetic 13CO data from non-LTE radiative transfer calculations based on
density and velocity fields of a simulation of supersonic hydrodynamic
turbulence. We find that the method yields the correct power spectrum with good
accuracy. We then apply the method to the Five College Radio Astronomy
Observatory 13CO map of the Perseus region, from the COMPLETE website. We find
a power law power spectrum with slope beta=1.81+-0.10. The values of beta as a
function of velocity resolution are also confirmed using the lower resolution
map of the same region obtained with the AT&T Bell Laboratories antenna.
Because of its small uncertainty, this result provides a useful constraint for
numerical codes used to simulate molecular cloud turbulence.


  Most existing unsupervised domain adaptation methods mainly focused on
aligning the marginal distributions of samples between the source and target
domains. This setting does not sufficiently consider the class distribution
information between the two domains, which could adversely affect the reduction
of domain gap. To address this issue, we propose a novel approach called
Conditional ADversarial Image Translation (CADIT) to explicitly align the class
distributions given samples between the two domains. It integrates a
discriminative structure-preserving loss and a joint adversarial generation
loss. The former effectively prevents undesired label-flipping during the whole
process of image translation, while the latter maintains the joint distribution
alignment of images and labels. Furthermore, our approach enforces the
classification consistence of target domain images before and after adaptation
to aid the classifier training in both domains. Extensive experiments were
conducted on multiple benchmark datasets including Digits, Faces, Scenes and
Office31, showing that our approach achieved superior classification in the
target domain when compared to the state-of-the-art methods. Also, both
qualitative and quantitative results well supported our motivation that
aligning the class distributions can indeed improve domain adaptation.


  We present a novel method for single image depth estimation using surface
normal constraints. Existing depth estimation methods either suffer from the
lack of geometric constraints, or are limited to the difficulty of reliably
capturing geometric context, which leads to a bottleneck of depth estimation
quality. We therefore introduce a simple yet effective method, named Adaptive
Surface Normal (ASN) constraint, to effectively correlate the depth estimation
with geometric consistency. Our key idea is to adaptively determine the
reliable local geometry from a set of randomly sampled candidates to derive
surface normal constraint, for which we measure the consistency of the
geometric contextual features. As a result, our method can faithfully
reconstruct the 3D geometry and is robust to local shape variations, such as
boundaries, sharp corners and noises. We conduct extensive evaluations and
comparisons using public datasets. The experimental results demonstrate our
method outperforms the state-of-the-art methods and has superior efficiency and
robustness.


  We present a new approach for averaging in general relativity and cosmology.
After a short review of the theory originally taken from the equivalence
problem, we consider two ways how to deal with averaging based on Cartan
scalars. We apply the theory for two different LTB models. In the first one,
correlation term behaves as a positive cosmological constant, in the second
example leading correlation term behaves like spatial curvature. We also show
nontriviality of averaging for linearized monochromatic gravitational wave.


  We have carried out the first spatially-resolved investigation of the
multi-phase interstellar medium (ISM) at high redshift, using the z=4.24
strongly-lensed sub-millimetre galaxy H-ATLASJ142413.9+022303 (ID141). We
present high-resolution (down to ~350 pc) ALMA observations in dust continuum
emission and in the CO(7-6), H_2O (2_{1,1} - 2_{0,2}), CI(1-0) and CI(2-1)
lines, the latter two allowing us to spatially resolve the cool phase of the
ISM for the first time. Our modelling of the kinematics reveals that the system
appears to be dominated by a rotationally-supported gas disk with evidence of a
nearby perturber. We find that the CI(1-0) line has a very different
distribution to the other lines, showing the existence of a reservoir of cool
gas that might have been missed in studies of other galaxies. We have estimated
the mass of the ISM using four different tracers, always obtaining an estimate
in the range (3.2-3.8) x 10^{11} M_sol, significantly higher than our dynamical
mass estimate of (0.8-1.3) x 10^{11} M_sol. We suggest that this conflict and
other similar conflicts reported in the literature is because the gas-to-tracer
ratios are ~4 times lower than the Galactic values used to calibrate the ISM in
high-redshift galaxies. We demonstrate that this could result from a top-heavy
initial mass function and strong chemical evolution. Using a variety of
quantitative indicators, we show that, extreme though it is at z=4.24, ID141
will likely join the population of quiescent galaxies that appears in the
Universe at z~3.


  A proof is given for the explicit representations which have been formulated
in the author's previous work (nucl-th/9505028) for the Faddeev components of
three-body T-matrix continued analytically on unphysical sheets of the energy
Riemann surface. Also, the analogous representations for analytical
continuation of the three-body scattering matrices and resolvent are proved. An
algorithm to search for the three-body resonances on the base of the Faddeev
differential equations is discussed.


  This paper presents the reproduction of two studies focused on the perception
of micro and macro expressions of Virtual Humans (VHs) generated by Computer
Graphics (CG), first described in 2014 and replicated in 2021. The 2014 study
referred to a VH realistic, whereas, in 2021, it referred to a VH cartoon. In
our work, we replicate the study by using a realistic CG character. Our main
goals are to compare the perceptions of micro and macro expressions between
levels of realism (2021 cartoon versus 2023 realistic) and between realistic
characters in different periods (i.e., 2014 versus 2023). In one of our
results, people more easily recognized micro expressions in realistic VHs than
in a cartoon VH. In another result, we show that the participants' perception
was similar for both micro and macro expressions in 2014 and 2023.


  We analyse the influence of rotation on shapes of pulse profiles of
fast-rotating (millisecond) pulsars. Corotation has two opposing effects: 1)
the caustic enhancement of the trailing side (TS) by aberration and retardation
(AR), which squeezes the emission into a narrower phase interval; 2) the
weakening of the TS caused by the asymmetry of curvature radiation about the
dipole axis. Analysis of the radii of curvature of electron trajectories in the
inertial observer's frame (IOF) enables these two effects to be considered
together. We demonstrate that for dipolar magnetic field lines on the TS there
exists a `caustic phase' beyond which no emission can be observed. This phase
corresponds to the zero (or minimum) curvature of the IOF trajectories and
maximum bunching of the emission. The maximum gradient of polarisation angle
(PA) in the S-shaped PA curve is also associated with the curvature minimum and
occurs at exactly the same phase. The asymmetry of trajectory curvature with
respect to the dipole axis affects the curvature emissivity and the efficiency
of pair production, suggesting a minimum at the caustic phase. Emission over a
fixed range of altitudes, as expected in millisecond pulsars, leads to broad
leading profiles and sharp peaks with a cutoff phase on the TS. We apply our
results to the main pulse of the 5 ms pulsar J1012+5307.


  We investigate the dynamics of the Standard Model higgs with a minimal
coupling to gravity during and after inflation. In the regime where the
Standard Model vacuum is stable, we find that the higgs becomes a light
spectator field after about 30 efolds of inflation, irrespectively of its
initial value. Once the higgs has become light, its root-mean-square value h_*
relaxes to equilibrium in about 85 efolds for the inflationary scale of
H_*=10^4 GeV and in 20 efolds for H_*=10^10 GeV. The equilibrium value is given
by h_*~0.36 lambda^{-1/4}H_*, where lambda=0.09 ... 0.0005 is the higgs self
coupling at the scales H_*=10^4 ... 10^10 GeV. We show that the main decay
channel of the higgs condensate after inflation is the resonant production of
Standard Model gauge bosons. For a set of parameters we find that a significant
part of the condensate has decayed in between 340 and 630 Hubble times after
the onset of higgs oscillations, depending on H_* in a non-trivial way. The
higgs perturbations correspond to isocurvature modes during inflation but they
could generate significant adiabatic perturbations at a later stage for example
through a modulation of the reheating stage. However, this requires that the
inflaton(s) decay no later than a few hundred Hubble times after the onset of
higgs oscillations.


  We present results from 4.8 GHz VLA and Global-VLBI observations of the
northern half of the moderate FIR luminosity (median L_IR = 10^11.01 L_Sol)
COLA sample of star-forming galaxies. VLBI sources are detected in a high
fraction (20/90) of the galaxies observed. The radio luminosities of these
cores (~10^21 W/Hz) are too large to be explained by radio supernovae or
supernova remnants and we argue that they are instead powered by AGN. These
sub-parsec scale radio cores are preferentially detected toward galaxies whose
VLA maps show bright 100-500 parsec scale nuclear radio components. Since these
latter structures tightly follow the FIR to radio-continuum correlation for
star-formation we conclude that the AGN powered VLBI sources are associated
with compact nuclear starburst environments. The implications for possible
starburst-AGN connections are discussed. The detected VLBI sources have a
relatively narrow range of radio luminosity consistent with models in which
intense compact Eddington-limited starbursts regulate the gas supply onto a
central super-massive black hole. The high incidence of AGN radio cores in
compact starbursts suggests little or no delay between the starburst phase and
the onset of AGN activity.


  Joint understanding of video and language is an active research area with
many applications. Prior work in this domain typically relies on learning
text-video embeddings. One difficulty with this approach, however, is the lack
of large-scale annotated video-caption datasets for training. To address this
issue, we aim at learning text-video embeddings from heterogeneous data
sources. To this end, we propose a Mixture-of-Embedding-Experts (MEE) model
with ability to handle missing input modalities during training. As a result,
our framework can learn improved text-video embeddings simultaneously from
image and video datasets. We also show the generalization of MEE to other input
modalities such as face descriptors. We evaluate our method on the task of
video retrieval and report results for the MPII Movie Description and MSR-VTT
datasets. The proposed MEE model demonstrates significant improvements and
outperforms previously reported methods on both text-to-video and video-to-text
retrieval tasks. Code is available at:
https://github.com/antoine77340/Mixture-of-Embedding-Experts


  Motivated by the study of breathers in the disordered Discrete Non Linear
Schr\"odinger equation, we study the uniform probability over the intersection
of a simplex and an ellipsoid in $n$ dimensions, with quenched disorder in the
definition of either the simplex or the ellipsoid. Unless the disorder is too
strong, the phase diagram looks like the one without disorder, with a
transition separating a fluid phase, where all variables have the same order of
magnitude, and a condensed phase, where one variable is much larger than the
others. We then show that the condensed phase exhibits "intermediate symmetry
breaking": the site hosting the condensate is chosen neither uniformly at
random, nor is it fixed by the disorder realization. In particular, the model
mimicking the well-studied Discrete Non Linear Schr\"odinger model with
frequency disorder shows a very weak symmetry breaking: all variables have a
sizable probability to host the condensate (i.e. a breather in a DNLS setting),
but its localization is still biased towards variables with a large linear
frequency. Throughout the article, our heuristic arguments are complemented
with direct Monte Carlo simulations.


  The Hamiltonian constraint formalism is used to obtain the first explicit
complete analysis of non-trivial viable dynamic modes for the Poincar\'e gauge
theory of gravity. Two modes with propagating spin-zero torsion are analyzed.
The explicit form of the Hamiltonian is presented. All constraints are obtained
and classified. The Lagrange multipliers are derived. It is shown that a
massive spin-$0^-$ mode has normal dynamical propagation but the associated
massless $0^-$ is pure gauge. The spin-$0^+$ mode investigated here is also
viable in general. Both modes exhibit a simple type of ``constraint
bifurcation'' for certain special field/parameter values.


  Dialogue-based Intelligent Tutoring Systems (ITSs) have significantly
advanced adaptive and personalized learning by automating sophisticated human
tutoring strategies within interactive dialogues. However, replicating the
nuanced patterns of expert human communication remains a challenge in Natural
Language Processing (NLP). Recent advancements in NLP, particularly Large
Language Models (LLMs) such as OpenAI's GPT-4, offer promising solutions by
providing human-like and context-aware responses based on extensive pre-trained
knowledge. Motivated by the effectiveness of LLMs in various educational tasks
(e.g., content creation and summarization, problem-solving, and automated
feedback provision), our study introduces the Socratic Playground for Learning
(SPL), a dialogue-based ITS powered by the GPT-4 model, which employs the
Socratic teaching method to foster critical thinking among learners. Through
extensive prompt engineering, SPL can generate specific learning scenarios and
facilitates efficient multi-turn tutoring dialogues. The SPL system aims to
enhance personalized and adaptive learning experiences tailored to individual
needs, specifically focusing on improving critical thinking skills. Our pilot
experimental results from essay writing tasks demonstrate SPL has the potential
to improve tutoring interactions and further enhance dialogue-based ITS
functionalities. Our study, exemplified by SPL, demonstrates how LLMs enhance
dialogue-based ITSs and expand the accessibility and efficacy of educational
technologies.


  We report the existence of confined massless fermion states in a graphene
quantum well (QW) by means of analytical and numerical calculations. These
states show an unusual quasi-linear dependence on the momentum parallel to the
QW: their number depends on the wavevector and is constrained by electron-hole
conversion in the barrier regions. An essential difference with
non-relativistic electron states is a mixing between free and confined states
at the edges of the free-particle continua, demonstrated by the
direction-dependent resonant transmission across a potential well.


  We consider a stochastic partial differential equation with reflection at 0
and with the constraint of conservation of the space average. The equation is
driven by the derivative in space of a space--time white noise and contains a
double Laplacian in the drift. Due to the lack of the maximum principle for the
double Laplacian, the standard techniques based on the penalization method do
not yield existence of a solution. We propose a method based on infinite
dimensional integration by parts formulae, obtaining existence and uniqueness
of a strong solution for all continuous nonnegative initial conditions and
detailed information on the associated invariant measure and Dirichlet form.


  In this paper, we consider Fredlin-Wentzell type large deviation principle
(LDP) of multidimensional reflected stochastic partial differential equations
in a convex domain, allowing for oblique direction of reflection. To prove the
LDP, a sufficient condition for the weak convergence method and penalized
method plays an important role.


  The value of roadside perception, which could extend the boundaries of
autonomous driving and traffic management, has gradually become more prominent
and acknowledged in recent years. However, existing roadside perception
approaches only focus on the single-infrastructure sensor system, which cannot
realize a comprehensive understanding of a traffic area because of the limited
sensing range and blind spots. Orienting high-quality roadside perception, we
need Roadside Cooperative Perception (RCooper) to achieve practical
area-coverage roadside perception for restricted traffic areas. Rcooper has its
own domain-specific challenges, but further exploration is hindered due to the
lack of datasets. We hence release the first real-world, large-scale RCooper
dataset to bloom the research on practical roadside cooperative perception,
including detection and tracking. The manually annotated dataset comprises 50k
images and 30k point clouds, including two representative traffic scenes (i.e.,
intersection and corridor). The constructed benchmarks prove the effectiveness
of roadside cooperation perception and demonstrate the direction of further
research. Codes and dataset can be accessed at:
https://github.com/AIR-THU/DAIR-RCooper.


  Exact string solutions are presented, where moduli fields are varying with
time. They provide examples where a dynamical change of the topology of space
is occurring. Some other solutions give cosmological examples where some
dimensions are compactified dynamically or simulate pre-big bang type
scenarios. Some lessons are drawn concerning the region of validity of
effective theories and how they can be glued together, using stringy
information in the region where the geometry and topology are not well defined
from the low energy point of view. Other time dependent solutions are presented
where a hierarchy of scales is absent. Such solutions have dynamics which is
qualitatively different and resemble plane gravitational waves. (Talk given at
the Trieste Spring School and Workshop, 1994.)


  We have studied the high-frequency properties of the non-equilibrium electron
gas in GaN samples subjected to electric and magnetic fields. Spectra of the
complex tensor of the dynamical mobility have been calculated for THz frequency
range. For the compensated GaN and low temperatures, in the intervals of
electric fields of the few $kV/cm$ and magnetic fields of the few $T$ the
existence of the cyclotron and optical phonon transit-time resonances has been
identified. We have shown that interplay of two resonances gives rise to
specific spectra of THz transmission and absorption (or gain). We suggest that
experimental investigation of these effects will facilitate elaboration of
field controlled devices for THz optoelectronics.


  The multi-index model is a simple yet powerful high-dimensional regression
model which circumvents the curse of dimensionality assuming $ \mathbb{E} [ Y |
X ] = g(A^\top X) $ for some unknown index space $A$ and link function $g$. In
this paper we introduce a method for the estimation of the index space, and
study the propagation error of an index space estimate in the regression of the
link function. The proposed method approximates the index space by the span of
linear regression slope coefficients computed over level sets of the data.
Being based on ordinary least squares, our approach is easy to implement and
computationally efficient. We prove a tight concentration bound that shows
$N^{-1/2}$-convergence, but also faithfully describes the dependence on the
chosen partition of level sets, hence giving indications on the hyperparameter
tuning. The estimator's competitiveness is confirmed by extensive comparisons
with state-of-the-art methods, both on synthetic and real data sets. As a
second contribution, we establish minimax optimal generalization bounds for
k-nearest neighbors and piecewise polynomial regression when trained on samples
projected onto any $N^{-1/2}$-consistent estimate of the index space, thus
providing complete and provable estimation of the multi-index model.


  We present a convenient tool (ChaSES) which allows to search for extended
structures in Chandra X-ray Observatory Advanced CCD Imaging Spectrometer
(ACIS) images. The tool relies on DBSCAN clustering algorithm to detect regions
with overdensity of photons compared to the background. Here we describe the
design and functionality of the tool which we make publicly available on
GitHub. We also provide online extensive examples of its applications to the
real data.


  A single graphene sheet, when subjected to a perpendicular static magnetic
field provides Faraday rotation that, per atomic layer, greatly surpasses that
of any other known material. This Giant Faraday rotation originates from the
cyclotron resonance of massless electrons, which allows dynamical tuning
through either external electrostatic or magnetostatic setting. Furthermore,
the rotation direction can be controlled by changing the sign of the carriers
in graphene, which can be done by means of an external electric field. However,
despite these tuning possibilities, the requirement of large magnetic fields
hinders application of the Faraday effect in real devices, especially for
frequencies higher than few THz. In this work we demonstrate that, for a given
value of the static external magnetic field, giant Faraday rotation can be
achieved in arrays of graphene microribbons at frequencies much higher than the
corresponding cyclotron frequency. The main feature in the magneto-optical
response of graphene ribbons is not associated with the cyclotron resonance but
rather with the fundamental magnetoplasmon excitation of a single ribbon. The
magnetoplasmon nature of Faraday rotation in graphene ribbons opens great
possibilities, as the resonance frequency can be locally selected by
appropriately choosing the width of the ribbon while still preserving the
tuning capability through a (smaller) external magnetic field.


  We study a sequential decision problem where the learner faces a sequence of
$K$-armed bandit tasks.
  The task boundaries might be known (the bandit meta-learning setting), or
unknown (the non-stationary bandit setting). For a given integer $M\le K$, the
learner aims to compete with the best subset of arms of size $M$.
  We design an algorithm based on a reduction to bandit submodular
maximization, and show that, for $T$ rounds comprised of $N$ tasks, in the
regime of large number of tasks and small number of optimal arms $M$, its
regret in both settings is smaller than the simple baseline of
$\tilde{O}(\sqrt{KNT})$ that can be obtained by using standard algorithms
designed for non-stationary bandit problems. For the bandit meta-learning
problem with fixed task length $\tau$, we show that the regret of the algorithm
is bounded as $\tilde{O}(NM\sqrt{M \tau}+N^{2/3}M\tau)$. Under additional
assumptions on the identifiability of the optimal arms in each task, we show a
bandit meta-learning algorithm with an improved $\tilde{O}(N\sqrt{M
\tau}+N^{1/2}\sqrt{M K \tau})$ regret.


  We study the Maurer-Cartan equation of the pre-Lie algebra of graphs
controling the deformation theory of associative algebras and prove that there
is a canonical solution within the class of graphs without circuits, without
assuming the Jacobi identity. The proof is based on the unique factorization
property of graph insertions.


  The most recent data of NA49 on hadron production in nuclear collisions at
CERN SPS energies are presented. Anomalies in the energy dependence of pion and
kaon production in central Pb+Pb collisions are observed. They suggest that the
onset of deconfinement is located at about 30 AGeV. Large multiplicity and
transverse momentum fluctuations are measured for collisions of intermediate
mass systems at 158 AGeV. The need for a new experimental programme at the CERN
SPS is underlined.


  Three inverse boundary value problems for the heat equations in one space
dimension are considered. Those three problems are: extracting an unknown
interface in a heat conductive material, an unknown boundary in a layered
material or a material with a smooth heat conductivity by employing a single
set of the temperature and heat flux on a known boundary as the observation
data. Some extraction formulae of those discontinuities which suggest a
relationship between the travel time of a {\it virtual} signal and the
observation data are given by applying the enclosure method to the problems.


  To explain the large difference between the Planck scale and the electroweak
scale, models in which gravity propagates in more than four dimensions have
recently been proposed. In this paper, results from searches for extra
dimensions at the Tevatron ppbar collider are presented. Limits are set on the
higher dimensional Planck scale, and expectations for the LHC pp collider are
given.


  The last decade has seen a renewed exploration of semiconductor materials for
X-ray detection, foremost focusing on lead-based perovskites and other metal
halides as direct-conversion materials and scintillators. However, the reported
performance characteristics are often incomplete or misleading in assessing the
practical utility of materials. This Perspective offers guidelines for
choosing, estimating and presenting the relevant figures of merit. We also
provide ready-to-used tools for calculating these figures of merit: MATLAB
application, Mathcad worksheet and a website. The X-ray detectors for medical
imaging are at focus for their increasing societal value and since they bring
about the most stringent requirements as the image shall be acquired at as low
as reasonably attainable (i.e. ALARA principle) dose received by the patient.


  We establish an isomorphism between certain complex-valued and vector-valued
modular form spaces of half-integral weight, generalizing the well-known
isomorphism between modular forms for $\Gamma_0(4)$ with Kohnen's plus
condition and modular forms for the Weil representation associated to the
discriminant form for the lattice with Gram matrix $(2)$. With such an
isomorphism, we prove the Zagier duality and write down the Borcherds lifts
explicitly.


  We present a study of the Galactic Center region as a possible source of both
secondary gamma-ray and neutrino fluxes from annihilating dark matter. We have
studied the gamma-ray flux observed by the High Energy Stereoscopic System
(HESS) from the J1745-290 Galactic Center source. The data are well fitted as
annihilating dark matter in combination with an astrophysical background. The
analysis was performed by means of simulated gamma spectra produced by Monte
Carlo event generators packages. We analyze the differences in the spectra
obtained by the various Monte Carlo codes developed so far in particle physics.
We show that, within some uncertainty, the HESS data can be fitted as a signal
from a heavy dark matter density distribution peaked at the Galactic Center,
with a power-law for the background with a spectral index which is compatible
with the Fermi-Large Area Telescope (LAT) data from the same region. If this
kind of dark matter distribution generates the gamma-ray flux observed by HESS,
we also expect to observe a neutrino flux. We show prospective results for the
observation of secondary neutrinos with the Astronomy with a Neutrino Telescope
and Abyss environmental RESearch project (ANTARES), Ice Cube Neutrino
Observatory (Ice Cube) and the Cubic Kilometer Neutrino Telescope (KM3NeT).
Prospects solely depend on the device resolution angle when its effective area
and the minimum energy threshold are fixed.


  A perfect matching in a 4-uniform hypergraph is a subset of
$\lfloor\frac{n}{4}\rfloor$ disjoint edges. We prove that if $H$ is a
sufficiently large 4-uniform hypergraph on $n=4k$ vertices such that every
vertex belongs to more than ${n-1\choose 3} - {3n/4 \choose 3}$ edges then $H$
contains a perfect matching. This bound is tight and settles a conjecture of
H{\'a}n, Person and Schacht.


  We study continuous, equality knapsack problems with uniform separable,
non-convex objective functions that are continuous, antisymmetric about a
point, and have concave and convex regions. For example, this model captures a
simple allocation problem with the goal of optimizing an expected value where
the objective is a sum of cumulative distribution functions of identically
distributed normal distributions (i.e., a sum of inverse probit functions). We
prove structural results of this model under general assumptions and provide
two algorithms for efficient optimization: (1) running in linear time and (2)
running in a constant number of operations given preprocessing of the objective
function.


  The vortex-excitation energy on a sphere can be obtained by using the
stereographic projection. By applying this method, we calculate the energy
needed to create a vortex on a surface with a constant negative curvature. It
is found that the energy is a linear function of the radius of the vortex. In
accordance with this result, the interaction energy between a pair of vortices
is also found to change linearly with the vortex separation distance. Explicit
vortex configurations are obtained numerically with this interaction.


  Graph clustering is widely used in many data analysis applications. In this
paper we propose several parallel graph clustering algorithms based on Monte
Carlo simulations and expectation maximization in the context of stochastic
block models. We apply those algorithms to the specific problems of recommender
systems and social network anonymization. We compare the experimental results
to previous propositions.


  The martensitic transformation is one of the most important phenomena in
metals science due to its essential contribution to the strength of steels and
most engineering alloys. Yet the basic, atomistic mechanisms leading to
martensite nucleation and twin morphology are not yet known. A detailed picture
in this regard is required if the strengthening effects of martensite are to be
properly understood. This work presents molecular dynamics (MD) simulations of
the martensitic transformation using a model fcc/bcc semi-coherent interface
with Nishiyama-Wasserman orientation relationship. Significant insight into
this important phenomenon is detailed in this work which shows that the atomic
displacements that cause nucleation and twin morphology formation of the
martensitic phase originate at the fcc/bcc interface. The interface facilitates
the initial atomic shear during the transformation which in turn causes the
stress-induced homogeneous nucleation and twin morphology formation. The
understanding of the atomistic processes leading to the twin morphology
formation will allow the control of the twinning process for further
enhancement of mechanical properties.


  The current $\sim3.5\sigma$ discrepancy between the experimental measurement
and theoretical prediction of the muon magnetic anomaly, $a_{\mu}$, stands as a
potential indication of the existence of new physics. The Muon $g-2$ experiment
at Fermilab is set to measure $a_{\mu}$ with a four-fold improvement in the
uncertainty with respect to previous experiment, with an aim to determine
whether the $g-2$ discrepancy is well established. The experiment recently
completed its first physics run and a summer programme of essential upgrades,
before continuing on with its experimental programme. The Run-1 data alone are
expected to yield a statistical uncertainty of 350 ppb and the publication of
the first result is expected in late-2019.


  We derive a formula for the velocity distribution of an axially symmetric
galaxy where the mass density is corrected using the mass formula from special
relativity. We take some reasonable test mass densities and numerically compute
the resulting galaxy rotation curves. We then compare these to the rotation
curves obtained from a similar formula without a relativistic correction
factor. We find that the correction factor has a small dark-matter like effect.
Finally, we compare these to the corresponding Keplerian velocity curves. We
find that there is a large discrepancy in this case, where away from the
galactic center, up to the galactic edge, the curves computed using our
formulas give a noticeably higher velocity.


  Coronal bright points (CBPs) as characteristics of the solar corona are
small-scale bright features ubiquitously observed at extreme ultraviolet
passbands. Here, we focused on time series of CBPs in the periods of their
lifetimes. We used Solar Dynamic Observatory (SDO) / Atmospheric Imaging
Assembly (AIA) taken at 171 and 193 \AA. Using image processing methods, CBPs
were extracted from data and tracked during their lifetimes. We found that CBPs
observed in 171 \AA have more lifetimes than those of observed at 193 \AA. CBPs
at 171 \AA are more fluctuated in intensity and appeared as highly density
number (DN) features than 193 \AA CBPs. It was found that about $75 \%$ of CBPs
are firstly achieved their peak at 171 \AA, and then, after 12 seconds two
minutes are achieved to their peak at 193 \AA. Computing Pearson correlation
for time series of CBPs at both wavelengths where the correlation reaches at
maximum value gives information about time delay of magnetic reconnection about
40 seconds from one layer to the other one.


  Hot surface ignition and subsequent flame propagation of premixed n-hexane
air mixtures are shown in this fluid dynamics video. High speed schlieren
photography revealed 3 distinct behaviors of ignition and propagation as a
function of mixture composition and initial pressure.


  Percolation diffusion into long (11.5 cm) self-assembled, ordered mesoporous
microfibres is studied using optical transmission and laser ablation inductive
coupled mass spectrometry (LA-ICP-MS). Optical transmission based diffusion
studies reveal rapid penetration (< 5 s, D > 80 um2.s-1) of Rhodamine B with
very little percolation of larger molecules such as zinc tetraphenylporphyrin
(ZnTPP) observed under similar loading conditions. The failure of ZnTPP to
enter the microfibre was confirmed, in higher resolution, using LA-ICP-MS. In
the latter case, LA-ICP-MS was used to determine the diffusion of zinc acetate
dihydrate, D ~ 3 x 10-4 nm2.s-1. The large differences between the molecules
are accounted for by proposing ordered solvent and structure assisted
accelerated diffusion of the Rhodamine B based on its hydrophilicity relative
to the zinc compounds. The broader implications and applications for
filtration, molecular sieves and a range of devices and uses are described.


  We present the results of high resolution spectra of seven stars in the field
of NGC 2482, an open star cluster of age 447 Myr. We confirm the previously
published values of the radial velocity and metallicity of one giant star. This
gives us confidence that another giant star is a bona fide cluster member, and
that three stars significantly above the main sequence in a color-magnitude
diagram are not members, on the basis of discordant radial velocities. Another
star ~1.7 mag above the main sequence may or may not be a member. Its [Fe/H]
value is ~0.1 dex more positive than two giant stars studied, and its radial
velocity is 3-4 km/s less than that of the two giant stars, which is a
significant difference if the velocity dispersion of the cluster is less than
+/-1 km/s. To a large extent the width of the main sequence seems to be due to
the presence of foreground and background stars in the same general direction,
stars that masquerade as main sequence stars in the cluster.


  Portable Consumer Electronics has made a mark in the industry. With the ease
of use at an accessible price range, they have experienced significant growth
in the market. Our idea is to develop a portable wireless music player and text
reader using a Cortex-M series microcontroller and bare-metal programming
techniques. We chose to use an SD card as the storage device. The resulting
electronic device is similar to a consumer grade music player available in a
car. The system comprises an MCU, an MP3 encoder/decoder, an LCD, an audio
output jack, an SD card and a remote control. We also present various
challenges involved in developing the system and solutions we used to overcome
the challenges. The intricacy of the work lies in the fact that the system was
developed to be consumer-centric by providing a rich User Experience. It can be
used as a personal entertainment system in a car.


  Let M be a simply connected Riemannian symmetric space, with at most one flat
direction. We show that every Riemannian (or unitary) vector bundle with
parallel curvature over M is an associated vector bundle of a canonical
principal bundle, with the connection inherited from the principal bundle. The
problem of finding Riemannian (or unitary) vector bundles with parallel
curvature then reduces to finding representations of the structure group of the
canonical principal bundle.


  The nonlinear $\sigma$-model is considered to be useful in describing hadrons
(Skyrmions) in low energy hadron physics and the approximate behavior of the
global texture. Here we investigate the properties of the static solution of
the nonlinear $\sigma$-model equation coupled with gravity. As in the case
where gravity is ignored, there is still no scale parameter that determines the
size of the static solution and the winding number of the solution is $1/2$.
The geometry of the spatial hyperspace in the asymptotic region of large $r$ is
explicitly shown to be that of a flat space with some missing solid angle.


  This paper proposes a method for hiding the least-important samples during
the training of deep neural networks to increase efficiency, i.e., to reduce
the cost of training. Using information about the loss and prediction
confidence during training, we adaptively find samples to exclude in a given
epoch based on their contribution to the overall learning process, without
significantly degrading accuracy. We explore the converge properties when
accounting for the reduction in the number of SGD updates. Empirical results on
various large-scale datasets and models used directly in image classification
and segmentation show that while the with-replacement importance sampling
algorithm performs poorly on large datasets, our method can reduce total
training time by up to 22% impacting accuracy only by 0.4% compared to the
baseline. Code available at https://github.com/TruongThaoNguyen/kakurenbo


  We present a theoretical model which accounts for recent angle resolved
photoemission data in bilayer cuprate superconductors. Lineshapes and
dispersions of the various bonding and antibonding features in the spectra are
quantitatively reproduced. The observed dispersion anomalies are consistent
with the interaction of electrons with a bosonic mode which is odd with respect
to the layer indices, a unique property of the magnetic resonance observed by
inelastic neutron scattering.


  We present an optical conductivity study of the multifold semimetal PdGa,
performed in a broad spectral range (100 - 20000 cm-1; 12 meV - 2.5 eV) down to
T = 10 K. The conductivity at frequencies below 4000 cm-1 is dominated by free
carriers while at higher frequencies interband transitions provide the major
contribution. The spectra do not demonstrate a significant temperature
evolution: only the intraband part changes as a function of temperature with
the plasma frequency remaining constant. The interband contribution to the
conductivity exhibits a broad peak at around 5500 cm-1 and increases basically
monotonously at frequencies above 9000 cm-1. The band-structure-based
computations reproduce these features of the interband conductivity and predict
its linear-in-frequency behavior as frequency diminishes.


  Equations of motion for the D0-brane on AdS_4 x CP^3 superbackground are
shown to be classically integrable by extending the argument previously
elaborated for the massless superparticle model.


  Analogy to Cherenkov radiation, when a particle moves faster than the
propagation velocity of gravitational wave in matter ($v>c_{\rm{g}}$), we
expect gravitational wave-Cherenkov radiation (GWCR). In the situation that a
photon travels across diffuse dark matters, the GWCR condition is always
satisfied, photon will thence loss its energy all the path. This effect is long
been ignored in the practice of astrophysics and cosmology, without
justification with serious calculation. We study this effect for the first
time, and shows that this energy loss time of the photon is far longer than the
Hubble time, therefore justify the practice of ignoring this effect in
astrophysics context.


  Many exponential speedups that have been achieved in quantum computing are
obtained via hidden subgroup problems (HSPs). We show that the HSP over
Weyl-Heisenberg groups can be solved efficiently on a quantum computer. These
groups are well-known in physics and play an important role in the theory of
quantum error-correcting codes. Our algorithm is based on non-commutative
Fourier analysis of coset states which are quantum states that arise from a
given black-box function. We use Clebsch-Gordan decompositions to combine and
reduce tensor products of irreducible representations. Furthermore, we use a
new technique of changing labels of irreducible representations to obtain
low-dimensional irreducible representations in the decomposition process. A
feature of the presented algorithm is that in each iteration of the algorithm
the quantum computer operates on two coset states simultaneously. This is an
improvement over the previously best known quantum algorithm for these groups
which required four coset states.


  We investigate single ions of $^{40}Ca^+$ in Paul traps for quantum
information processing. Superpositions of the S$_{1/2}$ electronic ground state
and the metastable D$_{5/2}$ state are used to implement a qubit. Laser light
on the S$_{1/2} \leftrightarrow$ D$_{5/2}$ transition is used for the
manipulation of the ion's quantum state. We apply sideband cooling to the ion
and reach the ground state of vibration with up to 99.9% probability. Starting
from this Fock state $|n=0>$, we demonstrate coherent quantum state
manipulation. A large number of Rabi oscillations and a ms-coherence time is
observed. Motional heating is measured to be as low as one vibrational quantum
in 190 ms. We also report on ground state cooling of two ions.


  This article deals with the study of predicting the confinement effect of
carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using
metaheuristics-based artificial neural networks. A detailed database of 708
CFRP confined concrete cylinders is developed from previously published
research with information on 8 parameters including geometrical parameters like
the diameter (d) and height (h) of a cylinder, unconfined compressive strength
of concrete (fco'), thickness (nt), the elastic modulus of CFRP (Ef),
unconfined concrete strain confined concrete strain and the ultimate
compressive strength of confined concrete fcc'. Three metaheuristic models are
implemented including particle swarm optimization (PSO), grey wolf optimizer
(GWO), and bat algorithm (BA). These algorithms are trained on the data using
an objective function of mean square error and their predicted results are
validated against the experimental studies and finite element analysis. The
study shows that the hybrid model of PSO predicted the strength of
CFRP-confined concrete cylinders with maximum accuracy of 99.13% and GWO
predicted the results with an accuracy of 98.17%. The high accuracy of axial
compressive strength predictions demonstrated that these prediction models are
a reliable solution to the empirical methods. The prediction models are
especially suitable for avoiding full-scale time-consuming experimental tests
that make the process quick and economical.


  Though very popular, it is well known that the EM for GMM algorithm suffers
from non-Gaussian distribution shapes, outliers and high-dimensionality. In
this paper, we design a new robust clustering algorithm that can efficiently
deal with noise and outliers in diverse data sets. As an EM-like algorithm, it
is based on both estimations of clusters centers and covariances. In addition,
using a semi-parametric paradigm, the method estimates an unknown scale
parameter per data-point. This allows the algorithm to accommodate for heavier
tails distributions and outliers without significantly loosing efficiency in
various classical scenarios. We first derive and analyze the proposed algorithm
in the context of elliptical distributions, showing in particular important
insensitivity properties to the underlying data distributions. We then study
the convergence and accuracy of the algorithm by considering first synthetic
data. Then, we show that the proposed algorithm outperforms other classical
unsupervised methods of the literature such as k-means, the EM for Gaussian
mixture models and its recent modifications or spectral clustering when applied
to real data sets as MNIST, NORB, and 20newsgroups.


  Current models of the spectrum of X-rays reflected from accretion disks
around black holes and other compact objects are commonly calculated assuming
that the density of the disk atmosphere is constant within several Thomson
depths from the irradiated surface. An important simplifying assumption of
these models is that the ionization structure of the gas is completely
specified by a single, fixed value of the ionization parameter $\xi$, which is
the ratio of the incident flux to the gas density. The density is typically
fixed at $n_e=10^{15}$ cm$^{-3}$. Motivated by observations, we consider higher
densities in the calculation of the reflected spectrum. We show by computing
model spectra for $n_e \gtrsim 10^{17}$ cm$^{-3}$ that high-density effects
significantly modify reflection spectra. The main effect is to boost the
thermal continuum at energies $\lesssim 2$ keV. We discuss the implications of
these results for interpreting observations of both AGN and black hole
binaries. We also discuss the limitations of our models imposed by the quality
of the atomic data currently available.


  We provide some new properties of entanglement of formation. In particular,
we obtain an additive lower bound for entanglement of formation. Subsequently
we develop the concept of local orthogonality of ensembles which leads to the
mixed states with distillable entanglement equal to entanglement of formation.
Then we consider thermodynamical analogies within the entanglement processing
domain. Especially, we exploit analogy entanglement -- energy. In this scheme
the total entanglement i.e. the amount of singlet pairs needed for local
preparation of a state corresponds to internal energy while the free
entanglement defined as the number of pairs which can be recovered from the
state (distillable entanglement) is the counterpart of free energy. In
particular, it leads us to the question about ``temperature'' of entanglement.
We also propose a scheme of the search of representative state for given
entanglement which can be viewed as an analogue of the Jaynes maximum entropy
principle.


  Einstein's Equivalence Principle is used with the electromagnetic spectrum to
translate meters and seconds into radians and seconds. Based on a unique
geometric relationship, a new transformation of velocities and a changed
Lorentz transformation result. The physical angle of parallelism is quantified.
The way we measure distance and time is standardized, constructing a theory of
time and geometry to the universe.


  In this paper we deal with a feedback control design for the action potential
of a neuronal membrane in relation with the non-linear dynamics of the
Hodgkin-Huxley mathematical model. More exactly, by using an external current
as a control expressed by a relay graph in the equation of the potential, we
aim at forcing it to reach a certain manifold in finite time and to slide on it
after that. From the mathematical point of view we solve a system involving a
parabolic differential inclusion and three nonlinear differential equations via
an approximating technique and a fixed point result. The existence of the
sliding mode and the determination of the time at which the potential reaches
the prescribed manifold is proved by a maximum principle argument. Numerical
simulations are presented.


  Using Watson's terminating $_8\phi_7$ transformation formula, we prove a
family of $q$-congruences modulo the square of a cyclotomic polynomial, which
were originally conjectured by the author and Zudilin [J. Math. Anal. Appl. 475
(2019), 1636--646]. As an application, we deduce two supercongruences modulo
$p^4$ ($p$ is an odd prime) and their $q$-analogues. This also partially
confirms a special case of Swisher's (H.3) conjecture.


  The discovery of HDF~J123652+621227, a candidate gravitational lens in the
HDF, is reported. This lens may be multiply imaging several optical sources at
different redshifts. If follow-up spectroscopy of the lens and the brightest
image confirms this hypothesis, observations of this system alone can be used
to obtain an estimate of the redshift distribution at extremely faint flux
levels.


  In 2012, a family of benzenoids was introduced by Cruz, Gutman, and Rada,
which they called convex benzenoids. In this paper we introduce the convexity
deficit, a new topological index intended for benzenoids and, more generally,
fusenes. This index measures by how much a given fusene departs from convexity.
It is defined in terms of the boundary-edges code. In particular, convex
benzenoids are exactly the benzenoids having convexity deficit equal to 0.
Quasi-convex benzenoids form the family of non-convex benzenoids that are
closest to convex, i.e., they have convexity deficit equal to 1. Finally, we
investigate convexity deficit of several important families of benzenoids.


  The identification of Alzheimer's disease (AD) and its early stages using
structural magnetic resonance imaging (MRI) has been attracting the attention
of researchers. Various data-driven approaches have been introduced to capture
subtle and local morphological changes of the brain accompanied by the disease
progression. One of the typical approaches for capturing subtle changes is
patch-level feature representation. However, the predetermined regions to
extract patches can limit classification performance by interrupting the
exploration of potential biomarkers. In addition, the existing patch-level
analyses have difficulty explaining their decision-making. To address these
problems, we propose the BrainBagNet with a position-based gate
(PG-BrainBagNet), a framework for jointly learning pathological region
localization and AD diagnosis in an end-to-end manner. In advance, as all scans
are aligned to a template in image processing, the position of brain images can
be represented through the 3D Cartesian space shared by the overall MRI scans.
The proposed method represents the patch-level response from whole-brain MRI
scans and discriminative brain-region from position information. Based on the
outcomes, the patch-level class evidence is calculated, and then the
image-level prediction is inferred by a transparent aggregation. The proposed
models were evaluated on the ADNI datasets. In five-fold cross-validation, the
classification performance of the proposed method outperformed that of the
state-of-the-art methods in both AD diagnosis (AD vs. normal control) and mild
cognitive impairment (MCI) conversion prediction (progressive MCI vs. stable
MCI) tasks. In addition, changes in the identified discriminant regions and
patch-level class evidence according to the patch size used for model training
are presented and analyzed.


  We study the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state of spin
fluctuation mediated pairing, and focus on the effect of coexisting charge
fluctuations. We find that (i) consecutive transitions from singlet pairing to
FFLO and further to $S_z=1$ triplet pairing can generally take place upon
increasing the magnetic field when strong charge fluctuations coexist with spin
fluctuations, and (ii) the enhancement of the charge fluctuations lead to a
significant increase of the parity mixing in the FFLO state, where the
triplet/singlet component ratio in the gap function can be close to unity.
  We propose that such consecutive pairing state transition and strong parity
mixing in the FFLO state may take place in a quasi-one-dimensional organic
superconductor (TMTSF)$_2X$.


  The SPI instrument has been launched on-board the INTEGRAL observatory on
October 17, 2002. SPI is a spectrometer devoted to the sky observation in the
20 keV-8 MeV energy range using 19 germanium detectors. The performance of the
cryogenic system is nominal and allows to cool the 19 kg of germanium down to
85 K with a comfortable margin. The energy resolution of the whole camera is
2.5 keV at 1.1 MeV. This resolution degrades with time due to particle
irradiation in space. We show that the annealing process allows the recovery of
the initial performance. The anticoincidence shield works as expected, with a
low threshold at 75 keV, reducing the GeD background by a factor of 20. The
digital front-end electronics system allows the perfect alignement in time of
all the signals as well as the optimisation of the dead time (12%). We
demonstrate that SPI is able to map regions as complex as the galactic plane.
The obtained spectrum of the Crab nebula validates the present version of our
response matrix. The 3 $\sigma$ sensitivity of the instrument at 1 MeV is 8
10$^{-7}$ph$\cdot$cm$^{-2}\cdot$s$^{-1}\cdot$keV$^{-1}$ for the continuum and 3
10$^{-5}$ph$\cdot$cm$^{-2}\cdot$s$^{-1}$ for narrow lines.


  Deep learning-based Multi-Task Classification (MTC) is widely used in
applications like facial attributes and healthcare that warrant strong privacy
guarantees. In this work, we aim to protect sensitive information in the
inference phase of MTC and propose a novel Multi-Trigger-Key (MTK) framework to
achieve the privacy-preserving objective. MTK associates each secured task in
the multi-task dataset with a specifically designed trigger-key. The true
information can be revealed by adding the trigger-key if the user is
authorized. We obtain such an MTK model by training it with a newly generated
training set. To address the information leakage malaise resulting from
correlations among different tasks, we generalize the training process by
incorporating an MTK decoupling process with a controllable trade-off between
the protective efficacy and the model performance. Theoretical guarantees and
experimental results demonstrate the effectiveness of the privacy protection
without appreciable hindering on the model performance.


  In this paper, the out-of-time-order correlators (OTOC) in quantum harmonic
oscillators are calculated analytically by second quantization method in
perturbative approximation. We consider the coupled harmonic oscillators and
anharmonic (quartic) oscillators. The analytic formulas of microcanonical OTOC
in the leading order of interaction are obtained. From these results we clearly
see that the interactions can enhance the correlation to very large values over
time and is proportional to the energy level. The growth of OTOC signatures the
quantum chaos therein. The microcanonical OTOC which is an increasing
oscillation becomes a simple increasing function in thermal OTOC after sum over
all energy level, as different level oscillates with different frequency. The
property that, at late time stage OTOC saturates to a constant value, however,
does not show in the first-order perturbation studied in this paper.


  We study baryon and diquark correlation functions in the framework of an
instanton model for the QCD vacuum. The model naturally accomodates a light
scalar diquark. As a consequence, the correlation functions for the nucleon and
delta are found to be qualitatively different. Using a complete set of all
available correlation functions we determine masses and coupling constants for
these particles. We compare our results with predictions from QCD sum rules and
recent lattice measurement of point-to-point correlators.


  In a previous paper, the convergence of the effective field theory approach
of Furnstahl, Serot and Tang to the nuclear many-body problem was studied by
applying it to selected doubly-magic, and neighboring single-particle and
single-hole, nuclei far from stability. The success of that approach,
interpreted through density functional theory, would imply reliable densities.
In this paper, the single-particle (Kohn-Sham) wave functions are probed using
weak transitions near the Fermi surface. The weak currents are the Noether
currents derived from the effective Lagrangian. The general single-particle
transition matrix elements, from which any semi-leptonic weak rate can be
calculated, are obtained in terms of upper and lower components of the Dirac
wave functions. Here beta-decays in nuclei neighboring 132-Sn are studied and
compared with available experimental data. Calibration of the theoretical
results for such decays may also have useful application in element formation.


  High-temperature (high-Tc) superconductivity in the copper oxides arises from
electron or hole doping of their antiferromagnetic (AF) insulating parent
compounds. The evolution of the AF phase with doping and its spatial
coexistence with superconductivity are governed by the nature of charge and
spin correlations and provide clues to the mechanism of high-Tc
superconductivity. Here we use a combined neutron scattering and scanning
tunneling spectroscopy (STS) to study the Tc evolution of electron-doped
superconducting Pr0.88LaCe0.12CuO4-delta obtained through the oxygen annealing
process. We find that spin excitations detected by neutron scattering have two
distinct modes that evolve with Tc in a remarkably similar fashion to the
electron tunneling modes in STS. These results demonstrate that
antiferromagnetism and superconductivity compete locally and coexist spatially
on nanometer length scales, and the dominant electron-boson coupling at low
energies originates from the electron-spin excitations.


  One of the greatest scientific challenges in network neuroscience is to
create a representative map of a population of heterogeneous brain networks,
which acts as a connectional fingerprint. The connectional brain template
(CBT), also named network atlas, presents a powerful tool for capturing the
most representative and discriminative traits of a given population while
preserving its topological patterns. The idea of a CBT is to integrate a
population of heterogeneous brain connectivity networks, derived from different
neuroimaging modalities or brain views (e.g., structural and functional), into
a unified holistic representation. Here we review current state-of-the-art
methods designed to estimate well-centered and representative CBT for
populations of single-view and multi-view brain networks. We start by reviewing
each CBT learning method, then we introduce the evaluation measures to compare
CBT representativeness of populations generated by single-view and multigraph
integration methods, separately, based on the following criteria: centeredness,
biomarker-reproducibility, node-level similarity, global-level similarity, and
distance-based similarity. We demonstrate that the deep graph normalizer (DGN)
method significantly outperforms other multi-graph and all single-view
integration methods for estimating CBTs using a variety of healthy and
disordered datasets in terms of centeredness, reproducibility (i.e.,
graph-derived biomarkers reproducibility that disentangle the typical from the
atypical connectivity variability), and preserving the topological traits at
both local and global graph-levels.


  We present a new compound in the silver cobalt fluoride system, featuring
paramagnetic silver (d9) and high-spin cobalt (d6), synthesized by solid state
method in an autoclave under F2 overpressure. Based on powder X ray
diffraction, we determined that AgIICoIIIF5 crystallizes in a monoclinic system
with space group C2/c. The calculated fundamental band gap falls in the visible
range of the electromagnetic spectrum, and the compound has the character of
charge-transfer insulator. AgCoF5 is a ferrimagnet with one predominant
superexchange magnetic interaction constant between mixed spin cations (Ag ...
Co) of minus 62 meV (SCAN result). Magnetometric measurements conducted on a
powdered sample allowed the identification of a transition at 128 K, which
could indicate magnetic ordering.


  Arctic sea ice extent has declined continuously for the past decade, owing
partially to light absorption by black carbon (BC) and other impurities
deposited on snow and the underlying pack. We present simulations for the
contemporary period showing that the optical depth contributed by Arctic ice
algal chlorophyll may be comparable during Boreal Spring to the corresponding
values attributable to BC. The largest chlorophyll attenuation is obtained in
the bottom layer, which supports pigment concentrations of about 300 to 1000
mg/m3 in the Bering Sea and Sea of Okhotsk. However, chlorophyll concentrations
for the ice interior in regions north of 75{\deg} N and across the Canadian
Archipelago are less than 0.1 micro g/m3. Freeboard and infiltration
communities lead to intermediate levels of light removal. Since BC works its
way downward from the atmospheric interface, there will be regions where it
appropriates photosynthetic capability. Where ice thicknesses permit
significant penetration through the pack column the ice algae may be crucial
absorbers. We propose a continuous increase in relative chlorophyll activity
and attenuation in the future, as biological activity becomes stronger in thin
ice toward the center of the Arctic basin. A shift in relative importance of
the two absorber types could occur as total BC mixing ratios are reduced
because of environmental advocacy.


  Equivariant T-duality triples of locally compact abelian groups are
considered. The motivating example dealing with the group $\R^n$ containing a
lattice $\Z^n$ comes with an isomorphism in twisted equivariant K-theory.


  Pulsar timing arrays (PTAs), aimed at detecting gravitational waves (GWs) in
the $1\sim 100$ nHz range, have recently made significant strides. Compelling
evidence has emerged for a common spectrum signal spatially correlated among
pulsars, following a Hellings-Downs (HD) pattern, which is crucial for
detecting a gravitational-wave background (GWB). However, the HD curve is
expected for discrete and non-interfering sources, which is unlikely to hold in
realistic scenarios with potential interference among numerous GW sources, such
as the supermassive black-hole binaries. Incorporating interference was
previously expected to introduce an irreducible uncertainty (known as "cosmic
variance") in discerning the HD correlation; however, our work reveals how this
interference generates measurable frequency-dependent spatial correlations
distinct from the HD curve. The spatial correlations for interfering sources
(referred to as "ISC") still exhibit contributions in the quadrupole and higher
orders, resembling the HD correlation and encoding the nature of GW radiations.
We apply these novel correlations to search for a GWB in the NANOGrav 15-year
data set. In an optimistic estimation, our findings show a Bayes factor of
$33.7\pm 3.2$ comparing ISC to the HD correlation, and an improvement in
optimal statistic signal-to-noise ratio from $4.9\pm 1.1$ for the HD
correlation to $6.6\pm 1.7$ for the ISC, highlighting the significant
enhancement in evidence for detecting a GWB through incorporating interference.


  In this work, we show the characterization of quantum iterations that would
generally construct quantum amplitude amplification algorithms with a quadratic
speedup, namely, quantum amplitude amplification operators (QAAOs). Exact
quantum search algorithms that find a target with certainty and with a
quadratic speedup can be composed of sequential applications of QAAO: existing
quantum amplitude amplification algorithms thus turn out to be sequences of
QAAOs. We show that an optimal and exact quantum amplitude amplification
algorithm corresponds to the Grover algorithm together with a single iteration
of QAAO. We then realize 3-qubit QAAOs with the current quantum technologies
via cloud-based quantum computing services, IBMQ and IonQ. Finally, our results
find that fixed-point quantum search algorithms known so far are not a sequence
of QAAOs, e.g. the amplitude of a target state may decrease during quantum
iterations.


  The space of smotth functions and vector fields on $\R^d$ is a Lie subalgebra
of the (graded) Lie algebra $T\_{poly}(\R^d)$, equipped with the Scouten
bracket. In this paper, we compute the cohomology of this subalgebra for the
adjoint representation in $T\_{poly}(\R^d)$, restricting ourselves to the case
of cochains defined with purely aerial Kontsevich's graphs, as in [AGM]. We
find results which are very similar to the classical Gelfand-Fuchs and de
Wilde-Lecomte one.


  In this paper we consider the problem of classification of the nilpotent
class 2 finitely generated torsion free groups up to the geometric equivalence.
By a very easy technique it is proved that this problem is equivalent to the
problem of classification of the complete (in the Maltsev sense) nilpotent
torsion free finite rank groups up to the isomorphism. This result, allows us
to once more comprehend the complication of the problem of the classification
of the quasi-varieties of nilpotent class 2 groups. It is well known that the
variety of a nilpotent class s (for every s) groups is Noetherian. So the
problem of the classification of the quasi-varieties generated even by a single
nilpotent class 2 finitely generated torsion free group, is equivalent to the
problem of classification of complete (in the Maltsev sense) nilpotent torsion
free finite rank groups up to the isomorphism.


  Let ${\cal P}=\{h_1, ..., h_s\}\subset \Z[Y_1, ..., Y_k]$, $D\geq \deg(h_i)$
for $1\leq i \leq s$, $\sigma$ bounding the bit length of the coefficients of
the $h_i$'s, and $\Phi$ be a quantifier-free ${\cal P}$-formula defining a
convex semi-algebraic set. We design an algorithm returning a rational point in
${\cal S}$ if and only if ${\cal S}\cap \Q\neq\emptyset$. It requires
$\sigma^{\bigO(1)}D^{\bigO(k^3)}$ bit operations. If a rational point is
outputted its coordinates have bit length dominated by $\sigma D^{\bigO(k^3)}$.
Using this result, we obtain a procedure deciding if a polynomial $f\in \Z[X_1,
>..., X_n]$ is a sum of squares of polynomials in $\Q[X_1, ..., X_n]$. Denote
by $d$ the degree of $f$, $\tau$ the maximum bit length of the coefficients in
$f$, $D={{n+d}\choose{n}}$ and $k\leq D(D+1)-{{n+2d}\choose{n}}$. This
procedure requires $\tau^{\bigO(1)}D^{\bigO(k^3)}$ bit operations and the
coefficients of the outputted polynomials have bit length dominated by $\tau
D^{\bigO(k^3)}$.


  We have studied the acoustic Faraday effect in superfluid 3He up to
significantly larger magnetic fields than in previous experiments achieving
rotations of the polarization of transverse sound as large as 1710^{\circ}. We
report nonlinear field effects, and use the linear results to determine the
Zeeman splitting of the imaginary squashing mode (ISQ) frequency in 3He-B.


  Quantum revivals are investigated for the dynamics of an atom in a driven
gravitational cavity. It is demonstrated that the external driving field
influences the revival time significantly. Analytical expressions are presented
which are based on second order perturbation theory and semiclassical secular
theory. These analytical results explain the dependence of the revival time on
the characteristic parameters of the problem quantitatively in a simple way.
They are in excellent agreement with numerical results.


  The goal of the Semantic Scene Completion (SSC) task is to simultaneously
predict a completed 3D voxel representation of volumetric occupancy and
semantic labels of objects in the scene from a single-view observation. Since
the computational cost generally increases explosively along with the growth of
voxel resolution, most current state-of-the-arts have to tailor their framework
into a low-resolution representation with the sacrifice of detail prediction.
Thus, voxel resolution becomes one of the crucial difficulties that lead to the
performance bottleneck.
  In this paper, we propose to devise a new geometry-based strategy to embed
depth information with low-resolution voxel representation, which could still
be able to encode sufficient geometric information, e.g., room layout, object's
sizes and shapes, to infer the invisible areas of the scene with well
structure-preserving details. To this end, we first propose a novel 3D
sketch-aware feature embedding to explicitly encode geometric information
effectively and efficiently. With the 3D sketch in hand, we further devise a
simple yet effective semantic scene completion framework that incorporates a
light-weight 3D Sketch Hallucination module to guide the inference of occupancy
and the semantic labels via a semi-supervised structure prior learning
strategy. We demonstrate that our proposed geometric embedding works better
than the depth feature learning from habitual SSC frameworks. Our final model
surpasses state-of-the-arts consistently on three public benchmarks, which only
requires 3D volumes of 60 x 36 x 60 resolution for both input and output. The
code and the supplementary material will be available at
https://charlesCXK.github.io.


  A recently derived approach to the tensor reduction of 5-point one-loop
Feynman integrals expresses the tensor coefficients by scalar 1-point to
4-point Feynman integrals completely algebraically. In this letter we derive
extremely compact algebraic expressions for the contractions of the tensor
integrals with external momenta. This is based on sums over signed minors
weighted with scalar products of the external momenta. With these contractions
one can construct the invariant amplitudes of the matrix elements under
consideration, and the evaluation of one-loop contributions to massless and
massive multi-particle production at high energy colliders like LHC and ILC is
expected to be performed very efficiently.


  The CNIa0.02 project aims to collect a complete, nearby sample of Type Ia
supernovae (SNe Ia) light curves, and the SNe are volume-limited with
host-galaxy redshifts z_host < 0.02. The main scientific goal is to infer the
distributions of key properties (e.g., the luminosity function) of local SNe Ia
in a complete and unbiased fashion in order to study SN explosion physics. We
spectroscopically classify any SN candidate detected by the All-Sky Automated
Survey for Supernovae (ASAS-SN) that reaches peak brightness < 16.5 mag. Since
ASAS-SN scans the full sky and does not target specific galaxies, our target
selection is effectively unbiased by host-galaxy properties. We perform
multi-band photometric observations starting from the time of discovery. In the
first data release (DR1), we present the optical light curves obtained for 247
SNe from our project (including 148 SNe in the complete sample), and we derive
parameters such as the peak fluxes, dm15 and s_BV.


  Risk assessment of roadways is commonly practiced based on historical crash
data. Information on driver behaviors and real-time traffic situations is
sometimes missing. In this paper, the Safe Route Mapping (SRM) model, a
methodology for developing dynamic risk heat maps of roadways, is extended to
consider driver behaviors when making predictions. An Android App is designed
to gather drivers' information and upload it to a server. On the server, facial
recognition extracts drivers' data, such as facial landmarks, gaze directions,
and emotions. The driver's drowsiness and distraction are detected, and driving
performance is evaluated. Meanwhile, dynamic traffic information is captured by
a roadside camera and uploaded to the same server. A
longitudinal-scanline-based arterial traffic video analytics is applied to
recognize vehicles from the video to build speed and trajectory profiles. Based
on these data, a LightGBM model is introduced to predict conflict indices for
drivers in the next one or two seconds. Then, multiple data sources, including
historical crash counts and predicted traffic conflict indicators, are combined
using a Fuzzy logic model to calculate risk scores for road segments. The
proposed SRM model is illustrated using data collected from an actual traffic
intersection and a driving simulation platform. The prediction results show
that the model is accurate, and the added driver behavior features will improve
the model's performance. Finally, risk heat maps are generated for
visualization purposes. The authorities can use the dynamic heat map to
designate safe corridors and dispatch law enforcement and drivers for early
warning and trip planning.


  We obtain asymptotic estimates for the best approximations by trigonometric
polynomials in the metric space $C$ $(L_p)$ of classes of periodic functions
that can be represented as a convolution of kernels $\Psi_\beta$, which Fourier
coefficients tend to zero faster than any power sequence, with functions
$\varphi\in C (\varphi\in L_p),$ which moduli of continuity do not exceed a
fixed majorant $\omega(t)$. It is proved that in the spaces $C$ and $L_1$ the
obtained estimates are asymptotically exact for convex moduli of continuity
$\omega(t)$.


  Long-slit spectroscopy has been obtained for a sample of 22 blue dwarf
galaxies selected in the direction of the Virgo Cluster as part of a larger
sample of Virgo blue dwarf galaxies for which deep Halpha imaging has been
collected. Most of the galaxies in the present sample appear classified as BCDs
or dwarf Irregulars in the Virgo Cluster Catalog. Line fluxes, Hbeta equivalent
width and extinction coefficients, spatial emission profiles, ionization
structure and physical conditions are presented for the galaxies of the sample.
Chemical abundances have been derived either using a direct determination of
the electron temperature or after detailed examination of the predictions of
different empirical calibrations. The oxygen abundance derived for the sample
of Virgo dwarf galaxies span in the range 7.6 < 12 + log O/H < 8.9, and the
corresponding nitrogen to oxygen abundance ratio cover from values typical of
low metallicity field BCD galaxies up to near solar.


  In this paper, a family of low-density parity-check (LDPC) degree
distributions, whose decoding threshold on the binary erasure channel (BEC)
admits a simple closed form, is presented. These degree distributions are a
subset of the check regular distributions (i.e. all the check nodes have the
same degree), and are referred to as $p$-positive distributions. It is given
proof that the threshold for a $p$-positive distribution is simply expressed by
$[\lambda'(0)\rho'(1)]^{-1}$. Besides this closed form threshold expression,
the $p$-positive distributions exhibit three additional properties. First, for
given code rate, check degree and maximum variable degree, they are in some
cases characterized by a threshold which is extremely close to that of the best
known check regular distributions, under the same set of constraints. Second,
the threshold optimization problem within the $p$-positive class can be solved
in some cases with analytic methods, without using any numerical optimization
tool. Third, these distributions can achieve the BEC capacity. The last
property is shown by proving that the well-known binomial degree distributions
belong to the $p$-positive family.


  Domain specific languages (DSL) have been used in a variety of fields to
express complex scientific problems in a concise manner and provide automated
performance optimization for a range of computational architectures. As such
DSLs provide a powerful mechanism to speed up scientific Python computation
that goes beyond traditional vectorization and pre-compilation approaches,
while allowing domain scientists to build applications within the comforts of
the Python software ecosystem. In this paper we present Devito, a new finite
difference DSL that provides optimized stencil computation from high-level
problem specifications based on symbolic Python expressions. We demonstrate
Devito's symbolic API and performance advantages over traditional Python
acceleration methods before highlighting its use in the scientific context of
seismic inversion problems.


  We consider the space of solutions of the Bethe ansatz equations of the
$\widehat{\frak{sl}_N}$ XXX quantum integrable model, associated with the
trivial representation of $\widehat{\frak{sl}_N}$. We construct a family of
commuting flows on this space and identify the flows with the flows of coherent
rational Ruijesenaars-Schneider systems. For that we develop in full generality
the spectral transform for the rational Ruijesenaars-Schneider system.


  Instead of the invariant theory approach employed by Beloshaoka and Mamai for
constructing the moduli spaces of Beloshapka's universal CR-models, we consider
two alternative approaches borrowed from the theories of equivalence problem
and Lie symmetries, each of them having its own advantages. Also the moduli
space M(1,4) associated to the class of universal CR-models of CR-dimension 1
and codimension 4 is computed by means of the presented methods.


  In this paper we propose several novel distributed gradient-based temporal
difference algorithms for multi-agent off-policy learning of linear
approximation of the value function in Markov decision processes with strict
information structure constraints, limiting inter-agent communications to small
neighborhoods. The algorithms are composed of: 1) local parameter updates based
on single-agent off-policy gradient temporal difference learning algorithms,
including eligibility traces with state dependent parameters, and 2) linear
stochastic time varying consensus schemes, represented by directed graphs. The
proposed algorithms differ by their form, definition of eligibility traces,
selection of time scales and the way of incorporating consensus iterations. The
main contribution of the paper is a convergence analysis based on the general
properties of the underlying Feller-Markov processes and the stochastic time
varying consensus model. We prove, under general assumptions, that the
parameter estimates generated by all the proposed algorithms weakly converge to
the corresponding ordinary differential equations (ODE) with precisely defined
invariant sets. It is demonstrated how the adopted methodology can be applied
to temporal-difference algorithms under weaker information structure
constraints. The variance reduction effect of the proposed algorithms is
demonstrated by formulating and analyzing an asymptotic stochastic differential
equation. Specific guidelines for communication network design are provided.
The algorithms' superior properties are illustrated by characteristic
simulation results.


  We present a numerically cheap approximation to super-sample covariance (SSC)
of large scale structure cosmological probes, first in the case of angular
power spectra. It necessitates no new elements besides those used for the
prediction of the considered probes, thus relieving analysis pipelines from
having to develop a full SSC modeling, and reducing the computational load. The
approximation is asymptotically exact for fine redshift bins $\Delta z
\rightarrow 0$. We furthermore show how it can be implemented at the level of a
Gaussian likelihood or a Fisher matrix forecast, as a fast correction to the
Gaussian case without needing to build large covariance matrices. Numerical
application to a Euclid-like survey show that, compared to a full SSC
computation, the approximation recovers nicely the signal-to-noise ratio as
well as Fisher forecasts on cosmological parameters of the $w$CDM cosmological
model. Moreover it allows for a fast prediction of which parameters are going
to be the most affected by SSC and at which level. In the case of photometric
galaxy clustering with Euclid-like specifications, we find that $\sigma_8$,
$n_s$ and the dark energy equation of state $w$ are particularly heavily
affected. We finally show how to generalize the approximation for probes other
than angular spectra (correlation functions, number counts and bispectra), and
at the likelihood level, allowing for the latter to be non-Gaussian if needs
be. We release publicly a Python module allowing to implement the SSC
approximation, as well as a notebook reproducing the plots of the article, at
https://github.com/fabienlacasa/PySSC