  In this paper we investigate the dependency of characteristic distance on
energy dissipation model. Both the many-to-one and any-to-any communication
paradigm have been presented for performance analysis. Characteristic distance
has been derived for three different cases. This study will be useful for
designing an energy-efficient wireless network where nodes are
energy-constrained.


  A novel singular gauge transformation is developed for quasiparticles in the
mixed state of a strongly type-II superconductor which permits a full solution
of the problem at low and intermediate fields. For a periodic vortex lattice
the natural low-energy quasiparticle states are Bloch waves rather than Landau
levels discussed in the recent literature. The new representation elucidates
the physics considerably and provides fresh insights into the spectral
properties of such systems.


  Bose-Einstein symmetrization can lead to correlations between out going
identical particles which reflect the space-time extent of the collision
process. At LEP and LEPII these correlations have been studied as a function of
a single variable Q, the invariant momentum separation. Assuming a simple form
for the correlation function the experiments find source radii dependent on the
hadron mass. In this note, I point out that such effects can arise from purely
kinematic considerations, although these are unlikely to explain the observed
effects completely.


  Analogical reasoning methods have been built over various resources,
including commonsense knowledge bases, lexical resources, language models, or
their combination. While the wide coverage of knowledge about entities and
events make Wikidata a promising resource for analogical reasoning across
situations and domains, Wikidata has not been employed for this task yet. In
this paper, we investigate whether the knowledge in Wikidata supports
analogical reasoning. Specifically, we study whether relational knowledge is
modeled consistently in Wikidata, observing that relevant relational
information is typically missing or modeled in an inconsistent way. Our further
experiments show that Wikidata can be used to create data for analogy
classification, but this requires much manual effort. To facilitate future work
that can support analogies, we discuss key desiderata, and devise a set of
metrics to guide an automatic method for extracting analogies from Wikidata.


  We introduce a deterministic model defined on a two dimensional hyperbolic
lattice. This model provides an example of a non random system whose
multifractal behaviour has a number theoretic origin. We determine the
multifractal exponents, discuss the termination of multifractality and
conjecture the geometric origin of the multifractal behavior in Liouville
quasi--classical field theory.


  First we report that the adjacency matrices of real-world complex networks
systematically have null eigenspaces with much higher dimensions than that of
random networks. These null eigenvalues are caused by duplication mechanisms
leading to structures with local symmetries which should be more present in
complex organizations. The associated eigenvectors of these states are strongly
localized. We then evaluate these microstructures in the context of quantum
mechanics, demonstrating the previously mentioned localization by studying the
spread of continuous-time quantum walks. This null-eigenvalue localization is
essentially different from the Anderson localization in the following points:
first, the eigenvalues do not lie on the edges of the density of states but at
its center; second, the eigenstates do not decay exponentially and do not leak
out of the symmetric structures. In this sense, it is closer to the bound state
in continuum.


  The temperature gradient of microwave background radiation (CMBR) is
calculated in the Self Consistent Model. An expected values for Hubble
parameter have been presented in two different cases. In the first case the
temperature is treated as a function of time only, while in the other one the
temperature depends on relaxation of isotropy condition in the self-consistent
model and the assumption that the universe expands adiabatically. The COBE's or
WMAP's fluctuations in temperature of CMBR may be used to predict a value for
Hubble parameter.


  The new astronomical technique of gravitational microlensing enables
measurements of high precision to be made in certain circumstances. Useful
advances have been made in the fields of galactic astronomy, stellar astronomy
and planetary science. The technique is best suited to the southern sky, and
several observations have been made from Australasia. A sample of these
observations is described here. A case is also made for a telescope at the
Antarctic dedicated to gravitational microlensing.


  We present numerical studies of a quantum ``projected'' SO(5) model which
aims at a unifying description of antiferromagnetism and superconductivity in
the high-T$_c$ cuprates, while properly taking into account the Mott insulating
gap. Our numerical results, obtained by the Quantum Monte Carlo technique of
Stochastic Series Expansion, show that this model can give a realistic
description of the global phase diagram of the high-$T_c$ superconductors and
accounts for many of their physical properties. Moreover, we address the
question of dynamic restoring of the SO(5) symmetry at the critical point.


  We consider the evolution of the neutron-nucleus scattering length for the
lightest nuclei. We show that, when increasing the number of neutrons in the
target nucleus, the strong Pauli repulsion is weakened and the balance with the
attractive nucleon-nucleon interaction results into a resonant virtual state in
$^{18}$B.
  We describe $^{19}$B in terms of a $^{17}$B-$n$-$n$ three-body system where
the two-body subsystems $^{17}$B-$n$ and $n$-$n$ are unbound (virtual) states
close to the unitary limit. The energy of $^{19}$B ground state is well
reproduced and two low-lying resonances are predicted. Their eventual link with
the Efimov physics is discussed. This model can be extended to describe the
recently discovered resonant states in $^{20,21}$B.


  An autostereogram is a single image that encodes depth information that pops
out when looking at it. The trick is achieved by replicating a vertical strip
that sets a basic two-dimensional pattern with disparity shifts that encode a
three-dimensional scene. It is of interest to explore the dependency between
the ease of perceiving depth in autostereograms and the choice of the basic
pattern used for generating them. In this work we confirm a theory proposed by
Bruckstein et al. to explain the process of autostereographic depth perception,
providing a measure for the ease of "locking into" the depth profile, based on
the spectral properties of the basic pattern used. We report the results of
three sets of psychophysical experiments using autostereograms generated from
two-dimensional random noise patterns having power spectra of the form
$1/f^\beta$. The experiments were designed to test the ability of human
subjects to identify smooth, low resolution surfaces, as well as detail, in the
form of higher resolution objects in the depth profile, and to determine limits
in identifying small objects as a function of their size. In accordance with
the theory, we discover a significant advantage of the $1/f$ noise pattern
(pink noise) for fast depth lock-in and fine detail detection, showing that
such patterns are optimal choices for autostereogram design. Validating the
theoretical model predictions strengthens its underlying assumptions, and
contributes to a better understanding of the visual system's binocular
disparity mechanisms.


  We investigate Dark Energy by associating it with vacuum energy or
Cosmological constant ${\Lambda}$ which is taken to be dynamic in nature. Our
approach is phenomenological and falls within the domain of variable-$\Lambda$
Cosmology. However, motivated by quantum theory of metastable vacuum decay, we
proposed a new phenomenological decay law of $\Lambda$(t) where $\Lambda$(t) is
a superposition of constant and variable components viz. $\Lambda$(t) =
$\Lambda_{C}$ + $\Lambda_{v}$ which is indicated by the word $"$hybrid
dynamic$"$ in the title. By taking a simplified two-fluid scenario with the
Universe consisting of Dark Energy and another major component, we found the
solutions for three particular phenomenological expressions and made a
parametrization of the model in terms of dilution parameter (the dilution
parameter has been defined in the text as the exponent of scale factor in the
expression of density of the other major component, representing the dilution
of the component with the expansion of Universe in the presence of dynamic Dark
Energy). For pressureless Dust and dynamic Dark Energy Universe, we found the
present-day matter density ($\Omega_{m0}$) and dilution parameter (u) to be
$\Omega_{m0}$ = 0.29 $\pm$ 0.03, u = 2.90 $\pm$ 0.54 at 1 $\sigma$ by analysing
580 supernova from Union 2.1 catalogue. The physical features of the model in
regard to scale factor evolution, deceleration parameter, cosmic age has also
been studied and parallels have been drawn with $\Lambda$CDM model. The status
of Cosmological problems in the model has also been checked which showed that
the model solves the Cosmological Constant Problem but the Coincidence problem
still exists in the model.


  We present the generic junction conditions obeyed by a co-dimension one brane
in an arbitrary background spacetime. As well as the usual Darmois-Israel
junction conditions which relate the discontinuity in the extrinsic curvature
to the to the energy-momentum tensor of matter which is localized to the brane,
we point out that another condition must also be obeyed. This condition, which
is the analogous to Newton's second law for a point particle, is trivially
satisfied when $Z_2$ symmetry is enforced by hand, but in more general
circumstances governs the evolution of the brane world-volume. As an
illustration of its effect we compute the force on the brane due to a form
field.


  As a key low-power communication technique, backscatter communication has
received significant attention since the rising of the Internet of Things
(IoT). We revisit the state-of-the-art backscatter system, RBLE [1]. It solves
several key reliability issues of backscatter system including unreliable
two-step modulation, productive-data dependency, and lack of interference
countermeasures. We implement a Matlab simulation version of this. It uses the
reverse whiten technique to generate a single tone signal, operates direct
frequency on it and calculates the bit error rate (BER) to evaluate. We give
the spectrograms of the middle waveform results, compare the influence of
different modulation methods and analyze the cause of high BER. In the end, we
discuss the future prospects of the applications using RBLE.


  We use the IllustrisTNG simulations to explore the dynamic scaling relation
between massive clusters and their central galaxies (BCGs). The Illustris
TNG300-1 simulation we use includes 280 massive clusters with $M_{200} >
10^{14}$ M$_{\odot}$ enabling a robust statistical analysis. We derive the
line-of-sight velocity dispersion of the stellar particles of the BCGs
($\sigma_{*, BCG}$), analogous to the observed BCG stellar velocity dispersion.
We also compute the subhalo velocity dispersion to measure the cluster velocity
dispersion ($\sigma_{cl}$). Both $\sigma_{*, BCG}$ and $\sigma_{cl}$ are
proportional to the cluster halo mass, but the slopes differ slightly. Thus
like the observed relation, $\sigma_{*, BCG} / \sigma_{cl}$ declines as a
function of $\sigma_{cl}$, but the scatter is large. We explore the redshift
evolution of $\sigma_{*, BCG} - \sigma_{cl}$ scaling relation for $z \lesssim
1$ in a way that can be compared directly with observations. The scaling
relation has a similar slope at high redshift, but the scatter increases
because of the large scatter in $\sigma_{*, BCG}$. The simulations imply that
high redshift BCGs are dynamically more complex than their low redshift
counterparts.


  DiffExp is a Mathematica package for integrating families of Feynman
integrals order-by-order in the dimensional regulator from their systems of
differential equations, in terms of one-dimensional series expansions along
lines in phase-space, which are truncated at a given order in the line
parameter. DiffExp is based on the series expansion strategies that were
explored in recent literature for the computation of families of Feynman
integrals relevant for Higgs plus jet production with full heavy quark mass
dependence at next-to-leading order. The main contribution of this paper, and
its associated package, is to provide a public implementation of these series
expansion methods, which works for any family of integrals for which the user
provides a set of differential equations and boundary conditions (and for which
the program is not computationally constrained.) The main functions of the
DiffExp package are discussed, and its use is illustrated by applying it to the
three loop equal-mass and unequal-mass banana graph families.


  We obtain the equation of state (EoS) for two-color QCD at low temperature
and high density from the lattice Monte Carlo simulation. We find that the
velocity of sound exceeds the relativistic limit ($c_s^2/c^2=1/3$) after
BEC-BCS crossover in the superfluid phase. Such an excess of the sound velocity
is previously unknown from any lattice calculations for QCD-like theories. This
finding might have a possible relevance to the EoS of neutron star matter
revealed by recent measurements of neutron star masses and radii.


  Context: The Galactic Center IRS 13E cluster is located ~3.2'' from SgrA*. It
is an extremely dense stellar association containing several Wolf-Rayet and
O-type stars, at least four of which show a common velocity. Only half an
arcsecond north from IRS 13E there is a complex of extremely red sources,
so-called IRS 13N. Their nature is still unclear. Based on the analysis of
their colors, there are two main possibilities: (1) dust embedded sources older
than few Myr, or (2) extremely young objects with ages less than 1Myr.
  Aims: We present the first proper motion measurements of IRS 13N members, and
additionally give proper motions of four of IRS 13E stars resolved in the
L'-band.
  Methods: The L'-band (3.8 micron) observations have been carried out using
the NACO adaptive optics system at the ESO VLT. Proper motions have been
obtained by linear fitting of the stellar positions extracted by StarFinder as
a function of time, weighted by positional uncertainties.
  Results: We show that six of seven resolved northern sources show a common
proper motion, thus revealing a new comoving group of stars in the central half
parsec of the Milky Way. The common proper motions of IRS 13E and IRS 13N
clusters are significantly (>5sigma) different. We also performed a fitting of
the positional data for those stars onto Keplerian orbits, assuming SgrA* as
the center of the orbit. Our results favor the very young stars hypothesis.


  Hierarchical clustering is a popular method for analyzing data which
associates a tree to a dataset. Hartigan consistency has been used extensively
as a framework to analyze such clustering algorithms from a statistical point
of view. Still, as we show in the paper, a tree which is Hartigan consistent
with a given density can look very different than the correct limit tree.
Specifically, Hartigan consistency permits two types of undesirable
configurations which we term over-segmentation and improper nesting. Moreover,
Hartigan consistency is a limit property and does not directly quantify
difference between trees.
  In this paper we identify two limit properties, separation and minimality,
which address both over-segmentation and improper nesting and together imply
(but are not implied by) Hartigan consistency. We proceed to introduce a merge
distortion metric between hierarchical clusterings and show that convergence in
our distance implies both separation and minimality. We also prove that uniform
separation and minimality imply convergence in the merge distortion metric.
Furthermore, we show that our merge distortion metric is stable under
perturbations of the density.
  Finally, we demonstrate applicability of these concepts by proving
convergence results for two clustering algorithms. First, we show convergence
(and hence separation and minimality) of the recent robust single linkage
algorithm of Chaudhuri and Dasgupta (2010). Second, we provide convergence
results on manifolds for topological split tree clustering.


  We show that the spin-current response of a semiconductor crystal to an
external electric field is considerably more complex than previously assumed.
While in systems of high symmetry only the spin-Hall components are allowed, in
systems of lower symmetry other non-spin-Hall components may be present. We
argue that, when spin-orbit interactions are present only in the band
structure, the distinction between intrinsic and extrinsic contributions to the
spin current is not useful. We show that the generation of spin currents and
that of spin densities in an electric field are closely related, and that our
general theory provides a systematic way to distinguish between them in
experiment. We discuss also the meaning of vertex corrections in systems with
spin-orbit interactions.


  In this paper we obtain an algorithm towards solving the two-dimensional
moment problem. This algorithm gives the necessary and sufficient conditions
for the solvability of the moment problem. It is shown that all solutions of
the moment problem can be constructed using this algorithm. In a consequence,
analogous results are obtained for the complex moment problem.


  With the increasing scale, complexity, and heterogeneity of the next
generation networked systems, seamless control, management, and security of
such systems becomes increasingly challenging. Many diverse applications have
driven interest in networked systems, including large-scale distributed
learning, multi-agent optimization, 5G service provisioning, and network
slicing, etc. In this paper, we propose trust as a measure to evaluate the
status of network agents and improve the decision-making process. We interpret
trust as a relation among entities that participate in various protocols. Trust
relations are based on evidence created by the interactions of entities within
a protocol and may be a composite of multiple metrics such as availability,
reliability, resilience, etc. depending on application context. We first
elaborate on the importance of trust as a metric and then present a
mathematical framework for trust computation and aggregation within a network.
Then we show in practice, how trust can be integrated into network
decision-making processes by presenting two examples. In the first example, we
show how utilizing the trust evidence can improve the performance and the
security of Federated Learning. Second, we show how a 5G network resource
provisioning framework can be improved when augmented with a trust-aware
decision-making scheme. We verify the validity of our trust-based approach
through simulations. Finally, we explain the challenges associated with
aggregating the trust evidence and briefly explain our ideas to tackle them.


  The dynamics of systems of many degrees of freedom evolving on multiple
scales are often modeled in terms of stochastic differential equations. Usually
the structural form of these equations is unknown and the only manifestation of
the system's dynamics are observations at discrete points in time. Despite
their widespread use, accurately inferring these systems from sparse-in-time
observations remains challenging. Conventional inference methods either focus
on the temporal structure of observations, neglecting the geometry of the
system's invariant density, or use geometric approximations of the invariant
density, which are limited to conservative driving forces. To address these
limitations, here, we introduce a novel approach that reconciles these two
perspectives. We propose a path augmentation scheme that employs data-driven
control to account for the geometry of the invariant system's density.
Non-parametric inference on the augmented paths, enables efficient
identification of the underlying deterministic forces of systems observed at
low sampling rates.


  We study effect of magnetic field on massive dense core formation in
colliding unequal molecular clouds by performing magnetohydrodynamic
simulations with sub-parsec resolution (0.015 pc) that can resolve the
molecular cores. Initial clouds with the typical gas density of the molecular
clouds are immersed in various uniform magnetic fields. The turbulent magnetic
fields in the clouds consistent with the observation by Crutcher et al. (2010)
are generated by the internal turbulent gas motion before the collision, if the
uniform magnetic field strength is 4.0 $\mu$G. The collision speed of 10 km
s$^{-1}$ is adopted, which is much larger than the sound speeds and the
Alfv\'{e}n speeds of the clouds. We identify gas clumps with gas densities
greater than 5 $\times$ 10$^{-20}$ g cm$^{-3}$ as the dense cores and trace
them throughout the simulations to investigate their mass evolution and
gravitational boundness. We show that a greater number of massive,
gravitationally bound cores are formed in the strong magnetic field (4.0
$\mu$G) models than the weak magnetic field (0.1 $\mu$G) models. This is partly
because the strong magnetic field suppresses the spatial shifts of the shocked
layer that should be caused by the nonlinear thin shell instability. The
spatial shifts promote formation of low-mass dense cores in the weak magnetic
field models. The strong magnetic fields also support low-mass dense cores
against gravitational collapse. We show that the numbers of massive,
gravitationally bound cores formed in the strong magnetic field models are much
larger than the isolated, non-colliding cloud models, which are simulated for
comparison. We discuss the implications of our numerical results on massive
star formation.


  The measurement of large scale anisotropies in cosmic ray arrival directions
at energies above 10^13 eV is performed through the detection of Extensive Air
Showers produced by cosmic ray interactions in the atmosphere. The observed
anisotropies are small, so accurate measurements require small statistical
uncertainties, i.e. large datasets. These can be obtained by employing ground
detector arrays with large extensions (from 10^4 to 10^9 m^2) and long
operation time (up to 20 years). The control of such arrays is challenging and
spurious variations in the counting rate due to instrumental effects (e.g. data
taking interruptions or changes in the acceptance) and atmospheric effects
(e.g. air temperature and pressure effects on EAS development) are usually
present. These modulations must be corrected very precisely before performing
standard anisotropy analyses, i.e. harmonic analysis of the counting rate
versus local sidereal time. In this paper we discuss an alternative method to
measure large scale anisotropies, the "East-West method", originally proposed
by Nagashima in 1989. It is a differential method, as it is based on the
analysis of the difference of the counting rates in the East and West
directions. Besides explaining the principle, we present here its mathematical
derivation, showing that the method is largely independent of experimental
effects, that is, it does not require corrections for acceptance and/or for
atmospheric effects. We explain the use of the method to derive the amplitude
and phase of the anisotropy and we demonstrate its power under different
conditions of detector operation.


  We study the expectation value of a nonplanar Wilson graph operator in
SL(2,C) Chern-Simons theory on $S^3$. In particular we analyze its asymptotic
behaviour in the double-scaling limit in which both the representation labels
and the Chern-Simons coupling are taken to be large, but with fixed ratio. When
the Wilson graph operator has a specific form, motivated by loop quantum
gravity, the critical point equations obtained in this double-scaling limit
describe a very specific class of flat connection on the graph complement
manifold. We find that flat connections in this class are in correspondence
with the geometries of constant curvature 4-simplices. The result is fully
non-perturbative from the perspective of the reconstructed geometry. We also
show that the asymptotic behavior of the amplitude contains at the leading
order an oscillatory part proportional to the Regge action for the single
4-simplex in the presence of a cosmological constant. In particular, the
cosmological term contains the full-fledged curved volume of the 4-simplex.
Interestingly, the volume term stems from the asymptotics of the Chern-Simons
action. This can be understood as arising from the relation between
Chern-Simons theory on the boundary of a region, and a theory defined by an
$F^2$ action in the bulk. Another peculiarity of our approach is that the sign
of the curvature of the reconstructed geometry, and hence of the cosmological
constant in the Regge action, is not fixed a priori, but rather emerges
semiclassically and dynamically from the solution of the equations of motion.
In other words, this work suggests a relation between 4-dimensional loop
quantum gravity with a cosmological constant and SL(2,C) Chern-Simons theory in
3-dimensions with knotted graph defects.


  To democratize large language models (LLMs) to most natural languages, it is
imperative to make these models capable of understanding and generating texts
in many languages, in particular low-resource ones. While recent multilingual
LLMs demonstrate remarkable performance in such capabilities, these LLMs still
support a limited number of human languages due to the lack of training data
for low-resource languages. Moreover, these LLMs are not yet aligned with human
preference for downstream tasks, which is crucial for the success of LLMs in
English. In this paper, we introduce xLLaMA-100 and xBLOOM-100 (collectively
xLLMs-100), which scale the multilingual capabilities of LLaMA and BLOOM to 100
languages. To do so, we construct two datasets: a multilingual instruction
dataset including 100 languages, which represents the largest language coverage
to date, and a cross-lingual human feedback dataset encompassing 30 languages.
We perform multilingual instruction tuning on the constructed instruction data
and further align the LLMs with human feedback using the DPO algorithm on our
cross-lingual human feedback dataset. We evaluate the multilingual
understanding and generating capabilities of xLLMs-100 on five multilingual
benchmarks. Experimental results show that xLLMs-100 consistently outperforms
its peers across the benchmarks by considerable margins, defining a new
state-of-the-art multilingual LLM that supports 100 languages.


  The article is devoted to a further study of the Compton camera method of
passive detection of small amounts of special nuclear materials, developed by
the authors in their previous work. Various cargo scenarios, detector errors,
and other issues are addressed.


  This is a survey on recent progress in algebraic deformation theory and the
application of algebraic operads to its study. We review the classical
homotopical tools in the theory of algebraic operads, namely Koszul duality. We
give concrete examples of applications of such tools to various flavours of
problems related to deformations of algebraic structures. We also study formal
moduli problems and related notions from the operadic point of view.


  We determine the Betti numbers for the (degenerate and normalized)
set-theoretic Yang-Baxter (co)homology groups of cyclic biquandles and estimate
their torsion subgroups. This partially settles the conjecture presented by
Przytycki, Vojtechovsky, and Yang. We also obtain cocycles which are
representatives of the elements of a basis for the free part of the cohomology
group of a cyclic biquandle.


  A positive integer $n$ is called a tiling number if the equilateral triangle
can be dissected into $nk^2$ congruent triangles for some integer $k$. An
integer $n>3$ is tiling number if and only if at least one of the elliptic
curves $E^{(\pm n)}:\pm ny^2=x(x-1)(x+3)$ has positive Mordell-Weil rank. Let
$A$ denote one of the two curves. In this paper, using Waldspurger formula and
an induction method, for $n\equiv 3,7\mod 24$ positive square-free, as well as
some other residue classes, we express the parity of analytic Sha of $A$ in
terms of the genus number $g(m):=\#2\mathrm{Cl}(\mathbb{Q}(\sqrt{-m}))$ as $m$
runs over factors of $n$. Together with $2$-descent method which express
$\mathrm{dim}_{\mathbb{F}_2}\mathrm{Sel}_2(A/\mathbb{Q})/A[2]$ in terms of the
corank of a matrix of $\mathbb{F}_2$-coefficients, we show that for $n\equiv
3,7\mod 24$ positive square-free, the analytic Sha of $A$ being odd is
equivalent to that $\mathrm{Sel}_2(A/\mathbb{Q})/A[2]$ being trivial, as
predicted by the BSD conjecture.
  We also show that, among the residue classes $3$, resp. $7\mod 24$, the
subset of $n$ such that both of $E^{(n)}$ and $E^{(-n)}$ have analytic Sha odd
is of limit density $0.288\cdots$ and $0.144\cdots$, respectively, in
particular, they are non-tiling numbers. This exhibits two new phenomena on
tiling number elliptic curves: firstly, the limit density is different from the
general phenomenon on elliptic curves predicted by
Bhargava-Kane-Lenstra-Poonen-Rains; secondly, the joint distribution has
different behavior among different residue classes.


  Gaussian elimination with no pivoting and block Gaussian elimination are
attractive alternatives to the customary but communication intensive Gaussian
elimination with partial pivoting (hereafter we use the acronyms GENP, BGE, and
GEPP} provided that the computations proceed safely and numerically safely},
that is, run into neither division by 0 nor numerical problems. Empirically,
safety and numerical safety of GENP have been consistently observed in a number
of papers where an input matrix was pre-processed with various structured
multipliers chosen ad hoc. Our present paper provides missing formal support
for this empirical observation and explains why it was elusive so far. Namely
we prove that GENP is numerically unsafe for a specific class of input matrices
in spite of its pre-processing with some well-known and well-tested structured
multipliers, but we also prove that GENP and BGE are safe and numerically safe
for the average input matrix pre-processed with any nonsingular and
well-conditioned multiplier. This should embolden search for sparse and
structured multipliers, and we list and test some new classes of them. We also
seek randomized pre-processing that universally (that is, for all input
matrices) supports (i) safe GENP and BGE with probability 1 and/or (ii)
numerically safe GENP and BGE with a probability close to 1.We achieve goal (i)
with a Gaussian structured multiplier and goal (ii) with a Gaussian
unstructured multiplier and alternatively with Gaussian structured
augmentation. We consistently confirm all these formal results with our tests
of GENP for benchmark inputs. We have extended our approach to other
fundamental matrix computations and keep working on further extensions.


  Adversarial examples can be useful for identifying vulnerabilities in AI
systems before they are deployed. In reinforcement learning (RL), adversarial
policies can be developed by training an adversarial agent to minimize a target
agent's rewards. Prior work has studied black-box versions of these attacks
where the adversary only observes the world state and treats the target agent
as any other part of the environment. However, this does not take into account
additional structure in the problem. In this work, we study white-box
adversarial policies and show that having access to a target agent's internal
state can be useful for identifying its vulnerabilities. We make two
contributions. (1) We introduce white-box adversarial policies where an
attacker observes both a target's internal state and the world state at each
timestep. We formulate ways of using these policies to attack agents in
2-player games and text-generating language models. (2) We demonstrate that
these policies can achieve higher initial and asymptotic performance against a
target agent than black-box controls. Code is available at
https://github.com/thestephencasper/lm_white_box_attacks


  Identifying strong lensing gravitational wave (SLGW) events is of utmost
importance in astrophysics as we approach the historic first detection of SLGW
amidst the growing number of gravitational wave (GW) events. Currently, one
crucial method for identifying SLGW signals involves assessing the overlap of
parameters between two GWs. However, the distribution of discrete matter, such
as stars and sub-halos, within the strong lensing galaxy can imprint a wave
optical (WO) effect on the SLGW waveform. These frequency dependent imprints
introduce biases in parameter estimation and impact SLGW identification. In
this study, we assess the influence of the stellar microlensing field embedded
in a strong lensing galaxy. Our finding demonstrate that the WO effect reduces
the detection efficiency of SLGW by $5\%\sim 50\%$ for various false alarm
probabilities per pair (${\rm FAP}_{\rm per~pair}$). Specifically, at an ${\rm
FAP}_{\rm per~pair}$ of $10^{-5}$, the detection efficiency decreases from
$\sim 10\%$ to $\sim 5\%$. Consequently, the presence of the microlensing field
can result in missing half of the strong lensing candidates. Additionally, the
microlensing WO effect introduces a noticeable bias in intrinsic parameters,
particularly for chirp mass and mass ratio. However, it has tiny influence on
extrinsic parameters. Considering all parameters, $\sim 30\%$ of events exhibit
a $1\sigma$ parameter bias, $\sim 12\%$ exhibit a $2\sigma$ parameter bias, and
$\sim 5\%$ exhibit a $3\sigma$ parameter bias.


  We present deep optical spectroscopy of eight HII regions located in the
anticentre of the Milky Way. The spectra were obtained at the 10.4m GTC and
8.2m VLT. We determined Te([NII]) for all objects and Te([OIII]) for six of
them. We also included in our analysis an additional sample of 13 inner-disc
Galactic Hii regions from the literature that have excellent T_e
determinations. We adopted the same methodology and atomic dataset to determine
the physical conditions and ionic abundances for both samples. We also detected
the CII and OII optical recombination lines in Sh 2-100, which enables
determination of the abundance discrepancy factor for this object. We found
that the slopes of the radial oxygen gradients defined by the HII regions from
R_25 (= 11.5 kpc) to 17 kpc and those within R_25 are similar within the
uncertainties, indicating the absence of flattening in the radial oxygen
gradient in the outer Milky Way. In general, we found that the scatter of the
O/H ratios of Hii regions is not substantially larger than the observational
uncertainties. The largest possible local inhomogeneities of the oxygen
abundances are of the order of 0.1 dex. We also found positive radial gradients
in Te([O III]) and Te([N II]) across the Galactic disc. The shapes of these
temperature gradients are similar and also consistent with the absence of
flattening of the metallicity distribution in the outer Galactic disc.


  In this technical report, we present TeleChat, a collection of large language
models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It
includes pretrained language models as well as fine-tuned chat models that is
aligned with human preferences. TeleChat is initially pretrained on an
extensive corpus containing a diverse collection of texts from both English and
Chinese languages, including trillions of tokens. Subsequently, the model
undergoes fine-tuning to align with human preferences, following a detailed
methodology that we describe. We evaluate the performance of TeleChat on
various tasks, including language understanding, mathematics, reasoning, code
generation, and knowledge-based question answering. Our findings indicate that
TeleChat achieves comparable performance to other open-source models of similar
size across a wide range of public benchmarks. To support future research and
applications utilizing LLMs, we release the fine-tuned model checkpoints of
TeleChat's 7B and 12B variant, along with code and a portion of our pretraining
data, to the public community.


  The measurements of hadron and lepton-pair production cross sections and
leptonic forward-backward asymmetries performed with the L3 detector at
centre-of-mass energies between 130 GeV and 189 GeV are used to search for new
physics phenomena such as: contact interactions, exchange of virtual
leptoquarks, scalar quarks and scalar neutrinos, effects of TeV strings in
models of quantum gravity with large extra dimensions and non-zero sizes of the
fermions. No evidence for these phenomena is found and new limits on their
parameters are set.


  Solving feedback Stackelberg games with nonlinear dynamics and coupled
constraints, a common scenario in practice, presents significant challenges.
This work introduces an efficient method for computing approximate local
feedback Stackelberg equilibria in multi-player general-sum dynamic games, with
continuous state and action spaces. Different from existing (approximate)
dynamic programming solutions that are primarily designed for unconstrained
problems, our approach involves reformulating a feedback Stackelberg dynamic
game into a sequence of nested optimization problems, enabling the derivation
of Karush-Kuhn-Tucker (KKT) conditions and the establishment of a second-order
sufficient condition for local feedback Stackelberg equilibria. We propose a
Newton-style primal-dual interior point method for solving constrained linear
quadratic (LQ) feedback Stackelberg games, offering provable convergence
guarantees. Our method is further extended to compute local feedback
Stackelberg equilibria for more general nonlinear games by iteratively
approximating them using LQ games, ensuring that their KKT conditions are
locally aligned with those of the original nonlinear games. We prove the
exponential convergence of our algorithm in constrained nonlinear games. In a
feedback Stackelberg game with nonlinear dynamics and (nonconvex) coupled costs
and constraints, our experimental results reveal the algorithm's ability to
handle infeasible initial conditions and achieve exponential convergence
towards an approximate local feedback Stackelberg equilibrium.


  We prove both the group version and the Lie algebra version of the
Fundamental Lemma appearing in a relative trace formula of Jacquet-Rallis in
the function field case when the characteristic is greater than the rank of the
relevant groups.


  Humans can learn many novel tasks from a very small number (1--5) of
demonstrations, in stark contrast to the data requirements of nearly tabula
rasa deep learning methods. We propose an expressive class of policies, a
strong but general prior, and a learning algorithm that, together, can learn
interesting policies from very few examples. We represent policies as logical
combinations of programs drawn from a domain-specific language (DSL), define a
prior over policies with a probabilistic grammar, and derive an approximate
Bayesian inference algorithm to learn policies from demonstrations. In
experiments, we study five strategy games played on a 2D grid with one shared
DSL. After a few demonstrations of each game, the inferred policies generalize
to new game instances that differ substantially from the demonstrations. Our
policy learning is 20--1,000x more data efficient than convolutional and fully
convolutional policy learning and many orders of magnitude more computationally
efficient than vanilla program induction. We argue that the proposed method is
an apt choice for tasks that have scarce training data and feature significant,
structured variation between task instances.


  We report the first results of our ongoing campaign to discover the first
brown dwarfs (BD) in NGC 2264, a young (3 Myr), populous star forming region
for which our optical studies have revealed a very high density of potential
candidates - 236 in $<$ 1 deg$^2$ - from the substellar limit down to at least
$\sim$ 20 M$_{\rm Jup}$ for zero reddening. Candidate BD were first selected
using wide field ($I,z$) band imaging with CFH12K, by reference to current
theoretical isochrones. Subsequently, 79 (33%) of the $I,z$ sample were found
to have near-infrared 2MASS photometry ($JHK_s$ $\pm$ 0.3 mag. or better),
yielding dereddened magnitudes and allowing further investigation by comparison
with the location of NextGen and DUSTY isochrones in colour-colour and
colour-magnitude diagrams involving various combinations of $I$,$J$,$H$ and
$K_s$. We discuss the status and potential substellarity of a number of
relatively unreddened (A$_{\rm v}$ $<$ 5) likely low-mass members in our
sample, but in spite of the depth of our observations in $I,z$, we are as yet
unable to unambiguously identify substellar candidates using only 2MASS data.
Nevertheless, there are excellent arguments for considering two faint (observed
$I$ $\sim$ 18.4 and 21.2) objects as cluster candidates with masses
respectively at or rather below the hydrogen burning limit. More current
candidates could be proven to be cluster members with masses around 0.1
M$_{\odot}$ {\it via} gravity-sensitive spectroscopy, and deeper near-infrared
imaging will surely reveal a hitherto unknown population of young brown dwarfs
in this region, accessible to the next generation of deep near-infrared
surveys.


  Water distribution networks are a key component of modern infrastructure for
housing and industry. They transport and distribute water via widely branched
networks from sources to consumers. In order to guarantee a working network at
all times, the water supply company continuously monitors the network and takes
actions when necessary -- e.g. reacting to leakages, sensor faults and drops in
water quality. Since real world networks are too large and complex to be
monitored by a human, algorithmic monitoring systems have been developed. A
popular type of such systems are residual based anomaly detection systems that
can detect events such as leakages and sensor faults. For a continuous high
quality monitoring, it is necessary for these systems to adapt to changed
demands and presence of various anomalies.
  In this work, we propose an adaption of the incremental SAM-kNN classifier
for regression to build a residual based anomaly detection system for water
distribution networks that is able to adapt to any kind of change.


  A measurement of the top quark pole mass $m_\mathrm{t}^\text{pole}$ in events
where a top quark-antiquark pair ($\mathrm{t\bar{t}}$) is produced in
association with at least one additional jet ($\mathrm{t\bar{t}}$+jet) is
presented. This analysis is performed using proton-proton collision data at
$\sqrt{s}$ = 13 TeV collected by the CMS experiment at the CERN LHC,
corresponding to a total integrated luminosity of 36.3 fb$^{-1}$. Events with
two opposite-sign leptons in the final state (e$^+$e$^-$, $\mu^+\mu^-$,
e$^\pm\mu^\mp$) are analyzed. The reconstruction of the main observable and the
event classification are optimized using multivariate analysis techniques based
on machine learning. The production cross section is measured as a function of
the inverse of the invariant mass of the $\mathrm{t\bar{t}}$+jet system at the
parton level using a maximum likelihood unfolding. Given a reference parton
distribution function (PDF), the top quark pole mass is extracted using the
theoretical predictions at next-to-leading order. For the ABMP16NLO PDF, this
results in $m_\mathrm{t}^\text{pole}$ = 172.93 $\pm$ 1.36 GeV.


  A fundamental challenge in synthesis from examples is designing a learning
algorithm that poses the minimal number of questions to an end user while
guaranteeing that the target hypothesis is discovered. Such guarantees are
practically important because they ensure that end users will not be
overburdened with unnecessary questions.
  We present SPEX -- a learning algorithm that addresses the above challenge.
SPEX considers the hypothesis space of formulas over first-order predicates and
learns the correct hypothesis by only asking the user simple membership queries
for concrete examples. Thus, SPEX is directly applicable to any learning
problem that fits its hypothesis space and uses membership queries.
  SPEX works by iteratively eliminating candidate hypotheses from the space
until converging to the target hypothesis. The main idea is to use the
implication order between hypotheses to guarantee that in each step the
question presented to the user obtains maximal pruning of the space. This
problem is particularly challenging when predicates are potentially correlated.
  To show that SPEX is practically useful, we expressed two rather different
applications domains in its framework: learning programs for the domain of
technical analysts (stock trading) and learning data structure specifications.
The experimental results show that SPEX's optimality guarantee is effective: it
drastically reduces the number of questions posed to the user while
successfully learning the exact hypothesis.


  3-dimensional BF theory with gauge group $G$ (= Chern-Simons theory with
non-compact gauge group $TG$) is a deceptively simple yet subtle topological
gauge theory. Formally, its partition function is a sum/integral over the
moduli space $\mathcal{M}$ of flat connections, weighted by the Ray-Singer
torsion. In practice, however, this formal expression is almost invariably
singular and ill-defined.
  In order to improve upon this, we perform a direct evaluation of the path
integral for certain classes of 3-manifolds (namely integral and rational
Seifert homology spheres). By a suitable choice of gauge, we sidestep the issue
of having to integrate over $\mathcal{M}$ and reduce the partition function to
a finite-dimensional Abelian matrix integral which, however, itself requires a
definition. We offer 3 definitions of this integral, firstly via residues, and
then via a large $k$ limit of the corresponding $G\times G$ or $G_C$
Chern-Simons matrix integrals (obtained previously). We then check and discuss
to which extent the results capture the expected sum/integral over all flat
connections.


  We study the population of satellite galaxies formed in a suite of
N-body/gasdynamical simulations of galaxy formation in a LCDM universe. We find
little spatial or kinematic bias between the dark matter and the satellite
population. The velocity dispersion of the satellites is a good indicator of
the virial velocity of the halo: \sigma_{sat}/V_{vir}=0.9 +/- 0.2. Applied to
the Milky Way and M31 this gives V_{vir}^{MW}=109 +/- 22$ km/s and
V_{vir}^{M31} = 138 +/- 35 km/s, respectively, substantially lower than the
rotation speed of their disk components. The detailed kinematics of simulated
satellites and dark matter are also in good agreement. By contrast, the stellar
halo of the simulated galaxies is kinematically and spatially distinct from the
population of surviving satellites. This is because the survival of a satellite
depends on mass and on time of accretion; surviving satellites are biased
toward low-mass systems that have been recently accreted by the galaxy. Our
results support recent proposals for the origin of the systematic differences
between stars in the Galactic halo and in Galactic satellites: the elusive
``building blocks'' of the Milky Way stellar halo were on average more massive,
and were accreted (and disrupted) earlier than the population of dwarfs that
has survived self-bound until the present.


  Cardiovascular disease has become one of the most significant threats
endangering human life and health. Recently, Electrocardiogram (ECG) monitoring
has been transformed into remote cardiac monitoring by Holter surveillance.
However, the widely used Holter can bring a great deal of discomfort and
inconvenience to the individuals who carry them. We developed a new wireless
ECG patch in this work and applied a deep learning framework based on the
Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) models.
However, we find that the models using the existing techniques are not able to
differentiate two main heartbeat types (Supraventricular premature beat and
Atrial fibrillation) in our newly obtained dataset, resulting in low accuracy
of 58.0 %. We proposed a semi-supervised method to process the badly labelled
data samples with using the confidence-level-based training. The experiment
results conclude that the proposed method can approach an average accuracy of
90.2 %, i.e., 5.4 % higher than the accuracy of conventional ECG classification
methods.


  We consider the one-band Hubbard model on the square lattice by using
variational and Green's function Monte Carlo methods, where the variational
states contain Jastrow and backflow correlations on top of an uncorrelated wave
function that includes BCS pairing and magnetic order. At half filling, where
the ground state is antiferromagnetically ordered for any value of the on-site
interaction $U$, we can identify a hidden critical point $U_{\rm Mott}$, above
which a finite BCS pairing is stabilized in the wave function. The existence of
this point is reminiscent of the Mott transition in the paramagnetic sector and
determines a separation between a Slater insulator (at small values of $U$),
where magnetism induces a potential energy gain, and a Mott insulator (at large
values of $U$), where magnetic correlations drive a kinetic energy gain. Most
importantly, the existence of $U_{\rm Mott}$ has crucial consequences when
doping the system: We observe a tendency to phase separation into a hole-rich
and a hole-poor region only when doping the Slater insulator, while the system
is uniform by doping the Mott insulator. Superconducting correlations are
clearly observed above $U_{\rm Mott}$, leading to the characteristic dome
structure in doping. Furthermore, we show that the energy gain due to the
presence of a finite BCS pairing above $U_{\rm Mott}$ shifts from the potential
to the kinetic sector by increasing the value of the Coulomb repulsion.


  In this article we derive gradient estimation for positive solution of the
equation \begin{equation*} (\partial_t-\Delta_f)u = A(u)p(x,t) + B(u)q(x,t) +
\mathcal{G}(u) \end{equation*} on a weighted Riemannian manifold evolving along
the $(k,m)$ super Perelman-Ricci flow \begin{equation*} \frac{\partial
g}{\partial t}(x,t)+2Ric_f^m(g)(x,t)\ge -2kg(x,t). \end{equation*} As an
application of gradient estimation we derive a Harnack type inequality along
with a Liouville type theorem.


  Recent lattice determinations of the hadronic vacuum polarization
contribution to the muon anomalous magnetic moment $a_{\mu}^{\rm HVP}$ have
confirmed the discrepancy with the data-driven dispersive method. In the
meanwhile the CMD-3 collaboration has reported a result for the $e^+e^-\to
\pi^+\pi^-$ cross section considerably larger than previous experimental
results (and close to the lattice determinations) exacerbating the discordance
between different $e^+e^-$ datasets. We explore to what extent these
disagreements can be accounted for by some new physics effect altering
selectively the individual experimental determinations of $\sigma(e^+e^-
\to\;$hadrons). We find that specific effects of GeV-scale new particles are
able to shift upwards the KLOE and BaBar results in the low and intermediate
energy windows, while leaving unaffected the CMD-3 energy scan. Although these
new physics effects cannot fully explain all the discrepancies among the
different $\sigma(e^+e^- \to\;$hadrons) datasets, they succeed in mitigating
the overall tension between data-driven and lattice estimates of $a_{\mu}^{\rm
HVP}$. Remarkably, the additional loop corrections involving the new particles
concur to solve the residual discrepancy with the experimental value of
$(g-2)_\mu$.


  (abridged) CIT 3 is an oxygen-rich long-period variable evolving along the
AGB and one of the most extreme infrared AGB objects. We present the first
bispectrum speckle-interferometry observations of CIT 3 in the J-, H-, and
K'-band (resolution: 48mas, 56mas, and 73mas). The interferograms were obtained
with the Russian SAO 6m telescope. While CIT 3 appears almost spherically
symmetric in the H- and K'-band, it is clearly elongated in the J-band along a
symmetry axis of position angle -28 degr. Two structures can be identified: a
compact elliptical core (eccentricity ~0.8) and a fainter north-western
fan-like structure (full opening angle ~40 degr). Extensive radiative transfer
calculations have been carried out and confronted with the spectral energy
distribution, our 1.24, 1.65 and 2.12 micron visibility functions, and 11micron
ISI interferometry. The best model refers to a cool central star (Teff=2250K)
surrounded by an optically thick dust shell (tau_V = 30). The central-star
diameter is 10.9mas and the inner dust shell diameter 71.9mas. The inner
dust-shell rim is located at r_1=6.6 Rstar and has a temperature of T_1=900K. A
two-component model existing of an inner uniform-outflow shell region
(rho~1/r^2; r < 20.5 r_1) and an outer region with rho ~ 1/r^1.5 proved to give
the best overall match of the observations. Provided the outflow velocity kept
constant, the more shallow density distribution in the outer shell indicates
that mass-loss has decreased with time in the past of CIT 3. Adopting
vexp=20km/s, the termination of that mass-loss decrease and the begin of the
uniform-outflow phase took place 87yr ago. The present-day mass-loss rate can
be determined to be Mdot = (1.3-2.1) x 10^-5 Msol/yr for d=500-800pc.


  Algorithm parallelization to leverage multi-core platforms for improving the
efficiency of Electronic Design Automation~(EDA) tools plays a significant role
in enhancing the scalability of Integrated Circuit (IC) designs. Logic
optimization is a key process in the EDA design flow to reduce the area and
depth of the circuit graph by finding logically equivalent graphs for
substitution, which is typically time-consuming. To address these challenges,
in this paper, we first analyze two types of conflicts that need to be handled
in the parallelization framework of refactoring And-Inverter Graph~(AIG). We
then present a fine-grained parallel AIG refactoring method, which strikes a
balance between the degree of parallelism and the conflicts encountered during
the refactoring operations. Experiment results show that our parallel refactor
is 28x averagely faster than the sequential algorithm on large benchmark tests
with 64 physical CPU cores, and has comparable optimization quality.


  Fix an arbitrary compact orientable surface with a boundary and consider a
uniform bipartite random quadrangulation of this surface with $n$ faces and
boundary component lengths of order $\sqrt n$ or of lower order. Endow this
quadrangulation with the usual graph metric renormalized by $n^{-1/4}$, mark it
on each boundary component, and endow it with the counting measure on its
vertex set renormalized by $n^{-1}$, as well as the counting measure on each
boundary component renormalized by $n^{-1/2}$. We show that, as $n\to\infty$,
this random marked measured metric space converges in distribution for the
Gromov--Hausdorff--Prokhorov topology, toward a random limiting marked measured
metric space called a Brownian surface.
  This extends known convergence results of uniform random planar
quadrangulations with at most one boundary component toward the Brownian sphere
and toward the Brownian disk, by considering the case of quadrangulations on
general compact orientable surfaces. Our approach consists in cutting a
Brownian surface into elementary pieces that are naturally related to the
Brownian sphere and the Brownian disk and their noncompact analogs, the
Brownian plane and the Brownian half-plane, and to prove convergence results
for these elementary pieces, which are of independent interest.


  Turing computability is the standard computability paradigm which captures
the computational power of digital computers. To understand whether one can
create physically realistic devices which have super-Turing power, one needs to
understand whether it is possible to obtain systems with super-Turing
capabilities which also have other desirable properties such as robustness to
perturbations. In this paper we introduce a framework for analyzing whether a
non-computability result is robust over continuous spaces. Then we use this
framework to study the degree of robustness of several non-computability
results which involve the wave equation, differentiation, and basins of
attraction.


  Cavitation is a phenomenon in which bubbles form and collapse in liquids due
to pressure or temperature changes. Even common tools like a rubber popper can
be used to create cavitation at home. As a rubber popper toy slams a solid wall
underwater, toroidal cavitation forms. As part of this project, we aim to
explain how an elastic shell causes cavitation and to describe the bubble
morphology. High-speed imaging reveals that a fast fluid flow between a
snapping popper and a solid glass reduces the fluid pressure to cavitate.
Cavitation occurs on the popper surface in the form of sheet cavitation. Our
study uses two-dimensional Rayleigh-Plesset equations and the energy balance to
capture the relationship between the bubble lifetime and the popper
deformability. The initial distance between the popper and the wall is an
important parameter for determining the cavitation dynamics. Presented results
provide a deeper understanding of cavitation mechanics, which involves the
interaction between fluid and elastic structure.


  Interfaces between the perovskite active layer and the charge-transport
layers (CTLs) play a critical role in both efficiency and stability of
halide-perovskite photovoltaics. One of the major concerns is that surface
defects of perovskite could cause detrimental nonradiative recombination and
material degradation. In this work, we addressed this challenging problem by
inserting ultrathin alkali-fluoride (AF) films between the tri-cation
lead-iodide perovskite layer and both CTLs. This bilateral inorganic walls
strategy makes use of both physical-blocking and chemical-anchoring
functionalities of the continuous, uniform and compact AF framework: on the one
hand, the uniformly distributed alkali-iodine coordination at the perovskite-AF
interfaces effectively suppresses the formation of iodine-vacancy defects at
the surfaces and grain boundaries of the whole perovskite film, thus reducing
the trap-assisted recombination at the perovskite-CTL interfaces and therewith
the open-voltage loss; on the other hand, the impermeable AF buffer layers
effectively prevent the bidirectional ion migration at the perovskite-CTLs
interfaces even under harsh working conditions. As a result, a power-conversion
efficiency (PCE) of 22.02% (certified efficiency 20.4%) with low open-voltage
deficit (< 0.4V) was achieved for the low-temperature processed inverted planar
perovskite solar cells. Exceptional operational stability (500 h, ISOS-L-2) and
thermal stability (1000 h, ISOS-D-2) were obtained. Meanwhile, a 35.7% PCE was
obtained under dim-light source (1000 lux white LED light) with the optimized
device, which is among the best records in perovskite indoor photovoltaics.


  Users of social networks tend to post and share content with little
restraint. Hence, rumors and fake news can quickly spread on a huge scale. This
may pose a threat to the credibility of social media and can cause serious
consequences in real life. Therefore, the task of rumor detection and
verification has become extremely important. Assessing the veracity of a social
media message (e.g., by fact checkers) involves analyzing the text of the
message, its context and any multimedia attachment. This is a very
time-consuming task that can be much helped by machine learning. In the
literature, most message veracity verification methods only exploit textual
contents and metadata. Very few take both textual and visual contents, and more
particularly images, into account. In this paper, we second the hypothesis that
exploiting all of the components of a social media post enhances the accuracy
of veracity detection. To further the state of the art, we first propose using
a set of advanced image features that are inspired from the field of image
quality assessment, which effectively contributes to rumor detection. These
metrics are good indicators for the detection of fake images, even for those
generated by advanced techniques like generative adversarial networks (GANs).
Then, we introduce the Multimodal fusiON framework to assess message veracIty
in social neTwORks (MONITOR), which exploits all message features (i.e., text,
social context, and image features) by supervised machine learning. Such
algorithms provide interpretability and explainability in the decisions taken,
which we believe is particularly important in the context of rumor
verification. Experimental results show that MONITOR can detect rumors with an
accuracy of 96% and 89% on the MediaEval benchmark and the FakeNewsNet dataset,
respectively. These results are significantly better than those of
state-of-the-art machine learning baselines.


  Given a bounded open connected Lipschitz set $\Omega \subset \mathbb R^2$, we
show that the relaxed Cartesian area functional $\overline{\mathcal
A}(u,\Omega)$ of a map $u\in W^{1,1}(\Omega;\mathbb S^1)$ is finite, and
provide a useful upper bound for its value. Using this estimate, we prove a
modified version of a De Giorgi conjecture [17] adapted to
$W^{1,1}(\Omega;\mathbb S^1)$, on the largest countably subadditive set
function $\overline {\overline{\mathcal A}}(u, \cdot)$ smaller than or equal to
$\overline{\mathcal A}(u,\cdot)$.


  Fourier ptychography microscopy (FPM), sharing its roots with synthetic
aperture technique and phase retrieval method, is a recently developed
computational microscopic super-resolution technique. By turning on the
light-emitting diode (LED) elements sequentially and acquiring the
corresponding images that contain different spatial frequencies, FPM can
achieve a wide field-of-view (FOV), high-spatial-resolution imaging, and phase
recovery simultaneously. Conventional FPM assumes that the sample is
sufficiently thin and strictly in focus. Nevertheless, even for a relatively
thin sample, the non-planar distribution characteristics and the non-ideal
position/posture of the sample will cause all or part of FOV to be defocused.
In this paper, we proposed a fast digital refocusing and depth-of-field (DOF)
extended FPM strategy by taking the advantages of image lateral shift caused by
sample defocusing and varied-angle illuminations. The lateral shift amount is
proportional to the defocus distance and the tangent of the illumination angle.
Instead of searching the optimal defocus distance in optimization strategy,
which is time-consuming, the defocus distance of each subregion of the sample
can be precisely and quickly obtained by calculating the relative lateral shift
amounts corresponding to different oblique illuminations. And then, the digital
refocusing strategy rooting in the Fresnel propagator is integrated into the
FPM framework to achieve the high-resolution and phase information
reconstruction for each part of the sample, which means the DOF the FPM is
effectively extended. The feasibility of the proposed method in fast digital
refocusing and FOV extending is verified in the actual experiments with the
USAF chart and biological samples.


  We prove that there exists no window function $g \in L^2(\mathbb{R})$ and no
lattice $\mathcal{L} \subset \mathbb{R}^2$ such that every $f \in
L^2(\mathbb{R})$ is determined up to a global phase by spectrogram samples
$|V_gf(\mathcal{L})|$ where $V_gf$ denotes the short-time Fourier transform of
$f$ with respect to $g$. Consequently, the forward operator $f \mapsto
|V_gf(\mathcal{L})|$ mapping a square-integrable function to its spectrogram
samples on a lattice is never injective on the quotient space $L^2(\mathbb{R})
/ {\sim}$ with $f \sim h$ identifying two functions which agree up to a
multiplicative constant of modulus one. We will further elaborate this result
and point out that under mild conditions on the lattice $\mathcal{L}$,
functions which produce identical spectrogram samples but do not agree up to a
unimodular constant can be chosen to be real-valued. The derived results
highlight that in the discretization of the STFT phase retrieval problem from
lattice measurements, a prior restriction of the underlying signal space to a
proper subspace of $L^2(\mathbb{R})$ is inevitable.


  We study the algebra ${\cal A}_n$ and the basis of the Hilbert space ${\cal
H}_n$ in terms of the $\theta$ functions of the positions of $n$ solitons. Then
we embed the Heisenberg group as the quantum operator factors in the
representation of the transfer matrice of various integrable models. Finally we
generalize our result to the generic $\theta$ case.


  A burgeoning paradigm in algorithm design is the field of algorithms with
predictions, in which algorithms can take advantage of a possibly-imperfect
prediction of some aspect of the problem. While much work has focused on using
predictions to improve competitive ratios, running times, or other performance
measures, less effort has been devoted to the question of how to obtain the
predictions themselves, especially in the critical online setting. We introduce
a general design approach for algorithms that learn predictors: (1) identify a
functional dependence of the performance measure on the prediction quality and
(2) apply techniques from online learning to learn predictors, tune
robustness-consistency trade-offs, and bound the sample complexity. We
demonstrate the effectiveness of our approach by applying it to bipartite
matching, ski-rental, page migration, and job scheduling. In several settings
we improve upon multiple existing results while utilizing a much simpler
analysis, while in the others we provide the first learning-theoretic
guarantees.


  The usual way of testing probability forecasts in game-theoretic probability
is via construction of test martingales. The standard assumption is that all
forecasts are output by the same forecaster. In this paper I will discuss
possible extensions of this picture to testing probability forecasts output by
several forecasters. This corresponds to multiple hypothesis testing in
statistics. One interesting phenomenon is that even a slight relaxation of the
requirement of family-wise validity leads to a very significant increase in the
efficiency of testing procedures. The main goal of this paper is to report
results of preliminary simulation studies and list some directions of further
research.


  On-chip integration of quantum optical systems could be a major factor
enabling photonic quantum technologies. Unlike the case of electronics, where
the essential device is a transistor and the dominant material is silicon, the
toolbox of elementary devices required for both classical and quantum photonic
integrated circuits is vast. Therefore, many material platforms are being
examined to host the future quantum photonic computers and network nodes. We
discuss the pros and cons of several platforms for realizing various elementary
devices, compare the current degrees of integration achieved in each platform
and review several composite platform approaches.


  We introduce a new class of stable light bullets that form in twisted
waveguide arrays pumped withultrashort pulses, where twisting offers a powerful
knob to tune the properties of localized states.We find that above a critical
twist, three-dimensional wavepackets are unambiguously stabilized,with no
minimum energy threshold. As a consequence, when the higher order perturbations
thataccompany ultrashort pulse propagation are at play, the bullets dynamically
adjust and sweep alongstable branches. Therefore, they are predicted to feature
an unprecedented experimental robustness.


  The emerging applications of silicon photonics in free space, such as LiDARs
and quantum photonics, urge versatile emission shaping beyond the capabilities
of conventional grating couplers. A platform that offers arbitrary shaping of
free-space emission while maintaining the CMOS compatibility and monolithic
integration is in pressing need. Here we demonstrate a platform that integrates
metasurfaces monolithically on silicon photonic integrated circuits. The
metasurfaces consist of amorphous silicon nanopillars evanescently coupled to
silicon waveguides. We demonstrate experimentally diffraction-limited beam
focusing with a Strehl ratio of 0.82, where the focused spot can be switched
between two positions. We also realize a meta-hologram experimentally that
projects an image above the silicon photonic chip. This platform can add a
highly versatile interface to the existing silicon photonic ecosystems for
precise delivery of free-space emission.


  A new mechanism for generating neutrino masses without a high-energy mass
scale is proposed. The mechanism needs a fundamental mass scale M in the
100-1000 TeV region and a minimal field content beyond the Standard Model one
containing a pair of fermion singlets and a pair of weak doublet fermions for
each neutrino mass, all of them with a mass of order M. The neutrino mass
appears by a multiple seesaw-type tree-level diagram. We provide an explicit
model based on supersymmetry and an abelian symmetry which provides the
required fermion mass matrix. The mechanism is natural in the context of string
theories with a low fundamental scale. Within an explicit example where the
abelian symmetry is also responsible for the generation of fermion masses and
mixings, we give a hint relating the fermion mass matrices and the weak mixing
angle. By assuming the weak-strong couplings unification, one naturally finds
$\sin^2 \theta_w = 1/4$ at the fundamental scale.


  The higher-spin (HS) algebras so far known can be interpreted as the
symmetries of the minimal representation of the isometry algebra. After
discussing this connection briefly, we generalize this concept to any classical
Lie algebras and consider the corresponding HS algebras. For sp(2N) and so(N),
the minimal representations are unique so we get unique HS algebras. For sl(N),
the minimal representation has one-parameter family, so does the corresponding
HS algebra. The so(N) HS algebra is what underlies the Vasiliev theory while
the sl(2) one coincides with the 3D HS algebra hs[lambda]. Finally, we derive
the explicit expression of the structure constant of these algebras --- more
precisely, their bilinear and trilinear forms. Several consistency checks are
carried out for our results.


  Although mass and energy in Jupiter's magnetosphere mostly come from the
innermost Galilean moon Io's volcanic activities, solar wind perturbations can
play crucial roles in releasing the magnetospheric energy and powering aurorae
in Jupiter's polar regions. The systematic response of aurora to solar wind
compression remains poorly understood. Here we report the analysis of a set of
auroral images with contemporaneous in situ magnetopause detections. We
distinguish two types of auroral enhancements: a transient localized one and a
long-lasting global one. We show that only the latter systematically appears
under a compressed magnetopause, while the localized auroral expansion could
occur during an expanded magnetopause. Moreover, we directly examine previous
theories on how solar wind compressions enhance auroral emissions. Our results
demonstrate that auroral morphologies can be diagnostic of solar wind
conditions at planets when in situ measurements are not possible.


  Algorithms which sort lists of real numbers into ascending order have been
studied for decades. They are typically based on a series of pairwise
comparisons and run entirely on chip. However people routinely sort lists which
depend on subjective or complex judgements that cannot be automated. Examples
include marketing research; where surveys are used to learn about customer
preferences for products, the recruiting process; where interviewers attempt to
rank potential employees, and sporting tournaments; where we infer team
rankings from a series of one on one matches. We develop a novel sorting
algorithm, where each pairwise comparison reflects a subjective human judgement
about which element is bigger or better. We introduce a finite and large error
rate to each judgement, and we take the cost of each comparison to
significantly exceed the cost of other computational steps. The algorithm must
request the most informative sequence of comparisons from the user; in order to
identify the correct sorted list with minimum human input. Our Discrete
Adiabatic Monte Carlo approach exploits the gradual acquisition of information
by tracking a set of plausible hypotheses which are updated after each
additional comparison.


  In this work we investigate the process of pattern formation induced by
nonlinear diffusion in a reaction-diffusion system with Lotka-Volterra
predator-prey kinetics. We show that the cross-diffusion term is responsible of
the destabilizing mechanism that leads to the emergence of spatial patterns.
Near marginal stability we perform a weakly nonlinear analysis to predict the
amplitude and the form of the pattern, deriving the Stuart-Landau amplitude
equations. Moreover, in a large portion of the subcritical zone, numerical
simulations show the emergence of oscillating patterns, which cannot be
predicted by the weakly nonlinear analysis. Finally when the pattern invades
the domain as a travelling wavefront, we derive the Ginzburg-Landau amplitude
equation which is able to describe the shape and the speed of the wave.


  Polymer quantization is a non-standard representation of the quantum
mechanics that inspired by loop quantum gravity. To study the associated
statistical mechanics, one needs to find microstates' energies which are
eigenvalues of the Hamiltonian operator in the polymer framework. But, this is
not an easy task at all since the Hamiltonian takes a nonlinear form in polymer
picture. In this paper, we introduce a semiclassical method in which it is not
necessary to solve the eigenvalue problem. Instead, we work with the classical
Hamiltonian function and the deformed density of states in the polymeric phase
space. Implementing this method, we obtain the canonical partition function for
the polymerized systems and we show that our results are in good agreement with
those arising from full quantum considerations. Using the partition function,
we study the thermodynamics of quantum Schwarzschild black hole and we obtain
corrections to the Bekenstein-Hawking entropy due to loop quantum gravity
effects.


  Chatbots systems, despite their popularity in today's HCI and CSCW research,
fall short for one of the two reasons: 1) many of the systems use a rule-based
dialog flow, thus they can only respond to a limited number of pre-defined
inputs with pre-scripted responses; or 2) they are designed with a focus on
single-user scenarios, thus it is unclear how these systems may affect other
users or the community. In this paper, we develop a generalizable chatbot
architecture (CASS) to provide social support for community members in an
online health community. The CASS architecture is based on advanced neural
network algorithms, thus it can handle new inputs from users and generate a
variety of responses to them. CASS is also generalizable as it can be easily
migrate to other online communities. With a follow-up field experiment, CASS is
proven useful in supporting individual members who seek emotional support. Our
work also contributes to fill the research gap on how a chatbot may influence
the whole community's engagement.


  Treating class with a single center may hardly capture data distribution
complexities. Using multiple sub-centers is an alternative way to address this
problem. However, highly correlated sub-classes, the classifier's parameters
grow linearly with the number of classes, and lack of intra-class compactness
are three typical issues that need to be addressed in existing multi-subclass
methods. To this end, we propose to use Fixed Sub-Center (F-SC), which allows
the model to create more discrepant sub-centers while saving memory and cutting
computational costs considerably. The F-SC specifically, first samples a class
center Ui for each class from a uniform distribution, and then generates a
normal distribution for each class, where the mean is equal to Ui. Finally, the
sub-centers are sampled based on the normal distribution corresponding to each
class, and the sub-centers are fixed during the training process avoiding the
overhead of gradient calculation. Moreover, F-SC penalizes the Euclidean
distance between the samples and their corresponding sub-centers, it helps
remain intra-compactness. The experimental results show that F-SC significantly
improves the accuracy of both image classification and fine-grained recognition
tasks.


  We perform a global fit to the structure function F_2 measured in
lepton-proton experiments at small values of Bjorken-x, x\le 0.01, for all
experimentally available values of Q^2, 0.045 GeV^2\le Q^2 \le 800 GeV^2. We
show that the recent improvements resulting from the inclusion of running
coupling corrections allow for a description of data in terms of non-linear QCD
evolution equations. In this approach F_2 is calculated within the dipole model
with all Bjorken-x dependence described by the running coupling
Balitsky-Kovchegov equation. Two different initial conditions for the evolution
are used, both yielding excellent fits to data with \chi^2/d.o.f.<1.1. Data for
the proton longitudinal structure function F_L, not included in the fits, are
also well described. We provide predictions for F_2 and F_L in the kinematical
regions of interest for future colliders and ultra-high energy cosmic rays. Our
analysis allows to perform a first principle extrapolation of the proton-dipole
scattering amplitude. A numerical implementation of our results down to
x=10^{-12} is released as a computer code for public use.


  The quantized thermal Hall effect is an important probe for detecting chiral
topological order and revealing the nature of chiral gapless edge states. The
standard Kubo formula approach for the thermal Hall conductance $\kappa_{xy}$
based on the linear-response theory faces difficulties in practical application
due to the lack of a reliable numerical method for calculating dynamical
quantities in microscopic models at finite temperature. In this work, we
propose an approach for calculating $\kappa_{xy}$ in two-dimensional lattice
models displaying chiral topological order. Our approach targets at the edge
current localized at the boundary which involves only thermal averages of local
operators in equilibrium, thus drastically lowering the barrier for the
calculation of $\kappa_{xy}$. We use the chiral $p$-wave superconductor (with
and without disorder) and the Hofstadter model as benchmark examples to
illustrate several sources of finite-size effects, and we suggest the infinite
(or sufficiently long) strip as the best geometry for carrying out numerical
simulations.


  We study the decoherence of a coupled quantum system consisting of a central
spin and its correlated environment described by a general $XY$ spin-chain
model. We make it clear that the evolution of the coherence factor sensitively
depends on the initial states of the environment spin-chain. Specially, the
dynamical evolution of the coherence factor of the central spin is numerically
and analytically investigated in both weak and strong coupling cases for
different initial states including thermal equilibrium state. In both weak and
strong coupling regimes, the decay of the coherence factor can be approximated
by a Gaussian and in the strong coupling regime the coherence factor oscillate
rapidly under a Gaussian envelope. The width of the Gaussian decay (envelope)
has been studied in details and we explained the origin of the so-called
universal regime.


  It is often advantageous to train models on a subset of the available train
examples, because the examples are of variable quality or because one would
like to train with fewer examples, without sacrificing performance. We present
Gradient Information Optimization (GIO), a scalable, task-agnostic approach to
this data selection problem that requires only a small set of (unlabeled)
examples representing a target distribution. GIO begins from a natural,
information-theoretic objective that is intractable in practice. Our
contribution is in showing that it can be made highly scalable through a simple
relaxation of the objective and a highly efficient implementation. In
experiments with machine translation, spelling correction, and image
recognition, we show that GIO delivers outstanding results with very small
train sets. These findings are robust to different representation models and
hyperparameters for GIO itself. GIO is task- and domain-agnostic and can be
applied out-of-the-box to new datasets and domains. We open source a
pip-installable implementation of the algorithm as "pip install grad-info-opt".


  Protocols provide the unifying glue in concurrent and distributed software
today; verifying that message-passing programs conform to such governing
protocols is important but difficult. Static approaches based on multiparty
session types (MPST) use protocols as types to avoid protocol violations and
deadlocks in programs. An elusive problem for MPST is to ensure both protocol
conformance and deadlock freedom for implementations with interleaved and
delegated protocols.
  We propose a decentralized analysis of multiparty protocols, specified as
global types and implemented as interacting processes in an asynchronous
$\pi$-calculus. Our solution rests upon two novel notions: router processes and
relative types. While router processes use the global type to enable the
composition of participant implementations in arbitrary process networks,
relative types extract from the global type the intended interactions and
dependencies between pairs of participants. In our analysis, processes are
typed using APCP, a type system that ensures protocol conformance and deadlock
freedom with respect to binary protocols, developed in prior work. Our
decentralized, router-based analysis enables the sound and complete
transference of protocol conformance and deadlock freedom from APCP to
multiparty protocols.


  Experimental results from literature show equidistant energy levels in thin
Bi films on surfaces, suggesting a harmonic oscillator description. Yet this
conclusion is by no means imperative, especially considering that any
measurement only yields energy levels in a finite range and with a nonzero
uncertainty. Within this study we review isospectral potentials from the
literature and investigate the applicability of the harmonic oscillator
hypothesis to recent measurements. First, we describe experimental results from
literature by a harmonic oscillator model, obtaining a realistic size and depth
of the resulting quantum well. Second, we use the shift-operator approach to
calculate anharmonic non-polynomial potentials producing (partly) equidistant
spectra. We discuss different potential types and interpret the possible
modeling applications. Finally, by applying $n$th order perturbation theory we
show that \textbf{exactly} equidistant eigenenergies cannot be achieved by
polynomial potentials, except by the harmonic oscillator potential. In summary,
we aim to give an overview over which conclusions may be drawn from the
experimental determination of energy levels and which may not.


  Suppose that $\calak$ is a $C^*$-algebra acting on a Hilbert space $\calhk$,
and that $\phi, \psi$ are mappings from $\calak$ into $B(\calhk)$ which are not
assumed to be necessarily linear or continuous. A $(\phi, \psi)$-derivation is
a linear mapping $d: \calak \to B(\calhk)$ such that
$$d(ab)=\phi(a)d(b)+d(a)\psi(b)\quad (a,b\in \calak).$$ We prove that if $\phi$
is a multiplicative (not necessarily linear) $*$-mapping, then every
$*$-$(\phi,\phi)$-derivation is automatically continuous. Using this fact, we
show that every $*$-$(\phi,\psi)$-derivation $d$ from $\calak$ into $B(\calhk)$
is continuous if and only if the $*$-mappings $\phi$ and $\psi$ are left and
right $d$-continuous, respectively.


  We analyze the scenario where the IceCube high energy neutrino events are
explained in terms of an extraterrestrial flux due to two different components:
a contribution coming from know astrophysical sources for energies up to few
hundreds TeV and a top-down contribution originated by the decay of heavy dark
matter particles with a mass of few PeV. Contrary to previous approaches, we
consider a leptophilic three-body decay that dominates at PeV energies due to
the absence of quarks in the final state. We find that the theoretical
predictions of such a scenario are in a slightly better agreement with the
IceCube data if the astrophysical component has a cut-off at about 100 TeV.
This interpretation of IceCube data can be easily tested in the near future
since the decaying dark matter scenario predicts a sharp cut-off at PeV energy
scale and the observation of an anisotropy towards Galactic Center of our
Galaxy in contrast with the isotropic astrophysical flux.


  This paper proposes an adaptive sparse polynomial chaos expansion(PCE)-based
method to quantify the impacts of uncertainties on critical clearing time (CCT)
that is an important index in transient stability analysis. The proposed method
can not only give fast and accurate estimations for the probabilistic
characteristics (e.g., mean, variance, probability density function) of the
probabilistic CCT (PCCT), but also provides crucial information about the
sensitivity of random inputs with respect to the variance of PCCT. Utilizing
the sensitivity information, mitigation measures can be developed for transient
stability enhancement. Numerical studies on the WSCC 9-bus system demonstrate
the high accuracy and efficiency of the proposed method compared to the Monte
Carlo simulation method. The provided sensitivity information and the
effectiveness of mitigation measures in transient stability enhancement are
also verified.


  Partial-monitoring games constitute a mathematical framework for sequential
decision making problems with imperfect feedback: The learner repeatedly
chooses an action, opponent responds with an outcome, and then the learner
suffers a loss and receives a feedback signal, both of which are fixed
functions of the action and the outcome. The goal of the learner is to minimize
his total cumulative loss. We make progress towards the classification of these
games based on their minimax expected regret. Namely, we classify almost all
games with two outcomes and finite number of actions: We show that their
minimax expected regret is either zero, $\widetilde{\Theta}(\sqrt{T})$,
$\Theta(T^{2/3})$, or $\Theta(T)$ and we give a simple and efficiently
computable classification of these four classes of games. Our hope is that the
result can serve as a stepping stone toward classifying all finite
partial-monitoring games.


  Dynamic instability -- the growth, catastrophe, and shrinkage of
quasi-one-dimensional filaments -- has been observed in multiple biopolymers.
Scientists have long understood the catastrophic cessation of growth and
subsequent depolymerization as arising from the interplay of hydrolysis and
polymerization at the tip of the polymer. Here, we show that for a broad class
of catastrophe models, the expected catastrophe time distribution is
exponential. We show that the distribution shape is insensitive to noise, but
that depletion of monomers from a finite pool can dramatically change the
distribution shape by reducing the polymerization rate. We derive a form for
this finite-pool catastrophe time distribution and show that finite-pool
effects can be important even when the depletion of monomers does not greatly
alter the polymerization rate.


  We have derived the radiative-transfer equation for a point source with a
specified intensity and spectrum, originating in the early Universe between the
epochs of annihilation and recombination, at redshifts $z_\s =10^8\div 10^4$.
The direct radiation of the source is separated from the diffuse radiation it
produces. Optical distances from the source for Thomson scattering and
bremsstrahlung absorption at the maximum of the thermal background radiation
are calculated as a function of the redshift z.The distances grow sharply with
decreasing z, approaching asymptotic values, the absorption distance increasing
more slowly and reaching their limiting values at lower z. For the adopted z
values, the optical parameters of the Universe can be described in a flat model
with dusty material and radiation, and radiative transfer can be treated in a
grey approximation.


  Federated Learning (FL) enables edge devices or clients to collaboratively
train machine learning (ML) models without sharing their private data. Much of
the existing work in FL focuses on efficiently learning a model for a single
task. In this paper, we study simultaneous training of multiple FL models using
a common set of clients. The few existing simultaneous training methods employ
synchronous aggregation of client updates, which can cause significant delays
because large models and/or slow clients can bottleneck the aggregation. On the
other hand, a naive asynchronous aggregation is adversely affected by stale
client updates. We propose FedAST, a buffered asynchronous federated
simultaneous training algorithm that overcomes bottlenecks from slow models and
adaptively allocates client resources across heterogeneous tasks. We provide
theoretical convergence guarantees for FedAST for smooth non-convex objective
functions. Extensive experiments over multiple real-world datasets demonstrate
that our proposed method outperforms existing simultaneous FL approaches,
achieving up to 46.0% reduction in time to train multiple tasks to completion.


  This work provides two statistical Gaussian forecasting methods for
predicting First Daily Departure Times (FDDTs) of everyday use electric
vehicles. This is important in smart grid applications to understand
disconnection times of such mobile storage units, for instance to forecast
storage of non dispatchable loads (e.g. wind and solar power). We provide a
review of the relevant state-of-the-art driving behavior features towards FDDT
prediction, to then propose an approximated Gaussian method which qualitatively
forecasts how many vehicles will depart within a given time frame, by assuming
that departure times follow a normal distribution. This method considers
sampling sessions as Poisson distributions which are superimposed to obtain a
single approximated Gaussian model. Given the Gaussian distribution assumption
of the departure times, we also model the problem with Gaussian Mixture Models
(GMM), in which the priorly set number of clusters represents the desired time
granularity. Evaluation has proven that for the dataset tested, low error and
high confidence ($\approx 95\%$) is possible for 15 and 10 minute intervals,
and that GMM outperforms traditional modeling but is less generalizable across
datasets, as it is a closer fit to the sampling data. Conclusively we discuss
future possibilities and practical applications of the discussed model.


  Rajagopal and Wilczek have proposed that relativistic nuclear collisions can
generate domains in which the chiral condensate is disoriented. If sufficiently
large ({\it i.e.} nucleus sized), such domains can yield measurable
fluctuations in the number of neutral and charged pions. However, by numerical
simulation of the zero-temperature two-flavor linear sigma model, we find that
domains are essentially {\it pion} sized. Nevertheless, we show that large
domains can occur if the effective mesons masses are much lighter.


  Human walking and gaits involve several complex body parts and are influenced
by personality, mood, social and cultural traits, and aging. These factors are
reflected in shoeprints, which in turn can be used to predict age, a problem
not systematically addressed using any computational approach. We collected
100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data
to develop a deep learning end-to-end model ShoeNet to analyze age-related
patterns and predict age. The model integrates various convolutional neural
network models together using a skip mechanism to extract age-related features,
especially in pressure and abrasion regions from pair-wise shoeprints. The
results show that 40.23% of the subjects had prediction errors within 5-years
of age and the prediction accuracy for gender classification reached 86.07%.
Interestingly, the age-related features mostly reside in the asymmetric
differences between left and right shoeprints. The analysis also reveals
interesting age-related and gender-related patterns in the pressure
distributions on shoeprints; in particular, the pressure forces spread from the
middle of the toe toward outside regions over age with gender-specific
variations on heel regions. Such statistics provide insight into new methods
for forensic investigations, medical studies of gait-pattern disorders,
biometrics, and sport studies.


  Recent advances in neural forecasting have produced major improvements in
accuracy for probabilistic demand prediction. In this work, we propose novel
improvements to the current state of the art by incorporating changes inspired
by recent advances in Transformer architectures for Natural Language
Processing. We develop a novel decoder-encoder attention for context-alignment,
improving forecasting accuracy by allowing the network to study its own history
based on the context for which it is producing a forecast. We also present a
novel positional encoding that allows the neural network to learn
context-dependent seasonality functions as well as arbitrary holiday distances.
Finally we show that the current state of the art MQ-Forecaster (Wen et al.,
2017) models display excess variability by failing to leverage previous errors
in the forecast to improve accuracy. We propose a novel decoder-self attention
scheme for forecasting that produces significant improvements in the excess
variation of the forecast.


  The problem of the quantizations of the $L$-shaped billiards and the like
ones, i.e. each angle of which is equal to $\pi/2$ or $3\pi/2$, is considered
using as a tool the Fourier series expansion method. The respective wave
functions and the quantization conditions are written and discussed looking for
and discussing about the superscars effects in such multi-rectangular billiards
(MRB). It is found that a special set of POC modes effect the superscars
phenomena in MRB in which the billiards are excited as a whole to the modes
closest to the semiclassical ones existing in their approximated copies being
MRB in which their parallel sides remain in rational relations between
themselves.


  We present zoom-in simulations of fuzzy dark matter (FDM) halos including
baryons and star-formation with sufficient resolution to follow the formation
and evolution of central solitons. We find that their properties are determined
by the local dark matter velocity dispersion in the combined dark matter-baryon
gravitational potential. This motivates a simple prescription to estimate the
radial density profiles of FDM cores in the presence of baryons. As cores
become more massive and compact if baryons are included, galactic rotation
curve measurements are likely harder to reconcile with FDM.


  There is a Paley graph for each prime power $q$ such that $q\equiv 1\pmod 4$.
The vertex set is the field $\mathbb Fq$ and two vertices $x$ and $y$ are
joined by an edge if and only if $x-y$ is a nonzero square of $\mathbb Fq$. We
compute the Smith normal forms of the adjacency matrix and Laplacian matrix of
a Paley graph.


  Dropout is a simple yet effective algorithm for regularizing neural networks
by randomly dropping out units through Bernoulli multiplicative noise, and for
some restricted problem classes, such as linear or logistic regression, several
theoretical studies have demonstrated the equivalence between dropout and a
fully deterministic optimization problem with data-dependent Tikhonov
regularization. This work presents a theoretical analysis of dropout for matrix
factorization, where Bernoulli random variables are used to drop a factor,
thereby attempting to control the size of the factorization. While recent work
has demonstrated the empirical effectiveness of dropout for matrix
factorization, a theoretical understanding of the regularization properties of
dropout in this context remains elusive. This work demonstrates the equivalence
between dropout and a fully deterministic model for matrix factorization in
which the factors are regularized by the sum of the product of the norms of the
columns. While the resulting regularizer is closely related to a variational
form of the nuclear norm, suggesting that dropout may limit the size of the
factorization, we show that it is possible to trivially lower the objective
value by doubling the size of the factorization. We show that this problem is
caused by the use of a fixed dropout rate, which motivates the use of a rate
that increases with the size of the factorization. Synthetic experiments
validate our theoretical findings.


  The nonlinear spin-dependent transport properties in zigzag graphene
nanoribbons (ZGNRs) edge doped by an atom of group III and V elements are
studied systematically using density functional theory combined with
non-equilibrium Greens functions. The dopant type, acceptor or donor, and the
geometrical symmetry, odd or even, are found critical in determining the spin
polarization of the current and the current-voltage characteristics. For ZGNRs
substitutionally doped on the lower-side edge, the down (up) spin current
dominates in odd-(even-)width ZGNRs under a bias voltage around 1V. Remarkably,
in even-width ZGNRs, doped by group III elements (B and Al), negative
differential resistance (NDR) occurs only for down spins. The bias range of the
spin NDR increases with the width of ZGNRs. The clear spin NDR is not observed
in any odd-width ZGNRs nor in even-width ZGNRs doped by group V elements (N,
and P). This peculiar spin NDR of edge doped ZGNRs suggests potential
applications in spintronics.