  We prove the existence of a Galois closure for towers of torsors under finite
group schemes over a proper, geometrically connected and geometrically reduced
algebraic stack $X$ over a field $k$. This is done by describing the Nori
fundamental gerbe of an essentially finite cover of $X$. A similar result is
also obtained for the $S$-fundamental gerbe.


  We propose a practical scheme for deterministically teleporting an arbitrary
multipartite state, either product or entangled, using Faraday rotation of the
photonic polarization. Our scheme, based on the input-output process of
single-photon pulses regarding cavities, works in low-Q cavities and only
involves virtual excitation of the atoms, which is insensitive to both cavity
decay and atomic spontaneous emission. Besides, the Bell-state measurement is
accomplished by the Faraday rotation plus product-state measurements, which
could much relax the experimental difficulty to realize the Bell-state
measurement by the CNOT operation.


  We consider the problem of predicting power outages in an electrical power
grid due to hazards produced by convective storms. These storms produce extreme
weather phenomena such as intense wind, tornadoes and lightning over a small
area. In this paper, we discuss the application of state-of-the-art machine
learning techniques, such as random forest classifiers and deep neural
networks, to predict the amount of damage caused by storms. We cast this
application as a classification problem where the goal is to classify storm
cells into a finite number of classes, each corresponding to a certain amount
of expected damage. The classification method use as input features estimates
for storm cell location and movement which has to be extracted from the raw
data.
  A main challenge of this application is that the training data is heavily
imbalanced as the occurrence of extreme weather events is rare. In order to
address this issue, we applied SMOTE technique.


  Mobile malware are malicious programs that target mobile devices. They are an
increasing problem, as seen in the rise of detected mobile malware samples per
year. The number of active smartphone users is expected to grow, stressing the
importance of research on the detection of mobile malware. Detection methods
for mobile malware exist but are still limited.
  In this paper, we provide an overview of the performance of machine learning
(ML) techniques to detect malware on Android, without using privileged access.
The ML-classifiers use device information such as the CPU usage, battery usage,
and memory usage for the detection of 10 subtypes of Mobile Trojans on the
Android Operating System (OS).
  We use a real-life dataset containing device and malware data from 47 users
for a year (2016). We examine which features, i.e. aspects, of a device, are
most important to monitor to detect (subtypes of) Mobile Trojans. The focus of
this paper is on dynamic hardware features. Using these dynamic features we
apply state-of-the-art machine learning classifiers: Random Forest, K-Nearest
Neighbour, and AdaBoost. We show classification results on different feature
sets, making a distinction between global device features, and specific app
features. None of the measured feature sets require privileged access.
  Our results show that the Random Forest classifier performs best as a general
malware classifier: across 10 subtypes of Mobile Trojans, it achieves an F1
score of 0.73 with a False Positive Rate (FPR) of 0.009 and a False Negative
Rate (FNR) of 0.380. The Random Forest, K-Nearest Neighbours, and AdaBoost
classifiers achieve F1 scores above 0.72, an FPR below 0.02 and, an FNR below
0.33, when trained separately to detect each subtype of Mobile Trojans.


  In this paper we analyze the problem of tadpole cancellation in open
topological strings. We prove that the inclusion of unorientable worldsheet
diagrams guarantees a consistent decoupling of A and B model for open
superstring amplitudes at all genera. This is proven by direct microscopic
computation in Super Conformal Field Theory. For the B-model we explicitly
calculate one loop amplitudes in terms of analytic Ray-Singer torsions of
appropriate vector bundles and obtain that the decoupling corresponds to the
cancellation of D-brane and orientifold charges. Local tadpole cancellation on
the worldsheet then guarantees the decoupling at all loops. The holomorphic
anomaly equations for open topological strings at one loop are also obtained
and compared with the results of the Quillen formula.


  Traditionally, the transverse Ising model is mapped to the fermionic c-cycle
problem, which neglects the boundary effect due to thermodynamic limit. If
persisting on a perfect periodic boundary condition, we can get a so-called
a-cycle problem that has not been treated seriously so far (Lieb et al., 1961
\textit{Ann. of Phys.} \textbf{16} 407). In this work, we show a little
surprising but exact result in this respect. We find the odevity of the number
of lattice sites, $N$, in the a-cycle problem plays an unexpected role even in
the thermodynamic limit, $N\rightarrow\infty$, due to the boundary constraint.
We pay a special attention to the system with $N(\in Odd)\rightarrow\infty$,
which is in contrast to the one with $N(\in Even)\rightarrow\infty$, because
the former suffers a ring frustration. As a new effect, we find the ring
frustration induces a low-energy gapless spectrum above the ground state. By
proving a theorem for a new type of Toeplitz determinant, we demonstrate that
the ground state in the gapless region exhibits a peculiar longitudinal
spin-spin correlation. The entangled nature of the ground state is also
disclosed by the evaluation of its entanglement entropy. At low temperatures,
new behavior of specific heat is predicted. We also propose an experimental
protocol for observing the new phenomenon due to the ring frustration.


  We present a branching ratio measurement for the beta decay of neutral
Cascade hyperon to a positive Sigma, electron and neutrino. Data were collected
during 1997 with the KTeV detector at Fermilab. We used the principal decay
mode, Cascade to a Lambda and a neutral pion, as the flux normalization mode.
The status of the measurement of the ratio of the axial-vector to vector
coupling (g_1/f_1) for this decay will also be discussed. Furthermore, we
present the preliminary branching ratio measurement for the muonic channel of
Cascade beta decay.


  Due to its rotation, Earth traps a few equatorial ocean and atmospheric
waves, including Kelvin, Yanai, Rossby, and Poincare modes. It has been
recently demonstrated that the mathematical origin of equatorial waves is
intricately related to the nontrivial topology of hydrodynamic equations
describing oceans or the atmosphere. In the present work, we consider plasma
oscillations supported by a two-dimensional electron gas confined at the
surface of a sphere or a cylinder. We argue that in the presence of a uniform
magnetic field, these systems host a set of equatorial magnetoplasma waves that
are counterparts to the equatorial waves trapped by Earth. For a spherical
geometry, the equatorial modes are well developed only if their penetration
length is smaller than the radius of the sphere. For a cylindrical geometry,
the spectrum of equatorial modes is weakly dependent on the cylinder radius and
overcomes finite-size effects. We argue that this exceptional robustness can be
explained by destructive interference effects. We discuss possible experimental
setups, including grains and rods composed of topological insulators (e.g.,
Bi2Se3) or metal-coated dielectrics (e.g., Au2S).


  Research on the cosmic microwave background (CMB) is progressing rapidly. New
experimental groups are popping up and two new satellites will be launched. The
current enthusiasm to measure fluctuations in the CMB power spectrum at angular
scales between 0.1 and 1 degree is largely motivated by the expectation that
CMB determinations of cosmological parameters will be of unprecedented
precision: cosmological gold. In this article I will try to answer the
following questions: 1) What is the CMB? 2) What are cosmological parameters?
3) What is the CMB power spectrum? 4) What are all those bumps in the power
spectrum? 5) What are the current CMB constraints on cosmological parameters?


  This study endeavors to conceptualize and execute a sophisticated
agricultural greenhouse control system grounded in the amalgamation of the
Internet of Things (IoT) and machine learning. Through meticulous monitoring of
intrinsic environmental parameters within the greenhouse and the integration of
machine learning algorithms, the conditions within the greenhouse are aptly
modulated. The envisaged outcome is an enhancement in crop growth efficiency
and yield, accompanied by a reduction in resource wastage. In the backdrop of
escalating global population figures and the escalating exigencies of climate
change, agriculture confronts unprecedented challenges. Conventional
agricultural paradigms have proven inadequate in addressing the imperatives of
food safety and production efficiency. Against this backdrop, greenhouse
agriculture emerges as a viable solution, proffering a controlled milieu for
crop cultivation to augment yields, refine quality, and diminish reliance on
natural resources [b1]. Nevertheless, greenhouse agriculture contends with a
gamut of challenges. Traditional greenhouse management strategies, often
grounded in experiential knowledge and predefined rules, lack targeted
personalized regulation, thereby resulting in resource inefficiencies. The
exigencies of real-time monitoring and precise control of the greenhouse's
internal environment gain paramount importance with the burgeoning scale of
agriculture. To redress this challenge, the study introduces IoT technology and
machine learning algorithms into greenhouse agriculture, aspiring to institute
an intelligent agricultural greenhouse control system conducive to augmenting
the efficiency and sustainability of agricultural production.


  Recent years have seen a renewed interest in using `edge modes' to extend the
pre-symplectic structure of gauge theory on manifolds with boundaries. Here we
further the investigation undertaken in \cite{FP2018} by using the formalism of
homotopy pullback and Deligne-Beilinson cohomology to describe an
electromagnetic (EM) duality on the boundary of $M=B^{3}\times\mathbb{R}$. Upon
breaking a generalized global symmetry, the duality is implemented by a BF-like
topological boundary term. We then introduce Wilson line singularities on
$\partial M$ and show that these induce the existence of dual edge modes, which
we identify as connections over a $\left(-1\right)$-gerbe. We derive the
pre-symplectic structure that yields the central charge in \cite{FP2018} and
show that the central charge is related to a non-trivial class of the
$\left(-1\right)$-gerbe.


  We newly apply the improved Batalin-Fradkin-Tyutin(BFT) Hamiltonian method to
the O(3) nonlinear sigma model, and directly obtain the compact form of
nontrivial first class Hamiltonian by introducing the BFT physical fields.
Furthermore, following the BFV formalism, we derive the BRST invariant gauge
fixed Lagrangian through the standard path-integral procedure. Finally, by
introducing collective coordinates, we also study a semi-classical quantization
of soliton background to conclude that the spectrum of zero modes are unchanged
through the BFT procedure.


  We propose a new model for the nature of the nucleation of solid from the
superfluid phases of $^4$He and $^3$He. Unique to the superfluid phases the
solid nucleation involves an extremely fast solidification front. This results
in a local release of pressure and a velocity field in the superfluid. The
superfluid velocity in turn facilitates the nucleation of vortex-loops. The
kinetic energy gain of this process balances the surface tension, as the solid
surface is quickly covered by many vortex-loops ("hairy snow-ball"). We show
that this scenario gives good agreement with experiments on heterogeneous
nucleation, which differ with the classical theory of homogeneous nucleation by
8 orders of magnitude. We propose several experiments that could show the
involvement of vortices with solid nucleation.


  We show that fully-localized, three-dimensional,
time-reversal-symmetry-broken insulators do not belong to a single phase of
matter but can realize topologically distinct phases that are labelled by
integers. The phase transition occurs only when the system becomes conducting
at some filling. We find that these novel topological phases are fundamentally
distinct from insulators without disorder: they are guaranteed to host
delocalized boundary states giving rise to the quantized boundary Hall
conductance, whose value is equal to the bulk topological invariant.


  This is the first paper of a series aimed to understand the formation and
evolution of bars in early-type spirals and their influence in the evolution of
the galaxy. Optical long-slit spectra along the major axis of the bar of a
sample of 20 galaxies are analyzed. Line-strength indices in the bar region are
measured to derive stellar mean-age and metallicity distributions along the
bars using stellar population models. We find three different types of bars
according to their metallicity and age distribution along the radius: 1) Bars
with negative metallicity gradients. They show mean young/intermediate
population (< 2 Gyr), and have amongst the lowest stellar maximum central
velocity dispersion of the sample. 2) Bars with null metallicity gradients.
These galaxies tend to have negative age gradients. 3) Bars with positive
metallicity gradients. These galaxies are predominantly those with higher
velocity dispersion and older mean population. We found no significant
correlation between the age and metallicity distribution, and bar/galaxy
parameters such as the AGN presence, size or the bar strength. From the
kinematics, we find that all the galaxies show a disk-like central component.
The results from the metallicity and age gradients indicate that most galaxies
with high central stellar velocity dispersion host bars that could have been
formed more than 3 Gyrs ago, while galaxies with lower central velocity
dispersions show a wider distribution in their population and age gradients. A
few bars show characteristics compatible with having been formed less than <2
Gy ago. These results place strong constrains to models of bar formation and
evolution. The disk-like central components also show the important role played
by bars in the secular evolution of the central structure.


  Skyrmion racetrack memory has a lots of potential in future non-volatile
solid state devices. In general such devices require current to nucleate
skyrmions via spin transfer torque (STT) effect. Further the current is also
required to drive the skyrmions in the nanowire device. However the current
applied during nucleation of successive skyrmions may have unwanted
perturbation \emph{viz.} Joule heating and skyrmion Hall effect, on the
propagation of previously generated skyrmions. Therefore new methodology is
required to decouple the generation and propagation of skyrmions. Here we
present a novel route via micromagnetic simulation for generation of skyrmions
from triangular antidot structure in a ferromagnetic nanotrack using local
oersted field. Antidots are holes in a magnetic nanoelement. Controlled
skyrmion injection can be achieved by tuning the dimensions of the antidots
that are placed at either end of the nanotrack. Multiple skyrmions can be
simultaneously generated by incorporating more number of antidots. Here we
propose a novel design to realise skyrmionic racetrcak memory where one can
individually generate and manipulate the skyrmions within the nanotrack.


  The concepts of topology have a profound impact on physics research spanning
the fields of condensed matter, photonics and acoustics and predicting
topological states that provide unprecedented versatility in routing and
control of waves of various nature. Higher-order topological insulators further
expand this plethora of possibilities towards extended range of structure
dimensionalities. Here, we put forward a novel class of two-dimensional
multipolar higher-order topological insulators that arise due to the
interference of the degenerate modes of the individual meta-atoms generalizing
the mechanism of spin-orbit coupling in condensed matter systems. We prove that
this model features disorder-robust corner modes and cannot be reduced to the
known crystalline topological phases or conventional quadrupole insulators,
providing the first example of multipolar topology in a $C_3$-symmetric lattice
featuring quantized octupole moment. The multimode nature of the lattice gives
rise to flat bands and corner states with extreme localization enabling
coherent control of the topological modes. We support our predictions by
assembling the designed structure, observing multipolar topological corner
states and experimentally demonstrating their coherent control.


  Supra-arcade downflows (SADs) are dark, teardrop-shaped features descending
upon flare arcades. They are thought to be the results of magnetic
reconnection, but the detailed formation mechanism of SADs and their
relationship to flare energy release are still unclear. In this work, we
explore the thermodynamical properties of SADs in the 2011 October 22 limb
flare using the observations of the Solar Dynamics Observatory/Atmospheric
Imaging Assembly and an improved Differential Emission Measure (DEM) technique.
Different heating events around SADs are identified and the propagation speeds
of plasma heating are calculated. The first heating event starts with the
increase of the emission measure at temperatures higher than 5 MK, about 2.8
minutes before the arrival of the first studied SAD. Its propagation speed is
about 140 km/s, a little faster than the speed of the SAD. However, the other
two events have fast propagation speeds more than 700 km/s. We suspect that the
first heating event can be explained by adiabatic compression, but the others
may have different causes. Besides, we observed that SADs can push away their
surrounding spikes. The formation of SADs is further explained on the basis of
patchy and bursty magnetic reconnection that reconnection outflows may push
away surrounding plasma and leave dark lanes behind them. The reliability of
the DEM results, heating and cooling mechanisms, and other SAD explanations are
discussed.


  This work presents blind constrained constant modulus (CCM) adaptive
algorithms based on the set-membership filtering (SMF) concept and incorporates
dynamic bounds {for interference suppression} applications. We develop
stochastic gradient and recursive least squares type algorithms based on the
CCM design criterion in accordance with the specifications of the SMF concept.
We also propose a blind framework that includes channel and amplitude
estimators that take into account parameter estimation dependency, multiple
access interference (MAI) and inter-symbol interference (ISI) to address the
important issue of bound specification in multiuser communications. A
convergence and tracking analysis of the proposed algorithms is carried out
along with the development of analytical expressions to predict their
performance. Simulations for a number of scenarios of interest with a DS-CDMA
system show that the proposed algorithms outperform previously reported
techniques with a smaller number of parameter updates and a reduced risk of
overbounding or underbounding.


  Diverse approaches to HFCG's inductance,resistance and armature expansion
calculating are evaluated. Comparison of simulated and experimentally obtained
results is provided. Validity criteria for different simulation models are
proposed. Consideration of armature acceleration under the pressure of
detonation products is shown to be beneficial for accuracy of HFCG simulation.
Control of HFCG temperature during simulation enables detecting critical points
of system operation.


  This paper addresses channel estimation and data equalization on
frequency-selective 1-bit quantized Multiple Input-Multiple Output (MIMO)
systems. No joint processing or Channel State Information is assumed at the
transmitter, and therefore our findings are also applicable to the uplink of
Multi-User MIMO systems. System models for both Orthogonal Division Frequency
Multiplexing (OFDM) and single-carrier schemes are developed. A Cram\'er-Rao
Lower Bound for the estimation problems is derived. The two nonlinear
algorithms Expectation Maximization (EM) and Generalized Approximate Message
Passing (GAMP) are adapted to the problems, and a linear method based on the
Bussgang theorem is proposed. In the OFDM case, the linear method enables
subcarrier-wise estimation, greatly reducing computational complexity.
Simulations are carried out to compare the algorithms with different settings.
The results turn out to be close to the Cram\'er-Rao bound in the low Signal to
Noise Ratio (SNR) region. The OFDM setting is more suitable for the nonlinear
algorithms, and that the linear methods incur a performance loss with respect
to the nonlinear approaches. In the relevant low and medium SNR regions, the
loss amounts to 2-3 dB and might well be justified in exchange for the reduced
computational effort, especially in Massive MIMO settings.


  The spectral properties of two products $AB$ and $BA$ of possibly unbounded
operators $A$ and $B$ in a Banach space are considered. The results are applied
in the comparison of local spectral properties of the operators $T^{[*]} T$ and
$TT^{[*]} $ in a Krein space. It is shown that under the assumption that both
operators $T^{[*]} T$ and $T^{[*]} $ have non-empty resolvent sets, the
operator $T^{[*]} T$ is locally definitizable if and only if $TT^{[*]} $ is. In
this context the critical points of both operators are compared.


  Solar disinfection by photocatalysis is one of the promising methods used for
drinking water disinfection. It leads to the destruction of bacteria like
$Escherichia$ $Coli$ ($E.$ $Coli$). In this paper, we compare our theoretical
results with experimental ones done previously by A.G. Rinc\'on and his
colleagues concerning the order of decay of C$_{6}$H$_{4}$(OH)$_{2}$ isomers in
the presence of titanium dioxide TiO$_{2}$, and show the influence of optical
properties of those molecules on $E.$ $Coli$ inactivation. According to the
adsorption energy parameter, we find that catechol has the highest adsorption
degree on titanium dioxide, followed by resorcinol, and finally hydroquinone.
Three dihydroxybenzene isomers absorb photons belonging to ultraviolet (UV)
range. The lowest absorption energies of resorcinol, catechol and hydroquinone
are respectively 3.42, 4.44 and 4.49 eV.


  An expression of the filtered Eulerian drag force is proposed based on the
second order Taylor polynomial approximation of the microscopic Eulerian drag
coefficient. Theoretical computations of the expression are performed at low
Reynolds numbers based on an available microscopic drag model. It shows that
four sub-grid quantities, i.e., the gas drift velocity, the solid drift
velocity, the scalar variance of solid volume fraction and a third-order
moment, defined as the covariance of squared solid volume fraction and the slip
velocity, are significant for an accurate estimation of the filtered drag force
at low Reynolds numbers. The gas drift velocity is nothing but the drift
velocity defined by Parmentier et al. (AIChE Journal, 2012, 58 (4): 1084-1098),
and in analogy to the gas drift velocity, we defines the solid drift velocity
in the present work. The great relevance of the gas drift velocity and the
scalar variance of solid volume fraction to the filtered drag force have been
demonstrated through numerous correlative analyses of fully resolved
simulations. Present theoretical analysis shows that the gas drift velocity
term is primarily responsible for the drag reduction, whereas the solid drift
velocity term is prone to attenuate this reduction, especially at moderate and
high solid volume fractions. The scalar variance of solid volume fraction term
is found to increase the filtered drag force in the full range of solid volume
fractions. The third-order term has exactly the same coefficient with that of
the variance term, and it mostly attains negative values with the trend to
decrease the filtered drag force.


  We prove the existence of the local weak limit of the measure obtained by
sampling random triangulations of size $n$ decorated by an Ising configuration
with a weight proportional to the energy of this configuration. To do so, we
establish the algebraicity and the asymptotic behaviour of the partition
functions of triangulations with spins for any boundary condition. In
particular, we show that these partition functions all have the same phase
transition at the same critical temperature. Some properties of the limiting
object -- called the Infinite Ising Planar Triangulation -- are derived,
including the recurrence of the simple random walk at the critical temperature.


  We investigate the phase enhancement of quantum states subject to non-linear
phase shifts. The optimal phase estimation of even entangled coherent states
(ECSs) is shown to be better than that of NOON states and of odd ECS states
with the same average particle number <n> and non-linearity exponent k. We
investigate the creation of approximate ECSs (AECSs) from a squeezed vacuum
with current optical technology methods and find that a pure AECS is even
slightly better than an even ECS for large <n>. We also examine simple but
physically relevant cases of loss in the non-linear interferometer arm, for a
fixed <n>.


  We use high resolution 2D hydrodynamic simulations to study the formation of
spiral substructure in the gaseous disk of a galaxy. The obtained gaseous
response is driven by a self-consistent non-axisymmetric potential obtained
from an imposed spiral mass distribution. We highlight the importance of
ultraharmonic resonances in generating these features. The temporal evolution
of the system is followed with the parallel ZEUS-MP code, and we follow the
steepening of perturbations induced by the spiral potential until large-scale
shocks emerge. These shocks exhibit bifurcations that protrude from the gaseous
arms and continue to steepen until new shocks are formed. When the contribution
from the spiral potential relative to the axisymmetric background is increased
from our default value, spurs protrude from the main arms after several
revolutions of the gaseous disk. Such spurs overlap on top of the
aforementioned shocks. These results support the hypothesis that a complicated
gaseous response can coexist with an orderly spiral potential term, in the
sense that the underlying background potential can be smooth yet drive a
gaseous response that is far more spatially complex.


  By combining the features of CSMA and TDMA, fully decentralised WLAN MAC
schemes have recently been proposed that converge to collision-free schedules.
In this paper we describe a MAC with optimal long-run throughput that is almost
decentralised. We then design two \changed{schemes} that are practically
realisable, decentralised approximations of this optimal scheme and operate
with different amounts of sensing information. We achieve this by (1)
introducing learning algorithms that can substantially speed up convergence to
collision free operation; (2) developing a decentralised schedule length
adaptation scheme that provides long-run fair (uniform) access to the medium
while maintaining collision-free access for arbitrary numbers of stations.


  In order to shed light on the Vertical-Axis Wind Turbines (VAWT) wake
characteristics, in this paper we present high-fidelity CFD simulations of the
flow around an exemplary H-shaped VAWT turbine, and we propose to apply Proper
Orthogonal Decomposition (POD) to the computed flow field in the near wake of
the rotor. The turbine under consideration was widely studied in previous
experimental and computational investigations. In the first part of the study,
multiple Reynolds-Averaged Navier-Stokes (RANS) simulations were performed at
the Tip Speed Ratio (TSR) of peak power coefficient, to select the most
accurate turbulence model with respect to available data. In the following
step, further RANS numerical simulations were performed at different TSRs to
compare the power coefficient against experimental data. Then, Large Eddy
Simulation (LES) was applied for multiple TSR conditions. The spatial and
temporal POD modes along with modal energy for the RANS and LES results were
extracted, and the performance of the turbulence models was assessed. Also, an
interpretation of the POD modes with respect to the flow structures was given
to highlight the most significant time and length scales of the predictions
considering the different dynamical levels of approximations of the
computational models.


  This work is devoted to the logical proof of the Goodenough and Khomskii idea
of the existence of spin-orbit transitions in transition magnetic crystals. In
agreement with the basics of the Landau theory of phase transitions the
phenomenological theory of spin-orbit transitions is constructed. The general
scheme of the theory is illustrated by the application to the description of
magnetic and structural transformations in the copper ferrite CuFe2O4.


  The charged multiplicity fluctuations in deep-inelastic scattering (DIS) are
investigated to test perturbative QCD and local parton hadron duality (LPHD).
The fluctuations were measured with the ZEUS detector at HERA in restricted
phase space domains in the current Breit frame region. The measurements are
compared to analytic pQCD + LPHD predictions and QCD-based Monte Carlo models.


  The World Year of Physics 2005 celebrates Einstein 1905. Too much celebration
of a single character may be hazardous as an example for the younger
generation. With such a motto in mind, I comment on some episodes in Einstein's
scientific career that are at some extent characterized by what could be dubbed
{\it blundering behavior}. The final purpose is obviously to {\it humanize} the
personage as opposed tore the current trend of {\it deification}.


  This paper has been removed by arXiv administrators because it plagiarizes
gr-qc/0602105, gr-qc/0307010, and gr-qc/9802050.


  The promise of AI is huge. AI systems have already achieved good enough
performance to be in our streets and in our homes. However, they can be brittle
and unfair. For society to reap the benefits of AI systems, society needs to be
able to trust them. Inspired by decades of progress in trustworthy computing,
we suggest what trustworthy properties would be desired of AI systems. By
enumerating a set of new research questions, we explore one approach--formal
verification--for ensuring trust in AI. Trustworthy AI ups the ante on both
trustworthy computing and formal methods.


  A theorem of Leibman asserts that a polynomial orbit
$(g(1),g(2),g(3),\ldots)$ on a nilmanifold $G/\Gamma$ is always equidistributed
in a union of closed sub-nilmanifolds of $G/\Gamma$. In this paper we give a
quantitative version of Leibman's result, describing the uniform distribution
properties of a finite polynomial orbit $(g(1),\ldots,g(N))$ in a nilmanifold.
More specifically we show that there is a factorization $g = \epsilon
g'\gamma$, where $\epsilon(n)$ is "smooth", $\gamma(n)$ is periodic and
"rational", and $(g'(a),g'(a+d),\ldots,g'(a + d(l-1)))$ is uniformly
distributed (up to a specified error $\delta$) inside some subnilmanifold
$G'/\Gamma'$ of $G/\Gamma$, for all sufficiently dense arithmetic progressions
$a,a+d,\ldots,a+d(l-1)$ inside $\{1,..,N\}$.
  Our bounds are uniform in $N$ and are polynomial in the error tolerance
delta. In a subsequent paper we shall use this theorem to establish the Mobius
and Nilsequences conjecture from our earlier paper "Linear equations in
primes".


  We describe how the procedure of calculating approximate solitons from
instanton holonomies may be extended to the case of soliton crystals. It is
shown how sine-Gordon kink chains may be obtained from CP1 instantons on a
torus. These kink chains turn out to be remarkably accurate approximations to
the true solutions. Some remarks on the relevance of this work to Skyrme
crystals are also made.


  Words have been represented in a high-dimensional vector space that encodes
their semantic similarities, enabling downstream applications such as
retrieving synonyms, antonyms, and relevant contexts. However, despite recent
advances in multilingual language models (LMs), the effectiveness of these
models' representations in semantic retrieval contexts has not been
comprehensively explored. To fill this gap, this paper introduces the MINERS, a
benchmark designed to evaluate the ability of multilingual LMs in semantic
retrieval tasks, including bitext mining and classification via
retrieval-augmented contexts. We create a comprehensive framework to assess the
robustness of LMs in retrieving samples across over 200 diverse languages,
including extremely low-resource languages in challenging cross-lingual and
code-switching settings. Our results demonstrate that by solely retrieving
semantically similar embeddings yields performance competitive with
state-of-the-art approaches, without requiring any fine-tuning.


  The present study numerically explores the mixed convection phenomena in a
differentially heated ventilated square cavity with active flow modulation via
a rotating plate. Forced convection flow in the cavity is attained by
maintaining an external fluid flow through an opening at the bottom of the left
cavity wall while leaving it through another opening at the right cavity wall.
A counter-clockwise rotating plate at the center of the cavity acts as active
flow modulator. Moving mesh approach is used for the rotation of the plate and
the numerical solution is achieved using Arbitrary Lagrangian-Eulerian (ALE)
finite element formulation with a quadrilateral discretization scheme.
Transient parametric simulations have been performed for various frequency of
the rotating plate for a fixed Reynolds number (Re) of 100 based on maximum
inlet flow velocity while the Richardson number (Ri) is maintained at unity.
Heat transfer performance has been evaluated in terms of spatially averaged
Nusselt number and time-averaged Nusselt number along the heated wall. Power
spectrum analysis in the frequency domain obtained from the fast Fourier
transform (FFT) analysis indicates that thermal frequency and plate frequency
start to deviate from each other at higher values of velocity ratio (> 4).


  A pronounced xy-anisotropy was observed in recent neutron scattering
experiments for magnetic excitations in untwinned YBa_{2}Cu_{3}O_{6+y} (YBCO).
The small anisotropy of the bare band structure due to the orthorhombic crystal
symmetry seems to be enhanced by correlation effects. A natural possibility is
that the system is close to a Pomeranchuk instability associated with a d-wave
Fermi surface deformation (dFSD). We investigate this possibility in the
bilayer t-J model within a self-consistent slave-boson mean-field theory. We
show that the dFSD correlations drive a pronounced xy-anisotropy of magnetic
excitations at low doping and at relatively high temperatures, providing a
scenario for the observed xy-anisotropy in optimally doped as well as
underdoped YBCO, including the pseudogap phase.


  In 2006, K\"{u}hn and Osthus showed that if a 3-graph H on n vertices has
minimum co-degree at least (1/4 +o(1))n and n is even then H has a loose
Hamilton cycle. In this paper, we prove that the minimum co-degree of n/4
suffices. The result is tight.


  Previous research established that the maximal rank of the abstract regular
polytopes whose automorphism group is a transitive proper subgroup of $\Sym_n$
is $n/2 + 1$, with only two polytopes attaining this rank, both of which having
odd ranks.
  In this paper, we investigate the case where the rank is equal to $n/2$
($n\geq 14$). Our analysis reveals that reducing the rank by one results in a
substantial increase in the number of regular polytopes ($33$ distinct families
are discovered) covering all possible ranks (even and odd).


  The stability of some spatial asymmeric games is discussed. Both linear and
nonlinear asymptotic stability of asymmetric hawk-dove and prisoner's dilemma
are studied. Telegraph reaction diffusion equations for the asymmetric spatial
games are presented. Asymmetric game of parental investment is studied in the
presence of both ordinary and cross diffusions.


  We investigate the chaotic inflationary model using the two-loop effective
potential of a self-interacting scalar field theory in curved spacetime. We use
the potential which contains a non-minimal scalar curvature coupling and a
quartic scalar self-interaction. We analyze the Lyapunov stability of de Sitter
solution and show the stability bound. Calculating the inflationary parameters,
we systematically explore the spectral index $n_s$ and the tensor-to-scalar
ratio $r$, with varying the four parameters, the scalar-curvature coupling
$\xi_0$, the scalar quartic coupling $\lambda_0$, the renormalization scale
$\mu$ and the e-folding number $N$. It is found that the two-loop correction on
$n_s$ is much larger than the leading-log correction, which has previously been
studied. We show that the model is consistent with the observation by Planck
with WMAP and a recent joint analysis of BICEP2.


  Transition metal chalcogenide tin sulfide (SnS) films as alternative
noncontact alignment layer for liquid crystals, have been demonstrated and
investigated. The SnS has an anisotropic atomic chain structure similar to
black Phosphorous which causes the liquid crystal molecules to align without
the need for any additional surface treatments. The high anisotropic nature of
SnS promotes the alignment of the easy axis of liquid crystal molecules along
the periodic atomic grooves of the SnS layer. The atomically thin SnS layers
were deposited on indium tin oxide films on glass substrates, at room
temperature by chemical vapor deposition. The device characteristics are
comparable to those commercially available, which use photo-aligning polymer
materials. We measured threshold voltage of 0.92V, anchoring energy of
1.573x10^(-6) J/m^2, contrast ratio better than 71:1 and electro-optical
rise/fall times of 80/390ms, respectively for ~11 micron thick liquid crystal
device as expected.


  We solve the problem of discrete translocation of a polymer through a pore,
driven by the irreversible, random sequential adsorption of particles on one
side of the pore. Although the kinetics of the wall motion and the deposition
are coupled, we find the exact steady-state distribution for the gap between
the wall and the nearest deposited particle. This result enables us to
construct the mean translocation velocity demonstrating that translocation is
faster when the adsorbing particles are smaller. Monte-Carlo simulations also
show that smaller particles gives less dispersion in the ratcheted motion. We
also define and compare the relative efficiencies of ratcheting by deposition
of particles with different sizes and we describe an associated
"zone-refinement" process.


  Large language models (LLMs) are the result of a massive experiment in
bottom-up, data-driven reverse engineering of language at scale. Despite their
utility in a number of downstream NLP tasks, ample research has shown that LLMs
are incapable of performing reasoning in tasks that require quantification over
and the manipulation of symbolic variables (e.g., planning and problem
solving); see for example [25][26]. In this document, however, we will focus on
testing LLMs for their language understanding capabilities, their supposed
forte. As we will show here, the language understanding capabilities of LLMs
have been widely exaggerated. While LLMs have proven to generate human-like
coherent language (since that's how they were designed), their language
understanding capabilities have not been properly tested. In particular, we
believe that the language understanding capabilities of LLMs should be tested
by performing an operation that is the opposite of 'text generation' and
specifically by giving the LLM snippets of text as input and then querying what
the LLM "understood". As we show here, when doing so it will become apparent
that LLMs do not truly understand language, beyond very superficial inferences
that are essentially the byproduct of the memorization of massive amounts of
ingested text.


  We introduce OpportunityFinder, a code-less framework for performing a
variety of causal inference studies with panel data for non-expert users. In
its current state, OpportunityFinder only requires users to provide raw
observational data and a configuration file. A pipeline is then triggered that
inspects/processes data, chooses the suitable algorithm(s) to execute the
causal study. It returns the causal impact of the treatment on the configured
outcome, together with sensitivity and robustness results. Causal inference is
widely studied and used to estimate the downstream impact of individual's
interactions with products and features. It is common that these causal studies
are performed by scientists and/or economists periodically. Business
stakeholders are often bottle-necked on scientist or economist bandwidth to
conduct causal studies. We offer OpportunityFinder as a solution for commonly
performed causal studies with four key features: (1) easy to use for both
Business Analysts and Scientists, (2) abstraction of multiple algorithms under
a single I/O interface, (3) support for causal impact analysis under binary
treatment with panel data and (4) dynamic selection of algorithm based on scale
of data.


  Today, image classification is a common way for systems to process visual
content. Although neural network approaches to classification have seen great
progress in reducing error rates, it is not clear what this means for a
cognitive system that needs to make sense of the multiple and competing
predictions from its own classifiers. As a step to address this, we present a
novel framework that uses meta-reasoning and meta-operations to unify
predictions into abstractions, properties, or relationships. Using the
framework on images from ImageNet, we demonstrate systems that unify 41% to 46%
of predictions in general and unify 67% to 75% of predictions when the systems
can explain their conceptual differences. We also demonstrate a system in "the
wild" by feeding live video images through it and show it unifying 51% of
predictions in general and 69% of predictions when their differences can be
explained conceptually by the system. In a survey given to 24 participants, we
found that 87% of the unified predictions describe their corresponding images.


  Contradiction is often seen as a defect of intelligent systems and a
dangerous limitation on efficiency. In this paper we raise the question of
whether, on the contrary, it could be considered a key tool in increasing
intelligence in biological structures. A possible way of answering this
question in a mathematical context is shown, formulating a proposition that
suggests a link between intelligence and contradiction.
  A concrete approach is presented in the well-defined setting of cellular
automata. Here we define the models of ``observer'', ``entity'',
``environment'', ``intelligence'' and ``contradiction''. These definitions,
which roughly correspond to the common meaning of these words, allow us to
deduce a simple but strong result about these concepts in an unbiased,
mathematical manner. Evidence for a real-world counterpart to the demonstrated
formal link between intelligence and contradiction is provided by three
computational experiments.


  In Task Oriented Dialogue (TOD) system, detecting and inducing new intents
are two main challenges to apply the system in the real world. In this paper,
we suggest the semantic multi-view model to resolve these two challenges: (1)
SBERT for General Embedding (GE), (2) Multi Domain Batch (MDB) for dialogue
domain knowledge, and (3) Proxy Gradient Transfer (PGT) for cluster-specialized
semantic. MDB feeds diverse dialogue datasets to the model at once to tackle
the multi-domain problem by learning the multiple domain knowledge. We
introduce a novel method PGT, which employs the Siamese network to fine-tune
the model with a clustering method directly.Our model can learn how to cluster
dialogue utterances by using PGT. Experimental results demonstrate that our
multi-view model with MDB and PGT significantly improves the Open Intent
Induction performance compared to baseline systems.


  We consider the problem of shotgun identification of patterns on groups,
which extends previous work on shotgun identification of DNA sequences and
labeled graphs. A shotgun identification problem on a group $G$ is specified by
two finite subsets $C \subset G$ and $K \subset G$ and a finite alphabet
$\mathcal{A}$. In such problems, there is a ``global" pattern $w \in
\mathcal{A}^{CK}$, and one would like to be able to identify this pattern (up
to translation) based only on observation of the ``local" $K$-shaped
subpatterns of $w$, called reads, centered at the elements of $C$. We consider
an asymptotic regime in which the size of $w$ tends to infinity and the symbols
of $w$ are drawn in an i.i.d. fashion. Our first general result establishes
sufficient conditions under which the random pattern $w$ is identifiable from
its reads with probability tending to one, and our second general result
establishes sufficient conditions under which the random pattern $w$ is
non-identifiable with probability tending to one. Additionally, we illustrate
our main results by applying them to several families of examples.


  In systems biomedicine, an experimenter encounters different potential
sources of variation in data such as individual samples, multiple experimental
conditions, and multi-variable network-level responses. In multiparametric
cytometry, which is often used for analyzing patient samples, such issues are
critical. While computational methods can identify cell populations in
individual samples, without the ability to automatically match them across
samples, it is difficult to compare and characterize the populations in typical
experiments, such as those responding to various stimulations or distinctive of
particular patients or time-points, especially when there are many samples.
Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous
modeling and registration of populations across a cohort. JCM models every
population with a robust multivariate probability distribution. Simultaneously,
JCM fits a random-effects model to construct an overall batch template -- used
for registering populations across samples, and classifying new samples. By
tackling systems-level variation, JCM supports practical biomedical
applications involving large cohorts.


  A well-known theorem of Wedderburn asserts that a finite division ring is
commutative. In a division ring the group of invertible elements is as large as
possible. Here we will be particularly interested in the case where this group
is as small as possible, namely reduced to 1. We will show that, if this is the
case, then the ring is boolean. Thus, here too, the ring is commutative.


  We consider Markov decision processes (MDPs) which are a standard model for
probabilistic systems. We focus on qualitative properties for MDPs that can
express that desired behaviors of the system arise almost-surely (with
probability 1) or with positive probability. We introduce a new simulation
relation to capture the refinement relation of MDPs with respect to qualitative
properties, and present discrete graph theoretic algorithms with quadratic
complexity to compute the simulation relation. We present an automated
technique for assume-guarantee style reasoning for compositional analysis of
MDPs with qualitative properties by giving a counter-example guided
abstraction-refinement approach to compute our new simulation relation. We have
implemented our algorithms and show that the compositional analysis leads to
significant improvements.


  We propose an automated procedure to prove polyhedral abstractions (also
known as polyhedral reductions) for Petri nets. Polyhedral abstraction is a new
type of state space equivalence, between Petri nets, based on the use of linear
integer constraints between the marking of places. In addition to defining an
automated proof method, this paper aims to better characterize polyhedral
reductions, and to give an overview of their application to reachability
problems.
  Our approach relies on encoding the equivalence problem into a set of SMT
formulas whose satisfaction implies that the equivalence holds. The difficulty,
in this context, arises from the fact that we need to handle infinite-state
systems. For completeness, we exploit a connection with a class of Petri nets,
called flat nets, that have Presburger-definable reachability sets. We have
implemented our procedure, and we illustrate its use on several examples.


  Deep neural networks have gained tremendous success in a broad range of
machine learning tasks due to its remarkable capability to learn semantic-rich
features from high-dimensional data. However, they often require large-scale
labelled data to successfully learn such features, which significantly hinders
their adaption into unsupervised learning tasks, such as anomaly detection and
clustering, and limits their applications into critical domains where obtaining
massive labelled data is prohibitively expensive. To enable unsupervised
learning on those domains, in this work we propose to learn features without
using any labelled data by training neural networks to predict data distances
in a randomly projected space. Random mapping is a theoretically proven
approach to obtain approximately preserved distances. To well predict these
random distances, the representation learner is optimised to learn genuine
class structures that are implicitly embedded in the randomly projected space.
Empirical results on 19 real-world datasets show that our learned
representations substantially outperform a few state-of-the-art competing
methods in both anomaly detection and clustering tasks. Code is available at
https://git.io/RDP


  Several widely used methods for the calculation of band structures and photo
emission spectra, such as the GW approximation, rely on Many-Body Perturbation
Theory. They can be obtained by iterating a set of functional differential
equations relating the one-particle Green's function to its functional
derivative with respect to an external perturbing potential. In the present
work we apply a linear response expansion in order to obtain insights in
various approximations for Green's functions calculations. The expansion leads
to an effective screening, while keeping the effects of the interaction to all
orders. In order to study various aspects of the resulting equations we
discretize them, and retain only one point in space, spin, and time for all
variables. Within this one-point model we obtain an explicit solution for the
Green's function, which allows us to explore the structure of the general
family of solutions, and to determine the specific solution that corresponds to
the physical one. Moreover we analyze the performances of established
approaches like $GW$ over the whole range of interaction strength, and we
explore alternative approximations. Finally we link certain approximations for
the exact solution to the corresponding manipulations for the differential
equation which produce them. This link is crucial in view of a generalization
of our findings to the real (multidimensional functional) case where only the
differential equation is known.


  We describe a quantitative construction of almost-normal diffeomorphisms
between embedded orientable manifolds with boundary to be used in the study of
geometric variational problems with stratified singular sets. We then apply
this construction to isoperimetric problems for planar bubble clusters. In this
setting we develop an improved convergence theorem, showing that a sequence of
almost-minimizing planar clusters converging in $L^1$ to a limit cluster has
actually to converge in a strong $C^{1,\alpha}$-sense. Applications of this
improved convergence result to the classification of isoperimetric clusters and
the qualitative description of perturbed isoperimetric clusters are also
discussed. Analogous results for three-dimensional clusters are presented in
part two, while further applications are discussed in some companion papers.


  A remarkably high value of specific capacitance of 450 F/g has been observed
through electrochemical measurements in the electrode made of multiferroic
Bismuth Ferrite (BFO) in the form of nanorods protruding out. These BFO
nanorods were developed on porous Anodised Alumina (AAO) templates using wet
chemical technique. Diameters of nanorods were in the range of 20-100 nm. The
high capacitance is attributed to the nanostructure. The active surface charge
has been evaluated electrochemically by cyclic voltammetry (CV) at different
scanning rates and charge-discharge studies. The specific capacitances were
constant after several cycles of charge-discharge leading to their useful
application in devices. The mechanism of accumulation of charge on the
electrode surface has been studied.


  An effective model is identified for thin perfectly plastic plates whose
microstructure consists of the periodic assembling of two elastoplastic phases,
as the periodicity parameter converges to zero. Assuming that the thickness of
the plates and the periodicity of the microstructure are comparably small, a
limiting description is obtained in the quasistatic regime via simultaneous
homogenization and dimension reduction by means of evolutionary
$\Gamma$-convergence, two-scale convergence, and periodic unfolding.


  With the rapid development of social network and multimedia technology,
customized image and video stylization has been widely used for various
social-media applications. In this paper, we explore the problem of
exemplar-based photo style transfer, which provides a flexible and convenient
way to invoke fantastic visual impression. Rather than investigating some fixed
artistic patterns to represent certain styles as was done in some previous
works, our work emphasizes styles related to a series of visual effects in the
photograph, e.g. color, tone, and contrast. We propose a photo stylistic brush,
an automatic robust style transfer approach based on Superpixel-based BIpartite
Graph (SuperBIG). A two-step bipartite graph algorithm with different
granularity levels is employed to aggregate pixels into superpixels and find
their correspondences. In the first step, with the extracted hierarchical
features, a bipartite graph is constructed to describe the content similarity
for pixel partition to produce superpixels. In the second step, superpixels in
the input/reference image are rematched to form a new superpixel-based
bipartite graph, and superpixel-level correspondences are generated by a
bipartite matching. Finally, the refined correspondence guides SuperBIG to
perform the transformation in a decorrelated color space. Extensive
experimental results demonstrate the effectiveness and robustness of the
proposed method for transferring various styles of exemplar images, even for
some challenging cases, such as night images.


  Future electron-positron Higgs factories could provide excellent
opportunities to examine the Standard Model and search for new physics with
much higher precision than the LHC. A precise particle identification is
crucial for the physics program at these future colliders and can be achieved
via precise time-of-flight (TOF) measurements of the final state particles. In
this paper, we propose a conceptual design of TOF system based on the multigap
resistive plate chamber (MRPC) technology for future electron-positron Higgs
factories. This TOF system has a time resolution of < 35 ps, a total active
area of 77m2, and a construction budget of the order of 5 million USD.
Keywords: MRPC, TOF, PID, CEPC


  We show that the spontaneous breakdown of U(1) symmetry in a Higgs model can
be described in discretized light cone formulation even by neglecting zero
mode. We obtain correctly the energy of a ground state with the symmetry
breakdown. We also show explicitly the presence of a Goldstone mode and its
absence when the U(1) symmetry is gauged. In spite of obtaining the favorable
results, we lose a merit in the formulation without zero modes that a naive
Fock vacuum is the true ground state.


  Time-domain (TD) spectroscopic data from the Large Sky Area Multi-Object
Fiber Spectroscopic Telescope (LAMOST) can provide accurate and high-cadence
radial velocities (RVs). In this work, we search for binaries with compact
components with RV monitoring method by using the LAMOST TD survey of four $K$2
plates. Three binary systems including an unseen white dwarf or neutron star
are found. For each binary system, we estimate the stellar parameters of the
visible star and orbital parameters, and finally calculate the binary mass
function and the minimum mass of the unseen star. No obvious double-lined
feature is seen from the LAMOST medium-resolution spectra of the three sources.
In addition, we found no X-ray counterpart for all these sources but UV
companions for two of them. Spectral disentangling also shows no additional
component with optical absorption spectra, supporting that these systems
contain compact objects.


  The aim of the present dissertation is to analyze the meaning of the entropy
bounds of the holographic sector once tested for statistical ensembles of
particles, in order to deeper investigate the nature of these constraints and
their mutual links. From the Universal Energy Bound (UEB) simple time
constraints can be argued, which are manifestations of the discrete nature of
the space-time and of the presence of ultimate space-time scales. From the
combined effort of the UEB and of the Holographic bound by 't Hooft and
Susskind, an entropy density bound as a function of temperature is achieved.


  Multifidelity forward uncertainty quantification (UQ) problems often involve
multiple quantities of interest and heterogeneous models (e.g., different
grids, equations, dimensions, physics, surrogate and reduced-order models).
While computational efficiency is key in this context, multi-output strategies
in multilevel/multifidelity methods are either sub-optimal or non-existent. In
this paper we extend multilevel best linear unbiased estimators (MLBLUE) to
multi-output forward UQ problems and we present new semidefinite programming
formulations for their optimal setup. Not only do these formulations yield the
optimal number of samples required, but also the optimal selection of
low-fidelity models to use. While existing MLBLUE approaches are single-output
only and require a non-trivial nonlinear optimization procedure, the new
multi-output formulations can be solved reliably and efficiently. We
demonstrate the efficacy of the new methods and formulations in practical UQ
problems with model heterogeneity.


  A new network model is proposed to describe the $1/f^\alpha$ resistance noise
in disordered materials for a wide range of $\alpha$ values ($0< \alpha < 2$).
More precisely, we have considered the resistance fluctuations of a thin
resistor with granular structure in different stationary states: from nearly
equilibrium up to far from equilibrium conditions. This system has been
modelled as a network made by different species of resistors, distinguished by
their resistances, temperature coefficients and by the energies associated with
thermally activated processes of breaking and recovery. The correlation
behavior of the resistance fluctuations is analyzed as a function of the
temperature and applied current, in both the frequency and time domains. For
the noise frequency exponent, the model provides $0< \alpha < 1$ at low
currents, in the Ohmic regime, with $\alpha$ decreasing inversely with the
temperature, and $1< \alpha <2$ at high currents, in the non-Ohmic regime.
Since the threshold current associated with the onset of nonlinearity also
depends on the temperature, the proposed model qualitatively accounts for the
complicate behavior of $\alpha$ versus temperature and current observed in many
experiments. Correspondingly, in the time domain, the auto-correlation function
of the resistance fluctuations displays a variety of behaviors which are tuned
by the external conditions.


  (abridged) We present uniform, detailed spectral analyses of gamma-ray burst
(GRB) X-ray afterglows observed with ASCA, Beppo-SAX, Chandra, and XMM-Newton,
and critically evaluate the statistical significances of X-ray emission and
absorption features in these spectra. The sample consists of 21 X-ray afterglow
observations up to and including that of GRB 040106 with spectra of sufficient
statistical quality to allow meaningful line searches, chosen here somewhat
arbitrarily to be detections with more than 100 total (source plus background)
counts. This sample includes all nine X-ray afterglows with published claims of
line detections. Moderate resolution spectra are available for 16 of the 21
sources, and for the remaining five the Chandra transmission grating
spectrometers obtained high-resolution data. All of the data are available from
the public archive. We test a simple hypothesis in which the observed spectra
are produced by a power-law continuum model modified by photoelectric
absorption by neutral material both in our Galaxy and possibly also local to
the burst. As a sample, these afterglow spectra are consistent with this
relatively simple model. However, since the chi-squared statistic is not
sensitive to weak and/or localized fluctuations, we have performed Monte Carlo
simulations to search for discrete features and to estimate their
significances. In all but a few cases, the observed deviations are entirely
consistent with statistical fluctuations such as those typically observed in
spectra with a low number of counts.


  We present ab-initio calculations of the excited state properties of liquid
water in the framework of Many-Body Green's function formalism. Snapshots taken
from molecular dynamics simulations are used as input geometries to calculate
electronic and optical spectra, and the results are averaged over the different
configurations. The optical absorption spectra with the inclusion of excitonic
effects are calculated by solving the Bethe-Salpeter equation. These
calculations are made possible by exploiting the insensitivity of screening
effects to a particular configuration. The resulting spectra are strongly
modified by many-body effects, both concerning peak energies and lineshapes,
and are in good agreement with experiments.


  We consider a scenario where an agent has multiple available strategies to
explore an unknown environment. For each new interaction with the environment,
the agent must select which exploration strategy to use. We provide a new
strategy-agnostic method that treat the situation as a Multi-Armed Bandits
problem where the reward signal is the diversity of effects that each strategy
produces. We test the method empirically on a simulated planar robotic arm, and
establish that the method is both able discriminate between strategies of
dissimilar quality, even when the differences are tenuous, and that the
resulting performance is competitive with the best fixed mixture of strategies.


  In the paper, I consider appearance of unit's digits in minor totals of a few
integer sequences. The sequences include the sequence of even integers,
sequence of odd integers and Faulhaber polynomial at $p = 2$. Application of
difference tables allows predicting of which digits can appear as unit's digits
in minor totals of the sequences. Absence of some digits ("gaps" in frequency
distributions) depends often on numbering system applied. However, in case of
odd numbers' integers the gaps are found under all numbering systems with bases
from 3 to 10.


  In this work, we study a class of hybrid dynamical systems called hybrid gene
regulatory networks (HGRNs) which was proposed to model gene regulatory
networks. In HGRNs, there exist well-behaved trajectories that reach a fixed
point or converge to a limit cycle, as well as chaotic trajectories that behave
non-periodic or indeterministic. In our work, we investigate these irregular
behaviors of HGRNs and present theoretical results about the decidability of
the reachability problem, the probability of indeterministic behavior of HGRNs,
and chaos especially in 2-dimensional HGRNs.


  We calculate the maximal non-equilibrium work that can be extracted from any
mass using information about its micro-state. This is done through the use of
black hole thermodynamics in the context of the thermodynamics of information.
The non-equilibrium work that can be obtained by knowing the exact micro-state
is found to be $\frac{1}{2} Mc^{2}$. This is calculated using the entropy of an
eternal, uncharged, non rotating black hole. This is derivation helps elucidate
the relativity of entropy and its relationship to general relativity and time.


  Equivariance has been a long-standing concern in various fields ranging from
computer vision to physical modeling. Most previous methods struggle with
generality, simplicity, and expressiveness -- some are designed ad hoc for
specific data types, some are too complex to be accessible, and some sacrifice
flexible transformations. In this work, we propose a novel and simple framework
to achieve equivariance for point cloud analysis based on the message passing
(graph neural network) scheme. We find the equivariant property could be
obtained by introducing an orientation for each point to decouple the relative
position for each point from the global pose of the entire point cloud.
Therefore, we extend current message passing networks with a module that learns
orientations for each point. Before aggregating information from the neighbors
of a point, the networks transforms the neighbors' coordinates based on the
point's learned orientations. We provide formal proofs to show the equivariance
of the proposed framework. Empirically, we demonstrate that our proposed method
is competitive on both point cloud analysis and physical modeling tasks. Code
is available at https://github.com/luost26/Equivariant-OrientedMP .


  We report on surface and bulk spin density measurements of diamond, using
ultra-sensitive magnetic force microscopy with magnetic field gradients up to
0.5 T/$\mu$m. At temperatures between 25 and 800 mK, we measure the shifts in
the resonance frequency and quality factor of a cantilever with a micromagnet
attached to it. A recently developed theoretical analysis allows us to extract
a surface spin density of 0.072 spins/nm$^2$ and a bulk spin density of 0.4 ppm
from this data. In addition, we find an increase of the $T_1$ time of the
surface spins in high magnetic field gradients due to the suppression of spin
diffusion. Our technique is applicable to a variety of samples other than
diamond, and could be of interest for several research fields where surface,
interface or impurity bulk spin densities are an important factor.


  This article is based on the Planckon densely piled vacuum model and the
principle of cosmology. With the Planck era as initial conditions and including
the early inflation, we have solved the Einstein-Friedmann equations to
describe the evolution of the universe. The results are: 1) the ratio of the
dark energy density to the vacuum quantum fluctuation energy density is
$\frac{{{\rho }_{de}}}{{{\rho
}_{vac}}}\sim{{(\frac{{{t}_{P}}}{{{T}_{0}}})}^{2}}\sim{{10}^{-122}} $; 2) at
the inflation time ${{t}_{\inf }}={{10}^{-35}}s$, the calculated universe
radiation energy density is $\rho ({{t}_{\inf }})\sim{{10}^{-16}}{{\rho
}_{vac}}$ and the corresponding temperature is ${{E}_{c}}\sim{{10}^{15}}GeV$
consistent with the GUT phase transition temperature; 3) the expanding universe
with vacuum as its environment is a non-equilibrium open system constantly
exchanging energy with vacuum; during its expansion, the Planckons in the
universe lose quantum fluctuation energy and create the cosmic expansion
quanta-cosmons, the energy of cosmons is the lost part of the vacuum quantum
fluctuation energy and contributes to the universe energy with the calculated
value ${{E}_{\cos mos}}={{10}^{22}}{{M}_{\otimes }}{{c}^{2}}$ (where
${{M}_{\otimes }}$ is solar mass); 4) the total energy of the universe, namely
the negative gravity energy plus the positive universe energy is zero; 5) the
negative gravity potential and the gravity acceleration related to the creation
of cosmons are derived with the nature of outward repulsive force, indicating
that the cosmon may be the candidate of the dark energy quantum; 6) both the
initial Planck era solution and the infinite asymptotic solution of the
Einstein-Friedman equations are unstable: the former tends to expand and the
latter tends to shrink, so that the Einstein-Friedman universe will undergo a
cyclic evolution of successive expansion and shrinking.


  The mass-metallicity relation (MZR) provides crucial insights into the baryon
cycle in galaxies and provides strong constraints on galaxy formation models.
We use JWST NIRSpec observations from the UNCOVER program to measure the
gas-phase metallicity in a sample of eight galaxies during the epoch of
reionization at $z=6-8$. Thanks to strong lensing of the galaxy cluster Abell
2744, we are able to probe extremely low stellar masses between $10^{6}$ and
$10^{8} M_{\odot}$. Using strong lines diagnostics and the most recent JWST
calibrations, we derive extremely-low oxygen abundances ranging from
12+log(O/H)=6.7 to 7.8. By combining this sample with more massive galaxies at
similar redshifts, we derive a best-fit relation of 12+{\rm
log(O/H)}=$0.39_{-0.02}^{+0.02} \times$ log(\mstar) $+ 4.52_{-0.17}^{+0.17}$,
which is steeper than determinations at $z \sim 3$. Our results show a clear
redshift evolution in the overall normalization of the relation, galaxies at
higher redshift having significantly lower metallicities at a given mass. A
comparison with theoretical models provides important constraints on which
physical processes, such as metal mixing, star formation or feedback recipes,
are important in reproducing the observations. Additionally, these galaxies
exhibit star formation rates that are higher by a factor of a few to tens
compared to extrapolated relations at similar redshifts or theoretical
predictions of main-sequence galaxies, pointing to a recent burst of star
formation. All these observations are indicative of highly stochastic star
formation and ISM enrichment, expected in these low-mass systems, suggesting
that feedback mechanisms in high-$z$ dwarf galaxies might be different from
those in place at higher masses.


  We discuss in detail the problem of counting BPS gauge invariant operators in
the chiral ring of quiver gauge theories living on D-branes probing generic
toric CY singularities. The computation of generating functions that include
counting of baryonic operators is based on a relation between the baryonic
charges in field theory and the Kaehler moduli of the CY singularities. A study
of the interplay between gauge theory and geometry shows that given geometrical
sectors appear more than once in the field theory, leading to a notion of
"multiplicities". We explain in detail how to decompose the generating function
for one D-brane into different sectors and how to compute their relevant
multiplicities by introducing geometric and anomalous baryonic charges. The
Plethystic Exponential remains a major tool for passing from one D-brane to
arbitrary number of D-branes. Explicit formulae are given for few examples,
including C^3/Z_3, F_0, and dP_1.


  We have performed a detailed experimental study of resistive cooling of large
ensembles of highly charged ions such as Ar$^{13+}$ in a cryogenic Penning
trap. Different from the measurements reported in [M. Vogel et al., Phys. Rev.
A, 043412 (2014)], we observe purely exponential cooling behavior when
conditions are chosen to allow collisional thermalization of the ions. We
provide evidence that in this situation, resistive cooling time constants and
final temperatures are independent of the initial ion energy, and that the
cooling time constant of a thermalized ion ensemble is identical to the
single-ion cooling time constant. For sufficiently high ion number densities,
our measurements show discontinuities in the spectra of motional resonances
which indicate a transition of the ion ensemble to a fluid-like state when
cooled to temperatures below approximately 14 K. With the final ion temperature
presently being 7.5 K, ions of the highest charge states are expected to form
ion crystals by mere resistive cooling, in particular not requiring the use of
laser cooling.


  We investigate new contributions to the neutron electric dipole moment
induced by colored scalars.
  As an example, we evaluate contributions coming from the color octet, weak
doublet scalar, accommodated within a modified Minimal Flavor Violating
framework. These flavor non-diagonal couplings of the color octet scalar might
account for the $A_{CP} (D^0 \to K^- K^+) - A_{CP} (D^0 \to \pi^+ \pi^-)$
asymmetry on the tree level. Using these charm asymmetry constrained couplings,
we calculate two-loop contributions to the neutron electric dipole moment and
find that they are of the same order as the experimental bound. We comment also
on contributions of higher dimensional operators to the neutron electric dipole
moment within this framework.


  Ab initio calculations using the APW+lo method as implemented in the WIEN2k
code have been used to describe the electronic structure of the
quasi-one-dimensional system BaCoO3. Both, GGA and LDA+U approximations were
employed to study different orbital and magnetic orderings. GGA predicts a
metallic ground state whereas LDA+U calculations yield an insulating and
ferromagnetic ground state (in a low-spin state) with an alternating orbital
ordering along the Co-Co chains, consistent with the available experimental
data.


  We provide a list of particle physics models at the TeV-scale that are
compatible with neutrino masses and dark matter. In these models, the Standard
Model particle content is extended with a small number (\leq 4) of scalar and
fermion fields transforming as singlets, doublets or triplets under SU(2), and
neutrino masses are generated radiatively via 1-loop diagrams. The dark matter
candidates are stabilized by a Z_2 symmetry and are in general mixtures of the
neutral components of such new multiplets. We describe the particle content of
each of these models and determine the conditions under which they are
consistent with current data. We find a total of 35 viable models, most of
which have not been previously studied in the literature. There is a great
potential to test these models at the LHC not only due to the TeV-scale masses
of the new fields but also because about half of the viable models contain
particles with exotic electric charges, which give rise to background-free
signals. Our results should serve as a first step for detailed analysis of
models that can simultaneously account for dark matter and neutrino masses.


  Following Ref. [Oriols X 2007 Phys. Rev. Lett., 98 066803], an algorithm to
deal with the exchange interaction in non-separable quantum systems is
presented. The algorithm can be applied to fermions or bosons and, by
construction, it exactly ensures that any observable is totally independent
from the interchange of particles. It is based on the use of conditional
Bohmian wave functions which are solutions of single-particle
pseudo-Schr\"odinger equations. The exchange symmetry is directly defined by
demanding symmetry properties of the quantum trajectories in the configuration
space with a universal algorithm, rather than through a particular
exchange-correlation functional introduced into the single-particle
pseudo-Schr\"odinger equation. It requires the computation of N^2 conditional
wave functions to deal with N identical particles. For separable Hamiltonians,
the algorithm reduces to the standard Slater determinant for fermions, or
permanent for bosons. A numerical test for a two-particle system, where exact
solutions for non-separable Hamiltonians are computationally accessible, is
presented. The numerical viability of the algorithm for quantum electron
transport (in a far-from equilibrium time-dependent open system) is
demonstrated by computing the current and fluctuations in a nano-resistor, with
exchange and Coulomb interactions among electrons.


  Rich, open-domain textual data available on the web resulted in great
advancements for language processing. However, while that data may be suitable
for language processing tasks, they are mostly non-conversational, lacking many
phenomena that appear in human interactions and this is one of the reasons why
we still have many unsolved challenges in conversational AI. In this work, we
attempt to address this by using Generative Conversational Networks to
automatically generate data and train social conversational agents. We evaluate
our approach on TopicalChat with automatic metrics and human evaluators,
showing that with 10% of seed data it performs close to the baseline that uses
100% of the data.


  We undertake a theoretical study of edge spin-vortex excitations in
fractional quantum Hall fluid. This is done in view of quantised Euler
hydrodynamics theory. The dispersions of true excitations for fractions within
$0\leq \nu \leq 1$ are simulated which exhibit universal similarities and
differences in behaviour. The differences arise from different edge smoothness
and spin (pseudo-spin) polarisations, in addition to spin-charge competition.
In particular, tuning the spin-charge factor causes coherent spin flipping
associated with partial and total polarisations of edge spin-vortices. This
observation is tipped as an ideal mechanism for realisation of functional
spintronic devices.


  We give a simplified proof for the perturbative renormalizability of theories
with massive vector particles. For renormalizability it is sufficient that the
vector particle is treated as an gauge field, corresponding to an Abelian gauge
group. Contrary to the non-Abelian case one does not need the Higgs mechanism
to create the appropriate mass terms. The proof uses ``Stueckelberg's trick''
and the Ward-Takahashi identities from local Abelian gauge invariance. The
simplification is due to the fact that, again contrary to the non-Abelian case,
no BRST analysis is needed.


  Devices exhibiting the integer quantum Hall effect can be modeled by
one-electron Schroedinger operators describing the planar motion of an electron
in a perpendicular, constant magnetic field, and under the influence of an
electrostatic potential. The electron motion is confined to bounded or
unbounded subsets of the plane by confining potential barriers. The edges of
the confining potential barriers create edge currents. This is the second of
two papers in which we review recent progress and prove explicit lower bounds
on the edge currents associated with one- and two-edge geometries. In this
paper, we study various unbounded and bounded, two-edge geometries with soft
and hard confining potentials. These two-edge geometries describe the electron
confined to unbounded regions in the plane, such as a strip, or to bounded
regions, such as a finite length cylinder. We prove that the edge currents are
stable under various perturbations, provided they are suitably small relative
to the magnetic field strength, including perturbations by random potentials.
The existence of, and the estimates on, the edge currents are independent of
the spectral type of the operator.


  Edge Computing (EC) offers an infrastructure that acts as the mediator
between the Cloud and the Internet of Things (IoT). The goal is to reduce the
latency that we enjoy when relying on Cloud. IoT devices interact with their
environment to collect data relaying them towards the Cloud through the EC.
Various services can be provided at the EC for the immediate management of the
collected data. One significant task is the management of missing values. In
this paper, we propose an ensemble based approach for data imputation that
takes into consideration the spatio-temporal aspect of the collected data and
the reporting devices. We propose to rely on the group of IoT devices that
resemble to the device reporting missing data and enhance its data imputation
process. We continuously reason on the correlation of the reported streams and
efficiently combine the available data. Our aim is to `aggregate' the local
view on the appropriate replacement with the `opinion' of the group. We adopt
widely known similarity techniques and a statistical modelling methodology to
deliver the final outcome. We provide the description of our model and evaluate
it through a high number of simulations adopting various experimental
scenarios.


  We investigate Isaacson's high-frequency gravitational waves which propagate
in some relevant cosmological models, in particular the FRW spacetimes. Their
time evolution in Fourier space is explicitly obtained for various metric forms
of (anti--)de Sitter universe. Behaviour of high-frequency waves in the
anisotropic Kasner spacetime is also described.


  In ths paper we study the VOA $V_{\mathcal{J},r}$ constructed by Ashihara and
Miyamoto, We construct simple quotients of
$V_{\mathcal{J},r},r\in\mathbb{Z}_{\neq 0}$ explicitly using dual-pair type
constructions. We also compute the character formula of the simple quotients
when $r=-2n,n\geq 1$.


  Root Cause Analysis (RCA) plays a pivotal role in the incident diagnosis
process for cloud services, requiring on-call engineers to identify the primary
issues and implement corrective actions to prevent future recurrences.
Improving the incident RCA process is vital for minimizing service downtime,
customer impact and manual toil. Recent advances in artificial intelligence
have introduced state-of-the-art Large Language Models (LLMs) like GPT-4, which
have proven effective in tackling various AIOps problems, ranging from code
authoring to incident management. Nonetheless, the GPT-4 model's immense size
presents challenges when trying to fine-tune it on user data because of the
significant GPU resource demand and the necessity for continuous model
fine-tuning with the emergence of new data. To address the high cost of
fine-tuning LLM, we propose an in-context learning approach for automated root
causing, which eliminates the need for fine-tuning. We conduct extensive study
over 100,000 production incidents, comparing several large language models
using multiple metrics. The results reveal that our in-context learning
approach outperforms the previous fine-tuned large language models such as
GPT-3 by an average of 24.8\% across all metrics, with an impressive 49.7\%
improvement over the zero-shot model. Moreover, human evaluation involving
actual incident owners demonstrates its superiority over the fine-tuned model,
achieving a 43.5\% improvement in correctness and an 8.7\% enhancement in
readability. The impressive results demonstrate the viability of utilizing a
vanilla GPT model for the RCA task, thereby avoiding the high computational and
maintenance costs associated with a fine-tuned model.


  Many of the planets discovered by the Kepler satellite are close orbiting
Super-Earths or Mini-Neptunes. Such objects exhibit a wide spread of densities
for similar masses. One possible explanation for this density spread is giant
collisions stripping planets of their atmospheres. In this paper we present the
results from a series of smoothed particle hydrodynamics (SPH) simulations of
head-on collisions of planets with significant atmospheres and bare projectiles
without atmospheres. Collisions between planets can have sufficient energy to
remove substantial fractions of the mass from the target planet. We find the
fraction of mass lost splits into two regimes -- at low impact energies only
the outer layers are ejected corresponding to atmosphere dominated loss, at
higher energies material deeper in the potential is excavated resulting in
significant core and mantle loss. Mass removal is less efficient in the
atmosphere loss dominated regime compared to the core and mantle loss regime,
due to the higher compressibility of atmosphere relative to core and mantle. We
find roughly twenty per cent atmosphere remains at the transition between the
two regimes. We find that the specific energy of this transition scales
linearly with the ratio of projectile to target mass for all projectile-target
mass ratios measured. The fraction of atmosphere lost is well approximated by a
quadratic in terms of the ratio of specific energy and transition energy. We
provide algorithms for the incorporation of our scaling law into future
numerical studies.


  For each closed, orientable surface F, we construct a local, diffeomorphism
invariant trace on the Kauffman bracket skein module K_t(F x [0,1]). The trace
is defined when |t| is neither 0 nor 1, and at certain roots of unity. At t = -
1, the trace is integration against the symplectic measure on the SU(2)
character variety of the fundamental group of F.


  We study black hole formation in a model of two dimensional dilaton gravity
and 24 massless scalar fields with a boundary. We find the most general
boundary condition consistent with perfect reflection of matter and the
constraints. We show that in the semiclassical approximation and for the
generic value of the parameter which characterizes the boundary conditions, the
boundary starts receeding to infinity at the speed of light whenever the total
energy of the incoming matter flux exceeds a certain critical value. This is
also the critical energy which marks the onset of black hole formation. We then
compute the quantum fluctuations of the boundary and of the rescaled scalar
curvature and show that as soon as the incoming energy exceeds this critical
value, an asymptotic observer using normal time resolutions will always measure
large fluctuations of space-time near the horizon, even though the freely
falling observer does not. This is an aspect of black hole complementarity
relating directly the quantum gravity effects.


  This note is focused on a novel technique in order to establish the
boundedness in more regular spaces for global attractors of dissipative
dynamical systems, without appealing to uniform-in-time estimates. As an
application of the abstract result, the semigroup generated by the strongly
damped wave equation $$u_{tt}-\Delta u_t-\Delta u+\phi(u)=f$$ with critical
nonlinearity is considered, whose attractor is shown to possess the optimal
regularity.


  We revisit the discovery outburst of the X-ray transient XTE J1550-564 during
which relativistic jets were observed in 1998 September, and review the radio
images obtained with the Australian Long Baseline Array, and lightcurves
obtained with the Molonglo Observatory Synthesis Telescope and the Australia
Telescope Compact Array. Based on HI spectra, we constrain the source distance
to between 3.3 and 4.9 kpc. The radio images, taken some two days apart, show
the evolution of an ejection event. The apparent separation velocity of the two
outermost ejecta is at least 1.3c and may be as large as 1.9c; when
relativistic effects are taken into account, the inferred true velocity is
>0.8c. The flux densities appear to peak simultaneously during the outburst,
with a rather flat (although still optically thin) spectral index of -0.2.


  Failure restoration at the IP layer in IP-over-WDM networks requires to map
the IP topology on the WDM topology in such a way that a failure at the WDM
layer leaves the IP topology connected. Such a mapping is called $survivable$.
As finding a survivable mapping is known to be NP-complete, in practice it
requires a heuristic approach. We have introduced in [1] a novel algorithm
called ``SMART'', that is more effective and scalable than the heuristics known
to date. Moreover, the formal analysis of SMART [2] has led to new
applications: the formal verification of the existence of a survivable mapping,
and a tool tracing and repairing the vulnerable areas of the network. In this
paper we extend the theoretical analysis in [2] by considering $multiple
failures$.


  We give an analog of triangle comparison for Kaehler manifolds with a lower
bound on the holomorphic bisectional curvature. We show that the condition
passes to noncollapsed Gromov-Hausdorff limits. We discuss tangent cones and
singular Kaehler spaces.


  Near-field electromagnetic heat transfer holds great potential for the
advancement of nanotechnology. Whereas far-field electromagnetic heat transfer
is constrained by Planck's blackbody limit, the increased density of states in
the near-field enhances heat transfer rates by orders of magnitude relative to
the conventional limit. Such enhancement opens new possibilities in numerous
applications, including thermal-photo-voltaics, nano-patterning, and imaging.
The advancement in this area, however, has been hampered by the lack of
rigorous theoretical treatment, especially for geometries that are of direct
experimental relevance. Here we introduce an efficient computational strategy,
and present the first rigorous calculation of electromagnetic heat transfer in
a sphere-plate geometry, the only geometry where transfer rate beyond blackbody
limit has been quantitatively probed at room temperature. Our approach results
in a definitive picture unifying various approximations previously used to
treat this problem, and provides new physical insights for designing
experiments aiming to explore enhanced thermal transfer.


  This document describes rules to take into account when implementing
SOAP-based web services. It explains also how to check conformance to these
rules. It can be read as a "Guideline to VO Web Service Interoperability" or a
"How to provide interoperable VO web services".


  Pretraining has become a standard technique in computer vision and natural
language processing, which usually helps to improve performance substantially.
Previously, the most dominant pretraining method is transfer learning (TL),
which uses labeled data to learn a good representation network. Recently, a new
pretraining approach -- self-supervised learning (SSL) -- has demonstrated
promising results on a wide range of applications. SSL does not require
annotated labels. It is purely conducted on input data by solving auxiliary
tasks defined on the input data examples. The current reported results show
that in certain applications, SSL outperforms TL and the other way around in
other applications. There has not been a clear understanding on what properties
of data and tasks render one approach outperforms the other. Without an
informed guideline, ML researchers have to try both methods to find out which
one is better empirically. It is usually time-consuming to do so. In this work,
we aim to address this problem. We perform a comprehensive comparative study
between SSL and TL regarding which one works better under different properties
of data and tasks, including domain difference between source and target tasks,
the amount of pretraining data, class imbalance in source data, and usage of
target data for additional pretraining, etc. The insights distilled from our
comparative studies can help ML researchers decide which method to use based on
the properties of their applications.


  Previous studies of the vacuum polarization on de Sitter have demonstrated
that there is a simple, noncovariant representation of it in which the physics
is transparent. There is also a cumbersome, covariant representation in which
the physics is obscure. Despite being unwieldy, the latter form has a powerful
appeal for those who are concerned about de Sitter invariance. We show that
nothing is lost by employing the simple, noncovariant representation because
there is a closed form procedure for converting its structure functions to
those of the covariant representation. We also present a vastly improved
technique for reading off the noncovariant structure functions from the
primitive diagrams. And we discuss the issue of representing the vacuum
polarization for a general metric background.


  We present a preliminary lattice calculation of the leading-order
electromagnetic and strong isospin-breaking corrections to the Hadronic Vacuum
Polarization (HVP) contribution of the light quarks to the anomalous magnetic
moment of the muon. The results are obtained in the quenched-$QED$
approximation using the $QCD$ gauge configurations generated by the European
Twisted Mass Collaboration (ETMC) with $N_f = 2 + 1 + 1$ dynamical quarks, at
three values of the lattice spacing varying from $0.089$ to $0.062 ~
\mbox{fm}$, at several lattice volumes and with pion masses in the range $M_\pi
\simeq 220 \div 490 ~ \mbox{MeV}$.


  Liquid droplets impacting a superhydrophobic surface decorated with
micro-scale posts often bounce off the surface. However, by decreasing the
impact velocity droplets may land on the surface in a fakir state, and by
increasing it posts may impale droplets that are then stuck on the surface. We
use a two-phase lattice-Boltzmann model to simulate droplet impact on
superhydrophobic surfaces, and show that it may result in a fakir state also
for reasonable high impact velocities. This happens more easily if the surface
is made more hydrophobic or the post height is increased, thereby making the
impaled state energetically less favourable.


  Let $G$ be $U(n)$, $SU(n)$, $Sp(n)$ or $Spin(n)$. In this short note we give
explicit general formulas for Adams operations on $K^*(G)$, and eigenvectors of
Adams operations on $K^*(U(n))$.


  Some time ago, Rideau and Winternitz introduced a realization of the quantum
algebra su_q(2) on a real two-dimensional sphere, or a real plane, and
constructed a basis for its representations in terms of q-special functions,
which can be expressed in terms of q-Vilenkin functions. In their study, the
values of q were implicitly restricted to q in R^+. In the present paper, we
extend their work to the case of generic values of q in S^1 (i.e., q values
different from a root of unity). In addition, we unitarize the representations
for both types of q values, q in R^+ and generic q in S^1, by determining some
appropriate scalar products.


  We present a deep learning approach for learning the joint semantic
embeddings of images and captions in a Euclidean space, such that the semantic
similarity is approximated by the L2 distances in the embedding space. For
that, we introduce a metric learning scheme that utilizes multitask learning to
learn the embedding of identical semantic concepts using a center loss. By
introducing a differentiable quantization scheme into the end-to-end trainable
network, we derive a semantic embedding of semantically similar concepts in
Euclidean space. We also propose a novel metric learning formulation using an
adaptive margin hinge loss, that is refined during the training phase. The
proposed scheme was applied to the MS-COCO, Flicke30K and Flickr8K datasets,
and was shown to compare favorably with contemporary state-of-the-art
approaches.


  Analyzing job hopping behavior is important for the understanding of job
preference and career progression of working individuals. When analyzed at the
workforce population level, job hop analysis helps to gain insights of talent
flow and organization competition. Traditionally, surveys are conducted on job
seekers and employers to study job behavior. While surveys are good at getting
direct user input to specially designed questions, they are often not scalable
and timely enough to cope with fast-changing job landscape. In this paper, we
present a data science approach to analyze job hops performed by about 490,000
working professionals located in a city using their publicly shared profiles.
We develop several metrics to measure how much work experience is needed to
take up a job and how recent/established the job is, and then examine how these
metrics correlate with the propensity of hopping. We also study how job hop
behavior is related to job promotion/demotion. Finally, we perform network
analyses at the job and organization levels in order to derive insights on
talent flow as well as job and organizational competitiveness.


  This paper considers the requirements to ensure bounded mean queuing delay
and non-starvation in a slotted Aloha network operating the exponential backoff
protocol. It is well-known that the maximum possible throughput of a slotted
Aloha system with a large number of nodes is 1/e = 0.3679. Indeed, a saturation
throughput of 1/e can be achieved with an exponential backoff factor of r =
e/(e-1)=1.5820. The binary backoff factor of r = 2 is assumed in the majority
of prior work, and in many practical multiple-access networks such as the
Ethernet and WiFi. For slotted Aloha, the saturation throughput 0.3466 for r =
2 is reasonably close to the maximum of 1/e, and one could hardly raise
objection to adopting r = 2 in the system. However, this paper shows that if
mean queuing delay is to be bounded, then the sustainable throughput when r = 2
is only 0.2158, a drastic 41% drop from 1/e . Fortunately, the optimal setting
of r = 1.3757 under the bounded mean-delay requirement allows us to achieve
sustainable throughput of 0.3545, a penalty of only less than 4% relative to
1/e. A general conclusion is that the value of r may significantly affect the
queuing delay performance. Besides analyzing mean queuing delay, this paper
also delves into the phenomenon of starvation, wherein some nodes are deprived
of service for an extended period of time while other nodes hog the system.
Specifically, we propose a quantitative definition for starvation and show that
the conditions to guarantee bounded mean delay and non-starved operation are
one of the same, thus uniting these two notions. Finally, we show that when
mean delay is large and starvation occurs, the performance results obtained
from simulation experiments may not converge. A quantitative discussion of this
issue is provided in this paper.


  We investigate the numerical implementation of the limiting equation for the
phonon transport equation in the small Knudsen number regime. The main
contribution is that we derive the limiting equation that achieves the second
order convergence, and provide a numerical recipe for computing the Robin
coefficients. These coefficients are obtained by solving an auxiliary
half-space equation. Numerically the half-space equation is solved by a
spectral method that relies on the even-odd decomposition to eliminate
corner-point singularity. Numerical evidences will be presented to justify the
second order asymptotic convergence rate.