  We show that a quantum particle subjected to a positive force in one path of
a Mach-Zehnder interferometer and a null force in the other path may receive a
negative average momentum transfer when it leaves the interferometer by a
particular exit. In this scenario, an ensemble of particles may receive an
average momentum in the opposite direction of the applied force due to quantum
interference, a behavior with no classical analogue. We discuss some
experimental schemes that could verify the effect with current technology, with
electrons or neutrons in Mach-Zehnder interferometers in free space and with
atoms from a Bose-Einstein condensate.


  By using high-resolution 1D hydrodynamical simulations, we investigate the
effects of purely mechanical feedback from super massive black holes (SMBHs) in
the evolution of elliptical galaxies for a broad range of feedback efficiencies
and compare the results to four major observational constraints. In particular,
we focus on 1) the central black hole to stellar mass ratio of the host galaxy,
2) the lifetime of the luminous quasar phase, 3) the mass of stars formed in
the host galaxy within the last Gyr, and 4) the X-ray luminosity of the hot
diffuse gas. As a result, we try to pin down the most successful range of
mechanical feedback efficiencies. We find that while low feedback efficiencies
result in too much growth of the SMBH, high efficiencies totally blow out the
hot interstellar gas, and the models are characterized by very low thermal
X-ray luminosity well below the observed range. The net lifetime of the quasar
phase is strongly coupled to the mass ratio between SMBH and its host galaxy,
while the X-ray luminosity is generally correlated to the recent star formation
within the last Gyr. When considering the popularly adopted model of the
constant feedback efficiency, the feedback energy deposited into the ambient
medium should be more than 0.01% of the SMBH accretion energy to be consistent
with the SMBH mass to stellar mass ratio in the local universe. Yet, the X-ray
luminosity of the hot gas favors about 0.005% of the accretion energy as the
mechanical AGN feedback energy. We conclude that the purely mechanical feedback
mode is unlikely to be simultaneously compatible with all four observable
tests, even allowing a broad range of feedback efficiencies, and that including
both radiative and mechanical feedback together may be a solution to comply the
observational constraints. [abridged]


  We present the discovery and analysis of SN\,2022oqm, a Type Ic supernova
(SN) detected $<1$\,day after explosion. The SN rises to a blue and short-lived
(2\,days) initial peak. Early-time spectral observations of SN\,2022oqm show a
hot (40,000\,K) continuum with high-ionization C and O absorption features at
velocities of 4000\,km\,s$^{-1}$, while its photospheric radius expands at
20,000\,\kms, indicating a pre-existing distribution of expanding C/O material.
After $\sim2.5$\,days, both the spectrum and light curves evolve into those of
a typical SN Ic, with line velocities of $\sim10,000$\,km\,s$^{-1}$, in
agreement with the photospheric radius evolution. The optical light curves
reach a second peak at $t\approx15$\,days. By $t=60$\,days, the spectrum of
\oqm\ becomes nearly nebular, displaying strong \ion{Ca}{2} and [\ion{Ca}{2}]
emission with no detectable [\ion{O}{1}], marking this event as Ca-rich. The
early behavior can be explained by $10^{-3}$\,\msun\ of optically thin
circumstellar material (CSM) surrounding either (1) a massive compact
progenitor such as a Wolf-Rayet star, (2) a massive stripped progenitor with an
extended envelope, or (3) a binary system with a white dwarf. We propose that
the early-time light curve is powered by both interaction of the ejecta with
the optically thin CSM and shock cooling (in the massive-star scenario). The
observations can be explained by CSM that is optically thick to X-ray photons,
is optically thick in the lines as seen in the spectra, and is optically thin
to visible-light continuum photons that come either from downscattered X-rays
or from the shock-heated ejecta. Calculations show that this scenario is
self-consistent.


  In this paper we prove short-time existence of a smooth solution in the plane
to the surface diffusion equation with an elastic term and without an
additional curvature regularization. We also prove the asymptotic stability of
strictly stable stationary sets.


  It is shown that the classical quadratic and cubic transformation identities
satisfied by the hypergeometric function ${}_3F_2$ can be extended to include
additional parameter pairs, which differ by integers. In the extended
identities, which involve hypergeometric functions of arbitrarily high order,
the added parameters are nonlinearly constrained: in the quadratic case, they
are the negated roots of certain orthogonal polynomials of a discrete argument
(dual Hahn and Racah ones). Specializations and applications of the extended
identities are given, including an extension of Whipple's identity relating
very well poised ${}_7F_6(1)$ series and balanced ${}_4F_3(1)$ series, and
extensions of other summation identities.


  Classical novae are thermonuclear explosions that take place in the envelopes
of accreting white dwarfs in binary systems. The material piles up under
degenerate conditions, driving a thermonuclear runaway. The energy released by
the suite of nuclear processes operating at the envelope heats the material up
to peak temperatures about 100 - 400 MK. During these events, about 10-3 - 10-7
Msun, enriched in CNO and, sometimes, other intermediate-mass elements (e.g.,
Ne, Na, Mg, Al) are ejected into the interstellar medium. To account for the
gross observational properties of classical novae (in particular, the large
concentrations of metals spectroscopically inferred in the ejecta), models
require mixing between the (solar-like) material transferred from the secondary
and the outermost layers (CO- or ONe-rich) of the underlying white dwarf.
Recent multidimensional simulations have demonstrated that Kelvin-Helmholtz
instabilities can naturally produce self-enrichment of the accreted envelope
with material from the underlying white dwarf at levels that agree with
observations. However, the feasibility of this mechanism has been explored in
the framework of CO white dwarfs, while mixing with different substrates still
needs to be properly addressed. Three-dimensional simulations of mixing at the
core-envelope interface during nova outbursts have been performed with the
multidimensional code FLASH, for two types of substrates: CO- and ONe-rich. We
show that the presence of an ONe-rich substrate, as in "neon novae", yields
larger metallicity enhancements in the ejecta, compared to CO,rich substrates
(i.e., non-neon novae). A number of requirements and constraints for such 3-D
simulations (e.g., minimum resolution, size of the computational domain) are
also outlined.


  A perspective of statistical language models which emphasizes their
collocational aspect is advocated. It is suggested that strings be generalized
in terms of classes of relationships instead of classes of objects. The single
most important characteristic of such a model is a mechanism for comparing
patterns. When patterns are fully generalized a natural definition of syntactic
class emerges as a subset of relational class. These collocational syntactic
classes should be an unambiguous partition of traditional syntactic classes.


  This investigation is directed to understand the asymmetry in $\Delta$X
variations caused due to the relative roles played by IMF Bz and IMF By in a
particular interval (22:22 - 22:55 UT), during the main phase of a strong
geomagnetic storm event of April 06, 2000 (Ap = 236). Two pairs of antipodal
stations, being part of the SuperMAG network, are considered here. Ionospheric
convection maps from SuperDARN network are used to understand spatio-temporal
evolution of the DP2 ionospheric convection patterns over high-latitudes. The
two-dimensional maps of equivalent currents are used to show signatures of
global DP2 currents associated with the interplay effect between the two IMF
components. Observations show increases in the difference in $\Delta$X
variations between nearly antipodal stations from the Japanese-European/African
sector with respect to the same between the nearly antipodal stations from the
Pacific/American-Indian sector. This asymmetry is observed during the period
when the absolute magnitude of IMF By is larger than that of IMF Bz resulting
in a significant and conspicuous enhancement in IMF |By/Bz|. It is suggested
that the distortions in DP2 cells and associated rotation of electrodynamic
day-night divider, bring one pair of stations under the same DP2 cell and one
station of the other pair under a different DP2 cell and throat flow region
leading to the asymmetry in $\Delta$X variations between the antipodal
stations. Therefore, the work highlights the importance of the interplay
between IMF Bz and IMF By in determining the ionospheric impact over low
latitudes during strong geomagnetic conditions.


  Starting from the boundary CFT definition for the D-branes in vacuum string
field theory (VSFT) given in hep-th/0105168, we derive the oscillator
expression for the D24-brane solution in the VSFT on D25-brane. We show that
the state takes the form of a squeezed state, similar to the one found directly
in terms of the oscillators and reported in hep-th/0102112. Both the solutions
are actually one parameter families of solutions. We also find numerical
evidence that at least for moderately large values of the parameter $(b)$ in
the oscillator construction the two families of solutions are same under a
suitable redefinition of the parameter. Finally we generalize the method to
computing the oscillator expression for a D-brane solution with constant gauge
field strength turned on along the world volume.


  The nonlinear Fourier transform (NFT) has recently gained significant
attention in fiber optic communications and other engineering fields. Although
several numerical algorithms for computing the NFT have been published, the
design of highly accurate low-complexity algorithms remains a challenge. In
this paper, we present new fast forward NFT algorithms that achieve accuracies
that are orders of magnitudes better than current methods, at comparable run
times and even for moderate sampling intervals. The new algorithms are compared
to existing solutions in multiple, extensive numerical examples.


  The detection of GW190521 gains huge attention because it is the most massive
binary that LIGO and Virgo ever confidently detected until the release of
GWTC-3 (GW190426_190642 is more massive), and it is the first black hole merger
whose remnant is believed to be an intermediate mass black hole. Furthermore,
the primary black hole mass falls in the black hole mass gap, where the
pair-instability supernova prevents the formation of astrophysical black holes
in this range. In this paper, we systematically explore the prospect of TianQin
on detecting GW190521-like sources. For sources with small orbital
eccentricities, (i) TianQin could resolve up to a dozen of sources with
signal-to-noise ratio (SNR) larger than 8. Even if the signal-to-noise ratio
threshold increases to 12, TianQin could still detect GW190521-like binaries.
(ii) The parameters of sources merging within several years would be precisely
recovered. The precision of coalescence time and sky localization closes to $1\
{\rm s}$ and $1\ {\rm deg^{2}}$ respectively. This indicates that TianQin could
provide early warnings for ground-based gravitational waves detectors and
electromagnetic telescopes for these sources. Furthermore, TianQin could
distinguish the formation channels of these sources by measuring the orbital
eccentricities with a relative precision of $10^{-4}$. (iii) TianQin could
constrain the Hubble constant with a $10\%$ precision with GW190521-like
sources. Finally, for very eccentric GW190521-like sources, although their
gravitational wave signal might be too weak for TianQin to detect, even the
null detection of TianQin could still present a significant contribution to the
understanding of the underlying science.


  We present results on hadron masses and quark condensate from Neuberger's
overlap fermion. The scaling and chiral properties and finite volume effects
from this new Dirac operator are studied. We find that the generalized
Gell-Mann-Oakes-Renner relation is well satisfied down to the physical u and d
quark mass range. We find that in the range of the lattice spacing we consider,
the $\pi$ and $\rho$ masses at a fixed $m_{\pi}/m_{\rho}$ ratio have weak
$O(a^2)$ dependence.


  We study the N\'eel-paramagnetic quantum phase transition in two-dimensional
dimerized $S=1/2$ Heisenberg antiferromagnets using finite-size scaling of
quantum Monte Carlo data. We resolve the long standing issue of the role of
cubic interactions arising in the bond-operator representation when the dimer
pattern lacks a certain symmetry. We find non-monotonic (monotonic) size
dependence in the staggered (columnar) dimerized model, where cubic
interactions are (are not) present. We conclude that there is an irrelevant
field in the staggered model that is not present in the columnar case, but, at
variance with previous claims, it is not the leading irrelevant field. The new
exponent is $\omega_2 \approx 1.25$ and the prefactor of the correction
$L^{-\omega_2}$ is large and comes with a different sign from that of the
formally leading conventional correction with exponent $\omega_1 \approx 0.78$.
Our study highlights the possibility of competing scaling corrections at
quantum critical points.


  We apply the Hurst exponent idea for investigation of DJIA index time-series
data. The behavior of the local Hurst exponent prior to drastic changes in
financial series signal is analyzed. The optimal length of the time-window over
which this exponent can be calculated in order to make some meaningful
predictions is discussed. Our prediction hypothesis is verified with examples
of '29 and '87 crashes, as well as with more recent phenomena in stock market
from the period 1995-2003.Some interesting agreements are found.


  To reduce the typical time-consuming routines of plant modeling for
model-based controller designs, the fictitious reference iterative tuning
(FRIT) has been proposed and has proven to be effective in many applications.
However, it is generally difficult to select a reference model properly without
information on the plant, which significantly affects the control performance
and sometimes leads to considerable performance degradation. To address this
problem, we propose a pseudo-linearization (PL) method using FRIT and design a
new controller for nonlinear systems that combines data-driven and model-based
control. This design considers the input constraints using model predictive
control. The effectiveness of the proposed method was evaluated according to
several practical references using numerical simulations for nonlinear classes
and experiments involving artificial muscles with hysteresis characteristics.


  We investigate in detail the circular motion of test particles on the
equatorial plane of the ergoregion in the Kerr spacetime. We consider all the
regions where circular motion is allowed, and we analyze the stability
properties and the energy and angular momentum of the test particles. We show
that the structure of the stability regions has definite features that make it
possible to distinguish between black holes and naked singularities. The naked
singularity case presents a very structured non-connected set of regions of
orbital stability, where the presence of counterrotating particles and zero
angular momentum particles for a specific class of naked singularities is
interpreted as due to the presence of a repulsive field generated by the
central source of gravity. In particular, we analyze the effects of the
dynamical structure of the ergoregion (the union of the orbital regions for
different attractor spins) on the behavior of accretion disks around the
central source. The properties of the circular motion turn out to be so
distinctive that they allow the introduction of a complete classification of
Kerr spacetimes, each class of which is characterized by different physical
effects that could be of especial relevance in observational Astrophysics. We
also identify some special black hole spacetimes where these effects could be
relevant.


  We present the latest developments to the phase modulation method for finding
binaries among pulsating stars. We demonstrate how the orbital elements of a
pulsating binary star can be obtained analytically, that is, without converting
time delays to radial velocities by numerical differentiation. Using the time
delays directly offers greater precision, and allows the parameters of much
smaller orbits to be derived. The method is applied to KIC9651065, KIC10990452,
and KIC8264492, and a set of the orbital parameters is obtained for each
system. Radial velocity curves for these stars are deduced from the orbital
elements thus obtained.


  In this paper we study the second integral cohomology of moduli spaces of
semistable sheaves on projective K3 surfaces. If $S$ is a projective K3
surface, $v$ a Mukai vector and $H$ a $v-$generic polarization on $S$, we show
that $H^{2}(M_{v},\mathbb{Z})$ is a free $\mathbb{Z}-$module of rank 23
carrying a pure weight-two Hodge structure and a lattice structure, with
respect to which $H^{2}(M_{v},\mathbb{Z})$ is Hodge isometric to the Hodge
sublattice $v^{\perp}$ of the Mukai lattice of $S$. Similar results are proved
for Abelian surfaces.


  Machine learning has been highly successful in data-intensive applications
but is often hampered when the data set is small. Recently, Few-Shot Learning
(FSL) is proposed to tackle this problem. Using prior knowledge, FSL can
rapidly generalize to new tasks containing only a few samples with supervised
information. In this paper, we conduct a thorough survey to fully understand
FSL. Starting from a formal definition of FSL, we distinguish FSL from several
relevant machine learning problems. We then point out that the core issue in
FSL is that the empirical risk minimized is unreliable. Based on how prior
knowledge can be used to handle this core issue, we categorize FSL methods from
three perspectives: (i) data, which uses prior knowledge to augment the
supervised experience; (ii) model, which uses prior knowledge to reduce the
size of the hypothesis space; and (iii) algorithm, which uses prior knowledge
to alter the search for the best hypothesis in the given hypothesis space. With
this taxonomy, we review and discuss the pros and cons of each category.
Promising directions, in the aspects of the FSL problem setups, techniques,
applications and theories, are also proposed to provide insights for future
research.


  The recent major modification, r$^2$SCAN, of the SCAN (strongly constrained
and appropriately normed) meta-GGA exchange-correlation functional is shown to
give substantially better spin-crossover electronic energies (high spin minus
low spin) on a benchmark data set than the original SCAN. The deorbitalized
counterpart r$^2$SCAN-L is almost as good as SCAN and much faster in
periodically bounded systems. A combination strategy for balanced treatment of
molecular and periodic spin-crossover therefore is recommended.


  If a collection of identical particles is poured into a container, different
shapes will fill to different densities. But what is the shape that fills a
container as close as possible to a pre-specified, desired density? We
demonstrate a solution to this inverse-packing problem by framing it in the
context of artificial evolution. By representing shapes as bonded spheres, we
show how shapes may be mutated, simulated, and selected to produce particularly
dense or loose packing aggregates, both with and without friction. Moreover, we
show how motifs emerge linking these shapes together. The result is a set of
design rules that function as an effective solution to the inverse packing
problem for given packing procedures and boundary conditions. Finally, we show
that these results are verified by experiments on 3D-printed prototypes used to
make packings in the real world.


  We discuss dilepton production from a quark-gluon plasma which has a
time-dependent anisotropy in momentum space. A phenomenological model for the
hard momentum scale, p_hard(tau) and the plasma anisotropy parameter, xi(tau),
is presented. The model interpolates between early-time 1+1 free streaming
behavior (tau << tau_iso) and late-time ideal 1+1 hydrodynamical behavior (tau
>> tau_iso). Using this model, we find that the dilepton rate arising from
electromagnetic annihilations of quarks in the kinematic range 3 <p_T< 8 GeV is
sensitive to the assumed isotropization time of the system, tau_iso. Therefore
high-energy dilepton production can be used to probe the degree of
momentum-space isotropy of a quark-gluon plasma produced in relativistic heavy
ion collisions and the time of onset of hydrodynamic expansion of the QGP.


  Un des r\'esultats fondamentaux de la th\'eorie de Bass-Serre est le
th\'eor\`eme suivant : un groupe est amalgam\'e si et seulement si il agit sur
un arbre avec comme domaine fondamental un segment. Dans cet article nous
donnons un analogue pour les groupes profinis de ce r\'esultat en utilisant la
th\'eorie des prographes de Deschamps et Suarez introduite.
  A fundamental result of Bass-Serre theory is the following theorem: an
abstract group is a free product with amalgamation if and only if it acts on a
tree with a segment as fundamental domain. In this article, an analogous result
for profinite groups will be given, using the theory of prographs of Deschamps
and Suarez


  Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable
success in various image generation tasks compared with Generative Adversarial
Nets (GANs). Recent work on semantic image synthesis mainly follows the
\emph{de facto} GAN-based approaches, which may lead to unsatisfactory quality
or diversity of generated images. In this paper, we propose a novel framework
based on DDPM for semantic image synthesis. Unlike previous conditional
diffusion model directly feeds the semantic layout and noisy image as input to
a U-Net structure, which may not fully leverage the information in the input
semantic mask, our framework processes semantic layout and noisy image
differently. It feeds noisy image to the encoder of the U-Net structure while
the semantic layout to the decoder by multi-layer spatially-adaptive
normalization operators. To further improve the generation quality and semantic
interpretability in semantic image synthesis, we introduce the classifier-free
guidance sampling strategy, which acknowledge the scores of an unconditional
model for sampling process. Extensive experiments on three benchmark datasets
demonstrate the effectiveness of our proposed method, achieving
state-of-the-art performance in terms of fidelity (FID) and diversity (LPIPS).


  Microwave absorption experiments employing a phase-sensitive external
resistive detection are performed for a topical van der Waals antiferromagnet
CrSBr. The field dependence of two resonance modes is measured in an applied
field parallel to the three principal crystallographic directions, revealing
anisotropies and magnetic transitions in this material. To account for the
observed results, we formulate a microscopic spin model with a bi-axial
single-ion anisotropy and inter-plane exchange. Theoretical calculations give
an excellent description of full magnon spectra enabling us to precisely
determine microscopic interaction parameters for CrSBr.


  Using only implicit data, many recommender systems fail in general to provide
a precise set of recommendations to users with limited interaction history.
This issue is regarded as the "Cold Start" problem and is typically resolved by
switching to content-based approaches where extra costly information is
required. In this paper, we use a dimensionality reduction algorithm, Word2Vec
(W2V), originally applied in Natural Language Processing problems under the
framework of Collaborative Filtering (CF) to tackle the "Cold Start" problem
using only implicit data. This combined method is named Embedded Collaborative
Filtering (ECF). An experiment is conducted to determine the performance of ECF
on two different implicit data sets. We show that the ECF approach outperforms
other popular and state-of-the-art approaches in "Cold Start" scenarios.


  Language style transferring rephrases text with specific stylistic attributes
while preserving the original attribute-independent content. One main challenge
in learning a style transfer system is a lack of parallel data where the source
sentence is in one style and the target sentence in another style. With this
constraint, in this paper, we adapt unsupervised machine translation methods
for the task of automatic style transfer. We first take advantage of
style-preference information and word embedding similarity to produce
pseudo-parallel data with a statistical machine translation (SMT) framework.
Then the iterative back-translation approach is employed to jointly train two
neural machine translation (NMT) based transfer systems. To control the noise
generated during joint training, a style classifier is introduced to guarantee
the accuracy of style transfer and penalize bad candidates in the generated
pseudo data. Experiments on benchmark datasets show that our proposed method
outperforms previous state-of-the-art models in terms of both accuracy of style
transfer and quality of input-output correspondence.


  This paper gives a short review of the history of statistical physics
starting from D. Bernoulli's kinetic theory of gases in the 18th century until
the recent new developments in nonequilibrium kinetic theory in the last
decades of this century. The most important contributions of the great
physicists Clausius, Maxwell and Boltzmann are sketched. It is shown how the
reversibility and the recurrence paradox are resolved within Boltzmann's
statistical interpretation of the second law of thermodynamics. An approach to
classical and quantum statistical mechanics is outlined. Finally the progress
in nonequilibrium kinetic theory in the second half of this century is sketched
starting from the work of N.N. Bogolyubov in 1946 up to the progress made
recently in understanding the diffusion processes in dense fluids using
computer simulations and analytical methods.


  In the particle in the box problem, the particle is not in both boxes at the
same time as some would have you believe. It is a set definition situation with
the two boxes being part of a set that also contains a particle. Set and subset
differences are explored.
  Atomic electron orbitals can be mimicked by roulette wheel probability; thus
ELECTRONIC ROULETTE. 0 and 00 serve as boundary limits and are on opposite
sides of the central core - a point that quantum physics ignores. Considering a
stray marble on the floor as part of the roulette wheel menage is taking
assumptions a bit too far. Likewise, the attraction between a positive and
negative charge at distance does not make the negative charge part of the
positive charge's orbital system. This, of course, is contrary to the stance of
current quantum physics methodology that carries this orbital association a bit
too far.


  The nuclear saturation mechanism is discussed in terms of two-nucleon and
three-nucleon interactions in chiral effective field theory (Ch-EFT), using the
framework of lowest-order Brueckner theory. After the Coester band, which is
observed in calculating saturation points with various nucleon-nucleon (NN)
forces, is revisited using modern NN potentials and their low-momentum
equivalent interactions, detailed account of the saturation curve of the Ch-EFT
interaction is presented. The three-nucleon force (3NF) is treated by reducing
it to an effective two-body interaction by folding the third nucleon degrees of
freedom. Uncertainties due to the choice of the 3NF low-energy constants $c_D$
and $c_E$ are discussed. The reduction of the cutoff-energy dependence of the
NN potential is explained by demonstrating the effect of the 3NF in the
$^1$S$_0$ and $^3$S$_1$ states.


  We address the problem to estimate a Kronecker graphical model corresponding
to an autoregressive Gaussian stochastic process. The latter is completely
described by the power spectral density function whose inverse has support
which admits a Kronecker product decomposition. We propose a Bayesian approach
to estimate such a model. We test the effectiveness of the proposed method by
some numerical experiments. We also apply the procedure to urban pollution
monitoring data.


  The actions of a half Virasoro algebra have appeared in many integrable
systems. In this paper we show that there is an action of a (Half) Virasoro
algebra on the space of (2+0) harmonic maps into a Lie group. This action is
generated by a natural action on the frames. A similar calculation on the
space-time (1+1) harmonic maps yields formulas generated by John Schwarz.


  The topic of this study is the outage behavior of multiple-antenna slow
fading channels with quantized feedback and partial power control. A fixed-rate
communication system is considered. It is known from the literature that with
error-free feedback, the outage-optimal quantizer for power control has a
circular structure. Moreover, the diversity gain of the system increases
polynomially with the cardinality of the power control codebook. Here, a
similar system is studied, but when the feedback link is error-prone. We prove
that in the high-SNR regime, the optimal quantizer structure with noisy
feedback is still circular and the optimal Voronoi regions are contiguous
non-zero probability intervals. Furthermore, the optimal power control codebook
resembles a channel optimized scalar quantizer (COSQ), i.e., the Voronoi
regions merge with erroneous feedback information. Using a COSQ, the outage
performance of the system is superior to that of a no-feedback scheme. However,
asymptotic analysis shows that the diversity gain of the system is the same as
a no-CSIT scheme if there is a non-zero and non-vanishing feedback error
probability.


  Co-saliency detection (Co-SOD) aims to segment the common salient foreground
in a group of relevant images. In this paper, inspired by human behavior, we
propose a gradient-induced co-saliency detection (GICD) method. We first
abstract a consensus representation for the grouped images in the embedding
space; then, by comparing the single image with consensus representation, we
utilize the feedback gradient information to induce more attention to the
discriminative co-salient features. In addition, due to the lack of Co-SOD
training data, we design a jigsaw training strategy, with which Co-SOD networks
can be trained on general saliency datasets without extra pixel-level
annotations. To evaluate the performance of Co-SOD methods on discovering the
co-salient object among multiple foregrounds, we construct a challenging CoCA
dataset, where each image contains at least one extraneous foreground along
with the co-salient object. Experiments demonstrate that our GICD achieves
state-of-the-art performance. Our codes and dataset are available at
https://mmcheng.net/gicd/.


  Let $G$ be a compact simple Lie group equipped with the left invariant
framing $L$. It is known that there are several groups $G$ such that $(G, L)$
is non-null framed bordant. Previously we gave an alternative proof of these
results using the decomposition formula of its bordism class into a Kronecker
product by E. Ossa. In this note we propose a verification formula by
reconsidering it, through a little more ingenious in the use of this product
formula, and try to apply it to the non-null bordantness results above.


  The neutron transport equation (NTE) describes the flux of neutrons across a
planar cross-section in an inhomogeneous fissile medium when the process of
nuclear fission is active. Classical work on the NTE emerges from the applied
mathematics literature in the 1950s through the work of R. Dautray and
collaborators, [7, 8, 19]. The NTE also has a probabilistic representation
through the semigroup of the underlying physical process when envisaged as a
stochastic process; cf. [7, 17, 18, 20]. More recently, [6] and [16] have
continued the probabilistic analysis of the NTE, introducing more recent ideas
from the theory of spatial branching processes and quasi-stationary
distributions. In this paper, we continue in the same vein and look at a
fundamental description of stochastic growth in the supercritical regime. Our
main result provides a significant improvement on the last known contribution
to growth properties of the physical process in [20], bringing neutron
transport theory in line with modern branching process theory such as [14, 12].


  Perceptual distances between images, as measured in the space of pre-trained
deep features, have outperformed prior low-level, pixel-based metrics on
assessing perceptual similarity. While the capabilities of older and less
accurate models such as AlexNet and VGG to capture perceptual similarity are
well known, modern and more accurate models are less studied. In this paper, we
present a large-scale empirical study to assess how well ImageNet classifiers
perform on perceptual similarity. First, we observe a inverse correlation
between ImageNet accuracy and Perceptual Scores of modern networks such as
ResNets, EfficientNets, and Vision Transformers: that is better classifiers
achieve worse Perceptual Scores. Then, we examine the ImageNet
accuracy/Perceptual Score relationship on varying the depth, width, number of
training steps, weight decay, label smoothing, and dropout. Higher accuracy
improves Perceptual Score up to a certain point, but we uncover a Pareto
frontier between accuracies and Perceptual Score in the mid-to-high accuracy
regime. We explore this relationship further using a number of plausible
hypotheses such as distortion invariance, spatial frequency sensitivity, and
alternative perceptual functions. Interestingly we discover shallow ResNets and
ResNets trained for less than 5 epochs only on ImageNet, whose emergent
Perceptual Score matches the prior best networks trained directly on supervised
human perceptual judgements. The checkpoints for the models in our study are
available at
https://console.cloud.google.com/storage/browser/gresearch/perceptual_similarity.


  We consider SUSY extensions of the standard model where the gravitino is the
dark-matter particle and the stau is long lived. If there is a significant mass
gap with squarks and gluinos, the staus produced at hadron colliders tend to be
fast (beta>0.8), and the searches based on their delay in the time of flight or
their anomalous ionization become less effective. Such staus would be
identified as regular muons with the same linear momentum and a slightly
reduced energy. Compared to the usual SUSY models where a neutralino is the
LSP, this scenario implies (i) more leptons (the two staus at the end of the
decay chains), (ii) a strong e-mu asymmetry, and (iii) less missing E_T (just
from neutrinos, as the lightest neutralino decays into stau). We study the
bounds on this SUSY from current LHC analyses (same-sign dileptons and
multilepton events) and discuss the best strategy for its observation.


  Existing patient data analytics platforms fail to incorporate information
that has context, is personal, and topical to patients. For a recommendation
system to give a suitable response to a query or to derive meaningful insights
from patient data, it should consider personal information about the patient's
health history, including but not limited to their preferences, locations, and
life choices that are currently applicable to them. In this review paper, we
critique existing literature in this space and also discuss the various
research challenges that come with designing, building, and operationalizing a
personal health knowledge graph (PHKG) for patients.


  Though some years remain before quantum computation can fully outperform
conventional computation, it already provides resources that can be used for
exploratory purposes in various fields. This includes certain tasks for
procedural generation in computer games, music and art. The so-called `Quantum
Blur' method represents the first step on this journey, providing a simple
proof-of-principle example of how quantum software can be useful in these areas
today. Here we analyse the `Quantum Blur' method and compare it to conventional
blur effects. This investigation was guided by discussions with the most
prominent user of the method, to determine which features were found most
useful. In particular we determine how these features depend on the quantum
phenomena of superposition and entanglement.


  We study the quantum ground state phases of the one-dimensional disordered
Bose--Hubbard model with attractive interactions, realized by a chain of
superconducting transmon qubits or cold atoms. We map the phase diagram using
perturbation theory and exact diagonalization. Compared to the repulsive
Bose--Hubbard model, the quantum ground state behavior is dramatically
different. At strong disorder of the on-site energies, all the bosons localize
into the vicinity of a single site, contrary to the Bose glass behavior of the
repulsive model. At weak disorder, depending on hopping, the ground state is
either superfluid or a W state, which is a multi-site and multi-particle
entangled superposition of states where all the bosons occupy a single site. We
show that the robustness of the W phase against disorder diminishes as the
total number of bosons increases.


  All known string theory models may be obtained as partial fermionization,
projection and background Ans\"atze from the original, purely bosonic string
theory. The latter theory in turn has been recently shown to describe a
chirally and non-commutatively doubled and manifestly T-dual target spacetime.
We show herein that this, so-called metastring theory automatically includes
mirror symmetry.


  The reason for the present accelerated expansion of the Universe stands as
one of the most profound questions in the realm of science, with deep
connections to both cosmology and fundamental physics. From a cosmological
point of view, physical models aimed at elucidating the observed expansion can
be categorized into two major classes: dark energy and modified gravity. We
review various major approaches that employ a single scalar field to account
for the accelerating phase of our present Universe. Dynamical system analysis
is employed in several important models to seek for cosmological solutions that
exhibit an accelerating phase as an attractor. For scalar field models of dark
energy, we consistently focus on addressing challenges related to the
fine-tuning and coincidence problems in cosmology, as well as exploring
potential solutions to them. For scalar-tensor theories and their
generalizations, we emphasize the importance of constraints on theoretical
parameters to ensure overall consistency with experimental tests. Models or
theories that could potentially explain the Hubble tension are also emphasized
throughout this review.


  We show that the (random) Riemann surfaces of the Angel-Schramm Uniform
Infinite Planar Triangulation and of Sheffield's infinite necklace construction
are both parabolic. In other words, Brownian motion on these surfaces is
recurrent. We obtain this result as a corollary to a more general theorem on
subsequential distributional limits of random unbiased disc triangulations,
following work of Benjamini and Schramm.


  The DHCAL collaboration is assembling a large scale prototype Digital Hadron
Calorimeter (DHCAL). The calorimeter utilizes Resistive Plate Chambers (RPCs)
as active medium and features of the order of 400,000 1 \times 1 cm2 pads with
binary (or digital) electronic readout. The purpose of the prototype is to
provide detailed measurements of hadronic showers and to prove the concept of a
DHCAL with RPCs as active elements.


  We present a study of the numerical solution of the two dimensional
electrical impedance tomography problem, with noisy measurements of the
Dirichlet to Neumann map. The inversion uses parametrizations of the
conductivity on optimal grids. The grids are optimal in the sense that finite
volume discretizations on them give spectrally accurate approximations of the
Dirichlet to Neumann map. The approximations are Dirichlet to Neumann maps of
special resistor networks, that are uniquely recoverable from the measurements.
Inversion on optimal grids has been proposed and analyzed recently, but the
study of noise effects on the inversion has not been carried out. In this paper
we present a numerical study of both the linearized and the nonlinear inverse
problem. We take three different parametrizations of the unknown conductivity,
with the same number of degrees of freedom. We obtain that the parametrization
induced by the inversion on optimal grids is the most efficient of the three,
because it gives the smallest standard deviation of the maximum a posteriori
estimates of the conductivity, uniformly in the domain. For the nonlinear
problem we compute the mean and variance of the maximum a posteriori estimates
of the conductivity, on optimal grids. For small noise, we obtain that the
estimates are unbiased and their variance is very close to the optimal one,
given by the Cramer-Rao bound. For larger noise we use regularization and
quantify the trade-off between reducing the variance and introducing bias in
the solution. Both the full and partial measurement setups are considered.


  We study the properties of remnant function, which is a function of output
remnant versus amplitude of the input signal, of Preisach hysteresis operators.
The remnant behavior (or the leftover memory when the input reaches zero)
enables an energy-optimal application of piezoactuator systems where the
applied electrical field can be removed when the desired strain/displacement
has been attained. We show that when the underlying weight of Preisach
operators is positive, the resulting remnant curve is monotonically increasing
and accordingly a Newton and secant update laws for the iterative remnant
control are proposed that allows faster convergence to the desired remnant
value than the existing iterative remnant control algorithm in literature as
validated by numerical simulation.


  Let $(g_n)_{n\geq 1}$ be a sequence of independent and identically
distributed random elements with law $\mu$ on the general linear group
$\textrm{GL}(V)$, where $V=\mathbb R^d$. Consider the random walk $G_n : = g_n
\ldots g_1$, $n \geq 1$, and the coefficients $\langle f, G_n v \rangle$, where
$v \in V$ and $f \in V^*$. Under suitable moment assumptions on $\mu$, we prove
the strong and weak laws of large numbers and the central limit theorem for
$\langle f, G_n v \rangle$, which improve the previous results established
under the exponential moment condition on $\mu$. We further demonstrate the
Berry-Esseen bound, the Edgeworth expansion, the Cram\'{e}r type moderate
deviation expansion and the local limit theorem with moderate deviations for
$\langle f, G_n v \rangle$ under the exponential moment condition. Under a
subexponential moment condition on $\mu$, we also show a Berry-Esseen type
bound and the moderate deviation principle for $\langle f, G_n v \rangle$. Our
approach is based on various versions of the H\"older regularity of the
invariant measure of the Markov chain $G_n \!\cdot \! x = \mathbb R G_n v$ on
the projective space of $V$ with the starting point $x = \mathbb R v$.


  Let $K$ be a tunnel number one knot in $M$, where $M$ is either $S^3$, or a
connected sum of $S^2\times S^1$ with any lens space. (In particular this
includes $M = S^2\times S^1$.) We prove that if a Dehn surgery on $K$ yields a
non-trivial lens space, then $K$ is a doubly primitive knot in $M$. For $M =
S^3$ this resolves the tunnel number one Berge Conjecture. For $M = S^2\times
S^1$ this resolves a conjecture of Greene and Baker-Buck-Lecuona for tunnel
number one knots.


  We present the first mid-IR detection of the linear polarization toward the
star CygOB2-12, a luminous blue hypergiant that, with Av of ~10 mag of
foreground extinction, is a benchmark in the study of the properties of dust in
the diffuse interstellar medium. The 8-13 micrometer spectropolarimetry,
obtained with the CanariCam multi-mode camera at the Gran Telescopio Canarias
(GTC), shows clear trends with wavelength characteristic of silicate grains
aligned in the interstellar magnetic field. The maximum polarization, detected
with 7.8 statistical significance near 10.2 micrometers, is (1.24 +/- 0.28) %
with position angle (126 +/- 8) deg. We comment on these measurements in the
context of recent models for the dust composition in the diffuse interstellar
medium.


  Muography is an imaging tool based on the attenuation of cosmic muons to
observe the density distribution of large objects, such as underground caves or
fractured zones. Tomography based on muography measurements -- that is, three
dimensional reconstruction of density distribution from two dimensional muon
flux maps -- brings up special challenges. The detector field of view covering
must be as balanced as possible, considering the muon flux drop at higher
zenith angles and the detector placement possibilities. The inversion from
directional muon fluxes to 3D density map is usually underdetermined (more
voxels than measurements) which can be unstable due to partial coverage. This
can be solved by geologically relevant Bayesian constraints. The Bayesian
principle results in parameter bias and artifacts. In this work, the linearized
(density-length based) inversion is applied, the methodology is explained,
formulating the constraints associated with inversion to ensure the stability
of parameter fitting. After testing the procedure on synthetic examples, an
actual high quality muography measurement data set from 7 positions is used as
input for the inversion. The result demonstrates the tomographic imaging of a
complex karstic crack zone and provides details on the complicated internal
structures. The existence of low density zones in the imaged space was verified
by samples from core drills, which consist altered dolomite powder within the
intact high density dolomite.


  We investigate two models for the following setup: We consider a stochastic
process X \in C[0,1] whose distribution belongs to a parametric family indexed
by \vartheta \in {\Theta} \subset R. In case \vartheta = 0, X is a generalized
Pareto process. Based on n independent copies X(1),...,X(n) of X, we establish
local asymptotic normality (LAN) of the point process of exceedances among
X(1),...,X(n) above an increasing threshold line in each model.
  The corresponding central sequences provide asymptotically optimal sequences
of tests for testing H0 : \vartheta = 0 against a sequence of alternatives Hn :
\vartheta = \varthetan converging to zero as n increases. In one model, with an
underlying exponential family, the central sequence is provided by the number
of exceedances only, whereas in the other one the exceedances themselves
contribute, too. However it turns out that, in both cases, the test statistics
also depend on some additional and usually unknown model parameters.
  We, therefore, consider an omnibus test statistic sequence as well and
compute its asymptotic relative efficiency with respect to the optimal test
sequence.


  We extend the Marcus-Schaeffer bijection between orientable rooted bipartite
quadrangulations (equivalently: rooted maps) and orientable labeled one-face
maps to the case of all surfaces, that is orientable and non-orientable as
well. This general construction requires new ideas and is more delicate than
the special orientable case, but it carries the same information. In
particular, it leads to a uniform combinatorial interpretation of the counting
exponent $\frac{5(h-1)}{2}$ for both orientable and non-orientable rooted
connected maps of Euler characteristic $2-2h$, and of the algebraicity of their
generating functions, similar to the one previously obtained in the orientable
case via the Marcus-Schaeffer bijection. It also shows that the renormalization
factor $n^{1/4}$ for distances between vertices is universal for maps on all
surfaces: the renormalized profile and radius in a uniform random pointed
bipartite quadrangulation on any fixed surface converge in distribution when
the size $n$ tends to infinity. Finally, we extend the Miermont and
Ambj{\o}rn-Budd bijections to the general setting of all surfaces. Our
construction opens the way to the study of Brownian surfaces for any compact
2-dimensional manifold.


  It is relatively easy to construct a finitely generated group with infinite
asymptotic dimension: the restricted wreath product of $\mathbb{Z}$ by
$\mathbb{Z}$ provides an example. In light of this, it becomes interesting to
consider the rate of growth of the asymptotic dimension function of a group.
Loosely speaking, we measure the dimension on $\lambda$-scale and let $\lambda$
increase to infinity to recover the asymptotic dimension. In this paper we
consider how the asymptotic dimension function is affected by different
constructions involving groups.


  It is almost universal to regard attention as the facility that permits an
agent, human or machine, to give priority processing resources to relevant
stimuli while ignoring the irrelevant. The reality of how this might manifest
itself throughout all the forms of perceptual and cognitive processes possessed
by humans, however, is not as clear. Here we examine this reality with a broad
perspective in order to highlight the myriad ways that attentional processes
impact both perception and cognition. The paper concludes by showing two real
world problems that exhibit sufficient complexity to illustrate the ways in
which attention and cognition connect. These then point to new avenues of
research that might illuminate the overall cognitive architecture of spatial
cognition.


  We propose an approach for helping agents compose email replies to customer
requests. To enable that, we use LDA to extract latent topics from a collection
of email exchanges. We then use these latent topics to label our data,
obtaining a so-called "silver standard" topic labelling. We exploit this
labelled set to train a classifier to: (i) predict the topic distribution of
the entire agent's email response, based on features of the customer's email;
and (ii) predict the topic distribution of the next sentence in the agent's
reply, based on the customer's email features and on features of the agent's
current sentence. The experimental results on a large email collection from a
contact center in the tele- com domain show that the proposed ap- proach is
effective in predicting the best topic of the agent's next sentence. In 80% of
the cases, the correct topic is present among the top five recommended topics
(out of fifty possible ones). This shows the potential of this method to be
applied in an interactive setting, where the agent is presented a small list of
likely topics to choose from for the next sentence.


  We present a population study of low- and intermediate-mass X-ray binaries
(LMXBs) with neutron star accretors, performed using the detailed 1D stellar
evolution code MESA. We identify all plausible Roche-lobe overflowing binaries
at the start of mass transfer, and compare our theoretical mass transfer tracks
to the population of well-studied Milky Way LMXBs. The mass transferring
evolution depends on the accepted magnetic braking (MB) law for angular
momentum loss. The most common MB prescription ("Skumanich MB") originated from
observations of the time-dependence of rotational braking of Sun-type stars,
where the angular momentum loss rate depends on the donor mass $M_d$, donor
radius $R_d$, and rotation rate $\Omega$, $\dot{J} \propto M_d R_d^{\gamma}
\Omega^3$. The functional form of the Skumanich MB can be also obtained
theoretically assuming a radial magnetic field, isotropic isothermal winds, and
boosting of the magnetic field by rotation. Here we show that this simple form
of the Skumanich MB law gives mass transfer rates an order of magnitude too
weak to explain most observed persistent LMXBs. This failure suggests that the
standard Skumanich MB law should not be employed to interpret Galactic, or
extragalactic, LMXB populations, with either detailed stellar codes or rapid
binary population synthesis codes. We investigate modifications for the MB law,
and find that including a scaling of the magnetic field strength with the
convective turnover time, and a scaling of MB with the wind mass loss rate, can
reproduce persistent LMXBs, and does a better job at reproducing transient
LMXBs.


  A molecule is a complex of heterogeneous components, and the spatial
arrangements of these components determine the whole molecular properties and
characteristics. With the advent of deep learning in computational chemistry,
several studies have focused on how to predict molecular properties based on
molecular configurations. Message passing neural network provides an effective
framework for capturing molecular geometric features with the perspective of a
molecule as a graph. However, most of these studies assumed that all
heterogeneous molecular features, such as atomic charge, bond length, or other
geometric features always contribute equivalently to the target prediction,
regardless of the task type. In this study, we propose a dual-branched neural
network for molecular property prediction based on message-passing framework.
Our model learns heterogeneous molecular features with different scales, which
are trained flexibly according to each prediction target. In addition, we
introduce a discrete branch to learn single atom features without local
aggregation, apart from message-passing steps. We verify that this novel
structure can improve the model performance with faster convergence in most
targets. The proposed model outperforms other recent models with sparser
representations. Our experimental results indicate that in the chemical
property prediction tasks, the diverse chemical nature of targets should be
carefully considered for both model performance and generalizability.


  We apply the methods of Heegaard Floer homology to identify topological
properties of complex curves in the complex projective plane. As one
application, we resolve an open conjecture that constrains the Alexander
polynomial of the link of the singular point of the curve in the case that
there is exactly one singular point, having connected link, and the curve is of
genus 0. Generalizations apply in the case of multiple singular points.


  GRB 990123 is the most luminous event detected so far, and in an important
paper, Hjorth et al. (1999) reported an upper limit on the degree of linear
polarization of the optical afterglow for this burst (P <2.3%). One of the
interprtations for this small value of P was that the emission was probably due
a relativistic jet with ordered magnetic field, and the viewing angle in the
lab frame theta' <= Gamma^{-1}, where Gamma is the bulk Lorentz factor of the
jet at the time of the optical emission. We point out that this conclusion
resulted from a confusion between the angles measured in the lab frame and in
the plasma rest frame. For ordered magnetic field, one would actually obtain a
large value of P because the above mentioned angle would correspond to a very
large angle, theta <= \pi/2, in the plasma rest frame. And this is probably the
case with the blazars. On the other hand, it is indeed possible to have P ~ 0
if the magnetic field of GRB 990123 was completely chaotic and the viewing
angle was considerably smaller than the semi-angle of the jet.


  Given a boolean predicate $\Pi$ on labeled networks (e.g., proper coloring,
leader election, etc.), a self-stabilizing algorithm for $\Pi$ is a distributed
algorithm that can start from any initial configuration of the network (i.e.,
every node has an arbitrary value assigned to each of its variables), and
eventually converge to a configuration satisfying $\Pi$. It is known that
leader election does not have a deterministic self-stabilizing algorithm using
a constant-size register at each node, i.e., for some networks, some of their
nodes must have registers whose sizes grow with the size $n$ of the networks.
On the other hand, it is also known that leader election can be solved by a
deterministic self-stabilizing algorithm using registers of $O(\log \log n)$
bits per node in any $n$-node bounded-degree network. We show that this latter
space complexity is optimal. Specifically, we prove that every deterministic
self-stabilizing algorithm solving leader election must use $\Omega(\log \log
n)$-bit per node registers in some $n$-node networks. In addition, we show that
our lower bounds go beyond leader election, and apply to all problems that
cannot be solved by anonymous algorithms.


  We consider single-conflict colorings, a variant of graph colorings in which
each edge of a graph has a single forbidden color pair. We show that for any
assignment of forbidden color pairs to the edges of a $d$-degenerate graph $G$
on $n$ vertices of edge-multiplicity at most $\log \log n$, $O(\sqrt{ d } \log
n)$ colors are always enough to color the vertices of $G$ in a way that avoids
every forbidden color pair. This answers a question of Dvo\v{r}\'ak, Esperet,
Kang, and Ozeki for simple graphs (Journal of Graph Theory 2021).


  We develop the QCD description of the breakup of photons into forward dijets
in small-x deep inelastic scattering off nuclei in the saturation regime. Based
on the color dipole approach, we derive a multiple scattering expansion for
intranuclear distortions of the jet-jet transverse momentum spectrum. A special
attention is paid to the non-Abelian aspects of the propagation of color
dipoles in a nuclear medium. We report a nonlinear $k_{\perp}$-factorization
formula for the breakup of photons into dijets in terms of the collective
Weizs\"acker-Williams (WW) glue of nuclei as defined in ref.
\cite{Saturation,NSSdijet}. For hard dijets with the transverse momenta above
the saturation scale the azimuthal decorrelation (acoplanarity) momentum is of
the order of the nuclear saturation momentum QA. For minijets with the
transverse momentum below the saturation scale the nonlinear kT-factorization
predicts a complete disappearance of the jet-jet correlation. We comment on a
possible relevance of the nuclear decorrelation of jets to the experimental
data from the STAR-RHIC Collaboration.


  Recent all-sky surveys have led to the discovery of new types of transients.
These include stars disrupted by the central supermassive black hole, and
supernovae that are 10-100 times more energetic than typical ones. However, the
nature of even more energetic transients that apparently occur in the innermost
regions of their host galaxies is hotly debated. Here we report the discovery
of the most energetic of these to date: PS1-10adi, with a total radiated energy
of ~ 2.3 x 10^52 erg. The slow evolution of its light curve and persistently
narrow spectral lines over ~3 yr are inconsistent with known types of recurring
black hole variability. The observed properties imply powering by shock
interaction between expanding material and large quantities of surrounding
dense matter. Plausible sources of this expanding material are a star that has
been tidally disrupted by the central black hole, or a supernova. Both could
satisfy the energy budget. For the former, we would be forced to invoke a new
and hitherto unseen variant of a tidally disrupted star, while a supernova
origin relies principally on environmental effects resulting from its nuclear
location. Remarkably, we also discovered that PS1-10adi is not an isolated
case. We therefore surmise that this new population of transients has
previously been overlooked due to incorrect association with underlying central
black hole activity.


  In this paper, we prove that a rational map with a Cantor Julia set carries
no invariant line fields on its Julia set. It follows that a structurally
stable rational map with a Cantor Julia set is hyperbolic.


  We show how conformal partial waves (or conformal blocks) of spinor/tensor
correlators can be related to each other by means of differential operators in
four dimensional conformal field theories. We explicitly construct such
differential operators for all possible conformal partial waves associated to
four-point functions of arbitrary traceless symmetric operators. Our method
allows any conformal partial wave to be extracted from a few "seed"
correlators, simplifying dramatically the computation needed to bootstrap
tensor correlators.


  The objective is to investigate the global existence of solutions for a
degenerate chemotaxis system with logistic sources in a two-dimensional domain.
It is demonstrated that the inclusion of logistic sources can exclude the
occurrence of blow-up solutions, even in the presence of superlinear growth in
the cross-diffusion rate. Our proof relies on the application of elliptic and
parabolic regularity in Orlicz spaces and variational approach.


  With the rapid growth of real-time Internet of Things (IoT) applications, the
need for fresh information has surged. Age of Information (AoI) is a
tailor-made metric to characterize the information freshness perceived by the
devices. In this paper, we investigate the problem of scheduling updates to
minimize AoI in broadcast network. In this case, a central controller, e.g. a
base station, collects status updates from different sources and schedules them
to corresponding clients. Different from previous work, we consider both
stochastic status updates and unreliable links. The problem is first modeled as
an infinite horizon average constrained cost Markov decision problem (CMDP).
With Lagrangian relaxation, an approximation of Whittle's index is derived and
a scheduling policy is designed based on the approximate index. The results in
previous work can be view as degenerate cases of the approximation index policy
either with reliable links or periodic arrival constraints. Simulation results
demonstrate the near-optimal performance of the proposed policy.


  Emission and absorption line observations of molecules in late-type stars are
a vital component in our understanding of stellar evolution, dust formation and
mass loss in these objects. The molecular composition of the gas in the
circumstellar envelopes of AGB stars reflects chemical processes in gas whose
properties are strong functions of radius with density and temperature varying
by more than ten and two orders of magnitude, respectively. In addition, the
interstellar UV field plays a critical role in determining not only molecular
abundances but also their radial distributions. In this article, I shall
briefly review some recent successful approaches to describing chemistry in
both the inner and outer envelopes and outline areas of challenge for the
future.


  In this critical survey, we analyze typical claims on the relationship
between explainable AI (XAI) and fairness to disentangle the multidimensional
relationship between these two concepts. Based on a systematic literature
review and a subsequent qualitative content analysis, we identify seven
archetypal claims from 175 scientific articles on the alleged fairness benefits
of XAI. We present crucial caveats with respect to these claims and provide an
entry point for future discussions around the potentials and limitations of XAI
for specific fairness desiderata. Importantly, we notice that claims are often
(i) vague and simplistic, (ii) lacking normative grounding, or (iii) poorly
aligned with the actual capabilities of XAI. We suggest to conceive XAI not as
an ethical panacea but as one of many tools to approach the multidimensional,
sociotechnical challenge of algorithmic fairness. Moreover, when making a claim
about XAI and fairness, we emphasize the need to be more specific about what
kind of XAI method is used, which fairness desideratum it refers to, how
exactly it enables fairness, and who is the stakeholder that benefits from XAI.


  A $k$-plex of a latin square is a collection of cells representing each row,
column, and symbol precisely $k$ times. The classic case of $k=1$ is more
commonly known as a transversal. We introduce the concept of a $k$-weight, an
integral weight function on the cells of a latin square whose row, column, and
symbol sums are all $k$. We then show that several non-existence results about
$k$-plexes can been seen as more general facts about $k$-weights and that the
weight-analogues of several well-known existence conjectures for plexes
actually hold for $k$-weights.


  University evaluation is a topic of increasing concern in Italy as well as in
other countries. In empirical analysis, university activities and performances
are generally measured by means of indicator variables, summarizing the
available information under different perspectives. In this paper, we argue
that the evaluation process is a complex issue that can not be addressed by a
simple descriptive approach and thus association between indicators and
similarities among the observed universities should be accounted for.
Particularly, we examine faculty-level data collected from different sources,
covering 55 Italian Economics faculties in the academic year 2009/2010. Making
use of a clustering framework, we introduce a biclustering model that accounts
for both homogeneity/heterogeneity among faculties and correlations between
indicators. Our results show that there are two substantial different
performances between universities which can be strictly related to the nature
of the institutions, namely the Private and Public profiles . Each of the two
groups has its own peculiar features and its own group-specific list of
priorities, strengths and weaknesses. Thus, we suggest that caution should be
used in interpreting standard university rankings as they generally do not
account for the complex structure of the data.


  Learning-based methods have recently enabled performance leaps in analysis of
high-dimensional functional MRI (fMRI) time series. Deep learning models that
receive as input functional connectivity (FC) features among brain regions have
been commonly adopted in the literature. However, many models focus on
temporally static FC features across a scan, reducing sensitivity to dynamic
features of brain activity. Here, we describe a plug-in graph neural network
that can be flexibly integrated into a main learning-based fMRI model to boost
its temporal sensitivity. Receiving brain regions as nodes and
blood-oxygen-level-dependent (BOLD) signals as node inputs, the proposed
GraphCorr method leverages a node embedder module based on a transformer
encoder to capture temporally-windowed latent representations of BOLD signals.
GraphCorr also leverages a lag filter module to account for delayed
interactions across nodes by computing cross-correlation of windowed BOLD
signals across a range of time lags. Information captured by the two modules is
fused via a message passing algorithm executed on the graph, and enhanced node
features are then computed at the output. These enhanced features are used to
drive a subsequent learning-based model to analyze fMRI time series with
elevated sensitivity. Comprehensive demonstrations on two public datasets
indicate improved classification performance and interpretability for several
state-of-the-art graphical and convolutional methods that employ
GraphCorr-derived feature representations of fMRI time series as their input.


  The doping dependence of dry thermal oxidation rates in n-type 4H-SiC was
investigated. The oxidation was performed in the temperature range 1000C to
1200C for samples with nitrogen doping in the range of 6.5e15/cm3 to
9.3e18/cm3, showing a clear doping dependence. Samples with higher doping
concentrations displayed higher oxidation rates. The results were interpreted
using a modified Deal-Grove model. Linear and parabolic rate constants and
activation energies were extracted. Increasing nitrogen led to an increase in
linear rate constant pre-exponential factor from 10-6m/s to 10-2m/s and the
parabolic rate constant pre-exponential factor from 10e9m2/s to 10e6m2/s. The
increase in linear rate constant was attributed to defects from doping-induced
lattice mismatch, which tend to be more reactive than bulk crystal regions. The
increase in the diffusion-limited parabolic rate constant was attributed to
degradation in oxide quality originating from the doping-induced lattice
mismatch. This degradation was confirmed by the observation of a decrease in
optical density of the grown oxide films from 1.4 to 1.24. The linear
activation energy varied from 1.6eV to 2.8eV, while the parabolic activation
energy varied from 2.7eV to 3.3eV, increasing with doping concentration. These
increased activation energies were attributed to higher nitrogen content,
leading to an increase in effective bond energy stemming from the difference in
C-Si (2.82eV) and Si-N (4.26eV) binding energies. This work provides crucial
information in the engineering of SiO2 dielectrics for SiC MOS structures,
which typically involve regions of very different doping concentrations, and
suggests that thermal oxidation at high doping concentrations in SiC may be
defect mediated.


  The Guarded Negation Fragment (GNFO) is a fragment of first-order logic that
contains all positive existential formulas, can express the first-order
translations of basic modal logic and of many description logics, along with
many sentences that arise in databases. It has been shown that the syntax of
GNFO is restrictive enough so that computational problems such as validity and
satisfiability are still decidable. This suggests that, in spite of its
expressive power, GNFO formulas are amenable to novel optimizations. In this
paper we study the model theory of GNFO formulas. Our results include effective
preservation theorems for GNFO, effective Craig Interpolation and Beth
Definability results, and the ability to express the certain answers of queries
with respect to a large class of GNFO sentences within very restricted logics.
  This version of the paper contains streamlined and corrected versions of
results concerning entailment of a conjunctive query from a set of ground facts
and a theory consisting of GNFO sentences of a special form ("dependencies").


  Augmented reality is an emerging technology in many application domains.
Among them is the beauty industry, where live virtual try-on of beauty products
is of great importance. In this paper, we address the problem of live hair
color augmentation. To achieve this goal, hair needs to be segmented quickly
and accurately. We show how a modified MobileNet CNN architecture can be used
to segment the hair in real-time. Instead of training this network using large
amounts of accurate segmentation data, which is difficult to obtain, we use
crowd sourced hair segmentation data. While such data is much simpler to
obtain, the segmentations there are noisy and coarse. Despite this, we show how
our system can produce accurate and fine-detailed hair mattes, while running at
over 30 fps on an iPad Pro tablet.


  In the traditional two-rack distributed storage system (DSS) model, due to
the assumption that the storage capacity of each node is the same, the minimum
bandwidth regenerating (MBR) point becomes infeasible. In this paper, we design
a new non-homogeneous two-rack model by proposing a generalization of the
threshold function used to compute the tradeoff curve. We prove that by having
the nodes in the rack with higher regenerating bandwidth stores more
information, all the points on the tradeoff curve, including the MBR point,
become feasible. Finally, we show how the non-homogeneous two-rack model
outperforms the traditional model in the tradeoff curve between the storage per
node and the repair bandwidth.


  It is argued that the signals expected from the evaporation of mini black
holes - predicted in TeV-scale gravity models with large extra dimensions and
possibly produced in ultra high energy collisions in the atmosphere - have
characteristics quite similar to the ones of the Centauro events, an old
mystery of cosmic ray physics.


  In this work, we aim to address the 3D scene stylization problem - generating
stylized images of the scene at arbitrary novel view angles. A straightforward
solution is to combine existing novel view synthesis and image/video style
transfer approaches, which often leads to blurry results or inconsistent
appearance. Inspired by the high-quality results of the neural radiance fields
(NeRF) method, we propose a joint framework to directly render novel views with
the desired style. Our framework consists of two components: an implicit
representation of the 3D scene with the neural radiance fields model, and a
hypernetwork to transfer the style information into the scene representation.
In particular, our implicit representation model disentangles the scene into
the geometry and appearance branches, and the hypernetwork learns to predict
the parameters of the appearance branch from the reference style image. To
alleviate the training difficulties and memory burden, we propose a two-stage
training procedure and a patch sub-sampling approach to optimize the style and
content losses with the neural radiance fields model. After optimization, our
model is able to render consistent novel views at arbitrary view angles with
arbitrary style. Both quantitative evaluation and human subject study have
demonstrated that the proposed method generates faithful stylization results
with consistent appearance across different views.


  Under the assumption of a simple and time-invariant gravitational potential,
many Galactic dynamics techniques infer the Milky Way's mass and dark matter
distribution from stellar kinematic observations. These methods typically rely
on parameterized potential models of the Galaxy and must take into account
non-trivial survey selection effects, because they make use of the density of
stars in phase space. Large-scale spectroscopic surveys now supply information
beyond kinematics in the form of precise stellar label measurements (especially
element abundances). These element abundances are known to correlate with
orbital actions or other dynamical invariants. Here, we use the Orbital Torus
Imaging (OTI) framework that uses abundance gradients in phase space to map
orbits. In many cases these gradients can be measured without detailed
knowledge of the selection function. We use stellar surface abundances from the
APOGEE survey combined with kinematic data from the Gaia mission. Our method
reveals the vertical ($z$-direction) orbit structure in the Galaxy and enables
empirical measurements of the vertical acceleration field and orbital
frequencies in the disk. From these measurements, we infer the total surface
mass density, $\Sigma$, and midplane volume density, $\rho_0$, as a function of
Galactocentric radius and height. Around the Sun, we find
$\Sigma_{\odot}(z=1.1$ kpc)$=72^{+6}_{-9}$M$_{\odot}$pc$^{-2}$ and
$\rho_{\odot}(z=0)=0.081^{+0.015}_{-0.009}$ M$_{\odot}$pc$^{-3}$ using the most
constraining abundance ratio, [Mg/Fe]. This corresponds to a dark matter
contribution in surface density of $\Sigma_{\odot,\mathrm{DM}}(z=1.1$
kpc)$=24\pm4$ M$_{\odot}$pc$^{-2}$, and in total volume mass density of
$\rho_{\odot,\mathrm{DM}}(z=0)=0.011\pm0.002$ M$_{\odot}$pc$^{-3}$. Moreover,
using these mass density values we estimate the scale length of the
low-$\alpha$ disc to be $h_R=2.24\pm0.06$kpc.


  The traditional finite-difference time-domain (FDTD) method is constrained by
the Courant-Friedrich-Levy (CFL) condition and suffers from the notorious
staircase error in electromagnetic simulations. This paper proposes a
three-dimensional conformal locally-one-dimensional FDTD (CLOD-FDTD) method to
address the two issues for modeling perfectly electrical conducting (PEC)
objects. By considering the partially filled cells, the proposed CLOD-FDTD
method can significantly improve the accuracy compared with the traditional
LOD-FDTD method and the FDTD method. At the same time, the proposed method
preserves unconditional stability, which is analyzed and numerically validated
using the Von-Neuman method. Significant gains in Central Processing Unit (CPU)
time are achieved by using large time steps without sacrificing accuracy. Two
numerical examples include a PEC cylinder and a missile are used to verify its
accuracy and efficiency with different meshes and time steps. It can be found
from these examples, the CLOD-FDTD method show better accuracy and can improve
the efficiency compared with those of the traditional FDTD method and the
traditional LOD-FDTD method.


  A search engine usually outputs a list of $K$ web pages. The user examines
this list, from the first web page to the last, and chooses the first
attractive page. This model of user behavior is known as the cascade model. In
this paper, we propose cascading bandits, a learning variant of the cascade
model where the objective is to identify $K$ most attractive items. We
formulate our problem as a stochastic combinatorial partial monitoring problem.
We propose two algorithms for solving it, CascadeUCB1 and CascadeKL-UCB. We
also prove gap-dependent upper bounds on the regret of these algorithms and
derive a lower bound on the regret in cascading bandits. The lower bound
matches the upper bound of CascadeKL-UCB up to a logarithmic factor. We
experiment with our algorithms on several problems. The algorithms perform
surprisingly well even when our modeling assumptions are violated.


  The Gemini Planet Imager (GPI) is an instrument designed to directly image
planets and circumstellar disks from 0.9 to 2.5 microns (the $YJHK$ infrared
bands) using high contrast adaptive optics with a lenslet-based integral field
spectrograph. We develop an extraction algorithm based on a least-squares
method to disentangle the spectra and systematic noise contributions
simultaneously. We utilize two approaches to adjust for the effect of flexure
of the GPI optics which move the position of light incident on the detector.
The first method is to iterate the extraction to achieve minimum residual and
the second is to cross-correlate the detector image with a model image in
iterative extraction steps to determine an offset. Thus far, this process has
made clear qualitative improvements to the cube extraction by reducing the
Moir\'{e} pattern. There are also improvements to the automated routines for
finding flexure offsets which are reliable to with $\sim0.5$ pixel accuracy
compared to pixel accuracy prior. Further testing and optimization will follow
before implementation into the GPI pipeline.


We produce a new, shorter construction of a minor-universal planar graph.


  Permissioned blockchains like Hyperledger Fabric have become quite popular
for implementation of enterprise applications. Recent research has mainly
focused on improving performance of permissioned blockchains without any
consideration of their power/energy consumption. In this paper, we conduct a
comprehensive empirical study to understand energy efficiency
(throughput/energy) of validator peer in Hyperledger Fabric (a major bottleneck
node). We pick a number of optimizations for validator peer from literature
(allocated CPUs, software block cache and FPGA based accelerator). First, we
propose a methodology to measure power/energy consumption of the two resulting
compute platforms (CPU-only and CPU+FPGA). Then, we use our methodology to
evaluate energy efficiency of a diverse set of validator peer configurations,
and present many useful insights. With careful selection of software
optimizations and FPGA accelerator configuration, we improved energy efficiency
of validator peer by 10$\times$ compared to vanilla validator peer (i.e.,
energy-aware provisioning of validator peer can deliver 10$\times$ more
throughput while consuming the same amount of energy). In absolute terms, this
means 23,000 tx/s with power consumption of 118W from a validator peer using
software block cache running on a 4-core server with AMD/Xilinx Alveo U250 FPGA
card.


  We establish improved upper limits on branching fractions for B0 decays to
final States 10 where the decay products are purely invisible (i.e., no
observable final state particles) and for final states where the only visible
product is a photon. Within the Standard Model, these decays have branching
fractions that are below the current experimental sensitivity, but various
models of physics beyond the Standard Model predict significant contributions
for these channels. Using 471 million BB pairs collected at the Y(4S) resonance
by the BABAR experiment at the PEP-II e+e- storage ring at the SLAC National
Accelerator Laboratory, we establish upper limits at the 90% confidence level
of 2.4x10^-5 for the branching fraction of B0-->Invisible and 1.7x10^-5 for the
branching fraction of B0-->Invisible+gamma


  Structures of X-ray emitting magnetic polar regions on neutron stars in X-ray
pulsars are studied in a range of the accretion rate, 10$^{17}$ g s$^{-1} \sim
10^{18}$ g s$^{-1}$. It is shown that a thin but tall, radiation energy
dominated, X-ray emitting polar cone appears at each of the polar regions. The
height of the polar cone is several times as large as the neutron star radius.
The energy gain due to the gravity of the neutron star in the polar cone
exceeds the energy loss due to photon diffusion in the azimuthal direction of
the cone, and a significant amount of energy is advected to the neutron star
surface. Then, the radiation energy carried with the flow should become so
large for the radiation pressure to overcome the magnetic pressure at the
bottom of the cone. As a result, the matter should expand in the tangential
direction along the neutron star surface, dragging the magnetic lines of force,
and form a mound-like structure. The advected energy to the bottom of the cone
should finally be radiated away from the surface of the polar mound and the
matter should be settled on the neutron star surface there. From such
configurations, we can expect an X-ray spectrum composed of a multi-color
blackbody spectrum from the polar cone region and a quasi-single blackbody
spectrum from the polar mound region. These spectral properties agree with
observations. A combination of a fairly sharp pencil beam and a broad fan beam
is expected from the polar cone region, while a broad pencil beam is expected
from the polar mound region. With these X-ray beam properties, basic patterns
of pulse profiles of X-ray pulsars can be explained too.


  The quantized magnetic flux $\Phi=-4\pi N\sk, N=0,\pm1,...$ of
non-topological vortices in the non-relativistic Chern-Simons theory is related
to the topological degree of the $S^2\to S^2$ mapping defined by lifting the
problem to the Riemann spheres. Regular solutions with finite degree only arise
for rational functions, whose topological degree, $N$, is the commun number of
their zeros and poles on the Riemann sphere, also called their algebraic order.


  We study a gas of repulsively interacting bosons in the first excited band of
an optical lattice. We explore this p-band physics both within the framework of
a standard mean-field theory as well as with the more accurate generalized
Gutzwiller ansatz. We find the phase diagrams for two- and three-dimensional
systems and characterize the first Mott-states in detail. Furthermore, we find
that even though the p-band model has strongly anisotropic kinetic energies and
inter-flavor interaction terms are missing in the lowest band theory, the
mean-field theory becomes useful quite rapidly once the transition from the
Mott-insulator to the superfluid is crossed.


  In this article we study some geometric properties of a non-trivial square
tile (a non-trivial square tile is a non-constant function on a square).
  Consider infinitely many copies of this single square tile and cover the
plane with them, without gaps and without overlaps (a tiling of the plane),
with the vertices making a square point lattice. The question we ask ourselves
in this article is the following: if there is a rotation center of order 4 what
kind of geometric properties has the drawing in the tile?
  We prove that the tiles of this kind that one can see belong to one of four
types. However, there are eight exceptions to these general rules.
  This work was inspired by a pattern tile, with remarkable properties,
designed in 1966 by the portuguese artist Eduardo Nery. It can be seen in many
beautiful panels in several locations in Portugal.


  An error was made when calculating the amplitude of the perturbed scalar mode
after Hubble-radius crossing. When corrected, it eliminates the leading term in
the statistical anisotropy of the power spectrum, reducing it from the
previously claimed upper estimate of $\lesssim 10^{-2}$ to the level of at most
$10^{-6}$, which is beyond the possibility of detection. The general conclusion
is that a single-field inflationary scenario cannot produce statistical
anisotropy of appreciable magnitude.


  The mass of the lenses giving rise to Galactic microlensing events can be
constrained by measuring the relative lens-source proper motion and lens flux.
The flux of the lens can be separated from that of the source, companions to
the source, and unrelated nearby stars with high-resolution images taken when
the lens and source are spatially resolved. For typical ground-based adaptive
optics (AO) or space-based observations, this requires either inordinately long
time baselines or high relative proper motions. We provide a list of
microlensing events toward the Galactic Bulge with high relative lens-source
proper motion that are therefore good candidates for constraining the lens mass
with future high-resolution imaging. We investigate all events from 2004 --
2013 that display detectable finite-source effects, a feature that allows us to
measure the proper motion. In total, we present 20 events with mu >~ 8 mas/yr.
Of these, 14 were culled from previous analyses while 6 are new, including
OGLE-2004-BLG-368, MOA-2005-BLG-36, OGLE-2012-BLG-0211, OGLE-2012-BLG-0456,
MOA-2012-BLG-532, and MOA-2013-BLG-029. In <~12 years the lens and source of
each event will be sufficiently separated for ground-based telescopes with AO
systems or space telescopes to resolve each component and further characterize
the lens system. Furthermore, for the most recent events, comparison of the
lens flux estimates from images taken immediately to those estimated from
images taken when the lens and source are resolved can be used to empirically
check the robustness of the single-epoch method currently being used to
estimate lens masses for many events.


  Patient-specific hemodynamics assessment could support diagnosis and
treatment of neurovascular diseases. Currently, conventional medical imaging
modalities are not able to accurately acquire high-resolution hemodynamic
information that would be required to assess complex neurovascular pathologies.
Therefore, computational fluid dynamics (CFD) simulations can be applied to
tomographic reconstructions to obtain clinically relevant information. However,
three-dimensional (3D) CFD simulations require enormous computational resources
and simulation-related expert knowledge that are usually not available in
clinical environments. Recently, deep-learning-based methods have been proposed
as CFD surrogates to improve computational efficiency. Nevertheless, the
prediction of high-resolution transient CFD simulations for complex vascular
geometries poses a challenge to conventional deep learning models. In this
work, we present an architecture that is tailored to predict high-resolution
(spatial and temporal) velocity fields for complex synthetic vascular
geometries. For this, an octree-based spatial discretization is combined with
an implicit neural function representation to efficiently handle the prediction
of the 3D velocity field for each time step. The presented method is evaluated
for the task of cerebral hemodynamics prediction before and during the
injection of contrast agent in the internal carotid artery (ICA). Compared to
CFD simulations, the velocity field can be estimated with a mean absolute error
of 0.024 m/s, whereas the run time reduces from several hours on a
high-performance cluster to a few seconds on a consumer graphical processing
unit.


  The operator nabla, introduced by Garsia and the author, plays a crucial role
in many aspect of the study of diagonal harmonics. Besides giving several new
formulas involving this operator, we show how one is lead to representation
theoretic explanations for conjectures about the effect of this operator on
Schur functions.


  Aging in spin glasses is analyzed via the Probability Density Function (PDF)
of the heat transfer between system and bath over a small time $\delta t$. The
PDF contains a Gaussian part, describing reversible fluctuations, and an
exponential tail caused by intermittent events. We find that the relative
weight of these two parts depends, for fixed $\delta t$, on the ratio of the
total sampling time to the age $t_w$. Fixing this ratio, the intensity of the
intermittent events is proportional to $\delta t/t_w$ and independent of the
temperature. The Gaussian part has a variance with the same temperature
dependence as the variance of the equilibrium energy in a system with an
exponential density of states. All these observations are explained assuming
that, for any $t_w$, intermittent events are triggered by local energy
fluctuations exceeding those previously occurred.


  This document is about the multi-document Von-Mises-Fisher mixture model with
a Dirichlet prior, referred to as VMFMix. VMFMix is analogous to Latent
Dirichlet Allocation (LDA) in that they can capture the co-occurrence patterns
acorss multiple documents. The difference is that in VMFMix, the topic-word
distribution is defined on a continuous n-dimensional hypersphere. Hence VMFMix
is used to derive topic embeddings, i.e., representative vectors, from multiple
sets of embedding vectors. An efficient Variational Expectation-Maximization
inference algorithm is derived. The performance of VMFMix on two document
classification tasks is reported, with some preliminary analysis.


  In this paper we consider a SEIRD epidemic model for a population composed by
two groups with asymmetric interaction. Given two sets of parameters for the
model, we estimate the distance of the solutions for the second group based on
the distance of solutions for the first one. We also study the final size of
the epidemic for each group. We illustrate our results with the spread of
coronavirus disease 2019 (COVID-19) pandemic in the New York County (USA) for
the initial stage of the contamination, and in the the cities of Petrolina and
Juazeiro (Brazil).


  Following our work presented in Dai et al. (2016), we report our detailed
data analysis for another 38 Fermi gamma-ray un-associated sources. These
sources are selected from the Fermi Large Area Telescope (LAT) third source
catalog on the basis of the properties of known gamma-ray millisecond pulsars
(MSPs) and for the purpose of finding likely candidate MSPs. From our analysis
of the LAT data, we identify that among the 38 sources, 28 of them are single
point-like sources with clean background and their spectra show significant
curvature. We also conduct analysis of archival X-ray data available for 24 of
the 28 sources. In the fields of 10 sources, there are at least one X-ray
object, and in those of the other 14 sources, no X-ray object is detected but
probably due to the X-ray observations being short. We discuss the possible MSP
nature for these sources. Six of them(J0514.6-4406, J1035.7-6720, J1624.2-4041,
J1744.1-7619, J1946.4-5403, and J2039.6-5618) are most likely associated with
pulsars because of multi-wavelength identifications including direct radio or
gamma-ray detection of pulsations. To firmly establish the associations or
verify the MSP nature for other sources, deep X-ray and/or optical observations
are needed.


  The paper deals with the existence of nonstationary collision-free periodic
solutions of singular first order Hamiltonian systems of $N$-vortex type in a
domain $\Omega\subset\mathbb{C}$. These are solutions
$z(t)=(z_1(t),\dots,z_N(t))$ of \[ \dot{z}_j(t)=-i\nabla_{z_j}
H_\Omega\big(z(t)\big),\quad j=1,\dots,N, \tag{HS} \] where the Hamiltonian
$H_\Omega$ has the form \[ H_\Omega(z_1,\dots,z_N)
  = -\sum_{{j,k=1}\over{j\ne k}}^N \frac{1}{2\pi}\log|z_j-z_k| -\sum_{j,k=1}^N
g(z_j,z_k). \] The function $g:\Omega\times\Omega\to\mathbb{R}$ is required to
be of class $C^3$ and symmetric, the regular part of a hydrodynamic Green
function being our model. The Hamiltonian is unbounded from above and below,
and the associated action integral is not defined on an open subset of the
space of periodic $H^{1/2}$ functions.
  Given a closed connected component $\Gamma\subset\partial\Omega$ of class
$C^3$ we are interested in periodic solutions of (HS) near $\Gamma$. We present
quite general conditions on the behavior of $g$ near $\Gamma$ which imply that
there exists a family of periodic solutions $z^{(r)}(t)$, $0<r<\overline{r}$,
with arbitrarily small minimal period $T_r\to0$ as $r\to0$, and such that the
"point vortices" $z_j^{(r)}(t)$ approach $\Gamma$ as $r\to0$. The solutions are
choreographies, i.e.\ $z_j^{(r)}(t)$ moves on the same trajectory as
$z_1^{(r)}(t)$ with a phase shift. We can also relate the speed of each vortex
with the curvature of $\Gamma$.


  Presently online video games have become a progressively favorite source of
recreation and Counter Strike: Global Offensive (CS: GO) is one of the
top-listed online first-person shooting games. Numerous competitive games are
arranged every year by Esports. Nonetheless, (i) No study has been conducted on
video analysis and action recognition of CS: GO game-play which can play a
substantial role in the gaming industry for prediction model (ii) No work has
been done on the real-time application on the actions and results of a CS: GO
match (iii) Game data of a match is usually available in the HLTV as a CSV
formatted file however it does not have open access and HLTV tends to prevent
users from taking data. This manuscript aims to develop a model for accurate
prediction of 4 different actions and compare the performance among the five
different transfer learning models with our self-developed deep neural network
and identify the best-fitted model and also including major voting later on,
which is qualified to provide real time prediction and the result of this model
aids to the construction of the automated system of gathering and processing
more data alongside solving the issue of collecting data from HLTV.


  These notes are based on a series of lectures given by the first author at
the school of `Poisson 2010', held at IMPA, Rio de Janeiro. They contain an
exposition of the theory of super- and graded manifolds, cohomological vector
fields, graded symplectic structures, reduction and the AKSZ-formalism.


  The world faces a shortage of radiologists, leading to longer treatment times
and increased stress, negatively impacting patient safety and workforce morale.
Integrating artificial intelligence to interpret radiographic images and
generate descriptive reports offers a promising solution. However, limited
research exists on generating natural language descriptions for volumetric
medical images. This study introduces a deep learning-based proof of concept
model to accurately identify abnormalities in volumetric CT data and generate
narrative-style reports. Various encoder-decoder models were assessed for their
efficacy in clinically relevant and surrogate tasks. Clinically relevant tasks
involved identifying and describing pulmonary nodules and pleural effusions,
while surrogate tasks involved recognizing and describing artificial
abnormalities such as mirroring, rotation, and lung lobe occlusion. The results
show high accuracy in detecting combinations of artificial abnormalities, with
the best model achieving a classification accuracy of 0.97 on an independent
dataset with a homogeneously distributed 11-class problem. Furthermore, the
best model consistently generated coherent radiology reports in natural
language, with a next-word prediction accuracy of 0.84. Additionally, 65% of
these reports were factually accurate regarding the identified artificial
abnormalities. Unfortunately, these models did not replicate this success for
clinically relevant tasks. Overall, this study provides a working proof of
concept model for a challenge yet to be fully addressed by the scientific
community. Given the success on surrogate tasks, the leap to clinically
relevant tasks seems feasible. Acquiring a significantly larger high-quality
dataset appears to be the most promising path forward, alongside more
computational resources for end-to-end model training.


  Intuitively, the filter dimension of an algebra or a module measures how
`close' standard filtrations of the algebra or the module are. In particular,
for a simple algebra it also measures the growth of how `fast' one can prove
that the algebra is simple.
  The filter dimension appears naturally when one wants to generalize the
Bernstein's inequality for the Weyl algebras to the class of simple finitely
generated algebras.
  This paper is a review of the author's results on the filter dimension and
its connections with the Gelfand-Kirillov dimension, the Krull dimension, the
representation theory of simple finitely generated algebras and the `size' of
their maximal commutative subalgebras.


  An argument of Y. Nikonorov completes the proof of Theorem 2.5 in "Bounded
Isometries and Homogeneous Quotients", JGA 27 (2017), 56--64
[arXiv:1502.04276].