  A general procedure to find static and axially symmetric, interior solutions
to the Einstein equations is presented. All the so obtained solutions, verify
the energy conditions for a wide range of values of the parameters, and match
smoothly to some exterior solution of the Weyl family, thereby representing
globally regular models describing non spherical sources of gravitational
field. In the spherically symmetric limit, all our models converge to the well
known incompressible perfect fluid solution.The key stone of our approach is
based on an ansatz allowing to define the interior metric in terms of the
exterior metric functions evaluated at the boundary source. Some particular
sources are obtained, and the physical variables of the energy-momentum tensor
are calculated explicitly, as well as the geometry of the source in terms of
the relativistic multipole moments. The total mass of different configurations
is also calculated, it is shown to be equal to the monopole of the exterior
solution.


  Neutron star tidal deformability extracted from gravitational wave data
provides a novel probe to the interior neutron star structures and the
associated nuclear equation of state (EOS). Instead of the popular composition
of nucleons and leptons in neutron stars, we include hyperons and examine the
role of hyperons in the tidal deformability and its impact on the symmetry
energy in a relativistic mean-field approach with the density-dependent
parametrizations. The hyperons are found to have significant impact on the
deformability, correlated sensitively with the onset density and fraction of
hyperons in neutron star matter. Moderately lower onset density of hyperons can
yield considerable modification to the tidal deformability and shift its
inference on the nuclear EOS. The future measurements of the tidal
deformability at multi-fiducial star masses are anticipated to lift the
degeneracy between the contributions from the hyperon component and symmetry
energy.


  The Large Hadron Collider will provide an unprecedented quantity of collision
data right from the start-up. The challenge for the LHC experiments is the
quick use of these data for the final commissioning of the detectors, including
calibration, alignment, measuring of detector and trigger efficiencies. A new
energy frontier will open up, and measurement of basic Standard Model processes
will build a solid basement for any discovery studies.


  The response of inertial particle detectors coupled to a scalar field
satisfying nonlocal dynamics described by nonanalytic functions of the
d'Alembertian operator $\Box$ is studied. We show that spontaneous emission
processes of a low energy particle detector are very sensitive to high-energy
nonlocality scales. This allows us to suggest a nuclear physics experiment
($\sim$ MeV energy scales) that outperforms the sensitivity of LHC experiments
by many orders of magnitude. This may have implications for the falsifiability
of theoretical proposals of quantum gravity.


  This paper studies the consensus control problem faced with three essential
demands, namely, discrete control updating for each agent, discrete-time
communications among neighboring agents, and the fully distributed fashion of
the controller implementation without requiring any global information of the
whole network topology. Noting that the existing related results only meeting
one or two demands at most are essentially not applicable, in this paper we
establish a novel framework to solve the problem of fully distributed consensus
with discrete communication and control. The first key point in this framework
is the design of controllers that are only updated at discrete event instants
and do not depend on global information by introducing time-varying gains
inspired by the adaptive control technique. Another key point is the invention
of novel dynamic triggering functions that are independent of relative
information among neighboring agents. Under the established framework, we
propose fully distributed state-feedback event-triggered protocols for
undirected graphs and also further study the more complexed cases of
output-feedback control and directed graphs. Finally, numerical examples are
provided to verify the effectiveness of the proposed event-triggered protocols.


  This article presents a generic method to solve 2D multi-objective placement
problem for free-form components. The proposed method is a relaxed placement
technique combined with an hybrid algorithm based on a genetic algorithm and a
separation algorithm. The genetic algorithm is used as a global optimizer and
is in charge of efficiently exploring the search space. The separation
algorithm is used to legalize solutions proposed by the global optimizer, so
that placement constraints are satisfied. A test case illustrates the
application of the proposed method. Extensions for solving the 3D problem are
given at the end of the article.


  Large Transformer models routinely achieve state-of-the-art results on a
number of tasks but training these models can be prohibitively costly,
especially on long sequences. We introduce two techniques to improve the
efficiency of Transformers. For one, we replace dot-product attention by one
that uses locality-sensitive hashing, changing its complexity from O($L^2$) to
O($L\log L$), where $L$ is the length of the sequence. Furthermore, we use
reversible residual layers instead of the standard residuals, which allows
storing activations only once in the training process instead of $N$ times,
where $N$ is the number of layers. The resulting model, the Reformer, performs
on par with Transformer models while being much more memory-efficient and much
faster on long sequences.


  A dimer of bound atoms cannot melt, only dissociate. Bulk metals show a well
defined first order transition between their solid and liquid phases. The
appearance of the melting transition is explored for increasing clusters sizes
via the signatures in the specific heat and the root mean square of the bond
lengths $\delta_{\rm B}$ (Berry parameter) by means of Monte-Carlo simulations
of Al clusters modelled by Gupta potentials. Clear signatures of a melting
transition appear for $N\sim 6$ atoms. Closed-shell effects are shown for
clusters with up to 56 atoms. The melting transition is compared in detail with
the dissociation transition, which induces a second and possibly much larger
local maximum in the specific heat at higher temperatures. Larger clusters are
shown to fragment into dimers and trimers, which in turn dissociate at higher
temperatures.


  In a recent FTC by Tremblay {\sl et al} (2009 {\sl J. Phys. A: Math. Theor.}
{\bf 42} 205206), it has been conjectured that for any integer value of $k$,
some novel exactly solvable and integrable quantum Hamiltonian $H_k$ on a plane
is superintegrable and that the additional integral of motion is a $2k$th-order
differential operator $Y_{2k}$. Here we demonstrate the conjecture for the
infinite family of Hamiltonians $H_k$ with odd $k \ge 3$, whose first member
corresponds to the three-body Calogero-Marchioro-Wolfes model after elimination
of the centre-of-mass motion. Our approach is based on the construction of some
$D_{2k}$-extended and invariant Hamiltonian $\chh_k$, which can be interpreted
as a modified boson oscillator Hamiltonian. The latter is then shown to possess
a $D_{2k}$-invariant integral of motion $\cyy_{2k}$, from which $Y_{2k}$ can be
obtained by projection in the $D_{2k}$ identity representation space.


  We present a model for one-dimensional (1D) matter power spectra in redshift
space as estimated from data provided along individual lines of sight. We
derive analytic expressions for these power spectra in the linear and nonlinear
regimes, focusing on redshift distortions arising from peculiar velocities. In
the linear regime, redshift distortions enhance the 1D power spectra only on
small scales, and do not affect the power on large scales. This is in contrast
to the effect of distortions on three-dimensional (3D) power spectra estimated
from data in 3D space, where the enhancement is independent of scale. For CDM
cosmologies, the 1D power spectra in redshift and real space are similar for
wavenumbers $q<0.1h/Mpc$ where both have a spectral index close to unity,
independent of the details of the 3D power spectrum. Nonlinear corrections
drive the 1D power spectrum in redshift space into a nearly universal shape
over scale $q<10h/Mpc$, and suppress the power on small scales as a result of
the strong velocity shear and random motions. The redshift space, 1D power
spectrum is mostly sensitive to the amplitude of the initial density
perturbations. Our results are useful in particular for power spectra computed
from the SDSS quasars sample.


  We introduce a randomized topological augmentor based on Schur complements
for Graph Contrastive Learning (GCL). Given a graph laplacian matrix, the
technique generates unbiased approximations of its Schur complements and treats
the corresponding graphs as augmented views. We discuss the benefits of our
approach, provide theoretical justifications and present connections with graph
diffusion. Unlike previous efforts, we study the empirical effectiveness of the
augmentor in a controlled fashion by varying the design choices for subsequent
GCL phases, such as encoding and contrasting. Extensive experiments on node and
graph classification benchmarks demonstrate that our technique consistently
outperforms pre-defined and adaptive augmentation approaches to achieve
state-of-the-art results.


  The analog of the Schwarzschild metric is explored in the context of
Non-Singular Gravity. Analytic results are developed describing redshifts,
curvatures and topological features of the spacetime. All curvatures and
redshifts are finite so there are no Black Holes, no singularities and no
Hawking radiation.


  We investigate the properties of high density compact objects in a vector
type theory, inspired by Einstein's 1919 theory of elementary particles, in
which Einstein assumed that elementary particles are held together by
gravitational as well as electromagnetic type forces. From a modern
perspective, Einstein's theory can be interpreted as a vector type model, with
the gravitational action constructed as a linear combination of the Ricci
scalar, of the trace of the matter energy-momentum tensor, and of a massive
self-interacting vector type field. To obtain the properties of stellar models
we consider the field equations for a static, spherically symmetric system, and
we investigate numerically their solutions for different equations of state of
quark and neutron matter, by assuming that the self-interaction potential of
the vector field either vanishes or is quadratic in the vector field potential.
We consider quark stars described by the MIT bag model equation of state and in
the Color Flavor Locked phase, as well as compact stars consisting of a
Bose-Einstein Condensate of neutron matter, with neutrons forming Cooper pairs.
Constant density stars, representing a generalization of the Interior
Schwarzschild solution of general relativity, are also analyzed. Also, we
consider the Douchin-Haensel (SLy) equation of state. The numerical solutions
are explicitly obtained in both standard general relativity, and the Einstein
dark energy model and an in depth comparison between the astrophysical
predictions of these two theories are performed. As a general conclusion of our
study, we find that for all the considered equations of state a much larger
variety of stellar structures can be obtained in the Einstein dark energy
model, including classes of stars that are more massive than their general
relativistic counterparts.


  Electronic and magnetic structures of iron selenide compounds Ce2O2FeSe2
(2212\ast) and BaFe2Se3(123\ast) are studied by the first-principles
calculations. We find that while all these compounds are composed of
one-dimensional (1D) Fe chain (or ladder) structures, their electronic
structures are not close to be quasi-1D. The magnetic exchange couplings
between two nearest-neighbor (NN) chains in 2212\ast and between two NN
two-leg-ladders in 123\ast are both antiferromagnetic (AFM), which is
consistent with the presence of significant third NN AFM coupling, a common
feature shared in other iron-chalcogenides, FeTe (11\ast) and KyFe2-xSe2
(122\ast). In magnetic ground states, each Fe chain of 2212\ast is
ferromagnetic and each two-leg ladder of 123\ast form a block-AFM structure. We
suggest that all magnetic structures in iron-selenide compounds can be unified
into an extended J1-J2-J3 model. Spin-wave excitations of the model are
calculated and can be tested by future experiments on these two systems.


  As individuals communicate, their exchanges form a dynamic network. We
demonstrate, using time series analysis of communication in three online
settings, that network structure alone can be highly revealing of the diversity
and novelty of the information being communicated. Our approach uses both
standard and novel network metrics to characterize how unexpected a network
configuration is, and to capture a network's ability to conduct information. We
find that networks with a higher conductance in link structure exhibit higher
information entropy, while unexpected network configurations can be tied to
information novelty. We use a simulation model to explain the observed
correspondence between the evolution of a network's structure and the
information it carries.


  Diffusion processes are central to human interactions. One common prediction
of the current modeling frameworks is that initial spreading dynamics follow
exponential growth. Here, we find that, ranging from mobile handsets to
automobiles, from smart-phone apps to scientific fields, early growth patterns
follow a power law with non-integer exponents. We test the hypothesis that
mechanisms specific to substitution dynamics may play a role, by analyzing a
unique data tracing 3.6M individuals substituting for different mobile
handsets. We uncover three generic ingredients governing substitutions,
allowing us to develop a minimal substitution model, which not only explains
the power-law growth, but also collapses diverse growth trajectories of
individual constituents into a single curve. These results offer a mechanistic
understanding of power-law early growth patterns emerging from various domains
and demonstrate that substitution dynamics are governed by robust
self-organizing principles that go beyond the particulars of individual
systems.


  We highlight the role of the light elements (Li, Be, B) in the evolution of
massive single and binary stars, which is largely restricted to a diagnostic
value, and foremost so for the element boron. However, we show that the boron
surface abundance in massive early type stars contains key information about
their foregoing evolution which is not obtainable otherwise. In particular, it
allows to constrain internal mixing processes and potential previous mass
transfer event for binary stars (even if the companion has disappeared). It may
also help solving the mystery of the slowly rotating nitrogen-rich massive main
sequence stars.


  To learn about a physical system of interest, experimental results must be
able to discriminate among models. We introduce a geometrical measure to
quantify the distance between models for pseudoscalar-meson photoproduction in
amplitude space. Experimental observables, with finite accuracy, map to
probability distributions in amplitude space, and the characteristic width
scale of such distributions needs to be smaller than the distance between
models if the observable data are going to be useful. We therefore also
introduce a method for evaluating probability distributions in amplitude space
that arise as a result of one or more measurements, and show how one can use
this to determine what further measurements are going to be necessary to be
able to discriminate among models.


  In the context of combined model of evolution-dominated hydrodynamics +
leading particles, we discuss the pseudorapidity distributions of charged
particles produced in p-p collisions. A comparison is made between the
theoretical predictions and experimental measurements. The combined model works
well in p-p collisions in the whole available energy region from
sqrt(s_NN)=23.6 to 900 GeV.


  Effective cross-sections of nano-objects are fundamental properties that
determine their ability to interact with light. However, measuring them for
individual resonators directly and quantitatively remains challenging,
particularly because of the very low signals involved. Here, we experimentally
measure the thermal emission cross-section of metal-insulator-metal
nano-resonators using a stealthy hyperuniform distribution based on a
hierarchical Poisson-disk algorithm. In such distributions, there are no
long-range interactions between antennas, and we show that the light emitted by
the metasurface behaves as the sum of cross-sections of independent
nanoantennas, enabling direct retrieval of the single resonator contribution.
The emission cross-section at resonance is found to be of the order of
$\mathbf{\lambda_0^2/3}$, a value that is nearly three times larger than the
theroretical maximal absorption cross-section of a single particle but remains
smaller than the maximal extinction cross-section. This measurement technique
can be generalized to any single resonator cross-section, and we also apply it
here to the extinction cross-section.


  We study the transition to runaway expansion of an initially stalled
core-collapse supernova shock. The neutrino luminosity, mass accretion rate,
and neutrinospheric radius are all treated as free parameters. In spherical
symmetry, this transition is mediated by a global non-adiabatic instability
that develops on the advection time and reaches non-linear amplitude. Here we
perform high-resolution, time-dependent hydrodynamic simulations of stalled
supernova shocks with realistic microphysics to analyze this transition. We
find that radial instability is a sufficient condition for runaway expansion if
the neutrinospheric parameters do not vary with time and if heating by the
accretion luminosity is neglected. For a given unstable mode, transition to
runaway occurs when fluid in the gain region reaches positive specific energy.
We find approximate instability criteria that accurately describe the behavior
of the system over a wide region of parameter space. The threshold neutrino
luminosities are in general different than the limiting value for a
steady-state solution. We hypothesize that multidimensional explosions arise
from the excitation of unstable large-scale modes of the turbulent background
flow, at threshold luminosities that are lower than in the laminar case.


  We consider the problem of construction of determinant formulas for the
partition function of the six-vertex model with domain wall boundary
conditions. In pioneering works of Korepin and Izergin a determinant formula
was proposed and proved using a recursion relation. In later works, another
determinant formulas were given by Kostov for the rational case and by Foda and
Wheeler for the trigonometric case. Here, we develop an approach in which the
recursion relation is replaced by a system of algebraic equations with respect
to one set of spectral parameters. We prove that this system has a unique
solution. The result can be easily given as a determinant parametrized by an
arbitrary basis of polynomials. In particular, the choice of the basis of
Lagrange polynomials with respect to the second set of spectral parameters
leads to the Izergin-Korepin representation, and the choice of the monomial
basis leads to the Kostov and Foda-Wheeler representations.


  In this paper, we are primarily concerned with the study of entire and
analytical solutions of abstract degenerate (multi-term) fractional
differential equations with Caputo time-fractional derivatives. We also analyze
systems of such equations and furnish several illustrative examples to
demonstrate usage of obtained theoretical results.


  We study the properties of the broad line region in blazars by comparing the
virial estimate of black hole masses with that derived from the mass of the
host galaxies. The former is sensitive to the width of broad lines, i.e., to
the projection of the velocity of line-emitting clouds along the line of sight;
the latter is not. This comparison allows us to constrain the deprojection
factor f, thus revealing general properties of the geometry of the broad line
region. We show that blazars tend to have 1) higher f values than the quasars
of our reference sample: <f_{BLLacs}>=6.9 +/- 2.3, <f_{blazars}>=5.6 +/- 1.3
and <f_{quasars}>=2.0 +/- 0.3; 2) relatively narrow broad emission lines; 3)
modest equivalent widths, as expected because of the occurrence of jet emission
at very low inclination angles. In a disc-like sketch of the broad line region,
these results indicate a pole-on view of a flat geometry in blazars. This
consistently extends the orientation--dependent unified model of active nuclei
to the geometry of the broad line region.


  We consider pairing in a three-component gas of degenerate fermions. In
particular, we solve the finite temperature mean-field theory of an interacting
gas for a system where both interaction strengths and fermion masses can be
unequal. At zero temperature we find a a possibility of a quantum phase
transition between states associated with pairing between different pairs of
fermions. On the other hand, finite temperature behavior of the three-component
system reveals some qualitative differences from the two-component gas: for a
range of parameters it is possible to have two different critical temperatures.
The lower one corresponds to a transition between different pairing channels,
while the higher one corresponds to the usual superfluid-normal transition. We
discuss how these phase transitions could be observed in ultracold gases of
fermionic atoms.


  We investigate the KdV-Burgers and Gardner equations with dissipation and
external perturbation terms by the approach of dynamical systems and
Shil'nikov's analysis. The stability of the equilibrium point is considered,
and Hopf bifurcations are investigated after a certain scaling that reduces the
parameter space of a three-mode dynamical system which now depends only on two
parameters. The Hopf curve divides the two-dimensional space into two regions.
On the left region the equilibrium point is stable leading to dissapative
periodic orbits. While changing the bifurcation parameter given by the velocity
of the traveling waves, the equilibrium point becomes unstable and a unique
stable limit cycle bifurcates from the origin. This limit cycle is the result
of a supercritical Hopf bifurcation which is proved using the Lyapunov
coefficient together with the Routh-Hurwitz criterion. On the right side of the
Hopf curve, in the case of the KdV-Burgers, we find homoclinic chaos by using
Shil'nikov's theorem which requires the construction of a homoclinic orbit,
while for the Gardner equation the supercritical Hopf bifurcation leads only to
a stable periodic orbit.


  Accurate and automatic segmentation of fibroglandular tissue in breast MRI
screening is essential for the quantification of breast density and background
parenchymal enhancement. In this retrospective study, we developed and
evaluated a transformer-based neural network for breast segmentation (TraBS) in
multi-institutional MRI data, and compared its performance to the well
established convolutional neural network nnUNet. TraBS and nnUNet were trained
and tested on 200 internal and 40 external breast MRI examinations using manual
segmentations generated by experienced human readers. Segmentation performance
was assessed in terms of the Dice score and the average symmetric surface
distance. The Dice score for nnUNet was lower than for TraBS on the internal
testset (0.909$\pm$0.069 versus 0.916$\pm$0.067, P<0.001) and on the external
testset (0.824$\pm$0.144 versus 0.864$\pm$0.081, P=0.004). Moreover, the
average symmetric surface distance was higher (=worse) for nnUNet than for
TraBS on the internal (0.657$\pm$2.856 versus 0.548$\pm$2.195, P=0.001) and on
the external testset (0.727$\pm$0.620 versus 0.584$\pm$0.413, P=0.03). Our
study demonstrates that transformer-based networks improve the quality of
fibroglandular tissue segmentation in breast MRI compared to
convolutional-based models like nnUNet. These findings might help to enhance
the accuracy of breast density and parenchymal enhancement quantification in
breast MRI screening.


  We present a logical framework to represent and reason about fuzzy
optimization problems based on fuzzy answer set optimization programming. This
is accomplished by allowing fuzzy optimization aggregates, e.g., minimum and
maximum in the language of fuzzy answer set optimization programming to allow
minimization or maximization of some desired criteria under fuzzy environments.
We show the application of the proposed logical fuzzy optimization framework
under the fuzzy answer set optimization programming to the fuzzy water
allocation optimization problem.


  An analysis of glass formation for polymer melts that are diluted by
structured molecular additives is derived by using the generalized entropy
theory, which involves a combination of the Adam-Gibbs model and the direct
computation of the configurational entropy based on a lattice model of polymer
melts that includes monomer structural effects. Antiplasticization is
accompanied by a "toughening" of the glass mixture relative to the pure
polymer, and this effect is found to occur when the diluents are small species
with strongly attractive interactions with the polymer matrix. Plasticization
leads to a decreased glass transition temperature T_g and a "softening" of the
fragile host polymer in the glass state. Plasticization is prompted by small
additives with weakly attractive interactions with the polymer matrix. The
shifts in T_g of polystyrene diluted by fully flexible short oligomers are
evaluated from the computations, along with the relative changes in the
isothermal compressibility at T_g to characterize the extent to which the
additives act as antiplasticizers or plasticizers. The theory predicts that a
decreased fragility can accompany both antiplasticization and plasticization of
the glass by molecular additives. The general reduction in the T_g and
fragility of polymers by these molecular additives is rationalized by analyzing
the influence of the diluent's properties (cohesive energy, chain length, and
stiffness) on glass formation in diluted polymer melts. The description of
glass formation at fixed temperature that is induced upon change the fluid
composition directly implies the Angell equation for the structural relaxation
time as function of the polymer concentration, and the computed "zero mobility
concentration" scales linearly with the inverse polymerization index N.


  Describing a scene in terms of primitives -- geometrically simple shapes that
offer a parsimonious but accurate abstraction of structure -- is an established
vision problem. This is a good model of a difficult fitting problem: different
scenes require different numbers of primitives and primitives interact
strongly, but any proposed solution can be evaluated at inference time. The
state of the art method involves a learned regression procedure to predict a
start point consisting of a fixed number of primitives, followed by a descent
method to refine the geometry and remove redundant primitives. Methods are
evaluated by accuracy in depth and normal prediction and in scene segmentation.
This paper shows that very significant improvements in accuracy can be obtained
by (a) incorporating a small number of negative primitives and (b) ensembling
over a number of different regression procedures. Ensembling is by refining
each predicted start point, then choosing the best by fitting loss. Extensive
experiments on a standard dataset confirm that negative primitives are useful
in a large fraction of images, and that our refine-then-choose strategy
outperforms choose-then-refine, confirming that the fitting problem is very
difficult.


  A novel phase field material point method is introduced for robust simulation
of dynamic fracture in elastic media considering the most general case of
anisotropic surface energy. Anisotropy is explicitly introduced through a
properly defined crack density functional. The particular case of impact driven
fracture is treated by employing a discrete field approach within the material
point method setting. In this, the equations of motion and phase field
governing equations are solved independently for each discrete field using a
predictor-corrector algorithm. Contact at the interface is resolved through
frictional contact conditions. The proposed method is verified using analytical
predictions. The influence of surface energy anisotropy and loading conditions
on the resulting crack paths is assessed through a set of benchmark problems.
Comparisons are made with the standard Phase Field Finite Element Method and
experimental observations.


  Hog (HDL on Git) is an open-source tool designed to manage Git-based HDL
repositories. It aims to simplify HDL project development, maintenance, and
versioning by using Git to guarantee synthesis and implementation
reproducibility and binary file traceability. This is ensured by linking each
produced binary file to a specific Git commit, embedding the Git commit hash
(SHA) into the binary file via HDL generics stored in firmware registers. Hog
is released twice a year, in January and in June. We present here the latest
stable version 2023.1, which introduces major novel features, such as the
support for Microchip Libero IDE, and the capability to run the Hog Continuous
Integration (Hog-CI) workflow with GitHub Actions. A plan to integrate Hog with
the OpenCores repository is also described, which is expected to be completed
for Hog release 2023.2


  Runtime enforcers can be used to ensure that running applications satisfy
desired correctness properties. Although runtime enforcers that are
correct-by-construction with respect to abstract behavioral models are
relatively easy to specify, the concrete software enforcers generated from
these specifications may easily introduce issues in the target application.
Indeed developers can generate test suites to verify the functional behavior of
the enforcers, for instance exploiting the same models used to specify them.
However, it remains challenging and tedious to verify the behavior of enforcers
in terms of non-functional performance characteristics. This paper describes a
practical approach to reveal runtime enforcers that may introduce
inefficiencies in the target application. The approach relies on a combination
of automatic test generation and runtime monitoring of multiple key performance
indicators. We designed our approach to reveal issues in four indicators for
mobile systems: responsiveness, launch time, memory, and energy consumption.
Experimental results show that our approach can detect performance issues that
might be introduced by automatically generated enforcers.


  Cold Dark Matter (CDM) is a crucial constituent of the current concordance
cosmological model. Having a vanishing equation of state (EoS), its energy
density scales with the inverse cosmic volume and is thus uniquely described by
a single number, its present abundance. We test the inverse cosmic volume law
for Dark Matter (DM) by allowing its EoS to vary independently in eight
redshift bins in the range $z=10^5$ and $z=0$. We use the latest measurements
of the Cosmic Microwave Background radiation from the Planck satellite and
supplement them with Baryon Acoustic Oscillation (BAO) data from the 6dF and
SDSS-III BOSS surveys, and with the Hubble Space Telescope (HST) key project
data. We find no evidence for nonzero EoS in any of the eight redshift bins.
With Planck data alone, the DM abundance is most strongly constrained around
matter-radiation equality $\omega^{\rm eq}_g = 0.1193^{+0.0036}_{-0.0035}$ (95%
c.l.), whereas its present day value is more weakly constrained $\omega^{(0)}_g
= 0.16^{+0.12}_{-0.10}$ (95% c.l.). Adding BAO or HST data does not
significantly change the $\omega^{\rm eq}_g$ constraint, while $\omega^{(0)}_g$
tightens to $0.160^{+0.069}_{-0.065} $ (95% c.l.) and $0.124^{+0.081}_{-0.067}$
(95% c.l.) respectively. Our results constrain for the first time the level of
"coldness" required of the DM across various cosmological epochs and show that
the DM abundance is strictly positive at all times.


  Threshold values in population dynamics can be formulated as spectral bounds
of matrices, determining the dichotomy of population persistence and
extinction. For a square matrix $\mu A + Q$, where $A$ is a quasi-positive
matrix describing population dispersal among patches in a heterogeneous
environment and $Q$ is a diagonal matrix encoding within-patch population
dynamics, the monotonicy of its spectral bound with respect to dispersal
speed/coupling strength/travel frequency $\mu$ is established via two methods.
The first method is an analytic derivation utilizing a graph-theoretic approach
based on Kirchhoff's Matrix-Tree Theorem; the second method employs
Collatz-Wielandt formula from matrix theory and complex analysis arguments. It
turns out that our established result is a slightly strengthen version of
Karlin-Altenberg's Theorem, which has previously been discovered independently
while investigating reduction principle in evolution biology and evolution
dispersal in patchy landscapes. Nevertheless, our result provides a new and
effective approach in stability analysis of complex biological systems in a
heterogeneous environment. We illustrate this by applying our result to
well-known ecological models of single species, predator-prey and competition,
and an epidemiological model of susceptible-infected-susceptible (SIS) type. We
successfully solve some open problems in the literature of population dynamics.


  We consider the computation by simulation and neural net regression of
conditional expectations, or more general elicitable statistics, of functionals
of processes $(X, Y )$. Here an exogenous component $Y$ (Markov by itself) is
time-consuming to simulate, while the endogenous component $X$ (jointly Markov
with $Y$) is quick to simulate given $Y$, but is responsible for most of the
variance of the simulated payoff. To address the related variance issue, we
introduce a conditionally independent, hierarchical simulation scheme, where
several paths of $X$ are simulated for each simulated path of $Y$. We analyze
the statistical convergence of the regression learning scheme based on such
block-dependent data. We derive heuristics on the number of paths of $Y$ and,
for each of them, of $X$, that should be simulated. The resulting algorithm is
implemented on a graphics processing unit (GPU) combining Python/CUDA and
learning with PyTorch. A CVA case study with a nested Monte Carlo benchmark
shows that the hierarchical simulation technique is key to the success of the
learning approach.


  Resampling methods such as the bootstrap have proven invaluable in the field
of machine learning. However, the applicability of traditional bootstrap
methods is limited when dealing with large streams of dependent data, such as
time series or spatially correlated observations. In this paper, we propose a
novel bootstrap method that is designed to account for data dependencies and
can be executed online, making it particularly suitable for real-time
applications. This method is based on an autoregressive sequence of
increasingly dependent resampling weights. We prove the theoretical validity of
the proposed bootstrap scheme under general conditions. We demonstrate the
effectiveness of our approach through extensive simulations and show that it
provides reliable uncertainty quantification even in the presence of complex
data dependencies. Our work bridges the gap between classical resampling
techniques and the demands of modern data analysis, providing a valuable tool
for researchers and practitioners in dynamic, data-rich environments.


  At sufficiently high baryon density, a quark matter is expected to become a
color superconductor because of the pairing forces mediated by gluons. The
theoretical aspect of this novel phase of the strong interaction is reviewed
with the emphasis on the perturbation theory of QCD at high chemical potential
and low temperature. The derivation of the scaling formula of the
superconducting transition temperature at weak coupling is explained in detail.
The Ginzburg-Landau theory of the color superconductivity is also discussed.


  The transmission eigenvalue problem arises from the inverse scattering theory
for inhomogeneous media and has important applications in many qualitative
methods. The problem is posted as a system of two second order partial
differential equations and is essentially nonlinear, non-selfadjoint, and of
higher order. It is nontrivial to develop effective numerical methods and the
proof of convergence is challenging. In this paper, we formulate the
transmission eigenvalue problem for anisotropic media as an eigenvalue problem
of a holomorphic Fredholm operator function of index zero. The Lagrange finite
elements are used for discretization and the convergence is proved using the
abstract approximation theory for holomorphic operator functions. A spectral
indicator method is developed to compute the eigenvalues. Numerical examples
are presented for validation.


  The changes in excitation dependence of efficiency with temperature is
modeled for a wurtzite InGaN light-emitting diode. The model incorporates
bandstructure changes with carrier density arising from screening of
quantum-confined Stark effect. Bandstructure is computed by solving Poisson and
k.p equations in the envelop approximation. The information is used in a
dynamical model for populations in momentum-resolved electron and hole states.
Application of the approach shows the interplay of quantum-well and barrier
emissions giving rise to shape changes in efficiency versus current density
with changing temperature, as observed in some experiments.


  We have investigated the reduction of the glass transition temperature,
$T_g$, for thin supported films of particularly small molecular weight ($M_W$ =
2 kg/mol) polystyrene, and found good agreement with earlier studies on larger
molecules. By combining the eigenmodes of the films with mode coupling theory,
we arrive at a simple and predictive model which is in quantitative agreement
with the data. Its only fitting parameter is the shear modulus governing the
relevant fluctuations. The weak dependence of the latter upon the molecular
weight of the polymer is in accordance with the general observation that the
reduction of $T_g$ is largely independent of chain length at sufficiently low
molecular weight.


  In paper I (Yu et al. [1]), we show through N-body simulation that a local
monotonic Gaussian transformation can significantly reduce non-Gaussianity in a
noise-free lensing convergence field. This makes the Gaussianization a
promising theoretical tool to understand high-order lensing statistics. Here we
present a study of its applicability in lensing data analysis, in particular
when shape measurement noise is presented in lensing convergence maps. (i) We
find that shape measurement noise significantly degrades the Gaussianization
performance and the degradation increases for shallower surveys. (ii) The
Wiener filter is efficient in reducing the impact of shape measurement noise.
The Gaussianization of the Wiener-filtered lensing maps is able to suppress
skewness, kurtosis, and the 5th- and 6th-order cumulants by a factor of 10 or
more. It also works efficiently to reduce the bispectrum to zero.


  Recently, many datasets have been proposed to test the systematic
generalization ability of neural networks. The companion baseline Transformers,
typically trained with default hyper-parameters from standard tasks, are shown
to fail dramatically. Here we demonstrate that by revisiting model
configurations as basic as scaling of embeddings, early stopping, relative
positional embedding, and Universal Transformer variants, we can drastically
improve the performance of Transformers on systematic generalization. We report
improvements on five popular datasets: SCAN, CFQ, PCFG, COGS, and Mathematics
dataset. Our models improve accuracy from 50% to 85% on the PCFG productivity
split, and from 35% to 81% on COGS. On SCAN, relative positional embedding
largely mitigates the EOS decision problem (Newman et al., 2020), yielding 100%
accuracy on the length split with a cutoff at 26. Importantly, performance
differences between these models are typically invisible on the IID data split.
This calls for proper generalization validation sets for developing neural
networks that generalize systematically. We publicly release the code to
reproduce our results.


  The existence of charged black holes has suggested that wormholes may also be
charged. The purpose of this paper is to construct a general model of a charged
wormhole that proves to be a natural extension of the original Morris-Thorne
wormhole. This goal is achieved by means of the classical embedding theory that
has played a major role in the general theory of relativity.


  Visual perception tasks often require vast amounts of labelled data,
including 3D poses and image space segmentation masks. The process of creating
such training data sets can prove difficult or time-intensive to scale up to
efficacy for general use. Consider the task of pose estimation for rigid
objects. Deep neural network based approaches have shown good performance when
trained on large, public datasets. However, adapting these networks for other
novel objects, or fine-tuning existing models for different environments,
requires significant time investment to generate newly labelled instances.
Towards this end, we propose ProgressLabeller as a method for more efficiently
generating large amounts of 6D pose training data from color images sequences
for custom scenes in a scalable manner. ProgressLabeller is intended to also
support transparent or translucent objects, for which the previous methods
based on depth dense reconstruction will fail. We demonstrate the effectiveness
of ProgressLabeller by rapidly create a dataset of over 1M samples with which
we fine-tune a state-of-the-art pose estimation network in order to markedly
improve the downstream robotic grasp success rates. ProgressLabeller is
open-source at https://github.com/huijieZH/ProgressLabeller.


  Human pose estimation (HPE) is a central part of understanding the visual
narration and body movements of characters depicted in artwork collections,
such as Greek vase paintings. Unfortunately, existing HPE methods do not
generalise well across domains resulting in poorly recognized poses. Therefore,
we propose a two step approach: (1) adapting a dataset of natural images of
known person and pose annotations to the style of Greek vase paintings by means
of image style-transfer. We introduce a perceptually-grounded style transfer
training to enforce perceptual consistency. Then, we fine-tune the base model
with this newly created dataset. We show that using style-transfer learning
significantly improves the SOTA performance on unlabelled data by more than 6%
mean average precision (mAP) as well as mean average recall (mAR). (2) To
improve the already strong results further, we created a small dataset
(ClassArch) consisting of ancient Greek vase paintings from the 6-5th century
BCE with person and pose annotations. We show that fine-tuning on this data
with a style-transferred model improves the performance further. In a thorough
ablation study, we give a targeted analysis of the influence of style
intensities, revealing that the model learns generic domain styles.
Additionally, we provide a pose-based image retrieval to demonstrate the
effectiveness of our method.


  Mixing of the pseudoscalar mesons is discussed in the quark-flavor basis with
the hypothesis that the basis decay constants follow the pattern of particle
state mixing. The divergences of the axial vector currents which embody the
axial vector anomaly, combined with this hypothesis provide a calculational
scheme for the parameters describing the mixing of the pion, eta and eta'
mesons. Phenomenological applications of this mixing scheme are presented with
particular interest focussed on isospin symmetry breaking in QCD estimated as
eta and eta' admixtures to the pion. In contrast to previous work a possible
difference in the basis decay constants f_u and f_d is considered and
consequences of this potentially large effect on the strength of isospin
symmetry breaking is discussed.


  This article presents the CODYRUN software developped by University of La
R\'eunion. It is a multizone thermal software, with detailled airflow and
humidity transfer calculations. One of its specific aspects is that it
constitutes a research tool, a design tool used by the lab and professionnals
and also a teaching tool. After a presentation of the multiple model aspect,
some details of the tree modules associated to physical phenomenons are given.
Elements of validation are exposed in next paraghaph, and then a few details of
the front end.


  We introduce a definition of the fidelity function for multi-round quantum
strategies, which we call the strategy fidelity, that is a generalization of
the fidelity function for quantum states. We provide many properties of the
strategy fidelity including a Fuchs-van de Graaf relationship with the strategy
norm. We also provide a general monotinicity result for both the strategy
fidelity and strategy norm under the actions of strategy-to-strategy linear
maps. We illustrate an operational interpretation of the strategy fidelity in
the spirit of Uhlmann's Theorem and discuss its application to the security
analysis of quantum protocols for interactive cryptographic tasks such as
bit-commitment and oblivious string transfer. Our analysis is general in the
sense that the actions of the protocol need not be fully specified, which is in
stark contrast to most other security proofs. Lastly, we provide a semidefinite
programming formulation of the strategy fidelity.


  We demonstrate reliable machine-learned tuning of quantum-dot-based
artificial Kitaev chains to Majorana sweet spots, using the covariance matrix
adaptation algorithm. We show that a loss function based on local
tunnelling-spectroscopy features of a chain with two additional sensor dots
added at its ends provides a reliable metric to navigate parameter space and
find points where crossed Andreev reflection and elastic cotunneling between
neighbouring sites balance in such a way to yield near-zero-energy modes with
very high Majorana quality. We simulate tuning of two- and three-site Kitaev
chains, where the loss function is found from calculating the low-energy
spectrum of a model Hamiltonian that includes Coulomb interactions and finite
Zeeman splitting. In both cases, the algorithm consistently converges towards
high-quality sweet spots. Since tunnelling spectroscopy provides one global
metric for tuning all on-site potentials simultaneously, this presents a
promising way towards tuning longer Kitaev chains, which are required for
achieving topological protection of the Majorana modes.


  We present an analysis of 15 Type Ia supernovae (SNe Ia) at redshift z > 1 (9
at 1.5 < z < 2.3) recently discovered in the CANDELS and CLASH Multi-Cycle
Treasury programs using WFC3 on the Hubble Space Telescope. We combine these
SNe Ia with a new compilation of 1050 SNe Ia, jointly calibrated and corrected
for simulated survey biases to produce accurate distance measurements. We
present unbiased constraints on the expansion rate at six redshifts in the
range 0.07 < z < 1.5 based only on this combined SN Ia sample. The added
leverage of our new sample at z > 1.5 leads to a factor of ~3 improvement in
the determination of the expansion rate at z = 1.5, reducing its uncertainty to
~20%, a measurement of H(z=1.5)/H0=2.67 (+0.83,-0.52). We then demonstrate that
these six measurements alone provide a nearly identical characterization of
dark energy as the full SN sample, making them an efficient compression of the
SN Ia data. The new sample of SNe Ia at z > 1 usefully distinguishes between
alternative cosmological models and unmodeled evolution of the SN Ia distance
indicators, placing empirical limits on the latter. Finally, employing a
realistic simulation of a potential WFIRST SN survey observing strategy, we
forecast optimistic future constraints on the expansion rate from SNe Ia.


  We provide a novel upper-bound on Witsenhausen's rate, the rate required in
the zero-error analogue of the Slepian-Wolf problem; our bound is given in
terms of a new information-theoretic functional defined on a certain graph. We
then use the functional to give a single letter lower-bound on the error
exponent for the Slepian-Wolf problem under the vanishing error probability
criterion, where the decoder has full (i.e. unencoded) side information. Our
exponent stems from our new encoding scheme which makes use of source
distribution only through the positions of the zeros in the `channel' matrix
connecting the source with the side information, and in this sense is
`semi-universal'. We demonstrate that our error exponent can beat the
`expurgated' source-coding exponent of Csisz\'{a}r and K\"{o}rner,
achievability of which requires the use of a non-universal maximum-likelihood
decoder. An extension of our scheme to the lossy case (i.e. Wyner-Ziv) is
given. For the case when the side information is a deterministic function of
the source, the exponent of our improved scheme agrees with the sphere-packing
bound exactly (thus determining the reliability function). An application of
our functional to zero-error channel capacity is also given.


  A mixed-symmetry nuclear shell-model scheme for carrying out calculations in
regimes where there is a competition between two or more modes is proposed. A
one-dimensional toy model is used to demonstrate the concept. The theory is
then applied to $^{24}Mg$ and $^{44}Ti$. For lower pf-shell nuclei such as
$^{44-48}Ti$ and $^{48}Cr$ there is strong SU(3) symmetry breaking due to the
spin-orbit interaction. However, the quadrupole collectivity as measured by
B(E2) transition strengths in the yrast band remain high even though SU(3)
appears to be broken. Some results for the so-called X(5) symmetry that falls
along the U(5) $\leftrightarrow$ SU(3) leg of the Interacting Boson Model are
also considered. The results show that the mixed-symmetry concept is effective,
even when strong symmetry breaking occurs.


  A charge-symmetry-breaking nucleon-nucleon force due to the up-down quark
mass difference is evaluated in the quark cluster model. It is applied to the
shell-model calculation for the isovector mass shifts of isospin multiplets and
the isospin-mixing matrix elements in 1s0d-shell nuclei. We find that the
contribution of the quark mass difference effect is large and agrees with
experiment. This contribution may explain the Okamoto-Nolen-Schiffer anomaly,
alternatively to the meson-mixing contribution, which is recently predicted to
be reduced by the large off-shell correction.


  We derive two systems of boundary-domain integral equations (BDIEs)
equivalent to the Dirichlet problem for the compressible Stokes system using
the potential method with an explicit parametrix (Levi function). The BDIEs are
given in terms of surface and volume hydrodynamic potentials. We study the
mapping properties of these integral potential operators and used them to prove
existence and uniqueness of solution of the two systems of BDIEs obtained
taking into account the non-trivial kernels of the single layer and
hypersingular hydrodynamic surface potentials.


  We construct a subalgebra of the Hecke algebra of type A. This is a
generalization of the group algebra of the alternating groups. All the
equivalent classes of irreducible representations of the subalgebra and the
q-analogue of the branching rule between symmetric groups and alternating
groups is obtained.


  We show that every probability-measure-preserving action of a countable
amenable group G can be tiled, modulo a null set, using finitely many finite
subsets of G ("shapes") with prescribed approximate invariance so that the
collection of tiling centers for each shape is Borel. This is a dynamical
version of the Downarowicz--Huczek--Zhang tiling theorem for countable amenable
groups and strengthens the Ornstein--Weiss Rokhlin lemma. As an application we
prove that, for every countably infinite amenable group G, the crossed product
of a generic free minimal action of G on the Cantor set is Z-stable.


  Neutrino masses are yet unknown. We discuss the present state of effective
electron anti-neutrino mass from $\beta$ decay experiments; effective Majorana
neutrino mass from neutrinoless double-beta decay experiments; neutrino mass
squared differences from neutrino oscillation: solar, atmospheric, reactor and
accelerator based experiments; sum of neutrino masses from cosmological
observations. Current experimental challenges in the determination of neutrino
masses are briefly discussed. The main focus is devoted to contemporary
experiments.


  COVID-19 (Coronavirus disease 2019) has been quickly spreading since its
outbreak, impacting financial markets and healthcare systems globally.
Countries all around the world have adopted a number of extraordinary steps to
restrict the spreading virus, where early COVID-19 diagnosis is essential.
Medical images such as X-ray images and Computed Tomography scans are becoming
one of the main diagnostic tools to combat COVID-19 with the aid of deep
learning-based systems. In this survey, we investigate the main contributions
of deep learning applications using medical images in fighting against COVID-19
from the aspects of image classification, lesion localization, and severity
quantification, and review different deep learning architectures and some image
preprocessing techniques for achieving a preciser diagnosis. We also provide a
summary of the X-ray and CT image datasets used in various studies for COVID-19
detection. The key difficulties and potential applications of deep learning in
fighting against COVID-19 are finally discussed. This work summarizes the
latest methods of deep learning using medical images to diagnose COVID-19,
highlighting the challenges and inspiring more studies to keep utilizing the
advantages of deep learning to combat COVID-19.


  We present a comparison of the performance of two non-local update algorithms
for path integral Monte Carlo (PIMC) simulations, the multigrid Monte Carlo
method and the staging algorithm. Looking at autocorrelation times for the
internal energy we show that both refined algorithms beat the slowing down
which is encountered for standard local update schemes in the continuum limit.
We investigate the conditions under which the staging algorithm performs
optimally and give a brief discussion of the mutual merits of the two
algorithms.


  In this article, we show that $\alpha$-fractal functions defined on
Sierpi\'nski gasket (denoted by $\triangle$) depend continuously on the
parameters involved in the construction. In the latter part of this article,
the continuous dependence of parameters on $\alpha$-fractal functions defined
on $\triangle$ is shown graphically.


  Sparse subspace clustering (SSC) is a state-of-the-art method for segmenting
a set of data points drawn from a union of subspaces into their respective
subspaces. It is now well understood that SSC produces subspace-preserving data
affinity under broad geometric conditions but suffers from a connectivity
issue. In this paper, we develop a novel geometric analysis for a variant of
SSC, named affine SSC (ASSC), for the problem of clustering data from a union
of affine subspaces. Our contributions include a new concept called affine
independence for capturing the arrangement of a collection of affine subspaces.
Under the affine independence assumption, we show that ASSC is guaranteed to
produce subspace-preserving affinity. Moreover, inspired by the phenomenon that
the $\ell_1$ regularization no longer induces sparsity when the solution is
nonnegative, we further show that subspace-preserving recovery can be achieved
under much weaker conditions for all data points other than the extreme points
of samples from each subspace. In addition, we confirm a curious observation
that the affinity produced by ASSC may be subspace-dense---which could
guarantee the subspace-preserving affinity of ASSC to produce correct
clustering under rather weak conditions. We validate the theoretical findings
on carefully designed synthetic data and evaluate the performance of ASSC on
several real data sets.


  We show the necessary part of the following theorem : a finitely generated,
residually finite group has property $PL^p$ (i.e. it admits a proper isometric
affine action on some $L^p$ space) if, and only if, one (or equivalently, all)
of its box spaces admits a fibred coarse embedding into some $L^p$ space. We
also prove that coarse embeddability of a box space of a group into a $L^p$
space implies property $PL^p$ for this group.


  The quest for safer and more efficient transportation through cooperative,
connected and automated mobility (CCAM) calls for realistic performance
analysis tools, especially with respect to wireless communications. While the
simulation of existing and emerging communication technologies is an option,
the most realistic results can be obtained by employing real hardware, as done
for example in field operational tests (FOTs). For CCAM, however, performing
FOTs requires vehicles, which are generally expensive. and performing such
tests can be very demanding in terms of manpower, let alone considering safety
issues. Mobility simulation with hardware-in-the-loop (HIL) serves as a middle
ground, but current solutions lack flexibility and reconfigurability. This work
thus proposes ColosSUMO as a way to couple Colosseum, the world's largest
wireless network emulator, with the SUMO mobility simulator, showing its design
concept, how it can be exploited to simulate realistic vehicular environments,
and its flexibility in terms of communication technologies.


  Cold-start item recommendation is a long-standing challenge in recommendation
systems. A common remedy is to use a content-based approach, but rich
information from raw contents in various forms has not been fully utilized. In
this paper, we propose a domain/data-agnostic item representation learning
framework for cold-start recommendations, naturally equipped with multimodal
alignment among various features by adopting a Transformer-based architecture.
Our proposed model is end-to-end trainable completely free from classification
labels, not just costly to collect but suboptimal for recommendation-purpose
representation learning. From extensive experiments on real-world movie and
news recommendation benchmarks, we verify that our approach better preserves
fine-grained user taste than state-of-the-art baselines, universally applicable
to multiple domains at large scale.


  Maximum mean discrepancy (MMD) has been widely adopted in domain adaptation
to measure the discrepancy between the source and target domain distributions.
Many existing domain adaptation approaches are based on the joint MMD, which is
computed as the (weighted) sum of the marginal distribution discrepancy and the
conditional distribution discrepancy; however, a more natural metric may be
their joint probability distribution discrepancy. Additionally, most metrics
only aim to increase the transferability between domains, but ignores the
discriminability between different classes, which may result in insufficient
classification performance. To address these issues, discriminative joint
probability MMD (DJP-MMD) is proposed in this paper to replace the
frequently-used joint MMD in domain adaptation. It has two desirable
properties: 1) it provides a new theoretical basis for computing the
distribution discrepancy, which is simpler and more accurate; 2) it increases
the transferability and discriminability simultaneously. We validate its
performance by embedding it into a joint probability domain adaptation
framework. Experiments on six image classification datasets demonstrated that
the proposed DJP-MMD can outperform traditional MMDs.


  The aim of the ArDM project is the development and operation of a one ton
double-phase liquid argon detector for direct Dark Matter searches. The
detector measures both the scintillation light and the ionization charge from
ionizing radiation using two independent readout systems. This paper briefly
describes the detector concept and presents preliminary results from the ArDM
R&D program, including a 3 l prototype developed to test the charge readout
system.


  The success of Artificial Intelligence (AI) in multiple disciplines and
vertical domains in recent years has promoted the evolution of mobile
networking and the future Internet toward an AI-integrated Internet-of-Things
(IoT) era. Nevertheless, most AI techniques rely on data generated by physical
devices (e.g., mobile devices and network nodes) or specific applications
(e.g., fitness trackers and mobile gaming). To bypass this circumvent,
Generative AI (GAI), a.k.a. AI-generated content (AIGC), has emerged as a
powerful AI paradigm; thanks to its ability to efficiently learn complex data
distributions and generate synthetic data to represent the original data in
various forms. This impressive feature is projected to transform the management
of mobile networking and diversify the current services and applications
provided. On this basis, this work presents a concise tutorial on the role of
GAIs in mobile and wireless networking. In particular, this survey first
provides the fundamentals of GAI and representative GAI models, serving as an
essential preliminary to the understanding of the applications of GAI in mobile
and wireless networking. Then, this work provides a comprehensive review of
state-of-the-art studies and GAI applications in network management, wireless
security, semantic communication, and lessons learned from the open literature.
Finally, this work summarizes the current research on GAI for mobile and
wireless networking by outlining important challenges that need to be resolved
to facilitate the development and applicability of GAI in this edge-cutting
area.


  We present a protocol to deterministically prepare the electromagnetic field
in a large photon number state. The field starts in a coherent state and,
through resonant interaction with one or few two-level systems, it evolves into
a coherently displaced Fock state, without any post-selection. We show the
feasibility of the scheme under realistic parameters. The presented method
opens a door to reach Fock states with $n\sim100$ and optimal fidelities above
$70$%, blurring the line between macroscopic and quantum states of the field.


  We study single-sample prophet inequalities (SSPIs), i.e., prophet
inequalities where only a single sample from each prior distribution is
available. Besides a direct, and optimal, SSPI for the basic single choice
problem [Rubinstein et al., 2020], most existing SSPI results were obtained via
an elegant, but inherently lossy, reduction to order-oblivious secretary (OOS)
policies [Azar et al., 2014]. Motivated by this discrepancy, we develop an
intuitive and versatile greedy-based technique that yields SSPIs directly
rather than through the reduction to OOSs. Our results can be seen as
generalizing and unifying a number of existing results in the area of prophet
and secretary problems. Our algorithms significantly improve on the competitive
guarantees for a number of interesting scenarios (including general matching
with edge arrivals, bipartite matching with vertex arrivals, and certain
matroids), and capture new settings (such as budget additive combinatorial
auctions). Complementing our algorithmic results, we also consider mechanism
design variants. Finally, we analyze the power and limitations of different
SSPI approaches by providing a partial converse to the reduction from SSPI to
OOS given by Azar et al.


  In this letter, we have considered generalized Ricci dark energy in the
Horava-Lifshitz gravity. We have reconstructed the Hubble's parameter in terms
of fractional densities. We have viewed the equation of state parameter in this
situation. Also, we have examined the behavior of deceleration parameter and
investigated the nature of the statefinder diagnostics. The equation of state
parameter has exhibited quintessence-like behavior and from the plot of the
deceleration parameter we have observed an ever accelerating universe.


  We give meaning to linear and semi-linear (possibly degenerate) parabolic
partial differential equations with (affine) linear rough path noise and
establish stability in a rough path metric. In the case of enhanced Brownian
motion (Brownian motion with its L\'evy area) as rough path noise the solution
coincides with the standard variational solution of the SPDE.


  In this paper, we propose a novel color constancy approach, called Bag of
Color Features (BoCF), building upon Bag-of-Features pooling. The proposed
method substantially reduces the number of parameters needed for illumination
estimation. At the same time, the proposed method is consistent with the color
constancy assumption stating that global spatial information is not relevant
for illumination estimation and local information ( edges, etc.) is sufficient.
Furthermore, BoCF is consistent with color constancy statistical approaches and
can be interpreted as a learning-based generalization of many statistical
approaches. To further improve the illumination estimation accuracy, we propose
a novel attention mechanism for the BoCF model with two variants based on
self-attention. BoCF approach and its variants achieve competitive, compared to
the state of the art, results while requiring much fewer parameters on three
benchmark datasets: ColorChecker RECommended, INTEL-TUT version 2, and NUS8.


  We consider eigenvectors of the Hamiltonian $H_0$ perturbed by a generic
perturbation $V$ modelled by a random matrix from the Gaussian Unitary Ensemble
(GUE). Using the supersymmetry approach we derive analytical results for the
statistics of the eigenvectors, which are non-perturbative in $V$ and valid for
an arbitrary deterministic $H_0$. Further we generalise them to the case of a
random $H_0$, focusing, in particular, on the Rosenzweig-Porter model. Our
analytical predictions are confirmed by numerical simulations.


  We investigate spin-vortex excitations in the two-dimensional antiferromagnet
on the basis of the nonlinear sigma model. The model of two-dimensional
Heisenberg quantum antiferromagnet is mapped onto the (2+1)D nonlinear sigma
model. The 2D nonlinear sigma model has an instanton (or skyrmion) solution
which describes an excitation of spin-vortex type. Quantum fluctuations of
instantons are reduced to the study of the Coulomb gas, and the gas of
instantons of the 2D nonlinear sigma model is in the plasma phase. We
generalize this picture of instanton gas to the (2+1)D nonlinear sigma model.
We show, using some approximation, that there is a Kosterlitz-Thouless
transition from the plasma phase to the molecular phase as the temperature is
lowered.


  The $\mathrm{SU}(N)$ symmetric antiferromagnetic Heisenberg model with
multi-column representations on the two-dimensional square lattice is
investigated by quantum Monte Carlo simulations. For the representation of
Young diagram with two columns, we confirm that a valence-bond solid order
appears as soon as the N\'eel order disappears at $N = 10$ indicating no
intermediate phase. In the case of the representation with three columns, there
is no evidence for both of the N\'eel and the valence-bond solid ordering for
$N\ge 15$. This is actually consistent with the large-$N$ theory, which
predicts that the VBS state immediately follows the N\'eel state, because the
expected spontaneous order is too weak to be detected.


  Early detection and diagnosis of coronary artery disease (CAD) could save
lives and reduce healthcare costs. In this study, we propose a 3D Resnet-50
deep learning model to directly classify normal subjects and CAD patients on
computed tomography coronary angiography images. Our proposed method
outperforms a 2D Resnet-50 model by 23.65%. Explainability is also provided by
using a Grad-GAM. Furthermore, we link the 3D CAD classification to a 2D
two-class semantic segmentation for improved explainability and accurate
abnormality localisation.


  Humans have the capability, aided by the expressive compositionality of their
language, to learn quickly by demonstration. They are able to describe unseen
task-performing procedures and generalize their execution to other contexts. In
this work, we introduce DescribeWorld, an environment designed to test this
sort of generalization skill in grounded agents, where tasks are linguistically
and procedurally composed of elementary concepts. The agent observes a single
task demonstration in a Minecraft-like grid world, and is then asked to carry
out the same task in a new map. To enable such a level of generalization, we
propose a neural agent infused with hierarchical latent language--both at the
level of task inference and subtask planning. Our agent first generates a
textual description of the demonstrated unseen task, then leverages this
description to replicate it. Through multiple evaluation scenarios and a suite
of generalization tests, we find that agents that perform text-based inference
are better equipped for the challenge under a random split of tasks.


  It it shown that the Bloch-Kato conjecture on the norm residue homomorphism
$K^M(F)/l \to H^*(G_F,Z/l)$ follows from its (partially known) low-degree part
under the assumption that the Milnor K-theory algebra $K^M(F)/l$ modulo $l$ is
Koszul. This conclusion is a case of a general result on the cohomology of
nilpotent (co-)algebras and Koszulity.


  A cursory reading of the literature suggests that we have made a lot of
progress in designing effective adversarial defenses for Graph Neural Networks
(GNNs). Yet, the standard methodology has a serious flaw - virtually all of the
defenses are evaluated against non-adaptive attacks leading to overly
optimistic robustness estimates. We perform a thorough robustness analysis of 7
of the most popular defenses spanning the entire spectrum of strategies, i.e.,
aimed at improving the graph, the architecture, or the training. The results
are sobering - most defenses show no or only marginal improvement compared to
an undefended baseline. We advocate using custom adaptive attacks as a gold
standard and we outline the lessons we learned from successfully designing such
attacks. Moreover, our diverse collection of perturbed graphs forms a
(black-box) unit test offering a first glance at a model's robustness.


  We measure reddenings to HII regions in NGC 2903, NGC 1512, M51, NGC 4449 and
NGC 6946 from Hubble Space Telescope Pa alpha and H alpha images. Extinctions
range from A_V ~ 5 - 0 depending upon the galaxy. For the galaxies with HST
images in both lines, NGC 2903, NGC 1512 and M51, the Pa alpha and H alpha
emission are almost identical in morphology which implies that little emission
from bright HII regions is hidden from view by regions of comparatively high
extinction. The scatter in the measured extinctions is only +- 0.5 mag.
  We compare the reddenings we measure in five galaxies using the Pa alpha to H
alpha ratios to those measured previously from the Balmer decrement in the LMC
and as a function of radius in M101 and M51. We find that luminosity weighted
mean extinctions of these ensembles of HI regions are correlated with gas
surface density and metallicity. The correlation is consistent with the mean
extinction depending on dust density where the dust to gas mass ratio scales
with the metallicity. This trend is expected if HII regions tend to be located
near the mid-plane of a gas disk and emerge from their parent molecular clouds
soon after birth. In environments with gas densities below a few hundred
Msol/pc^2 star formation rates estimated from integrated line fluxes and mean
extinctions are likely to be fairly accurate.


  For data-driven control of nonlinear systems, the basis functions
characterizing the dynamics are usually essential. In existing works, the basis
functions are often carefully chosen based on pre-knowledge of the dynamics so
that the system can be expressed or well-approximated by the basis functions
and the experimental data. For a more general setting where explicit
information on the basis functions is not available, this paper presents a
data-driven approach for stabilizer design and closed-loop analysis via the
Lyapunov method. First, based on Taylor's expansion and using input-state data,
a stabilizer and a Lyapunov function are designed to render the known
equilibrium locally asymptotically stable. Then, data-driven conditions are
derived to check whether a given sublevel set of the found Lyapunov function is
an invariant subset of the region of attraction. One of the main challenges is
how to handle Taylor's remainder in the design of the local stabilizers and the
analysis of the closed-loop performance.


  Recent progress in neural implicit functions has set new state-of-the-art in
reconstructing high-fidelity 3D shapes from a collection of images. However,
these approaches are limited to closed surfaces as they require the surface to
be represented by a signed distance field. In this paper, we propose NeAT, a
new neural rendering framework that can learn implicit surfaces with arbitrary
topologies from multi-view images. In particular, NeAT represents the 3D
surface as a level set of a signed distance function (SDF) with a validity
branch for estimating the surface existence probability at the query positions.
We also develop a novel neural volume rendering method, which uses SDF and
validity to calculate the volume opacity and avoids rendering points with low
validity. NeAT supports easy field-to-mesh conversion using the classic
Marching Cubes algorithm. Extensive experiments on DTU, MGN, and Deep Fashion
3D datasets indicate that our approach is able to faithfully reconstruct both
watertight and non-watertight surfaces. In particular, NeAT significantly
outperforms the state-of-the-art methods in the task of open surface
reconstruction both quantitatively and qualitatively.


  In the recent study of correlation functions for the infinite XXZ spin chain,
a new pair of anti-commuting operators $b(z), c(z)$ was introduced. They act on
the space of quasi-local operators, which are local operators multiplied by the
disorder operator. For the inhomogeneous chain with the spectral parameters
$\xi_{k}$, these operators have simple poles at $z^2=\xi_{k}^2$. The residues
are denoted by $b_{k}, c_{k}$. At $q=i$, we show that the operators $b_{k},
c_{k}$ are cubic monomials in free fermions. In other words, the action of
these operators is very simple in the fermion basis. We give an explicit
construction of these fermions. Then, we show that the existence of the
fermionic basis is a consequence of the Grassmann relation, the equivariance
with respect to the action of the symmetric group and the reduction property,
which are all valid for the operators $b_{k}, c_{k}$ in the case of generic
$q$.


  We propose a simple procedure for evaluating the main thermodynamical
attributes of a Schwarzschild's black hole: Bekenstein-Hawking entropy,
Hawking's temperature and Bekenstein's quantization of the surface area. We
make use of the condition that the circumference of a great circle on the black
hole horizon contains finite number of the corresponding reduced Compton's
wavelength. It is essentially analogous to Bohr's quantization postulate in
Bohr's atomic model interpreted by de Broglie's relation. We present black hole
radiation in the form conceptually analogous to Bohr's postulate on the photon
emission by discrete quantum jump of the electron within the Old quantum
theory. It enables us, in accordance with Heisenberg's uncertainty relation and
Bohr's correspondence principle, to make a rough estimate of the time interval
for black hole evaporation, which turns out very close to time interval
predicted by the standard Hawking's theory. Our calculations confirm
Bekenstein's semiclassical result for the energy quantization, in variance with
Frasca's (2005) calculations. Finally we speculate about the possible
source-energy distribution within the black hole horizon.


  The concept of the maximal contact is the key in Hironaka's resolution
theory. It treats local theory, and it is not effective in positive
characteristics. This is the essential reason why Hironaka's theory treats only
the case of characteristic zero. In this article we propose the substitute for
the maximal contact, which is effective in any characteristics of the ground
field. We replace the maximal contact by a theorem in the theory of torus
embeddings. Using essential ideas here, we would like to establish the theory
of resolution of singularities in arbitrary characteristics in a global sense
in the forthcoming articles.


  Recently, Li and Liu have studied global monoole of tachyon in a four
dimensional static space-time. We analyze the motion of massless and massive
particles around tachyon monopole. Interestingly, for the bending of light rays
due to tachyon monopole instead of getting angle of deficit we find angle of
surplus. Also we find that the tachyon monopole exerts an attractive
gravitational force towards matter.


  Intrinsic wavelet transforms and wavelet estimation methods are introduced
for curves in the non-Euclidean space of Hermitian positive definite matrices,
with in mind the application to Fourier spectral estimation of multivariate
stationary time series. The main focus is on intrinsic average-interpolation
wavelet transforms in the space of positive definite matrices equipped with an
affine-invariant Riemannian metric, and convergence rates of linear wavelet
thresholding are derived for intrinsically smooth curves of Hermitian positive
definite matrices. In the context of multivariate Fourier spectral estimation,
intrinsic wavelet thresholding is equivariant under a change of basis of the
time series, and nonlinear wavelet thresholding is able to capture localized
features in the spectral density matrix across frequency, always guaranteeing
positive definite estimates. The finite-sample performance of intrinsic wavelet
thresholding is assessed by means of simulated data and compared to several
benchmark estimators in the Riemannian manifold. Further illustrations are
provided by examining the multivariate spectra of trial-replicated brain signal
time series recorded during a learning experiment.


  Wrinkling, creasing and folding are frequent phenomena encountered in
biological and man-made bilayers made by thin films bonded to thicker and
softer substrates often containing fibers. Paradigmatic examples of the latter
are the skin, the brain, and arterial walls, for which wiggly cross-sections
are detected. Although experimental investigations on corrugation of these and
analog bilayers would greatly benefit from scaling laws for prompt comparison
of the wrinkling features, neither are they available nor have systematic
approaches yielding to such laws ever been provided before. This gap is filled
in this paper, where a uniaxially compressed bilayer formed by a thin elastic
film bonded on a hyperelastic fiber-reinforced substrate is considered. The
force balance at the film-substrate interface is here analytically and
numerically investigated for highly mismatched film-substrates. The onset of
wrinkling is then characterized in terms of both the critical strain and its
corresponding wavenumber. Inspired by the asymptotic laws available for
neo-Hookean bilayers, the paper then provides a systematic way to achieve novel
scaling laws for the wrinkling features for fiber-reinforced highly mismatched
hyperelastic bilayers. Such novel scaling laws shed light on the key
contributions defining the response of the bilayer, as it is characterized by a
fiber-induced complex anisotropy. Results are compared with Finite Element
Analyses and also with outcomes of both existing linear models and available
adhoc scalings. Furthermore, the amplitude, the global maximum and minimum of
ruga occurring under increasing compression spanning the wrinkling, period
doubling and folding regimes are also obtained.


  So far essentially all black hole masses in X-ray binaries have been obtained
by observing the companion star's velocity and light curves as functions of the
orbital phase. However a major uncertainty is the estimate of the orbital
inclination angle of an X-ray binary. Here we suggest to measure the black hole
mass in an X-ray binary by measuring directly the black hole's orbital motion,
thus obtaining the companion to black hole mass ratio. In this method we assume
that accretion disk wind moves with the black hole and thus the black hole's
orbital motion can be obtained from the Doppler velocity of the absorption
lines produced in the accretion disk wind. We validate this method by analyzing
the Chandra/HETG observations of GRO J1655-40, in which the black hole orbital
motion with line of sight velocity of 90.8 (+-11.3) km/s, inferred from the
Doppler velocity of disk-wind absorption lines, is consistent with the
prediction from its previously measured system parameters. We obtain the black
hole mass of 5.41 (+0.98, -0.57) solar masses and system inclination of 72.0
(+7.8, -7.5) degrees in GRO J1655-40. Additional observations of this source
covering more orbital phases can improve estimates on its system parameters
substantially. We then apply the method to the black hole X-ray binary LMC X-3
observed with HST/COS near orbital phase 0.75. We find that the disk-wind
absorption lines of CIV doublet were shifted to about 50 km/s, which yields a
companion-to-black-hole mass ratio of 0.6 for an assumed disk wind velocity of
-400 km/s. Additional observations covering other orbital phases (0.25 in
particular) are crucial to ease this assumption and then to directly constrain
the mass ratio. This method in principle can also be applied to any accreting
compact objects with detectable accretion disk wind absorption line features.


  Atmospheric muon neutrino in Deep Core (whose rate and spectra might be soon
available) should exhibit a suppression (due to tens GeV up-going muon neutrino
converted into tau flavor) that must be imprinted in out-coming rate spectra.
We estimate here our independent muon neutrino spectra based on SK and its
projected record on Deep Core Channels. Our estimate (based on cosmic rays,
muon records and tested Super-Kamiokande (SK) data) differs both in shape and
in rate from other previous published spectra. The expected rate might exhibit
a minimum near channel 6 of Deep Core strings and it should manifest strong
signature for flavor mixing (mostly between channel 4--15)and a relevant
anomaly for eventual CPT violation (MINOS like) written at channel 3--6,whose
statistical weight (mainly at channel 5) might soon confirm or dismiss MINOS
CPT claim. At the flux minimum around channel 6, (a flux suppressed respect the
non oscillated case at least by an order of magnitude) the atmospheric neutrino
paucity offers a better windows to a twenty GeV Neutrino Astronomy. Therefore
by doubling the string array we may foresee a richer rate and a more complete
(zenith and azimuth) atmospheric neutrino distribution and an exciting first
twenty GeV Astronomy.


  In live and studio recordings unexpected sound events often lead to
interferences in the signal. For non-stationary interferences, sound source
separation techniques can be used to reduce the interference level in the
recording. In this context, we present a novel approach combining the strengths
of two algorithmic families: NMF and KAM. The recent KAM approach applies
robust statistics on frames selected by a source-specific kernel to perform
source separation. Based on semi-supervised NMF, we extend this approach in two
ways. First, we locate the interference in the recording based on detected NMF
activity. Second, we improve the kernel-based frame selection by incorporating
an NMF-based estimate of the clean music signal. Further, we introduce a
temporal context in the kernel, taking some musical structure into account. Our
experiments show improved separation quality for our proposed method over a
state-of-the-art approach for interference reduction.


  We prove some uniform and pointwise gradient estimates for the Dirichlet and
the Neumann evolution operators $G_{\mathcal{D}}(t,s)$ and
$G_{\mathcal{N}}(t,s)$ associated with a class of nonautonomous elliptic
operators $\A(t)$ with unbounded coefficients defined in $I\times \Rd_+$ (where
$I$ is a right-halfline or $I=\R$). We also prove the existence and the
uniqueness of a tight evolution system of measures $\{\mu_t^{\mathcal{N}}\}_{t
\in I}$ associated with $G_{\mathcal{N}}(t,s)$, which turns out to be
sub-invariant for $G_{\mathcal{D}}(t,s)$, and we study the asymptotic behaviour
of the evolution operators $G_{\mathcal{D}}(t,s)$ and $G_{\mathcal{N}}(t,s)$ in
the $L^p$-spaces related to the system $\{\mu_t^{\mathcal{N}}\}_{t \in I}$.


  We study a class of weakly coupled systems of Hamilton-acobi equations using
the random frame introduced in a previous paper of
Mitake-Siconolfi-Tran-Yamada. We provide a cycle condition characterizing the
points of Aubry set. This generalizes a property already known in the scalar
case.


  The mean field limit of large-population symmetric stochastic differential
games is derived in a general setting, with and without common noise, on a
finite time horizon. Minimal assumptions are imposed on equilibrium strategies,
which may be asymmetric and based on full information. It is shown that
approximate Nash equilibria in the $n$-player games admit certain weak limits
as $n$ tends to infinity, and every limit is a weak solution of the mean field
game (MFG). Conversely, every weak MFG solution can be obtained as the limit of
a sequence of approximate Nash equilibria in the $n$-player games. Thus, the
MFG precisely characterizes the possible limiting equilibrium behavior of the
$n$-player games. Even in the setting without common noise, the empirical state
distributions may admit stochastic limits which cannot be described by the
usual notion of MFG solution.


  With teams growing in all areas of scientific and scholarly research, we
explore the relationship between team structure and the character of knowledge
they produce. Drawing on 89,575 self-reports of team member research activity
underlying scientific publications, we show how individual activities cohere
into broad roles of (1) leadership through the direction and presentation of
research and (2) support through data collection, analysis and discussion. The
hidden hierarchy of a scientific team is characterized by its lead (or L)-ratio
of members playing leadership roles to total team size. The L-ratio is
validated through correlation with imputed contributions to the specific paper
and to science as a whole, which we use to effectively extrapolate the L-ratio
for 16,397,750 papers where roles are not explicit. We find that relative to
flat, egalitarian teams, tall, hierarchical teams produce less novelty and more
often develop existing ideas; increase productivity for those on top and
decrease it for those beneath; increase short-term citations but decrease
long-term influence. These effects hold within-person -- the same person on the
same-sized team produces science much more likely to disruptively innovate if
they work on a flat, high L-ratio team. These results suggest the critical role
flat teams play for sustainable scientific advance and the training and
advancement of scientists.


  A major objective in photonics is to tailor the emission properties of
microcavities which is usually achieved with specific cavity shapes. Yet, the
dynamical change of the emission properties during operation would often be
advantageous. The implementation of such a method is still a challenging issue.
We present an effective procedure for the dynamical control of the emission
lobes which relies on the selection of a specific coherent superposition of
degenerate modes belonging to different symmetry classes. It is generally
applicable to systems exhibiting pairs of degenerate modes. We explored it
experimentally and analytically with organic square microlasers, which emit
narrow lobes parallel to their sidewalls. By means of the pump polarization,
emission lobes are switched on and off selectively with an extinction ratio
better 1/50.


  We present some general results on properties of the bihamiltonian
cohomologies associated to bihamiltonian structures of hydrodynamic type, and
compute the third cohomology for the bihamiltonian structure of the
dispersionless KdV hierarchy. The result of the computation enables us to prove
the existence of bihamiltonian deformations of the dispersionless KdV hierarchy
starting from any of its infinitesimal deformations.


  We consider sound waves in superfluid nucleon-hyperon matter of massive
neutron-star cores. We calculate and analyze the speeds of sound modes and
their damping times due to the shear viscosity and non-equilibrium weak
processes of particle transformations. For that, we employ the dissipative
relativistic hydrodynamics of a superfluid nucleon-hyperon mixture, formulated
recently [M.E. Gusakov and E.M. Kantor, Phys. Rev. D78, 083006 (2008)]. We
demonstrate that the damping times of sound modes calculated using this
hydrodynamics and the ordinary (nonsuperfluid) one, can differ from each other
by several orders of magnitude.


  This paper estimates the blow-up time for the heat equation $u_t=\Delta u$
with a local nonlinear Neumann boundary condition: The normal derivative
$\partial u/\partial n=u^{q}$ on $\Gamma_{1}$, one piece of the boundary, while
on the rest part of the boundary, $\partial u/\partial n=0$. The motivation of
the study is the partial damage to the insulation on the surface of space
shuttles caused by high speed flying subjects. We prove the solution blows up
in finite time and estimate both upper and lower bounds of the blow-up time in
terms of the area of $\Gamma_1$. In many other work, they need the convexity of
the domain $\Omega$ and only consider the problem with
$\Gamma_1=\partial\Omega$. In this paper, we remove the convexity condition and
only require $\partial\Omega$ to be $C^{2}$. In addition, we deal with the
local nonlinearity, namely $\Gamma_1$ can be just part of $\partial\Omega$.


  We report on two Chandra observations of the 3-Myr pulsar B1929+10, which
reveal a faint compact (~9"x5") nebula elongated in the direction perpendicular
to the pulsar's proper motion, two patchy wings, and a possible short (~3") jet
emerging from the pulsar. In addition, we detect a tail extending up to at
least 4' in the direction opposite to the pulsar's proper motion, aligned with
the 15'-long tail detected in ROSAT and XMM-Newton observations. The overall
morphology of the nebula suggests that the shocked pulsar wind is confined by
the ram pressure due to the pulsar's supersonic speed. The shape of the compact
nebula in the immediate vicinity of the pulsar seems to be consistent with the
current MHD models. However, since these models do not account yet for the
change of the flow velocity at larger distances from the pulsar, they are not
able to constrain the extent of the long pulsar tail. The luminosity of the
whole nebula as seen by Chandra is ~10^30 ergs/s in the 0.3-8 keV band, for the
distance of 361 pc. Using the Chandra and XMM-Newton data, we found that the
pulsar spectrum is comprised of non-thermal (magnetospheric) and thermal
components. The non-thermal component can be described by a power-law model
with photon index ~1.7 and luminosity 1.7x10^30 ergs/s in the 0.3-10 keV band.
The blackbody fit for the thermal component, which presumably emerges from hot
polar caps, gives the temperature kT~0.3 keV and projected emitting area 3x10^3
m^2, corresponding to the bolometric luminosity ~(1-2)x10^30 ergs/s.


  We correct the proof of Theorem 5 of the paper Homology stability for outer
automorphism groups of free groups, by the first two authors.


  Advances in space weather science and small satellite (SmallSat) technology
have proceeded in parallel over the past two decades, but better communication
and coordination is needed among the respective worldwide communities
contributing to this rapid progress. We identify six areas where improved
international coordination is especially desirable, including: (1) orbital
debris mitigation; (2) spectrum management; (3) export control regulations; (4)
access to timely and low-cost launch opportunities; (5) inclusive data
policies; and (6) education. We argue the need for internationally coordinated
policies and programs to promote the use of SmallSats for space weather
research and forecasting while realizing maximum scientific and technical
advances through the integration of these two increasingly important endeavors.


  It is shown how to construct a successful co-adapted coupling of two copies
of an $n$-dimensional Brownian motion $(B_1,...,B_n)$ while simultaneously
coupling all corresponding copies of L\'{e}vy stochastic areas $\int B_i
dB_j-\int B_j dB_i$. It is conjectured that successful co-adapted couplings
still exist when the L\'{e}vy stochastic areas are replaced by a finite set of
multiply iterated path- and time-integrals, subject to algebraic compatibility
of the initial conditions.


  Justified representation (JR) is a standard notion of representation in
multiwinner approval voting. Not only does a JR committee always exist, but
previous work has also shown through experiments that the JR condition can
typically be fulfilled by groups of fewer than $k$ candidates. In this paper,
we study such groups -- known as $n/k$-justifying groups -- both theoretically
and empirically. First, we show that under the impartial culture model,
$n/k$-justifying groups of size less than $k/2$ are likely to exist, which
implies that the number of JR committees is usually large. We then present
efficient approximation algorithms that compute a small $n/k$-justifying group
for any given instance, and a polynomial-time exact algorithm when the instance
admits a tree representation. In addition, we demonstrate that small
$n/k$-justifying groups can often be useful for obtaining a gender-balanced JR
committee even though the problem is NP-hard.


  This paper addresses the problem of scheduling tasks with different
criticality levels in the presence of I/O requests. In mixed-criticality
scheduling, higher criticality tasks are given precedence over those of lower
criticality when it is impossible to guarantee the schedulability of all tasks.
While mixed-criticality scheduling has gained attention in recent years, most
approaches typically assume a periodic task model. This assumption does not
always hold in practice, especially for real-time and embedded systems that
perform I/O. For example, many tasks block on I/O requests until devices signal
their completion via interrupts; both the arrival of interrupts and the waking
of blocked tasks can be aperiodic. In our prior work, we developed a scheduling
technique in the Quest real-time operating system, which integrates the
time-budgeted management of I/O operations with Sporadic Server scheduling of
tasks. This paper extends our previous scheduling approach with support for
mixed-criticality tasks and I/O requests on the same processing core. Results
show the effective schedulability of different task sets in the presence of I/O
requests is superior in our approach compared to traditional methods that
manage I/O using techniques such as Sporadic Servers.


  We consider collaborative systems where users make contributions across
multiple available projects and are rewarded for their contributions in
individual projects according to a local sharing of the value produced. This
serves as a model of online social computing systems such as online Q&A forums
and of credit sharing in scientific co-authorship settings. We show that the
maximum feasible produced value can be well approximated by simple local
sharing rules where users are approximately rewarded in proportion to their
marginal contributions and that this holds even under incomplete information
about the player's abilities and effort constraints. For natural instances we
show almost 95% optimality at equilibrium. When players incur a cost for their
effort, we identify a threshold phenomenon: the efficiency is a constant
fraction of the optimal when the cost is strictly convex and decreases with the
number of players if the cost is linear.


  We derive fundamental bounds on the maximal achievable precision in
multiparameter noisy quantum metrology, valid under the most general
entanglement-assisted adaptive strategy, which are tighter than the bounds
obtained by a direct use of single-parameter results. This allows us to study
the issue of the optimal probe incompatibility in the simultaneous estimation
of multiple parameters in generic noisy channels, while so far the issue has
been studied mostly in effectively noiseless scenarios (where the Heisenberg
scaling is possible). We apply our results to the estimation of both unitary
and noise parameters, and indicate models where the fundamental probe
incompatibility is present. In particular, we show that in lossy multiple arm
interferometry the probe incompatibility is as strong as in the noiseless
scenario. Finally, going beyond the multiple-parameter estimation paradigm, we
introduce the concept of \emph{random quantum sensing} and show how the tools
developed may be applied to multiple channel discrimination problems. As an
illustration, we provide a simple proof of the loss of the quadratic advantage
of time-continuous Grover algorithm in presence of dephasing or erasure noise.


  We study critical points of the Ginzburg-Landau (GL) functional and the
abelian Yang-Mills-Higgs (YMH) functional on the sphere and the complex
projective space, both equipped with the standard metrics. For the GL
functional we prove that on $S^{n}$ with $n \geq 2$ and $\mathbb{CP}^{n}$ with
$n \geq 1$, stable critical points must be constants. In addition, for GL
critical points on $S^{n}$ for $n \geq 3$ we obtain a lower bound on the Morse
index under suitable assumptions. On the other hand, for the abelian YMH
functional we prove that on $S^{n}$ with $n \geq 4$ there are no stable
critical points unless the line bundle is isomorphic to $S^n \times
\mathbb{C}$, in which case the only stable critical points are the trivial
ones. Our methods come from the work of Lawson--Simons.