  In this paper, we propose a tightly-coupled, multi-modal simultaneous
localization and mapping (SLAM) framework, integrating an extensive set of
sensors: IMU, cameras, multiple lidars, and Ultra-wideband (UWB) range
measurements, hence referred to as VIRAL (visual-inertial-ranging-lidar) SLAM.
To achieve such a comprehensive sensor fusion system, one has to tackle several
challenges such as data synchronization, multi-threading programming, bundle
adjustment (BA), and conflicting coordinate frames between UWB and the onboard
sensors, so as to ensure real-time localization and smooth updates in the state
estimates. To this end, we propose a two stage approach. In the first stage,
lidar, camera, and IMU data on a local sliding window are processed in a core
odometry thread. From this local graph, new key frames are evaluated for
admission to a global map. Visual feature-based loop closure is also performed
to supplement the global factor graph with loop constraints. When the global
factor graph satisfies a condition on spatial diversity, the BA process will be
triggered to update the coordinate transform between UWB and onboard SLAM
systems. The system then seamlessly transitions to the second stage where all
sensors are tightly integrated in the odometry thread. The capability of our
system is demonstrated via several experiments on high-fidelity
graphical-physical simulation and public datasets.


  We investigate the $\gamma$-ray emission from a outer-gap accelerator, which
is located in the outer-magnetosphere of a pulsar. The charge depletion from
the Goldreich-Julian density causes a large electric field along the magnetic
field lines. The electric field accelerates electrons and positrons to high
energies. We solve the formation of the electric field self-consistently with
the radiation and the pair-creation processes in one-dimensional geometry along
the magnetic field lines, and calculate curvature spectrum to compare with the
observations. We find that because the particles escape from the accelerating
region with large Lorentz factors as $10^{7.5}$, curvature radiation from the
outside of the gap, in which there is no electric field, still has a important
contribution to the spectrum. Including the outside emission, the model spectra
are improved between 100 MeV and 1 GeV for the Vela-type pulsars. Since the
field line curvature close to the light cylinder affects the spectrum in
several hundred MeV, there is a possibility to diagnose the field structure by
using the spectrum in these bands. The difference found in the $\gamma$-ray
spectra of the twin pulsars, PSR B0833-45 (Vela) and B1706-44, is deduced from
the dimensionless electric current running through the gap. The extension to
the three-dimensional modeling with self-consistent electrodynamics is
necessary to understand the difference in the observed flux between the twin
pulsars.


  Code Language Models have been trained to generate accurate solutions,
typically with no regard for runtime. On the other hand, previous works that
explored execution optimisation have observed corresponding drops in functional
correctness. To that end, we introduce Code-Optimise, a framework that
incorporates both correctness (passed, failed) and runtime (quick, slow) as
learning signals via self-generated preference data. Our framework is both
lightweight and robust as it dynamically selects solutions to reduce
overfitting while avoiding a reliance on larger models for learning signals.
Code-Optimise achieves significant improvements in pass@k while decreasing the
competitive baseline runtimes by an additional 6% for in-domain data and up to
3% for out-of-domain data. As a byproduct, the average length of the generated
solutions is reduced by up to 48% on MBPP and 23% on HumanEval, resulting in
faster and cheaper inference. The generated data and codebase will be
open-sourced at www.open-source.link.


  We prove the uniqueness of H\"older continuous weak solutions via duality
argument and vanishing viscosity method for the Keller-Segel system of porous
medium type equations coupled to the Stokes system in dimensions three. An
important step is the estimate of the Green function of parabolic equations
with lower order terms of variable coefficients, which seems to be of
independent interest.


  The present work proves that the folk-lore of the pathology of
non-conservation of probability in quantum anisotropic models is wrong. It is
shown in full generality that all operator ordering can lead to a Hamiltonian
with a self-adjoint extension as long as it is constructed to be a symmetric
operator, thereby making the problem of non-unitarity in context of anisotropic
homogeneous model a ghost. Moreover, it is indicated that the self-adjoint
extension is not unique and this non-uniqueness is suspected not to be a
feature of Anisotropic model only, in the sense that there exists operator
orderings such that Hamiltonian for an isotropic homogeneous cosmological model
does not have unique self-adjoint extension, albeit for isotropic model, there
is a special unique extension associated with quadratic form of Hamiltonian i.e
{\it Friedrichs extension}. Details of calculations are carried out for a
Bianchi III model.


  Predicting the winner of an election is of importance to multiple
stakeholders. To formulate the problem, we consider an independent sequence of
categorical data with a finite number of possible outcomes in each. The data is
assumed to be observed in batches, each of which is based on a large number of
such trials and can be modeled via multinomial distributions. We postulate that
the multinomial probabilities of the categories vary randomly depending on
batches. The challenge is to predict accurately on cumulative data based on
data up to a few batches as early as possible. On the theoretical front, we
first derive sufficient conditions of asymptotic normality of the estimates of
the multinomial cell probabilities and present corresponding suitable
transformations. Then, in a Bayesian framework, we consider hierarchical priors
using multivariate normal and inverse Wishart distributions and establish the
posterior convergence. The desired inference is arrived at using these results
and ensuing Gibbs sampling. The methodology is demonstrated with election data
from two different settings -- one from India and the other from the United
States of America. Additional insights of the effectiveness of the proposed
methodology are attained through a simulation study.


  We study varieties with a finitely generated Cox ring. In a first part, we
generalize a combinatorial approach developed in earlier work for varieties
with a torsion free divisor class group to the case of torsion. Then we turn to
modifications, e.g., blow ups, and the question how the Cox ring changes under
such maps. We answer this question for a certain class of modifications induced
from modifications of ambient toric varieties. Moreover, we show that every
variety with finitely generated Cox ring can be explicitly constructed in a
finite series of toric ambient modifications from a combinatorially minimal
one.


  Ensemble inequivalence, i.e. the possibility of observing different
thermodynamic properties depending on the statistical ensemble which describes
the system, is one of the hallmarks of long-range physics, which has been
demonstrated in numerous classical systems. Here, an example of ensemble
inequivalence of a long-range quantum ferromagnet is presented. While the $T=0$
microcanonical quantum phase-diagram coincides with that in the canonical
ensemble, the phase-diagrams of the two ensembles are different at finite
temperature. This is in contrast with the common lore of statistical mechanics
of systems with short-range interactions where thermodynamic properties are
bound to coincide for macroscopic systems described by different ensembles. The
consequences of these findings in the context of atomic, molecular and optical
(AMO) setups are delineated.


  We develop a Melnikov method for volume-preserving maps with codimension one
invariant manifolds. The Melnikov function is shown to be related to the flux
of the perturbation through the unperturbed invariant surface. As an example,
we compute the Melnikov function for a perturbation of a three-dimensional map
that has a heteroclinic connection between a pair of invariant circles. The
intersection curves of the manifolds are shown to undergo bifurcations in
homology


  Using the Kirschner-Lipatov equation as a basis, we study high energy elastic
quark-quark amplitudes, which correspond to secondary Reggeons and pion
exchange in the double-logarithmic approximation of perturbative QCD.


  We define the quantization structures for Poisson algebras necessary to
generalise Groenewold and Van Hove's result that there is no consistent
quantization for the Poisson algebra of Euclidean phase space. Recently a
similar obstruction was obtained for the sphere, though surprising enough there
is no obstruction to the quantization of the torus. In this paper we want to
analyze the circumstances under which such obstructions appear. In this context
we review the known results for the Poisson algebras of Euclidean space, the
sphere and the torus.


  In this paper, we investigate certain graded-commutative rings which are
related to the reciprocal plane compactification of the coordinate ring of a
complement of a hyperplane arrangement. We give a presentation of these rings
by generators and defining relations. This presentation was used by Holler and
I. Kriz to calculate the $\mathbb{Z}$-graded coefficients of localizations of
ordinary $RO((\mathbb{Z}/p)^n)$-graded equivariant cohomology at a given set of
representation spheres, and also more recently by the author in a
generalization to the case of an arbitrary finite group. We also give an
interpretation of these rings in terms of superschemes, which can be used to
further illuminate their structure.


  We investigate proper conformal vector fields in non conformally flat
Kantowski-Sachs and Bianchi type III space-times using direct integration
technique. Using the above mentioned technique we show that very special
classes of the above space-times admit proper conformal vector fields.


  Let C and D be digraphs. A mapping $f:V(D)\to V(C)$ is a C-colouring if for
every arc $uv$ of D, either $f(u)f(v)$ is an arc of C or $f(u)=f(v)$, and the
preimage of every vertex of C induces an acyclic subdigraph in D. We say that D
is C-colourable if it admits a C-colouring and that D is uniquely C-colourable
if it is surjectively C-colourable and any two C-colourings of D differ by an
automorphism of C. We prove that if a digraph D is not C-colourable, then there
exist digraphs of arbitrarily large girth that are D-colourable but not
C-colourable. Moreover, for every digraph D that is uniquely D-colourable,
there exists a uniquely D-colourable digraph of arbitrarily large girth. In
particular, this implies that for every rational number $r\geq 1$, there are
uniquely circularly r-colourable digraphs with arbitrarily large girth.


  Relativistic jets carry a significant fraction of the total energy budget of
a radio source, rivaling the power that is extracted through accretion. A minor
part of this bulk kinetic power is transformed to radiation, possibly through
internal shocks if the plasma is accelerated, at the base of the jet, to a
velocity which changes in time. In this way we can understand why some
radiation is produced all along the jet even if most of it originates at a
preferred location, and why the efficiency of conversion of bulk to random
energy is small. The recent observations by Chandra of intense jet X-ray
emission at large scales suggest that at least the "spine" of jets continues to
be highly relativistics even up to hundreds of kiloparsecs away from the
nucleus and give tight lower limits on the jet bulk kinetic power.


  Pseudopure "cat" state, a superposition of quantum states with all spins up
and all spins down, is experimentally demonstrated for a system of twelve
dipolar-coupled nuclear spins of fully 13C-labeled benzene molecule oriented in
a liquid-crystalline matrix.


  Given a semigroup $S$, for each Green's relation
$\mathcal{K}\in\{\mathcal{L},\mathcal{R},\mathcal{J},\mathcal{H}\}$ on $S,$ the
$\mathcal{K}$-height of $S,$ denoted by $H_{\mathcal{K}}(S),$ is the height of
the poset of $\mathcal{K}$-classes of $S.$ More precisely, if there is a finite
bound on the sizes of chains of $\mathcal{K}$-classes of $S,$ then
$H_{\mathcal{K}}(S)$ is defined as the maximum size of such a chain; otherwise,
we say that $S$ has infinite $\mathcal{K}$-height. We discuss the relationships
between these four $\mathcal{K}$-heights. The main results concern the class of
stable semigroups, which includes all finite semigroups. In particular, we
prove that a stable semigroup has finite $\mathcal{L}$-height if and only if it
has finite $\mathcal{R}$-height if and only if it has finite
$\mathcal{J}$-height. In fact, for a stable semigroup $S,$ if
$H_{\mathcal{L}}(S)=n$ then $H_{\mathcal{R}}(S)\leq2^n-1$ and
$H_{\mathcal{J}}(S)\leq2^n-1,$ and we exhibit a family of examples to prove
that these bounds are sharp. Furthermore, we prove that if $2\leq
H_{\mathcal{L}}(S)<\infty$ and $2\leq H_{\mathcal{R}}(S)<\infty,$ then
$H_{\mathcal{J}}(S)\leq H_{\mathcal{L}}(S)+H_{\mathcal{R}}(S)-2.$ We also show
that for each $n\in\mathbb{N}$ there exists a semigroup $S$ such that
$H_{\mathcal{L}}(S)=H_{\mathcal{R}}(S)=2^n+n-3$ and
$H_{\mathcal{J}}(S)=2^{n+1}-4.$ By way of contrast, we prove that for a regular
semigroup the $\mathcal{L}$-, $\mathcal{R}$- and $\mathcal{H}$-heights coincide
with each other, and are greater or equal to the $\mathcal{J}$-height.
Moreover, in a stable, regular semigroup the $\mathcal{L}$-, $\mathcal{R}$-,
$\mathcal{H}$- and $\mathcal{J}$-heights are all equal.


  In this paper, we propose feature-based federated transfer learning as a
novel approach to improve communication efficiency by reducing the uplink
payload by multiple orders of magnitude compared to that of existing approaches
in federated learning and federated transfer learning. Specifically, in the
proposed feature-based federated learning, we design the extracted features and
outputs to be uploaded instead of parameter updates. For this distributed
learning model, we determine the required payload and provide comparisons with
the existing schemes. Subsequently, we analyze the robustness of feature-based
federated transfer learning against packet loss, data insufficiency, and
quantization. Finally, we address privacy considerations by defining and
analyzing label privacy leakage and feature privacy leakage, and investigating
mitigating approaches. For all aforementioned analyses, we evaluate the
performance of the proposed learning scheme via experiments on an image
classification task and a natural language processing task to demonstrate its
effectiveness.


  The local stellar mass density is observed to be significantly lower than the
value obtained from integrating the cosmic star formation history (SFH),
assuming that all the stars formed with a Salpeter initial mass function (IMF).
Even other favoured IMFs, more successful in reconciling the observed $z=0$
stellar mass density with that inferred from the SFH, have difficulties in
reproducing the stellar mass density observed at higher redshift. In this study
we investigate to what extent this discrepancy can be alleviated for any
universal power-law IMF. We find that an IMF with a high-mass slope shallower
(2.15) than the Salpeter slope (2.35) reconciles the observed stellar mass
density with the cosmic star formation history, but only at low redshifts. At
higher redshifts $z>0.5$ we find that observed stellar mass densities are
systematically lower than predicted from the cosmic star formation history, for
any universal power-law IMF.


  Millions of people use platforms such as YouTube, Facebook, Twitter, and
other mass media. Due to the accessibility of these platforms, they are often
used to establish a narrative, conduct propaganda, and disseminate
misinformation. This work proposes an approach that uses state-of-the-art NLP
techniques to extract features from video captions (subtitles). To evaluate our
approach, we utilize a publicly accessible and labeled dataset for classifying
videos as misinformation or not. The motivation behind exploring video captions
stems from our analysis of videos metadata. Attributes such as the number of
views, likes, dislikes, and comments are ineffective as videos are hard to
differentiate using this information. Using caption dataset, the proposed
models can classify videos among three classes (Misinformation, Debunking
Misinformation, and Neutral) with 0.85 to 0.90 F1-score. To emphasize the
relevance of the misinformation class, we re-formulate our classification
problem as a two-class classification - Misinformation vs. others (Debunking
Misinformation and Neutral). In our experiments, the proposed models can
classify videos with 0.92 to 0.95 F1-score and 0.78 to 0.90 AUC ROC.


  Let $\alpha$ be an endomorphism of a ring $R$. We introduce the notion of
weak $\alpha$-skew McCoy rings which are a generalization of the $\alpha$-skew
McCoy rings and the weak McCo rings. Some properties of this generalization are
established, and connections of properties of a weak $\alpha$-skew McCoy ring
$R$ with $n\times n$ upper triangular $T_n(R)$ are investigated. We study
relationship between the weak skew McCoy property of a ring $R$ and it's
polynomial ring, $R[x]$. Among applications, we show that a number of
interesting properties of a weak $\alpha$-skew McCoy ring $R$ such as weak skew
McCoy property in a ring $R$.


  Simulating properties of quantum materials is one of the most promising
applications of quantum computation, both near- and long-term. While real-time
dynamics can be straightforwardly implemented, the finite temperature ensemble
involves non-unitary operators that render an implementation on a near-term
quantum computer extremely challenging. Recently, [Lu, Ba\~nuls and Cirac, PRX
Quantum 2, 020321 (2021)] suggested a "time-series quantum Monte Carlo method"
which circumvents this problem by extracting finite temperature properties from
real-time simulations via Wick's rotation and Monte Carlo sampling of easily
preparable states. In this paper, we address the challenges associated with the
practical applications of this method, using the two-dimensional transverse
field Ising model as a testbed. We demonstrate that estimating Boltzmann
weights via Wick's rotation is very sensitive to time-domain truncation and
statistical shot noise. To alleviate this problem, we introduce a technique
that imposes constraints on the density of states, most notably its
non-negativity, and show that this way, we can reliably extract Boltzmann
weights from noisy time series. In addition, we show how to reduce the
statistical errors of Monte Carlo sampling via a reweighted version of the
Wolff cluster algorithm. Our work enables the implementation of the time-series
algorithm on present-day quantum computers to study finite temperature
properties of many-body quantum systems.


  B. Y. Chen establish the relationship between the Ricci curvature and the
squared mean curvature for submanifolds of Riemannian space form with arbitrary
codimension. In this paper, we generalize the relationship between the Ricci
curvature and the squared norm of mean curvature vector for submanifolds of
Bochner Kahler manifolds.


  We present a time-dependent quantum calculation of the van der Waals
interaction between a pair of dissimilar atoms, one of which is initially
excited while the other one is in its ground state. For small detuning, the
interaction is predominantly mediated at all distances by the exchange of
doubly resonant photons between the two atoms. We find that it presents both
temporal and spatial oscillations. Spatially oscillating terms depend on the
resonant frequencies of both atoms, while the frequency of the time
oscillations is given by their detuning. We analyse the physical content of our
findings and discuss to what extent previous conflicting stationary approaches
provide compatible results. A proper account of causality is found essential in
order to obtain the correct result.


  In this paper we consider the quantization of a scalar field coupled to
gravity at one loop order. We investigate the divergences appearing in the mass
(i.e. phi^2) term in the effective action. We use the Vilkovisky-DeWitt
effective action technique which guarantees that the result is gauge invariant
as well as gauge condition independent in contrast to traditional calculations.
Our final result is to identify the complete pole part of the effective action.


  We study the critical region of the $O(2)\,\phi^4$ theory by means of Monte
Carlo simulations on the lattice. In particular we determine the ratio
$\Delta\langle\phi^2\rangle_c/g$ in order to estimate the first correction to
the critical temperature of a weak interacting Bose gas.


  Two years' worth of u-, g-, r-, i-, and z-band optical light curves were
obtained for the massive type 1 quasar PG 2308+098 at z=0.433 using the 1.05-m
Kiso Schmidt telescope/Kiso Wide Field Camera, and inter-band time lags of the
light curves were measured. Wavelength-dependent continuum reverberation lag
signals of several tens of days relative to the u-band were detected at g-, r-,
i-, and z-bands, where the longer wavelength bands showed larger lags. From the
wavelength-dependent lags, and assuming the standard disk temperature radial
profile $T \propto R_{\rm disk}^{-3/4}$ and an X-ray/far-ultraviolet
reprocessing picture, a constraint on the radius of the accretion disk
responsible for the rest-frame 2500 \AA\ disk continuum emission was derived as
$R_{\rm disk} = 9.46^{+0.29}_{-3.12}$ light-days. The derived disk size is
slightly (1.2-1.8 times) larger than the theoretical disk size of $R_{\rm disk}
= 5.46$ light-days predicted from the black hole mass ($M_{\rm BH}$) and
Eddington ratio estimates of PG 2308+098. This result is roughly in accordance
with previous studies of lower mass active galactic nuclei (AGNs), where
measured disk sizes have been found to be larger than the standard disk model
predictions by a factor of $\sim 3$; however, the disk size discrepancy is more
modest in PG 2308+098. By compiling literature values of the disk size
constraints from continuum reverberation and gravitational microlensing
observations for AGNs/quasars, we show that the $M_{\rm BH}$ dependence of
$R_{\rm disk}$ is weaker than that expected from the standard disk model. These
observations suggest that the standard Shakura-Sunyaev accretion disk theory
has limitations in describing AGN/quasar accretion disks.


  We propose a generic, Bayesian, information geometric approach to the
exploration--exploitation trade-off in multi-armed bandit problems. Our
approach, BelMan, uniformly supports pure exploration,
exploration--exploitation, and two-phase bandit problems. The knowledge on
bandit arms and their reward distributions is summarised by the barycentre of
the joint distributions of beliefs and rewards of the arms, the
\emph{pseudobelief-reward}, within the beliefs-rewards manifold. BelMan
alternates \emph{information projection} and \emph{reverse information
projection}, i.e., projection of the pseudobelief-reward onto beliefs-rewards
to choose the arm to play, and projection of the resulting beliefs-rewards onto
the pseudobelief-reward. It introduces a mechanism that infuses an exploitative
bias by means of a \emph{focal distribution}, i.e., a reward distribution that
gradually concentrates on higher rewards. Comparative performance evaluation
with state-of-the-art algorithms shows that BelMan is not only competitive but
can also outperform other approaches in specific setups, for instance involving
many arms and continuous rewards.


  Regular inspection of rail valves and engines is an important task to ensure
the safety and efficiency of railway networks around the globe. Over the past
decade, computer vision and pattern recognition based techniques have gained
traction for such inspection and defect detection tasks. An automated
end-to-end trained system can potentially provide a low-cost, high throughput,
and cheap alternative to manual visual inspection of these components. However,
such systems require a huge amount of defective images for networks to
understand complex defects. In this paper, a multi-phase deep learning based
technique is proposed to perform accurate fault detection of rail-valves. Our
approach uses a two-step method to perform high precision image segmentation of
rail-valves resulting in pixel-wise accurate segmentation. Thereafter, a
computer vision technique is used to identify faulty valves. We demonstrate
that the proposed approach results in improved detection performance when
compared to current state-of-theart techniques used in fault detection.


  HR 8799 is an hF0 mA5 gamma Doradus, lambda Bootis, Vega-type star best known
for hosting four directly imaged candidate planetary companions. Using the
CHARA Array interferometer, we measure HR 8799's limb-darkened angular diameter
to be 0.342 +/- 0.008 mas; this is the smallest interferometrically measured
stellar diameter to date, with an error of only 2%. By combining our
measurement with the star's parallax and photometry from the literature, we
greatly improve upon previous estimates of its fundamental parameters,
including stellar radius (1.44 +/- 0.06 R_Sun), effective temperature (7193 +/-
87 K, consistent with F0), luminosity (5.05 +/- 0.29 L_Sun), and the extent of
the habitable zone (1.62 AU to 3.32 AU). These improved stellar properties
permit much more precise comparisons with stellar evolutionary models, from
which a mass and age can be determined, once the metallicity of the star is
known. Considering the observational properties of other lambda Bootis stars
and the indirect evidence for youth of HR 8799, we argue that the internal
abundance, and what we refer to as the effective abundance, is most likely
near-solar. Finally, using the Yonsei-Yale evolutionary models with uniformly
scaled solar-like abundances, we estimate HR 8799's mass and age considering
two possibilities: 1.516 +0.038/-0.024 M_Sun and 33 +7/-13 Myr if the star is
contracting toward the zero age main-sequence or 1.513 +0.023/-0.024 M_Sun and
90 +381/-50 Myr if it is expanding from it. This improved estimate of HR 8799's
age with realistic uncertainties provides the best constraints to date on the
masses of its orbiting companions, and strongly suggests they are indeed
planets. They nevertheless all appear to orbit well outside the habitable zone
of this young star.


  The role of gravity is crucial in astrophysics. It determines the evolution
of any system, over an enormous range of time and space scales. Astronomical
stellar systems as composed by N interacting bodies represent examples of
self-gravitating systems, usually treatable with the aid of newtonian gravity
but for particular cases. In this note I will briefly discuss some of the open
problems in the dynamical study of classic self-gravitating N-body systems,
over the astronomical range of N. I will also point out how modern research in
this field compulsorily requires a heavy use of large scale computations, due
to the contemporary requirement of high precision and high computational speed.


  We derive a closed-form (infinite series) representation for the distribution
of the ruin time for the Sparre Andersen model with exponentially distributed
claims. This extends a recent result of Dickson et al. (2005) for such
processes with Erlang inter-claim times. We illustrate our result in the cases
of gamma and mixed exponential inter-claim time distributions.


  Electrical switching has been observed in carefully designed
metal-insulator-metal devices built at small geometries. These devices are also
commonly known as memristors and consist of specific materials such as
transition metal oxides, chalcogenides, perovskites, oxides with valence
defects, or a combination of an inert and an electrochemically active
electrode. No simple physical device has been reported to exhibit electrical
switching. We have discovered that a simple point-contact or a granular
arrangement formed of metal pieces exhibits bipolar switching. These devices,
referred to as coherers, were considered as one-way electrical fuses. We have
identified the state variable governing the resistance state and can program
the device to switch between multiple stable resistance states. Our
observations render previously postulated thermal mechanisms for their
resistance-change as inadequate. These devices constitute the missing canonical
physical implementations for memristor, often referred as the fourth passive
element. Apart from the theoretical advance in understanding metallic contacts,
the current discovery provides a simple memristor to physicists and engineers
for widespread experimentation, hitherto impossible.


  Using Monte Carlo simulations, we study a Higgs transition in several
three-dimensional lattice realizations of the noncompact CP$^1$ model
(NCCP$^1$), a gauge theory with two complex matter fields with SU(2)
invariance. By comparing with a one-component theory, which is well understood
and has continuous transition in the inverted XY universality class, we argue
that the two-component case also has continuous Higgs transition with a larger
correlation length exponent (i.e., it is ``more continuous''). The transition
can become first order in the vicinity of a new ``Molecular'' phase, which
occurs in one of our models, but is continuous in a wide range of parameters
away from this phase. The situation is significantly clarified by studying a
model where the Molecular phase is entirely absent, and a wide regime with a
continuous transition can be readily established. The two-component theory is
also an effective description of the hedgehog-suppressed O(3) universality, and
results are relevant for the recently discussed ``deconfined quantum
criticality'' scenario for the continuous Valence Bond Solid to Neel quantum
phase transition.


  C*-categories are essentially norm-closed *-categories of bounded linear
operators between Hilbert spaces. The purpose of this work is to identify
suitable axioms defining Krein C*-categories, i.e. those categories that play
the role of C*-categories whenever Hilbert spaces are replaced by more general
indefinite inner product Krein spaces, and provide some basic examples. Finally
we provide a Gel'fand-Naimark representation theorem for Krein C*-algebras and
Krein C*-categories.


  The 3d spin-3 gravity theory is holographically dual to a 2d ${\cal
W}_3$-extended CFT. In a large-c limit the symmetry algebra of the CFT reduces
to $SU(1,2) \times SU(1,2)$. On the ground of symmetry the dual bulk space-time
will be given by an 8d group manifold $SU(1,2)$. Hence we need to introduce
five extra coordinates in addition to three ordinary ones. The 3d space-time is
a 3d hyper-surface $\Sigma$ embedded at constant values of the extra variables.
Operators in the CFT at the boundary of $\Sigma$ are expressed in terms of
${\cal W}$ descendants of the operators at the boundary of $\Sigma_0$, where
the extra variables vanish. In this paper it is shown that AdS/CFT
correspondence for a scalar field coupled to 3d spin-3 gravity is realized in
this auxiliary 8d space. A bulk-to-boundary propagator of a scalar field is
found and a generating functional of boundary two-point functions of scalar
${\cal W}$-descendant operators is obtained by using the classical action for
the scalar field. Classically, the scalar field must satisfy both Klein-Gordon
equation and a third-order differential equation, which are related to the
quadratic and cubic Casimir operators of $su(1,2)$. It is found that the
coefficient function of the derivatives of the scalar field in the latter
equation is the spin-3 gauge field, when restricted to the hypersurface. An
action integral in the 8d auxiliary space for the 3d spin-3 gravity coupled to
a scalar field is presented. An 8d local frame is introduced and the equations
of motion for the 8d connections $A_{\mu}$, $\overline{A}_{\mu}$ are solved. By
restricting those solutions onto $\Sigma$, flat connections in 3d
$SL(3,\mathbb{R}) \times SL(3,\mathbb{R})$ Chern-Simons theory are obtained and
new 3d black hole solutions with and without spin-3 charge are found by this
method.


  The critical behavior in the short-time dynamics for the random-bond Potts
ferromagnet in two-dimensions is investigated by short-time dynamic Monte Carlo
simulations. The numerical calculations show that this dynamic approach can be
applied efficiently to study the scaling characteristic, which is used to
estimate the critical exponents theta, beta/nu and z for the quenched disorered
systems from the power-law behavior of the kth moments of magnetizations.


  Robots that cooperate with humans must be effective at communicating with
them. However, people have varied preferences for communication based on many
contextual factors, such as culture, environment, and past experience. To
communicate effectively, robots must take those factors into consideration. In
this work, we present the Robot Signal Design (RoSiD) tool to empower people to
easily self-specify communicative preferences for collaborative robots. We show
through a participatory design study that the RoSiD tool enables users to
create signals that align with their communicative preferences, and we
illuminate how this tool can be further improved.


  The method of numerical symmetrization of state of identical particles
proposed by us before is clarified and discussed.


  We show that topological phases include disordered materials if the
underlying invariant is interpreted as originating from coarse geometry. This
coarse geometric framework, grounded in physical principles, offers a natural
setting for the bulk-boundary correspondence, reproduces physical knowledge,
and leads to an efficient and tractable numerical approach for calculating
invariants. As a showcase, we give a detailed discussion of the framework for
three-dimensional systems with time-reversal symmetry. We numerically reproduce
the known disorder-free phase diagram of a tunable, effective tight-binding
model and analyze the evolution of the topological phase under disorder.


  In this paper, a transmission protocol is studied for a two relay wireless
network in which simple repetition coding is applied at the relays.
Information-theoretic achievable rates for this transmission scheme are given,
and a space-time V-BLAST signalling and detection method that can approach them
is developed. It is shown through the diversity multiplexing tradeoff analysis
that this transmission scheme can recover the multiplexing loss of the
half-duplex relay network, while retaining some diversity gain. This scheme is
also compared with conventional transmission protocols that exploit only the
diversity of the network at the cost of a multiplexing loss. It is shown that
the new transmission protocol offers significant performance advantages over
conventional protocols, especially when the interference between the two relays
is sufficiently strong.


  We show that every automorphism of the congruence completion of the extended
mapping class group which preserves the set of conjugacy classes of procyclic
groups generated by Dehn twists is inner and that its automorphism group is
naturally isomorphic to the automorphism group of the procongruence pants
complex. In the genus $0$ case, we prove the stronger result that all
automorphisms of the profinite completion of the extended mapping class group
are inner.


  In this video, Ray-tracing data visualization technique was used to obtain
realistic and detailed flow motions during droplet collision. The differences
of collision outcome between Newtonian and non-Newtonian were compared. Various
types of droplet collision were presented, including bouncing, coalescence, and
stretching separation. Because of the reducing of equivalent viscosity caused
by shear stress, the gas film between shear-thinning droplet is thinner than
Newtonian liquid. Since thinner gas film promotes coalescence, shear thinning
liquid has smaller area of bouncing regime in the diagram of Weber number and
impact parameter. During the ligament/thread breakup process of stretching
separation, two kinds of instabilities are identified, helical and buckling
instabilities. Helical instability is analogous to a viscous rotating liquid
jet, while the buckling instability is analogous to electrically charged liquid
jets of polymer solutions.


  In this article, we study the following parabolic equation involving the
fractional Laplacian with singular nonlinearity \begin{equation*}
  \quad (P_{t}^s) \left\{ \begin{split}
  \quad u_t + (-\Delta)^s u &= u^{-q} + f(x,u), \;u >0\; \text{in}\; (0,T)
\times \Omega,
  u &= 0 \; \mbox{in}\; (0,T) \times (\mb R^n \setminus\Omega),
  \quad \quad \quad \quad u(0,x)&=u_0(x) \; \mbox{in} \; {\mb R^n}, \end{split}
\quad \right. \end{equation*} where $\Omega$ is a bounded domain in $\mb{R}^n$
with smooth boundary $\partial \Omega$, $n> 2s, \;s \in (0,1)$, $q>0$,
${q(2s-1)<(2s+1)}$, $u_0 \in L^\infty(\Omega)\cap X_0(\Omega)$ and $T>0$. We
suppose that the map $(x,y)\in \Omega \times \mb R^+ \mapsto f(x,y)$ is a
bounded below Carath\'eodary function, locally Lipschitz with respect to second
variable and uniformly for $x \in \Omega$ it satisfies
  \begin{equation}\label{cond_on_f} { \limsup_{y \to +\infty}
\frac{f(x,y)}{y}<\lambda_1^s(\Omega)},
  \end{equation}
  where $\la_1^s(\Omega)$ is the first eigenvalue of $(-\Delta)^s$ in $\Omega$
with homogeneous Dirichlet boundary condition in $\mathbb{R}^n \setminus
\Omega$. We prove the existence and uniqueness of weak solution to $(P_t^s)$ on
assuming $u_0$ satisfies an appropriate cone condition. We use the
semi-discretization in time with implicit Euler method and study the stationary
problem to prove our results. We also show additional regularity on the
solution of $(P_t^s)$ when we regularize our initial function $u_0$.


  The magnetic interactions that determine magnetic order and magnon energies
typically involve only two spins. While rare, multi-spin interactions can also
appear in quantum magnets and be the driving force in the ground state
selection and in the nature of its excitations. By performing time-domain
terahertz and magneto-Raman spectroscopy measurements combined with theoretical
modeling, we determine the origin of the magnon excitation gap in Dirac
antiferromagnet CoTiO$_3$. By adding a ring-exchange interaction in a hexagonal
plaquette of the honeycomb lattice to both an XXZ spin model and to a low
energy spin-orbital flavor wave model, a gap is generated in the magnon
spectrum at the Brillouin zone center. With this addition, the flavor wave
model reproduces a large swath of experimental results including terahertz,
Raman, inelastic neutron scattering, and magnetization experiments.


  In the framework of the inverse problem of Dynamics we investigate the
compatibility between a two-parametric family of precessing orbits and
autonomous force fields. In a previous work, for a specific choice of the two
parameters in the precessing family of orbits, we obtained that if the family
is compatible with a central force, than this is derived from a Manev's type
potential. In this work we consider another choice of the parameters in the
two-parametric family of precessing orbits, for which we know that there is no
central force compatible and study its compatibility with an autonomous force
field.


  A proposed method for preparing the superposition states of qubits using
different axes of the Bloch sphere. This method utilizes the Y-axis of the
Bloch sphere using IBM native (square root of X) gates, instead of utilizing
the X-axis of the Bloch sphere using IBM non-native Hadamard gates, for
transpiling cost-effective quantum circuits on IBM quantum computers. In this
paper, our presented method ensures that the final transpiled quantum circuits
always have a lower quantum cost than that of the transpiled quantum circuits
using the Hadamard gates.


  Algebras with identity $(a\star b)\star (c\star d) -(a\star d)\star(c\star
b)$ $=(a,b,c)\star d-(a,d,c)\star b$ are studied. Novikov algebras under Jordan
multiplication and Leibniz dual algebras satisfy this identity. If algebra with
such identity has unit, then it is associative and commutative.


  The quantum approximate optimization algorithm (QAOA) has been introduced as
a heuristic digital quantum computing scheme to find approximate solutions of
combinatorial problems with shallow circuits. We present a scheme to
parallelize this approach for arbitrary all-to-all connected problem graphs in
a layout of quantum bits (qubits) with nearest neighbor interactions. The
protocol consisting of single qubit operations that encode the optimization
problem and all interactions are problem-independent pair-wise CNOT gates among
nearest neighbors. This allows for a parallelizable implementation in quantum
devices with a square lattice geometry. The basis of this proposal is a lattice
gauge model which also introduces additional parameters and protocols for QAOA
to improve the efficiency.


  We describe Machine-Aided Script Curator (MASC), a system for human-machine
collaborative script authoring. Scripts produced with MASC include (1) English
descriptions of sub-events that comprise a larger, complex event; (2) event
types for each of those events; (3) a record of entities expected to
participate in multiple sub-events; and (4) temporal sequencing between the
sub-events. MASC automates portions of the script creation process with
suggestions for event types, links to Wikidata, and sub-events that may have
been forgotten. We illustrate how these automations are useful to the script
writer with a few case-study scripts.


  The role of Poincar\'e covariant space-time translations is investigated in
the case of the pseudoscalar-meson charge form factors calculated within a
relativistic quantum mechanics framework. It is shown that this role extends
beyond the standard energy-momentum conservation, which is accounted for in all
works based on this general approach. It implies constraints that have been
largely ignored until now but should be nevertheless fulfilled to ensure the
full Poincar\'e covariance. The violation of these constraints, which is more
or less important depending on the form of relativistic quantum mechanics that
is employed, points to the validity of using a single-particle current, which
is generally assumed in calculations of form factors. In short, these
constraints concern the relation of the momentum transferred to the
constituents to the one transferred to the system, which most often differ in
relativistic quantum mechanics while they are equal in field theory. How to
account for the constraints, as well as restoring the equivalence of different
relativistic quantum mechanics approaches in estimating form factors, is
discussed. It is mentioned that the result so obtained can be identified to a
dispersion-relation one. A short conclusion relative to the underlying dynamics
is given in the pion case.


  In this paper, we present Sch\"utzenberger's factorization in different
combinatorial contexts and show that its validity is not restricted to these
cases but can be extended to every Lie algebra endowed with an ordered basis.
We also expose some elements of the relations between the
Poincar\'e-Birkhoff-Witt bases of the enveloping algebra and their dual
families.


  We present the first reconstruction of dark matter maps from weak lensing
observational data using deep learning. We train a convolution neural network
(CNN) with a Unet based architecture on over $3.6\times10^5$ simulated data
realizations with non-Gaussian shape noise and with cosmological parameters
varying over a broad prior distribution. We interpret our newly created DES SV
map as an approximation of the posterior mean $P(\kappa | \gamma)$ of the
convergence given observed shear. Our DeepMass method is substantially more
accurate than existing mass-mapping methods. With a validation set of 8000
simulated DES SV data realizations, compared to Wiener filtering with a fixed
power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11
per cent. With N-body simulated MICE mock data, we show that Wiener filtering
with the optimal known power spectrum still gives a worse MSE than our
generalized method with no input cosmological parameters; we show that the
improvement is driven by the non-linear structures in the convergence. With
higher galaxy density in future weak lensing data unveiling more non-linear
scales, it is likely that deep learning will be a leading approach for mass
mapping with Euclid and LSST.


  Utilizing difference in temperature dependencies we decoupled BCS and
residual components of the microwave surface resistance of superconducting
niobium at all rf fields up to $B_\mathrm{rf}\sim110$ mT. We reveal that the
residual resistance decreases with field at $B_\mathrm{rf} \lesssim$ 40 mT and
strongly increases in chemically treated niobium at $B_\mathrm{rf} > 80$ mT. We
find that BCS surface resistance is weakly dependent on field in the clean
limit, whereas a strong and peculiar field dependence emerges after
$120^\circ$C vacuum baking.


  The advancement of object detection (OD) in open-vocabulary and open-world
scenarios is a critical challenge in computer vision. This work introduces
OmDet, a novel language-aware object detection architecture, and an innovative
training mechanism that harnesses continual learning and multi-dataset
vision-language pre-training. Leveraging natural language as a universal
knowledge representation, OmDet accumulates a "visual vocabulary" from diverse
datasets, unifying the task as a language-conditioned detection framework. Our
multimodal detection network (MDN) overcomes the challenges of multi-dataset
joint training and generalizes to numerous training datasets without manual
label taxonomy merging. We demonstrate superior performance of OmDet over
strong baselines in object detection in the wild, open-vocabulary detection,
and phrase grounding, achieving state-of-the-art results. Ablation studies
reveal the impact of scaling the pre-training visual vocabulary, indicating a
promising direction for further expansion to larger datasets. The effectiveness
of our deep fusion approach is underscored by its ability to learn jointly from
multiple datasets, enhancing performance through knowledge sharing.


  In image recovery problems, one seeks to infer an image from distorted,
incomplete, and/or noise-corrupted measurements. Such problems arise in
magnetic resonance imaging (MRI), computed tomography, deblurring,
super-resolution, inpainting, phase retrieval, image-to-image translation, and
other applications. Given a training set of signal/measurement pairs, we seek
to do more than just produce one good image estimate. Rather, we aim to rapidly
and accurately sample from the posterior distribution. To do this, we propose a
regularized conditional Wasserstein GAN that generates dozens of high-quality
posterior samples per second. Our regularization comprises an $\ell_1$ penalty
and an adaptively weighted standard-deviation reward. Using quantitative
evaluation metrics like conditional Fr\'{e}chet inception distance, we
demonstrate that our method produces state-of-the-art posterior samples in both
multicoil MRI and large-scale inpainting applications. The code for our model
can be found here: https://github.com/matt-bendel/rcGAN


  The coherent states are viewed as a powerful tool in differential geometry.
It is shown that some objects in differential geometry can be expressed using
quantities which appear in the construction of the coherent states. The
following subjects are discussed via the coherent states: the geodesics, the
conjugate locus and the cut locus; the divisors; the Calabi's diastasis and its
domain of definition; the Euler-Poincar\'e characteristic of the manifold, the
number of Borel-Morse cells, Kodaira embeding theorem....


  Recent developments on the MicroMegas prototypes built by use of the bulk
technology with analog and digital readout electronics are presented. The main
test beam results of a stack of several MicroMegas prototypes fully comply with
the needs of a hadronic calorimeter for future particle physics experiments. A
technical solution for a large scale prototype is also introduced.


  Conjucyclic codes are part of a family of codes that includes cyclic,
constacyclic, and quasi-cyclic codes, among others. Despite their importance in
quantum error correction, they have not received much attention in the
literature. This paper focuses on additive conjucyclic (ACC) codes over
$\mathbb{F}_4$ and investigates their properties. Specifically, we derive the
duals of ACC codes using a trace inner product and obtain the trace hull and
its dimension. Also, establish a necessary and sufficient condition for an
additive code to have a complementary dual (ACD). Additionally, we identify a
necessary condition for an additive conjucyclic complementary pair of codes
over $\mathbb{F}_4$. Furthermore, we show that the trace code of an ACC code is
cyclic and provide a condition for the trace code of an ACC code to be LCD. To
demonstrate the practical application of our findings, we construct some good
entanglement-assisted quantum error-correcting (EAQEC) codes using the trace
code of ACC codes.


  To solve two problems of Bergman stated in 1981, we construct a group $G$
such that $G$ contains a free noncyclic subgroup (hence, $G$ satisfies no group
identity) and $G$, as a group, is generated by its subsemigroup that satisfies
a nontrivial semigroup identity.


  Voltage controlled magnetoresistive random access memory (VC MRAM) is a
promising candidate for a future low-power high-density memory. The main causes
of bit errors in VC MRAM are write error and retention error. As the size of
the memory cell decreases, the data retention time decreases, which causes a
transition from the write-error-dominant region to the retention-error-dominant
region at a certain operating time. Here we introduce the characteristic time
of the transition from the write-error-dominant region to the
retention-error-dominant region and analyze how the characteristic time depends
on the effective anisotropy constant, $K_{0}$. The characteristic time is
approximately expressed as $t_{\rm c} = 2\, w\, \tau$, where $w$ is the write
error rate, and $\tau$ is the relaxation time derived by Kalmkov [J. Appl.
Phys. 96, (2004) 1138-1145]. We show that for large $K_{0}$, $t_{\rm c}$
increases with increase of $K_{0}$ similar to $\tau$. The characteristic time
is a key parameter for designing the VC MRAM for the variety of applications
such as machine learning and artificial intelligence.


  The prevalence of binary stars at close separations implies that many of
these systems will interact or merge during the binary's lifetime. This paper
presents hydrodynamic simulations of the scenario of binary coalescence through
unstable mass transfer, which drives the pair to closer separations. When the
donor star does not rotate synchronously with respect to the orbit, dynamical
tidal waves are excited in its envelope. We show that resonance crossings with
high azimuthal-order $(m\sim3$ to $6$) fundamental modes induce a visible
"polygram" distortion to the star. As the binary orbit tightens, the system
sweeps through resonance with modes of decreasing azimuthal order, which are
selectively excited. We compare our hydrodynamic simulations to predictions
from linear theory of resonant mode excitation. The linear theory provides an
estimate of mode amplitudes to within a factor of two, even as the oscillations
become quite non-linear as the stars coalesce. We estimate that a wave with 10%
radial amplitude generates approximately 1% photometric variability; this may
be detectible if such a binary coalescence is caught in action by future
photometric all-sky surveys.


  This paper reviews the X-ray, optical, radio and IR observations of galactic
X-ray binaries suspected to contain black hole compact objects, with particular
emphasis on the supporting dynamical evidence.


  We construct exact localised solutions of the PT-symmetric Gross-Pitaevskii
equation with an attractive cubic nonlinearity. The trapping potential has the
form of two $\delta$-function wells, where one well loses particles while the
other one is fed with atoms at an equal rate. The parameters of the constructed
solutions are expressible in terms of the roots of a system of two
transcendental algebraic equations. We also furnish a simple analytical
treatment of the linear Schr\"odinger equation with the PT-symmetric
double-$\delta$ potential.


  We present a study of the relative energy loss of quark and gluon jets that
traverse a weakly-coupled medium. The study is carried out with the jet
quenching Monte-Carlo event generator JEWEL which has been validated for a
large set of jet observables. We find that the relative energy loss of quark
and gluon jets approaches a constant value at large jet energy and that value
is significantly larger (closer to 1) than the single parton expectation of
$C_F/C_A$. Within JEWEL quark and gluon jets lose energy similarly.


  Recently, deep convolutional neural networks (CNNs) have been demonstrated
remarkable progress on single image super-resolution. However, as the depth and
width of the networks increase, CNN-based super-resolution methods have been
faced with the challenges of computational complexity and memory consumption in
practice. In order to solve the above questions, we propose a deep but compact
convolutional network to directly reconstruct the high resolution image from
the original low resolution image. In general, the proposed model consists of
three parts, which are feature extraction block, stacked information
distillation blocks and reconstruction block respectively. By combining an
enhancement unit with a compression unit into a distillation block, the local
long and short-path features can be effectively extracted. Specifically, the
proposed enhancement unit mixes together two different types of features and
the compression unit distills more useful information for the sequential
blocks. In addition, the proposed network has the advantage of fast execution
due to the comparatively few numbers of filters per layer and the use of group
convolution. Experimental results demonstrate that the proposed method is
superior to the state-of-the-art methods, especially in terms of time
performance.


  Facial Emotion Recognition is an inherently difficult problem, due to vast
differences in facial structures of individuals and ambiguity in the emotion
displayed by a person. Recently, a lot of work is being done in the field of
Facial Emotion Recognition, and the performance of the CNNs for this task has
been inferior compared to the results achieved by CNNs in other fields like
Object detection, Facial recognition etc. In this paper, we propose a
multi-task learning algorithm, in which a single CNN detects gender, age and
race of the subject along with their emotion. We validate this proposed
methodology using two datasets containing real-world images. The results show
that this approach is significantly better than the current State of the art
algorithms for this task.


  Hanbury Brown-Twiss (HBT) results of the nucleon-nucleon correlation function
have been presented for the nuclear reactions with neutron-rich projectiles (Be
isotopes) using an event-generator, the Isospin-Dependent Quantum Molecular
Dynamics model. We explore that the relationship between the binding energy per
nucleon of the projectiles and the strength of the neutron-proton HBT at small
relative momentum. Moreover, we reveal the relationship between the single
neutron separation energy and the strength of the halo neutron-proton HBT.
Results show that neutron-proton HBT results are sensitive to binding energy or
separation energy.


  About half of the heavy elements beyond iron are known to be produced by the
rapid neutron capture process, known as r-process. However, the astrophysical
site producing the r-process is still uncertain. Chemical abundances observed
in several cosmic sites indicate that different mechanisms should be at play.
For instance, the abundances around silver measured in a subset of metal-poor
stars indicate the presence of a weak r-process. This process may be active in
neutrino-driven winds of core collapse supernovae where (${\alpha}$,n)
reactions dominate the synthesis of Z ~ 40 elements in the expelled materials.
Scarcely measured, the rates of (${\alpha}$,n) reactions are determined from
statistical Hauser-Feshbach calculations with ${\alpha}$-optical-model
potentials, which are still poorly constrained. The uncertainties of the
(${\alpha}$,n) reaction rates therefore make a significant contribution to the
uncertainties of the abundances determined from stellar modeling. In this work,
the $^{88}$Sr(${\alpha}$,n)$^{91}$Zr reaction which impacts the weak r-process
abundances has been probed at astrophysics energy for the first time; directly
measuring the total cross sections at astrophysical energies of 8.37 - 13.09
MeV in the center of mass (3.8 - 7.5 GK). Two measurements were performed at
ATLAS with the electrically-segmented ionization chamber MUSIC, in inverse
kinematics, while following the active target technique. The cross sections of
this ${\alpha}$-induced reaction on $^{88}$Sr, located at the shell closure N =
50, have been found to be lower than expected, by a factor of 3, despite recent
statistical calculations validated by measurements on neighboring nuclei. This
result encourages more experimental investigations of (${\alpha}$,n) reactions,
at N = 50 and towards the neutron-rich side, to further test the predictive
power and reliability of such calculations.


  Understanding the localization properties of eigenvectors of complex networks
is important to get insight into various structural and dynamical properties of
the corresponding systems. Here, we analytically develop a scheme to construct
a highly localized network for a given set of networks parameters that is the
number of nodes and the number of interactions. We find that the localization
behavior of the principal eigenvector (PEV) of such a network is sensitive
against a single edge rewiring. We find evidences for eigenvalue crossing
phenomena as a consequence of the single edge rewiring, in turn providing an
origin to the sensitive behavior of the PEV localization. These insights were
then used to analytically construct the highly localized network for a given
set of networks parameters. The analysis provides fundamental insight into
relationships between the structural and the spectral properties of networks
for PEV localized networks. Further, we substantiate the existence of the
eigenvalue crossing phenomenon by considering a linear-dynamical process,
namely the ribonucleic acid (RNA) neutral network population dynamical model.
The analysis presented here on model networks aids in understanding the
steady-state behavior of a broad range of linear-dynamical processes, from
epidemic spreading to biochemical dynamics associated with the adjacency
matrices.


  Real-time object detection in videos using lightweight hardware is a crucial
component of many robotic tasks. Detectors using different modalities and with
varying computational complexities offer different trade-offs. One option is to
have a very lightweight model that can predict from all modalities at once for
each frame. However, in some situations (e.g., in static scenes) it might be
better to have a more complex but more accurate model and to extrapolate from
previous predictions for the frames coming in at processing time. We formulate
this task as a sequential decision making problem and use reinforcement
learning (RL) to generate a policy that decides from the RGB input which
detector out of a portfolio of different object detectors to take for the next
prediction. The objective of the RL agent is to maximize the accuracy of the
predictions per image. We evaluate the approach on the Waymo Open Dataset and
show that it exceeds the performance of each single detector.


Contains various tools for preferential and related logics


  Short wave equations were introduced in connection with the nonlinear
reflection of weak shock waves. They also relate to the modulation of a
gas-fluid mixture. Khokhlov-Zabolotskaya equation are used to describe the
propagation of a diffraction sound beam in a nonlinear medium. We give a new
algebraic method of solving these equations by using certain finite-dimensional
stable range of the nonlinear terms and obtain large families of new explicit
exact solutions parameterized by several functions for them. These parameter
functions enable one to find the solutions of some related practical models and
boundary value problems.


  We report numerical and analytical studies of the donor-acceptor
recombination in compensated semiconductors. Our calculations take into account
random electric fields of charged impurities which are important in non zero
compensation case. We show that the donor-acceptor optical spectrum can be
described as a sum of two components: monomolecular and bimolecular. In the low
compensation limit we develop two analytical models for both types of the
recombination. Also our numerical simulation predicts that these two components
of the photoluminescence spectra can be resolved under certain experimental
conditions.


  The heat kernel and quasinormal mode methods of computing 1-loop partition
functions of spin $s$ fields on hyperbolic quotient spacetimes
$\mathbb{H}^{3}/\mathbb{Z}$ are related via the Selberg zeta function. We
extend that analysis to thermal $\text{AdS}_{2n+1}$ backgrounds, with quotient
structure $\mathbb{H}^{2n+1}/\mathbb{Z}$. Specifically, we demonstrate the
zeros of the Selberg function encode the normal mode frequencies of spin fields
upon removal of non-square-integrable modes. With this information we construct
the 1-loop partition functions for symmetric transverse traceless tensors in
terms of the Selberg zeta function and find exact agreement with the heat
kernel method.


  The three major theoretical principles of quantum mechanics relevant to its
interpretation are: (T1), linearity; (T2), invariance under certain groups; and
(T3) the orthogonality and isolation of the different branches of the state
vector. These three imply the particle-like properties of mass, energy,
momentum, spin, charge, and locality are actually properties of the state
vector; and this in turn implies there is no evidence for the existence of
particles. Experimentally there is no evidence for collapse (E1) and
theoretically linearity prohibits collapse. One also has the experimentally
verified probability law (E2), which is found to rule out the many-worlds
interpretation. The failure of these three major interpretation, particles,
collapse, and many-worlds, apparently implies an acceptable interpretation must
be based on perception. Rather than being a separate principle, probability
follows in this interpretation from a weak assumption on perception plus the
combinatorics when an experiment is run many times. This suggests a relatively
simple experimental test of the perception interpretation.


  This paper investigates the zero-shot object goal visual navigation problem.
In the object goal visual navigation task, the agent needs to locate navigation
targets from its egocentric visual input. "Zero-shot" means that the target the
agent needs to find is not trained during the training phase. To address the
issue of coupling navigation ability with target features during training, we
propose the Class-Independent Relationship Network (CIRN). This method combines
target detection information with the relative semantic similarity between the
target and the navigation target, and constructs a brand new state
representation based on similarity ranking, this state representation does not
include target feature or environment feature, effectively decoupling the
agent's navigation ability from target features. And a Graph Convolutional
Network (GCN) is employed to learn the relationships between different objects
based on their similarities. During testing, our approach demonstrates strong
generalization capabilities, including zero-shot navigation tasks with
different targets and environments. Through extensive experiments in the
AI2-THOR virtual environment, our method outperforms the current
state-of-the-art approaches in the zero-shot object goal visual navigation
task. Furthermore, we conducted experiments in more challenging cross-target
and cross-scene settings, which further validate the robustness and
generalization ability of our method. Our code is available at:
https://github.com/SmartAndCleverRobot/ICRA-CIRN.


  Ice cores are known to yield information about astronomical phenomena as well
as information about past climate. We report time series analyses of annually
resolved nitrate variations in an ice core, drilled at the Dome Fuji station in
East Antarctica, corresponding to the period from CE 1610 to 1904. Our analyses
revealed clear evidence of ~11, ~22, and ~90 year periodicities, comparable to
the respective periodicities of the well-known Schwabe, Hale, and Gleissberg
solar cycles. Our results show for the first time that nitrate concentrations
in an ice core can be used as a proxy for past solar activity on decadal to
multidecadal time scales. Furthermore, 11-year and 22-year periodicities were
detected in nitrate variations even during the Maunder Minimum (1645-1715),
when sunspots were almost absent. This discovery may support cyclic behavior of
the solar dynamo during the grand solar minimum.


  Normalizing Flows are a powerful technique for learning and modeling
probability distributions given samples from those distributions. The current
state of the art results are built upon residual flows as these can model a
larger hypothesis space than coupling layers. However, residual flows are
extremely computationally expensive both to train and to use, which limits
their applicability in practice. In this paper, we introduce a simplification
to residual flows using a Quasi-Autoregressive (QuAR) approach. Compared to the
standard residual flow approach, this simplification retains many of the
benefits of residual flows while dramatically reducing the compute time and
memory requirements, thus making flow-based modeling approaches far more
tractable and broadening their potential applicability.


  We examine the response of an inertia-less viscoelastic channel flow to
localized perturbations. A simplified model shows that the non-linear
interaction between the velocity and elastic stress fields can split an initial
pulsed disturbance into two separate pulses if the initial disturbance and the
base elastic stress are sufficiently high. In accordance, we demonstrate that a
transition to a pulse-splitting regime can be achieved experimentally. These
results suggest a possible new direction for studying the elastic instability
of viscoelastic channel flows at high elasticity through the growth of
localized perturbations.


  This paper proposes a sequential design for maximizing a stochastic computer
simulator output, y(x), over an unknown optimization domain. The training data
used to estimate the optimization domain are a set of (historical) inputs,
often from a physical system modeled by the simulator. Two methods are provided
for estimating the simulator input domain. An extension of the well-known
efficient global optimization algorithm is presented to maximize y(x). The
domain estimation/maximization procedure is applied to two readily understood
analytic examples. It is also used to solve a problem in nuclear safety by
maximizing the k-effective "criticality coefficient" of spent fuel rods,
considered as one-dimensional heterogeneous fissile media. One of the two
domain estimation methods relies on expertise-type constraints. We show that
these constraints, initially chosen to address the spent fuel rod example, are
robust in that they also lead to good results in the second analytic
optimization example. Of course, in other applications, it could be necessary
to design alternative constraints that are more suitable for these
applications.


  Given a piece of speech and its transcript text, text-based speech editing
aims to generate speech that can be seamlessly inserted into the given speech
by editing the transcript. Existing methods adopt a two-stage approach:
synthesize the input text using a generic text-to-speech (TTS) engine and then
transform the voice to the desired voice using voice conversion (VC). A major
problem of this framework is that VC is a challenging problem which usually
needs a moderate amount of parallel training data to work satisfactorily. In
this paper, we propose a one-stage context-aware framework to generate natural
and coherent target speech without any training data of the target speaker. In
particular, we manage to perform accurate zero-shot duration prediction for the
inserted text. The predicted duration is used to regulate both text embedding
and speech embedding. Then, based on the aligned cross-modality input, we
directly generate the mel-spectrogram of the edited speech with a
transformer-based decoder. Subjective listening tests show that despite the
lack of training data for the speaker, our method has achieved satisfactory
results. It outperforms a recent zero-shot TTS engine by a large margin.


  We have designed a low-cost radio telescope system named the Bose Horn
Antenna Radio Telescope (BHARAT) to detect the 21 cm hydrogen line emission
from our Galaxy. The system is being used at the Radio Physics Laboratory
(RPL), Inter-University Centre for Astronomy and Astrophysics (IUCAA), India,
for laboratory sessions and training students and teachers. It is also a part
of the laboratory curriculum at several universities and colleges. Here, we
present the design of a highly efficient, easy to build, and cost-effective
dual-mode conical horn used as a radio telescope and describe the calibration
procedure. We also present some model observation data acquired using the
telescope for facilitating easy incorporation of this experiment in the
laboratory curriculum of undergraduate or post-graduate programs. We have named
the antenna after Acharya Jagadish Chandra Bose, honoring a pioneer in
radio-wave science and an outstanding teacher, who inspired several world
renowned scientists.


  As a proof of principle, self-consistent Kohn--Sham calculations are
performed with the exact exchange-correlation functional. Finding the exact
functional for even one trial density requires solving the interacting
Schr\"odinger equation many times. The density matrix renormalization group
method makes this possible for one-dimensional, real-space systems of more than
two interacting electrons. We illustrate and explore the convergence properties
of the exact KS scheme for both weakly and strongly correlated systems. We also
explore the spin-dependent generalization and densities for which the
functional is ill defined.


  Electric vhicles and autonomous driving dominate current research efforts in
the automotive sector. The two topics go hand in hand in terms of enabling
safer and more environmentally friendly driving. One fundamental building block
of an autonomous vehicle is the ability to build a map of the environment and
localize itself on such a map. In this paper, we make use of a stereo camera
sensor in order to perceive the environment and create the map. With live
Simultaneous Localization and Mapping (SLAM), there is a risk of
mislocalization, since no ground truth map is used as a reference and errors
accumulate over time. Therefore, we first build up and save a map of visual
features of the environment at low driving speeds with our extension to the
ORB-SLAM\,2 package. In a second run, we reload the map and then localize on
the previously built-up map. Loading and localizing on a previously built map
can improve the continuous localization accuracy for autonomous vehicles in
comparison to a full SLAM. This map saving feature is missing in the original
ORB-SLAM\,2 implementation.
  We evaluate the localization accuracy for scenes of the KITTI dataset against
the built up SLAM map. Furthermore, we test the localization on data recorded
with our own small scale electric model car. We show that the relative
translation error of the localization stays under 1\% for a vehicle travelling
at an average longitudinal speed of 36 m/s in a feature-rich environment. The
localization mode contributes to a better localization accuracy and lower
computational load compared to a full SLAM. The source code of our contribution
to the ORB-SLAM2 will be made public at:
https://github.com/TUMFTM/orbslam-map-saving-extension.


  With the increasing global popularity of self-driving cars, there is an
immediate need for challenging real-world datasets for benchmarking and
training various computer vision tasks such as 3D object detection. Existing
datasets either represent simple scenarios or provide only day-time data. In
this paper, we introduce a new challenging A*3D dataset which consists of RGB
images and LiDAR data with significant diversity of scene, time, and weather.
The dataset consists of high-density images ($\approx~10$ times more than the
pioneering KITTI dataset), heavy occlusions, a large number of night-time
frames ($\approx~3$ times the nuScenes dataset), addressing the gaps in the
existing datasets to push the boundaries of tasks in autonomous driving
research to more challenging highly diverse environments. The dataset contains
$39\text{K}$ frames, $7$ classes, and $230\text{K}$ 3D object annotations. An
extensive 3D object detection benchmark evaluation on the A*3D dataset for
various attributes such as high density, day-time/night-time, gives interesting
insights into the advantages and limitations of training and testing 3D object
detection in real-world setting.


  Let $(M,g)$ be a compact manifold without conjugate points and with
visibility universal covering. We show that its geodesic flow has a
time-preserving expansive factor which is topologically mixing and has a local
product structure. As an application, assuming further the so-called
entropy-gap we prove the uniqueness of the measure of maximal entropy for the
geodesic flow. For the other results we restrict our setting assuming
furthermore the continuity of Green bundles and the existence of a hyperbolic
closed geodesic. In this new context, we deduce that Green bundles are uniquely
integrable and are tangent to the smooth leaves of the horospherical
foliations. Moreover, we prove the above expansive factor acts on a compact
topological manifold and its geodesic flow has a unique measure of maximal
entropy which has full support.


  The paper shows that matching without replacement on propensity scores
produces estimators that generally are inconsistent for the average treatment
effect of the treated. To achieve consistency, practitioners must either assume
that no units exist with propensity scores greater than one-half or assume that
there is no confounding among such units. The result is not driven by the use
of propensity scores, and similar artifacts arise when matching on other scores
as long as it is without replacement.


  The time evolution of collective modes in an expanding ultarelativistic and
(effectively) Abelian plasma is studied in the hard-loop approximation
semi-analytically by means of integro-differential equations. A previous
treatment is generalized to arbitrary orientation of wave vectors with respect
to the direction of anisotropy and thus to a fully 3+1 dimensional situation.
Moreover, initial fluctuations are allowed in both gauge fields and currents,
which is necessary in the case of (stable) longitudinal modes. For unstable
(Weibel) modes, this generalization of initial conditions reduces drastically
the lower bound on the delay in the onset of growth that was found previously
by considering only collective gauge fields as seeds. This makes it appear much
more likely that non-Abelian plasma instabilities seeded by small initial
rapidity fluctuations could play an important role in the early stage of
heavy-ion collisions at LHC energies.


  This paper proves some results concerning the polar factorisation of an
integrable vector-valued function u into the composition of the gradient of a
convex function with a measure-preserving mapping. Not every integrable
function has a polar factorisation; we extend the class of counterexamples. We
introduce a generalisation: u has a polar inclusion if u(x) belongs to the
subdifferential of the convex function at y for almost every pair (x,y) with
respect to a measure-preserving plan. Given a regularity assumption, we show
that such measure-preserving plans are exactly the minimisers of a
Monge-Kantorovich optimisation problem.


  We derive probabilistic limit theorems that reveal the intricate structure of
the phase transitions in a mean-field version of the Blume-Emery-Griffiths
model. These probabilistic limit theorems consist of scaling limits for the
total spin and moderate deviation principles (MDPs) for the total spin. The
model under study is defined by a probability distribution that depends on the
parameters $n$, $\beta$, and $K$, which represent, respectively, the number of
spins, the inverse temperature, and the interaction strength. The intricate
structure of the phase transitions is revealed by the existence of 18 scaling
limits and 18 MDPs for the total spin. These limit results are obtained as
$(\beta,K)$ converges along appropriate sequences to points belonging to
various subsets of the phase diagram, which include a curve of second-order
points and a tricritical point. The forms of the limiting densities in the
scaling limits and of the rate functions in the MDPs reflect the influence of
one or more sets that lie in neighborhoods of the critical points and the
tricritical point. Of all the scaling limits, the structure of those near the
tricritical point is by far the most complex, exhibiting new types of critical
behavior when observed in a limit-theorem phase diagram in the space of the two
parameters that parametrize the scaling limits.


  Generating a detailed near-field perceptual model of the environment is an
important and challenging problem in both self-driving vehicles and autonomous
mobile robotics. A Bird Eye View (BEV) map, providing a panoptic
representation, is a commonly used approach that provides a simplified 2D
representation of the vehicle surroundings with accurate semantic level
segmentation for many downstream tasks. Current state-of-the art approaches to
generate BEV-maps employ a Convolutional Neural Network (CNN) backbone to
create feature-maps which are passed through a spatial transformer to project
the derived features onto the BEV coordinate frame. In this paper, we evaluate
the use of vision transformers (ViT) as a backbone architecture to generate BEV
maps. Our network architecture, ViT-BEVSeg, employs standard vision
transformers to generate a multi-scale representation of the input image. The
resulting representation is then provided as an input to a spatial transformer
decoder module which outputs segmentation maps in the BEV grid. We evaluate our
approach on the nuScenes dataset demonstrating a considerable improvement in
the performance relative to state-of-the-art approaches.


  We present magnetoresistivity measurements on the heavy-fermion
superconductor UTe$_{2}$ in pulsed magnetic fields $\mu_0H$ up to 68~T and
temperatures $T$ from 1.4 to 80~K. Magnetic fields applied along the three
crystallographic directions $\mathbf{a}$ (easy magnetic axis), $\mathbf{b}$,
and $\mathbf{c}$ (hard magnetic axes), are found to induce different phenomena
- depending on the field direction - beyond the low-field suppression of the
superconducting state. For $\mathbf{H}\parallel\mathbf{a}$, a broad anomaly in
the resistivity is observed at $\mu_0H^*\simeq10$~T and $T = 1.4$~K. For
$\mathbf{H}\parallel\mathbf{c}$, no magnetic transition nor crossover are
observed. For $\mathbf{H}\parallel\mathbf{b}$, a sharp first-order-like step in
the resistivity indicates a metamagnetic transition at the field $\mu_0H_m
\simeq 35$~T. When the temperature is raised signature of first-order
metamagnetism is observed up to a critical endpoint at $T_{CEP}\simeq7$~K. At
higher temperatures a crossover persists up to 28~K, i.e., below the
temperature $T_\chi^{max} = 35$~K where the magnetic susceptibility is maximal.
A sharp maximum in the Fermi-liquid quadratic coefficient $A$ of the
low-temperature resistivity is found at $H_m$. It indicates an enhanced
effective mass associated with critical magnetic fluctuations, possibly coupled
with a Fermi surface instability. Similarly to the URhGe case, we show that
UTe$_{2}$ is a candidate for field-induced reentrant superconductivity in the
proximity of $H_m$.


  Starting from the leading soft term of the 5-point amplitude, involving a
graviton and two Kerr black holes, that factorises into the product of the
elastic amplitude without the graviton and the leading soft factor, we compute
the infrared divergent contribution to the imaginary part of the two-loop
eikonal. Then, using analyticity and crossing symmetry, we determine the
radiative contribution to the real part of the two-loop eikonal and from it the
radiative part of the deflection angle for spins aligned to the orbital angular
momentum, the loss of angular momentum and the zero frequency limit of the
energy spectrum for any spin and for any spin orientation. For spin one we find
perfect agreement with recent results obtained with the supersymmetric
worldline formalism.


  We preform large-scale \emph{ab initio} density functional theory
calculations to study the lattice strain and the vibrational properties of
colloidal semiconductor core-shell nanoclusters with up to one thousand atoms
(radii up to 15.6~\AA). For all the group IV, III-V and II-VI semiconductors
studied, we find that the atom positions of the shell atoms, seem unaffected by
the core material. In particular, for group IV core-shell clusters the shell
material remains unstrained, while the core adapts to the large lattice
mismatch (compressive or tensile strain). For InAs-InP and CdSe-CdS, both the
cores and the shells are compressively strained corresponding to pressures up
to 20 GPa. We show that this compression, which contributes a large blue-shift
of the vibrational frequencies, is counterbalanced, to some degree, by the
undercoordination effect of the near-surface shell, which contributes a
red-shift to the vibrational modes. These findings lead to a different
interpretation of the frequency shifts of recent Raman experiments, while they
confirm the speculated interface nature of the low-frequency shoulder of the
high frequency Raman peak.


  The TT-PET collaboration is developing a small animal TOF-PET scanner based
on silicon detectors featuring 30 ps RMS time resolution and intended to be
inserted in an existing MRI scanner. The TT-PET scanner makes use of a stack of
layers of high-Z photon-converter and 100 $\mathrm{\mu m}$ thick silicon
sensors, to achieve a scanner with 0.5 $\mathrm{\times}$ 0.5 $\mathrm{\times}$
0.2 $\mathrm{mm^{3}}$ granularity, with precise depth-of-interaction
measurement. In this paper we present the results of the Monte Carlo studies
for the expected data rate, time resolution on the TOF measurements, spatial
resolution and image reconstruction with and without the use of the timing
information. Most of the studies have been performed according to the
international standards used to assess the performance of small-animal PET
system.


  Let $S(\sigma,t)=\frac{1}{\pi}\arg\zeta(\sigma+it)$ be the argument of the
Riemann zeta-function at the point $\sigma+it$ in the critical strip. For
$n\geq 1$ and $t>0$, we define \begin{equation*} S_{n}(\sigma,t) = \int_0^t
S_{n-1}(\sigma,\tau) \,d\tau + \delta_{n,\sigma\,}, \end{equation*} where
$\delta_{n,\sigma}$ is a specific constant depending on $\sigma$ and $n$. Let
$0\leq \beta<1$ be a fixed real number. Assuming the Riemann hypothesis, we
establish lower bounds for the maximum of $S_n(\sigma,t+h)-S_n(\sigma,t)$ near
the critical line, on the interval $T^\beta\leq t \leq T$ and in a small range
of $h$. This improves some results of the first author and generalizes a result
of the authors on $S(t)$. We also give new omega results for $S_n(t)$,
improving a result by Selberg.


  The simulation of human neurons and neurotransmission mechanisms has been
realized in deep neural networks based on the theoretical implementations of
activation functions. However, recent studies have reported that the threshold
potential of neurons exhibits different values according to the locations and
types of individual neurons, and that the activation functions have limitations
in terms of representing this variability. Therefore, this study proposes a
simple yet effective activation function that facilitates different thresholds
and adaptive activations according to the positions of units and the contexts
of inputs. Furthermore, the proposed activation function mathematically
exhibits a more generalized form of Swish activation function, and thus we
denoted it as Adaptive SwisH (ASH). ASH highlights informative features that
exhibit large values in the top percentiles in an input, whereas it rectifies
low values. Most importantly, ASH exhibits trainable, adaptive, and
context-aware properties compared to other activation functions. Furthermore,
ASH represents general formula of the previously studied activation function
and provides a reasonable mathematical background for the superior performance.
To validate the effectiveness and robustness of ASH, we implemented ASH into
many deep learning models for various tasks, including classification,
detection, segmentation, and image generation. Experimental analysis
demonstrates that our activation function can provide the benefits of more
accurate prediction and earlier convergence in many deep learning applications.


  We prove that the correction to exponential decay of the truncated two points
function in the homogeneous positive field Ising model is $c\|x\|^{-(d-1)/2}$.
The proof is based on the development in the random current representation of a
"modern" Ornstein-Zernike theory, as developed by Campanino, Ioffe and Velenik.


  In this paper, we derive corrections to the subleading logarithmic term of
the entanglement entropy in systems with spontaneous broken continuous
symmetry. Using quantum Monte Carlo simulations, we show that the improved
scaling formula leads to much better estimations of the number of Goldstone
modes in the two-dimensional square lattice spin-1/2 Heisenberg model and
bilayer spin-1/2 Heisenberg model in systems of rather small sizes, compared
with previous results. In addition, the universal geometry-dependent finite
constant in the entanglement entropy scaling is also obtained in good agreement
with the theoretical value.


  The adoption of deep learning techniques in genomics has been hindered by the
difficulty of mechanistically interpreting the models that these techniques
produce. In recent years, a variety of post-hoc attribution methods have been
proposed for addressing this neural network interpretability problem in the
context of gene regulation. Here we describe a complementary way of approaching
this problem. Our strategy is based on the observation that two large classes
of biophysical models of cis-regulatory mechanisms can be expressed as deep
neural networks in which nodes and weights have explicit physiochemical
interpretations. We also demonstrate how such biophysical networks can be
rapidly inferred, using modern deep learning frameworks, from the data produced
by certain types of massively parallel reporter assays (MPRAs). These results
suggest a scalable strategy for using MPRAs to systematically characterize the
biophysical basis of gene regulation in a wide range of biological contexts.
They also highlight gene regulation as a promising venue for the development of
scientifically interpretable approaches to deep learning.


  We identify the key features of Kardar-Parisi-Zhang universality class in the
fluctuations of the wave density logarithm, in a two-dimensional Anderson
localized wave packet. In our numerical analysis, the fluctuations are found to
exhibit an algebraic scaling with distance characterized by an exponent of
$1/3$, and a Tracy-Widom probability distribution of the fluctuations.
Additionally, within a directed polymer picture of KPZ physics, we identify the
dominant contribution of a directed path to the wave packet density and find
that its transverse fluctuations are characterized by a roughness exponent
$2/3$. Leveraging on this connection with KPZ physics, we verify that an
Anderson localized wave packet in 2D exhibits a stretched-exponential
correction to its well-known exponential localization.


  Difference families are traditionally built using groups as their basis. This
paper looks at what sort of generalised difference family constructions could
be made, using the standard basis of translation and difference.
  The main result is that minimal requirements on the structure force nothing
that groups cannot give, at least in the finite case. Thus all difference
families arise from groups.


  The electromigration (EM) of metallic lines is studied in terms of
competition between two percolative processes taking place in a random resistor
network. The effects associated with the transport of mass and with the
consequent growth of the internal stress are accounted for by stochastic
generation and recovery of voids, driven by the external current. Monte Carlo
simulations enable us to investigate within a unified theoretical framework a
variety of relevant aspects of EM degradation as: damage patterns, the
distribution of the time to failures, early stage resistance change in the
presence of compositional effects, Black's law, geometrical effects, etc. A
general agreement is found with the experimental findings.


  Deep X-ray surveys have shown that the cosmic X-ray background (XRB) is
largely due to the accretion onto supermassive black holes, integrated over
cosmic time. The ROSAT, Chandra and XMM-Newton satellites have resolved more
than 80% of the 0.1-10 keV X-ray background into discrete sources. Optical
spectroscopic identifications are about 90% and 60% complete for the deepest
ROSAT and Chandra/ XMM-Newton surveys, respectively, and show that the sources
producing the bulk of the X-ray background are a mixture of obscured (type-2)
and unobscured (type-1) AGNs, as predicted by the XRB population synthesis
models, following the unified AGN scenarios. The characteristic hard spectrum
of the XRB can be explained if most of the AGN are heavily absorbed, and in
particular a class of highly luminous type-2 AGN, so called QSO-2s exist. The
deep Chandra and XMM-Newton surveys have recently detected several examples of
QSO-2s. The space density of the X-ray selected AGN, as determined from ROSAT
surveys does not seem to decline as rapidly as that of optically selected QSO,
however, the statistics of the high-redshift samples is still rather poor. The
new Chandra and XMM-Newton surveys at significantly fainter fluxes are starting
to provide additional constraints here, but the preliminary observed redshift
distribution peaks at much lower redshifts (z=0.5-0.7) than the predictions
based on the ROSAT luminosity function.


  Evaluating natural language generation (NLG) is a vital but challenging
problem in artificial intelligence. Traditional evaluation metrics mainly
capturing content (e.g. n-gram) overlap between system outputs and references
are far from satisfactory, and large language models (LLMs) such as ChatGPT
have demonstrated great potential in NLG evaluation in recent years. Various
automatic evaluation methods based on LLMs have been proposed, including
metrics derived from LLMs, prompting LLMs, and fine-tuning LLMs with labeled
evaluation data. In this survey, we first give a taxonomy of LLM-based NLG
evaluation methods, and discuss their pros and cons, respectively. We also
discuss human-LLM collaboration for NLG evaluation. Lastly, we discuss several
open problems in this area and point out future research directions.


  We present full-star simulations of Type Ia supernova explosions on the basis
of the standard Chandrasekhar-mass deflagration model. Most simulations so far
considered only one spatial octant and assumed mirror symmetry to the other
octants. Two full-star models are evolved to homologous expansion and compared
with previous single-octant simulations. Therefrom we analyze the effect of
abolishing the artificial symmetry constraint on the evolution of the flame
surface. It turns out that the development of asymmetries depends on the chosen
initial flame configuration. Such asymmetries of the explosion process could
possibly contribute to the observed polarization of some Type Ia supernova
spectra.


  In this paper, we investigate the effects of dimension six operators in the
framework of the standard model (SM) effective field theory on using
$t\bar{t}b\bar{b}$ channel at the LHC with the center-of-mass energy of 13 TeV.
In this analysis, we only assume leptonic top-quarks. Once the events were
simulated, the CMS-measured cross-section was used to constrain the
corresponding effective Wilson coefficients at $95\%$ confidence level.
Considering the SM effective Lagrangian, the $t\bar{t}b\bar{b}$ events in
proton-proton collision were very sensitive to the effective Wilson
coefficients $ \bar{C}_{g},\tilde{C_{g}},\bar{C}_{uG},\bar{C}_{uW}$. We show
that using $t\bar{t}b\bar{b}$ events, we can provide stronger constraints on
these Wilson coefficients than the current ones obtained from other channels.


  Strong laser-induced magnetization of oxygen gas at room temperature and
atmospheric pressure is achieved experimentally on the sub-nanosecond time
scale. The method is based on controlling the electronic spin of paramagnetic
molecules by means of manipulating their rotation with an optical centrifuge.
Spin-rotational coupling results in high degree of spin polarization on the
order of one Bohr magneton per centrifuged molecule. Owing to the non-resonant
interaction with the laser pulses, the demonstrated technique is applicable to
a broad class of paramagnetic rotors. Executed in a high-density gas, it may
offer an efficient way of generating macroscopic magnetic fields remotely (as
shown in this work), producing large amount of polarized electrons and
converting electronic to nuclear spin polarization.


  We study the dynamics of the de Sitter resonance, namely the stable
equilibrium configuration of the first three Galilean satellites. We clarify
the relation between this family of configurations and the more general Laplace
resonant states. In order to describe the dynamics around the de Sitter stable
equilibrium, a one-degree of freedom Hamiltonian normal form is constructed and
exploited to identify initial conditions leading to the two families.
  The normal form Hamiltonian is used to check the accuracy in the location of
the equilibrium positions. Besides, it gives a measure of how sensitive it is
with respect to the different perturbations acting on the system. By looking at
the phase-plane of the normal form, we can identify a \sl Laplace-like \rm
configuration, which highlights many substantial aspects of the observed one.


  We introduce a symplectic no-core configuration interaction (SpNCCI)
framework for ab initio nuclear structure calculations, in a correlated
many-body basis which encodes an approximate Sp(3,R) symmetry of the nucleus.
Such a scheme potentially provides a means of restricting the many-body space
to include only those highly-excited configurations which dominantly contribute
to the nuclear wave function. We examine the symplectic symmetry structure
arising in an illustrative ab initio SpNCCI calculation for 6Li. We observe
both the dominance of symplectic symmetry in individual wave functions and the
emergence of families of states related by symplectic symmetry.


  The paper is the second part of the work devoted to the problem of time in
quantum cosmology. Here we consider in detail two approaches within the scope
of Feynman path integration scheme: The first, by Simeone and collaborators, is
gauge-invariant and lies within the unitary approach to a consistent
quantization of gravity. It is essentially based on the idea of
deparametrization (reduction to physical degrees of freedom) as a first step
before quantization. The other approach by Savchenko, Shestakova and Vereshkov
is rather radical. It is an attempt to take into account peculiarities of the
Universe as a system without asymptotic states that leads to the conclusion
that quantum geometrodynamics constructed for such a system is, in general, a
gauge-noninvariant theory. However, this theory is shown to be mathematically
consistent and the problem of time is solved in this theory in a natural way.