  For any real pair i, j geq 0 with i+j=1 let Bad(i, j) denote the set of (i,
j)-badly approximable pairs. That is, Bad(i, j) consists of irrational vectors
x:=(x_1, x_2) in R^2 for which there exists a positive constant c(x) such that
max {||qx_1||^(-i), ||qx_2||^(-j)} > c(x)/q for all q in N. A new
characterization of the set Bad(i, j) in terms of `well-approximable' vectors
in the area of 'twisted' inhomogeneous Diophantine approximation is
established. In addition, it is shown that Bad^x(i, j), the `twisted'
inhomogeneous analogue of Bad(i, j), has full Hausdorff dimension 2 when x is
chosen from the set Bad(i, j). The main results naturally generalise the
i=j=1/2 work of Kurzweil.


  Mathematical knowledge is a central component in science, engineering, and
technology (documentation). Most of it is represented informally, and -- in
contrast to published research mathematics -- subject to continual change.
Unfortunately, machine support for change management has either been very
coarse grained and thus barely useful, or restricted to formal languages, where
automation is possible. In this paper, we report on an effort to extend change
management to collections of semi-formal documents which flexibly intermix
mathematical formulas and natural language and to integrate it into a semantic
publishing system for mathematical knowledge. We validate the long-standing
assumption that the semantic annotations in these flexiformal documents that
drive the machine-supported interaction with documents can support semantic
impact analyses at the same time. But in contrast to the fully formal setting,
where adaptations of impacted documents can be automated to some degree, the
flexiformal setting requires much more user interaction and thus a much tighter
integration into document management workflows.


  We study quantified propositional logics from the complexity theoretic point
of view. First we introduce alternating dependency quantified boolean formulae
(ADQBF) which generalize both quantified and dependency quantified boolean
formulae. We show that the truth evaluation for ADQBF is
AEXPTIME(poly)-complete. We also identify fragments for which the problem is
complete for the levels of the exponential hierarchy. Second we study
propositional team-based logics. We show that DQBF formulae correspond
naturally to quantified propositional dependence logic and present a general
NEXPTIME upper bound for quantified propositional logic with a large class of
generalized dependence atoms. Moreover we show AEXPTIME(poly)-completeness for
extensions of propositional team logic with generalized dependence atoms.


  We present results of our $R-$band polarimetry of a cometary globule, LBN 437
(Gal96-15, $\ell$ $=$ 96$\degree$, \textit{b} $=-15\degree$), to study magnetic
field geometry of the cloud. We estimated a distance of $360\pm65$ pc to LBN
437 (also one additional cloud, CB 238) using near-IR photometric method.
Foreground contribution to the observed polarisation values was subtracted by
making polarimetric observations of stars that are located in the direction of
the cloud and with known distances from the Hipparcos parallax measurements.
The magnetic field geometry of LBN 437 is found to follow the curved shape of
the globule head. This could be due to the drag that the magnetic field lines
could have experienced because of the ionisation radiation from the same
exciting source that caused the cometary shape of the cloud. The orientation of
the outflow from the Herbig A4e star, LkH$\alpha$ 233 (or V375 Lac), located at
the head of LBN 437, is found to be parallel to both the initial (prior to the
ionising source was turned on) ambient magnetic field (inferred from a star HD
214243 located just in front of the cloud) and the Galactic plane.


  In this paper, we construct and analyse the symmetries and conservation laws
(conserved densities) of a model of a nonlinear Scrodinger equation with
PT-symmetric potentials and inhomogeneity.


  We use first principles density functional theory to calculate electronic and
magnetic properties of TiF3 using the full potential linearized augmented plane
wave method. The LDA approximation predicts a fully saturated ferromagnetic
metal and finds degenerate energy minima for high and low symmetry structures.
The experimentally observed Jahn-Teller phase transition at Tc=370K can not be
driven by the electron-phonon interaction alone, which is usually described
accurately by LDA.
  Electron correlations beyond LDA are essential to lift the degeneracy of the
singly occupied Ti t2g orbital. Although the on-site Coulomb correlations are
important, the direction of the t2g-level splitting is determined by the
dipole-dipole interactions. The LDA+U functional predicts an aniferromagnetic
insulator with an orbitally ordered ground state. The input parameters U=8.1 eV
and J=0.9 eV for the Ti 3d orbital were found by varying the total charge on
the TiF$_6^{2-}$ ion using the molecular NRLMOL code. We estimate the
Heisenberg exchange constant for spin-1/2 on a cubic lattice to be
approximately 24 K. The symmetry lowering energy in LDA+U is about 900 K per
TiF3 formula unit.


  Upcoming Intelligent Transportation Systems (ITSs) will transform roads from
static resources to dynamic Cyber Physical Systems (CPSs) in order to satisfy
the requirements of future vehicular traffic in smart city environments.
Up-to-date information serves as the basis for changing street directions as
well as guiding individual vehicles to a fitting parking slot. In this context,
not only abstract indicators like traffic flow and density are required, but
also data about mobility parameters and class information of individual
vehicles. Consequently, accurate and reliable systems that are capable of
providing these kinds of information in real-time are highly demanded. In this
paper, we present a system for classifying vehicles based on their
radio-fingerprints which applies cutting-edge machine learning models and can
be non-intrusively installed into the existing road infrastructure in an ad-hoc
manner. In contrast to other approaches, it is able to provide accurate
classification results without causing privacy-violations or being vulnerable
to challenging weather conditions. Moreover, it is a promising candidate for
large-scale city deployments due to its cost-efficient installation and
maintenance properties. The proposed system is evaluated in a comprehensive
field evaluation campaign within an experimental live deployment on a German
highway, where it is able to achieve a binary classification success ratio of
more than 99% and an overall accuracy of 89.15% for a fine-grained
classification task with nine different classes.


  We consider the problem of learning in a non-stationary reinforcement
learning (RL) environment, where the setting can be fully described by a
piecewise stationary discrete-time Markov decision process (MDP). We introduce
a variant of the Restarted Bayesian Online Change-Point Detection algorithm
(R-BOCPD) that operates on input streams originating from the more general
multinomial distribution and provides near-optimal theoretical guarantees in
terms of false-alarm rate and detection delay. Based on this, we propose an
improved version of the UCRL2 algorithm for MDPs with state transition kernel
sampled from a multinomial distribution, which we call R-BOCPD-UCRL2. We
perform a finite-time performance analysis and show that R-BOCPD-UCRL2 enjoys a
favorable regret bound of $O\left(D O \sqrt{A T K_T \log\left (\frac{T}{\delta}
\right) + \frac{K_T \log \frac{K_T}{\delta}}{\min\limits_\ell \:
\mathbf{KL}\left(
{\mathbf{\theta}^{(\ell+1)}}\mid\mid{\mathbf{\theta}^{(\ell)}}\right)}}\right)$,
where $D$ is the largest MDP diameter from the set of MDPs defining the
piecewise stationary MDP setting, $O$ is the finite number of states (constant
over all changes), $A$ is the finite number of actions (constant over all
changes), $K_T$ is the number of change points up to horizon $T$, and
$\mathbf{\theta}^{(\ell)}$ is the transition kernel during the interval
$[c_\ell, c_{\ell+1})$, which we assume to be multinomially distributed over
the set of states $\mathbb{O}$. Interestingly, the performance bound does not
directly scale with the variation in MDP state transition distributions and
rewards, ie. can also model abrupt changes. In practice, R-BOCPD-UCRL2
outperforms the state-of-the-art in a variety of scenarios in synthetic
environments. We provide a detailed experimental setup along with a code
repository (upon publication) that can be used to easily reproduce our
experiments.


  Matrices often represent important information in scientific applications and
are involved in performing complex calculations. But systematically testing
these applications is hard due to the oracle problem. Metamorphic testing is an
effective approach to test such applications because it uses metamorphic
relations to determine whether test cases have passed or failed. Metamorphic
relations are typically identified with the help of a domain expert and is a
labor intensive task. In this work we use a graph kernel based machine learning
approach to predict metamorphic relations for matrix calculation programs.
Previously, this graph kernel based machine learning approach was used to
successfully predict metamorphic relations for programs that perform numerical
calculations. Results of this study show that this approach can be used to
predict metamorphic relations for matrix calculation programs as well.


  We observe the actions of a $K$ sub-sample of $N$ individuals up to time $t$
for some large $K<N$. We model the relationships of individuals by i.i.d.
Bernoulli($p$)-random variables, where $p\in (0,1]$ is an unknown parameter.
The rate of action of each individual depends on some unknown parameter $\mu>
0$ and on the sum of some function $\phi$ of the ages of the actions of the
individuals which influence him. The function $\phi$ is unknown but we assume
it rapidly decays. The aim of this paper is to estimate the parameter $p$
asymptotically as $N\to \infty$, $K\to \infty$, and $t\to \infty$. Let $m_t$ be
the average number of actions per individual up to time $t$. In the subcritical
case, where $m_t$ increases linearly, we build an estimator of $p$ with the
rate of convergence
$\frac{1}{\sqrt{K}}+\frac{N}{m_t\sqrt{K}}+\frac{N}{K\sqrt{m_t}}$. In the
supercritical case, where $m_{t}$ increases exponentially fast, we build an
estimator of $p$ with the rate of convergence
$\frac{1}{\sqrt{K}}+\frac{N}{m_{t}\sqrt{K}}$.


  "The hardest logic puzzle ever" presented by George Boolos became a target
for philosophers and logicians who tried to modify it and make it even tougher.
I propose further modification of the original puzzle where part of the
available information is eliminated but the solution is still possible. The
solution also gives interesting ideas on logic behind discovery of unknown
language.


  We construct a family of complete, properly embedded, annular translators $M$
such that $M$ lies in a slab and is invariant under reflections in the vertical
coordinate planes. Each translator in the family is asymptotic as $z\to
-\infty$ to four vertical planes $\{y= \pm b\}$ and $\{y= \pm B\}$, where $0<b
\le B < \infty$. We call $b$ and $B$ the inner width and the (outer) width of
the translator. We show that for each $b \ge \pi/2$ and each $s>0$, there is a
translator in the family with inner width $b$ and with necksize $s$. (We also
show that there are no translators with inner width $<\pi/2$ having the
properties of the examples we construct.)


  This paper is a generalization of the previous work (Yang et.al, J. Comput.
Phys. 330 (2017), 863-883) to the 3-D irregular convex domains. The analytical
calculation formula of fractional derivatives of finite element basis functions
are given and a path searching method is developed to find the integration
paths corresponding to the Gaussian points. Moreover, a template matrix is
introduced to speed up the procedures. Numerical experiments on a steady
problem are presented verifying the efficiency of proposed techniques.


  Data compression and decompression have become vital components of big-data
applications to manage the exponential growth in the amount of data collected
and stored. Furthermore, big-data applications have increasingly adopted GPUs
due to their high compute throughput and memory bandwidth. Prior works presume
that decompression is memory-bound and have dedicated most of the GPU's threads
to data movement and adopted complex software techniques to hide memory latency
for reading compressed data and writing uncompressed data. This paper shows
that these techniques lead to poor GPU resource utilization as most threads end
up waiting for the few decoding threads, exposing compute and synchronization
latencies.
  Based on this observation, we propose CODAG, a novel and simple kernel
architecture for high throughput decompression on GPUs. CODAG eliminates the
use of specialized groups of threads, frees up compute resources to increase
the number of parallel decompression streams, and leverages the ample compute
activities and the GPU's hardware scheduler to tolerate synchronization,
compute, and memory latencies. Furthermore, CODAG provides a framework for
users to easily incorporate new decompression algorithms without being burdened
with implementing complex optimizations to hide memory latency. We validate our
proposed architecture with three different encoding techniques, RLE v1, RLE v2,
and Deflate, and a wide range of large datasets from different domains. We show
that CODAG provides 13.46x, 5.69x, and 1.18x speed up for RLE v1, RLE v2, and
Deflate, respectively, when compared to the state-of-the-art decompressors from
NVIDIA RAPIDS.


  Reconstructing the structural connectivity between interacting units from
observed activity is a challenge across many different disciplines. The
fundamental first step is to establish whether or to what extent the
interactions between the units can be considered pairwise and, thus, can be
modeled as an interaction network with simple links corresponding to pairwise
interactions. In principle this can be determined by comparing the maximum
entropy given the bivariate probability distributions to the true joint
entropy. In many practical cases this is not an option since the bivariate
distributions needed may not be reliably estimated, or the optimization is too
computationally expensive. Here we present an approach that allows one to use
mutual informations as a proxy for the bivariate distributions. This has the
advantage of being less computationally expensive and easier to estimate. We
achieve this by introducing a novel entropy maximization scheme that is based
on conditioning on entropies and mutual informations. This renders our approach
typically superior to other methods based on linear approximations. The
advantages of the proposed method are documented using oscillator networks and
a resting-state human brain network as generic relevant examples.


  We examine the free energy and the thermodynamic properties in the
three-chain Hubbard model for Ta2NiSe5 to clarify the phase transitions between
the uniform and the FFLO excitonic states which are expected to be observed in
Ta2NiSe5 under high pressure.


  We prove trace inequalities for a self-adjoint operator on an abstract
Hilbert space. These inequalities lead to universal bounds on spectral gaps and
on moments of eigenvalues lambda_k that are analogous to those known for
Schroedinger operators and the Dirichlet Laplacian, on which the operators of
interest are modeled. In addition we produce inequalities that are new even in
the model case. These include a family of differential inequalities for
generalized Riesz means and theorems stating that arithmetic means of
lambda_k^p for p <= 3 are universally bounded from above by multiples of the
geometric mean of the lambda_k. For Schroedinger operators and the Dirichlet
Laplacian these bounds are Weyl-sharp, i.e., saturated by the standard
semiclassical estimates for lambda_k at large k.


  It is well known that R^N has subspaces of dimension proportional to N on
which the \ell_1 norm is equivalent to the \ell_2 norm; however, no explicit
constructions are known. Extending earlier work by Artstein--Avidan and Milman,
we prove that such a subspace can be generated using O(N) random bits.


  A computable structure A is x-computably categorical for some Turing degree
x, if for every computable structure B isomorphic to A there is an isomorphism
f:B -> A with f computable in x. A degree x is a degree of categoricity if
there is a computable A such that A is x-computably categorical, and for all y,
if A is y-computably categorical then y computes x.
  We construct a Sigma_2 set whose degree is not a degree of categoricity. We
also demonstrate a large class of degrees that are not degrees of categoricity
by showing that every degree of a set which is 2-generic relative to some
perfect tree is not a degree of categoricity. Finally, we prove that every
noncomputable hyperimmune-free degree is not a degree of categoricity.


  Fake base station (FBS) attack is a great security challenge to wireless user
equipment (UE). During the cell selection stage, the UE receives multiple
synchronization signals (SSs) from multiple nearby base stations (BSs), and
then synchronizes itself with the strongest SS. A FBS also can transmit a SS
with sufficient power to confuse the UE, which makes the UE connect to the FBS,
and may lead to the leakage of private information. In this letter,
countermeasure to the FBS attack by utilizing the location information is
investigated. Two location awareness based FBS-resistance schemes are proposed
by checking the received signal strength according to the position of the UE
and a legitimate BS map. The successful cheating rate (SCR) definded as the
probability that the UE will connect to the FBS is investigated. Numeric
results show that with the two proposed schemes, the SCR can be greatly reduced
especially when the transmit power of the FBS is large. Beyond that, a
cooperation aided method is further proposed to improve the performance, and we
show that the cooperation aided method can further suppress the SCR when the
signal strength from the FBS is similar to that from the legitimate BS.


  We establish the phase diagram of the strongly-interacting Bose-Hubbard model
defined on a two-leg ladder geometry in the presence of a homogeneous flux. Our
work is motivated by a recent experiment [Atala et al., Nature Phys. 10, 588
(2014)], which studied the same system, in the complementary regime of weak
interactions. Based on extensive density matrix renormalization group
simulations and a bosonization analysis, we fully explore the parameter space
spanned by filling, inter-leg tunneling, and flux. As a main result, we
demonstrate the existence of gapless and gapped Meissner and vortex phases,
with the gapped states emerging in Mott-insulating regimes. We calculate
experimentally accessible observables such as chiral currents and vortex
patterns.


  We study open-world multi-label text classification under extremely weak
supervision (XWS), where the user only provides a brief description for
classification objectives without any labels or ground-truth label space.
Similar single-label XWS settings have been explored recently, however, these
methods cannot be easily adapted for multi-label. We observe that (1) most
documents have a dominant class covering the majority of content and (2)
long-tail labels would appear in some documents as a dominant class. Therefore,
we first utilize the user description to prompt a large language model (LLM)
for dominant keyphrases of a subset of raw documents, and then construct a
(initial) label space via clustering. We further apply a zero-shot multi-label
classifier to locate the documents with small top predicted scores, so we can
revisit their dominant keyphrases for more long-tail labels. We iterate this
process to discover a comprehensive label space and construct a multi-label
classifier as a novel method, X-MLClass. X-MLClass exhibits a remarkable
increase in ground-truth label space coverage on various datasets, for example,
a 40% improvement on the AAPD dataset over topic modeling and keyword
extraction methods. Moreover, X-MLClass achieves the best end-to-end
multi-label classification accuracy.


  Galactic centers are very dynamically active environments, often harbouring a
nuclear star cluster and supermassive black hole at their cores. Binaries in
these environments are subject to strong tidal fields that can efficiently
torque its orbit, exciting near unity eccentricities that ultimately lead to
their merger. In turn, frequent close interactions with passing stars
impulsively perturb the orbit of the binary, generally softening their orbit
until their evaporation, potentially hindering the role of tides to drive these
mergers. In this work, we study the evolution of compact object binaries in the
galactic center and their merger rates, focusing for the first time on the
combined effect of the cluster's tidal field and flyby interactions. We find a
significant synergy between both processes, where merger rates increase by a
factor of ~10-30 compared to models in which only flybys or tides are taken
into account. This synergy is a consequence of the persistent tides-driven
eccentricity excitation that is enhanced by the gradual diffusion of $j_z$
driven by flybys. The merger efficiency peaks when the diffusion rate is
~10-100 slower than the tides-driven torquing. Added to this synergy, we also
find that the gradual softening of the binary can lift the relativistic
quenching of initially tight binaries, otherwise unable to reach extreme
eccentricities, and thus expanding the available phase-space for mergers.
Cumulatively, we conclude that despite the gradual softening of binaries due to
flybys, these greatly enhance their merger rates in galaxy centers by promoting
the tidal field-driven eccentricity excitation.


  We consider a non-nestling random walk in a product random environment. We
assume an exponential moment for the step of the walk, uniformly in the
environment. We prove an invariance principle (functional central limit
theorem) under almost every environment for the centered and diffusively scaled
walk. The main point behind the invariance principle is that the quenched mean
of the walk behaves subdiffusively.


  We consider an extended theory of gravity with Lagrangian $\mathcal{L} =
f(R,{\bf T}^{(n)})$, with ${\bf T}^{(n)}$ being a $2n$-th order invariant made
of contractions of the energy-momentum tensor. When $n=1$ this theory reduces
to $f(R,T)$ gravity, where $T$ accounts for the trace of the energy-momentum
tensor. We study the gravitational wave polarization modes, from which it
results that when the matter Lagrangian contains dynamical scalar fields
minimally coupled to the geometry, further polarization modes arise with
respect to General Relativity. Finally we show that the motion for test
particles is non-geodesic and we explicitly obtain the extra-force.


  Large Language Models (LLMs) have created opportunities for designing
chatbots that can support complex question-answering (QA) scenarios and improve
news audience engagement. However, we still lack an understanding of what roles
journalists and readers deem fit for such a chatbot in newsrooms. To address
this gap, we first interviewed six journalists to understand how they answer
questions from readers currently and how they want to use a QA chatbot for this
purpose. To understand how readers want to interact with a QA chatbot, we then
conducted an online experiment (N=124) where we asked each participant to read
three news articles and ask questions to either the author(s) of the articles
or a chatbot. By combining results from the studies, we present alignments and
discrepancies between how journalists and readers want to use QA chatbots and
propose a framework for designing effective QA chatbots in newsrooms.


  In recent years, many molecular and environmental factors have been studied
to understand how synaptic plasticity is modulated. Sleep, as an evolutionary
conserved biological function, has shown to be a critical player for the
consolidation and filtering of synaptic circuitry underlying memory traces.
Although sleep disturbances do not alter normal memory consolidation, they may
reflect fundamental circuit malfunctions that can play a significant role in
exacerbating diseases, such as autism and Alzheimer's disease. Very recently,
scientists sought to answer part of this enigma and they identified p75
neurotrophic receptor (p75NTR) as a critical player in mediating impairments in
hippocampal-dependent associative plasticity upon sleep deprivation. This paper
will review the role of the p75NTR, critically discuss the impact and
implications of this research as the bridge for sleep research and neurological
diseases.


  The high-intensity, repetitive noise associated with functional magnetic
resonance imaging hinders on-line monitoring of subjects' speech and/or
recording speech signals suitable for off-line analysis. The proposed algorithm
enhances the speech signal by suppressing the scanner noise in the signal
recorded by a single-channel microphone. Significant increases in
signal-to-noise ratio are achieved using an adaptive filter that combines time
and frequency domain elements. In addition to providing a recording suitable
for speech analysis, such a real-time system provides an alternative means (to,
e.g., the "panic ball") for communication between the patient and the operator
during image acquisition.


  Multi-layer networks are networks on a set of entities (nodes) with multiple
types of relations (edges) among them where each type of relation/interaction
is represented as a network layer. As with single layer networks, community
detection is an important task in multi-layer networks. A large group of
popular community detection methods in networks are based on optimizing a
quality function known as the modularity score, which is a measure of presence
of modules or communities in networks. Hence a first step in community
detection is defining a suitable modularity score that is appropriate for the
network in question. Here we introduce several multi-layer network modularity
measures under different null models of the network, motivated by empirical
observations in networks from a diverse field of applications. In particular we
define the multi-layer configuration model, the multi-layer expected degree
model and their various modifications as null models for multi-layer networks
to derive different modularities. The proposed modularities are grouped into
two categories. The first category, which is based on degree corrected
multi-layer stochastic block model, has the multi-layer expected degree model
as their null model. The second category, which is based on multi-layer
extensions of Newman-Girvan modularity, has the multi-layer configuration model
as their null model. These measures are then optimized to detect the optimal
community assignment of nodes. We compare the effectiveness of the measures in
community detection in simulated networks and then apply them to four real
networks.


  Forecasting electricity demand plays a fundamental role in the operation and
planning procedures of power systems and the publications about electricity
demand forecasting increasing year by year. In this paper, we use Scientometric
analysis to analyze the current state and the emerging trends in the field of
electricity demand forecasting form 831 publications of web of science core
collection during 20 years: 1999-2018. Employing statistical description,
cooperative network analysis, keyword co-occurrence analysis, co-citation
analysis, cluster analysis, and emerging trend analysis techniques, this
article gives the most critical countries, institutions, journals, authors and
publications in this field, cooperative networks relationships, research
hotspots and emerging trends. The results of this article can provide
meaningful guidance and some insights for researchers to find out crucial
research, emerging trends and new developments in this area.


  Combining hydrodynamic planet-disk interaction simulations with dust
evolution models, we show that protoplanetary disks having a giant planet can
reveal diverse morphology in (sub-)millimeter continuum, including a full disk
without significant radial structure, a transition disk with an inner cavity, a
disk with a single gap and a central continuum peak, and a disk with multiple
rings and gaps. Such a diversity originates from (1) the level of viscous
transport in the disk which determines the number of gaps a planet can open;
(2) the size and spatial distributions of grains determined by the coagulation,
fragmentation, and radial drift, which in turn affects the emmisivity of the
disk at (sub-)millimeter wavelengths; and (3) the angular resolution used to
observe the disk. In particular, our results show that disks having the same
underlying gas distribution can have very different grain size/spatial
distributions and thus appearance in continuum, depending on the interplay
among coagulation, fragmentation, and radial drift. This suggests that proper
treatments for the grain growth have to be included in models of protoplanetary
disks concerning continuum properties and that complementary molecular line
observations are highly desired in addition to continuum observations to reveal
the true nature of disks. The fact that a single planet can produce diverse
disk morphology emphasizes the need to search for more direct, localized
signatures of planets in order to confirm (or dispute) the planetary origin of
observed ringed substructures.


  V1494 Aql was a very fast nova which reached a visual maximum of about 4.0 by
the end of 1999 December 3. We report observations from 4 to 284 days after
discovery, including sub-mm- and cm-band fluxes, a single MERLIN image, and
optical spectroscopy in the 410 to 700 nm range. The extent of the radio
continuum emission is consistent with a recent lower distance estimate of 1.6
kpc. We conclude that the optical and radio emission arises from the same
expanding ejecta. We show that these observations are not consistent with
simple kinematical spherical shell models used in the past to explain the rise
and fall of the radio flux density in these objects. The resolved remnant
structure is consistent with an inclined ring of enhanced density within the
ejecta. Optical spectroscopy indicates likely continued mass ejection for over
195 days, with the material becoming optically thin in the visible sometime
between 195 and 285 days after outburst.


  We demonstrate conclusively that there is no "Open Access Advantage" for
papers from the Astrophysical Journal. The two to one citation advantage
enjoyed by papers deposited in the arXiv e-print server is due entirely to the
nature and timing of the deposited papers. This may have implications for other
disciplines.


  For partial wetting, motion of the triple liquid-gas-solid contact line is
influenced by heterogeneities of the solid surface. This influence can be
strong in the case of inertial (e.g. oscillation) flows where the line can be
pinned or move intermittently. A model that takes into account both surface
defects and fluid inertia is proposed. The viscous dissipation in the bulk of
the fluid is assumed to be negligible as compared to the dissipation in the
vicinity of the contact line. The equations of motion and the boundary
condition at the contact line are derived from Hamilton's principle. The rapid
capillary rise along a vertical inhomogeneous wall is treated as an example.


  In this study, a novel approach is demonstrated for converting calorimeter
images from fast simulations to those akin to comprehensive full simulations,
utilizing conditional Generative Adversarial Networks (GANs). The concept of
pix2pix is tailored for CALPAGAN, where images from fast simulations serve as
the basis(condition) for generating outputs that closely resemble those from
detailed simulations. The findings indicate a strong correlation between the
generated images and those from full simulations, especially in terms of key
observables like jet transverse momentum distribution, jet mass, jet
subjettiness, and jet girth. Additionally, the paper explores the efficacy of
this method and its intrinsic limitations. This research marks a significant
step towards exploring more efficient simulation methodologies in High Energy
Particle Physics.


  In interacting galaxies, strong tidal forces disturb the global morphology of
the progenitors and give birth to the long stellar, gaseous and dusty tails
often observed. In addition to this destructive effect, tidal forces can morph
into a transient, protective setting called compressive mode. Such modes then
shelter the matter in their midst by increasing its gravitational binding
energy. This thesis focuses on the study of this poorly known regime by
quantifying its properties thanks to numerical and analytical tools applied to
a spectacular merging system of two galaxies, commonly known as the Antennae
galaxies. N-body simulations of this pair yield compressive modes in the
regions where observations reveal a burst of star formation. Furthermore,
characteristic time- and energy scales of these modes match well those of
self-gravitating substructures such as star clusters and tidal dwarf galaxies.
These results suggest that the compressive modes of tidal fields plays an
important role in the formation and evolution of young clusters, at least in a
statistical sense, over a lapse of ~10 million years. Preliminary results from
simulations of stellar associations highlight the importance of embedding the
clusters in the evolving background galaxies to account precisely for their
morphology and internal evolution.


  An infinitely divisible distribution on $\mathbb{R}$ is a probability measure
$\mu$ such that the characteristic function $\hat{\mu}$ has a
L\'{e}vy-Khintchine representation with characteristic triplet $(a,\gamma,
\nu)$, where $\nu$ is a L\'{e}vy measure, $\gamma\in\mathbb{R}$ and $a\ge 0$. A
natural extension of such distributions are quasi-infinitely distributions.
Instead of a L\'{e}vy measure, we assume that $\nu$ is a "signed L\'{e}vy
measure", for further information on the definition see [\ref{Lindner}]. We
show that a distribution $\mu=p\delta_{x_0}+(1-p)\mu_{ac}$ with $p>0$ and $x_0
\in \mathbb{R}$, where $\mu_{ac}$ is the absolutely continuous part, is
quasi-infinitely divisible if and only if $\hat{\mu}(z)\neq0$ for every
$z\in\mathbb{R}$. We apply this to show that certain variance mixtures of mean
zero normal distributions are quasi-infinitely divisible distributions, and we
give an example of a quasi-infinitely divisible distribution that is not
continuous but has infinite quasi-L\'{e}vy measure. Furthermore, it is shown
that replacing the signed L\'{e}vy measure by a seemingly more general complex
L\'{e}vy measure does not lead to new distributions. Last but not least it is
proven that the class of quasi-infinitely divisible distributions is not open,
but path-connected in the space of probability measures with the Prokhorov
metric.


  This paper presents a new approach to prevent transportation accidents and
monitor driver's behavior using a healthcare AI system that incorporates
fairness and ethics. Dangerous medical cases and unusual behavior of the driver
are detected. Fairness algorithm is approached in order to improve
decision-making and address ethical issues such as privacy issues, and to
consider challenges that appear in the wild within AI in healthcare and
driving. A healthcare professional will be alerted about any unusual activity,
and the driver's location when necessary, is provided in order to enable the
healthcare professional to immediately help to the unstable driver. Therefore,
using the healthcare AI system allows for accidents to be predicted and thus
prevented and lives may be saved based on the built-in AI system inside the
vehicle which interacts with the ER system.


  Video-language embeddings are a promising avenue for injecting semantics into
visual representations, but existing methods capture only short-term
associations between seconds-long video clips and their accompanying text. We
propose HierVL, a novel hierarchical video-language embedding that
simultaneously accounts for both long-term and short-term associations. As
training data, we take videos accompanied by timestamped text descriptions of
human actions, together with a high-level text summary of the activity
throughout the long video (as are available in Ego4D). We introduce a
hierarchical contrastive training objective that encourages text-visual
alignment at both the clip level and video level. While the clip-level
constraints use the step-by-step descriptions to capture what is happening in
that instant, the video-level constraints use the summary text to capture why
it is happening, i.e., the broader context for the activity and the intent of
the actor. Our hierarchical scheme yields a clip representation that
outperforms its single-level counterpart as well as a long-term video
representation that achieves SotA results on tasks requiring long-term video
modeling. HierVL successfully transfers to multiple challenging downstream
tasks (in EPIC-KITCHENS-100, Charades-Ego, HowTo100M) in both zero-shot and
fine-tuned settings.


  I review, from an observational perspective, the interactions of accretion
discs with magnetic fields in cataclysmic variable stars. I start with systems
where the accretion flows via a stream, and discuss the circumstances in which
the stream forms into an accretion disc, pointing to stars which are close to
this transition. I then turn to disc-fed systems and discuss what we know about
how material threads on to field lines, as deduced from the pattern of
accretion footprints on the white dwarf. I discuss whether distortions of the
field lines are related to accretion torques and the changing spin periods of
the white dwarfs. I also review the effect on the disc-magnetosphere
interaction of disc-instability outbursts. Lastly, I discuss the temporary,
dynamo-driven magnetospheres thought to occur in dwarf-nova outbursts, and
whether slow-moving waves are excited at the inner edges of the disc.


  Context. Galaxy clusters are the last and largest objects to form in the
standard hierarchical structure formation scenario through merging of smaller
systems. The substructure frequency in the past and present epoch provides
excellent means for studying the underlying cosmological model. Aims. Using
X-ray observations, we study the substructure frequency as a function of
redshift by quantifying and comparing the fraction of dynamically young
clusters at different redshifts up to z=1.08. We are especially interested in
possible biases due to the inconsistent data quality of the low-z and high-z
samples. Methods. Two well-studied morphology estimators, power ratio P3/P0 and
center shift w, were used to quantify the dynamical state of 129 galaxy
clusters, taking into account the different observational depth and noise
levels of the observations. Results. Owing to the sensitivity of P3/P0 to
Poisson noise, it is essential to use datasets with similar photon statistics
when studying the P3/P0-z relation. We degraded the high-quality data of the
low-redshift sample to the low data quality of the high-z observations and
found a shallow positive slope that is, however, not significant, indicating a
slightly larger fraction of dynamically young objects at higher redshift. The
w-z relation shows no significant dependence on the data quality and gives a
similar result. Conclusions. We find a similar trend for P3/P0 and w, namely a
very mild increase of the disturbed cluster fraction with increasing redshifts.
Within the significance limits, our findings are also consistent with no
evolution.


  We present a new mechanism for baryogenesis, which links the baryon asymmetry
of the universe to the dark matter density. The mechanism arises naturally in
the Pentagon model of TeV scale physics. In that context, it forces a
re-evaluation of some of the assumptions of the model, and we detail the
changes that are required in order to fit observations.


  The nature of fast magnetoacoustic and Alfv\'en waves is investigated in a
zero $\beta$ plasma in the neighbourhood of a pair of two-dimensional null
points. This gives an indication of wave propagation in the low $\beta$ solar
corona, for a more complicated magnetic configuration than that looked at by
McLaughlin & Hood (2004). It is found that the fast wave is attracted to the
null points and that the front of the wave slows down as it approaches the null
point pair, with the wave splitting and part of the wave accumulating at one
null and the rest at the other. Current density will then accumulate at these
points and ohmic dissipation will then extract the energy in the wave at these
points. This suggests locations where wave heating will occur in the corona.
The Alfv\'en wave behaves in a different manner in that the wave accumulates
along the separatrices. Hence, the current density will accumulate at this part
of the topology and this is where wave heating will occur. However, the
phenomenon of wave accumulation at a specific place is a feature of both wave
types, and illustrates the importance of studying the topology of the corona
when considering MHD wave propagation.


  Recently, Nadir and Occultation for Mars Discovery (NOMAD) ultraviolet and
visible spectrometer instrument on board the European Space Agency's ExoMars
Trace Gas Orbiter (TGO) simultaneously measured the limb emission intensities
for both [OI] 2972 and 5577 {\AA} (green) emissions in the dayside of Martian
upper atmosphere. We aim to explore the photochemistry of all these forbidden
atomic oxygen emissions ([OI] 2972, 5577, 6300, 6464 {\AA}) in the Martian
daylight upper atmosphere and suitable conditions for the simultaneous
detection of these emissions lines in the dayside visible spectra. A
photochemical model is developed to study the production and loss processes of
O(1S) and O(1D) by incorporating various chemical reactions of different
O-bearing species in the upper atmosphere of Mars. By reducing Fox (2004)
modelled neutral density profiles by a factor of 2, the calculated limb
intensity profiles for [OI] 5577 and 2972 {\AA} emissions are found to be
consistent with the NOMAD-TGO observations. In this case, at altitudes below
120 km, our modelled limb intensity for [OI] 6300 {\AA} emission is smaller by
a factor 2 to 5 compared to that of NOMAD-TGO observation for [OI] 2972 {\AA}
emission, and above this distance it is comparable with the upper limit of the
observation. We studied various parameters which can influence the limb
intensities of these atomic oxygen forbidden emission lines. Our calculated
limb intensity for [OI] 6300 {\AA} emission, when the Mars is at near
perihelion and for solar maximum condition, suggests that all these forbidden
emissions should be observable in the NOMAD-TGO visible spectra taken on the
dayside of Martian upper atmosphere. More simultaneous observations of
forbidden atomic oxygen emission lines will help to understand the
photochemical processes of oxygen-bearing species in the dayside Martian upper
atmosphere.


  The structure of real and virtual photons has been studied in ep collisions
at HERA using dijet production. Measurements of differential dijet cross
sections as function of the fraction of photon's momentum invested in the dijet
system are presented. The dependence of the cross sections on the virtuality of
the photon and mean transverse energy squared of the two leading jets has been
investigated. QCD calculations based on current parametrizations of the real
and virtual photon parton distribution functions have been compared to the
data.


  Gamma-ray bursts (GRBs) are tremendous explosions visible across most of the
Universe, certainly out to redshifts of z=4.5 and likely out to z~10. Recently,
GRBs have been found to have a roughly constant explosive energy as well as to
have two luminosity indicators (the spectral lag time and the variability) that
can be used to derive the burst's luminosity distance from the gamma-ray light
curve alone. There currently exists enough information to calibrate luminosity
distances and independent redshifts for nine bursts. From these, a GRB Hubble
diagram can be constructed, where the observed shape of the curve provides a
record of the expansion history of our Universe. The current nine burst diagram
is sparse, yet formal limits can be placed on the mass density of a flat
Universe. This first GRB Hubble diagram provides a proof of concept for a new
technique in cosmology at very high redshifts. With the launch of the SWIFT
satellite in 2003, we should get ~120 bursts to produce a Hubble diagram
impervious to all effects of dust extinction and out to redshifts impossible to
reach by any other method.


  This article consists of some loosely related remarks about the geometry of
G_2-structures on 7-manifolds and is partly based on old unpublished joint work
with two other people: F. Reese Harvey and Steven Altschuler. Much of this work
has since been subsumed in the work of Hitchin \cite{MR02m:53070} and Joyce
\cite{MR01k:53093}. I am making it available now mainly because of interest
expressed by others in seeing these results written up since they do not seem
to have all made it into the literature.
  A formula is derived for the scalar curvature and Ricci curvature of a
G_2-structure in terms of its torsion. When the fundamental 3-form of the
G_2-structure is closed, this formula implies, in particular, that the scalar
curvature of the underlying metric is nonpositive and vanishes if and only if
the structure is torsion-free. This version contains some new results on the
pinching of Ricci curvature for metrics associated to closed G_2-structures.
  Some formulae are derived for closed solutions of the Laplacian flow that
specify how various related quantities, such as the torsion and the metric,
evolve with the flow. These may be useful in studying convergence or long-time
existence for given initial data.


  We propose that dark matter is stable as a consequence of an accidental Z2
that results from a flavour-symmetry group which is the double-cover group of
the symmetry group of one of the regular geometric solids. Although
model-dependent, the phenomenology resembles that of a generic Higgs portal
dark matter scheme.


  We consider a theoretical problem of electron-electron scattering time in a
quasi-one-dimensional (Q1D) conductor in a magnetic field, perpendicular to its
conducting axis. We show that inverse electron-electron scattering time becomes
of the order of characteristic electron energy, $1/\tau \sim \epsilon \sim T$,
in a high magnetic field, directed far from the main crystallographic axes,
which indicates breakdown of the Fermi liquid theory. In a magnetic field,
directed close to one of the main crystallographic axis, inverse
electron-electron scattering time becomes much smaller than characteristic
electron energy and, thus, applicability of Fermi liquid theory restores. We
suggest that there exist crossovers between Fermi liquid and some non Fermi
liquid states in a strong enough tilted magnetic field. Application of our
results to the Q1D conductor (Per)$_2$Au(mnt)$_2$ shows that it has to be
possible to observe the above mentioned phenomenon in feasibly high magnetic
fields of the order of $H \geq H^* \simeq 25 \ T$.


  The research of metamaterials has achieved enormous success in the
manipulation of light in an artificially prescribed manner using delicately
designed sub-wavelength structures, so-called meta-atoms. Even though modern
numerical methods allow to accurately calculate the optical response of complex
structures, the inverse design of metamaterials is still a challenging task due
to the non-intuitive and non-unique relationship between physical structures
and optical responses. To better unveil this implicit relationship and thus
facilitate metamaterial design, we propose to represent metamaterials and model
the inverse design problem in a probabilistically generative manner. By
employing an encoder-decoder configuration, our deep generative model
compresses the meta-atom design and optical response into a latent space, where
similar designs and similar optical responses are automatically clustered
together. Therefore, by sampling in the latent space, the stochastic latent
variables function as codes, from which the candidate designs are generated
upon given requirements in a decoding process. With the effective latent
representation of metamaterials, we can elegantly model the complex
structure-performance relationship in an interpretable way, and solve the
one-to-many mapping issue that is intractable in a deterministic model.
Moreover, to alleviate the burden of numerical calculation in data collection,
we develop a semi-supervised learning strategy that allows our model to utilize
unlabeled data in addition to labeled data during training, simultaneously
optimizing the generative inverse design and deterministic forward prediction
in an end-to-end manner. On a data-driven basis, the proposed model can serve
as a comprehensive and efficient tool that accelerates the design,
characterization and even new discovery in the research domain of metamaterials
and photonics in general.


  We present the next-to-next-to-leading order (NNLO) contributions to the main
splitting functions for the evolution of longitudinally polarized parton
densities of hadrons in perturbative QCD. The quark-quark and gluon-quark
splitting functions have been obtained by extending our previous all Mellin-N
calculations to the structure function g_1 in electromagnetic deep-inelastic
scattering (DIS). Their quark-gluon and gluon-gluon counterparts have been
derived using third-order fixed-N calculations of structure functions in
graviton-exchange DIS, relations to the unpolarized case and mathematical tools
for systems of Diophantine equations. The NNLO corrections to the splitting
functions are small outside the region of small momentum fractions x where they
exhibit a large double-logarithmic enhancement, yet the corrections to the
evolution of the parton densities can be unproblematic down to at least x about
10^{-4}.


  The resolution and contrast of microscope imaging is often affected by
aberrations introduced by imperfect optical systems and inhomogeneous
refractive structures in specimens. Adaptive optics (AO) compensates these
aberrations and restores diffraction limited performance. A wide range of AO
solutions have been introduced, often tailored to a specific microscope type or
application. Until now, a universal AO solution -- one that can be readily
transferred between microscope modalities -- has not been deployed. We propose
versatile and fast aberration correction using a physics-based machine learning
(ML) assisted wavefront-sensorless AO control method. Unlike previous ML
methods, we used a bespoke neural network (NN) architecture, designed using
physical understanding of image formation, that was embedded in the control
loop of the microscope. The approach means that not only is the resulting NN
orders of magnitude simpler than previous NN methods, but the concept is
translatable across microscope modalities. We demonstrated the method on a
two-photon, a three-photon and a widefield three-dimensional (3D) structured
illumination microscope. Results showed that the method outperformed
commonly-used modal-based sensorless AO methods. We also showed that our
ML-based method was robust in a range of challenging imaging conditions, such
as extended 3D sample structures, specimen motion, low signal to noise ratio
and activity-induced fluorescence fluctuations. Moreover, as the bespoke
architecture encapsulated physical understanding of the imaging process, the
internal NN configuration was no-longer a ``black box'', but provided physical
insights on internal workings, which could influence future designs.


  We examine two positions, ON1 and ON2, within the Ophiuchus cloud LDN 1688
using observations made with the ISOPHOT instrument aboard the ISO satellite.
The data include mid-IR spectra (~6-12{\mu}m) and several photometric bands up
to 200{\mu}m. The data probe the emission from molecular PAH-type species,
transiently-heated Very Small Grains (VSGs), and large classical dust grains.
We compare the observations to earlier studies, especially those carried out
towards an isolated translucent cloud in Chamaeleon (Paper I). The spectra
towards the two LDN 1688 positions are very similar to each other, in spite of
position ON1 having a larger column density and probably being subjected to a
stronger radiation field. The ratios of the mid-IR features are similar to
those found in other diffuse and translucent clouds. Compared to paper I, the
7.7/11.3{\mu}m band ratios are lower, ~2.0, at both LDN 1688 positions. A
continuum is detected in the ~10{\mu}m region. This is stronger towards the
position ON1 but still lower than on any of the sightlines in Paper I. The
far-infrared opacities are higher than for diffuse medium. The value of the
position ON2, {\tau}200/N(H) = 3.9 x 10^{-25} cm^2/H, is twice the value found
for ON1. The radiation field of LDN 1688 is dominated by the two embedded B
type double stars, {\rho} Oph AB and HD 147889, with an additional contribution
from the Upper Sco OB association. The strong heating is reflected in the high
colour temperature, ~24 K, of the large grain emission. Radiative transfer
modelling confirms a high level of the radiation field and points to an
increased abundance of PAH grains. However, when the hardening of the radiation
field caused by the local B-stars is taken into account, the observations can
be fitted with almost no change to the standard dust models. However, all the
examined models underestimate the level of the mid-IR continuum.


  We propose a new method to apply the Lipschitz functional calculus of local
Dirichlet forms to Poisson random measures.


  This paper introduces a cooperative sensing framework designed for integrated
sensing and communication cellular networks. The framework comprises one base
station (BS) functioning as the sensing transmitter, while several nearby BSs
act as sensing receivers. The primary objective is to facilitate cooperative
target localization by enabling each receiver to share specific information
with a fusion center (FC) over a limited capacity backhaul link. To achieve
this goal, we propose an advanced cooperative sensing design that enhances the
communication process between the receivers and the FC. Each receiver
independently estimates the time delay and the reflecting coefficient
associated with the reflected path from the target. Subsequently, each receiver
transmits the estimated values and the received signal samples centered around
the estimated time delay to the FC. To efficiently quantize the signal samples,
a Karhunen-Lo\`eve Transform coding scheme is employed. Furthermore, an
optimization problem is formulated to allocate backhaul resources for
quantizing different samples, improving target localization. Numerical results
validate the effectiveness of our proposed advanced design and demonstrate its
superiority over a baseline design, where only the locally estimated values are
transmitted from each receiver to the FC.


  The nonbaryonic dark matter of the Universe is assumed to consist of new
stable particles. Stable particle candidates for cosmological dark matter are
usually considered as neutral and weakly interacting. However stable charged
leptons and quarks can also exist hidden in elusive dark atoms and can play a
role of dark matter. Such possibility is strongly restricted by the constraints
on anomalous isotopes of light elements that form positively charged heavy
species with ordinary electrons. This problem might be avoided, if stable
particles with charge -2 exist and there are no stable particles with charges
+1 and -1. These conditions cannot be realized in supersymmetric models, but
can be satisfied in several alternative scenarios, which are discussed in this
paper. The excessive -2 charged particles are bound with primordial helium in
O-helium atoms, maintaining specific nuclear-interacting form of the dark
matter. O-helium dark matter can provide solution for the puzzles of dark
matter searches. The successful development of composite dark matter scenarios
appeals to experimental search for doubly charged constituents of dark atoms.
Estimates of production cross section of such particles at LHC are presented
and discussed. Signatures of double charged particles in the ATLAS experiment
are outlined.


  We address the problem of reposing an image of a human into any desired novel
pose. This conditional image-generation task requires reasoning about the 3D
structure of the human, including self-occluded body parts. Most prior works
are either based on 2D representations or require fitting and manipulating an
explicit 3D body mesh. Based on the recent success in deep learning-based
volumetric representations, we propose to implicitly learn a dense feature
volume from human images, which lends itself to simple and intuitive
manipulation through explicit geometric warping. Once the latent feature volume
is warped according to the desired pose change, the volume is mapped back to
RGB space by a convolutional decoder. Our state-of-the-art results on the
DeepFashion and the iPER benchmarks indicate that dense volumetric human
representations are worth investigating in more detail.


  Recently a number of CNN-based techniques were proposed to remove image
compression artifacts. As in other restoration applications, these techniques
all learn a mapping from decompressed patches to the original counterparts
under the ubiquitous $\ell_\infty$ metric. However, this approach is incapable
of restoring distinctive image details which may be statistical outliers but
have high semantic importance (e.g., tiny lesions in medical images). To
overcome this weakness, we propose to incorporate an $\ell_\infty$ fidelity
criterion in the design of neural network so that no small, distinctive
structures of the original image can be dropped or distorted. Experimental
results demonstrate that the proposed method outperforms the state-of-the-art
methods in $\ell_\infty$ error metric and perceptual quality, while being
competitive in $\ell_2$ error metric as well. It can restore subtle image
details that are otherwise destroyed or missed by other algorithms. Our
research suggests a new machine learning paradigm of ultra high fidelity image
compression that is ideally suited for applications in medicine, space, and
sciences.


  We study dilute suspensions of magnetic nanoparticles in a nematic host, on
two-dimensional (2D) polygons. These systems are described by a nematic order
parameter and a spontaneous magnetization, in the absence of any external
fields. We study the stable states in terms of stable critical points of an
appropriately defined free energy, with a nemato-magnetic coupling energy. We
numerically study the interplay between the shape of the regular polygon, the
size of the polygon and the strength of the nemato-magnetic coupling for the
multistability of this prototype system. Our notable results include (i) the
co-existence of stable states with domain walls and stable interior and
boundary defects, (ii) the suppression of multistability for positive
nemato-magnetic coupling, and (iii) the enhancement of multistability for
negative nemato-magnetic coupling.


  We use new large area far infrared maps ranging from 65 - 500 microns
obtained with the AKARI and the Balloon-borne Large Aperture Submillimeter
Telescope (BLAST) missions to characterize the dust emission toward the
Cassiopeia A supernova remnant (SNR). Using the AKARI high resolution data we
find a new "tepid" dust grain population at a temperature of ~35K and with an
estimated mass of 0.06 solar masses. This component is confined to the central
area of the SNR and may represent newly-formed dust in the unshocked supernova
ejecta. While the mass of tepid dust that we measure is insufficient by itself
to account for the dust observed at high redshift, it does constitute an
additional dust population to contribute to those previously reported. We fit
our maps at 65, 90, 140, 250, 350, and 500 microns to obtain maps of the column
density and temperature of "cold" dust (near 16 K) distributed throughout the
region. The large column density of cold dust associated with clouds seen in
molecular emission extends continuously from the surrounding interstellar
medium to project on the SNR, where the foreground component of the clouds is
also detectable through optical, X-ray, and molecular extinction. At the
resolution available here, there is no morphological signature to isolate any
cold dust associated only with the SNR from this confusing interstellar
emission. Our fit also recovers the previously detected "hot" dust in the
remnant, with characteristic temperature 100 K.


  Networked Control Systems (NCSs) are often associated with problems like
random data losses which might lead to system instability. This paper proposes
a method based on the use of variable controller gains to achieve maximum
parametric robustness of the plant controlled over a network. Stability using
variable controller gains under data loss conditions is analyzed using a
suitable Linear Matrix Inequality (LMI) formulation. Also, a Particle Swarm
Optimization (PSO) based technique is used to maximize parametric robustness of
the plant.


  We investigate stau-antistau annihilation into heavy quarks in the
phenomenological Minimal Supersymmetric Standard Model within the DM@NLO
project. We present the calculation of the corresponding cross section
including corrections up to $\mathcal{O}(\alpha_s)$ and QED Sommerfeld
enhancement. The numerical impact of these corrections is discussed for the
cross section and the dark matter relic density, where we focus on top-quark
final states and consider either neutralino or gravitino dark matter. Similarly
to previous work, we find that the presented corrections should be included
when calculating the relic density or extracting parameters from cosmological
observations. Considering scheme and scale variations, we estimate the
theoretical uncertainty that affects the prediction of the annihilation cross
section and thus the prediction of the relic density.


  The recently developed Nambu-Jona-Lasinio (NJL) - Jet model is used as an
effective chiral quark theory to calculate the quark fragmentation functions to
pions, kaons, nucleons, and antinucleons. The effects of the vector mesons rho,
K* and phi on the production of secondary pions and kaons are included. The
fragmentation processes to nucleons and antinucleons are described by using the
quark-diquark picture, which has been shown to give a reasonable description of
quark distribution functions. We incorporate effects of next-to-leading order
(NLO) in the Q^2 evolution, and compare our results with the empirical
fragmentation functions.


  The concept of an intrinsic system can be extended to the case of collective
octupole degrees of freedom by exploiting the symmetry properties with respect
to transformations of the octahedral group O_h. Explicit formulas for scalar
invariants as polynomials of intrinsic variables are presented. A method of
constructing a basis in the space of functions on the octupole intrinsic space
is proposed.


  High-dimensional parabolic partial integro-differential equations (PIDEs)
appear in many applications in insurance and finance. Existing numerical
methods suffer from the curse of dimensionality or provide solutions only for a
given space-time point. This gave rise to a growing literature on deep learning
based methods for solving partial differential equations; results for
integro-differential equations on the other hand are scarce. In this paper we
consider an extension of the deep splitting scheme due to Beck, Becker,
Cheridito, Jentzen, Neufeld (2021) and Germain, Pham, Warin (2022) to PIDEs.
Our main contribution is a convergence analysis of the scheme. Moreover we
discuss several test case studies to show the viability of our approach.


  We describe a wavefunction engineering approach to the formation of textures
in a two-component nonrotated Bose-Einstein condensate. By controlling the
phases of wavepackets that combine in a three-wave interference process, a
ballistically-expanding regular lattice-texture is generated, in which the
phases determine the component textures. A particular example is presented of a
lattice-texture composed of half-quantum vortices and spin-2 textures. We
demonstrate the lattice formation with numerical simulations of a viable
experiment, identifying the textures and relating their locations to a linear
theory of wavepacket interference.


  Inference for functional linear models in the presence of heteroscedastic
errors has received insufficient attention given its practical importance; in
fact, even a central limit theorem has not been studied in this case. At issue,
conditional mean estimates have complicated sampling distributions due to the
infinite dimensional regressors, where truncation bias and scaling issues are
compounded by non-constant variance under heteroscedasticity. As a foundation
for distributional inference, we establish a central limit theorem for the
estimated conditional mean under general dependent errors, and subsequently we
develop a paired bootstrap method to provide better approximations of sampling
distributions. The proposed paired bootstrap does not follow the standard
bootstrap algorithm for finite dimensional regressors, as this version fails
outside of a narrow window for implementation with functional regressors. The
reason owes to a bias with functional regressors in a naive bootstrap
construction. Our bootstrap proposal incorporates debiasing and thereby attains
much broader validity and flexibility with truncation parameters for inference
under heteroscedasticity; even when the naive approach may be valid, the
proposed bootstrap method performs better numerically. The bootstrap is applied
to construct confidence intervals for centered projections and for conducting
hypothesis tests for the multiple conditional means. Our theoretical results on
bootstrap consistency are demonstrated through simulation studies and also
illustrated with a real data example.


  SDN and NFV have recently changed the way we operate networks. By decoupling
control and data plane operations and virtualising their components, they have
opened up new frontiers towards reducing network ownership costs and improving
usability and efficiency. Recently, their applicability has moved towards
public telecommunications networks, with concepts such as the cloud-CO that
have pioneered its use in access and metro networks: an idea that has quickly
attracted the interest of network operators. By merging mobile, residential and
enterprise services into a common framework, built around commoditised data
centre types of architectures, future embodiments of this CO virtualisation
concept could achieve significant capital and operational cost savings, while
providing customised network experience to high-capacity and low-latency future
applications.
  This tutorial provides an overview of the various frameworks and
architectures outlining current network disaggregation trends that are leading
to the virtualisation/cloudification of central offices. It also provides
insight on the virtualisation of the access-metro network, showcasing new
software functionalities like the virtual \ac{DBA} mechanisms for \acp{PON}. In
addition, we explore how it can bring together different network technologies
to enable convergence of mobile and optical access networks and pave the way
for the integration of disaggregated ROADM networks. Finally, this paper
discusses some of the open challenges towards the realisation of networks
capable of delivering guaranteed performance, while sharing resources across
multiple operators and services.


  In a space of $d $ Grassmann coordinates two types of generators of Lorentz
transformations can be defined, one of spinorial and the other of vectorial
character. Both kinds of operators appear as linear operators in Grassmann
space, definig the fundamental and the adjoint representations of the group $
SO(1,d$$-$$1) $, respectively. The eigenvalues of commuting operators belonging
to the subgroup $(SO(1,4)) $ can be identified with spins of either fermionic
or bosonic fields, while the operators belonging to subgroups of $ SO(d$$-$$5)
$ ${\supset SU(3)}$ $ {\times SU(2)}$ $ {\times U(1)} $, determine the
Yang-Mills charges. The theory offers unification of all the internal degrees
of freedom of fermionic and bosonic fields - spins and all Yang-Mills charges.
When accordingly all interactions are unified, Yang-Mills fields appear as part
of the gravitational field. The theory suggests that elementary particles are
either in the fundamental representations with respect to the groups
determining the spin and the charges, or they are in the adjoint
representations with respect to the groups, which determine the spin and the
charges.


  The availability and easy access of large scale experimental and
computational materials data have enabled the emergence of accelerated
development of algorithms and models for materials property prediction,
structure prediction, and generative design of materials. However, lack of
user-friendly materials informatics web servers has severely constrained the
wide adoption of such tools in the daily practice of materials screening,
tinkering, and design space exploration by materials scientists. Herein we
first survey current materials informatics web apps and then propose and
develop MaterialsAtlas.org, a web based materials informatics toolbox for
materials discovery, which includes a variety of routinely needed tools for
exploratory materials discovery, including materials composition and structure
check (e.g. for neutrality, electronegativity balance, dynamic stability,
Pauling rules), materials property prediction (e.g. band gap, elastic moduli,
hardness, thermal conductivity), and search for hypothetical materials. These
user-friendly tools can be freely accessed at \url{www.materialsatlas.org}. We
argue that such materials informatics apps should be widely developed by the
community to speed up the materials discovery processes.


  We construct an N=1 supersymmetric three-family flipped SU(5) model from type
IIA orientifolds on $T^6/(\Z_2\times \Z_2)$ with D6-branes intersecting at
general angles. The model is constrained by the requirement that Ramond-Ramond
tadpoles cancel, the supersymmetry conditions, and that the gauge boson coupled
to the $U(1)_X$ factor does not get a string-scale mass via a generalised
Green-Schwarz mechanism. The model is further constrained by requiring
cancellation of K-theory charges. The spectrum contains a complete grand
unified and electroweak Higgs sector, however the latter in a non-minimal
number of copies. In addition, it contains extra matter both in bi-fundamental
and vector-like representations as well as two copies of matter in the
symmetric representation of SU(5).


  Discoveries of terrestrial, Earth-sized exoplanets that lie within the
habitable zone (HZ) of their host stars continue to occur at increasing rates.
Transit spectroscopy can potentially enable the detection of molecular
signatures from such worlds, providing an indication of the presence of an
atmosphere and its chemical composition, including gases potentially indicative
of a biosphere. Such planets around nearby M-dwarf stars - such as TRAPPIST-1 -
provide relatively good signal, high signal/noise, and frequent transits for
follow-up spectroscopy. However, even with these advantages, transit
spectroscopy of terrestrial planets in the HZ of nearby M-stars will still be a
challenge. Herein, we examine the potential for future space observatories to
conduct such observations, using a Global Climate Model (GCM), a photochemical
model, and a radiative transfer suite to simulate modern-Earth-like atmospheric
boundary conditions on TRAPPIST-1e. The detectability of biosignatures on such
an atmosphere via transmission spectroscopy is modeled for JWST, LUVOIR, HabEx,
and Origins. We show that for any of these observatories, only CO2 would be
detectable at the 3 sigma level in transmission spectroscopy, when clouds are
included in our simulations. This is because the impacts of clouds on scale
height strongly limits the detectability of molecules in the atmosphere.
Synergies between space- and ground-based spectroscopy may be essential in
order to overcome these difficulties.


  The development of sensory receptive fields has been modeled in the past by a
variety of models including normative models such as sparse coding or
independent component analysis and bottom-up models such as spike-timing
dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic
plasticity. Here we show that the above variety of approaches can all be
unified into a single common principle, namely Nonlinear Hebbian Learning. When
Nonlinear Hebbian Learning is applied to natural images, receptive field shapes
were strongly constrained by the input statistics and preprocessing, but
exhibited only modest variation across different choices of nonlinearities in
neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse
network activity are necessary for the development of localized receptive
fields. The analysis of alternative sensory modalities such as auditory models
or V2 development lead to the same conclusions. In all examples, receptive
fields can be predicted a priori by reformulating an abstract model as
nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural
statistics can account for many aspects of receptive field formation across
models and sensory modalities.


  Acoustic wave modulation plays a pivotal role in various applications,
including sound-field reconstruction, wireless communication, and particle
manipulation, among others. However, current acoustic metamaterial and
metasurface designs typically focus on controlling either reflection or
transmission waves, often overlooking the coupling between amplitude and phase
of acoustic waves. To fulfill this gap, we propose and experimentally validate
a design enabling complete control of reflected and transmitted acoustic waves
individually across a frequency range of 4 kHz to 8 kHz, allowing arbitrary
combinations of amplitude and phase for reflected and transmitted sound in a
broadband manner. Additionally, we demonstrate the significance of our approach
for sound manipulation by achieving acoustic diffusion, reflection, focusing,
and generating a two-sided 3D hologram at three distinct frequencies. These
findings open an alternative avenue for extensively engineering sound waves,
promising applications in acoustics and related fields.


  The availability of powerful microprocessors and high-speed networks as
commodity components has enabled high performance computing on distributed
systems (wide-area cluster computing). In this environment, as the resources
are usually distributed geographically at various levels (department,
enterprise, or worldwide) there is a great challenge in integrating,
coordinating and presenting them as a single resource to the user; thus forming
a computational grid. Another challenge comes from the distributed ownership of
resources with each resource having its own access policy, cost, and mechanism.
  The proposed Nimrod/G grid-enabled resource management and scheduling system
builds on our earlier work on Nimrod and follows a modular and component-based
architecture enabling extensibility, portability, ease of development, and
interoperability of independently developed components. It uses the Globus
toolkit services and can be easily extended to operate with any other emerging
grid middleware services. It focuses on the management and scheduling of
computations over dynamic resources scattered geographically across the
Internet at department, enterprise, or global level with particular emphasis on
developing scheduling schemes based on the concept of computational economy for
a real test bed, namely, the Globus testbed (GUSTO).


  A key drawback to using a Open Source third-party library is the risk of
introducing malicious attacks. In recently times, these threats have taken a
new form, when maintainers turn their Open Source libraries into protestware.
This is defined as software containing political messages delivered through
these libraries, which can either be malicious or benign. Since developers are
willing to freely open-up their software to these libraries, much trust and
responsibility are placed on the maintainers to ensure that the library does
what it promises to do. Using different frameworks commonly used in AI ethics,
we illustrate how an open-source maintainer's decision to protest is influenced
by different stakeholders (viz., their membership in the OSS community, their
personal views, financial motivations, social status, and moral viewpoints),
making protestware a multifaceted and intricate matter.


  Within the Friedmann-Lema\^itre-Robertson-Walker (FLRW) framework, the Hubble
constant $H_0$ is an integration constant. Thus, consistency of the model
demands observational constancy of $H_0$. We demonstrate redshift evolution of
best fit $\Lambda$CDM parameters $(H_0, \Omega_{m})$ in Pantheon+ supernove
(SNe). Redshift evolution of best fit cosmological parameters is a prerequisite
to finding a statistically significant evolution as well as identifying
alternative models that are competitive with $\Lambda$CDM in a Bayesian model
comparison. To assess statistical significance, we employ three different
methods: i) Bayesian model comparison, ii) mock simulations and iii) profile
distributions. The first shows a marginal preference for the vanilla
$\Lambda$CDM model over an ad hoc model with 3 additional parameters and an
unphysical jump in cosmological parameters at $z=1$. From mock simulations, we
estimate the statistical significance of redshift evolution of best fit
parameters and negative dark energy density ($\Omega_m > 1$) to be in the $1-2
\sigma$ range, depending on the criteria employed. Importantly, in direct
comparison to the same analysis with the earlier Pantheon sample we find that
statistical significance of redshift evolution of best fit parameters has
increased, as expected for a physical effect. Our profile distribution analysis
demonstrates a shift in $(H_0, \Omega_m)$ in excess of $95\%$ confidence level
for SNe with redshifts $z > 1$ and also shows that a degeneracy in MCMC
posteriors is not equivalent to a curve of constant $\chi^2$. Our findings can
be interpreted as a statistical fluctuation or unexplored systematics in
Pantheon+ or $\Lambda$CDM model breakdown. The first two possibilities are
disfavoured by similar trends in independent probes.


  Double-diffusive convection in porous media is a common phenomenon in nature,
and has received considerable attention in a wide variety of engineering
applications. In this paper, a multiple-relaxation-time (MRT) lattice Boltzmann
(LB) model is developed for simulating double-diffusive convection in porous
media at the representative elementary volume scale. The MRT-LB model is
constructed in the framework of the triple-distribution-function approach: the
velocity field, the temperature and concentration fields are solved separately
by three different MRT-LB equations. The present model has two distinctive
features. First, the equilibrium moments of the temperature and concentration
distributions have been modified, which makes the effective thermal diffusivity
and heat capacity ratio as well as the effective mass diffusivity and porosity
decoupled . This feature is very useful in practical applications. Second,
source terms have been added into the MRT-LB equations of the temperature and
concentration fields so as to recover the macroscopic temperature and
concentration equations. Numerical tests demonstrate that the present model can
serve as an accurate and efficient numerical method for simulating
double-diffusive convection in porous media.


  This paper points out that the differential quadrature (DQ) and differential
cubature (DC) methods due to their global domain property are more efficient
for nonlinear problems than the traditional numerical techniques such as finite
element and finite difference methods. By introducing the Hadamard product of
matrices, we obtain an explicit matrix formulation for the DQ and DC solutions
of nonlinear differential and integro-differential equations. Due to its
simplicity and flexibility, the present Hadamard product approach makes the DQ
and DC methods much easier to be used. Many studies on the Hadamard product can
be fully exploited for the DQ and DC nonlinear computations. Furthermore, we
first present SJT product of matrix and vector to compute accurately and
efficiently the Frechet derivative matrix in the Newton-Raphson method for the
solution of the nonlinear formulations. We also propose a simple approach to
simplify the DQ or DC formulations for some nonlinear differential operators
and thus the computational efficiency of these methods is improved
significantly. We give the matrix multiplication formulas to compute
efficiently the weighting coefficient matrices of the DC method. The spherical
harmonics are suggested as the test functions in the DC method to handle the
nonlinear differential equations occurring in global and hemispheric weather
forecasting problems. Some examples are analyzed to demonstrate the simplicity
and efficiency of the presented techniques. It is emphasized that innovations
presented are applicable to the nonlinear computations of the other numerical
methods as well.


  Recent observations by the Large Area Telescope (LAT) onboard the Fermi
satellite have revealed bright gamma-ray emission from middle-aged supernova
remnants (SNRs) inside our Galaxy. These remnants, which also possess bright
non-thermal radio shells, are often found to be interacting directly with
surrounding gas clouds. We explore the non-thermal emission mechanism at these
dynamically evolved SNRs by constructing a hydrodynamical model. Two scenarios
of particle acceleration, either a re-acceleration of Galactic cosmic rays
(CRs) or an efficient nonlinear diffusive shock acceleration (NLDSA) of
particles injected from downstream, are considered. Using parameters inferred
from observations, our models are contrasted with the observed spectra of SNR
W44. For the re-acceleration case, we predict a significant enhancement of
radio and GeV emission as the SNR undergoes a transition into the radiative
phase. If sufficiently strong magnetic turbulence is present in the molecular
cloud, the re-acceleration scenario can explain the observed broadband spectral
properties. The NLDSA scenario also succeeds in explaining the $\gamma$-ray
spectrum but fails to reproduce the radio spectral index. Efficient NLDSA also
results in a significant post-shock non-thermal pressure that limits the
compression during cooling and prevents the formation of a prominent dense
shell. Some other interesting differences between the two models in
hydrodynamical behavior and resulting spectral features are illustrated.


  The stability of large-scale magnetic fields in rotating stars is explored,
using 3D numerical hydrodynamics to follow the evolution of an initial poloidal
field. It is found that the field is subject to an instability, located
initially on the magnetic equator, whereby the gas is displaced in a direction
parallel to the magnetic axis. If the magnetic axis is parallel to the rotation
axis, the rotation does not affect the initial linear growth of the
instability, but does restrict the growth of the instability outside of the
equatorial zone. The magnetic energy decays on a timescale which is a function
of the Alfv\'en crossing time and the rotation speed, but short compared to any
evolutionary timescale. No evidence is found for a possible stable end state to
evolve from an initial axisymmetric poloidal field. The field of an oblique
rotator is similarly unstable, in both cases regardless of the rotation speed.


  Photo-acoustic Tomography (PAT) and Thermo-acoustic Tomography (TAT) are
medical imaging modalities that combine the high contrast of radiative
properties of tissues with the high resolution of ultrasound. In both
modalities, a first step concerns the reconstruction of the radiation-induced
source of ultrasound. Transient Elastography (TE) and Magnetic Resonance
Elastography (MRE) combine the high elastic contrast of tissues with the high
resolution of ultrasound and magnetic resonance, respectively. In both
modalities, a first step concerns the reconstruction of the elastic
displacement.
  The result of this first step, which is not considered in this paper, is the
availability of internal functionals of the unknown tissue properties. All
imaging modalities are recast as the reconstruction of parameters in elliptic
equations from knowledge of solutions to such equations. This paper provides a
characterization of the parameters that may or may not be reconstructed from
such internal functionals. We provide explicit reconstruction procedures and
indicate how stable they are with respect to errors in the available
measurements.
  The modalities PAT, TAT, TE, and MRE allow us to reconstruct high-contrast
optical and elastic properties of tissues with the high resolution of
ultrasound or magnetic resonance imaging. They provide a means to reconstruct
second-order tensors modeling tissue anisotropy as well as complex-valued
coefficients modeling absorbing and dissipative effects.


  Photoacoustic computed tomography (PACT) is a rapidly emerging bioimaging
modality that seeks to reconstruct an estimate of the absorbed optical energy
density within an object. Conventional PACT image reconstruction methods assume
a constant speed-of-sound (SOS), which can result in image artifacts when
acoustic aberrations are significant. It has been demonstrated that
incorporating knowledge of an object's SOS distribution into a PACT image
reconstruction method can improve image quality. However, in many cases, the
SOS distribution cannot be accurately and/or conveniently estimated prior to
the PACT experiment. Because variations in the SOS distribution induce
aberrations in the measured photoacoustic wavefields, certain information
regarding an object's SOS distribution is encoded in the PACT measurement data.
Based on this observation, a joint reconstruction (JR) problem has been
proposed in which the SOS distribution is concurrently estimated along with the
sought-after absorbed optical energy density from the photoacoustic measurement
data. A broad understanding of the extent to which the JR problem can be
accurately and reliably solved has not been reported. In this work, a series of
numerical experiments is described that elucidate some important properties of
the JR problem that pertain to its practical feasibility. To accomplish this,
an optimization-based formulation of the JR problem is developed that yields a
non-linear iterative algorithm that alternatingly updates the two image
estimates. Heuristic analytic insights into the reconstruction problem are also
provided. These results confirm the ill-conditioned nature of the joint
reconstruction problem that will present significant challenges for practical
applications.


  Entanglement entropy plays a variety of roles in quantum field theory,
including the connections between quantum states and gravitation through the
holographic principle. This article provides a review of entanglement entropy
from a mixed viewpoint of field theory and holography. A set of basic methods
for the computation is developed and illustrated with simple examples such as
free theories and conformal field theories. The structures of the ultraviolet
divergences and the universal parts are determined and compared with the
holographic descriptions of entanglement entropy. The utility of quantum
inequalities of entanglement are discussed and shown to derive the C-theorem
that constrains renormalization group flows of quantum field theories in
diverse dimensions.


  Using the Expanded Very Large Array, we have mapped the vicinity of the Orion
BN/KL Hot Core with sub-arcsecond angular resolution in seven metastable
inversion transitions of ammonia: (J,K)=(6,6) to (12,12). This emission comes
from levels up to 1500 K above the ground state, enabling identification of
source(s) responsible for heating the region. We used this multi-transition
dataset to produce images of the rotational/kinetic temperature and the column
density of ammonia for ortho and para species separately and on a
position-by-position basis. We find rotational temperature and column density
in the range 160-490 K and (1-4)x10^17 cm^-2, respectively. Our
spatially-resolved images show that the highest (column) density and hottest
gas is found in a northeast-southwest elongated ridge to the southeast of
Source I. We have also measured the ortho-para ratio of ammonia, estimated to
vary in the range 0.9-1.6. Enhancement of ortho with respect to para and the
offset of hot ammonia emission peaks from known (proto)stellar sources provide
evidence that the ammonia molecules have been released from dust grains into
the gas-phase through the passage of shocks and not by stellar radiation. We
propose that the combined effect of Source I's proper motion and its
low-velocity outflow impinging on a pre-existing dense medium is responsible
for the excitation of ammonia and the Orion Hot Core. Finally, we found for the
first time evidence of a slow (5 km/s) and compact (1000 AU) outflow towards
IRc7.


  Diffusion preserves the positivity of concentrations, therefore,
multicomponent diffusion should be nonlinear if there exist non-diagonal terms.
The vast variety of nonlinear multicomponent diffusion equations should be
ordered and special tools are needed to provide the systematic construction of
the nonlinear diffusion equations for multicomponent mixtures with significant
interaction between components. We develop an approach to nonlinear
multicomponent diffusion based on the idea of the reaction mechanism borrowed
from chemical kinetics.
  Chemical kinetics gave rise to very seminal tools for the modeling of
processes. This is the stoichiometric algebra supplemented by the simple
kinetic law. The results of this invention are now applied in many areas of
science, from particle physics to sociology. In our work we extend the area of
applications onto nonlinear multicomponent diffusion.
  We demonstrate, how the mechanism based approach to multicomponent diffusion
can be included into the general thermodynamic framework, and prove the
corresponding dissipation inequalities. To satisfy thermodynamic restrictions,
the kinetic law of an elementary process cannot have an arbitrary form. For the
general kinetic law (the generalized Mass Action Law), additional conditions
are proved. The cell--jump formalism gives an intuitively clear representation
of the elementary transport processes and, at the same time, produces kinetic
finite elements, a tool for numerical simulation.


  Mueller polarized bi-directional scattering distribution functions (pBSDFs)
are 4x4 matrix-valued functions which depend on acquisition geometry. The most
popular pBSDF is a weighted sum between a Fresnel matrix and an ideal
depolarizer. This work's main contribution is relating the relative weight
between an ideal depolarizer and Fresnel matrix to a single depolarization
parameter. Rather than a 16-dimensional matrix norm, this parameter can form a
one-dimensional merit function. Then, instead of a full Mueller matrix
measurement, a scheme for pBSDF fitting to only two polarimetric measurements
is introduced. Depolarization can be mathematically expressed as the incoherent
addition of coherent states. This work shows that, for a Mueller matrix to be
in the span of a Fresnel matrix and an ideal depolarizer, the weights in the
incoherent addition are triply degenerate. This triple degeneracy is observed
in five different colored opaque plastics treated with nine different surface
textures and measured at varying acquisition geometries and wavebands.


  With the rapid development of urbanization, the boom of vehicle numbers has
resulted in serious traffic accidents, which led to casualties and huge
economic losses. The ability to predict the risk of traffic accident is
important in the prevention of the occurrence of accidents and to reduce the
damages caused by accidents in a proactive way. However, traffic accident risk
prediction with high spatiotemporal resolution is difficult, mainly due to the
complex traffic environment, human behavior, and lack of real-time
traffic-related data. In this study, we collected big traffic accident data. By
analyzing the spatial and temporal patterns of traffic accident frequency, we
presented the spatiotemporal correlation of traffic accidents. Based on the
patterns we found in analysis, we proposed a high accurate deep learning model
based on recurrent neural network toward the prediction of traffic accident
risk. The predictive accident risk can be potential applied to the traffic
accident warning system. The proposed method can be integrated into an
intelligent traffic control system toward a more reasonable traffic prediction
and command organization.


  Reconfigurable intelligent surface (RIS)-empowered communication is being
considered as an enabling technology for sixth generation (6G) wireless
networks. The key idea of RIS-assisted communication is to enhance the
capacity, coverage, energy efficiency, physical layer security, and many other
aspects of modern wireless networks. At the same time, mobile edge computing
(MEC) has already shown its huge potential by extending the computation,
communication, and caching capabilities of a standalone cloud server to the
network edge. In this article, we first provide an overview of how MEC and RIS
can benefit each other. We envision that the integration of MEC and RIS will
bring an unprecedented transformation to the future evolution of wireless
networks. We provide a system-level perspective on the MEC-aided RIS (and
RIS-assisted MEC) that will evolve wireless network towards 6G. We also outline
some of the fundamental challenges that pertain to the implementation of
MEC-aided RIS (and RIS-assisted MEC) networks. Finally, the key research trends
in the RIS-assisted MEC are discussed.


  A rich variety of physical effects in spin dynamics arises at the interface
between different magnetic materials. Engineered systems with interlaced
magnetic structures have been used to implement spin transistors, memories and
other spintronic devices. However, experiments in solid state systems can be
difficult to interpret because of disorder and losses. Here, we realize
analogues of magnetic junctions using a coherently-coupled mixture of ultracold
bosonic gases. The spatial inhomogeneity of the atomic gas makes the system
change its behavior from regions with oscillating magnetization -- resembling a
magnetic material in the presence of an external transverse field -- to regions
with a defined magnetization, as in magnetic materials with a ferromagnetic
anisotropy stronger than external fields. Starting from a far-from-equilibrium
fully polarized state, magnetic interfaces rapidly form. At the interfaces, we
observe the formation of short-wavelength magnetic waves. They are generated by
a quantum torque contribution to the spin current and produce strong spatial
anticorrelations in the magnetization. Our results establish ultracold gases as
a platform for the study of far-from-equilibrium spin dynamics in regimes that
are not easily accessible in solid-state systems.


  We present simulations of the evolution of a proto-neutron star in which
kaon-condensed matter might exist, including the effects of finite temperature
and trapped neutrinos. The phase transition from pure nucleonic matter to the
kaon condensate phase is described using Gibbs' rules for phase equilibrium,
which permit the existence of a mixed phase. A general property of neutron
stars containing kaon condensates, as well as other forms of strangeness, is
that the maximum mass for cold, neutrino-free matter can be less than the
maximum mass for matter containing trapped neutrinos or which has a finite
entropy. A proto-neutron star formed with a baryon mass exceeding that of the
maximum mass of cold, neutrino-free matter is therefore metastable, that is, it
will collapse to a black hole at some time during the Kelvin-Helmholtz cooling
stage.
  The effects of kaon condensation on metastable stars are dramatic. In these
cases, the neutrino signal from a hypothetical galactic supernova (distance
$\sim8.5$ kpc) will stop suddenly, generally at a level above the background in
the SuperK and SNO detectors, which have low energy thresholds and backgrounds.
This is in contrast to the case of a stable star, for which the signal
exponentially decays, eventually disappearing into the background. We find the
lifetimes of kaon-condensed metastable stars to be restricted to the range
40--70 s and weakly dependent on the proto-neutron star mass, in sharp contrast
to the significantly larger mass dependence and range (1--100 s) of
hyperon-rich metastable stars.


  Predicting gender by the first name is not a simple task. In many
applications, especially in the natural language processing (NLP) field, this
task may be necessary, mainly when considering foreign names. In this paper, we
examined and implemented several machine learning algorithms, such as extra
trees, KNN, Naive Bayes, SVM, random forest, gradient boosting, light GBM,
logistic regression, ridge classifier, and deep neural network models, such as
MLP, RNN, GRU, CNN, and BiLSTM, to classify gender through the first name. A
dataset of Brazilian names is used to train and evaluate the models. We
analyzed the accuracy, recall, precision, f1 score, and confusion matrix to
measure the models' performances. The results indicate that the gender
prediction can be performed from the feature extraction strategy looking at the
names as a set of strings. Some models accurately predict gender in more than
95% of the cases. The recurrent models overcome the feedforward models in this
binary classification problem.