  We present the GRjunction package which allows boundary surfaces and
thin-shells in general relativity to be studied with a computer algebra system.
Implementing the Darmois-Israel thin shell formalism requires a careful
selection of definitions and algorithms to ensure that results are generated in
a straight-forward way. We have used the package to correctly reproduce a wide
variety of examples from the literature. We present several of these
verifications as a means of demonstrating the packages capabilities. We then
use GRjunction to perform a new calculation - joining two Kerr solutions with
differing masses and angular momenta along a thin shell in the slow rotation
limit.


  We use Quantum Monte Carlo methods to determine $T=0$ Green functions,
$G(\vec{r}, \omega)$, on lattices up to $16 \times 16$ for the 2D Hubbard model
at $U/t =4$. For chemical potentials, $\mu$, within the Hubbard gap, $ |\mu | <
\mu_c$, and at {\it long} distances, $\vec{r}$, $G(\vec{r}, \omega = \mu) \sim
e^{ -|\vec{r}|/\xi_l}$ with critical behavior: $\xi_l \sim | \mu - \mu_c
|^{-\nu}$, $ \nu = 0.26 \pm 0.05$. This result stands in agreement with the
assumption of hyperscaling with correlation exponent $\nu = 1/4$ and dynamical
exponent $z = 4$. In contrast, the generic band insulator as well as the
metal-insulator transition in the 1D Hubbard model are characterized by $\nu =
1/2$ and $z = 2$.


  This is the manuscript version of the Physics ViewPoint article introducing
the paper "Observation of half-quantum vortices in topological superfluid
$^3$He", by S. Autti et al., which appeared in Physical Review Letters,
December 14, 2016.


  This paper applies energy conservation principles to the Daala video codec
using gain-shape vector quantization to encode a vector of AC coefficients as a
length (gain) and direction (shape). The technique originates from the CELT
mode of the Opus audio codec, where it is used to conserve the spectral
envelope of an audio signal. Conserving energy in video has the potential to
preserve textures rather than low-passing them. Explicitly quantizing a gain
allows a simple contrast masking model with no signaling cost. Vector
quantizing the shape keeps the number of degrees of freedom the same as scalar
quantization, avoiding redundancy in the representation. We demonstrate how to
predict the vector by transforming the space it is encoded in, rather than
subtracting off the predictor, which would make energy conservation impossible.
We also derive an encoding of the vector-quantized codewords that takes
advantage of their non-uniform distribution. We show that the resulting
technique outperforms scalar quantization by an average of 0.90 dB on still
images, equivalent to a 24.8% reduction in bitrate at equal quality, while for
videos, the improvement averages 0.83 dB, equivalent to a 13.7% reduction in
bitrate.


  All beams of electromagnetic radiation are made of photons. Therefore, it is
important to find a precise relationship between the classical properties of
the beam and the quantum characteristics of the photons that make a particular
beam. It is shown that this relationship is best expressed in terms of the
Riemann-Silberstein vector -- a complex combination of the electric and
magnetic field vectors -- that plays the role of the photon wave function. The
Whittaker representation of this vector in terms of a single complex function
satisfying the wave equation greatly simplifies the analysis. Bessel beams,
exact Laguerre-Gauss beams, and other related beams of electromagnetic
radiation can be described in a unified fashion. The appropriate photon quantum
numbers for these beams are identified. Special emphasis is put on the angular
momentum of a single photon and its connection with the angular momentum of the
beam.


  Memory effects in open quantum dynamics are often incorporated in the
equation of motion through a superoperator known as the memory kernel, which
encodes how past states affect future dynamics. However, the usual prescription
for determining the memory kernel requires information about the underlying
system-environment dynamics. Here, by deriving the transfer tensor method from
first principles, we show how a memory kernel master equation, for any quantum
process, can be entirely expressed in terms of a family of completely positive
dynamical maps. These can be reconstructed through quantum process tomography
on the system alone, either experimentally or numerically, and the resulting
equation of motion is equivalent to a generalised Nakajima-Zwanzig equation.
For experimental settings, we give a full prescription for the reconstruction
procedure, rendering the memory kernel operational. When simulation of an open
system is the goal, we show how our procedure yields a considerable advantage
for numerically calculating dynamics, even when the system is arbitrarily
periodically (or transiently) driven or initially correlated with its
environment. Namely, we show that the long time dynamics can be efficiently
obtained from a set of reconstructed maps over a much shorter time.


  A probabilistic frame is a Borel probability measure with finite second
moment whose support spans $\mathbb{R}^d$. A Parseval probabilistic frame is
one for which the associated matrix of the second moments is the identity
matrix in $\mathbb{R}^d$. Each probabilistic frame is canonically associated to
a Parseval probabilistic frame. In this paper, we show that this canonical
Parseval probabilistic frame is the closest Parseval probabilistic frame to a
given probabilistic frame in the $2-$Wasserstein distance. Our proof is based
on two main ingredients. On the one hand, we show that a probabilistic frame
can be approximated in the $2-$Wasserstein metric with (compactly supported)
finite frames whose bounds can be controlled. On the other hand, we establish
some fine continuity properties of the function that maps a probabilistic frame
to its canonical Parseval probabilistic frame. Our results generalize similar
ones for finite frames and their associated Parseval frames.


  Identifying differential equation governing dynamical system is an important
problem with wide applications. Copula Entropy (CE) is a mathematical concept
for measuring statistical independence in information theory. In this paper we
propose a method for identifying differential equation of dynamical systems
with CE. The problem is considered as a variable selection problem and solved
with the previously proposed CE-based method for variable selection. The
proposed method composed of two components: the difference operator and the CE
estimator. Since both components can be done non-parametrically, the proposed
method is therefore model-free and hyperparameter-free. The simulation
experiment with the 3D Lorenz system verified the effectiveness of the proposed
method.


  The effect of the Hall force on the pinning of vortices in type II
superconductors is considered. A field theoretic formulation of the pinning
problem allows a non-perturbative treatment of the influence of quenched
disorder. A self-consistent theory is constructed using the diagrammatic
functional method for the effective action, and an expression for the pinning
force for independent vortices as well as vortex lattices is obtained. We find
that the pinning force for a single vortex is suppressed by the Hall force at
low temperatures while it is increased at high temperatures. The effect of the
Hall force is more pronounced on a single vortex than on a vortex lattice. The
results of the self-consistent theory are shown to be in good agreement with
numerical simulations.


  The Dirac quantization is performed for the constrained system of the open
string with different charges located at both ends in the constant background B
field. Noncommutativity reveals to commutators [X, X], [P, P] and also [X,P] at
both ends of the string. We consider a dependence on the change of the
"cyclotron frequency" of the charged string. The g-factor of the charged string
is also calculated in the framework of our formulation.


  Total factor productivity (TFP) is a key determinant of farm development, a
sector that receives substantial public support. The issue has taken on great
importance today, where the conflict in Ukraine has led to repercussions on the
cereal markets. This paper investigates the effects of different subsidies on
the productivity of cereal farms, accounting that farms differ according to the
level of TFP. We relied on a three-step estimation strategy: i) estimation of
production functions, ii) evaluation of TFP, and iii) assessment of the
relationship between CAP subsidies and TFP. To overcome multiple endogeneity
problems, the System-GMM estimator is adopted. The investigation embraces farms
in France, Germany, Italy, Poland, Spain and the United Kingdom using the FADN
samples from 2008 to 2018. Adding to previous analyses, we compare results from
different countries and investigate three subsets of farms with varying levels
of TFP. The outcomes confirm how CAP negatively impacts farm TFP, but the
extent differs according to the type of subsidies, the six countries and,
within these, among farms with different productivity groups. Therefore there
is room for policy improvements in order to foster the productivity of cereal
farms.


  We present extensive new \emph{ab initio} path integral Monte Carlo (PIMC)
results for the spin-resolved density response of the uniform electron gas
(UEG) at warm dense matter conditions. This allows us to unambiguously assess
the accuracy of previous theoretical approximations, thereby providing valuable
new insights for the future development of dielectric schemes. From a physical
perspective, we observe a nontrivial manifestation of an effective
electron--electron attraction that emerges in the spin-offdiagonal static
density response function at strong coupling, $r_s\gtrsim5$. All PIMC results
are freely available online and can be used to benchmark new approximations and
simulation schemes.


  The fraction r(t) of spins which have never flipped up to time t is studied
within a linear diffusion approximation to phase ordering. Numerical
simulations show that, even in this simple context, r(t) decays with time like
a power-law with a non-trival exponent $\theta$ which depends on the space
dimension. The local dynamics at a given point is a special case of a
stationary gaussian process of known correlation function and the exponent
$\theta$ is shown to be determined by the asymptotic behavior of the
probability distribution of intervals between consecutive zero-crossings of
this process. An approximate way of computing this distribution is proposed, by
taking the lengths of the intervals between successive zero-crossings as
independent random variables. The approximation gives values of the exponent
$\theta$ in close agreement with the results of simulations.


  A well known theorem of Shuzo Izumi, strengthened by David Rees, asserts that
all the divisorial valuations centered in an analytically irreducible local
noetherian ring are linearly comparable to each other. In the present paper we
generalize this theorem to the case of Abhyankar valuations with archimedian
value semigroup. Indeed, we prove that in a certain sense linear equivalence of
topologies characterizes Abhyankar valuations with archimedian semigroups,
centered in analytically irreducible local noetherian rings. Then we show that
some of the classical results on equivalence of topologies in noetherian rings
can be strengthened to include linear equivalence of topologies. We also prove
a new comparison result between the Krull topology and the topology defined by
the symbolic powers of an arbitrary ideal.


  MHD Turbulence is a critical component of the current paradigms of star
formation, particle transport, magnetic reconnection and evolution of the ISM.
Progress on this difficult subject is made via numerical simulations and
observational studies. However, due to limitations of resolution, scale
discrepancies, and complexity of the observations, the best approach for
connecting numerics to observations is not always obvious. Here we advocate for
a approach that invokes statistical techniques to understand the underlying
physics of turbulent astrophysical systems. The wealth of numerical and
observational data calls for new statistical tools to be developed in order to
study turbulence in the interstellar medium. We briefly review some of the
recently developed statistics that focus on characterizing gas compressibility
and magnetization and their uses to interstellar studies.


  Recently the presence of topologically protected edge-states in
Bi$_{14}$Rh$_3$I$_9$ was confirmed by scanning tunnelling microscopy
consolidating this compound as a weak 3D topological insulator (TI). Here, we
present a density-functional-theory-based study on a family of TIs derived from
the Bi$_{14}$Rh$_3$I$_9$ parent structure via substitution of Ru, Pd, Os, Ir
and Pt for Rh. Comparative analysis of the band-structures throughout the
entire series is done by means of a unified minimalistic tight-binding model
that evinces strong similarity between the quantum-spin-Hall (QSH) layer in
Bi$_{14}$Rh$_3$I$_9$ and graphene in terms of $p_z$-molecular orbitals.
Topologically non-trivial energy gaps are found for the Ir-, Rh-, Pt- and
Pd-based systems, whereas the Os- and Ru-systems remain trivial. Furthermore,
the energy position of the metal $d$-band centre is identified as the parameter
which governs the evolution of the topological character of the band structure
through the whole family of TIs. The $d$-band position is shown to correlate
with the chemical bonding within the QSH layers, thus revealing how the
chemical nature of the constituents affects the topological band character.


  The total cross sections for the $^{152}$Gd(p,$\gamma$)$^{153}$Tb and
$^{152}$Gd(p,n)$^{152}$Tb reactions have been measured by the activation method
at effective center-of-mass energies \mbox{$3.47 \leq
E_\mathrm{c.m.}^\mathrm{eff}\leq 7.94$ MeV} and \mbox{$4.96 \leq
E_\mathrm{c.m.}^\mathrm{eff} \leq 7.94$ MeV}, respectively. The targets were
prepared by evaporation of 30.6\% isotopically enriched $^{152}$Gd oxide on
aluminum backing foils, and bombarded with proton beams provided by a cyclotron
accelerator. The cross sections were deduced from the observed $\gamma$-ray
activity, which was detected off-line by a HPGe detector in a low background
environment. The results are presented and compared with predictions of
statistical model calculations. This comparison supports a modified optical
proton+$^{152}$Gd potential suggested earlier.


  Over the past years, many applications aim to assess the causal effect of
treatments assigned at the community level, while data are still collected at
the individual level among individuals of the community. In many cases, one
wants to evaluate the effect of a stochastic intervention on the community,
where all communities in the target population receive probabilistically
assigned treatments based on a known specified mechanism (e.g., implementing a
community-level intervention policy that target stochastic changes in the
behavior of a target population of communities). The tmleCommunity package is
recently developed to implement targeted minimum loss-based estimation (TMLE)
of the effect of community-level intervention(s) at a single time point on an
individual-based outcome of interest, including the average causal effect.
Implementations of the inverse-probability-of-treatment-weighting (IPTW) and
the G-computation formula (GCOMP) are also available. The package supports
multivariate arbitrary (i.e., static, dynamic or stochastic) interventions with
a binary or continuous outcome. Besides, it allows user-specified data-adaptive
machine learning algorithms through SuperLearner, sl3 and h2oEnsemble packages.
The usage of the tmleCommunity package, along with a few examples, will be
described in this paper.


  (Abridged) Modern studies and industrial applications related to the design,
radiation protection, and reliability of nuclear facilities, medical
applications, as well as space research and exploration are relying on
extensive simulations and modeling. Computer codes realizing semi-classical and
quantum molecular dynamics (QMD) approaches are often employed to make up for
the lack of accelerator data on many nuclear reactions at intermediate and high
energies (>10s of MeV/n) and are in high demand. This contribution focuses on
the methodology of generating reliable proton-induced cross sections in the
energy range below 1 GeV. We developed a problem-oriented computer framework
based on MCNPX and CASCADE/INPE codes to calculate activation cross section
data at intermediate and high energies. Goodness of the fits of nucleon-nucleus
interaction models to the existing data is evaluated based on elaborated
algorithms. The method is based on the analysis of a large set of data and
calculated cross sections for different targets and residual nuclei in a wide
range of proton energies using numerous criteria. In practice, this could be
done by tuning the model parameters and algorithms to fit required experimental
data subset, or through achieving unification and consistency of fundamental
parameters for all considered reactions. The presented framework is pursuing
the latter approach. We use proton-induced reactions on Si and Fe nuclei to
illustrate the application of the developed multi-criteria algorithm, where we
use all data below 1 GeV available from the EXFOR data library and the IAEA CRP
"Benchmark of Spallation Models." We show that the analysis of the predictive
power of various intermediate and high-energy models based on the
multi-criteria algorithm allows more sophisticated selection of appropriate
models in a given energy range and residual nuclei domain.


  This paper explores the tasks of leveraging auxiliary modalities which are
only available at training to enhance multimodal representation learning
through cross-modal Knowledge Distillation (KD). The widely adopted mutual
information maximization-based objective leads to a short-cut solution of the
weak teacher, i.e., achieving the maximum mutual information by simply making
the teacher model as weak as the student model. To prevent such a weak
solution, we introduce an additional objective term, i.e., the mutual
information between the teacher and the auxiliary modality model. Besides, to
narrow down the information gap between the student and teacher, we further
propose to minimize the conditional entropy of the teacher given the student.
Novel training schemes based on contrastive learning and adversarial learning
are designed to optimize the mutual information and the conditional entropy,
respectively. Experimental results on three popular multimodal benchmark
datasets have shown that the proposed method outperforms a range of
state-of-the-art approaches for video recognition, video retrieval and emotion
classification.


  Simulations of nuclear magnetic resonance (NMR) experiments can be an
important tool for extracting information about molecular structure and
optimizing experimental protocols but are often intractable on classical
computers for large molecules such as proteins and for protocols such as
zero-field NMR. We demonstrate the first quantum simulation of an NMR spectrum,
computing the zero-field spectrum of the methyl group of acetonitrile using
four qubits of a trapped-ion quantum computer. We reduce the sampling cost of
the quantum simulation by an order of magnitude using compressed sensing
techniques. We show how the intrinsic decoherence of NMR systems may enable the
zero-field simulation of classically hard molecules on relatively near-term
quantum hardware and discuss how the experimentally demonstrated quantum
algorithm can be used to efficiently simulate scientifically and
technologically relevant solid-state NMR experiments on more mature devices.
Our work opens a practical application for quantum computation.


  The Density of electronic States (DoS) of a two--dimensional square lattice
with substitutional impurities is calculated in the presence of short--range
electron--electron interactions. In the middle of the energy band, the Bragg
reflections off the Brillouin zone boundary are shown to lead to additional
quantum corrections to the DoS, the sign of which is opposite to the sign of
the Altshuler--Aronov's logarithmic correction. The resulting quantum
correction to the DoS at half--filling is positive, i.e. the DoS increases
logarithmically as the Fermi energy is approached. However, far from the
commensurate points where the Bragg reflections are suppressed, the negative
logarithmic corrections to the DoS survive.


  We show an Uhlenbeck type estimate for closed simply connected manifolds
which provides the existence of certain exact sequences in K-area homology.
This leads to the behavior of the K-area homology under surgery. Moreover, we
give an index theoretic obstruction to positive scalar curvature on compact
spin manifolds with boundary.


  In recent years, the community of 'explainable artificial intelligence' (XAI)
has created a vast body of methods to bridge a perceived gap between model
'complexity' and 'interpretability'. However, a concrete problem to be solved
by XAI methods has not yet been formally stated. As a result, XAI methods are
lacking theoretical and empirical evidence for the 'correctness' of their
explanations, limiting their potential use for quality-control and transparency
purposes. At the same time, Haufe et al. (2014) showed, using simple toy
examples, that even standard interpretations of linear models can be highly
misleading. Specifically, high importance may be attributed to so-called
suppressor variables lacking any statistical relation to the prediction target.
This behavior has been confirmed empirically for a large array of XAI methods
in Wilming et al. (2022). Here, we go one step further by deriving analytical
expressions for the behavior of a variety of popular XAI methods on a simple
two-dimensional binary classification problem involving Gaussian
class-conditional distributions. We show that the majority of the studied
approaches will attribute non-zero importance to a non-class-related suppressor
feature in the presence of correlated noise. This poses important limitations
on the interpretations and conclusions that the outputs of these XAI methods
can afford.


  Deep convolutional neural networks (CNNs) have made impressive progress in
many video recognition tasks such as video pose estimation and video object
detection. However, CNN inference on video is computationally expensive due to
processing dense frames individually. In this work, we propose a framework
called Recurrent Residual Module (RRM) to accelerate the CNN inference for
video recognition tasks. This framework has a novel design of using the
similarity of the intermediate feature maps of two consecutive frames, to
largely reduce the redundant computation. One unique property of the proposed
method compared to previous work is that feature maps of each frame are
precisely computed. The experiments show that, while maintaining the similar
recognition performance, our RRM yields averagely 2x acceleration on the
commonly used CNNs such as AlexNet, ResNet, deep compression model (thus 8-12x
faster than the original dense models using the efficient inference engine),
and impressively 9x acceleration on some binary networks such as XNOR-Nets
(thus 500x faster than the original model). We further verify the effectiveness
of the RRM on speeding up CNNs for video pose estimation and video object
detection.


  We propose to go away from the electromagnetic wave description of light and
to explain through a purely corpuscular and neutral approach, the phenomenon of
the slowing down of light in a transparent medium. The quantum predictions and
ours are compared against experimental refractive indices. In our framework the
optical Kerr phenomenon finds a very natural interpretation and its numerical
values are obtained easily


  Due to generation retirements and growth of renewable energy sources
integration, there will be widespread changes in the real and reactive power
flow in nowadays power systems. These changes also bring about challenges on
system operation and stability. High voltage direct current (HVDC) technology
seems to be a promising solution to these new challenges. A study on the impact
of the replacement of 500KV AC transmission lines by VSC-HVDC transmission line
on system voltage security is conducted. The analysis is based on reactive load
margin method and implemented on one of the Dominion Energy planning models.
Different control schemes of HVDC are considered. Using k-means clustering
method, three representative zones within the network are selected. The results
corresponding to them are demonstrated and discussed. It is shown that HVDC
lines with P-V control remarkably improve system voltage security, while those
with P-PF control scheme have negative effects.


  We consider the problem of recovering a subhypergraph based on an observed
adjacency tensor corresponding to a uniform hypergraph. The uniform hypergraph
is assumed to contain a subset of vertices called as subhypergraph. The edges
restricted to the subhypergraph are assumed to follow a different probability
distribution than other edges. We consider both weak recovery and exact
recovery of the subhypergraph, and establish information-theoretic limits in
each case. Specifically, we establish sharp conditions for the possibility of
weakly or exactly recovering the subhypergraph from an information-theoretic
point of view. These conditions are fundamentally different from their
counterparts derived in hypothesis testing literature.


  We give an upper bound for the cactus rank of any multi-homogeneous
polynomial.


  We use SDSS+\textit{GALEX}+Galaxy Zoo data to study the quenching of star
formation in low-redshift galaxies. We show that the green valley between the
blue cloud of star-forming galaxies and the red sequence of quiescent galaxies
in the colour-mass diagram is not a single transitional state through which
most blue galaxies evolve into red galaxies. Rather, an analysis that takes
morphology into account makes clear that only a small population of blue
early-type galaxies move rapidly across the green valley after the morphologies
are transformed from disk to spheroid and star formation is quenched rapidly.
In contrast, the majority of blue star-forming galaxies have significant disks,
and they retain their late-type morphologies as their star formation rates
decline very slowly. We summarize a range of observations that lead to these
conclusions, including UV-optical colours and halo masses, which both show a
striking dependence on morphological type. We interpret these results in terms
of the evolution of cosmic gas supply and gas reservoirs. We conclude that
late-type galaxies are consistent with a scenario where the cosmic supply of
gas is shut off, perhaps at a critical halo mass, followed by a slow exhaustion
of the remaining gas over several Gyr, driven by secular and/or environmental
processes. In contrast, early-type galaxies require a scenario where the gas
supply and gas reservoir are destroyed virtually instantaneously, with rapid
quenching accompanied by a morphological transformation from disk to spheroid.
This gas reservoir destruction could be the consequence of a major merger,
which in most cases transforms galaxies from disk to elliptical morphology, and
mergers could play a role in inducing black hole accretion and possibly AGN
feedback.


  Antihydrogen is at the forefront of antimatter research at the CERN
Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry
and antigravity effects require the efficient detection of antihydrogen
annihilation events, which is performed using highly granular tracking
detectors installed around an antimatter trap. Improving the efficiency of the
antihydrogen annihilation detection plays a central role in the final
sensitivity of the experiments. We propose deep learning as a novel technique
to analyze antihydrogen annihilation data, and compare its performance with a
traditional track and vertex reconstruction method. We report that the deep
learning approach yields significant improvement, tripling event coverage while
simultaneously improving performance by over 5% in terms of Area Under Curve
(AUC).


  Hot electrons formed in a graphene crystal by high-intensity short-duration
laser pulses can exist for a time that is less than an electron-phonon energy
relaxation time. During that time, electron-electron collisions cause the
electrons to thermalize to a local effective temperature that propagates
(diffuses) through graphene. The non-uniform nature of the electron temperature
leads to a force acting on the graphene lattice. This force is the result of
the electron-lattice interaction that exists even at times that are less than
the electron-phonon scattering time. The force causes the lattice to deform. A
Boltzmann equation description of the transient electron-lattice deformation
causes by hot electrons in graphene is presented.


  We revisit the parameter space of singlet fermionic cold dark matter model in
order to determine the role of the mixing angle between the standard model
Higgs and new singlet one. Furthermore, we restudy the direct detection
constraints with the updated and new experimental data. As an important
conclusion, this model is completely excluded by recent XENON100, PandaX II and
LUX data.


  Deep learning algorithms - typically consisting of a class of deep neural
networks trained by a stochastic gradient descent (SGD) optimization method -
are nowadays the key ingredients in many artificial intelligence (AI) systems
and have revolutionized our ways of working and living in modern societies. For
example, SGD methods are used to train powerful large language models (LLMs)
such as versions of ChatGPT and Gemini, SGD methods are employed to create
successful generative AI based text-to-image creation models such as
Midjourney, DALL-E, and Stable Diffusion, but SGD methods are also used to
train DNNs to approximately solve scientific models such as partial
differential equation (PDE) models from physics and biology and optimal control
and stopping problems from engineering. It is known that the plain vanilla
standard SGD method fails to converge even in the situation of several convex
optimization problems if the learning rates are bounded away from zero.
However, in many practical relevant training scenarios, often not the plain
vanilla standard SGD method but instead adaptive SGD methods such as the
RMSprop and the Adam optimizers, in which the learning rates are modified
adaptively during the training process, are employed. This naturally rises the
question whether such adaptive optimizers, in which the learning rates are
modified adaptively during the training process, do converge in the situation
of non-vanishing learning rates. In this work we answer this question
negatively by proving that adaptive SGD methods such as the popular Adam
optimizer fail to converge to any possible random limit point if the learning
rates are asymptotically bounded away from zero. In our proof of this
non-convergence result we establish suitable pathwise a priori bounds for a
class of accelerated and adaptive SGD methods, which are also of independent
interest.


  We study charge ordered d-wave resonating valence bond states (dRVB) in the
doped cuprates, and estimate the energies of these states in a generalized $t-J
$ model by using a renormalized mean field theory. The long range Coulomb
potential tends to modulate the charge density in favor of the charge ordered
RVB state. The possible relevance to the recently observed $4 \times 4$
checkerboard patterns in tunnelling conductance in high $T_c$ cuprates is
discussed.


  Despite tremendous success of modern neural networks, they are known to be
overconfident even when the model encounters inputs with unfamiliar conditions.
Detecting such inputs is vital to preventing models from making naive
predictions that may jeopardize real-world applications of neural networks. In
this paper, we address the challenging problem of devising a simple yet
effective measure of uncertainty in deep neural networks. Specifically, we
propose to utilize backpropagated gradients to quantify the uncertainty of
trained models. Gradients depict the required amount of change for a model to
properly represent given inputs, thus providing a valuable insight into how
familiar and certain the model is regarding the inputs. We demonstrate the
effectiveness of gradients as a measure of model uncertainty in applications of
detecting unfamiliar inputs, including out-of-distribution and corrupted
samples. We show that our gradient-based method outperforms state-of-the-art
methods by up to 4.8% of AUROC score in out-of-distribution detection and 35.7%
in corrupted input detection.


  A new deformed canonical commutation relation, generalizing various known
deformations, is defined together with its structure function of deformation.
Then, the related irreducible representations are characterized and classified.
Finally, the discrete spectrum of the corresponding deformed harmonic
oscillator Hamiltonian is investigated and discussed.


  This paper discusses the methods for evaluating the stored electromagnetic
energies and the radiation Q for an arbitrary lossless antenna. New expressions
for the stored electromagnetic energies are derived by using the Poynting
theorem in the complex frequency domain, and they are compared with previous
theory and are validated by numerical examples. The minimization of radiation Q
is also investigated. There exists an optimal current distribution that
minimizes the radiation Q for specified antenna geometry. The optimized Q and
the optimal current distribution may be determined by solving a generalized
eigenvalue equation obtained from the Rayleigh quotient for the radiation Q.


  We report on a timing and spectral analysis of a 50-ks NuSTAR observation of
IGR J16320-4751 (= AX J1631.9-4752); a high-mass X-ray binary hosting a
slowly-rotating neutron star. In this observation from 2015, the spin period
was 1,308.8+/-0.4 s giving a period derivative dP/dt ~ 2E-8 s s-1 when compared
with the period measured in 2004. In addition, the pulsed fraction decreased as
a function of energy, as opposed to the constant trend that was seen
previously. This suggests a change in the accretion geometry of the system
during the intervening 11 years. The phase-averaged spectra were fit with the
typical model for accreting pulsars: a power law with an exponential cutoff.
This left positive residuals at 6.4 keV attributable to the known iron K-alpha
line, as well as negative residuals around 14 keV from a candidate cyclotron
line detected at a significance of 5-sigma. We found no significant differences
in the spectral parameters across the spin period, other than the expected
changes in flux and component normalizations. A flare lasting around 5 ks was
captured during the first half of the observation where the X-ray emission
hardened and the local column density decreased. Finally, the binary orbital
period was refined to 8.9912+/-0.0078 d thanks to Swift/BAT monitoring data
from 2005-2022.


  We use distorted wave electron scattering calculations to extract the weak
charge form factor F_W(q), the weak charge radius R_W, and the point neutron
radius R_n, of 208Pb from the PREX parity violating asymmetry measurement. The
form factor is the Fourier transform of the weak charge density at the average
momentum transfer q=0.475 fm$^{-1}$. We find F_W(q) =0.204 \pm 0.028 (exp) \pm
0.001 (model). We use the Helm model to infer the weak radius from F_W(q). We
find R_W= 5.826 \pm 0.181 (exp) \pm 0.027 (model) fm. Here the exp error
includes PREX statistical and systematic errors, while the model error
describes the uncertainty in R_W from uncertainties in the surface thickness
\sigma of the weak charge density. The weak radius is larger than the charge
radius, implying a "weak charge skin" where the surface region is relatively
enriched in weak charges compared to (electromagnetic) charges. We extract the
point neutron radius R_n=5.751 \pm 0.175 (exp) \pm 0.026 (model) \pm 0.005
(strange) fm$, from R_W. Here there is only a very small error (strange) from
possible strange quark contributions. We find R_n to be slightly smaller than
R_W because of the nucleon's size. Finally, we find a neutron skin thickness of
R_n-R_p=0.302\pm 0.175 (exp) \pm 0.026 (model) \pm 0.005 (strange) fm, where
R_p is the point proton radius.


  The hadronic charmless B decays into a scalar meson and a vector meson are
studied within the framework of QCD factorization. The main results are: (i)
The decay rates for the $f_0(980)K^{*-}$ and $f_0(980)\ov K^{*0}$ modes depend
on the $f_0-\sigma$ mixing angle $\theta$. The experimental measurements can be
accommodated for $\theta\approx 20^\circ$. (ii) If the $a_0(980)$ is a $q\bar
q$ bound state, the predicted branching ratios for the channels $a_0^+\rho^-$
and $a_0^0\rho^-$ will be very large, of order $30\times 10^{-6}$ and $23\times
10^{-6}$, respectively. If the observation of or the experimental limit on
theses two modes is much smaller than the expectation of $\sim 25\times
10^{-6}$, this could hint at a four-quark nature for the $a_0(980)$. (iii) For
the $a_0(1450)$ channels, $a_0^+(1450)\rho^-$ and $a_0^0(1450)\rho^-$ are found
to have branching ratios of order $16\times 10^{-6}$ and $22\times 10^{-6}$,
respectively. A measurement of them at the predicted level will favor the
$q\bar q$ structure for the $a_0(1450)$. (iv) Contrary to the naive expectation
that $\Gamma(B^-\to a_0^0\rho^-)\sim {1\over 2}\Gamma(\bar B^0\to
a_0^+\rho^-)$, we found that they have comparable rates due to additional
contributions to the $a_0\rho^-$ mode from the $a_0^0$ emission. (v) The
predicted central value of $Br(\bar B^0\to\ov K^{*0}_0(1430)\phi)$ is larger
than experiment, though it can be accommodated within theoretical errors. The
decays $B\to K^{*}_0(1430)\rho$ are expected to have rates substantially larger
than that of $B\to K^{*}_0(1430)\pi$ owing to the constructive (destructive)
interference between the $a_4$ and $a_6$ penguin terms in the former (latter).
Experimentally, it is thus important to check if the $B\to K^{*}_0\rho$ modes
are enhanced relative to the corresponding $K_0^*\pi$ channels.


  We give sufficient conditions for essential self-adjointness of magnetic
Schr\"odinger operators on locally finite graphs. Two of the main theorems of
the present paper generalize recent results of Torki-Hamza.


  Description of periodically and resonantly driven quantum systems can lead to
solid state models where condensed matter phenomena can be investigated in time
lattices formed by periodically evolving Wannier-like states. Here, we show
that inseparable two-dimensional time lattices with the M\"obius strip geometry
can be realized for ultra-cold atoms bouncing between two periodically
oscillating mirrors. Effective interactions between atoms loaded to a lattice
can be long-ranged and can be controlled experimentally. As a specific example
we show how to realize a Lieb lattice model with a flat band and how to control
long-range hopping of pairs of atoms in the model.


  Parallel programming is emerging fast and intensive applications need more
resources, so there is a huge demand for on-chip multiprocessors. Accessing L1
caches beside the cores are the fastest after registers but the size of private
caches cannot increase because of design, cost and technology limits. Then
split I-cache and D-cache are used with shared LLC (last level cache). For a
unified shared LLC, bus interface is not scalable, and it seems that
distributed shared LLC (DSLLC) is a better choice. Most of papers assume a
distributed shared LLC beside each core in on-chip network. Many works assume
that DSLLCs are placed in all cores; however, we will show that this design
ignores the effect of traffic congestion in on-chip network. In fact, our work
focuses on optimal placement of cores, DSLLCs and even memory controllers to
minimize the expected latency based on traffic load in a mesh on-chip network
with fixed number of cores and total cache capacity. We try to do some
analytical modeling deriving intended cost function and then optimize the mean
delay of the on-chip network communication. This work is supposed to be
verified using some traffic patterns that are run on CSIM simulator.


  Statistically interpretable axioms are formulated that define a quantum
stochastic process (QSP) as a causally ordered operator field in an arbitrary
space-time region T of an open quantum system under a sequential observation at
a discrete space-time localization. It is shown that to every QSP described in
the weak sense by a self-consistent system of causally ordered correlation
kernels there corresponds a unique, up to unitary equivalence, minimal QSP in
the strong sense. It is shown that the proposed QSP construction, which reduces
in the case of the linearly ordered discrete T=Z to the construction of the
inductive limit of Lindblad's canonical representations, corresponds to
Kolmogorov's classical reconstruction if the order on T is ignored and leads to
Lewis construction if one uses the system of all (not only causal) correlation
kernels, regarding this system as lexicographically preordered on T. The
approach presented encompasses both nonrelativistic and relativistic
irreversible dynamics of open quantum systems and fields satisfying the
conditions of local commutativity and semigroup covariance. Also given are
necessary and sufficient conditions of dynamicity (or conditional Markovianity)
and regularity, these leading to the properties of complete mixing (relaxation)
and ergodicity of the QSP.


  In this paper, we give a general axiomatization of anomalies in closed and
open conformal field theories. In particular, we generalize Segal's notion of
modular functor to a setting where the ``set of labels'' is a 2-vector space.
In the case of open conformal field theory, the ``set of $D$-branes'' is a
3-vector space. We also define a ``topological group completion'' of the
symmetric bimonoidal category of finite-dimensional vector spaces, and propose
it as a candidate for labelling conformal field theories whose modular functors
are super-vector spaces.


  We provide complete source code for building a fundamental industry
classification based on publically available and freely downloadable data. We
compare various fundamental industry classifications by running a horserace of
short-horizon trading signals (alphas) utilizing open source heterotic risk
models (https://ssrn.com/abstract=2600798) built using such industry
classifications. Our source code includes various stand-alone and portable
modules, e.g., for downloading/parsing web data, etc.


  We analyze existence, stability, and symmetry of point vortex relative
equilibria with one dominant vortex and N vortices with infinitesimal
circulation. The dimension of the problem can be reduced by taking an
infinitesimal circulation limit, resulting in the so-called (1+N)-vortex
problem. In this work, we first generalize the reduction to allow for
circulations of varying signs and weights. We then prove that symmetric
configurations require equality of two circulation parameters in the
(1+3)-vortex problem and show that there are stable asymmetric relative
equilibria. In a number of examples, we use rigorous methods from algebraic
geometry to count all relative equilibria.


  The complex dielectric constant $\hat{\epsilon} = \epsilon_1 + i \epsilon_2$
of SrTiO$_3$ reaches high values $\epsilon_1 \approx 2*10^{4}$ at cryogenic
temperatures, while the dielectric losses ($\epsilon_2$) are much stronger than
for other crystalline dielectrics. SrTiO$_3$ is a common substrate for oxide
thin films, like the superconducting LaAlO$_3$/SrTiO$_3$ system, but the large
$\epsilon_1$ and $\epsilon_2$ restrict high-frequency quantum devices on
SrTiO$_3$. Here we present superconducting coplanar Nb resonators on SrTiO$_3$,
which we successfully operate in a distant-flip-chip geometry at frequencies
that exceed 1 GHz. We find a pronounced and unexpected increase in resonator
quality factor $Q$ at temperatures below 1 K, reaching up to $Q \approx 800$.
We attribute this to substantial changes of the dielectric losses in SrTiO$_3$
at mK temperatures, and we also detect non-monotonous changes in the
temperature-dependent $\epsilon_1$. These findings challenge our present
understanding of the dielectric properties of SrTiO$_3$ and at the same time
demonstrate that cryogenic high-frequency devices on SrTiO$_3$ are more
feasible than previously assumed.


  Collaborative filtering is amongst the most preferred techniques when
implementing recommender systems. Recently, great interest has turned towards
parallel and distributed implementations of collaborative filtering algorithms.
This work is a survey of the parallel and distributed collaborative filtering
implementations, aiming not only to provide a comprehensive presentation of the
field's development, but also to offer future research orientation by
highlighting the issues that need to be further developed.


  We have investigated the chemistry of dense interstellar clouds and found new
bistable solutions in the nitrogen and carbon chemistries. We identify the
autocatalytic processes that are present in the pure, reduced, chemical
networks and, as previously found for oxygen chemistry, that He$^+$ plays an
important role.
  The applicability of these results to astronomical environments is briefly
discussed. The bistable solutions found for carbon chemistry occur for low
densities and high ionization fractions that are not compatible with that found
cold, dense clouds. Bistability in the pure nitrogen chemistry occurs for
conditions that are relevant for prestellar cores in which significant CO
depletion has taken place. We conclude that several autocatalyses are embedded
in gas-phase interstellar chemistry and that many more are potentially present.


  We show that a symmetric superposition of five standing plane waves can be
expressed as an infinite series of terms of decreasing wavenumber, where each
term is a product of five plane waves. We show that this series converges
pointwise in R^2 and uniformly in any disk domain in R^2. Using this series, we
provide a heuristic argument for why the locations of the local extrema of a
symmetric superposition of five standing plane waves can be approximated by the
vertices of a Penrose tiling.


  In this paper, we construct a universal C*-algebraic quantum group out of an
algebraic one. We show that this universal C*-algebraic quantum group has the
same rich structure as its reduced companion. This universal C*-algebraic
quantum group also satifies an upcoming definition of Masuda, Nakagami &
Woronowicz except for the possible non-faithfulness of the left Haar weight.


  LSST promises to be the largest optical imaging survey of the sky. If we were
fortunate enough to have the equivalent of LSST today, it would represent a
"fire hose" of data that would be difficult to store, transfer, and analyze
with available compute resources.
  LSST parallels the SDSS compute task which was ambitious yet tractable. By
almost any measure relative to computers that will be available (thanks to the
steady progression of Moore's Law), LSST will be a small data set. LSST will
never fill more than 22 hard drives. Individual investigators will be able to
maintain their own data copies to analyze as they choose.


  To comprehensively gauge the capacity of current models for complex
reasoning, it is crucial to assess their step-by-step reasoning in a scalable
manner. Established reference-based evaluation metrics rely on human-annotated
reasoning chains as references to assess the model-derived chains. However,
such "gold-standard" human-written reasoning chains may not be unique and their
acquisition is often labor-intensive. Existing reference-free reasoning
evaluation metrics, while eliminating the need for human-crafted reasoning
chains as references, often require fine-tuning with human-derived chains
before evaluation, complicating the process and questioning their adaptability
to other datasets. To address these challenges, we harness GPT-4 to
automatically evaluate reasoning chain quality, thereby removing the dependency
on human-written reasoning chains for both model fine-tuning and evaluative
purposes. Leveraging the Socratic method, we develop SocREval ({\bf Soc}ratic
Method-Inspired {\bf R}easoning {\bf Eval}uation), a novel approach for prompt
design in reference-free reasoning evaluation. Empirical results from four
human annotated datasets reveal that SocREval significantly improves GPT-4's
performance, surpassing existing reference-free and reference-based reasoning
evaluation metrics. Beyond its demonstrated efficacy, SocREval, proves to be
both cost-efficient and robust to prompt writing and example selection, as
substantiated by our in-depth analysis.


  We study explosiveness of age-dependent branching processes describing the
early stages of an epidemic-spread: both forward- and backward process are
analysed. For the classical age-dependent branching process $(h,G)$, where the
offspring has probability generating function $h$ and all individuals have
life-lengths independently picked from a distribution $G$, we focus on the
setting $h = h_{\alpha}^L$, with $L$ a function varying slowly at infinity and
$\alpha \in (0,1)$. Here, $h^L_{\alpha}(s) = 1 - (1-s)^{\alpha}
L(\frac{1}{1-s}),$ as $s \to 1$. For a fixed $G$, the process
$(h^L_{\alpha},G)$ explodes either for all $\alpha \in (0,1)$ or for no $\alpha
\in (0,1)$, regardless of $L$. Next, we add contagious periods to all
individuals and let their offspring survive only if their life-length is
smaller than the contagious period of their mother: a forward process. An
explosive process $(h^L_{\alpha},G)$, as above, stays explosive when adding a
non-zero contagious period. We extend this setting to backward processes with
contagious periods. Further, we consider processes with incubation periods
during which an individual has already contracted the disease but is not able
yet to infect her acquaintances. We let these incubation periods follow a
distribution $I$. In the forward process $(h^L_{\alpha},G,I)_{f}$, every
individual possesses an incubation period and only her offspring with life-time
larger than this period survives. In the backward process
$(h^L_{\alpha},G,I)_{b}$, individuals survive only if their life-time exceeds
their own incubation period. These two processes are the content of the third
main result that we establish: under a mild condition on $G$ and $I$,
explosiveness of both $(h,G)$ and $(h,I)$ is necessary and sufficient for
processes $(h^L_{\alpha},G,I)_{f}$ and $(h^L_{\alpha},G,I)_{b}$ to explode.


  We consider optimal control problems, where the control appears in the main
part of the operator. We derive the Pontryagin maximum principle as a necessary
optimality condition. The proof uses the concept of topological derivatives. In
contrast to earlier works, we do not need continuity assumptions for the
coefficient or gradients of solutions of partial differential equations.
Following classical proofs, we consider perturbations of optimal controls by
multiples of characteristic functions of sets, whose scaling factor is send to
zero. For $2d$ problems, we can perform an optimization over the elliptic
shapes of such sets leading to stronger optimality conditions involving a
variational inequality of a new type.


  We introduce and analyze a task that we call symmetrization, in which a state
of a quantum system, associated with a symmetry group, is transformed by a
random unitary operation to a symmetric state. Each element of the unitary
ensemble is required to be symmetry preserving, in the sense that it keeps the
set of symmetric states invariant. We consider an asymptotic limit of
infinitely many copies and vanishingly small error, and analyze the
symmetrizing cost, that is, the minimum cost of randomness per copy required
for symmetrization. We prove that the symmetrizing cost of an arbitrary quantum
state is equal to the relative entropy of frameness, thereby providing it with
a direct operational meaning.


  In a speech recognition system, voice activity detection (VAD) is a crucial
frontend module. Addressing the issues of poor noise robustness in traditional
binary VAD systems based on DFSMN, the paper further proposes semantic VAD
based on multi-task learning with improved models for real-time and offline
systems, to meet specific application requirements. Evaluations on internal
datasets show that, compared to the real-time VAD system based on DFSMN, the
real-time semantic VAD system based on RWKV achieves relative decreases in CER
of 7.0\%, DCF of 26.1\% and relative improvement in NRR of 19.2\%. Similarly,
when compared to the offline VAD system based on DFSMN, the offline VAD system
based on SAN-M demonstrates relative decreases in CER of 4.4\%, DCF of 18.6\%
and relative improvement in NRR of 3.5\%.


  Training an accurate object detector is expensive and time-consuming. One
main reason lies in the laborious labeling process, i.e., annotating category
and bounding box information for all instances in every image. In this paper,
we examine ways to improve performance of deep object detectors without extra
labeling. We first explore to group existing categories of high visual and
semantic similarities together as one super category (or, a superclass). Then,
we study how this knowledge of hierarchical categories can be exploited to
better detect object using multi-grained RCNN top branches. Experimental
results on DeepFashion2 and OpenImagesV4-Clothing reveal that the proposed
detection heads with multi-grained branches can boost the overall performance
by 2.3 mAP for DeepFashion2 and 2.5 mAP for OpenImagesV4-Clothing with no
additional time-consuming annotations. More importantly, classes that have
fewer training samples tend to benefit more from the proposed multi-grained
heads with superclass grouping. In particular, we improve the mAP for last 30%
categories (in terms of training sample number) by 2.6 and 4.6 for DeepFashion2
and OpenImagesV4-Clothing, respectively.


  Anomaly detection in Minimally-Invasive Surgery (MIS) traditionally requires
a human expert monitoring the procedure from a console. Data scarcity, on the
other hand, hinders what would be a desirable migration towards autonomous
robotic-assisted surgical systems. Automated anomaly detection systems in this
area typically rely on classical supervised learning. Anomalous events in a
surgical setting, however, are rare, making it difficult to capture data to
train a detection model in a supervised fashion. In this work we thus propose
an unsupervised approach to anomaly detection for robotic-assisted surgery
based on deep residual autoencoders. The idea is to make the autoencoder learn
the 'normal' distribution of the data and detect abnormal events deviating from
this distribution by measuring the reconstruction error. The model is trained
and validated upon both the publicly available Cholec80 dataset, provided with
extra annotation, and on a set of videos captured on procedures using
artificial anatomies ('phantoms') produced as part of the Smart Autonomous
Robotic Assistant Surgeon (SARAS) project. The system achieves recall and
precision equal to 78.4%, 91.5%, respectively, on Cholec80 and of 95.6%, 88.1%
on the SARAS phantom dataset. The end-to-end system was developed and deployed
as part of the SARAS demonstration platform for real-time anomaly detection
with a processing time of about 25 ms per frame.


  This paper discusses a new variable equation of state parameter leading to
exact solutions of the Einstein field equations describing traversable
wormholes. In addition to generalizing the notion of phantom energy, the
equation of state generates a mathematical model that combines the generalized
phantom energy and the generalized Chaplygin gas models.


  Multi-junction solar cells provide a path to overcome the efficiency limits
of standard silicon solar cells by harvesting more efficiently a broader range
of the solar spectrum. However, Si-based multi-junction architectures are
hindered by incomplete harvesting in the near-infrared (near-IR) spectral
range, as Si sub-cells have weak absorption close to the band gap. Here, we
introduce an integrated near-field/far-field light trapping scheme to enhance
the efficiency of silicon-based multi-junction solar cells in the near-IR
range. To achieve this, we design a nanopatterned diffractive silver
back-reflector featuring a scattering matrix that optimizes trapping of
multiply-scattered light into a range of diffraction angles. We minimize
reflection to the 0th-order and parasitic plasmonic absorption in the silver by
engineering destructive interference in the patterned back contact. Numerical
and experimental assessment of the optimal design on the performance of
single-junction Si TOPCon solar cells highlights an improved external quantum
efficiency (EQE) over a planar back-reflector (+1.52 mA/cm2). Nanopatterned
metagrating back-reflectors are fabricated on GaInP/GaInAsP//Si two-terminal
triple-junction solar cells via Substrate Conformal Imprint Lithography (SCIL)
and characterized optically and electronically, demonstrating a power
conversion efficiency improvement of +0.9%abs over the planar reference.
Overall, our work demonstrates the potential of nanophotonic light trapping for
enhancing the efficiency of silicon-based multi-junction solar cells, paving
the way for more efficient and sustainable solar energy technologies.


  We consider the modified Korteveg de Vriez equation on the whole line.
Initial data is real and step-like, i.e. $q(x,0)=0$ for $x\geq0$ and $q(x,0)=c$
for $x<0$, where c is arbitrary real number. The goal of this paper is to study
the asymptotic behavior of the initial-value problem's solution by means of the
asymptotic behavior of the some Riemann\textendash Hilbert problem. In this
paper we show that the solution of this problem has different asymptotic
behavior in different regions. In the region $x<-6c^2t$ and $x>4c^2t$ the
solution is tend to $c$ and 0 correspondingly. In the region $-6c^2t<x<4c^2t$
the solution takes the form of a modulated elliptic wave.


  In this paper we propose a structural parameter of CNF formulas and use it to
identify instances of weighted MaxSAT and #SAT that can be solved in polynomial
time. Given a CNF formula we say that a set of clauses is precisely satisfiable
if there is some complete assignment satisfying these clauses only. Let the
ps-value of the formula be the number of precisely satisfiable sets of clauses.
Applying the notion of branch decompositions to CNF formulas and using ps-value
as cut function, we define the ps-width of a formula. For a formula given with
a decomposition of polynomial ps-width we show dynamic programming algorithms
solving weighted MaxSAT and #SAT in polynomial time. Combining with results of
'Belmonte and Vatshelle, Graph classes with structured neighborhoods and
algorithmic applications, Theor. Comput. Sci. 511: 54-65 (2013)' we get
polynomial-time algorithms solving weighted MaxSAT and #SAT for some classes of
structured CNF formulas. For example, we get $O(m^2(m + n)s)$ algorithms for
formulas $F$ of $m$ clauses and $n$ variables and size $s$, if $F$ has a linear
ordering of the variables and clauses such that for any variable $x$ occurring
in clause $C$, if $x$ appears before $C$ then any variable between them also
occurs in $C$, and if $C$ appears before $x$ then $x$ occurs also in any clause
between them. Note that the class of incidence graphs of such formulas do not
have bounded clique-width.


  3D virtual simulation, which generates diversified test scenarios and tests
full-stack of Autonomous Driving Systems (ADSes) modules dynamically as a
whole, is a promising approach for Safety of The Intended Functionality (SOTIF)
ADS testing. However, as different configurations of a test scenario will
affect the sensor perceptions and environment interaction, e.g. light pulses
emitted by the LiDAR sensor will undergo backscattering and attenuation, which
is usually overlooked by existing works, leading to false positives or wrong
results. Moreover, the input space of an ADS is extremely large, with infinite
number of possible initial scenarios and mutations, along both temporal and
spatial domains.
  This paper proposes a first-principles based sensor modeling and environment
interaction scheme, and integrates it into CARLA simulator. With this scheme, a
long-overlooked category of adverse weather related corner cases are
discovered, along with their root causes. Moreover, a meta-heuristic algorithm
is designed based on several empirical insights, which guide both seed
scenarios and mutations, significantly reducing the search dimensions of
scenarios and enhancing the efficiency of corner case identification.
Experimental results show that under identical simulation setups, our algorithm
discovers about four times as many corner cases as compared to state-of-the-art
work.


  Bifurcation phenomena are ubiquitous in elasticity, but their study is often
limited to linear perturbation or numerical analysis since second or higher
variations are often beyond an analytic treatment. Here, we review two key
mathematical ideas, namely, the splitting lemma and the determinacy of a
function, and show how they can be fruitfully used to derive a reduced
function, named Landau expansion in the paper, that allows us to give a simple
but rigorous description of the bifurcation scenario, including the stability
of the equilibrium solutions. We apply these ideas to a paradigmatic example
with potential applications to various softly constrained physical systems and
biological tissues: a stretchable elastic ring under pressure. We prove the
existence of a tricritical point and find bistability effects and hysteresis
when the stretching modulus is sufficiently small. These results seem to be in
qualitative agreement with some recent experiments on heart cells.


  This paper examines linear beamforming methods for secure communications in a
multiuser wiretap channel with a single transmitter, multiple legitimate
receivers, and a single eavesdropper, where all nodes are equipped with
multiple antennas. No information regarding the eavesdropper is presumed at the
transmitter, and we examine both the broadcast MIMO downlink with independent
information, and the multicast MIMO downlink with common information for all
legitimate receivers. In both cases the information signal is transmitted with
just enough power to guarantee a certain SINR at the desired receivers, while
the remainder of the power is used to broadcast artificial noise. The
artificial interference selectively degrades the passive eavesdropper's signal
while remaining orthogonal to the desired receivers. We analyze the
confidentiality provided by zero-forcing and optimal minimum-power beamforming
designs for the broadcast channel, and optimal minimum-MSE beamformers for the
multicast channel. Numerical simulations for the relative SINR and BER
performance of the eavesdropper demonstrate the effectiveness of the proposed
physical-layer security schemes.


  It is proved that if a Banach space $Y$ is a quotient of a Banach space
having a shrinking unconditional basis, then every normalized weakly null
sequence in $Y$ has an unconditional subsequence. The proof yields the
corollary that every quotient of Schreier's space is $c_o$-saturated.


  A survey of tessellations, which appeared in Italian in La Matematica vol 3
in 2010. The Tiling Lemma appears for the first time.


  Recent work in NLP has shown promising results in training models on large
amounts of tasks to achieve better generalization. However, it is not
well-understood how tasks are related, and how helpful training tasks can be
chosen for a new task. In this work, we investigate whether knowing task
relationships via pairwise task transfer improves choosing one or more source
tasks that help to learn a new target task. We provide TaskWeb, a large-scale
benchmark of pairwise task transfers for 22 NLP tasks using three different
model types, sizes, and adaptation methods, spanning about 25,000 experiments.
Then, we design a new method TaskShop based on our analysis of TaskWeb.
TaskShop uses TaskWeb to estimate the benefit of using a source task for
learning a new target task, and to choose a subset of helpful training tasks
for multi-task training. Our method improves overall rankings and top-k
precision of source tasks by 10% and 38%, respectively. We also use TaskShop to
build much smaller multi-task training sets that improve zero-shot performances
across 11 different target tasks by at least 4.3%.


  Macdonald studied a discrete volume measure for a rational polytope $P$,
called solid angle sum, that gives a natural discrete volume for $P$. We give a
local formula for the codimension two quasi-coefficient of the solid angle sum
of $P$. We also show how to recover the classical Ehrhart quasi-polynomial from
the solid angle sum and in particular we find a similar local formula for the
codimension one and codimension two quasi-coefficients. These local formulas
are naturally valid for all positive real dilates of $P$.
  An interesting open question is to determine necessary and sufficient
conditions on a polytope $P$ for which the discrete volume of $P$ given by the
solid angle sum equals its continuous volume: $A_P(t) = \mathrm{vol}(P) t^d$.
We prove that a sufficient condition is that $P$ tiles $\mathbb R^d$ by
translations, together with the Hyperoctahedral group.


  In this presentation I summarize recent work on Cepheid research carried out
at the National Central University. The mid-infrared period-luminosity (P-L)
relations for Cepheids are important in the James Webb Space Telescope era for
distance scale work, as the relations have potential to derive the Hubble
constant within ~2% accuracy - a critical constraint in the precision
cosmology. Consequently, we have derived the mid-infrared P-L relations for
Cepheids in the Large and Small Magellanic Clouds, using archival data from the
Spitzer Space Telescope. Kepler Space Telescope is a NASA mission to search for
Earth-size and larger planets around Sun-like stars, by observing continuously
the stars in a dedicated patch of the sky. As a result, the almost
un-interrupted observation is also used for stellar variability and
asteroseismological study. However, Kepler observations are carried out with a
single broad-band filter, hence ground-based follow-up observation needed to
complement Kepler light curves to fully characterize the properties of the
target stars. Here I present the ground-based optical follow-up observations
for two Cepheid candidates located within the Kepler's field-of-view. Together
with Kepler light curves, our ground-based data rule out V2279 Cyg being a
Cepheid. Hence V1154 Cyg is the only Cepheid in the Kepler's field.


  The widespread application of deep neural network (DNN) techniques is being
challenged by adversarial examples, the legitimate input added with
imperceptible and well-designed perturbations that can fool DNNs easily in the
DNN testing/deploying stage. Previous adversarial example generation algorithms
for adversarial white-box attacks used Jacobian gradient information to add
perturbations. This information is too imprecise and inexplicit, which will
cause unnecessary perturbations when generating adversarial examples. This
paper aims to address this issue. We first propose to apply a more informative
and distilled gradient information, namely integrated gradient, to generate
adversarial examples. To further make the perturbations more imperceptible, we
propose to employ the restriction combination of $L_0$ and $L_1/L_2$ secondly,
which can restrict the total perturbations and perturbation points
simultaneously. Meanwhile, to address the non-differentiable problem of $L_1$,
we explore a proximal operation of $L_1$ thirdly. Based on these three works,
we propose two Integrated gradient based White-box Adversarial example
generation algorithms (IWA): IFPA and IUA. IFPA is suitable for situations
where there are a determined number of points to be perturbed. IUA is suitable
for situations where no perturbation point number is preset in order to obtain
more adversarial examples. We verify the effectiveness of the proposed
algorithms on both structured and unstructured datasets, and we compare them
with five baseline generation algorithms. The results show that our proposed
algorithms do craft adversarial examples with more imperceptible perturbations
and satisfactory crafting rate. $L_2$ restriction is more suitable for
unstructured dataset and $L_1$ restriction performs better in structured
dataset.


  Fog or Edge computing has recently attracted broad attention from both
industry and academia. It is deemed as a paradigm shift from the current
centralized cloud computing model and could potentially bring a "Fog-IoT"
architecture that would significantly benefit the future ubiquitous Internet of
Things (IoT) systems and applications. However, it takes a series of key
enabling technologies including emerging technologies to realize such a vision.
In this article, we will survey these key enabling technologies with specific
focuses on security and scalability, which are two very important and
much-needed characteristics for future large-scale deployment. We aim to draw
an overall big picture of the future for the research and development in these
areas.


  We introduce the dimension monoid of a lattice L, denoted by Dim L. The
monoid Dim L is commutative and conical, the latter meaning that the sum of any
two nonzero elements is nonzero. Furthermore, Dim L is given along with the
dimension map, $\Dim$, from L x L to Dim L, which has the intuitive meaning of
a distance function. The maximal semilattice quotient of Dim L is isomorphic to
the semilattice Conc L of compact congruences of L; hence Dim L is a precursor
of the congruence lattice of L. Here are some additional features of this
construction: (1) Our dimension theory provides a generalization to all
lattices of the von Neumann dimension theory of continuous geometries. In
particular, if L is an irreducible continuous geometry, then Dim L is either
isomorphic to Z^+ or to R^+. (2) If L has no infinite bounded chains, then Dim
L embeds (as an ordered monoid) into a power of Z^+ $\cup$ {$\infty$}. (3) If L
is modular or if L has no infinite bounded chains, then Dim L is a refinement
monoid. (4) If L is a simple geometric lattice, then DimL is isomorphic to Z^+,
if L is modular, and to the two-element semilattice, otherwise. (5) If L is a
countably meet-continuous complemented modular lattice, then both Dim L and the
dimension function $\Dim$ satisfy (countable) completeness properties. If R is
a von Neumann regular ring and if L is the lattice of principal right ideals of
the matrix ring M\_2(R), then Dim L is isomorphic to the monoid V(R) of
isomorphism classes of finitely generated projective right R-modules. Hence the
dimension theory of lattices provides a wide lattice-theoretical generalization
of nonstable K-theory of regular rings.


  We consider a mechanism to generate controllable qudit-qudit interactions in
a charge-position paradigm for a quantum computer, through the use of auxiliary
states. By controlling the tunneling rates onto these auxiliaries from the
qudits proper, we can controllably switch the entangling operations. We
consider a practical architecture in which to realize such a computer and
examine the associated Hilbert space dimension.


  Text summarization is a downstream natural language processing (NLP) task
that challenges the understanding and generation capabilities of language
models. Considerable progress has been made in automatically summarizing short
texts, such as news articles, often leading to satisfactory results. However,
summarizing long documents remains a major challenge. This is due to the
complex contextual information in the text and the lack of open-source
benchmarking datasets and evaluation frameworks that can be used to develop and
test model performance. In this work, we use ChatGPT, the latest breakthrough
in the field of large language models (LLMs), together with the extractive
summarization model C2F-FAR (Coarse-to-Fine Facet-Aware Ranking) to propose a
hybrid extraction and summarization pipeline for long documents such as
business articles and books. We work with the world-renowned company
getAbstract AG and leverage their expertise and experience in professional book
summarization. A practical study has shown that machine-generated summaries can
perform at least as well as human-written summaries when evaluated using
current automated evaluation metrics. However, a closer examination of the
texts generated by ChatGPT through human evaluations has shown that there are
still critical issues in terms of text coherence, faithfulness, and style.
Overall, our results show that the use of ChatGPT is a very promising but not
yet mature approach for summarizing long documents and can at best serve as an
inspiration for human editors. We anticipate that our work will inform NLP
researchers about the extent to which ChatGPT's capabilities for summarizing
long documents overlap with practitioners' needs. Further work is needed to
test the proposed hybrid summarization pipeline, in particular involving GPT-4,
and to propose a new evaluation framework tailored to the task of summarizing
long documents.


  Chest X-ray is one of the most popular medical imaging modalities due to its
accessibility and effectiveness. However, there is a chronic shortage of
well-trained radiologists who can interpret these images and diagnose the
patient's condition. Therefore, automated radiology report generation can be a
very helpful tool in clinical practice. A typical report generation workflow
consists of two main steps: (i) encoding the image into a latent space and (ii)
generating the text of the report based on the latent image embedding. Many
existing report generation techniques use a standard convolutional neural
network (CNN) architecture for image encoding followed by a Transformer-based
decoder for medical text generation. In most cases, CNN and the decoder are
trained jointly in an end-to-end fashion. In this work, we primarily focus on
understanding the relative importance of encoder and decoder components.
Towards this end, we analyze four different image encoding approaches: direct,
fine-grained, CLIP-based, and Cluster-CLIP-based encodings in conjunction with
three different decoders on the large-scale MIMIC-CXR dataset. Among these
encoders, the cluster CLIP visual encoder is a novel approach that aims to
generate more discriminative and explainable representations. CLIP-based
encoders produce comparable results to traditional CNN-based encoders in terms
of NLP metrics, while fine-grained encoding outperforms all other encoders both
in terms of NLP and clinical accuracy metrics, thereby validating the
importance of image encoder to effectively extract semantic information. GitHub
repository: https://github.com/mudabek/encoding-cxr-report-gen


  Reconstruction of CT images from a limited set of projections through an
object is important in several applications ranging from medical imaging to
industrial settings. As the number of available projections decreases,
traditional reconstruction techniques such as the FDK algorithm and model-based
iterative reconstruction methods perform poorly. Recently, data-driven methods
such as deep learning-based reconstruction have garnered a lot of attention in
applications because they yield better performance when enough training data is
available. However, even these methods have their limitations when there is a
scarcity of available training data. This work focuses on image reconstruction
in such settings, i.e., when both the number of available CT projections and
the training data is extremely limited. We adopt a sequential reconstruction
approach over several stages using an adversarially trained shallow network for
'destreaking' followed by a data-consistency update in each stage. To deal with
the challenge of limited data, we use image subvolumes to train our method, and
patch aggregation during testing. To deal with the computational challenge of
learning on 3D datasets for 3D reconstruction, we use a hybrid 3D-to-2D mapping
network for the 'destreaking' part. Comparisons to other methods over several
test examples indicate that the proposed method has much potential, when both
the number of projections and available training data are highly limited.


  For a system of $n$ non-relativistic spinless bosons, we show by using a set
of suitable matching conditions that the quantum equations in the pilot-wave
limit can be translated into a geometric language for a Finslerian manifold. We
further link these equations to Euclidean timelike relative Funk geometry and
show that the two different metrics in both of these geometric frameworks lead
to the same coupling.


  When a blood vessel ruptures or gets inflamed, the human body responds by
rapidly forming a clot to restrict the loss of blood. Platelets aggregation at
the injury site of the blood vessel occurring via platelet-platelet adhesion,
tethering and rolling on the injured endothelium is a critical initial step in
blood clot formation. A novel three-dimensional multiscale model is introduced
and used in this paper to simulate receptor-mediated adhesion of deformable
platelets at the site of vascular injury under different shear rates of blood
flow. The novelty of the model is based on a new approach of coupling submodels
at three biological scales crucial for the early clot formation: novel hybrid
cell membrane submodel to represent physiological elastic properties of a
platelet, stochastic receptor-ligand binding submodel to describe cell adhesion
kinetics and Lattice Boltzmann submodel for simulating blood flow. The model
implementation on the GPUs cluster significantly improved simulation
performance. Predictive model simulations revealed that platelet deformation,
interactions between platelets in the vicinity of the vessel wall as well as
the number of functional GPIb{\alpha} platelet receptors played significant
roles in the platelet adhesion to the injury site. Variation of the number of
functional GPIb{\alpha} platelet receptors as well as changes of platelet
stiffness can represent effects of specific drugs reducing or enhancing
platelet activity. Therefore, predictive simulations can improve the search for
new drug targets and help to make treatment of thrombosis patient specific.


  We argue that dark energy with multiple fields is theoretically
well-motivated and predicts distinct observational signatures, in particular
when cosmic acceleration takes place along a trajectory that is highly
non-geodesic in field space. Such models provide novel physics compared to
$\Lambda$CDM and quintessence by allowing cosmic acceleration on steep
potentials. From the theoretical point of view, these theories can easily
satisfy the conjectured swampland constraints and may in certain cases be
technically natural, potential problems which are endemic to standard
single-field dark energy. Observationally, we argue that while such multi-field
models are likely to be largely indistinguishable from the concordance
cosmology at the background level, dark energy perturbations can cluster,
leading to an enhanced growth of large-scale structure that may be testable as
early as the next generation of cosmological surveys.


  Maintainers are now self-sabotaging their work in order to take political or
economic stances, a practice referred to as "protestware". In this poster, we
present our approach to understand how the discourse about such an attack went
viral, how it is received by the community, and whether developers respond to
the attack in a timely manner. We study two notable protestware cases, i.e.,
Colors.js and es5-ext, comparing with discussions of a typical security
vulnerability as a baseline, i.e., Ua-parser, and perform a thematic analysis
of more than two thousand protest-related posts to extract the different
narratives when discussing protestware.


  In this paper, we prove the edge universality of largest eigenvalues for
separable covariance matrices of the form $\mathcal Q :=A^{1/2}XBX^*A^{1/2}$.
Here $X=(x_{ij})$ is an $n\times N$ random matrix with $x_{ij}=N^{-1/2}q_{ij}$,
where $q_{ij}$ are $i.i.d.$ random variables with zero mean and unit variance,
and $A$ and $B$ are respectively $n \times n$ and $N\times N$ deterministic
non-negative definite symmetric (or Hermitian) matrices. We consider the
high-dimensional case, i.e. ${n}/{N}\to d \in (0, \infty)$ as $N\to \infty$.
Assuming $\mathbb E q_{ij}^3=0$ and some mild conditions on $A$ and $B$, we
prove that the limiting distribution of the largest eigenvalue of $\mathcal Q$
coincide with that of the corresponding Gaussian ensemble (i.e. the $\mathcal
Q$ with $X$ being an $i.i.d.$ Gaussian matrix) as long as we have $\lim_{s
\rightarrow \infty}s^4 \mathbb{P}(\vert q_{ij} \vert \geq s)=0$, which is a
sharp moment condition for edge universality. If we take $B=I$, then $\mathcal
Q$ becomes the normal sample covariance matrix and the edge universality holds
true without the vanishing third moment condition. So far, this is the
strongest edge universality result for sample covariance matrices with
correlated data (i.e. non-diagonal $A$) and heavy tails, which improves the
previous results in \cite{BPZ1,LS} (assuming high moments and diagonal $A$),
\cite{Anisotropic} (assuming high moments) and \cite{DY} (assuming diagonal
$A$).


  Isaac Newton is usually associated with the idea of absolute space and time,
and with ballistic light-corpuscle arguments. However, Newton was also a
proponent of wave/particle duality, and published a "new" variable-density
aether model in which light and matter trajectories were either bent by
gravitational fields, or deflected by an aether density gradient. Newton's
(flawed) aether model can be considered as an early attempt at a curved-space
model of gravity.


  The popularity of streaming videos with live, high-action content has led to
an increased interest in High Frame Rate (HFR) videos. In this work we address
the problem of frame rate dependent Video Quality Assessment (VQA) when the
videos to be compared have different frame rate and compression factor. The
current VQA models such as VMAF have superior correlation with perceptual
judgments when videos to be compared have same frame rates and contain
conventional distortions such as compression, scaling etc. However this
framework requires additional pre-processing step when videos with different
frame rates need to be compared, which can potentially limit its overall
performance. Recently, Generalized Entropic Difference (GREED) VQA model was
proposed to account for artifacts that arise due to changes in frame rate, and
showed superior performance on the LIVE-YT-HFR database which contains frame
rate dependent artifacts such as judder, strobing etc. In this paper we propose
a simple extension, where the features from VMAF and GREED are fused in order
to exploit the advantages of both models. We show through various experiments
that the proposed fusion framework results in more efficient features for
predicting frame rate dependent video quality. We also evaluate the fused
feature set on standard non-HFR VQA databases and obtain superior performance
than both GREED and VMAF, indicating the combined feature set captures
complimentary perceptual quality information.


  We investigate different opinion formation models on adaptive network
topologies. Depending on the dynamical process, rewiring can either (i) lead to
the elimination of interactions between agents in different states, and
accelerate the convergence to a consensus state or break the network in
non-interacting groups or (ii) counter-intuitively, favor the existence of
diverse interacting groups for exponentially long times. The mean-field
analysis allows to elucidate the mechanisms at play. Strikingly, allowing the
interacting agents to bear more than one opinion at the same time drastically
changes the model's behavior and leads to fast consensus.


  The popular view according to which Category theory provides a support for
Mathematical Structuralism is erroneous. Category-theoretic foundations of
mathematics require a different philosophy of mathematics. While structural
mathematics studies invariant forms (Awodey) categorical mathematics studies
covariant transformations which, generally, don t have any invariants. In this
paper I develop a non-structuralist interpretation of categorical mathematics
and show its consequences for history of mathematics and mathematics education.


  The traditional Internet has encountered a bottleneck in allocating network
resources for emerging technology needs. Network virtualization (NV) technology
as a future network architecture, the virtual network embedding (VNE) algorithm
it supports shows great potential in solving resource allocation problems.
Combined with the efficient machine learning (ML) algorithm, a neural network
model close to the substrate network environment is constructed to train the
reinforcement learning agent. This paper proposes a two-stage VNE algorithm
based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that
the mapping result of existing heuristic algorithm is easy to converge to the
local optimal solution. For the problem that the existing VNE algorithm based
on ML often ignores the importance of substrate network representation and
training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE)
is proposed. In view of the problem that the existing VNE algorithm often
ignores the underlying resource changes between virtual network requests, a DRL
VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed.
Experimental results show that the above algorithm is superior to other
algorithms.


  RHIC data on high-$p_T$ hadron production display strong suppression in a
wide rapidity region, indicating strong induced energy loss for both
transversally and longitudinally traveling partons. We investigate the
interplay of energy loss and rapidity dependence in a perturbative QCD improved
parton model, and estimate the opacity of the produced hot matter in $AuAu$
collisions at energies $\sqrt{s}=200$ $A$GeV and 63 $A$GeV at different
rapidity values. Direction-dependent suppression offers the possibility to
study the geometry of the hot matter.


  Rotation and orbital eccentricity both strongly influence planetary climate.
Eccentricities can often be measured for exoplanets, but rotation rates are
currently difficult or impossible to constrain. Here we examine how the
combined effects of rotation and eccentricity on observed emission from
ocean-rich terrestrial planets can be used to infer their rotation rates in
circumstances where their eccentricities are known. We employ an Earth climate
model with no land and a slab ocean, and consider two eccentricities ($e=0.3$
and 0.6) and two rotation rates: a fast Earth-like period of 24 hours, and a
slower pseudo-synchronous period that generalizes spin synchronization for
eccentric orbits. We adopt bandpasses of the Mid-Infrared Instrument on the
James Webb Space Telescope as a template for future photometry. At $e=0.3$ the
rotation rates can be distinguished if the planet transits near periastron,
because slow rotation produces a strong day-night contrast and thus an emission
minimum during periastron. However, light curves behave similarly if the planet
is eclipsed near periastron, as well as for either viewing geometry at $e=0.6$.
Rotation rates can nevertheless be distinguished using ratios of emission in
different bands, one in the water vapor window with another in a region of
strong water absorption. These ratios vary over an orbit by $\lesssim\!0.1$ dex
for Earth-like rotation, but by 0.3--0.5 dex for pseudo-synchronous rotation
because of large day-night contrast in upper-tropospheric water. For planets
with condensible atmospheric constituents in eccentric orbits, rotation regimes
might thus be distinguished with infrared observations for a range of viewing
geometries.


  Over any smooth algebraic variety over a $p$-adic local field $k$, we
construct the de Rham comparison isomorphisms for the \'etale cohomology with
partial compact support of de Rham $\mathbb Z_p$-local systems, and show that
they are compatible with Poincar\'e duality and with the canonical morphisms
among such cohomology. We deduce these results from their analogues for rigid
analytic varieties that are Zariski open in some proper smooth rigid analytic
varieties over $k$. In particular, we prove finiteness of \'etale cohomology
with partial compact support of any $\mathbb Z_p$-local systems, and establish
the Poincar\'e duality for such cohomology after inverting $p$.


  In this paper, we study the uniformly rotating vortex patch solutions for the
2D incompressible Euler equations. Specifically, we prove that if the patch
solution is close to the Rankine vortex in a certain weak topology, it is
either the Kirchhoff ellipses or the Rankine vortex.


  We study the dynamics of entanglement in a 1D coupled-cavity array, each
cavity containing a two-level atom, via the Jaynes-Cummings-Hubbard (JCH)
Hamiltonian in the single-excitation sector. The model features a rich variety
of dynamical regimes that can be harnessed for entanglement control. The
protocol is based on setting an excited atom above the ground state and further
letting it evolve following the natural dynamics of the Hamiltonian. Here we
focus on the concurrence between pairs of atoms and its relation to atom-field
correlations and the structure of the array. We show that the extension and
distribution pattern of pairwise entanglement can be manipulated through a
judicious tuning of the atom-cavity coupling strength only. Our work offers a
comprehensive account over the machinery of the single-excitation JCH
Hamiltonian as well as contributes to the design of hybrid light-matter quantum
networks.


  We compare the current effects of rotation in stellar evolution to those of
the magnetic field created by the Tayler instability. In stellar regions, where
magnetic field can be generated by the dynamo due to differential rotation
(Spruit 2002), we find that the growth rate of the magnetic instability is much
faster than for the thermal instability. Thus, meridional circulation is
negligible with respect to the magnetic fields, both for the transport of
angular momentum and of chemical elements. Also, the horizontal coupling by the
magnetic field, which reaches values of a few $10^5$ G, is much more important
than the effects of the horizontal turbulence. The field, however, is not
sufficient to distort the shape of the equipotentials. We impose the condition
that the energy of the magnetic field created by the Tayler--Spruit dynamo
cannot be larger than the energy excess present in the differential rotation.
This leads to a criterion for the existence of the magnetic field in stellar
interiors. Numerical tests are made in a rotating star model of 15 M$_{\odot}$
rotating with an initial velocity of 300 km$\cdot$s$^{-1}$.


  A pitchfork bifurcation of an $(m-1)$-dimensional invariant submanifold of a
dynamical system in $\mathbb{R}^m$ is defined analogous to that in
$\mathbb{R}$. Sufficient conditions for such a bifurcation to occur are stated
and existence of the bifurcated manifolds is proved under the stated
hypotheses. For discrete dynamical systems, the existence of locally attracting
manifolds $M_+$ and $M_-$, after the bifurcation has taken place is proved by
constructing a diffeomorphism of the unstable manifold $M$. For continuous
dynamical systems, the theorem is proved by transforming it to the discrete
case. Techniques used for proving the theorem involve differential topology and
analysis. The theorem is illustrated by means of a canonical example.


  This paper presents a study of improvement in stability in a single machine
connected to infinite bus (SMIB) power system by using static compensator
(STATCOM). The gains of Proportional-Integral-Derivative (PID) controller in
STATCOM are being optimized by heuristic technique based on Particle swarm
optimization (PSO). Further, Bacterial Foraging Optimization (BFO) as an
alternative heuristic method is also applied to select optimal gains of PID
controller. The performance of STATCOM with the above soft-computing techniques
are studied and compared with the conventional PID controller under various
scenarios. The simulation results are accompanied with performance indices
based quantitative analysis. The analysis clearly signifies the robustness of
the new scheme in terms of stability and voltage regulation when compared with
conventional PID.


  The turnover in the stellar initial mass function (IMF) at low mass suggests
the presence of two independent mass functions that combine in different ways
above and below a characteristic mass given by the thermal Jeans mass in the
cloud. In the random sampling model introduced earlier, the Salpeter IMF at
intermediate to high mass follows primarily from the hierarchical structure of
interstellar clouds, which is sampled by various star formation processes and
converted into stars at the local dynamical rate. This power law part is
independent of the details of star formation inside each clump and therefore
has a universal character. The flat part of the IMF at low mass is proposed
here to result from a second, unrelated, physical process that determines only
the probability distribution function for final star mass inside a clump of a
given mass, and is independent of both this clump mass and the overall cloud
structure. Both processes operate for all potentially unstable clumps in a
cloud, regardless of mass, but only the first shows up above the thermal Jeans
mass, and only the second shows up below this mass. Analytical and stochastic
models of the IMF that are based on the uniform application of these two
functions for all masses reproduce the observations well.


  During many years since the birth of quantum mechanics, instrumentalist
interpretations prevailed: the meaning of the theory was expressed in terms of
measurements results. But in the last decades, several attempts to interpret it
from a realist viewpoint have been proposed. Among them, modal interpretations
supply a realist non-collapse account, according to which the system always has
definite properties and the quantum state represents possibilities, not
actualities. However, the traditional modal interpretations faced some
conceptual problems when addressing imperfect measurements. The
modal-Hamiltonian interpretation, on the contrary, proved to be able to supply
an adequate account of the measurement problem, both in its ideal and its
non-ideal versions. Moreover, in the non-ideal case, it gives a precise
criterion to distinguish between reliable and non-reliable measurements.
Nevertheless, that criterion depends on the particular state of the measured
system, and this might be considered as a shortcoming of the proposal. In fact,
one could ask for a criterion of reliability that does not depend on the
features of what is measured but only on the properties of the measurement
device. The aim of this article is precisely to supply such a criterion: we
will adopt an informational perspective for this purpose.


  Object detection is a main task in computer vision. Template matching is the
reference method for detecting objects with arbitrary templates. However,
template matching computational complexity depends on the rotation accuracy,
being a limiting factor for large 3D images (tomograms). Here, we implement a
new algorithm called tensorial template matching, based on a mathematical
framework that represents all rotations of a template with a tensor field.
Contrary to standard template matching, the computational complexity of the
presented algorithm is independent of the rotation accuracy. Using both,
synthetic and real data from tomography, we demonstrate that tensorial template
matching is much faster than template matching and has the potential to improve
its accuracy


  Multi-voxel pattern analysis (MVPA) learns predictive models from task-based
functional magnetic resonance imaging (fMRI) data, for distinguishing when
subjects are performing different cognitive tasks -- e.g., watching movies or
making decisions. MVPA works best with a well-designed feature set and an
adequate sample size. However, most fMRI datasets are noisy, high-dimensional,
expensive to collect, and with small sample sizes. Further, training a robust,
generalized predictive model that can analyze homogeneous cognitive tasks
provided by multi-site fMRI datasets has additional challenges. This paper
proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning
(TL) approach that can functionally align homogeneous multi-site fMRI datasets,
and so improve the prediction performance in every site. SSTL first extracts a
set of common features for all subjects in each site. It then uses TL to map
these site-specific features to a site-independent shared space in order to
improve the performance of the MVPA. SSTL uses a scalable optimization
procedure that works effectively for high-dimensional fMRI datasets. The
optimization procedure extracts the common features for each site by using a
single-iteration algorithm and maps these site-specific common features to the
site-independent shared space. We evaluate the effectiveness of the proposed
method for transferring between various cognitive tasks. Our comprehensive
experiments validate that SSTL achieves superior performance to other
state-of-the-art analysis techniques.