  We examine the potential of the $e^-p \to e^-\gamma^*p \to e^-W^-q'X$
($\gamma^*$ is Weizsacker-Willams photon) reaction to probe the non-standard
$W^+W^-\gamma$ couplings at the Large Hadron electron Collider (LHeC) and the
Future Circular Collider-hadron electron (FCC-he). We find $95\%$ confidence
level bounds on the anomalous coupling $\Delta\kappa_\gamma$ and
$\lambda_\gamma$ parameters in the view of effective Lagrangian approach with
various values of the integrated luminosity. We assume center-of-mass energies
of the electron-proton system $\sqrt{s}= 1.30, 1.98, 7.07, 10\hspace{0.8mm}{\rm
TeV}$ and luminosities ${\cal L} = 10-1000 \hspace{0.8mm}{\rm fb^{-1}}$. The
best limits obtained from the process $e^-p \to e^-\gamma^*p \to e^-W^-q'X$ on
the anomalous $\Delta\kappa_\gamma$ and $\lambda_\gamma$ couplings are
$\Delta\kappa_\gamma = |0.00069|$ and $\lambda_\gamma = [-0.0099, 0.0054]$.
These bounds show that the process under consideration is a good prospect for
the searching of the non-standard $\Delta\kappa_\gamma$ and $\lambda_\gamma$
couplings at the LHeC and the FCC-he. In addition, our results provide
complementary information on other results for $\Delta\kappa_\gamma$ and
$\lambda_\gamma$ couplings.


  The main physics topics of interest at LEP2, the CERN electron-positron
collider with center-of-mass energy in the range (161-192) GeV, are reviewed.
Progresses in both the precision tests of the standard model and the field of
discovery physics attainable at this machine are discussed. A few results of
the analysis of data collected in the 1996 first runs of LEP2 are also
presented.


  We study relatively minimal surfaces equipped with an isotrivial elliptic
fibration in positive characteristic by means of the notion of equivariantly
normal curves introduced and developed recently by Brion. Such surfaces are
isomorphic to a contracted product $E\times^G X$, where $E$ is an elliptic
curve, $G$ is a finite subgroup scheme of $E$ and $X$ is a $G$-normal curve.
Using this description, we compute their Betti numbers to determine their
birational classes. This allow us to complete the classification of maximal
automorphism groups of surfaces in any characteristic. When $G$ is
diagonalizable, we compute additional invariants to study the structure of
their Picard schemes.


  Speech perception involves storing and integrating sequentially presented
items. Recent work in cognitive neuroscience has identified temporal and
contextual characteristics in humans' neural encoding of speech that may
facilitate this temporal processing. In this study, we simulated similar
analyses with representations extracted from a computational model that was
trained on unlabelled speech with the learning objective of predicting upcoming
acoustics. Our simulations revealed temporal dynamics similar to those in brain
signals, implying that these properties can arise without linguistic knowledge.
Another property shared between brains and the model is that the encoding
patterns of phonemes support some degree of cross-context generalization.
However, we found evidence that the effectiveness of these generalizations
depends on the specific contexts, which suggests that this analysis alone is
insufficient to support the presence of context-invariant encoding.


  This is a short note on generalized $G_2$-structures obtained as a
consequence of a $T$-dual construction given in a previous work of the authors
together with Leonardo Soriani. Given classical $G_2$-structure on certain
seven dimensional manifolds, either closed or co-closed, we obtain integrable
generalized $G_2$-structures which are no longer an usual one, and with
non-zero three form in general. In particular we obtain manifolds admitting
closed generalized $G_2$-structures not admitting closed (usual)
$G_2$-structures.


  The democratic approach to account for fermion masses and mixing is known to
be successful not only in the quark sector but also in the lepton sector. Here
we extend this ansatz to supersymmetric standard models, in which the K\"ahler
potential obeys underlying S_3 flavor symmetries. The requirement of neutrino
bi-large mixing angles constrains the form of the K\"ahler potential for
left-handed lepton multiplets. We find that right-handed sleptons can have
non-degenerate masses and flavor mixing, while left-handed sleptons are argued
to have universal and hence flavor-blind masses. This mass pattern is testable
in future collider experiments when superparticle masses will be measured
precisely. Lepton flavor violation arises in this scenario. In particular, \mu
\to e \gamma is expected to be observed in a planning future experiment if
supersymmetry breaking scale is close to the weak scale.


  The use of real clocks and measuring rods in quantum mechanics implies a
natural loss of unitarity in the description of the theory. We briefly review
this point and then discuss the implications it has for the measurement problem
in quantum mechanics. The intrinsic loss of coherence allows to circumvent some
of the usual objections to the measurement process as due to environmental
decoherence.


  Recent BESIII data indicate a significant rate of the process $e^+e^- \to h_c
\pi^+ \pi^-$ at the Y(4260) and Y(4360) resonances, implying a substantial
breaking of the heavy quark spin symmetry. We consider these resonances within
the picture of hadrocharmonium, i.e. of (relatively) compact charmonium
embedded in a light quark mesonic excitation. We suggest that the resonances
Y(4260) and Y(4360) are a mixture, with mixing close to maximal, of two states
of hadrochamonium, one containing a spin-triplet $c \bar c$ pair and the other
containing a spin-singlet heavy quark pair. We argue that this model is in a
reasonable agreement with the available data and produces distinctive and
verifiable predictions for the energy dependence of the production rate in
$e^+e^-$ annihilation of the final states $J/\psi \pi \pi$, $\psi' \pi \pi$ and
$h_c \pi \pi$, including the pattern of interference between the two
resonances.


  We evaluate zeta-functions $\zeta(s)$ at $s=0$ for invariant non-minimal
2nd-order vector and tensor operators defined on maximally symmetric even
dimensional spaces. We decompose the operators into their irreducible parts and
obtain their corresponding eigenvalues. Using these eigenvalues, we are able to
explicitly calculate $\zeta(0)$ for the cases of Euclidean spaces and
$N$-spheres. In the $N$-sphere case, we make use of the Euler-Maclaurin formula
to develop asymptotic expansions for the required sums. The resulting
$\zeta(0)$ values for dimensions 2 to 10 are given in the Appendix.


  We investigate spectral evolution in 37 bright, long gamma-ray bursts
observed with the BATSE Spectroscopy Detectors. High resolution spectra are
characterized by the energy of the peak of \nfn~and the evolution of this
quantity is examined relative to the emission intensity. In most cases it is
found that this peak energy either rises with or slightly precedes major
intensity increases and softens for the remainder of the pulse. Inter-pulse
emission is generally harder early in the burst. For bursts with multiple
intensity pulses, later spikes tend to be softer than earlier ones indicating
that the energy of the peak of \nfn~is bounded by an envelope which decays with
time. Evidence is found that bursts in which the bulk of the flux comes well
after the event which triggers the instrument tend to show less peak energy
variability and are not as hard as several bursts in which the emission occurs
promptly after the trigger. Several recently proposed burst models are examined
in light of these results and no qualitative conflicts with the observations
presented here are found.


  Let $X$ be an elliptic surface over ${\bf P}^1$ with $\kappa(X)=1$, and
$M=M(c_2)$ be the moduli scheme of rank-two stable sheaves $E$ on $X$ with
$(c_1(E),c_2(E))=(0,c_2)$ in $\operatorname{Pic}(X)\times\mathbb{Z}$. We look
into defining equations of $M$ at its singularity $E$, partly because if $M$
admits only canonical singularities, then the Kodaira dimension $\kappa(M)$ can
be calculated. We show the following.
  (A) $E$ is at worst canonical singularity of $M$ if the restriction of
$E_{\eta}$ to the generic fiber of $X$ has no rank-one subsheaf, and if the
number of multiple fibers of $X$ is a few.
  (B) We obtain that $\kappa(M)=\{1+\dim(M)\}/2$ and the Iitaka program of $M$
can be described in purely moduli-theoretic way for $c_2\gg 0$, when
$\chi({\mathcal O}_X)=1$, $X$ has just two multiple fibers, and one of its
multiplicities equals $2$.
  (C) On the other hand, when $E_{\eta}$ has a rank-one subsheaf, it may be
insufficient to look at only the degree-two part of defining equations to judge
whether $E$ is at worst canonical singularity or not.


  Capture and annihilation of WIMP-like dark matter in red giant stars can lead
to faster-than-expected ignition of the helium core, and thus a lower tip of
the red giant branch (TRGB) luminosity. We use Gaia data to place constraints
on the dark matter-nucleon cross section using 22 globular clusters with
measured TRGB luminosities, and place projections on the sensitivity resulting
from 161 clusters with full phase space distributions observed by Gaia.
Although limits remain weaker than those from Earth-based direct detection
experiments, they represent a constraint that is fully independent of dark
matter properties in the Solar neighbourhood, probing its properties across the
entire Milky Way galaxy. Based on our findings, it is likely that the use of
the TRGB as a standard candle in $H_0$ measurements is very robust against the
effects of dark matter.


  Let $S$ be a pool of $s$ parties and Alice be the dealer. In this paper, we
propose a scheme that allows the dealer to encrypt messages in such a way that
only one authorized coalition of parties (which the dealer chooses depending on
the message) can decrypt. At the setup stage, each of the parties involved in
the process receives an individual key from the dealer. To decrypt information,
an authorized coalition of parties must work together to use their keys. Based
on this scheme, we propose a threshold encryption scheme. For a given message
$f$ the dealer can choose any threshold $m = m(f).$ More precisely, any set of
parties of size at least $m$ can evaluate $f$; any set of size less than $m$
cannot do this. Similarly, the distribution of keys among the included parties
can be done in such a way that authorized coalitions of parties will be given
the opportunity to put a collective digital signature on any documents. This
primitive can be generalized to the dynamic setting, where any user can
dynamically join the pool $S$. In this case the new user receives a key from
the dealer. Also any user can leave the pool $S$. In both cases, already
distributed keys of other users do not change. The main feature of the proposed
schemes is that for a given $s$ the keys are distributed once and can be used
multiple times.
  The proposed scheme is based on the idea of hidden multipliers in encryption.
As a platform, one can use both multiplicative groups of finite fields and
groups of invertible elements of commutative rings, in particular,
multiplicative groups of residue rings. We propose two versions of this scheme.


  In this paper, we propose a novel fully unsupervised framework that learns
action representations suitable for the action segmentation task from the
single input video itself, without requiring any training data. Our method is a
deep metric learning approach rooted in a shallow network with a triplet loss
operating on similarity distributions and a novel triplet selection strategy
that effectively models temporal and semantic priors to discover actions in the
new representational space. Under these circumstances, we successfully recover
temporal boundaries in the learned action representations with higher quality
compared with existing unsupervised approaches. The proposed method is
evaluated on two widely used benchmark datasets for the action segmentation
task and it achieves competitive performance by applying a generic clustering
algorithm on the learned representations.


  Multi-Agent Path Finding (MAPF) is a problem of finding a sequence of
movements for agents to reach their assigned location without collision.
Centralized algorithms usually give optimal solutions, but have difficulties to
scale without employing various techniques - usually with a sacrifice of
optimality; but solving MAPF problems with the number of agents greater than a
thousand remains a challenge nevertheless. To tackle the scalability issue, we
present DMAPF - a decentralized and distributed MAPF solver, which is a
continuation of our recently published work, ros-dmapf. We address the issues
of ros-dmapf where it (i) only works in maps without obstacles; and (ii) has a
low success rate with dense maps. Given a MAPF problem, both ros-dmapf and
DMAPF divide the map spatially into subproblems, but the latter further divides
each subproblem into disconnected regions called areas. Each subproblem is
assigned to a distributed solver, which then individually creates an abstract
plan - a sequence of areas that an agent needs to visit - for each agent in it,
and interleaves agent migration with movement planning. Answer Set Programming,
which is known for its performance in small but complex problems, is used in
many parts including problem division, abstract planning, border assignment for
the migration, and movement planning. Robot Operating System is used to
facilitate communication between the solvers and to enable the opportunity to
integrate with robotic systems. DMAPF introduces a new interaction protocol
between the solvers, and mechanisms that together result in a higher success
rate and better solution quality without sacrificing much of the performance.
We implement and experimentally validate DMAPF by comparing it with other
state-of-the-art MAPF solvers and the results show that our system achieves
better scalability.


  We generalize the invariant imbedding theory of the wave propagation and
derive new invariant imbedding equations for the propagation of arbitrary
number of coupled waves of any kind in arbitrarily-inhomogeneous stratified
media, where the wave equations are effectively one-dimensional. By doing this,
we transform the original boundary value problem of coupled second-order
differential equations to an initial value problem of coupled first-order
differential equations, which makes the numerical solution of the coupled wave
equations much easier. Using the invariant imbedding equations, we are able to
calculate the matrix reflection and transmission coefficients and the wave
amplitudes inside the inhomogeneous media exactly and efficiently. We establish
the validity and the usefulness of our results by applying them to the
propagation of circularly-polarized electromagnetic waves in one-dimensional
photonic crystals made of isotropic chiral media. We find that there are three
kinds of bandgaps in these structures and clarify the nature of these bandgaps
by exact calculations.


  In the past decade, sparsity-driven regularization has led to significant
improvements in image reconstruction. Traditional regularizers, such as total
variation (TV), rely on analytical models of sparsity. However, increasingly
the field is moving towards trainable models, inspired from deep learning. Deep
image prior (DIP) is a recent regularization framework that uses a
convolutional neural network (CNN) architecture without data-driven training.
This paper extends the DIP framework by combining it with the traditional TV
regularization. We show that the inclusion of TV leads to considerable
performance gains when tested on several traditional restoration tasks such as
image denoising and deblurring.


  The eROSITA telescope array aboard the Spektrum Roentgen Gamma (SRG)
satellite began surveying the sky in December 2019, with the aim of producing
all-sky X-ray source lists and sky maps of an unprecedented depth. Here we
present catalogues of both point-like and extended sources using the data
acquired in the first six months of survey operations (eRASS1; completed June
2020) over the half sky whose proprietary data rights lie with the German
eROSITA Consortium. We describe the observation process, the data analysis
pipelines, and the characteristics of the X-ray sources. With nearly 930000
entries detected in the most sensitive 0.2-2.3 keV energy range, the eRASS1
main catalogue presented here increases the number of known X-ray sources in
the published literature by more than 60%, and provides a comprehensive
inventory of all classes of X-ray celestial objects, covering a wide range of
physical processes. A smaller catalogue of 5466 sources detected in the less
sensitive but harder 2.3-5 keV band is the result of the first true imaging
survey of the entire sky above 2 keV. We show that the number counts of X-ray
sources in eRASS1 are consistent with those derived over narrower fields by
past X-ray surveys of a similar depth, and we explore the number counts
variation as a function of the location in the sky. Adopting a uniform all-sky
flux limit (at 50% completeness) of F_{0.5-2 keV} > 5 \times 10^{-14}$
erg\,s$^{-1}$\,cm$^{-2}$, we estimate that the eROSITA all-sky survey resolves
into individual sources about 20% of the cosmic X-ray background in the 1-2 keV
range. The catalogues presented here form part of the first data release (DR1)
of the SRG/eROSITA all-sky survey. Beyond the X-ray catalogues, DR1 contains
all detected and calibrated event files, source products (light curves and
spectra), and all-sky maps. Illustrative examples of these are provided.


  The European Union has proposed the Artificial Intelligence Act which
introduces detailed requirements of transparency for AI systems. Many of these
requirements can be addressed by the field of explainable AI (XAI), however,
there is a fundamental difference between XAI and the Act regarding what
transparency is. The Act views transparency as a means that supports wider
values, such as accountability, human rights, and sustainable innovation. In
contrast, XAI views transparency narrowly as an end in itself, focusing on
explaining complex algorithmic properties without considering the
socio-technical context. We call this difference the ``transparency gap''.
Failing to address the transparency gap, XAI risks leaving a range of
transparency issues unaddressed. To begin to bridge this gap, we overview and
clarify the terminology of how XAI and European regulation -- the Act and the
related General Data Protection Regulation (GDPR) -- view basic definitions of
transparency. By comparing the disparate views of XAI and regulation, we arrive
at four axes where practical work could bridge the transparency gap: defining
the scope of transparency, clarifying the legal status of XAI, addressing
issues with conformity assessment, and building explainability for datasets.


  The Coronavirus pandemic has created complex challenges and adverse
circumstances. This research discovers public sentiment amidst problematic
socioeconomic consequences of the lockdown, and explores ensuing four potential
sentiment associated scenarios. The severity and brutality of COVID-19 have led
to the development of extreme feelings, and emotional and mental healthcare
challenges. This research identifies emotional consequences - the presence of
extreme fear, confusion and volatile sentiments, mixed along with trust and
anticipation. It is necessary to gauge dominant public sentiment trends for
effective decisions and policies. This study analyzes public sentiment using
Twitter Data, time-aligned to COVID-19, to identify dominant sentiment trends
associated with the push to 'reopen' the economy. Present research uses textual
analytics methodologies to analyze public sentiment support for two potential
divergent scenarios - an early opening and a delayed opening, and consequences
of each. Present research concludes on the basis of exploratory textual
analytics and textual data visualization, that Tweets data from American
Twitter users shows more trust sentiment support, than fear, for reopening the
US economy. With additional validation, this could present a valuable time
sensitive opportunity for state governments, the federal government,
corporations and societal leaders to guide the nation into a successful new
normal future.


  High-intensity laser fields acting on free electrons were proposed to create
sufficiently large accelerations to enable detection of Unruh radiation.
However, the currently achievable electron accelerations are not large enough.
Here we demonstrate that Unruh effect in high-intensity laser fields is
strongly enhanced near hyperbolic metamaterials, so that many orders of
magnitude smaller accelerations may be used to detect Unruh radiation.


  Magnetic field topology frozen in ideal magnetohydrodynamics (MHD) and its
breakage in near ideal MHD are reviewed in two parts. The first part gives a
physically complete description of the frozen in field topology, taking
magnetic flux conservation as fundamental and treating four topics, Eulerian
and Lagrangian descriptions of MHD, Chandrasekhar-Kendall and Euler-potential
field representations, magnetic helicity, and inviscid vortex dynamics in
comparison to ideal MHD. A corollary clarifies the challenge of achieving a
high degree of the frozen in condition in numerical MHD. The second part treats
field topology breakage centered on the Parker Magnetostatic Theorem on a
general incompatibility of a continuous magnetic field with the dual demand of
force free equilibrium and an arbitrarily prescribed, 3D field topology.
Preserving field topology as a global constraint readily results in formation
of tangential magnetic discontinuities, i.e., electric current sheets of zero
thickness. A similar incompatibility is present in the steady, force and
thermal balance of a heated radiating fluid subject to an anisotropic thermal
flux conducted strictly along the frozen in magnetic field in the low beta
limit. In a weakly resistive fluid the thinning of current sheets by these
incompatibilities inevitably results in sheet dissipation, resistive heating
and topological changes in the field despite the small resistivity. Faraday
induction drives but also macroscopically limits this mode of energy
dissipation, storing free energy in self organized, ideal MHD structures. This
property of MHD turbulence captured by the Taylor hypothesis is reviewed in
relation to the Sun's corona, calling for a basic quantitative description of
the breakdown of flux conservation in the low resistivity limit. A cylindrical,
initial boundary value problem provides specificity in the review.


  We classify Ulrich vector bundles that are not big on smooth complex surfaces
and threefolds.


  These are the proceedings of the 10th International Workshop on Formal
Engineering approaches to Software Components and Architectures (FESCA). The
workshop was held on March 23, 2013 in Rome (Italy) as a satellite event to the
European Joint Conference on Theory and Practice of Software (ETAPS'13).
  The aim of the FESCA workshop is to bring together both young and senior
researchers from formal methods, software engineering, and industry interested
in the development and application of formal modelling approaches as well as
associated analysis and reasoning techniques with practical benefits for
component-based software engineering.
  FESCA aims to address the open question of how formal methods can be applied
effectively to these new contexts and challenges. FESCA is interested in both
the development and application of formal methods in component-based
development and tries to cross-fertilize their research and application.


  We are interested in creating statistical methods to provide informative
summaries of random fields through the geometry of their excursion sets. To
this end, we introduce an estimator for the length of the perimeter of
excursion sets of random fields on $\mathbb{R}^2$ observed over regular square
tilings. The proposed estimator acts on the empirically accessible binary
digital images of the excursion regions and computes the length of a piecewise
linear approximation of the excursion boundary. The estimator is shown to be
consistent as the pixel size decreases, without the need of any normalization
constant, and with neither assumption of Gaussianity nor isotropy imposed on
the underlying random field. In this general framework, even when the domain
grows to cover $\mathbb{R}^2$, the estimation error is shown to be of smaller
order than the side length of the domain. For affine, strongly mixing random
fields, this translates to a multivariate Central Limit Theorem for our
estimator when multiple levels are considered simultaneously. Finally, we
conduct several numerical studies to investigate statistical properties of the
proposed estimator in the finite-sample data setting.


  The purpose of this article is to study the problem of finding sharp lower
bounds for the norm of the product of polynomials in the ultraproducts of
Banach spaces $(X_i)_{\mathfrak U}$. We show that, under certain hypotheses,
there is a strong relation between this problem and the same problem for the
spaces $X_i$.


  This report defines (plain) Dag-like derivations in the purely implicational
fragment of minimal logic $M_{\imply}$. Introduce the horizontal collapsing set
of rules and the algorithm {\bf HC}. Explain why {\bf HC} can transform any
polynomial height-bounded tree-like proof of a $M_{\imply}$ tautology into a
smaller dag-like proof. Sketch a proof that {\bf HC} preserves the soundness of
any tree-like ND in $M_{\imply}$ in its dag-like version after the horizontal
collapsing application. We show some experimental results about applying the
compression method to a class of (huge) propositional proofs and an example,
with non-hamiltonian graphs, for qualitative analysis. The contributions
include the comprehensive presentation of the set of horizontal compression
(HC), the (sketch) of the proof that HC rules preserve soundness and the
demonstration that the compressed dag-like proofs are polynomially
upper-bounded when the submitted tree-like proof is height and foundation
poly-bounded. Finally, in the appendix, we outline an algorithm that verifies
in polynomial time on the size of the dag-like proofs whether they are valid
proofs of their conclusions.


  We present a progress report on our study of the charmonium spectrum in full
QCD on anisotropic lattices generated by the Hadron Spectrum Collaboration. We
adopt a large basis of interpolating operators to extract the excited
charmonium states using the variational method. A detailed spectrum of excited
charmonium mesons in many $J^{PC}$ channels is obtained. Hybrid states with
exotic and non-exotic quantum numbers are determined and preliminary results
from a study of disconnected contributions to the $\eta_c$ are presented.


  In this paper we study the parabolic representations of 2-bridge links by
finiding arc coloring vectors on the Conway diagram. The method we use is to
convert the system of conjugation quandle equations to that of symplectic
quandle equations. In this approach, we have an integer coefficient monic
polynomial $P_K(u)$ for each 2-bridge link $K$, and each zero of this
polynomial gives a set of arc coloring vectors on the diagram of $K$ satisfying
the system of symplectic quandle equations, which gives an explicit formula for
a parabolic representation of $K$. We then explain how these arc coloring
vectors give us the closed form formulas of the complex volume and the cusp
shape of the representation. As other applications of this method, we show some
interesting arithmetic properties of the Riley polynomial and of the trace
field, and also describe a necessary and sufficient condition for the existence
of epimorphisms between 2-bridge link groups in terms of divisibility of the
corresponding Riley polynomials.


  Nambu-Goto action for bosonic string predicts the quark-antiquark potential
to be $V(r) = -\frac{\gamma}{r}+ \sigma r + \mu_0$. The coefficient
$\gamma=\frac{\pi(d-2)}{24}$ is the L\"{u}scher coefficient of the L\"{u}scher
term $\frac{\gamma}{r}$, which depends upon the space-time dimension `d'. Very
recently, we have developed meson wave functions in higher dimension with this
potential from higher dimensional Schrodinger equation by applying quantum
mechanical perturbation technique with both $L\ddot{u}scher$ term as parent and
as perturbation. In this letter, we analyze Isgur-Wise function for heavy-light
mesons using these wave functions in higher dimension and make a comparative
study on the status of the perturbation technique in both the cases.


  The search for and potential identification of dark matter (DM) will require
a simultaneous, multi-pronged approach with important roles to be played by the
LHC, both direct and indirect DM detection experiments and, eventually, the
ILC. The 19-parameter p(henomenological)MSSM can provide a broad framework for
complementarity studies of neutralino DM. In this talk, I summarized the
sensitivity of these searches at the 7, 8 and 14 TeV LHC, combined with those
from \Fermi, CTA, IceCube/DeepCore, COUPP, LZ and XENON. The strengths and
weaknesses of each of these searches is briefly discussed as is their
interdependent roles in probing the pMSSM model parameter space. I also comment
on the future role of the ILC in exploring the detailed nature of neutralino
DM.


  Previously, we proposed a data buffering system that makes use of a pair of
white light cavities. For application to telecommunication systems, it would be
convenient to realize such a device using fiber optic resonators. In this
paper, we present the design of such a system, where the white light cavity
effect is produced by using stimulated Brillouin scattering. The system
consists of a pair of fiber optic white light cavities placed in series. As in
the original proposal, the delay time can be controlled independently of the
bandwidth of the data pulses. Furthermore, we show how the bandwidth of the
system can be made as large as several times the Brillouin frequency shift. We
also show that the net delay achievable in such a buffer can be significantly
larger than what can be achieved using a conventional recirculating loop
buffer.


  Concurrent transcranial magnetic stimulation (TMS) and electroencephalography
(EEG), or TMS-EEG, holds the potential to broaden the clinical applications of
TMS beyond its traditional role in evaluating the cortico-spinal tract and
motor cortices. TMS-evoked potentials (TEPs) have emerged as valuable tools in
clinical research, enabling the assessment of cortical excitability and
effective connectivity between cortical regions in various psychiatric and
neurological disorders and are increasingly recognized as a promising candidate
biomarker for aiding in diagnosis and prognosis. Despite the well-established
diagnostic utility of TMS, the clinical implementation of TMS-EEG has yet to
meet the necessary standards. One critical aspect that often remains
unaddressed is the reliability of TEP measurements. In this context, we outline
the crucial reliability assessments required to determine the clinical
applicability of TEPs. Firstly, we conduct a comprehensive review of the
existing literature on reliability, encompassing both theoretical and
statistical considerations. Subsequently, we present the current state of
knowledge on TEP reliability. We emphasize the specific elements of reliability
that must be incorporated to facilitate a unified, evidence-derived assessment
of TMS-EEG as a clinical tool.


  Slurry transportation via pipelines is essential for global industries,
offering efficiency and environmental benefits. Specifically, the precise
calibration of physical parameters for transporting raw phosphate material to
fertilizer plants is crucial to minimize energy losses and ensure secure
operations. Computational fluid dynamics (CFD) is commonly employed to
understand solid concentration, velocity distributions, and flow pressure along
the pipeline. However, numerical solutions for slurry flows often entail
uncertainties from initial and boundary conditions, emphasizing the need for
quantification. This study addresses the challenge by proposing a framework
that combines proper orthogonal decomposition and polynomial chaos expansions
to quantify uncertainties in two-dimensional phosphate slurry flow simulations.
The use of surrogate modeling methods, like polynomial chaos expansion, proves
effective in reducing computational costs associated with direct stochastic
simulations, especially for complex flows with high spatial variability, as
observed in phosphate slurries. Numerical results demonstrate the accuracy of
the non-intrusive reduction method in reproducing mean and variance
distributions. Moreover, the uncertainty quantification analysis shows that the
reduced-order model significantly reduces computational costs compared to the
full-order model.


  Zechmeister et al. (2009) surveyed 38 nearby M dwarfs from March 2000 to
March 2007 with VLT2 and the UVES spectrometer. This data has recently been
reanalyzed (Butler et al. 2019), yielding a significant improvement in the
Doppler velocity precision. Spurred by this, we have combined the UVES data
with velocity sets from HARPS, Magellan/PFS, and Keck/HIRES. Sixteen planet
candidates have been uncovered orbiting nine M dwarfs. Five of them are new
planets corresponding to radial velocity signals, which are not sensitive to
the choice of noise models and are identified in multiple data sets over
various timespans. Eight candidate planets require additional observation to be
confirmed. We also confirm three previously reported planets. Among the new
planets, GJ 180 d and GJ 229A c are super-Earths located in the conservative
habitable zones of their host stars. We investigate their dynamical stability
using the Monte Carlo approach and find both planetary orbits are robust to the
gravitational perturbations of the companion planets. Due to their proximity to
the Sun, the angular separation between the host stars and the potentially
habitable planets in these two systems is 25 and 59 mas, respectively. They are
thus good candidates for future direct imaging by JWST and E-ELT. In addition
we find GJ 433 c, a cold super-Neptune belonging to an unexplored population of
Neptune-like planets. With a separation of 0.5 as from its host star, GJ 433 c
is probably the first realistic candidate for direct imaging of cold Neptunes.
A comprehensive survey of these planets is important for the studies of planet
formation.


  Thouless pump is a one-dimensional dynamic topological effect that stems from
the same topological mechanism as the renowned two-dimensional Chern
insulators, with one momentum dimension replaced by a time variant evolution
parameter. The underlying physics is abelian, concerning topologies of
individual bands. By introducing multi-band non-abelian topology into pumping,
much richer physics is expected that goes beyond the abelian counterpart. Here
we report the observation of a non-abelian Thouless pump in an acoustic
waveguide array with judiciously engineered coupling configurations. The
observed non-abelian effect originates from time-evolution of eigen-states in
Z_3 pump cycles that traverse across several band degeneracies in the parameter
space in a three-band system. Interestingly, the pump of the eigenstates
exhibits non-commutativity, i.e. the final state depends critically on the
order of the pumping paths. Our study paves ways for exploring and utilizing
non-abelian dynamical effects in classical systems.


  The classical action for pure Yang--Mills gauge theory can be formulated as a
deformation of the topological $BF$ theory where, beside the two-form field
$B$, one has to add one extra-field $\eta$ given by a one-form which transforms
as the difference of two connections. The ensuing action functional gives a
theory that is both classically and quantistically equivalent to the original
Yang--Mills theory. In order to prove such an equivalence, it is shown that the
dependency on the field $\eta$ can be gauged away completely. This gives rise
to a field theory that, for this reason, can be considered as semi-topological
or topological in some but not all the fields of the theory. The symmetry group
involved in this theory is an affine extension of the tangent gauge group
acting on the tangent bundle of the space of connections. A mathematical
analysis of this group action and of the relevant BRST complex is discussed in
details.


  The sensitivities to the anomalous quartic gauge boson coupling
$\gamma\gamma\gamma Z$ are estimated via $\gamma Z$ production with intact
protons in the forward region at the LHC. Proton tagging proves to be a
powerful tool to suppress the background, which allows consideration of the
hadronic decays of the $Z$ boson in addition to the leptonic ones. We discuss
the discovery potential for an integrated luminosity of $300\,\mathrm{fb}^{-1}$
and $3000\,\mathrm{fb}^{-1}$. The sensitivity we obtain at
$300\,\mathrm{fb}^{-1}$ goes beyond the one expected from LHC bounds on the
$Z\rightarrow \gamma\gamma\gamma$ decay by about three orders of magnitude. The
$\gamma Z$ channel provides important discriminatory information with respect
to the exclusive $\gamma\gamma$ channel, as many particles beyond the Standard
Model (such as a radion or Kaluza Klein gravitons) predict a signal in the
latter but not the former.


  In this article we study local constancy of the mod $p$ reduction of certain
$2$-dimensional crystalline representations of
$\mathrm{Gal}\left(\bar{\mathbb{Q}}_p/\mathbb{Q}_p\right)$ using the mod $p$
local Langlands correspondence. We prove local constancy in the weight space by
giving an explicit lower bound on the local constancy radius centered around
weights going up to $(p-1)^{2} +3$ and the slope fixed in $(0, \ p-1)$
satisfying certain constraints. We establish the lower bound by determining
explicitly the mod $p$ reductions at nearby weights and applying a local
constancy result of Berger.


  The network of 5823 cities of Mexico with a population more than 5000
inhabitants is studied. Our analysis is focused to the spectral properties of
the adjacency matrix, the small-world properties of the network, the
distribution of the clustering coefficients and the degree distribution of the
vertices. The connection of these features with the spread of epidemics on this
network is also discussed.


  We study solution methods for (strongly-)convex-(strongly)-concave
Saddle-Point Problems (SPPs) over networks of two type - master/workers (thus
centralized) architectures and meshed (thus decentralized) networks. The local
functions at each node are assumed to be similar, due to statistical data
similarity or otherwise. We establish lower complexity bounds for a fairly
general class of algorithms solving the SPP. We show that a given suboptimality
$\epsilon>0$ is achieved over master/workers networks in
$\Omega\big(\Delta\cdot \delta/\mu\cdot \log (1/\varepsilon)\big)$ rounds of
communications, where $\delta>0$ measures the degree of similarity of the local
functions, $\mu$ is their strong convexity constant, and $\Delta$ is the
diameter of the network. The lower communication complexity bound over meshed
networks reads $\Omega\big(1/{\sqrt{\rho}} \cdot {\delta}/{\mu}\cdot\log
(1/\varepsilon)\big)$, where $\rho$ is the (normalized) eigengap of the gossip
matrix used for the communication between neighbouring nodes. We then propose
algorithms matching the lower bounds over either types of networks (up to
log-factors). We assess the effectiveness of the proposed algorithms on a
robust logistic regression problem.


  A graph is locally irregular if any pair of adjacent vertices have distinct
degrees. A locally irregular decomposition of a graph $G$ is a decomposition
$\mathcal{D}$ of $G$ such that every subgraph $H \in \mathcal{D}$ is locally
irregular. A graph is said to be decomposable if it admits a locally irregular
decomposition. We prove that any decomposable split graph can be decomposed
into at most three locally irregular subgraphs and we characterize all split
graphs whose decomposition can be into one, two or three locally irregular
subgraphs.


  Contrary to commonly held belief, we show that one can obtain a low value for
$M_R$, the $SU(2)_R$ breaking scale, in grand unification theories based on
$SO(10)$. This possibility emerges in the supersymmetric version of $SO(10)$
with a judicious choice of Higgs content. The unification scale is found to be
consistent with the constraint from proton decay. This result is first
explicitly demonstrated using the one loop renormalization group equations, and
then a full two loop analysis is carried out.


  For which positive integers $n,k,r$ does there exist a linear $[n,k]$ code
$C$ over $\mathbb{F}_q$ with all codeword weights divisible by $q^r$ and such
that the columns of a generating matrix of $C$ are projectively distinct? The
motivation for studying this problem comes from the theory of partial spreads,
or subspace codes with the highest possible minimum distance, since the set of
holes of a partial spread of $r$-flats in $\operatorname{PG}(v-1,\mathbb{F}_q)$
corresponds to a $q^r$-divisible code with $k\leq v$. In this paper we provide
an introduction to this problem and report on new results for $q=2$.


  We report on magnetic and electrical transport properties of Sb2-xCrxTe3
single crystals with 0 <= x <= 0.095 over temperatures from 2 K to 300 K. A
ferromagnetic state develops in these crystals at low temperatures with Curie
temperatures that are proportional to x (for x > 0.014), attaining a maximum
value of 20 K for x = 0.095. Hysteresis below TC for applied field parallel to
the c-axis is observed in both magnetization and Hall effect measurements.
Magnetic as well as transport data indicate that Cr takes the 3+ (3d3) valence
state, substituting for antimony in the host lattice structure, and does not
significantly affect the background hole concentration. Analysis of the
anomalous Hall effect reveals that skew scattering is responsible for its
presence. These results broaden the scope of ferromagnetism in the V2-VI3
diluted magnetic semiconductors (DMS) and in ferromagnetic DMS structures
generally


  Shocked winds of massive stars in young stellar clusters have been proposed
as possible sites in which relativistic particles are accelerated. Electrons,
accelerated in such an environment, are expected to efficiently comptonize
optical radiation (from massive stars) and the infra-red radiation
(re-scattered by the dust within the cluster) producing GeV-TeV gamma-rays. We
investigate the time dependent process of acceleration, propagation and
radiation of electrons in the stellar wind of the massive star $\Theta^1$ Ori C
within the Trapezium cluster. This cluster is located within the nearby Orion
Nebula (M 42). We show that the gamma-ray emission expected from the Trapezium
cluster is consistent with the present observations of the Orion Molecular
Cloud by the Fermi-LAT telescope provided that the efficiency of energy
conversion from the stellar wind to relativistic electrons is very low, i.e.
$\chi < 10^{-4}$. For such low efficiencies, the gamma-ray emission from
electrons accelerated in the stellar wind of $\Theta^1$ Ori C can be only
barely observed by the future Cherenkov telescopes, e.g. the Cherenkov
Telescope Array (CTA).


  In the studies of iron-pnictides, a key question is whether their bad-metal
state from which the superconductivity emerges lies in close proximity with a
magnetically ordered insulating phase. Recently it was found that at low
temperatures, the heavily Cu-doped NaFe$_{1-x}$Cu$_x$As ($x > 0.3$)
iron-pnictide is an insulator with long-range antiferromagnetic order, similar
to the parent compound of cuprates but distinct from all other iron-pnictides.
Using angle-resolved photoemission spectroscopy, we determined the
momentum-resolved electronic structure of NaFe$_{1-x}$Cu$_x$As ($x = 0.44$) and
identified that its ground state is a narrow-gap insulator. Combining the
experimental results with density functional theory (DFT) and DFT+U
calculations, our analysis reveals that the on-site Coulombic (Hubbard) and
Hund's coupling energies play crucial roles in formation of the band gap about
the chemical potential. We propose that at finite temperatures charge carriers
are thermally excited from the Cu-As-like valence band into the conduction
band, which is of Fe $3d$-like character. With increasing temperature, the
number of electrons in the conduction band becomes larger and the hopping
energy between Fe sites increases, and finally the long-range antiferromagnetic
order is destroyed at $T > T_\mathrm{N}$. Our study provides a basis for
investigating the evolution of the electronic structure of a Mott insulator
transforming into a bad metallic phase, and eventually forming a
superconducting state in iron-pnictidesa superconducting state in
iron-pnictides.


  The central engine of gamma-ray bursts (GRBs) is believed to be a hot and
dense disk with hyperaccretion onto a few solar-mass black hole. We investigate
where the magnetorotational instability (MRI) actively operates in the
hyperaccretion disk, which can cause angular momentum transport in the disk.
The inner region of hyperaccretion disks can be neutrino opaque, and the
energy- and momentum-transport by neutrinos could affect the growth of the MRI
significantly. Assuming reasonable disk models and a weak magnetic field $B
\lesssim 10^{14} \rm{G}$, it is found that the MRI is strongly suppressed by
the neutrino viscosity in the inner region of hyperaccretion disks. On the
other hand, the MRI can drive active MHD turbulence in the outer
neutrino-transparent region regardless of the field strength. This suggests
that the baryonic matter is accumulated into the inner dead zone where the MRI
grows inactively and the angular momentum transport is inefficient. When the
dead zone gains a large amount of mass and becomes gravitationally unstable,
intense mass accretion onto the central black hole would occur episodically
through the gravitational torque. This process can be a physical mechanism of
the short-term variability in the prompt emission of GRBs. Finally, the origin
of flaring activities in the X-ray afterglow is predicted in the context of our
episodic accretion scenario.


  Enhancing interoperable data access to radio data has become a science
priority within the International Virtual Observatory Alliance (IVOA). This
lead to the foundation of the IVOA Radio astronomy Interest Group. Several
radio astronomers and project scientists enrolled in various projects (NRAO,
ASKAP, LOFAR, JIVE, ALMA, SKA, INAF, NenuFAR, etc.) have joined. Together they
are paving the way to a better integration of their services in the virtual
observatory (VO) infrastructure and propose extension of IVOA standards to help
achieving this goal. Calibrated radio datasets such as cubes, images, spectra
and time series can already be searched and retrieved using the ObsCore/ObsTAP
specification defined in the IVOA, or by data product-specific services like
SIAv2, SODA, SSA and ConeSearch. However, properties of radio visibility data
are not fully implemented in the VO landscape yet. We need specific features to
refine data discovery and selection that are adapted to radio astronomers'
need. In this context the VO team at the Centre de Donn\'{e}es astronomiques de
Strasbourg (CDS) proposes to consider the ObsCore/ObsTAP specification and to
establish cross-walks between the ObsCore and the existing Measurement Set (MS)
metadata profile for data discovery of radio visibility data (VD). In order to
account for the difference in granularity between radio VD datasets and
science-ready datasets of the VO, the approach splits a MS data file into a
list of datasets served by an ObsTAP service, thus enabling coarse grain
discovery in the multi-wavelength context. Radio specific metadata such as
number of antennae, frequency ranges, $uv$ plane coverage plots,
frequency-phase and frequency-amplitude plots, primary and synthesized beams
are also provided either by adding column metadata or by using the DataLink
technique.


  We propose a new method of pushing $Herschel$ to its faintest detection
limits using universal trends in the redshift evolution of the far infrared
over 24$\mu$m colours in the well-sampled GOODS-North field. An extension to
other fields with less multi-wavelength information is presented. This method
is applied here to raise the contribution of individually detected $Herschel$
sources to the cosmic infrared background (CIRB) by a factor 5 close to its
peak at 250$\mu$m and more than 3 in the 350$\mu$m and 500$\mu$m bands. We
produce realistic mock $Herschel$ images of the deep PACS and SPIRE images of
the GOODS-North field from the GOODS-$Herschel$ Key Program and use them to
quantify the confusion noise at the position of individual sources, i.e.,
estimate a "local confusion noise". Two methods are used to identify sources
with reliable photometric accuracy extracted using 24$\mu$m prior positions.
The clean index (CI), previously defined but validated here with simulations,
which measures the presence of bright 24$\mu$m neighbours and the photometric
accuracy index (PAI) directly extracted from the mock $Herschel$ images. After
correction for completeness, thanks to our mock $Herschel$ images, individually
detected sources make up as much as 54% and 60% of the CIRB in the PACS bands
down to 1.1 mJy at 100$\mu$m and 2.2 mJy at 160$\mu$m and 55, 33, and 13% of
the CIRB in the SPIRE bands down to 2.5, 5, and 9 mJy at 250$\mu$m, 350$\mu$m,
and 500$\mu$m, respectively. The latter depths improve the detection limits of
$Herschel$ by factors of 5 at 250$\mu$m, and 3 at 350$\mu$m and 500$\mu$m as
compared to the standard confusion limit. Interestingly, the dominant
contributors to the CIRB in all $Herschel$ bands appear to be distant siblings
of the Milky Way ($z$$\sim$0.96 for $\lambda$$<$300$\mu$m) with a stellar mass
of $M_{\star}$$\sim$9$\times$10$^{10}$M$_{\odot}$.


  We demonstrate Raman sideband thermometry of single carbyne chains confined
in double-walled carbon nanotubes. Our results show that carbyne's record-high
Raman scattering cross section enables anti-Stokes Raman measurements at the
single chain level. Using laser irradiation as a heating source, we exploit the
temperature dependence of the anti-Stokes/Stokes ratio for local temperature
sensing. Due to its molecular size and its large Raman cross section carbyne is
an efficient probe for local temperature monitoring, with applications ranging
from nanoelectronics to biology.


  Early-generation in-situ dust detectors in near-Earth space have reported the
occurrence of clusters of sub-micron dust particles that seemed unrelated to
human spaceflight activities. In particular, data from the impact ionization
detector onboard the HEOS-2 satellite indicate that such swarms of particles
occur throughout the Earth's magnetosphere up to altitudes of 60,000 km -- far
beyond regions typically used by spacecraft. Further account of high-altitude
clusters has since been given by the GEO-deployed GORID detector, however,
explanations for the latter have so far only been sought in GEO spaceflight
activity.
  This perspective piece reviews dust cluster detections in near-Earth space,
emphasizing the natural swarm creation mechanism conjectured to explain the
HEOS-2 data -- that is, the electrostatic disruption of meteoroids.
Highlighting this mechanism offers a novel viewpoint on more recent near-Earth
dust measurements. We further show that the impact clusters observed by both
HEOS-2 and GORID are correlated with increased geomagnetic activity. This
consistent correlation supports the notion that both sets of observations stem
from the same underlying phenomenon and aligns with the hypothesis of the
electrostatic breakup origin. We conclude that the nature of these peculiar
swarms remains highly uncertain, advocating for their concerted investigation
by forthcoming dust science endeavours, such as the JAXA/DLR DESTINY+ mission.


  We present fully-sampled maps of 461 GHz CO (4-3), 807 GHz CO (7-6), and 492
GHz [CI] (3P1-3P0) emission from the inner 3 degrees of the Galactic Center
region taken with the Antarctic Submillimeter Telescope and Remote Observatory
(AST/RO) in 2001--2002. The data cover -1.3 < l < 2, -0.3 < b < 0.2 with 0.5
arcmin spacing, resulting in spectra in 3 transitions at over 24,000 positions
on the sky. The CO (4-3) emission is found to be essentially coextensive with
lower-J transitions of CO. The CO (7-6) emission is spatially confined to a far
smaller region than the lower-J CO lines. The [CI] (3P1-3P0) emission has a
spatial extent similar to the low-J CO emission, but is more diffuse. Bright CO
(7-6) emission is detected in the well-known Galactic Center clouds Sgr A and
Sgr B. We also detect CO (4-3) and CO (7-6) absorption from spiral arms in the
galactic disk at velocities near 0 km s^-1 along the line of sight to the
Galactic Center. Analyzing our CO (7-6) and CO (4-3) data in conjunction with J
= 1 - 0 12CO and 13CO data previously observed with the Bell Laboratories 7-m
antenna, we apply a Large Velocity Gradient (LVG) model to estimate the kinetic
temperature and density of molecular gas in the inner 200 pc of the Galactic
Center region. We show maps of the derived distribution of gas density and
kinetic temperature as a function of position and velocity for the entire
region. Kinetic temperature was found to decrease from relatively high values
(>70K) at cloud edges to low values (<50K) in the interiors. Typical gas
pressures in the Galactic Center gas are n(H_2) T_kin approx 10^5.2 K cm^-3. We
also present an (l,b) map of molecular hydrogen column density derived from our
LVG results.


  The electromagnetic pion production reactions are investigated within the
dynamical coupled-channels model developed in {\bf Physics Reports, 439, 193
(2007)}. The meson-baryon channels included in this study are $\gamma N$, $\pi
N$, $\eta N$, and the $\pi\Delta$, $\rho N$ and $\sigma N$ resonant components
of the $\pi\pi N$ channel. With the hadronic parameters of the model determined
in a recent study of $\pi N$ scattering, we show that the pion photoproduction
data up to the second resonance region can be described to a very large extent
by only adjusting the bare $\gamma N \to N^*$ helicity amplitudes, while the
non-resonant electromagnetic couplings are taken from previous works. It is
found that the coupled-channels effects can contribute about 10 - 20 % of the
production cross sections in the $\Delta$ (1232) resonance region, and can
drastically change the magnitude and shape of the cross sections in the second
resonance region. The importance of the off-shell effects in a dynamical
approach is also demonstrated. The meson cloud effects as well as the
coupled-channels contributions to the $\gamma N \to N^*$ form factors are found
to be mainly in the low $Q^2$ region. For the magnetic M1 $\gamma N \to \Delta$
(1232) form factor, the results are close to that of the Sato-Lee Model.
Necessary improvements to the model and future developments are discussed.


  Originating in harmonic analysis, interpolation sets were first studied in
dynamics by Glasner and Weiss in the 1980s. A set $S \subset \mathbb{N}$ is an
interpolation set for a class of topological dynamical systems $\mathcal{C}$ if
any bounded sequence on $S$ can be extended to a sequence that arises from a
system in $\mathcal{C}$. In this paper, we provide combinatorial
characterizations of interpolation sets for:
  $\bullet$ (totally) minimal systems;
  $\bullet$ topologically (weak) mixing systems;
  $\bullet$ strictly ergodic systems; and
  $\bullet$ zero entropy systems. Additionally, we prove some results on a
slightly different notion, called weak interpolation sets, for several classes
of systems. We also answer a question of Host, Kra, and Maass concerning the
connection between sets of pointwise recurrence for distal systems and
$IP$-sets.


  A symmetry preserving framework for the study of continuum Quantum
Chromodynamics (QCD) is obtained from a truncated solution of the QCD equations
of motion or QCD's Dyson-Schwinger equations (DSEs). A nonperturbative solution
of the DSEs enables the study of, e.g., hadrons as composites of dressed-quarks
and dressed-gluons, the phenomena of confinement and dynamical chiral symmetry
breaking (DCSB), and therefrom an articulation of any connection between them.
It is within this context that we present a unified study of Nucleon, Delta and
Roper elastic and transition form factors, and compare predictions made using a
framework built upon a Faddeev equation kernel and interaction vertices that
possess QCD-like momentum dependence with results obtained using a
symmetry-preserving treatment of a vector$\,\otimes\,$vector
contact-interaction.


  Graphene nanoribbons are prone to in-plane bending even when supported on
flat substrates. However, the amount of bending that ribbons can stably
withstand remains poorly known. Here, by using molecular dynamics simulations,
we study the stability limits of 0.5-1.9 nm wide armchair and zigzag graphene
nanoribbons subject to bending. We observe that the limits for maximum stable
curvatures are below ~10 deg/nm, in case the bending is externally forced and
the limit is caused by buckling instability. Furthermore, it turns out that the
limits for maximum stable curvatures are also below ~10 deg/nm, in case the
bending is not forced and the limit arises only from the corrugated potential
energy landscape due to the substrate. Both of the stability limits lower
rapidly when ribbons widen. These results agree with recent experiments and can
be understood by means of transparent elasticity models.


  The study of the transitions among different regimes in thermal convection
has been an issue of paramount importance in fluid mechanics. While the
bifurcations at low Rayleigh number, when the flow is laminar or moderately
chaotic, have been fully understood for a long time, transitions at higher
Rayleigh number are much more difficult to be clearly identified. Here, through
a numerical study of the two-dimensional Rayleigh-B\'enard convection covering
four decades in Rayleigh number for two different Prandtl numbers, we find a
clear-cut transition by considering the fluctuations of the heat flux through a
horizontal plane, rather than its mean value. More specifically, we have found
that this sharp transition is displayed by a jump of the ratio of the
root-mean-square fluctuations of the heat flux to its mean value and occurs at
$Ra/Pr \approx 10^9$. Above the transition, this ratio is found to be constant
in all regions of the flow, while taking different values in the bulk and at
the boundaries. Below the transition instead, different behaviors are observed
at the boundaries and in the bulk: at the boundaries, this ratio decreases with
respect to the Rayleigh number whereas it is found to be constant in the bulk
for all values of the Rayleigh number. Through this numerical evidence and an
analytical reasoning we confirm what was already observed in experiments, that
is the decrease of the ratio of root-mean-square fluctuations of the heat flux
to its mean value, observed at the boundaries below the transition, can
understood in terms of the law of large numbers.


  We develop a theory for thermodynamic instabilities of complex fluids
composed of many interacting chemical species organised in families. This model
includes partially structured and partially random interactions and can be
solved exactly using tools from random matrix theory. The model exhibits three
kinds of fluid instabilities: one in which the species form a condensate with a
local density that depends on their family (family condensation); one in which
species demix in two phases depending on their family (family demixing); and
one in which species demix in a random manner irrespective of their family
(random demixing). We determine the critical spinodal density of the three
types of instabilities and find that the critical spinodal density is finite
for both family condensation and family demixing, while for random demixing the
critical spinodal density grows as the square root of the number of species. We
use the developed framework to describe phase-separation instability of the
cytoplasm induced by a change in pH.


  Invariant yields of neutral pions at midrapidity in the transverse momentum
range $0.6 < p_{T} < 12 GeV/c$ measured in Pb-Pb collisions at $\sqrt{s_{\rm
NN}} = 2.76$ TeV are presented for six centrality classes. The pp reference
spectrum was measured in the range $0.4 < p_{T} < 10 GeV/c$ at the same
center-of-mass energy. The nuclear modification factor, $R_{\rm AA}$, shows a
suppression of neutral pions in central Pb-Pb collisions by a factor of up to
about $8-10$ for $5 \lesssim p_{T} \lesssim 7 GeV/c$. The presented
measurements are compared with results at lower center-of-mass energies and
with theoretical calculations.


  We present a theoretical analysis of the training process for a single-layer
GAN fed by high-dimensional input data. The training dynamics of the proposed
model at both microscopic and macroscopic scales can be exactly analyzed in the
high-dimensional limit. In particular, we prove that the macroscopic quantities
measuring the quality of the training process converge to a deterministic
process characterized by an ordinary differential equation (ODE), whereas the
microscopic states containing all the detailed weights remain stochastic, whose
dynamics can be described by a stochastic differential equation (SDE). This
analysis provides a new perspective different from recent analyses in the limit
of small learning rate, where the microscopic state is always considered
deterministic, and the contribution of noise is ignored. From our analysis, we
show that the level of the background noise is essential to the convergence of
the training process: setting the noise level too strong leads to failure of
feature recovery, whereas setting the noise too weak causes oscillation.
Although this work focuses on a simple copy model of GAN, we believe the
analysis methods and insights developed here would prove useful in the
theoretical understanding of other variants of GANs with more advanced training
algorithms.


  Fake news detection in social media has become increasingly important due to
the rapid proliferation of personal media channels and the consequential
dissemination of misleading information. Existing methods, which primarily rely
on multimodal features and graph-based techniques, have shown promising
performance in detecting fake news. However, they still face a limitation,
i.e., sparsity in graph connections, which hinders capturing possible
interactions among tweets. This challenge has motivated us to explore a novel
method that densifies the graph's connectivity to capture denser interaction
better. Our method constructs a cross-modal tweet graph using CLIP, which
encodes images and text into a unified space, allowing us to extract potential
connections based on similarities in text and images. We then design a Feature
Contextualization Network with Label Propagation (FCN-LP) to model the
interaction among tweets as well as positive or negative correlations between
predicted labels of connected tweets. The propagated labels from the graph are
weighted and aggregated for the final detection. To enhance the model's
generalization ability to unseen events, we introduce a domain generalization
loss that ensures consistent features between tweets on seen and unseen events.
We use three publicly available fake news datasets, Twitter, PHEME, and Weibo,
for evaluation. Our method consistently improves the performance over the
state-of-the-art methods on all benchmark datasets and effectively demonstrates
its aptitude for generalizing fake news detection in social media.


  As we known, the {\it Seifert-Van Kampen theorem} handles fundamental groups
of those topological spaces $X=U\cup V$ for open subsets $U, V\subset X$ such
that $U\cap V$ is arcwise connected. In this paper, this theorem is generalized
to such a case of maybe not arcwise-connected, i.e., there are $C_1$,
$C_2$,$..., C_m$ arcwise-connected components in $U\cap V$ for an integer
$m\geq 1$, which enables one to find fundamental groups of combinatorial spaces
by that of spaces with theirs underlying topological graphs, particularly, that
of compact manifolds by their underlying graphs of charts.


  Light curves of young star systems show photometric variability due to
different kinematic, and physical processes. One of the main contributors to
the photometric variability is the changing mass accretion rate, which
regulates the interplay between the forming young star and the protoplanetary
disk. We collected high-resolution spectroscopy in eight different epochs, as
well as ground-based and space-borne multi-epoch optical and infrared
photometry of WX Cha, an M0 binary system, with an almost edge-on disk (i =
87degrees) in the Chamaeleon I star-forming region. Spectroscopic observations
cover 72 days, the ground-based optical monitoring covers 42 days while
space-borne TESS photometry extends for 56 days. The multi-wavelength light
curves exhibit quasi-periodic variability of 0.35 - 0.53 mag in the
near-infrared, and of 1.3 mag in g band. We studied the variability of selected
emission lines that trace the accretion, computed the accretion luminosity and
the mass accretion rate using empirical relations and obtained values of the
accretion luminosity between 1.6 and 3.2 Lsun and mass accretion rate between
3.31x10{-7} Msun/yr and 7.76x10^{-7} Msun/yr. Our results show that WX Cha is
accreting at a rate larger than what is typical for T Tauri stars in the same
star-forming region with the same stellar parameters. We theorize that this is
due to the higher disk mass of WX Cha than what is usual for stars with similar
stellar mass, and to the binary nature of the system. Daily changes in the
accretion luminosity and in the extinction can explain the photometric
variability.


  Understanding neural dynamics is a central topic in machine learning,
non-linear physics and neuroscience. However, the dynamics is non-linear,
stochastic and particularly non-gradient, i.e., the driving force can not be
written as gradient of a potential. These features make analytic studies very
challenging. The common tool is the path integral approach or dynamical
mean-field theory, but the drawback is that one has to solve the
integro-differential or dynamical mean-field equations, which is
computationally expensive and has no closed form solutions in general. From the
aspect of associated Fokker-Planck equation, the steady state solution is
generally unknown. Here, we treat searching for the steady states as an
optimization problem, and construct an approximate potential related to the
speed of the dynamics, and find that searching for the ground state of this
potential is equivalent to running an approximate stochastic gradient dynamics
or Langevin dynamics. Only in the zero temperature limit, the distribution of
the original steady states can be achieved. The resultant stationary state of
the dynamics follows exactly the canonical Boltzmann measure. Within this
framework, the quenched disorder intrinsic in the neural networks can be
averaged out by applying the replica method, which leads naturally to order
parameters for the non-equilibrium steady states. Our theory reproduces the
well-known result of edge-of-chaos, and further the order parameters
characterizing the continuous transition are derived, and the order parameters
are explained as fluctuations and responses of the steady states. Our method
thus opens the door to analytically study the steady state landscape of the
deterministic or stochastic high dimensional dynamics.


  Gravitational waveforms generated by unequal mass black hole binaries are
expected to be common sources for future gravitational wave detectors. We
derived the waveforms emitted by such systems during the last part of the
inspiral, when the larger spin dominates over the orbital angular momentum and
the smaller spin is negligible. These Spin-Dominated Waveforms (SDW) arise as a
double expansion in the post-Newtonian parameter and another parameter
proportional to the ratio of the orbital angular momentum and the dominant
spin. The time spent by the gravitational wave as an SDW in the sensitivity
range of the KAGRA detector is presented for the first time.


  The abundance of high-dimensional data in the modern sciences has generated
tremendous interest in penalized estimators such as the lasso, scaled lasso,
square-root lasso, elastic net, and many others. In this paper, we establish a
general oracle inequality for prediction in high-dimensional linear regression
with such methods. Since the proof relies only on convexity and continuity
arguments, the result holds irrespective of the design matrix and applies to a
wide range of penalized estimators. Overall, the bound demonstrates that
generic estimators can provide consistent prediction with any design matrix.
From a practical point of view, the bound can help to identify the potential of
specific estimators, and they can help to get a sense of the prediction
accuracy in a given application.


  The near resonant cooling mechanism have offered for the experiment with
laser induced excitation of Lithium isotopes and its ionization by collisions.


  We show that a whole class of quantum actions for dilaton-gravity, which
reduce to the CGHS theory in the classical limit, can be written as a
Liouville-like theory. In a sub-class of this, the field space singularity
observed by several authors is absent, regardless of the number of matter
fields, and in addition it is such that the dilaton-gravity functional
integration range (the real line) transforms into itself for the Liouville
theory fields. We also discuss some problems associated with the usual
calculation of Hawking radiation, which stem from the neglect of back reaction.
We give an alternative argument incorporating back reaction but find that the
rate is still asymptotically constant. The latter is due to the fact that the
quantum theory does not seem to have a lower bound in energy and Hawking
radiation takes positive Bondi (or ADM) mass solutions to arbitrarily large
negative mass.


  We propose a neural language model capable of unsupervised syntactic
structure induction. The model leverages the structure information to form
better semantic representations and better language modeling. Standard
recurrent neural networks are limited by their structure and fail to
efficiently use syntactic information. On the other hand, tree-structured
recursive networks usually require additional structural supervision at the
cost of human expert annotation. In this paper, We propose a novel neural
language model, called the Parsing-Reading-Predict Networks (PRPN), that can
simultaneously induce the syntactic structure from unannotated sentences and
leverage the inferred structure to learn a better language model. In our model,
the gradient can be directly back-propagated from the language model loss into
the neural parsing network. Experiments show that the proposed model can
discover the underlying syntactic structure and achieve state-of-the-art
performance on word/character-level language model tasks.


  We study perfusion by a multiscale model coupling diffusion in the tissue and
diffusion along the one-dimensional segments representing the vasculature. We
propose a block-diagonal preconditioner for the model equations and demonstrate
its robustness by numerical experiments. We compare our model to a macroscale
model by [P. Tofts, Modelling in DCE MRI, 2012].


  We consider the problem of obtaining dense 3D reconstructions of humans from
single and partially occluded views. In such cases, the visual evidence is
usually insufficient to identify a 3D reconstruction uniquely, so we aim at
recovering several plausible reconstructions compatible with the input data. We
suggest that ambiguities can be modelled more effectively by parametrizing the
possible body shapes and poses via a suitable 3D model, such as SMPL for
humans. We propose to learn a multi-hypothesis neural network regressor using a
best-of-M loss, where each of the M hypotheses is constrained to lie on a
manifold of plausible human poses by means of a generative model. We show that
our method outperforms alternative approaches in ambiguous pose recovery on
standard benchmarks for 3D humans, and in heavily occluded versions of these
benchmarks.


  Facial expression recognition (FER) algorithms work well in constrained
environments with little or no occlusion of the face. However, real-world face
occlusion is prevalent, most notably with the need to use a face mask in the
current Covid-19 scenario. While there are works on the problem of occlusion in
FER, little has been done before on the particular face mask scenario.
Moreover, the few works in this area largely use synthetically created masked
FER datasets. Motivated by these challenges posed by the pandemic to FER, we
present a novel dataset, the Masked Student Dataset of Expressions or MSD-E,
consisting of 1,960 real-world non-masked and masked facial expression images
collected from 142 individuals. Along with the issue of obfuscated facial
features, we illustrate how other subtler issues in masked FER are represented
in our dataset. We then provide baseline results using ResNet-18, finding that
its performance dips in the non-masked case when trained for FER in the
presence of masks. To tackle this, we test two training paradigms: contrastive
learning and knowledge distillation, and find that they increase the model's
performance in the masked scenario while maintaining its non-masked
performance. We further visualise our results using t-SNE plots and Grad-CAM,
demonstrating that these paradigms capitalise on the limited features available
in the masked scenario. Finally, we benchmark SOTA methods on MSD-E.


  We introduce an intuitive algorithmic methodology for enacting automated
rewriting of string diagrams within a general double-pushout (DPO) framework,
in which the sequence of rewrites is chosen in accordance with the causal
structure of the underlying diagrammatic calculus. The combination of the
rewriting structure and the causal structure may be elegantly formulated as a
weak 2-category equipped with both total and partial monoidal bifunctors, thus
providing a categorical semantics for the full multiway evolution causal graph
of a generic Wolfram model hypergraph rewriting system. As an illustrative
example, we show how a special case of this algorithm enables highly efficient
automated simplification of quantum circuits, as represented in the
ZX-calculus.


  Task-based, rather than vehicle-based, control architectures have been shown
to provide superior performance in certain human supervisory control missions.
These results motivate the need for the development of robust, reliable
usability metrics to aid in creating interfaces for use in this domain. To this
end, we conduct a pilot usability study of a particular task-based supervisory
control interface called the Research Environment for Supervisory Control of
Heterogenous Unmanned Vehicles (RESCHU). In particular, we explore the use of
eye-tracking metrics as an objective means of evaluating the RESCHU interface
and providing guidance in improving usability. Our main goals for this study
are to 1) better understand how eye-tracking can augment standard usability
metrics, 2) formulate initial models of operator behavior, and 3) identify
interesting areas of future research.


  We present a general scheme to realize a cold-atom quantum simulator of
bidimensional atomic crystals. Our model is based on the use of two
independently trapped atomic species: the first one, subject to a strong
in-plane confinement, constitutes a two-dimensional matter wave which interacts
only with atoms of the second species, deeply trapped around the nodes of a
two-dimensional optical lattice. By introducing a general analytic approach we
show that the system Green function can be exactly determined, allowing for the
investigation of the matter-wave transport properties. We propose some
illustrative applications to both Bravais (square, triangular) and non-Bravais
(graphene, kagom\'e) lattices, studying both ideal periodic systems and
experimental-size and disordered ones. Some remarkable spectral properties of
these atomic artificial lattices are pointed out, such as the emergence of
single and multiple gaps, flat bands, and Dirac cones. All these features can
be manipulated via the interspecies interaction, which proves to be widely
tunable due to the interplay between scattering length and confinements.


  We propose novel chemical reaction networks to translate levels of
concentration into unique DNA strand species, which we call concentration
translators. Our design of the concentration translators is based on
combination of two chemical reaction networks, consensus network and conversion
network with any number of chemical species. We give geometric analysis of the
proposed CRNs from the viewpoint of nonlinear dynamical systems and show that
the CRNs can actually operate as translator. Our concentration translators
exploit DNA strand displacement (DSD) reaction, which is known for a universal
reaction that can implement arbitrary chemical reaction networks. We
demonstrate two specific types of concentration translators (translator A and
B) with different switching behavior and biochemical cost and compared their
characteristics computationally. The proposed concentration translators have an
advantage of being able to readout the concentration of targeted nucleic acid
strand without any fluorescence-based techniques. These characteristics can be
tailored according to requirements from applications, including dynamic range,
sensitivity and implementation cost.


  In this work, we study the classification of differential graded algebras
over $\mathbb{Z}$ (DGAs) whose homology is $\mathbb{F}_p[x]$, i.e. the
polynomial algebra over $\mathbb{F}_p$ on a single generator. This
classification problem was left open in work of Dwyer, Greenlees and Iyengar.
  For $\lvert y_{2p-2} \rvert = 2p-2$, we show that there is a unique
non-formal DGA with homology $\mathbb{F}_p[y_{2p-2}]$ and a non-formal $2p-2$
Postnikov section. Among a classification result, this provides the first
example of a non-formal DGA with homology $\mathbb{F}_p[x]$. By duality, this
also shows that there is a non-formal DGA whose homology is an exterior algebra
over $\mathbb{F}_p$ with a generator in degree $-(2p-1)$.
  Considering the classification of the ring spectra corresponding to these
DGAs, we show that every $E_2$ DGA with homology $\mathbb{F}_p[x]$ (with no
restrictions on $\lvert x \rvert$) is topologically equivalent to the formal
DGA with homology $\mathbb{F}_p[x]$, i.e. they are topologically formal. This
follows by a theorem of Hopkins and Mahowald.


  We demonstrate, by solving numerically the time-dependent Schroedinger
equation, the physical character of electron localization in a disordered
two-dimensional lattice. We show, in agreement with the prediction of P. W.
Anderson, that the disorder prevents electron diffusion. The electron becomes
spatially localized in a specific area of the system. Our numerical analysis
confirms that the electron localization is a quantum effect caused by the wave
character of electron propagation and has no analogy in classical mechanics.


  In this paper we treat the so called clock paradox in an analytical way by
assuming that a constant and uniform force F of finite magnitude acts
continuously on the moving clock along the direction of its motion assumed to
be rectilinear. No inertial motion steps are considered. The rest clock is
denoted as (1), the to-and-fro moving clock is (2), the inertial frame in which
(1) is at rest in its origin and (2) is seen moving is I and, finally, the
accelerated frame in which (2) is at rest in its origin and (1) moves forward
and backward is A. We deal with the following questions: I) What is the effect
of the finite force acting on (2) on the proper time intervals measured by the
two clocks when they reunite? Does a differential aging between the two clocks
occur, as it happens when inertial motion and infinite values of the
accelerating force is considered? The Special Theory of Relativity is used in
order to describe the hyperbolic motion of (2) in the frame I II) Is this
effect an absolute one, i.e. does the accelerated observer A comoving with (2)
obtain the same results as that in I, both qualitatively and quantitatively, as
it is expected? We use the General Theory of Relativity in order to answer this
question.


  A collective system of atoms in a high quality cavity can be described by a
nonlinear interaction which arises due to the Lamb shift of the energy levels
due to the cavity vacuum [Agarwal et al., Phys. Rev. A 56, 2249 (1997)]. We
show how this collective interaction can be used to perform quantum logic. In
particular we produce schemes to realize CNOT gates not only for two-qubit but
also for three-qubit systems. We also discuss realizations of Toffoli gates.
Our effective Hamiltonian is also realized in other systems such as trapped
ions or magnetic molecules.


  Based on the Lorenz-Mie theory, we derive analytical expressions of radiative
and nonradiative transition rates for different orientations of a point dipole
emitter in the vicinity of an infinitely long circular cylinder of arbitrary
radius. Special attention is devoted to the spontaneous decay rate of a dipole
emitter near a subwavelength-diameter nanowire coated with a graphene
monolayer. We show that plasmonic Fano resonances associated with light
scattering by graphene-coated nanowires appear in the Purcell factor as a
function of transition wavelength. Furthermore, the Fano line shape of
transition rates can be tailored and electrically tuned by varying the distance
between emitter and cylinder and by modulating the graphene chemical potential,
where the Fano asymmetry parameter is proportional to the square root of the
chemical potential. This gate-voltage-tunable Fano resonance leads to a
resonant enhancement and suppression of light emission in the far-infrared
range of frequencies. This result could be explored in applications involving
ultrahigh-contrast switching for spontaneous emission in specifically designed
tunable plasmonic nanostructures.


  Rumors are often associated with newly emerging events, thus, an ability to
deal with unseen rumors is crucial for a rumor veracity classification model.
Previous works address this issue by improving the model's generalizability,
with an assumption that the model will stay unchanged even after the new
outbreak of an event. In this work, we propose an alternative solution to
continuously update the model in accordance with the dynamics of rumor domain
creations. The biggest technical challenge associated with this new approach is
the catastrophic forgetting of previous learnings due to new learnings. We
adopt continual learning strategies that control the new learnings to avoid
catastrophic forgetting and propose an additional strategy that can jointly be
used to strengthen the forgetting alleviation.


  Creative exploration is central to science, art and cognitive development.
However, research on creative exploration is limited by a lack of
high-resolution automated paradigms. To address this, we present such an
automated paradigm, the creative foraging game, in which people search for
novel and valuable solutions in a large and well-defined space made of all
possible shapes made of ten connected squares. Players discovered shape
categories such as digits, letters, and airplanes. They exploited each
category, then dropped it to explore once again, and so on. Aligned with a
prediction of optimal foraging theory (OFT) prediction, during exploration
phases, people moved along meandering paths that are about three times longer
than the minimal paths between shapes, when exploiting a category of related
shapes, they moved along the minimal paths. The moment of discovery of a new
category was usually done at a nonprototypical and ambiguous shape, which can
serve as an experimental proxy for creative leaps. People showed individual
differences in their search patterns, along a continuum between two strategies:
a mercurial quick-to-discover/quick-to-drop strategy and a thorough
slow-to-discover/slow-to-drop strategy. Contrary to optimal foraging theory,
players leave exploitation to explore again far before categories are depleted.
This paradigm opens the way for automated high-resolution study of creative
exploration.


  The classical Dynamic Programming (DP) approach to optimal control problems
is based on the characterization of the value function as the unique viscosity
solution of a Hamilton-Jacobi-Bellman (HJB) equation. The DP scheme for the
numerical approximation of viscosity solutions of Bellman equations is
typically based on a time discretization which is projected on a fixed
state-space grid. The time discretization can be done by a one-step scheme for
the dynamics and the projection on the grid typically uses a local
interpolation. Clearly the use of a grid is a limitation with respect to
possible applications in high-dimensional problems due to the curse of
dimensionality. Here, we present a new approach for finite horizon optimal
control problems where the value function is computed using a DP algorithm on a
tree structure algorithm (TSA) constructed by the time discrete dynamics. In
this way there is no need to build a fixed space triangulation and to project
on it. The tree will guarantee a perfect matching with the discrete dynamics
and drop off the cost of the space interpolation allowing for the solution of
very high-dimensional problems. Numerical tests will show the effectiveness of
the proposed method.


  In this paper, we solve various isoperimetric problems for the
quermassintegrals and the curvature integrals in the hyperbolic space $\H^n$,
by using quermassintegral preserving curvature flows. As a byproduct, we obtain
hyperbolic Alexandrov-Fenchel inequalities.


  Exploration in reinforcement learning (RL) suffers from the curse of
dimensionality when the state-action space is large. A common practice is to
parameterize the high-dimensional value and policy functions using given
features. However existing methods either have no theoretical guarantee or
suffer a regret that is exponential in the planning horizon $H$. In this paper,
we propose an online RL algorithm, namely the MatrixRL, that leverages ideas
from linear bandit to learn a low-dimensional representation of the probability
transition model while carefully balancing the exploitation-exploration
tradeoff. We show that MatrixRL achieves a regret bound ${O}\big(H^2d\log
T\sqrt{T}\big)$ where $d$ is the number of features. MatrixRL has an equivalent
kernelized version, which is able to work with an arbitrary kernel Hilbert
space without using explicit features. In this case, the kernelized MatrixRL
satisfies a regret bound ${O}\big(H^2\widetilde{d}\log T\sqrt{T}\big)$, where
$\widetilde{d}$ is the effective dimension of the kernel space. To our best
knowledge, for RL using features or kernels, our results are the first regret
bounds that are near-optimal in time $T$ and dimension $d$ (or $\widetilde{d}$)
and polynomial in the planning horizon $H$.


  Network scientists have shown that there is great value in studying pairwise
interactions between components in a system. From a linear algebra point of
view, this involves defining and evaluating functions of the associated
adjacency matrix. Recent work indicates that there are further benefits from
accounting directly for higher order interactions, notably through a hypergraph
representation where an edge may involve multiple nodes. Building on these
ideas, we motivate, define and analyze a class of spectral centrality measures
for identifying important nodes and hyperedges in hypergraphs, generalizing
existing network science concepts. By exploiting the latest developments in
nonlinear Perron-Frobenius theory, we show how the resulting constrained
nonlinear eigenvalue problems have unique solutions that can be computed
efficiently via a nonlinear power method iteration. We illustrate the measures
on realistic data sets.


  Solar filaments are an intriguing phenomenon, like cool clouds suspended in
the hot corona. Similar structures exist in the intergalactic medium as well.
Despite being a long-studied topic, solar filaments have continually attracted
intensive attention because of their link to the coronal heating, coronal
seismology, solar flares, and coronal mass ejections (CMEs). In this review
paper, by combing through the solar filament-related work done in the past
decade, we discuss several controversial topics, such as the fine structures,
dynamics, magnetic configurations, and helicity of filaments. With
high-resolution and high-sensitivity observations, combined with numerical
simulations, it is expected that resolving these disputes will definitely lead
to a huge leap in understanding the physics related to solar filaments, and
even shed light on galactic filaments.


  Two-dimensional group-VI transition metal dichalcogenide semiconductors, such
as MoS2, WSe2 and others, exhibit strong light-matter coupling and possess
direct band gaps in the infrared and visible spectral regimes, making them
potentially interesting candidates for various applications in optics and
optoelectronics. Here, we review their optical and optoelectronic properties
with emphasis on exciton physics and devices. As excitons are tightly bound in
these materials and dominate the optical response even at room-temperature,
their properties are examined in depth in the first part of this article. We
discuss the remarkably versatile excitonic landscape, including bright, dark,
localized and interlayer excitons. In the second part, we provide an overview
on the progress in optoelectronic device applications, such as electrically
driven light emitters, photovoltaic solar cells, photodetectors and
opto-valleytronic devices, again bearing in mind the prominent role of
excitonic effects. We conclude with a brief discussion on challenges that
remain to be addressed to exploit the full potential of transition metal
dichalcogenide semiconductors in possible exciton-based applications.


  Recently, pretext-task based methods are proposed one after another in
self-supervised video feature learning. Meanwhile, contrastive learning methods
also yield good performance. Usually, new methods can beat previous ones as
claimed that they could capture "better" temporal information. However, there
exist setting differences among them and it is hard to conclude which is
better. It would be much more convincing in comparison if these methods have
reached as closer to their performance limits as possible. In this paper, we
start from one pretext-task baseline, exploring how far it can go by combining
it with contrastive learning, data pre-processing, and data augmentation. A
proper setting has been found from extensive experiments, with which huge
improvements over the baselines can be achieved, indicating a joint
optimization framework can boost both pretext task and contrastive learning. We
denote the joint optimization framework as Pretext-Contrastive Learning (PCL).
The other two pretext task baselines are used to validate the effectiveness of
PCL. And we can easily outperform current state-of-the-art methods in the same
training manner, showing the effectiveness and the generality of our proposal.
It is convenient to treat PCL as a standard training strategy and apply it to
many other works in self-supervised video feature learning.


  We analyze the existence and stability of localized solutions in the
one-dimensional discrete nonlinear Schr\"{o}dinger (DNLS) equation with a
combination of competing self-focusing cubic and defocusing quintic onsite
nonlinearities. We produce a stability diagram for different families of
soliton solutions, that suggests the (co)existence of infinitely many branches
of stable localized solutions. Bifurcations which occur with the increase of
the coupling constant are studied in a numerical form. A variational
approximation is developed for accurate prediction of the most fundamental and
next-order solitons together with their bifurcations. Salient properties of the
model, which distinguish it from the well-known cubic DNLS equation, are the
existence of two different types of symmetric solitons and stable asymmetric
soliton solutions that are found in narrow regions of the parameter space. The
asymmetric solutions appear from and disappear back into the symmetric ones via
loops of forward and backward pitchfork bifurcations.


  As human-machine interaction systems are developing towards lightweight and
pervasive direction, the role of simultaneous and proportional control (SPC) in
human-machine interaction becomes increasingly prominent. However, existing
continuous joint angle prediction algorithms based on surface electromyography
(sEMG) typically incur high inference costs or are only applicable to specific
subjects rather than cross-subject scenarios. Therefore, we proposed a
hierarchical Spiking Attentional FEature decomposition Network (SAFE-Net) in
order to reduce inference costs and improve recognition accuracy in
cross-subject scenarios. This network first encodes the sEMG signals into
neural spiking forms through a Spiking Sparse Attention Encoder (SSAE). The
compressed features are then decomposed into kinematic features and biological
features by a Spiking Attentional Feature Decomposition (SAFD) module. Finally,
the kinematic features and biological features are decoded into joint angle
values and subject identity, respectively. We validated the effectiveness of
SAFE-Net on two datasets (SIAT-DB1 and SIAT-DB2) and compared it with two
state-of-the-art methods, Informer and Spikformer. Experimental results
demonstrate that, on the one hand, SSAE saves 39.1% and 37.5% power consumption
respectively over them in terms of inference costs. On the other hand, SAFE-Net
outperforms Informer and Spikformer in recognition accuracy on both datasets.
This study showcased that the proposed SAFE-Net can provide accurate
predictions in cross-subject scenarios, offering a promising vision for precise
continuous control of lower limb rehabilitation exoskeleton robots.


  In this work, we study the $\delta$-chromatic number of a graph which is the
chromatic number of the $\delta$-complement of a graph. We give a structure of
the $\delta$-complements and sharp bounds on the $\delta$-chromatic numbers of
the Cartesian products of graphs. Furthermore, we compute the
$\delta$-chromatic numbers of various classes of Cartesian product graphs,
including the Cartesian products between cycles, paths, and stars.


  Observations by the WMAP experiment have identified an excess of microwave
emission from the center of the Milky Way. It has previously been shown that
this "WMAP Haze" could be synchrotron emission from relativistic electrons and
positrons produced in the annihilations of dark matter particles. In
particular, the intensity, spectrum and angular distribution of the WMAP Haze
is consistent with an electroweak scale dark matter particle (such as a
supersymmetric neutralino or Kaluza-Klein dark matter in models with universal
extra dimensions) annihilating with a cross section on the order of sigma
v~3x10^-26 cm^3/s and distributed with a cusped halo profile. No further exotic
astrophysical or annihilation boost factors are required. If dark matter
annihilations are in fact responsible for the observed Haze, then other
annihilation products will also be produced, including gamma rays. In this
article, we study the prospects for the GLAST satellite to detect gamma rays
from dark matter annihilations in the Galactic Center region in this scenario.
We find that by studying only the inner 0.1 degrees around the Galactic Center,
GLAST will be able to detect dark matter annihilating to heavy quarks or gauge
bosons over astrophysical backgrounds with 5sigma (3sigma) significance if they
are lighter than approximately 320-500 GeV (500-750 GeV). If the angular window
is broadened to study the dark matter halo profile's angular extension (while
simultaneously reducing the astrophysical backgrounds), WIMPs as heavy as
several TeV can be identified by GLAST with high significance. Only if the dark
matter particles annihilate mostly to electrons or muons will GLAST be unable
to identify the gamma ray spectrum associated with the WMAP Haze.


  Quasi-rigidity means that one builds a theory for assemblies of grains under
a slowly changing external load by using the deformation of those grains as a
small parameter. Is quasi-rigidity a complete theory for these granular
assemblies? Does it provide unique predictions of the assembly's behavior, or
must some other process be invoked to decide between several possibilities? We
provide evidence that quasi-rigidity is a complete theory by showing that two
possible sources of indeterminacy do not exist for the case of disk shaped
grains. One possible source of indeterminacy arises from zero-frequency modes
present in the packing. This problem can be solved by considering the
conditions required to obtain force equilibrium. A second possible source of
indeterminacy is the necessity to choose the status (sliding or non-sliding) at
each contact. We show that only one choice is permitted, if contacts slide only
when required by Coulomb friction.


  Reliable prediction of surround vehicle motion is a critical requirement for
path planning for autonomous vehicles. In this paper we propose a unified
framework for surround vehicle maneuver classification and motion prediction
that exploits multiple cues, namely, the estimated motion of vehicles, an
understanding of typical motion patterns of freeway traffic and inter-vehicle
interaction. We report our results in terms of maneuver classification accuracy
and mean and median absolute error of predicted trajectories against the ground
truth for real traffic data collected using vehicle mounted sensors on
freeways. An ablative analysis is performed to analyze the relative importance
of each cue for trajectory prediction. Additionally, an analysis of execution
time for the components of the framework is presented. Finally, we present
multiple case studies analyzing the outputs of our model for complex traffic
scenarios


  It is shown that two ways of the description of the 1/2-spin precession in a
circularly polarized electromagnetic wave: on the basis of the
Bargmann-Michel-Telegdi equation and on the basis of the Dirac equation with
Pauli term give the same result in the second order on the wave's field for
arbitrary wave length.