  We find theoretically energy spectrum of a graphene monolayer in a strong
constant electric field using a tight-binding model. Within a single band, we
find quantized equidistant energy levels (Wannier-Stark ladder), separated by
the Bloch frequency. Singular interband coupling results in mixing of the
states of different bands and anticrossing of corresponding levels, which is
described analytically near Dirac points and is related to the
Pancharatnam-Berry phase. The rate of interband tunneling, which is
proportional to the anticrossing gaps in the spectrum, is only inversely
proportional to the tunneling distance, in a sharp contrast to conventional
solids where this dependence is exponential. This singularity will have major
consequences for graphene behavior in strong ultrafast optical fields, in
particular, leading to non-adiabaticity of electron excitation dynamics.


  Central Exclusive Production is a unique QCD process in which particles are
produced via colourless propagators. Several results have been obtained at LHCb
for the production of single charmonia, pairs of charmonia, and single
bottomonia.


  For ghost imaging, pursuing high resolution images and short acquisition
times required for reconstructing images are always two main goals. We report
an image reconstruction algorithm called compressive sampling (CS)
reconstruction to recover ghost images. By CS reconstruction, ghost imaging
with both super-resolution and a good signal-to-noise ratio can be obtained via
short acquisition times. Both effect influencing and approaches further
improving the resolution of ghost images via CS reconstruction, relationship
between ghost imaging and CS theory are also discussed.


  A novel design has been proposed for a facile and rapid build-up of highly
tailorable nanostructured multishells on metal oxide particles
(graphene/TiOx@TiO2) under a dry inert atmosphere to maximize the visible-light
photocatalytic performance. We also thoroughly and systematically investigated
the as-prepared nanostructured particle surface and the mechanisms of the
extraordinary in-situ graphene growth on the TiOx@TiO2 nanoparticles achieved
by a rapid-concomitant reaction in the metal oxide (TiO2)/polymer (polymethyl
methacrylate) interface under microwave irradiation. The as-prepared composite
materials are also found to perform a better photocatalytic activity in
comparison to the traditional synthesis pathway for degradation of organic
pollutants under visible light, associated with the synergetic effects of
homo-/hetero-junction on TiO2.


  The recalibration of the sunspot number series, the primary long-term record
of the solar cycle, requires the recovery of the entire collection of raw
sunspot counts collected by the Zurich Observatory for the production of this
index between 1849 and 1980. Here, we report about the major progresses
accomplished recently in the construction of this global digital sunspot number
database, and we derive global statistics of all the individual observers and
professional observatories who provided sunspot data over more than 130 years.
First, we can announce the full recovery of long-lost source-data tables
covering the last 34 years between 1945 and 1979, and we describe the unique
information available in those tables. We then also retrace the evolution of
the core observing team in Zurich and of the auxiliary stations. In 1947, we
find a major disruption in the composition of both the Zurich team and the
international network of auxiliary stations. This sharp transition is unique in
the history of the Zurich Observatory and coincides with the main scale-jump
found in the original Zurich sunspot number series, the so-called "Waldmeier"
jump. This adds key historical evidence explaining why methodological changes
introduced progressively in the early $20^{th}$ century could play a role
precisely at that time. We conclude on the remaining steps needed to fully
complete this new sunspot data resource.


  For $G$-monogenic mappings taking values in the algebra of complex quaternion
we prove a curvilinear analogue of the Cauchy integral theorem in the case
where a curve of integration lies on the boundary of a domain.


  Gas infall and accretion play a fundamental role in galaxy formation, and
several processes of accretion are reviewed. In particular the cold accretion
may solve to some extent the angular momentum problem in disk formation, while
it is aggravated by mergers. Gas accretion is one of the main actor in secular
evolution: it is required to account for recurrent bar formation, and to
explain the feedback cycles of formation of bulges and black holes, with
correlated masses. Infall is also required to fuel a regular and almost
stationary star formation history. Star formation is quenched for galaxy in
clusters when gas accretion is suppressed through stripping. The central
brighter central galaxy can benefit however of gas accretion through cooling
flows, moderated by AGN feedback. Hot and cold feedback scenarios can be
considered, to account for a stationary cooling flow, and explain the
filamentary CO and Halpha observed structures.


  In toroidal geometry, and prior to the establishment of a fully developed
turbulent state, the so-called topological instability of the
pressure-gradient-driven turbulence is observed. In this intermediate state, a
narrow spectral band of modes dominates the dynamics, giving rise to the
formation of iso-surfaces of electric potential with a complicated topology.
Since E x B advection of tracer particles takes place along these iso-surfaces,
their topological complexity affects the characteristic features of radial and
poloidal transport dramatically. In particular, they both become strongly
non-diffusive and non-Gaussian. Since radial transport determines the system
confinement properties and poloidal transport controls the equilibration
dynamics (on any magnetic surface), the development of non-diffusive models in
both directions is thus of physical interest. In previous work, a fractional
model to describe radial transport was constructed by the authors. In this
contribution, recent results on periodic fractional models are exploited for
the construction of an effective model of poloidal transport. Numerical
computations using a three-dimensional reduced magnetohydrodynamic set of
equations are compared with analytical solutions of the fractional periodic
model. It is shown that the aforementioned analytical solutions accurately
describe poloidal transport, which turns out to be superdiffusive with index
$\alpha=1$.


  We investigate the entanglement properties of finite spin rings, with
noncollinear Ising interaction between nearest neighbours. The orientations of
the Ising axes are determined either by the spin position within the ring
(model A) or by the direction of the bond (model B). In both cases, the
considered spin Hamiltonians have a point group symmetry, rather than a
translation invariance, as in spin rings with collinear Ising interaction. The
ground state of these models exhibit remarkable entanglement properties,
resembling GHZ-like states in the absence of an applied magnetic field (model
B). Besides, the application of an homogeneous magnetic field allows to modify
qualitatively the character of the ground state entanglement, switching from
multipartite to pairwise quantum correlations (both models A and B).


  For any fixed graph $G$, the subgraph isomorphism problem asks whether an
$n$-vertex input graph has a subgraph isomorphic to $G$. A well-known algorithm
of Alon, Yuster and Zwick (1995) efficiently reduces this to the "colored"
version of the problem, denoted $G$-$\mathsf{SUB}$, and then solves
$G$-$\mathsf{SUB}$ in time $O(n^{tw(G)+1})$ where $tw(G)$ is the treewidth of
$G$. Marx (2010) conjectured that $G$-$\mathsf{SUB}$ requires time
$\Omega(n^{\mathrm{const}\cdot tw(G)})$ and, assuming the Exponential Time
Hypothesis, proved a lower bound of $\Omega(n^{\mathrm{const}\cdot emb(G)})$
for a certain graph parameter $emb(G) \ge \Omega(tw(G)/\log tw(G))$. With
respect to the size of $\mathrm{AC}^0$ circuits solving $G$-$\mathsf{SUB}$ in
the average case, Li, Razborov and Rossman (2017) proved (unconditional) upper
and lower bounds of $O(n^{2\kappa(G)+\mathrm{const}})$ and
$\Omega(n^{\kappa(G)})$ for a different graph parameter $\kappa(G) \ge
\Omega(tw(G)/\log tw(G))$.
  Our contributions are as follows. First, we prove that $emb(G)$ is
$O(\kappa(G))$ for all graphs $G$. Next, we show that $\kappa(G)$ can be
asymptotically less than $tw(G)$; for example, if $G$ is a hypercube then
$\kappa(G)$ is $\Theta\big(tw(G)\big/\sqrt{\log tw(G)}\big)$. This implies that
the average-case complexity of $G$-$\mathsf{SUB}$ is $n^{o(tw(G))}$ when $G$ is
a hypercube. Finally, we construct $\mathrm{AC}^0$ circuits of size
$O(n^{\kappa(G)+\mathrm{const}})$ that solve $G$-$\mathsf{SUB}$ in the average
case, closing the gap between the upper and lower bounds of Li et al.


  The realization of topological superconductors (SCs) in one or two dimensions
is a highly pursued goal. Prominent proposed realization schemes include
semiconductor/superconductor heterostructures and set stringent constraints on
the chemical potential of the system. However, the ability to keep the chemical
potential in the required range while in the presence of an adjacent SC and its
accompanied screening effects, is a great experimental challenge. In this work,
we study a SC lattice structure in which the SC is deposited periodically on a
one- or two-dimensional sample. We demonstrate that this realization platform
overcomes the challenge of controlling the chemical potential in the presence
of the superconductor's electrostatic screening. We show how Majorana bound
states emerge at the ends of a one-dimensional system proximity coupled to a
one-dimensional SC lattice, and move on to present a SC-lattice-based
realization of the two-dimensional px+ipy SC, hosting chiral Majorana modes at
its edges. In particular, we establish that even when assuming the worst case
of absolute screening, in which the chemical potential under the SC is
completely unaffected by the external gate potential, the topological phase can
be reached by tuning the chemical potential in the area not covered by the SC.
Finally, we briefly discuss possible effects of Coulomb blockade on the
properties of the system.


  The Zintl compound AIn2X2 (A = Ca, Sr, and X = P, As), as a theoretically
predicted new non-magnetic topological insulator, requires experiments to
understand their electronic structure and topological characteristics. In this
paper, we systematically investigate the crystal structures and electronic
properties of the Zintl compound SrIn2As2 under both ambient and high-pressure
conditions. Based on systematic angle-resolved photoemission spectroscopy
(ARPES) measurements, we observed the topological surface states on its (001)
surface as predicted by calculations, indicating that SrIn2As2 is a strong
topological insulator. Interestingly, application of pressure effectively tuned
the crystal structure and electronic properties of SrIn2As2. Superconductivity
is observed in SrIn2As2 for pressure where the temperature dependence of the
resistivity changes from a semiconducting-like behavior to that of a metal. The
observation of nontrivial topological states and pressure-induced
superconductivity in SrIn2As2 provides crucial insights into the relationship
between topology and superconductivity, as well as stimulates further studies
of superconductivity in topological materials.


  Nanosecond laser ablation of p-type Si(111) wafer is presented under an
ambient, a water and a glycerin conditions. Effects of pulse energy, number of
pulses and type of environment are systematically analyzed with an Optical
Profilometer, a Scanning Electron Microscope and an Atomic Force Microscope.
Obtained results are compared with Zemax-EE simulations. Self-assembly
nano-ripple formation has been observed in an ambient condition. However, these
ripples and heat affected zone (HAZ) area disappeared in aqueous medium which
enhances the quality of ablation. Moreover, no remarkable oxidation was
obtained in ablation zone even in an ambient condition based on the SEM-EDX
analysis. Ablation profiles and their depths for different environments are
compared with false color technique. Although ablation diameters are comparable
each other, their depths are small enough in aqueous medium to control depth
profile of damaged area. According to our results, best ablation profiles
(precise edges without HAZ) are achieved under an aqueous medium especially in
glycerin conditions both in low and high pulse energy regime.


  In this paper, we introduce and study the fermionic concurrence in a two-site
extended Hubbard model. Its behaviors both at the ground state and finite
temperatures as function of Coulomb interaction $U$ (on-site) and $V$
(nearest-neighbor) are obtained analytically and numerically. We also
investigate the change of the concurrence under a nonuniform field, including
local potential and magnetic field, and find that the concurrence can be
modulated by these fields.


  Using various tools from representation theory and group theory, but without
using hard classification theorems such as the classification of finite simple
groups, we show that the Jones representations of braid groups are dense in the
complex Zariski topology when the parameter $t$ is not a root of unity. As
first established by Freedman, Larsen, and Wang, we the same result when t is a
non-lattice root of unity, other than one initial case when t has order 10. We
also compute the real Zariski closure of these representations. When such a
representation is indiscrete in the analytic topology, then its analytic
closure is the same as its real Zariski closure.


  Despite much progress being made in the field of object recognition with the
advances of deep learning, there are still several factors negatively affecting
the performance of deep learning models. Domain shift is one of these factors
and is caused by discrepancies in the distributions of the testing and training
data. In this paper, we focus on the problem of compact feature clustering in
domain generalization to help optimize the embedding space from multi-domain
data. We design a domainaware triplet loss for domain generalization to help
the model to not only cluster similar semantic features, but also to disperse
features arising from the domain. Unlike previous methods focusing on
distribution alignment, our algorithm is designed to disperse domain
information in the embedding space. The basic idea is motivated based on the
assumption that embedding features can be clustered based on domain
information, which is mathematically and empirically supported in this paper.
In addition, during our exploration of feature clustering in domain
generalization, we note that factors affecting the convergence of metric
learning loss in domain generalization are more important than the pre-defined
domains. To solve this issue, we utilize two methods to normalize the embedding
space, reducing the internal covariate shift of the embedding features. The
ablation study demonstrates the effectiveness of our algorithm. Moreover, the
experiments on the benchmark datasets, including PACS, VLCS and Office-Home,
show that our method outperforms related methods focusing on domain
discrepancy. In particular, our results on RegnetY-16 are significantly better
than state-of-the-art methods on the benchmark datasets. Our code will be
released at https://github.com/workerbcd/DCT


  Pre-trained language models have shown remarkable results on various NLP
tasks. Nevertheless, due to their bulky size and slow inference speed, it is
hard to deploy them on edge devices. In this paper, we have a critical insight
that improving the feed-forward network (FFN) in BERT has a higher gain than
improving the multi-head attention (MHA) since the computational cost of FFN is
2$\sim$3 times larger than MHA. Hence, to compact BERT, we are devoted to
designing efficient FFN as opposed to previous works that pay attention to MHA.
Since FFN comprises a multilayer perceptron (MLP) that is essential in BERT
optimization, we further design a thorough search space towards an advanced MLP
and perform a coarse-to-fine mechanism to search for an efficient BERT
architecture. Moreover, to accelerate searching and enhance model
transferability, we employ a novel warm-up knowledge distillation strategy at
each search stage. Extensive experiments show our searched EfficientBERT is
6.9$\times$ smaller and 4.4$\times$ faster than BERT$\rm_{BASE}$, and has
competitive performances on GLUE and SQuAD Benchmarks. Concretely,
EfficientBERT attains a 77.7 average score on GLUE \emph{test}, 0.7 higher than
MobileBERT$\rm_{TINY}$, and achieves an 85.3/74.5 F1 score on SQuAD v1.1/v2.0
\emph{dev}, 3.2/2.7 higher than TinyBERT$_4$ even without data augmentation.
The code is released at https://github.com/cheneydon/efficient-bert.


  In this paper, we are interested in explicit zero-free discs for some
Dirichlet series and we also study a general Beurling-Nyman criterion for
$L$-functions. Our results generalize and improve previous results obtained by
N. Nikolski and by A. de Roton. As a concrete application, we get, for example,
a Beurling-Nyman type criterion for the Siegel zero problem.


  Wind power as a renewable source of energy, has numerous economic,
environmental and social benefits. In order to enhance and control renewable
wind power, it is vital to utilize models that predict wind speed with high
accuracy. Due to neglecting of requirement and significance of data
preprocessing and disregarding the inadequacy of using a single predicting
model, many traditional models have poor performance in wind speed prediction.
In the current study, for predicting wind speed at target stations in the north
of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale
Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited
set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the
ten target stations, with the nine stations for training and tenth station for
testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh,
Kiyashahr, Lahijan, Masuleh, and Deylaman) to increase the accuracy of the
subsequent hybrid model. The capability of the hybrid model in wind speed
forecasting at each target station was compared with the MLP model without the
WOA optimizer. To determine definite results, numerous statistical performances
were utilized. For all ten target stations, the MLP-WOA model had precise
outcomes than the standalone MLP model. The hybrid model had acceptable
performances with lower amounts of the RMSE, SI and RE parameters and higher
values of NSE, WI, and KGE parameters. It was concluded that the WOA
optimization algorithm can improve the prediction accuracy of MLP model and may
be recommended for accurate wind speed prediction.


  This work proposes a training-free approach for the detection of
LLMs-generated codes, mitigating the risks associated with their indiscriminate
usage. To the best of our knowledge, our research is the first to investigate
zero-shot detection techniques applied to code generated by advanced black-box
LLMs like ChatGPT. Firstly, we find that existing training-based or zero-shot
text detectors are ineffective in detecting code, likely due to the unique
statistical properties found in code structures. We then modify the previous
zero-shot text detection method, DetectGPT (Mitchell et al., 2023) by utilizing
a surrogate white-box model to estimate the probability of the rightmost
tokens, allowing us to identify code snippets generated by language models.
Through extensive experiments conducted on the python codes of the CodeContest
and APPS dataset, our approach demonstrates its effectiveness by achieving
state-of-the-art detection results on text-davinci-003, GPT-3.5, and GPT-4
models. Moreover, our method exhibits robustness against revision attacks and
generalizes well to Java codes. We also find that the smaller code language
model like PolyCoder-160M performs as a universal code detector, outperforming
the billion-scale counterpart. The codes will be available at
https://github.com/ Xianjun-Yang/Code_detection.git


  An intense beam of muons is needed to provide a luminosity on the order of
10$^{34}$ cm$^{-2}$s$^{-1}$ for a multi-TeV collider. Because muons produced by
colliding a multi-MW proton beam with a target made of carbon or mercury have a
large phase space, significant six dimensional cooling is required. Through
ionization cooling - the only cooling method that works within the lifetime of
the muon - and emittance exchange, the desired emittances for a Higgs Factory
or higher energy collider are attainable. A cooling channel utilizing gas
filled radio frequency cavities has been designed to deliver the requisite cool
muon beam. Technology development of these RF cavities has progressed from
breakdown studies, through beam tests, to dielectric loaded and reentrant
cavity designs. The results of these experiments are summarized.


  The emission in the near ultraviolet Ca II H & K lines is modulated by
stellar magnetic activity. Although this emission, quantified via the S-index,
has been serving as a prime proxy of stellar magnetic activity for several
decades, many aspects of the complex relation between stellar magnetism and Ca
II H & K emission are still unclear. The amount of measured Ca II H & K
emission is suspected to be affected not only by the stellar intrinsic
properties but also by the inclination angle of the stellar rotation axis.
Until now such an inclination effect on S-index has remained largely
unexplored. To fill this gap, we develop a physics-based model to calculate
S-index, focusing on the Sun. Using the distributions of solar magnetic
features derived from observations together with Ca II H & K spectra
synthesized in non-local thermodynamic equilibrium, we validate our model by
successfully reconstructing the observed variations of solar S-index over four
activity cycles. Further, using the distribution of magnetic features over the
visible solar disk obtained from surface flux transport simulations, we obtain
S-index time series dating back to 1700 and investigate the effect of
inclination on S-index variability, both on the magnetic activity cycle and the
rotational timescales. We find that when going from an equatorial to a pole-on
view, the amplitude of S-index variations decreases weakly on the activity
cycle timescale and strongly on the rotational timescale (by about 22% and 81%,
respectively, for a cycle of intermediate strength). The absolute value of
S-index depends only weakly on the inclination. We provide analytical
expressions that model such dependencies.


  Gibbs-preserving operations have been studied as one of the standard free
processes in quantum thermodynamics. Although they admit a simple mathematical
structure, their operational significance has been unclear due to the potential
hidden cost to implement them using an operatioanlly motivated class of
operations, such as thermal operations. Here, we show that this hidden cost can
be infinite -- we present a family of Gibbs-preserving operations that cannot
be implemented by thermal operations aided by any finite amount of quantum
coherence. Our result implies that there are uncountably many Gibbs-preserving
operations that require unbounded thermodynamic resources to implement, raising
a question about employing Gibbs-preserving operations as available
thermodynamics processes. This finding is a consequence of the general lower
bounds we provide for the coherence cost of approximately implementing a
certain class of Gibbs-preserving operations with a desired accuracy. We find
that our lower bound is almost tight, identifying a quantity -- related to the
energy change caused by the channel to implement -- as a fundamental quantifier
characterizing the coherence cost for the approximate implementation of
Gibbs-preserving operations.


  We numerically investigate the transport properties of disordered interacting
electrons in three dimensions in the metallic as well as in the insulating
phases. The disordered many-particle problem is modeled by the quantum Coulomb
glass which contains a random potential, long-range unscreened Coulomb
interactions and quantum hopping between different sites. We have recently
developed the Hartree-Fock based diagonalization (HFD) method which amounts to
diagonalizing the Hamiltonian in a suitably chosen energetically truncated
basis. This method allows us to investigate comparatively large systems. Here
we calculate the combined effect of disorder and interactions on the
dissipative conductance. We find that the qualitative influence of the
interactions on the conductance depends on the relative disorder strength. For
strong disorder interactions can significantly enhance the transport while they
suppress the conductance for weak disorder.


  Photon-limited images are often seen in fields such as medical imaging.
Although the number of collected photons on an image sensor statistically
follows Poisson distribution, this type of noise is intractable, unlike
Gaussian noise. In this study, we propose a Bayesian restoration method of
Poisson corrupted image using Integrated Nested Laplace Approximation (INLA),
which is a computational method to evaluate marginalized posterior
distributions of latent Gaussian models (LGMs). When the original image can be
regarded as ICAR (intrinsic conditional auto-regressive) model reasonably, our
method performs very faster than well-known ones such as loopy belief
propagation-based method and Markov chain Monte Carlo (MCMC) without decreasing
the accuracy.


  In many real applications that use and analyze networked data, the links in
the network graph may be erroneous, or derived from probabilistic techniques.
In such cases, the node classification problem can be challenging, since the
unreliability of the links may affect the final results of the classification
process. If the information about link reliability is not used explicitly, the
classification accuracy in the underlying network may be affected adversely. In
this paper, we focus on situations that require the analysis of the uncertainty
that is present in the graph structure. We study the novel problem of node
classification in uncertain graphs, by treating uncertainty as a first-class
citizen. We propose two techniques based on a Bayes model and automatic
parameter selection, and show that the incorporation of uncertainty in the
classification process as a first-class citizen is beneficial. We
experimentally evaluate the proposed approach using different real data sets,
and study the behavior of the algorithms under different conditions. The
results demonstrate the effectiveness and efficiency of our approach.


  The renormalization paradigm for low-dimensional dynamical systems is that of
hyperbolic horseshoe dynamics. Does this paradigm survive a transition to more
physically relevant systems in higher dimensions? This article addresses this
question in the context of Lorenz dynamics which originates in homoclinic
bifurcations of flows in three dimensions and higher. A conjecture classifying
the dynamics of the Lorenz renormalization operator is stated and supported
with numerical evidence.


  Significant research has focused on low-power stochastic devices built from
memristive materials. These devices foster neuromorphic approaches to
computational efficiency enhancement in merged biomimetic and CMOS
architectures due to their ability to phase transition from a dielectric to a
metal at an increased temperature. Niobium dioxide has a volatile memristive
phase change that occurs $\sim$800$^\circ$C~that makes it an ideal candidate
for future neuromorphic electronics. A straightforward optical system has been
developed on a horizontal tube furnace for \emph{in situ} spectral measurements
as an as-grown \NbtOf\ film is annealed and ultimately crystallizes as \NbOt.
The system measures the changing spectral transmissivity of \NbtOf\ as it
undergoes both reduction and crystallization processes. We were also able to
measure the transition from metallic-to-non-metallic \NbOt\ during the cooldown
phase, which is shown to occur about 100$^\circ$C~ lower on a sapphire
substrate than fused silica. After annealing, the material properties of the
\NbtOf\ and \NbOt\ were assessed via X-ray photoelectron spectroscopy, X-ray
diffraction, and 4-point resistivity, confirming that we have made crystalline
\NbOt.


  It is well known that every Del Pezzo surface of degree 5 defined over k is
parametrizable over k. In this paper we give an efficient construction for
parametrizing, as well as algorithms for constructing examples in every
isomorphism class and for deciding equivalence.


  Due to circuit failures, defective elements that cannot adaptively adjust the
phase shifts of their impinging signals in a desired manner may exist on an
intelligent reflecting surface (IRS). Traditional way to find these defective
IRS elements requires a thorough diagnosis of all the circuits belonging to a
huge number of IRS elements, which is practically challenging. In this paper,
we will devise a novel approach under which a transmitter sends known pilot
signals and a receiver localizes all the defective IRS elements just based on
its over-the-air measurements reflected from the IRS. The key lies in the fact
that the over-the-air measurements at the receiver side are functions of the
set of defective IRS elements. Based on this observation, we propose a
bisection based method to localize all the defective IRS elements.
Specifically, at each time slot, we properly control the desired phase shifts
of all the IRS elements such that half of the considered regime that is not
useful to localize the defective elements can be found based on the received
signals and removed. Via numerical results, it is shown that our proposed
bisection method can exploit the over-the-air measurements to localize all the
defective IRS elements quickly and accurately.


  Recurrent neural networks are powerful models for sequential data, able to
represent complex dependencies in the sequence that simpler models such as
hidden Markov models cannot handle. Yet they are notoriously hard to train.
Here we introduce a training procedure using a gradient ascent in a Riemannian
metric: this produces an algorithm independent from design choices such as the
encoding of parameters and unit activities. This metric gradient ascent is
designed to have an algorithmic cost close to backpropagation through time for
sparsely connected networks. We use this procedure on gated leaky neural
networks (GLNNs), a variant of recurrent neural networks with an architecture
inspired by finite automata and an evolution equation inspired by
continuous-time networks. GLNNs trained with a Riemannian gradient are
demonstrated to effectively capture a variety of structures in synthetic
problems: basic block nesting as in context-free grammars (an important feature
of natural languages, but difficult to learn), intersections of multiple
independent Markov-type relations, or long-distance relationships such as the
distant-XOR problem. This method does not require adjusting the network
structure or initial parameters: the network used is a sparse random graph and
the initialization is identical for all problems considered.


  In this paper, we study the H\"older-type interpolation inequality and
observability inequality from measurable sets in time for parabolic equations
either with L^p unbounded potentials or with electric potentials. The parabolic
equations under consideration evolve in bounded C^{1,1} domains of R^N (N\geq3)
with homogeneous Neumann boundary conditions. The approach for the
interpolation inequality is based on a modified reduction method and some
stability estimates for the corresponding elliptic operator.


  In this article, we investigate the purity dynamics of entangled 2 two-level
atoms interacting with a single quantized electromagnetic field. We show that
the purity of the qubit pairs depends on the initial state of the atomic
system. It is found that the superposition case is the best choice to generate
entangled states with high purity and hence high entanglement. It is clear that
the purity of one qubit can be purified at the expense of the other pair
through the phenomena of purity swapping. The mean photon number plays an
important role in increasing the purity. The robustness of the quantum channel
is investigated in the presence of individual attacks, where we study the
separability of these channels and evaluate its fidelity. Finally, we use the
partial entangled states as quantum channels to perform the original coding
protocol. We find that Bob can obtain the coded information with reasonable
percentage. The inequality of security is tested, where we determine the
interval of times in which Alice and Bob can communicatesecurely. These
intervals depend on the type of error and the structure of the initial atomic
system


  Let $G$ be a countable group that admits a minimal equicontinuous action on
the Cantor set (for example, a residually finite group). We show that the
family of almost automorphic actions of $G$ on the Cantor set, is a test for
amenability for $G$. More precisely, we show that if $G$ is non-amenable, then
for every minimal equicontinuous system $(Y, G)$, such that $Y$ is a Cantor
set, there exists a Toeplitz subshift with no invariant probability measures,
whose maximal equicontinuous factor is $(Y,G)$. Consequently, we obtain that
$G$ is amenable if and only if every Toeplitz $G$-subshift has at least one
invariant probability measure.


  Causal inference plays a vital role in diverse domains like epidemiology,
healthcare, and economics. De-confounding and counterfactual prediction in
observational data has emerged as a prominent concern in causal inference
research. While existing models tackle observed confounders, the presence of
unobserved confounders remains a significant challenge, distorting causal
inference and impacting counterfactual outcome accuracy. To address this, we
propose a novel variational learning model of unobserved confounders for
counterfactual inference (VLUCI), which generates the posterior distribution of
unobserved confounders. VLUCI relaxes the unconfoundedness assumption often
overlooked by most causal inference methods. By disentangling observed and
unobserved confounders, VLUCI constructs a doubly variational inference model
to approximate the distribution of unobserved confounders, which are used for
inferring more accurate counterfactual outcomes. Extensive experiments on
synthetic and semi-synthetic datasets demonstrate VLUCI's superior performance
in inferring unobserved confounders. It is compatible with state-of-the-art
counterfactual inference models, significantly improving inference accuracy at
both group and individual levels. Additionally, VLUCI provides confidence
intervals for counterfactual outcomes, aiding decision-making in risk-sensitive
domains. We further clarify the considerations when applying VLUCI to cases
where unobserved confounders don't strictly conform to our model assumptions
using the public IHDP dataset as an example, highlighting the practical
advantages of VLUCI.


  We consider expressions of the form of an exponential of the sum of two
non-commuting operators of a single variable inside a path integration. We show
that it is possible to shift one of the non-commuting operators from the
exponential to other functions which are pre-factors and post-factors when the
domain of integration of the argument of that function is from -\infty to
+\infty. This shift theorem is useful to perform certain integrals and path
integrals involving the exponential of sum of two non-commuting operators.


  A Clar set of a benzenoid graph $B$ is a maximum set of independent
alternating hexagons over all perfect matchings of $B$. The Clar number of $B$,
denoted by ${\rm Cl}(B)$, is the number of hexagons in a Clar set for $B$. In
this paper, we first prove some results on the independence number of subcubic
trees to study the Clar number of catacondensed benzenoid graphs. As the main
result of the paper we prove an upper bound for the Clar number of
catacondensed benzenoid graphs and characterize the graphs that attain this
bound. More precisely, it is shown that for a catacondensed benzenoid graph $B$
with $n$ hexagons ${\rm Cl}(B) \leq [(2n+1)/3]$.


  The aims of our study are to improve the orbital elements of the giant and to
derive the spectroscopic orbit for the white dwarf companion. Spectral
variations related to the 2006 outburst are also studied. The spectroscopic
orbits have been obtained by measuring the radial velocities of the cool
component absorption lines and the broad Halpha emission wings, which seem to
be associated with the hot component. A set of cF-type absorption lines were
also analyzed for a possible connection with the hot component motion. A new
period of 453.6 days and a mass ratio, q=M_g/M_h=0.59 were determined. Assuming
a massive white dwarf as the hot component, M_h= 1.2-1.4 M_{\odot}, the red
giant mass is M_g= 0.68-0.80 M_{\odot} and the orbit inclination, i =
49-52^{\circ}. The cF-type lines are not associated with either binary
component, and are most likely formed in the material streaming towards the hot
component. We also confirm the presence of the LiI doublet in RS Oph and its
radial velocities fit very well the M-giant radial velocity curve. Regardless
of the mechanism involved to produce lithium, its origin is most likely from
within the cool giant rather than material captured by the giant at the time of
the nova explosion. In April 2006 most of the emission lines present a broad
pedestal with a strong and narrow component at about -20 km/s and two other
extended emission components at -200 and +150 km/s. These components could
originate in a bipolar gas outflow supporting the model of a bipolar
shock-heated shell expanding through the cool component wind perpendicularly to
the binary orbital plane.


  We conceptualize the process of understanding as information compression, and
propose a method for ranking large language models (LLMs) based on lossless
data compression. We demonstrate the equivalence of compression length under
arithmetic coding with cumulative negative log probabilities when using a large
language model as a prior, that is, the pre-training phase of the model is
essentially the process of learning the optimal coding length. At the same
time, the evaluation metric compression ratio can be obtained without actual
compression, which greatly saves overhead. In this paper, we use five large
language models as priors for compression, then compare their performance on
challenging natural language processing tasks, including sentence completion,
question answering, and coreference resolution. Experimental results show that
compression ratio and model performance are positively correlated, so it can be
used as a general metric to evaluate large language models.


  We present analytical and numerical evidence for the validity of an effective
S=1/2 approach to the description of random field generation in S>=1, and
especially in an S=1, dipolar spin glass models with strong uniaxial Ising
anisotropy and subject to weak external magnetic field Bx transverse to the
Ising direction. Explicitely Bx-dependent random fields are shown to naturally
emerge in the effective low-energy description of a microscopic S=1 toy model.
We discuss our results in relation to recent theoretical studies pertaining to
the topic of Bx-induced random fields in the LiHo$_x$Y$_{1-x}$F$_4$ magnetic
materials with the Ho$^{3+}$ Ising moments subject to a transverse field. We
show that the S_{eff}=1/2 approach is able to capture both the qualitative and
quantitative aspects of the physics at small Bx, giving results that agree with
those obtained using conventional second order perturbation theory.


  This paper presents a generalized technology of extraction of explicit
knowledge from data. The main ideas are 1) maximal reduction of network
complexity (not only removal of neurons or synapses, but removal all the
unnecessary elements and signals and reduction of the complexity of elements),
2) using of adjustable and flexible pruning process (the pruning sequence
shouldn't be predetermined - the user should have a possibility to prune
network on his own way in order to achieve a desired network structure for the
purpose of extraction of rules of desired type and form), and 3) extraction of
rules not in predetermined but any desired form. Some considerations and notes
about network architecture and training process and applicability of currently
developed pruning techniques and rule extraction algorithms are discussed. This
technology, being developed by us for more than 10 years, allowed us to create
dozens of knowledge-based expert systems. In this paper we present a
generalized three-step technology of extraction of explicit knowledge from
empirical data.


  We study the Neumann initial-boundary problem for the chemotaxis system
\begin{align*} \left\{\begin{array}{c@{\,}l@{\quad}l@{\,}c} u_{t}&=\Delta
u-\nabla\!\cdot(u\nabla v),\ &x\in\Omega,& t>0,\\ v_{t}&=\Delta v-v+u+f(x,t),\
&x\in\Omega,& t>0,\\ \frac{\partial u}{\partial\nu}&=\frac{\partial
v}{\partial\nu}=0,\ &x\in\partial\Omega,& t>0,\\ u(x,0)&=u_{0}(x),\
v(x,0)=v_{0}(x),\ &x\in\Omega&
  \end{array}\right. \end{align*} in a smooth, bounded domain
$\Omega\subset\mathbb{R}^n$ with $n\geq2$ and
$f\in\text{L}^\infty\left([0,\infty);\text{L}^{\frac{n}{2}+\delta_0}(\Omega)\right)\cap
C^\alpha(\Omega\times(0,\infty))$ with some $\alpha>0$ and
$\delta_0\in\left(0,1\right)$.
  First we prove local existence of classical solutions for reasonably regular
initial values. Afterwards we show that in the case of $n=2$ and $f$ being
constant in time, requiring the nonnegative initial data $u_0$ to fulfill the
property $\smallint_{\Omega} u_0\text{d} x<4\pi$ ensures that the solution is
global and remains bounded uniformly in time. Thereby we extend the well known
critical mass result by Nagai, Senba and Yoshida for the classical Keller-Segel
model (coinciding with $f\equiv 0$ in the system above) to the case
$f\not\equiv 0$.
  Under certain smallness conditions imposed on the initial data and $f$ we
furthermore show that for more general space dimension $n\geq2$ and $f$ not
necessarily constant in time, the solutions are also global and remain bounded
uniformly in time. Accordingly we extend a known result given by Winkler for
the classical Keller-Segel system to the present situation.


  In this paper, a new technique for camera calibration using only GPS data is
presented. A new way of tracking objects that move on a plane in a video is
achieved by using the location and size of the bounding box to estimate the
distance, achieving an average prediction error of 5.55m per 100m distance from
the camera. This solution can be run in real-time at the edge, achieving
efficient inference in a low-powered IoT environment while also being able to
track multiple different vessels.


  Quantum processors with sizes in the 10-100 qubit range are now increasingly
common. However, with increased size comes increased complexity for
benchmarking. The effectiveness of a given device may vary greatly between
different tasks, and will not always be easy to predict from single and two
qubit gate fidelities. For this reason, it is important to assess processor
quality for a range of important tasks. In this work we propose and implement
tests based on random quantum circuits. These are used to evaluate multiple
different superconducting qubit devices, with sizes from 5 to 19 qubits, from
two hardware manufacturers: IBM Research and Rigetti. The data is analyzed to
give a quantitive description of how the devices perform. We also describe how
it can be used for a qualititive description accessible to the layperson, by
being played as a game.


  Our current knowledge of star formation and accretion luminosity at
high-redshift (z>3-4), as well as the possible connections between them, relies
mostly on observations in the rest-frame ultraviolet (UV), which are strongly
affected by dust obscuration. Due to the lack of sensitivity of past and
current infrared (IR) instrumentation, so far it has not been possible to get a
glimpse into the early phases of the dust-obscured Universe. Among the next
generation of IR observatories, SPICA, observing in the 12-350 micron range,
will be the only facility that can enable us to make the required leap forward
in understanding the obscured star-formation rate and black-hole accretion rate
densities (SFRD and BHARD, respectively) with respect to what Spitzer and
Herschel achieved in the mid- and far-IR at z<3. In particular, SPICA will have
the unique ability to trace the evolution of the obscured SFRD and BHARD over
cosmic time, from the peak of their activity back to the reionisation epoch
(i.e., 3<z<6-7), where its predecessors had severe limitations. Here we discuss
the potential of both deep and shallow photometric surveys performed with the
SPICA mid-IR instrument (SMI), enabled by the very low level of impact of dust
obscuration in a band centred at 34 micron. These unique unbiased photometric
surveys that SPICA will perform will be followed up by observations both with
the SPICA spectrometers and with other facilities at shorter and longer
wavelengths, with the aim to fully characterise the evolution of AGNs and
star-forming galaxies after re-ionisation.


  We consider core-shell nanowires with conductive shell and insulating core,
and with polygonal cross section. We investigate the implications of this
geometry on Majorana states expected in the presence of proximity-induced
superconductivity and an external magnetic field. A typical prismatic nanowire
has a hexagonal profile, but square and triangular shapes can also be obtained.
The low-energy states are localized at the corners of the cross section, i.e.
along the prism edges, and are separated by a gap from higher energy states
localized on the sides. The corner localization depends on the details of the
shell geometry, i.e. thickness, diameter, and sharpness of the corners. We
study systematically the low-energy spectrum of prismatic shells using
numerical methods and derive the topological phase diagram as a function of
magnetic field and chemical potential for triangular, square, and hexagonal
geometries. A strong corner localization enhances the stability of Majorana
modes to various perturbations, including the orbital effect of the magnetic
field, whereas a weaker localization favorizes orbital effects and reduces the
critical magnetic field. The prismatic geometry allows the Majorana zero-energy
modes to be accompanied by low-energy states, which we call pseudo Majorana,
and which converge to real Majoranas in the limit of small shell thickness. We
include the Rashba spin-orbit coupling in a phenomenological manner, assuming a
radial electric field across the shell.


  Spin-wave dispersions in the antiferromagnetic state of single crystal
LiFePO$_4$ were determined by inelastic neutron scattering measurements. The
dispersion curves measured from the (010) reflection along both {\it a}$^\ast$
and {\it b}$^\ast$ reciprocal-space directions reflect the anisotropic coupling
of the layered Fe$^{2+}$ (S = 2) spin-system. The spin-wave dispersion curves
were theoretically modeled using linear spin-wave theory by including in the
spin-Hamiltonian in-plane nearest- and next-nearest-neighbor interactions ({\it
J}$_1$ and {\it J}$_2$), inter-plane nearest-neighbor interactions ({\it
J}$_\bot$) and a single-ion anisotropy ({\it D}). A weak (010) magnetic peak
was observed in elastic neutron scattering studies of the same crystal
indicating that the ground state of the staggered iron moments is not along
(010) direction, as previously reported from polycrystalline samples studies,
but slightly rotated away from this axis.


  The angular dependence of jet suppression encodes key information about the
process of energy and momentum hydrodynamization, and for this reason can be
used to greatly improve our understanding of fundamental aspects of the jet/QGP
interaction. In this work we study jet suppression from small to very large
radius, for low and very high energy jets at the LHC and RHIC. We use the
hybrid strong/weak coupling model for jet quenching that combines perturbative
shower evolution with an effective strongly coupled description of the energy
and momentum transfer from the jet into the QGP. Because of energy-momentum
conservation, the wake created by the jet enhances or depletes the yield of
particles generated at the freeze-out hypersurface depending on their
orientation with respect to the direction of the jet. We find that jet
suppression is remarkably independent of the anti-$k_T$ radius R, specially at
LHC, first slightly increasing by opening R, then at larger values of R slowly
decreasing. This nearly independence of jet suppression with R arises from two
competing effects, namely the larger energy loss of the energetic jet
components, which tends to increase suppression, against the partial recovery
of the lost energy due to medium response, which reduces suppression. We find
that the boosted medium from the recoiling jet depletes the amount of QGP in
the direction opposite to it in the transverse plane, inducing energy loss due
to an over-subtraction effect. We show that this unique signature of the
hydrodynamization of part of the jet energy can be scrutinized by selecting
samples of dijet configurations with different relative rapidities between the
leading and the subleading jet.


  In this paper, we perform an exhaustive evaluation of different
representations to address the intent classification problem in a Spoken
Language Understanding (SLU) setup. We benchmark three types of systems to
perform the SLU intent detection task: 1) text-based, 2) lattice-based, and a
novel 3) multimodal approach. Our work provides a comprehensive analysis of
what could be the achievable performance of different state-of-the-art SLU
systems under different circumstances, e.g., automatically- vs.
manually-generated transcripts. We evaluate the systems on the publicly
available SLURP spoken language resource corpus. Our results indicate that
using richer forms of Automatic Speech Recognition (ASR) outputs, namely
word-consensus-networks, allows the SLU system to improve in comparison to the
1-best setup (5.5% relative improvement). However, crossmodal approaches, i.e.,
learning from acoustic and text embeddings, obtains performance similar to the
oracle setup, a relative improvement of 17.8% over the 1-best configuration,
being a recommended alternative to overcome the limitations of working with
automatically generated transcripts.


  Surface resistance of HTS films is typically measured using Sapphire
dielectric rod resonators enclosed in a copper cavity. In this paper we present
surface resistance measurements of YBa2Cu3O7-{\delta} films using Strontium
Lanthanum Aluminate (SLAO) at a resonant frequency of 18.2 GHz. We have
performed the error analysis of the cavity loaded with SLAO dielectric rod and
also verification measurements using two Sapphire (Al2O3) rod resonators
operating at resonant frequencies of 24.6 GHz and 10 GHz respectively. Good
agreement between the values of Rs of two sets of YBa2Cu3O7-{\delta} films
measured using the SLAO and the Sapphire dielectrics has been obtained after a
frequency scaling of Rs was applied. Using different dielectric rods of the
same size in the same cavity for measurements of Rs of HTS films, it is
feasible to do microwave characterization of the same films at differing
frequencies.


  We prove a conjecture of Cautis and Sussan providing a categorification of
the Boson-Fermion correspondence as formulated by Frenkel and Kac. We lift the
Bernstein operators to infinite chain complexes in Khovanov's Heisenberg
category H and from them construct categorical analogues of the Kac-Frenkel
fermionic vertex operators. These fermionic functors are then shown to satisfy
categorical Clifford algebra relations, solving a conjecture of Cautis and
Sussan. We also prove another conjecture of Cautis and Sussan demonstrating
that the categorical Fock space representation of H is a direct summand of the
regular representation by showing that certain infinite chain complexes are
categorical Fock space idempotents. In the process, we enhance the graphical
calculus of H by lifting various Littlewood-Richardson branching isomorphisms
to the Karoubian envelope of H.


  Screening feature selection methods are often used as a preprocessing step
for reducing the number of variables before training step. Traditional
screening methods only focus on dealing with complete high dimensional
datasets. Modern datasets not only have higher dimension and larger sample
size, but also have properties such as streaming input, sparsity and concept
drift. Therefore a considerable number of online feature selection methods were
introduced to handle these kind of problems in recent years. Online screening
methods are one of the categories of online feature selection methods. The
methods that we proposed in this research are capable of handling all three
situations mentioned above. Our research study focuses on classification
datasets. Our experiments show proposed methods can generate the same feature
importance as their offline version with faster speed and less storage
consumption. Furthermore, the results show that online screening methods with
integrated model adaptation have a higher true feature detection rate than
without model adaptation on data streams with the concept drift property. Among
the two large real datasets that potentially have the concept drift property,
online screening methods with model adaptation show advantages in either saving
computing time and space, reducing model complexity, or improving prediction
accuracy.


  Flavordynamics chiral models of leptoquark fermions and bilepton gauge bosons
with masses up to about 1 TeV, although coincident at low energy with the
standard model, address the question of fermion mass hyerarchy and explain the
flavor question. The presence of charged right-handed weak currents coupled to
bileptons, which we point out herein, is another remarkable feature of these
chiral theories.


  A standard assumption in mean-field game (MFG) theory is that the coupling
between the Hamilton-Jacobi equation and the transport equation is
monotonically non-decreasing in the density of the population. In many cases,
this assumption implies the existence and uniqueness of solutions. Here, we
drop that assumption and construct explicit solutions for one-dimensional MFGs.
These solutions exhibit phenomena not present in monotonically increasing MFGs:
low-regularity, non-uniqueness, and the formation of regions with no agents.


  Chest X-rays (CXRs) are commonly utilized as a low-dose modality for lung
screening. Nonetheless, the efficacy of CXRs is somewhat impeded, given that
approximately 75% of the lung area overlaps with bone, which in turn hampers
the detection and diagnosis of diseases. As a remedial measure, bone
suppression techniques have been introduced. The current dual-energy
subtraction imaging technique in the clinic requires costly equipment and
subjects being exposed to high radiation. To circumvent these issues, deep
learning-based image generation algorithms have been proposed. However,
existing methods fall short in terms of producing high-quality images and
capturing texture details, particularly with pulmonary vessels. To address
these issues, this paper proposes a new bone suppression framework, termed
BS-Diff, that comprises a conditional diffusion model equipped with a U-Net
architecture and a simple enhancement module to incorporate an autoencoder. Our
proposed network cannot only generate soft tissue images with a high bone
suppression rate but also possesses the capability to capture fine image
details. Additionally, we compiled the largest dataset since 2010, including
data from 120 patients with high-definition, high-resolution paired CXRs and
soft tissue images collected by our affiliated hospital. Extensive experiments,
comparative analyses, ablation studies, and clinical evaluations indicate that
the proposed BS-Diff outperforms several bone-suppression models across
multiple metrics. Our code can be accessed at
https://github.com/Benny0323/BS-Diff.


  We analyze a compression scheme for large data sets that randomly keeps a
small percentage of the components of each data sample. The benefit is that the
output is a sparse matrix and therefore subsequent processing, such as PCA or
K-means, is significantly faster, especially in a distributed-data setting.
Furthermore, the sampling is single-pass and applicable to streaming data. The
sampling mechanism is a variant of previous methods proposed in the literature
combined with a randomized preconditioning to smooth the data. We provide
guarantees for PCA in terms of the covariance matrix, and guarantees for
K-means in terms of the error in the center estimators at a given step. We
present numerical evidence to show both that our bounds are nearly tight and
that our algorithms provide a real benefit when applied to standard test data
sets, as well as providing certain benefits over related sampling approaches.


  Online fraud often involves identity theft. Since most security measures are
weak or can be spoofed, we investigate a more nuanced and less explored avenue:
behavioral biometrics via handwriting movements. This kind of data can be used
to verify whether a user is operating a device or a computer application, so it
is important to distinguish between human and machine-generated movements
reliably. For this purpose, we study handwritten symbols (isolated characters,
digits, gestures, and signatures) produced by humans and machines, and compare
and contrast several deep learning models. We find that if symbols are
presented as static images, they can fool state-of-the-art classifiers (near
75% accuracy in the best case) but can be distinguished with remarkable
accuracy if they are presented as temporal sequences (95% accuracy in the
average case). We conclude that an accurate detection of fake movements has
more to do with how users write, rather than what they write. Our work has
implications for computerized systems that need to authenticate or verify
legitimate human users, and provides an additional layer of security to keep
attackers at bay.


  We study the effect of impurity pinning on a one-dimensional half-filled
electron system, which is expressed in terms of a phase Hamiltonian with the
charge degree of freedom. Within the classical treatment, the pinned state is
examined numerically. The Mott glass, which has been pointed out by Orignac et
al. [Phys. Rev. Lett 83 (1999) 2378], appears in the intermediate region where
the impurity potential competes with the commensurate potential. Such a state
is verified by calculating the soliton formation energy, the local restoring
force around the pinned state and the optical conductivity.


  Building off ideas developed by Agol, we construct a family of hyperbolic
knots $K_n$ whose complements contain no closed incompressible surfaces and
have Heegaard genus exactly $n$. These are the first known examples of small
knots having large Heegaard genus. Using work of Futer and Purcell, we are able
to bound the crossing number for each $K_n$ in terms of $n$.


  The era of technological change entails complex patterns of changes in wages
and employment. We develop a unified framework to measure the contribution of
technological change embodied in new capital to changes in the relative wages
and income shares of different types of labor. We obtain the aggregate
elasticities of substitution by estimating and aggregating sectoral production
function parameters with cross-country and cross-industry panel data from OECD
countries. We show that advances in information, communication, and computation
technologies contribute significantly to narrowing the gender wage gap,
widening the skill wage gap, and declining labor shares.


  Clustering is crucial for many computer vision applications such as robust
tracking, object detection and segmentation. This work presents a real-time
clustering technique that takes advantage of the unique properties of
event-based vision sensors. Since event-based sensors trigger events only when
the intensity changes, the data is sparse, with low redundancy. Thus, our
approach redefines the well-known mean-shift clustering method using
asynchronous events instead of conventional frames. The potential of our
approach is demonstrated in a multi-target tracking application using Kalman
filters to smooth the trajectories. We evaluated our method on an existing
dataset with patterns of different shapes and speeds, and a new dataset that we
collected. The sensor was attached to the Baxter robot in an eye-in-hand setup
monitoring real-world objects in an action manipulation task. Clustering
accuracy achieved an F-measure of 0.95, reducing the computational cost by 88%
compared to the frame-based method. The average error for tracking was 2.5
pixels and the clustering achieved a consistent number of clusters along time.


  It was pointed out long ago by Vainshtein [Phys. Lett. 39B, 393 (1972)] that
the weak-field perturbation expansion of generic theories (of the nonlinear
Fierz-Pauli type) involving massive spin-2 excitations breaks down below a
certain distance around a material source ("Vainshtein radius"), scaling as
some inverse power of the spin-2 mass $m_2$, i.e., some positive power of the
range $m_2^{-1}$. Here we prove that this conclusion does not apply in a
generalized Einstein-Cartan theory (called "torsion bigravity") whose spectrum
is made (like that of bimetric gravity) of a massless spin-2 excitation and a
massive spin-2 one. Working within a static spherically symmetric ansatz, we
prove, by reformulating the field equations in terms of new variables, that one
can construct an all-order weak-field perturbative expansion where no
denominators involving $m_2$ ever appear in the region $r \ll m_2^{-1}$. In
particular, we show how the formal large-range limit, $m_2 \to 0$, leads to a
well-defined, finite perturbation expansion, whose all-order structure is
discussed in some detail.


  In this paper, we construct a linear positive operators q-parametric
Szasz-Mirakjan operators generated by the q-Dunkl generalization of the
exponential function. We obtain Korovkin's type approximation theorem for these
operators and compute convergence of these operators by using the modulus of
continuity. Furthermore, the rate of convergence of the operators for functions
belonging to the Lipschitz class is presented.


  In this paper we study the geometric origin of non-invertible symmetries of
2d theories arising from the reduction of 6d $(2,0)$ theories on
four-manifolds. This generalizes and extends our previous results in the
context of class $\mathcal S$ theories to a wider realm of models. In
particular, we find that relative 2d field theories, such as the chiral boson,
have a higher dimensional origin in four-manifolds that are not null cobordant.
Moreover, we see that for the 2d theories with a 6d origin, the non-invertible
symmetries have a geometric origin as a sum over topologies from the
perspective of the 7d symmetry TFT. In particular, we show that the
Tambara-Yamagami non-invertible symmetries $TY(\mathbb Z_N)$ can be given a
geometric origin of this kind. We focus on examples that do not depend on spin
structures, but we analyse the simplest of such cases, finding an interesting
parallel between the extra choices arising in that context and symmetry
fractionalization in Maxwell theories.


  Recently we have developed a spin Hall magnetoresistance (SMR) sensor which
operates under AC bias and sense currents. Here we demonstrate both
theoretically and experimentally that the SMR sensor is uniquely suited for
eddy current testing applications because both the coil and sensor utilize AC
current as the excitation source. The use of SMR sensor effectively eliminates
the necessity of any demodulation or lock-in technique for detecting the eddy
current, which greatly simplifies the detection system. Furthermore, we show
that the combination of principal component analysis and decision tree model is
effective in classifying the metal cracks. The relatively clean signals
obtained by the SMR sensor greatly facilitates the subsequent signal analysis
and ensures high accuracy in the classification of different types of crack
features.


  We consider a phase retrieval problem, where the goal is to reconstruct a
$n$-dimensional complex vector from its phaseless scalar products with $m$
sensing vectors, independently sampled from complex normal distributions. We
show that, with a random initialization, the classical algorithm of alternating
minimization succeeds with high probability as $n,m\rightarrow\infty$ when
${m}/{\log^3m}\geq Mn^{3/2}\log^{1/2}n$ for some $M>0$. This is a step toward
proving the conjecture in \cite{Waldspurger2016}, which conjectures that the
algorithm succeeds when $m=O(n)$. The analysis depends on an approach that
enables the decoupling of the dependency between the algorithmic iterates and
the sensing vectors.


  App repackaging refers to the practice of customizing an existing mobile app
and redistributing it in the wild. In this way, the attacker aims to force some
mobile users to install the repackaged(likely malicious) app instead of the
original one. This phenomenon strongly affects Android, where apps are
available on public stores, and the only requirement for an app to execute
properly is to be digitally signed. Anti-repackaging techniques try
counteracting this attack by adding logical controls in the app at
compile-time. Such controls activate in case of repackaging and lead the
repackaged app to fail at runtime. On the other side, the attacker must detect
and bypass the controls to repackage safely. The high-availability of working
repackaged apps in the Android ecosystem suggests that the attacker's side is
winning. In this respect, this paper aims to bring out the main issues of the
current anti-repackaging approaches. The contribution of the paper is
three-fold: 1) analyze the weaknesses of the current state-of-the-art
anti-repackaging schemes (i.e., Self-Protection through Dex Encryption, AppIS,
SSN, SDC, BombDroid, and NRP), 2) summarize the main attack vectors to
anti-repackaging techniques composing those schemes, and 3) show how such
attack vectors allow circumventing the current proposals. The paper will also
show a full-fledged attack to NRP, the only publicly-available anti repackaging
tool to date.


  In this letter, N2 and forming gas (FG) were used during ohmic contact
annealing of InAlN/GaN HEMTs on Si. It is found that N2 annealing offers lower
ohmic contact resistance (RC) while FG annealing features lower sheet
resistance (Rsheet). Then FG/N2 two-step annealing was used to achieve a
subthreshold swing (SS) of 113 mV/dec, an on/off current (Ion/Ioff) ratio of ~
106, a transconductance (gm) peak of 415 mS/mm, a record low drain-inducing
barrier lowing (DIBL) of 65 mV/V, and a record high power gain cutoff frequency
(fmax) of 270 GHz on 50-nm InAlN/GaN HEMT on Si.


  For an abelian variety A over a number field k we discuss the maximal
divisibile subgroup of H^1(k,A) and its intersection with the subgroup
Sha(A/k). The results are most complete for elliptic curves over Q.


  A review of the status of the knowledge in the field of High-energy cosmic
particles is presented. The spectrum, arrival direction distribution and
composition measurements are summarized, together with some implications for
the understanding of the cosmic ray origin and their propagation. Special
emphasis is put in the ultra-high energy range, corresponding to particles of
extragalactic origin.


  We introduce the $q$-Hahn PushTASEP --- an integrable stochastic interacting
particle system which is a 3-parameter generalization of the PushTASEP, a
well-known close relative of the TASEP (Totally Asymmetric Simple Exclusion
Process). The transition probabilities in the $q$-Hahn PushTASEP are expressed
through the $_4\phi_3$ basic hypergeometric function. Under suitable limits,
the $q$-Hahn PushTASEP degenerates to all known integrable (1+1)-dimensional
stochastic systems with a pushing mechanism. One can thus view our new system
as a pushing counterpart of the $q$-Hahn TASEP introduced by Povolotsky (2013).
We establish Markov duality relations and contour integral formulas for the
$q$-Hahn PushTASEP.
  In a $q\to 1$ limit of our process we arrive at a random recursion which, in
a special case, appears to be similar to the inverse-Beta polymer model.
However, unlike in recursions for Beta polymer models, the weights (i.e., the
coefficients of the recursion) in our model depend on the previous values of
the partition function in a nontrivial manner.


  We provide an information-theoretic framework for studying the generalization
properties of machine learning algorithms. Our framework ties together existing
approaches, including uniform convergence bounds and recent methods for
adaptive data analysis. Specifically, we use Conditional Mutual Information
(CMI) to quantify how well the input (i.e., the training data) can be
recognized given the output (i.e., the trained model) of the learning
algorithm. We show that bounds on CMI can be obtained from VC dimension,
compression schemes, differential privacy, and other methods. We then show that
bounded CMI implies various forms of generalization.


  If two parties share an unknown quantum state, one can ask how much quantum
communication is needed for party A to send her share to party B. Recently, it
was found that the number of qubits which should be sent is given by the
conditional entropy. This quantifies the notion of partial information, and it
can even be negative. Here, we not only demand that A send her state to B, but
additionally, B should send his state to A. Paradoxically, we find that
requiring that the parties perform this additional task can lower the amount of
quantum communication required. This primitive, which we call quantum state
exchange, can be used to quantify the notion of uncommon information, since the
two parties only need to send each other the parts of their state they don't
hold in common. In the classical case, the concept of uncommon information
follows trivially from the concept of partial information. We find that for
quantum states, this is not so. We prove upper and lower bounds for the
uncommon information and find optimal protocols for several classes of states.


  The gamma-ray observation of the dwarf spheroidal galaxies (dSphs) is a
promising approach to search for the dark matter annihilation (or decay)
signal. The dSphs are the nearby satellite galaxies with a clean environment
and dense dark matter halo so that they give stringent constraints on the 1 TeV
dark matter. However, recent studies have revealed that current estimation of
astrophysical factors relevant for the dark matter searches are not
conservative, where the various non-negligible systematic uncertainties are not
taken into account. Among them, the effect of foreground stars on the
astrophysical factors has not been paid much attention, which becomes more
important for deeper and wider stellar surveys in the future. In this article,
we assess the effects of the foreground contamination by generating the mock
samples of stars and using a model of future spectrographs. We investigate
various data cuts to optimize the quality of the data and find that the cuts on
the velocity and surface gravity can efficiently eliminate the contamination.
We also propose a new likelihood function which includes the foreground
distribution function. We apply this likelihood function to the fit of the
three types of the mock data (Ursa Minor, Draco with large dark matter halo,
and Draco with small halo) and three cases of the observation. The likelihood
successfully reproduces the input $J$-factor value while the fit without
considering the foreground distribution gives large deviation from the input
value by a factor of three.


  In this work, we introduce a novel algorithm for the Biot problem based on a
Hybrid High-Order discretization of the mechanics and a Symmetric Weighted
Interior Penalty discretization of the flow. The method has several assets,
including, in particular, the support of general polyhedral meshes and
arbitrary space approximation order. Our analysis delivers stability and error
estimates that hold also when the specific storage coefficient vanishes, and
shows that the constants have only a mild dependence on the heterogeneity of
the permeability coefficient. Numerical tests demonstrating the performance of
the method are provided.


  A brief description of the work for which the first half of the Nobel prize
for physics for the year 2002 is presented.


  We study fillings of contact structures supported by planar open books by
analyzing positive factorizations of their monodromy. Our method is based on
Wendl's theorem on symplectic fillings of planar open books. We prove that
every virtually overtwisted contact structure on L(p,1) has a unique filling,
and describe fillable and non-fillable tight contact structures on certain
Seifert fibered spaces.


  When making an online purchase, it becomes important for the customer to read
the product reviews carefully and make a decision based on that. However,
reviews can be lengthy, may contain repeated, or sometimes irrelevant
information that does not help in decision making. In this paper, we introduce
MRCBert, a novel unsupervised method to generate summaries from product
reviews. We leverage Machine Reading Comprehension, i.e. MRC, approach to
extract relevant opinions and generate both rating-wise and aspect-wise
summaries from reviews. Through MRCBert we show that we can obtain reasonable
performance using existing models and transfer learning, which can be useful
for learning under limited or low resource scenarios. We demonstrated our
results on reviews of a product from the Electronics category in the Amazon
Reviews dataset. Our approach is unsupervised as it does not require any
domain-specific dataset, such as the product review dataset, for training or
fine-tuning. Instead, we have used SQuAD v1.1 dataset only to fine-tune BERT
for the MRC task. Since MRCBert does not require a task-specific dataset, it
can be easily adapted and used in other domains.


  In this paper, we present a new cross-architecture contrastive learning
(CACL) framework for self-supervised video representation learning. CACL
consists of a 3D CNN and a video transformer which are used in parallel to
generate diverse positive pairs for contrastive learning. This allows the model
to learn strong representations from such diverse yet meaningful pairs.
Furthermore, we introduce a temporal self-supervised learning module able to
predict an Edit distance explicitly between two video sequences in the temporal
order. This enables the model to learn a rich temporal representation that
compensates strongly to the video-level representation learned by the CACL. We
evaluate our method on the tasks of video retrieval and action recognition on
UCF101 and HMDB51 datasets, where our method achieves excellent performance,
surpassing the state-of-the-art methods such as VideoMoCo and MoCo+BE by a
large margin. The code is made available at https://github.com/guoshengcv/CACL.


  The distinct structures of contact ion pairs in water are identified.
Nonlinear infrared (IR) spectroscopy and theoretical calculations allow for the
separation and assignment of spectral features and interactions.


  In-memory computing on a reconfigurable architecture is the emerging field
which performs an application-based resource allocation for computational
efficiency and energy optimization. In this work, we propose a Ferroelectric
MirrorBit-integrated field-programmable reconfigurable memory. We show the
conventional 1-Bit FeFET, the MirrorBit, and MirrorBit-based Ternary
Content-addressable memory (MCAM or MirrorBit-based TCAM) within the same
field-programmable array. Apart from the conventional uniform Up and Down
polarization states, the additional states in the MirrorBit are programmed by
applying a non-uniform electric field along the transverse direction, which
produces a gradient in the polarization and the conduction band energy. This
creates two additional states, thereby, creating a total of 4 states or 2-bit
of information. The gradient in the conduction band resembles a Schottky
barrier (Schottky diode), whose orientation can be configured by applying an
appropriate field. The TCAM operation is demonstrated using the MirrorBit-based
diode on the reconfigurable array. The reconfigurable array architecture can
switch from AND-type to NOR-type and vice-versa. The AND-type array is
appropriate for programming the conventional bit and the MirrorBit. The
MirrorBit-based Schottky diode in the NOR-array resembles a crossbar structure,
which is appropriate for diode-based CAM operation. Our proposed memory system
can enable fast write via 1-bit FeFET, the dense data storage capability by
Mirror-bit technology and the fast search capability of the MCAM. Further, the
dual configurability enables power, area and speed optimization making the
reconfigurable Fe-Mirrorbit memory a compelling solution for In-memory and
associative computing.


  A new method of derivation of Lorentz Transformation (LT) is given based on
both axioms of special relativity (SR) and physical intuitions. The essence of
the transformation is established and the crucial role played by the
presumptions is presented for clarification. I consider the most general form
of transformations between two sets of events in two inertial reference frames
and use the most basic properties expected from such a transformation together
with the principle of the constancy of the velocity of light to derive LT. The
method is very simple, succinct and useful for students trying a better
understanding of the subject.


  A significant portion of transients measured by spacecraft at 1 AU does not
show the well-defined properties of magnetic clouds (MCs). Here, we propose a
new class of complex, non-MC ejecta resulting from the interaction of two CMEs
with different orientation, which differ from the previously studied
multiple-MC event. At 1 AU, they are associated with a smooth rotation of the
magnetic field vector over an extended duration and do not show clear signs of
interaction. We determine the characteristics of such events based on a
numerical simulation and identify and analyze a potential case in the
long-duration CME measured in situ in 2001 March 19-22. Such events may result
in intense, long-duration geo-magnetic storms, with sawtooth events, and may
sometimes be misidentified as isolated CMEs.


  At low temperatures the configurational phase space of a macroscopic complex
system (e.g., a spin-glass) of $N\sim 10^{23}$ interacting particles may split
into an exponential number $\Omega_s \sim \exp({\rm const} \times N)$ of
ergodic sub-spaces (thermodynamic states). Previous theoretical studies assumed
that the equilibrium collective behavior of such a system is determined by its
ground thermodynamic states of the minimal free-energy density, and that the
equilibrium free energies follow the distribution of exponential decay. Here we
show that these assumptions are not necessarily valid. For some complex
systems, the equilibrium free-energy values may follow a Gaussian distribution
within an intermediate temperature range, and consequently their equilibrium
properties are contributed by {\em excited} thermodynamic states. This work
will help improving our understanding of the equilibrium statistical mechanics
of spin-glasses and other complex systems.


  Let G be an arbitrary simple graph. The main results are explicit
representations of the edge cone of G as a finite intersection of closed
halfspaces. If G is bipartite and connected we determine the facets of the edge
cone and present a canonical irreducible representation.


  Moderate and high-resolution measurements (R ~ 40,000) of interstellar
resonance lines of D I, C II, N I, O I, Al II, and Si II (light ions) are
presented for all available observed targets located within 100 pc which also
have high-resolution observations of interstellar Fe II or Mg II (heavy ions)
lines. All spectra were obtained with the Goddard High Resolution Spectrograph
or the Space Telescope Imaging Spectrograph instruments aboard the Hubble Space
Telescope. Currently, there are 41 sightlines to targets within 100 pc with
observations that include a heavy ion at high resolution and at least one light
ion at moderate or high resolution. We present new measurements of light ions
along 33 of these sightlines, and collect from the literature results for the
remaining sightlines that have already been analyzed. We greatly increase the
number of sightlines with useful LISM absorption line measurements of light
ions by using knowledge of the kinematic structure along a line of sight
obtained from high resolution observations of intrinsically narrow absorption
lines, such as Fe II and Mg II. Because high resolution observations of heavy
ions are critical for understanding the kinematic structure of local absorbers
along the line of sight, we include 18 new measurements of Fe II and Mg II in
an appendix. We present a statistical analysis of the LISM absorption
measurements, which provides an overview of some physical characteristics of
warm clouds in the LISM, including temperature and turbulent velocity. This
complete collection and reduction of all available LISM absorption measurements
provides an important database for studying the structure of nearby warm
clouds, including ionization, abundances, and depletions.


  Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC)
dataset is a collection of automatically categorized and annotated sentences
obtained from Wikipedia. We constructed large-scale gazetteers by using a graph
crawler algorithm to extract relevant entity and domain information from a
semantic knowledge base, Freebase. The constructed gazetteers contains
approximately 300K entities with thousands of fine-grained entity types under
77 different domains. Since automated processes are prone to ambiguity, we also
introduce two new content specific noise reduction methodologies. Moreover, we
map fine-grained entity types to the equivalent four coarse-grained types:
person, loc, org, misc. Eventually, we construct six different dataset versions
and evaluate the quality of annotations by comparing ground truths from human
annotators. We make these datasets publicly available to support studies on
Turkish named-entity recognition (NER) and text categorization (TC).


  We consider the asymptotic behaviour of the effective thermal conductivity of
a two-phase composite obtained by introducing into an infinite homogeneous
matrix a periodic set of inclusions of a different material and of size
proportional to a positive parameter \epsilon. We are interested in the case of
imperfect thermal contact at the two-phase interface. Under suitable
assumptions, we show that the effective thermal conductivity can be continued
real analytically in the parameter \epsilon around the degenerate value
\epsilon=0, in correspondence of which the inclusions collapse to points. The
results presented here are obtained by means of an approach based on functional
analysis and potential theory and are also part of a forthcoming paper by the
authors.


  In evaluating the number of technological civilizations N in the Galaxy
through the Drake formula, emphasis is mostly put on the astrophysical and
biotechnological factors describing the emergence of a civilization and much
less on its the lifetime, which is intimately related to its demise. It is
argued here that this factor is in fact the most important regarding the
practical implications of the Drake formula, because it determines the maximal
extent of the "sphere of influence" of any technological civilization. The
Fermi paradox is studied in the terms of a simplified version of the Drake
formula, through Monte Carlo simulations of N civilizations expanding in the
Galaxy during their space faring lifetime L. In the framework of that scheme,
the probability of "direct contact" is determined as the fraction of the
Galactic volume occupied collectively by the "spheres of influence" of N
civilizations. The results of the analysis are used to determine regions in the
parameter space where the Fermi paradox holds. It is argued that in a large
region of the diagram the corresponding parameters suggest rather a "weak"
Fermi paradox. Future research may reveal whether a "strong" paradox holds in
some part of the parameter space. Finally, it is argued that the value of N is
not bound by N=1 from below, contrary to what is usually assumed, but it may
have a statistical interpretation.


  We consider two semi-infinite magnetoelecteric media separated by a planar
interface whose electromagnetic response is described by axion-electrodynamics.
The time dependent Green's function characterizing this geometry is obtained by
a method which can be directly generalized to cylindrical and spherical
configurations of two magnetoelectrics separated by an interface. We establish
the far-field approximation of the Green's function and apply these results to
the case of a charged particle moving from one medium to the other at a high
constant velocity perpendicular to the interface. From the resulting angular
distribution of the radiated energy per unit frequency we provide theoretical
evidence for the emergence of reversed Vavilov-\v{C}erenkov radiation in
naturally existing magnetoelectric media. In the case where one of the
magnetolectrics is a 3D topological insulator, ${\rm TlBiSe}_2$ for example,
located in front of a regular insulator, we estimate that an average forward
Vavilov-\v{C}erenkov radiation with frequency $\sim 2.5 \,\, {\rm eV}$ ($\sim
500\,\, {\rm nm}$) will produce a highly suppressed reversed
Vavilov-\v{C}erenkov radiation which can be characterized by an effective
frequency in the range of $\sim (4\times 10^{-3}-0.5) \,\, {\rm meV}$. However,
this value compares favorably with recent measurements in left-handed
metamaterials yielding reversed Vavilov-\v{C}erenkov radiation with frequencies
of the order of $(1.2-3.9)\times 10^{-2}\,\, {\rm meV}$.


  In this paper, we investigate the issue of maximizing the total equilibrium
population with respect to resources distribution m(x) and diffusion rates d
under the prescribed total amount of resources in a logistic model with
nonlocal dispersals. Among other things, we show that for $d\ge1$, there exist
$C_0, C_1>0$, depending on the $\|m\|_{L^1}$ only, such that
$$C_0\sqrt{d}<\mbox{supremum~ of~ total~ population}<C_1\sqrt{d}.$$ However,
when replaced by random diffusion, a conjecture, proposed by Ni and justified
in [3], indicates that in the one-dimensional case, supremum of total
population$=3\|m\|_{L^1}$. This reflects serious discrepancies between models
with local and nonlocal dispersal strategies.


  Allocation of (redundant) file chunks throughout a distributed storage system
affects important performance metrics such as the probability of file recovery,
data download time, or the service rate of the system under a given data access
model. This paper is concerned with the service rate under the assumption that
the stored data is large and its download time is not negligible. We focus on
quasi-uniform storage allocations and provide a service rate analysis for two
common data access models. We find that the optimal allocation varies in
accordance with different system parameters. This was not the case under the
assumption that the download time does not scale with the size of data, where
the minimal spreading allocation was previously found to be universally
optimal.


  We study and classify almost complex totally geodesic submanifolds of the
nearly Kaehler flag manifold $F_{1,2}(\mathbb C^3)$, and of its semi-Riemannian
counterpart. We also develop a structural approach to the nearly Kaehler flag
manifold $F_{1,2}(\mathbb C^3)$, expressing for example the curvature tensor in
terms of the nearly Kaehler structure $J$ and the three canonical orthogonal
complex structures.


  We present a theoretical study of the mixed-field orientation of molecules
without rotational symmetry. The time-dependent one-dimensional and
three-dimensional orientation of a thermal ensemble of
6-chloropyridazine-3-carbonitrile molecules in combined linearly or
elliptically polarized laser fields and tilted dc electric fields is computed.
The results are in good agreement with recent experimental results of
one-dimensional orientation for weak dc electric fields [J. Chem. Phys. 139,
234313 (2013)]. Moreover, they predict that using elliptically polarized laser
fields or strong dc fields three-dimensional orientation is obtained. The
field-dressed dynamics of excited rotational states is characterized by highly
non-adiabatic effects. We analyze the sources of these non-adiabatic effects
and investigate their impact on the mixed-field orientation for different field
configurations in mixed-field-orientation experiments.


  We propose and analyze the suitability of a spiking controller to engineer
the locomotion of a soft robotic crawler. Inspired by the FitzHugh-Nagumo model
of neural excitability, we design a bistable controller with an electrical
flipflop circuit representation capable of generating spikes on-demand when
coupled to the passive crawler mechanics. A proprioceptive sensory signal from
the crawler mechanics turns bistability of the controller into a rhythmic
spiking. The output voltage, in turn, activates the crawler's actuators to
generate movement through peristaltic waves. We show through geometric analysis
that this control strategy achieves endogenous crawling. The electro-mechanical
sensorimotor interconnection provides embodied negative feedback regulation,
facilitating locomotion. Dimensional analysis provides insights on the
characteristic scales in the crawler's mechanical and electrical dynamics, and
how they determine the crawling gait. Adaptive control of the electrical scales
to optimally match the mechanical scales can be envisioned to achieve further
efficiency, as in homeostatic regulation of neuronal circuits. Our approach can
scale up to multiple sensorimotor loops inspired by biological central pattern
generators.


  Complex materials encompassing different phases of matter can display new
photoinduced metastable states differing from those attainable under
equilibrium conditions. These states can be realized when energy is injected in
the material following a non-equilibrium pathway, unbalancing the unperturbed
energy landscape of the material. Guided by the fact that photoemission
experiments allow for detailed insights in the electronic band structure of
ordered systems, here we study bulk 1T-VSe$_2$ in its metallic and
charge-density-wave phase by time- and angle-resolved photoelectron
spectroscopy. After near-infrared optical excitation, the system shows a net
increase of the density of states in the energy range of the valence bands, in
the vicinity of the Fermi level, lasting for several picoseconds. We discuss
possible origins as band shifts or correlation effects on the basis of a band
structure analysis. Our results uncover the possibility of altering the
electronic band structure of bulk 1T-VSe$_2$ for low excitation fluences,
contributing to the understanding of light-induced electronic states.


  Information technology should have much to offer linguistics, not only
through the opportunities offered by large-scale data analysis and the stimulus
to develop formal computational models, but through the chance to use language
in systems for automatic natural language processing. The paper discusses these
possibilities in detail, and then examines the actual work that has been done.
It is evident that this has so far been primarily research within a new field,
computational linguistics, which is largely motivated by the demands, and
interest, of practical processing systems, and that information technology has
had rather little influence on linguistics at large. There are different
reasons for this, and not all good ones: information technology deserves more
attention from linguists.


  Quantum neural networks (QNNs) are a framework for creating quantum
algorithms that promises to combine the speedups of quantum computation with
the widespread successes of machine learning. A major challenge in QNN
development is a concentration of measure phenomenon known as a barren plateau
that leads to exponentially small gradients for a range of QNNs models. In this
work, we examine the assumptions that give rise to barren plateaus and show
that an unbounded loss function can circumvent the existing no-go results. We
propose a training algorithm that minimizes the maximal R\'enyi divergence of
order two and present techniques for gradient computation. We compute the
closed form of the gradients for Unitary QNNs and Quantum Boltzmann Machines
and provide sufficient conditions for the absence of barren plateaus in these
models. We demonstrate our approach in two use cases: thermal state learning
and Hamiltonian learning. In our numerical experiments, we observed rapid
convergence of our training loss function and frequently archived a $99\%$
average fidelity in fewer than $100$ epochs.


  We use the approach to generate spin foam models by an auxiliary field theory
defined on a group manifold (as recently developed in quantum gravity and
quantization of BF-theories) in the context of topological quantum field
theories with a 3-form field strength. Topological field theories of this kind
in seven dimensions are related to the superconformal field theories which live
on the worldvolumes of fivebranes in M-theory. The approach through an
auxiliary field theory for spinfoams gives a topology independent formulation
of such theories.


  The Hubble constant, $H_0$, tension is the tension among the local probes,
Supernovae Ia, and the Cosmic Microwave Background Radiation. It has been
almost a decade, and this tension still puzzles the community. Here, we add
intermediate redshift probes, such as Gamma-Ray Bursts (GRB) and Quasars
(QS0s), to check if and to what extent these higher redshift probes can reduce
this tension. We use the three-dimensional fundamental plane relation among the
prompt peak luminosity, the luminosity at the end of the plateau emission, and
its rest frame duration. We find similar trend in GRB intrinsic parameters as
previously seen in Pantheon-Plus intrinsic parameters. We find an apparent
$3.14\sigma$ tension for the GRB intrinsic parameter $b$. Indeed, this tension
disappears and the parameters are actually compatible within $2.26\sigma$.
Another interesting point is that the 3D relation plays an important role in
conjunction with Supernovae data with Pantheon Plus and that this apparent
discrepancy show how it is important the correction for selection biases and
redshift evolution. The incorporation of redshift evolution correction results
in a reduction of the GRB tension to $2.26\sigma$ when adjusting correction
parameters. We envision that with more data this indication of tension will
possibly disappear when the evolutionary parameters of GRBs are computed with
increased precision.


  We construct a Hermitian random matrix model that provides a stable
non-perturbative completion of Cangemi-Jackiw (CJ) gravity, a two-dimensional
theory of flat spacetimes. The matrix model reproduces, to all orders in the
topological expansion, the Euclidean partition function of CJ gravity with an
arbitrary number of boundaries. The non-perturbative completion enables the
exact computation of observables in flat space quantum gravity which we use to
explicitly characterize the Bondi Hamiltonian spectrum. We discuss the
implications of our results for the flat space S-matrix and black holes.


  In this paper we study a new ideal $\mathcal{WR}$. The main result is the
following: an ideal is not weakly Ramsey if and only if it is above
$\mathcal{WR}$ in the Kat\v{e}tov order. Weak Ramseyness was introduced by
Laflamme in order to characterize winning strategies in a certain game. We
apply result of Natkaniec and Szuca to conclude that $\mathcal{WR}$ is critical
for ideal convergence of sequences of quasi-continuous functions. We study
further combinatorial properties of $\mathcal{WR}$ and weak Ramseyness.
Answering a question of Filip\'ow et al. we show that $\mathcal{WR}$ is not
$2$-Ramsey, but every ideal on $\omega$ isomorphic to $\mathcal{WR}$ is Mon
(every sequence of reals contains a monotone subsequence indexed by a
$\mathcal{I}$-positive set).


  We offer a new way to look at the Large Magellanic Cloud through stellar
mono-abundance and mono-age-mono-abundance maps. These maps are based on
$\gtrsim 500\,000$ member stars with photo-spectroscopic [M/H] and age
estimates from Gaia DR3 data, and they are the first area-complete,
metallicity- and age-differentiated stellar maps of any disk galaxy.
Azimuthally averaged, these maps reveal a surprisingly simple picture of the
Milky Way's largest satellite galaxy. For any [M/H] below -0.1 dex, the LMC's
radial profile is well described by a simple exponential, but with a scale
length that steadily shrinks towards higher metallicities, from nearly 2.3~kpc
at [M/H]$=-1.8$ to only 0.75~kpc at [M/H]$=-0.25$. The prominence of the bar
decreases dramatically with [M/H], making it barely discernible at
[M/H]$\lesssim -1.5$. Yet, even for metal-rich populations, the bar has little
impact on the azimuthally averaged profile of the mono-abundance components.
Including ages, we find that the scale length is a greater function of age than
of metallicity, with younger populations far more centrally concentrated. At
old ages, the scale length decreases with increasing metallicity; at young
ages, the scale-length is independent of metallicity. These findings provide
quantitative support for a scenario where the LMC built its stellar structure
effectively outside in.