  In the braneworld scenario, the four dimensional effective Einstein equation
has extra terms which arise from the embedding of the 3-brane in the bulk.
These non-local effects, generated by the free gravitational field of the bulk,
may provide an explanation for the dynamics of the neutral hydrogen clouds at
large distances from the galactic center, which is usually explained by
postulating the existence of the dark matter. We obtain the exact galactic
metric, the dark radiation and the dark pressure in the flat rotation curves
region in the brane world scenario. Due to the presence of the bulk effects,
the flat rotation curves could extend several hundred kpc. The limiting radius
for which bulk effects are important is estimated and compared with the
numerical values of the truncation parameter of the dark matter halos, obtained
from weak lensing observations. There is a relatively good agreement between
the predictions of the model and observations. The deflection of photons is
also considered and the bending angle of light is computed. The bending angle
predicted by the brane world models is much larger than that predicted by
standard general relativistic and dark matter models. The angular radii of the
Einstein rings are obtained in the small angles approximation. The predictions
of the brane world model for the tangential shear are compared with the
observational data obtained in the weak lensing of galaxies in the Red-Sequence
Cluster Survey. Therefore the study of the light deflection by galaxies and the
gravitational lensing could discriminate between the different dynamical laws
proposed to model the motion of particles at the galactic level and the
standard dark matter models.


  We improve further the 2015 version of abcdSAT by various heuristics such as
at-least-one recently used strategy, learnt clause database approximation
reduction etc. Based on the requirement of different tracks at the SAT
Competition 2016, we develop three versions of abcdSAT: drup, inc and lim,
which participate in the competition of main (agile), incremental library and
no-limit track, respectively.


  By neglecting the effects of self-force and radiation, we investigate the
possibility of destroying the Kerr-MOG black hole through the point particle
absorption process. Using the instability of event horizon and equation of
particle motion, we get the upper and lower energy bounds allowed for a matter
particle to produce the naked singularity. We find that the energy gap always
exists between the upper and lower energy bounds for both extremal and
near-extremal black holes, which means some tailored particles can actually
lead to the violation of the weak cosmic censorship conjecture. However, when
considering the effect of the adiabatic process, the result shows that the
Kerr-MOG black hole gets more stable instead of a naked singularity, and thus
the weak cosmic censorship conjecture can be restored at some level.


  Let G be a compact Lie group or a complex reductive affine algebraic group.
We explore induced mappings between G-character varieties of surface groups by
mappings between corresponding surfaces. It is shown that these mappings are
generally Poisson. We also given an effective algorithm to compute the Poisson
bi-vectors when G=SL(2,C). We demonstrate this algorithm by explicitly
calculating the Poisson bi-vector for the 5-holed sphere, the first example for
an Euler characteristic -3 surface.


  The formalism recently introduced in arXiv:1610.08468 allows one to assign a
regularity structure, as well as a corresponding "renormalisation group", to
any subcritical system of semilinear stochastic PDEs. Under very mild
additional assumptions, it was then shown in arXiv:1612.08138 that large
classes of driving noises exhibiting the relevant small-scale behaviour can be
lifted to such a regularity structure in a robust way, following a
renormalisation procedure reminiscent of the BPHZ procedure arising in
perturbative QFT. The present work completes this programme by constructing an
action of the renormalisation group onto a suitable class of stochastic PDEs
which is intertwined with its action on the corresponding space of models. This
shows in particular that solutions constructed from the BPHZ lift of a smooth
driving noise coincide with the classical solutions of a modified PDE. This
yields a very general black box type local existence and stability theorem for
a wide class of singular nonlinear SPDEs.


  In this paper, we prove that $(-\infty, 0)$ is a zero-free interval for
chromatic polynomials of a family ${\cal L}_0$ of hypergraphs and $(0, 1)$ is a
zero-free interval for chromatic polynomials of a subfamily ${\cal L}_0'$ of
${\cal L}_0$ of hypergraphs. These results extend known results on zero-free
intervals of chromatic polynomials of graphs and hypergraphs.


  We present the joint threshold and recoil resummed transverse momentum
distributions for heavy quark hadroproduction, at next-to-leading logarithmic
accuracy. We exhibit their dependence on the production channel and the color
configurations, and compare these distributions to eachother and to NLO.


  We study nonlinear systems of hyperbolic (in a wider sense) PDE's in entire
d-dimensional space describing wave propagation with the initial data in the
form of a finite sum of wavepackets referred to as multi-wavepackets. The
problem involves two small parameters beta and rho where: (i) (1/beta) is a
factor describing spatial extension of the wavepackets; (ii) (1/rho) is a
factor describing the relative magnitude of the linear part of the evolution
equation compared to its nonlinearity. For a wide range of the small parameters
and on time intervals long enough for strong nonlinear effects we prove that
multi-wavepackets are preserved under the nonlinear evolution. In particular,
the corresponding wave vectors and the band numbers of involved wavepackets are
"conserved quantities". We also prove that the evolution of a multi-wavepacket
is described with high accuracy by a properly constructed system of envelope
equations with a universal nonlinearity which in simpler cases turn into
well-known Nonlinear Schrodinger or coupled modes equations. The universal
nonlinearity is obtained by a certain time averaging applied to the original
nonlinearity. This can be viewed as an extension of the well known averaging
method developed for finite-dimensional nonlinear oscillatory systems to the
case of a general translation invariant PDE systems with the linear part having
continuous spectrum.


  Tendon-driven continuum robots (TDCRs), with their flexible backbones, offer
the advantage of being used for navigating complex, cluttered environments.
However, to do so, they typically require multiple segments, often leading to
complex actuation and control challenges. To this end, we propose a novel
approach to navigate cluttered spaces effectively for a single-segment long
TDCR which is the simplest topology from a mechanical point of view. Our key
insight is that by leveraging contact with the environment we can achieve
multiple curvatures without mechanical alterations to the robot. Specifically,
we propose a search-based motion planner for a single-segment TDCR. This
planner, guided by a specially designed heuristic, discretizes the
configuration space and employs a best-first search. The heuristic, crucial for
efficient navigation, provides an effective cost-to-go estimation while
respecting the kinematic constraints of the TDCR and environmental
interactions. We empirically demonstrate the efficiency of our planner-testing
over 525 queries in environments with both convex and non-convex obstacles, our
planner is demonstrated to have a success rate of about 80% while baselines
were not able to obtain a success rate higher than 30%. The difference is
attributed to our novel heuristic which is shown to significantly reduce the
required search space.


  The phenomenon of flat bands pinned to the Fermi surface is analyzed on the
basis of the Landau-Pitaevskii relation, which is applicable to electron
systems of solids. It is shown that the gross properties of normal states of
high-$T_c$ superconductors, frequently called strange metals, are adequately
explained within the flat-band scenario. Most notably, we demonstrate that in
electron systems moving in a two-dimensional Brillouin zone, superconductivity
may exist in domains of the Lifshitz phase diagram lying far from lines of
critical antiferromagnetic fluctuations, even if the effective
electron-electron interaction in the Cooper channel is repulsive.


  Weakly supervised semantic segmentation is typically inspired by class
activation maps, which serve as pseudo masks with class-discriminative regions
highlighted. Although tremendous efforts have been made to recall precise and
complete locations for each class, existing methods still commonly suffer from
the unsolicited Out-of-Candidate (OC) error predictions that not belongs to the
label candidates, which could be avoidable since the contradiction with
image-level class tags is easy to be detected. In this paper, we develop a
group ranking-based Out-of-Candidate Rectification (OCR) mechanism in a
plug-and-play fashion. Firstly, we adaptively split the semantic categories
into In-Candidate (IC) and OC groups for each OC pixel according to their prior
annotation correlation and posterior prediction correlation. Then, we derive a
differentiable rectification loss to force OC pixels to shift to the IC group.
Incorporating our OCR with seminal baselines (e.g., AffinityNet, SEAM,
MCTformer), we can achieve remarkable performance gains on both Pascal VOC
(+3.2%, +3.3%, +0.8% mIoU) and MS COCO (+1.0%, +1.3%, +0.5% mIoU) datasets with
negligible extra training overhead, which justifies the effectiveness and
generality of our OCR.


  According to general relativity, the interaction of a matter field with
gravitation requires the simultaneous introduction of a tetrad field, which is
a field related to translations, and a spin connection, which is a field
assuming values in the Lie algebra of the Lorentz group. These two fields,
however, are not independent. By analyzing the constraint between them, it is
concluded that the relevant local symmetry group behind general relativity is
provided by the Lorentz group. Furthermore, it is shown that the minimal
coupling prescription obtained from the Lorentz covariant derivative coincides
exactly with the usual coupling prescription of general relativity. Instead of
the tetrad, therefore, the spin connection is to be considered as the
fundamental field representing gravitation.


  Metal nanostructures are key elements in nanooptics owing to their strong
resonant interaction with light through local plasmonic charge oscillations.
Their ability to shape light at the nanoscale have made them important across a
multitude of areas, including biosensing, energy conversion and ultrathin flat
metaoptics. Yet another dimension of avenues is foreseen for dynamic
nanoantennas, ranging from tuneable metalenses for miniaturized medical devices
to adaptable windows that control radiation flows in and out of buildings.
However, enabling nano-optical antennas to be dynamically controllable remains
highly challenging and particularly so for traditional metals with fixed
permittivity. Here we present state-of-the-art conductive polymers as a new
class of organic plasmonic materials for redox-tuneable nano-optics. Through
experiments and simulations, we show that nanodisks of highly conductive
polymers can provide clear optical extinction peaks via excitation of dipolar
localised surface plasmon resonances. Resonance frequencies redshift with
increasing nanodisk aspect ratio, in agreement with analytical calculations
based on dipolar polarizability theory. We furthermore demonstrate complete
switching of the optical response of the organic nanoantennas by chemical
tuning of the polymer's redox state, which effectively modulates the material
permittivity between plasmonic and non-plasmonic regimes. Our results thereby
show that conductive polymer nanostructures can act as redox-tuneable plasmonic
nanoantennas, based on bipolaronic charge carriers rather than electrons as in
conventional metals. Future directions may investigate different polymers and
geometries to further widen the plasmonic spectral range (here around 0.8 to
3.6 {\mu}m) as well as different ways of tuning.


  Many advances of deep learning techniques originate from the efforts of
addressing the image classification task on large-scale datasets. However, the
construction of such clean datasets is costly and time-consuming since the
Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In
this paper, we propose a Ubiquitous Reweighting Network (URNet) that learns an
image classification model from large-scale noisy data. By observing the web
data, we find that there are five key challenges, i.e., imbalanced class sizes,
high intra-classes diversity and inter-class similarity, imprecise instances,
insufficient representative instances, and ambiguous class labels. To alleviate
these challenges, we assume that every training instance has the potential to
contribute positively by alleviating the data bias and noise via reweighting
the influence of each instance according to different class sizes, large
instance clusters, its confidence, small instance bags and the labels. In this
manner, the influence of bias and noise in the web data can be gradually
alleviated, leading to the steadily improving performance of URNet.
Experimental results in the WebVision 2018 challenge with 16 million noisy
training images from 5000 classes show that our approach outperforms
state-of-the-art models and ranks the first place in the image classification
task.


  In this paper we consider the behavior of Kalman Filter state estimates in
the case of distribution with heavy tails .The simulated linear state space
models with Gaussian measurement noises were used. Gaussian noises in state
equation are replaced by components with alpha-stable distribution with di
erent parameters alpha and beta. We consider the case when "all parameters are
known" and two methods of parameters estimation are compared: the maximum
likelihood estimator (MLE) and the expectation- maximization algorithm (EM). It
was shown that in cases of large deviation from Gaussian distribution the total
error of states estimation rises dramatically. We conjecture that it can be
explained by underestimation of the state equation noises covariance matrix
that can be taken into account through the EM parameters estimation and ignored
in the case of ML estimation.


  ShinyItemAnalysis (SIA) is an R package and shiny application for an
interactive presentation of psychometric methods and analysis of multi-item
measurements in psychology, education, and social sciences in general. In this
article, we present a new feature introduced in the recent version of the
package, called "SIA modules", which allows researchers and practitioners to
offer new analytical methods for broader use via add-on extensions. We describe
how to build the add-on modules with the support of the new SIAtools package
and demonstrate the concepts using sample modules from the newly introduced
SIAmodules package. SIA modules are designed to integrate with and build upon
the SIA interactive application, enabling them to leverage the existing
infrastructure for tasks such as data uploading and processing. They can access
a range of outputs from various analyses, including item response theory
models, exploratory factor analysis, or differential item functioning models.
Because SIA modules come in R packages (or extend the existing ones), they may
come bundled with their datasets, use object-oriented systems, or even compiled
code. We discuss the possibility of broader use of the concept of SIA modules
in other areas.


  The outstanding performance capabilities of large language model have driven
the evolution of current AI system interaction patterns. This has led to
considerable discussion within the Human-AI Interaction (HAII) community.
Numerous studies explore this interaction from technical, design, and empirical
perspectives. However, the majority of current literature reviews concentrate
on interactions across the wider spectrum of AI, with limited attention given
to the specific realm of interaction with LLM. We searched for articles on
human interaction with LLM, selecting 110 relevant publications meeting
consensus definition of Human-AI interaction. Subsequently, we developed a
comprehensive Mapping Procedure, structured in five distinct stages, to
systematically analyze and categorize the collected publications. Applying this
methodical approach, we meticulously mapped the chosen studies, culminating in
a detailed and insightful representation of the research landscape. Overall,
our review presents an novel approach, introducing a distinctive mapping
method, specifically tailored to evaluate human-LLM interaction patterns. We
conducted a comprehensive analysis of the current research in related fields,
employing clustering techniques for categorization, which enabled us to clearly
delineate the status and challenges prevalent in each identified area.


  We establish Pohozaev identities and integration by parts type formulas for
anisotropic integro-differential operators of order $2s$, with $s\in(0,1)$.
  These identities involve local boundary terms, in which the quantity
$u/d^s|_{\partial\Omega}$ plays the role that $\partial u/\partial\nu$ plays in
the second order case. Here, $u$ is any solution to $Lu=f(x,u)$ in $\Omega$,
with $u=0$ in $\mathbb R^n\setminus\Omega$, and $d$ is the distance to
$\partial\Omega$.


  Channel charting is an emerging self-supervised method that maps channel
state information (CSI) to a low-dimensional latent space, which represents
pseudo-positions of user equipments (UEs). While this latent space preserves
local geometry, i.e., nearby UEs are nearby in latent space, the
pseudo-positions are in arbitrary coordinates and global geometry is not
preserved. In order to enable channel charting in real-world coordinates, we
propose a novel bilateration loss for multipoint wireless systems in which only
the access point (AP) locations are known--no geometrical models or
ground-truth UE position information is required. The idea behind this
bilateration loss is to compare the received power at pairs of APs in order to
determine whether a UE should be placed closer to one AP or the other in latent
space. We demonstrate the efficacy of our method using channel vectors from a
commercial ray-tracer.


  We test the reliability of two neural network interpretation techniques,
backward optimization and layerwise relevance propagation, within geoscientific
applications by applying them to a commonly studied geophysical phenomenon, the
Madden-Julian Oscillation. The Madden-Julian Oscillation is a multi-scale
pattern within the tropical atmosphere that has been extensively studied over
the past decades, which makes it an ideal test case to ensure the
interpretability methods can recover the current state of knowledge regarding
its spatial structure. The neural networks can, indeed, reproduce the current
state of knowledge and can also provide new insights into the seasonality of
the Madden-Julian Oscillation and its relationships with atmospheric state
variables.
  The neural network identifies the phase of the Madden-Julian Oscillation
twice as accurately as linear regression, which means that nonlinearities used
by the neural network are important to the structure of the Madden-Julian
Oscillation. Interpretations of the neural network show that it accurately
captures the spatial structures of the Madden-Julian Oscillation, suggest that
the nonlinearities of the Madden-Julian Oscillation are manifested through the
uniqueness of each event, and offer physically meaningful insights into its
relationship with atmospheric state variables. We also use the interpretations
to identify the seasonality of the Madden-Julian Oscillation, and find that the
conventionally defined extended seasons should be shifted later by one month.
More generally, this study suggests that neural networks can be reliably
interpreted for geoscientific applications and may there


  In this paper, we will prove that the local time of a L\'evy process is of
finite $p$-variation in the space variable in the classical sense, a.s. for any
$p>2$, $t\geq 0$, if the L\'evy measure satisfies $\int_{R\setminus
\{0\}}(|y|^{3\over 2}\wedge 1)n(dy)<\infty$, and is a rough path of roughness
$p$ a.s. for any $2<p<3$ under a slightly stronger condition for the L\'evy
measure. Then for any function $g$ of finite $q$-variation ($1\leq q <3$), we
establish the integral $\int_{-\infty}^{\infty}g(x)dL_t^x$ as a Young integral
when $1\leq q<2$ and a Lyons' rough path integral when $2\leq q<3$. We
therefore apply these path integrals to extend the Tanaka-Meyer formula for a
continuous function $f$ if $\nabla ^-f$ exists and is of finite $q$-variation
when $1\leq q<3$, for both continuous semi-martingales and a class of L\'evy
processes.


  We compute the motions of null infinity to which the components of the
angular momentum of the gravitational field, as defined by Penrose, are
conjugate. We find that the boosts are supplemented by anomalous translations.
If $c_{ab}$ is the skew bivector determining the component of the angular
momentum in question, the anomaly is proportional to $$c_{ab}t^b\phi ^{ad}\,
,$$ where $t^a$ is a unit vector in the direction of the Bondi--Sachs momentum
and $\phi _{ab}$ is the quadrupole moment of the shear of the cut at which the
angular momentum is evaluated. This effect persists in the weak--field limit.
This surprising result is a general consequence of the requirement that angular
momentum be supertranslation--invariant in a quiescent regime, and not some
essentially twistorial peculiarity.


  In this paper we study the problem of signal detection in Gaussian noise in a
distributed setting where the local machines in the star topology can
communicate a single bit of information. We derive a lower bound on the
Euclidian norm that the signal needs to have in order to be detectable.
Moreover, we exhibit optimal distributed testing strategies that attain the
lower bound.


  For a graph $G$, a hypergraph $\mathcal{H}$ is a Berge copy of $G$ (or a
Berge-$G$ in short), if there is a bijection $f : E(G) \rightarrow
E(\mathcal{H})$ such that for each $e \in E(G)$ we have $e \subseteq f(e)$. We
denote the family of $r$-uniform hypergraphs that are Berge copies of $G$ by
$B^rG$.
  For families of $r$-uniform hypergraphs $\mathbf{H}$ and $\mathbf{H}'$, we
denote by $R(\mathbf{H},\mathbf{H}')$ the smallest number $n$ such that in any
blue-red coloring of $\mathcal{K}_n^r$ (the complete $r$-uniform hypergraph on
$n$ vertices) there is a monochromatic blue copy of a hypergraph in
$\mathbf{H}$ or a monochromatic red copy of a hypergraph in $\mathbf{H}'$.
$R^c(\mathbf{H})$ denotes the smallest number $n$ such that in any coloring of
the hyperedges of $\mathcal{K}_n^r$ with $c$ colors, there is a monochromatic
copy of a hypergraph in $\mathbf{H}$.
  In this paper we initiate the general study of the Ramsey problem for Berge
hypergraphs, and show that if $r> 2c$, then $R^c(B^rK_n)=n$. In the case $r =
2c$, we show that $R^c(B^rK_n)=n+1$, and if $G$ is a non-complete graph on $n$
vertices, then $R^c(B^rG)=n$, assuming $n$ is large enough. In the case $r <
2c$ we also obtain bounds on $R^c(B^rK_n)$. Moreover, we also determine the
exact value of $R(B^3T_1,B^3T_2)$ for every pair of trees $T_1$ and $T_2$.


  In this paper, the second part of a survey of the geometric properties of
defects in quasicrystals studied from the Volterra viewpoint (see ref. [1]), we
show that: 1$- $ a {\sf disvection line} L$_{||} \subset \mathrm E_{||}$ of
Burgers vector $\textbf b =\textbf b_{||}+\textbf b_\bot $ splits naturally
along L$_{||}$ into a {\sf perfect dislocation} of Burgers vector $\textbf
b_{||}$ and an {\sf imperfect dislocation} of Burgers vector related to
$\textbf b_{\bot}$, akin to a stacking fault, (a 'phason' defect), 2$- $ the
'phason' defects are classified according to the relative position of
$\Sigma_{\bot}$ with respect to a partition of the acceptance window AW which
depends on the direction of $\textbf b_\bot $. The perpendicular cut surface
$\Sigma_{\bot}\subset \mathrm {AW}$ here introduced is a mapping of the usual
cut surface $\Sigma_{||}\subset\mathrm E_{||}$. Imperfect dislocations in QCs
are somewhat similar to Kronberg's synchroshear dislocations. It is also shown
that climb must generically be easier than glide.


  Secure and reliable management of identities has become one of the greatest
challenges facing cloud computing today, mainly due to the huge number of new
cloud-based applications generated by this model, which means more user
accounts, passwords, and personal information to provision, monitor, and
secure. Currently, identity federation is the most useful solution to overcome
the aforementioned issues and simplify the user experience by allowing
efficient authentication mechanisms and use of identity information from data
distributed across multiple domains. However, this approach creates
considerable complexity in managing trust relationships for both the cloud
service providers and their clients. Poor management of trust in federated
identity management systems brings with it many security, privacy and
interoperability issues, which contributes to the reluctance of organizations
to move their critical identity data to the cloud. In this paper, we aim to
address these issues by introducing a novel trust and identity management model
based on the Blockchain for cloud identity management with security and privacy
improvements.


  While Graph Neural Networks (GNNs) are powerful models for learning
representations on graphs, most state-of-the-art models do not have significant
accuracy gain beyond two to three layers. Deep GNNs fundamentally need to
address: 1). expressivity challenge due to oversmoothing, and 2). computation
challenge due to neighborhood explosion. We propose a simple "deep GNN, shallow
sampler" design principle to improve both the GNN accuracy and efficiency -- to
generate representation of a target node, we use a deep GNN to pass messages
only within a shallow, localized subgraph. A properly sampled subgraph may
exclude irrelevant or even noisy nodes, and still preserve the critical
neighbor features and graph structures. The deep GNN then smooths the
informative local signals to enhance feature learning, rather than
oversmoothing the global graph signals into just "white noise". We
theoretically justify why the combination of deep GNNs with shallow samplers
yields the best learning performance. We then propose various sampling
algorithms and neural architecture extensions to achieve good empirical
results. On the largest public graph dataset, ogbn-papers100M, we achieve
state-of-the-art accuracy with an order of magnitude reduction in hardware
cost.


  Progress in multiagent intelligence research is fundamentally limited by the
number and quality of environments available for study. In recent years,
simulated games have become a dominant research platform within reinforcement
learning, in part due to their accessibility and interpretability. Previous
works have targeted and demonstrated success on arcade, first person shooter
(FPS), real-time strategy (RTS), and massive online battle arena (MOBA) games.
Our work considers massively multiplayer online role-playing games (MMORPGs or
MMOs), which capture several complexities of real-world learning that are not
well modeled by any other game genre. We present Neural MMO, a massively
multiagent game environment inspired by MMOs and discuss our progress on two
more general challenges in multiagent systems engineering for AI research:
distributed infrastructure and game IO. We further demonstrate that standard
policy gradient methods and simple baseline models can learn interesting
emergent exploration and specialization behaviors in this setting.


  We show how, using different decompositions of E(11), one can calculate the
representations under the duality group of the so--called "de-form" potentials.
Evidence is presented that these potentials are in one-to-one correspondence to
the embedding tensors that classify the gaugings of all maximal gauged
supergravities. We supply the computer program underlying our calculations.


  We examine the dynamics of a quasi-two-dimensional spin-1 condensate in which
the quadratic Zeeman energy q is suddenly quenched to a value where the system
has a ferromagnetic ground state. There are two distinct types of ferromagnetic
phases, i.e. a range of q values where the magnetization prefers to be in the
direction of the external field (easy-axis), and a range of q values where it
prefers to be transverse to the field (easy-plane). We study the quench
dynamics for a variety of q values and show that there is a single dynamical
critical exponent to characterize the scale invariant domain growth for each
ferromagnetic phase. For both quenches we give simple analytic models that
capture the essential scale invariant dynamics, and correctly predict the
exponents. Because the order parameter for each phase is different, the natures
of the domains and the relevant topological defects in each type of coarsening
is also different. To explore these differences we characterize the fractal
dimension of the domain walls, and the relationship of polar-core spin vortices
to the domains in the easy-plane phase. Finally, we consider how the energy
liberated from the quench thermalizes in the easy-axis quench. We show that
local equilibrium is established in the spin waves on moderate time scales, but
continues to evolve as the domains anneal.


  - Both Lidars and Radars are sensors for obstacle detection. While Lidars are
very accurate on obstacles positions and less accurate on their velocities,
Radars are more precise on obstacles velocities and less precise on their
positions. Sensor fusion between Lidar and Radar aims at improving obstacle
detection using advantages of the two sensors. The present paper proposes a
real-time Lidar/Radar data fusion algorithm for obstacle detection and tracking
based on the global nearest neighbour standard filter (GNN). This algorithm is
implemented and embedded in an automative vehicle as a component generated by a
real-time multisensor software. The benefits of data fusion comparing with the
use of a single sensor are illustrated through several tracking scenarios (on a
highway and on a bend) and using real-time kinematic sensors mounted on the ego
and tracked vehicles as a ground truth.


  The energy dependence of the production cross section of a light Higgs boson
is studied at threshold and compared to the expectations of several spin
assumptions. Cross section measurements at three centre-of-mass energies with
an integrated luminosity of 20 fb-1 allow the confirmation of the scalar nature
of the Higgs Boson.


  We introduce Falcon2-11B, a foundation model trained on over five trillion
tokens, and its multimodal counterpart, Falcon2-11B-vlm, which is a
vision-to-text model. We report our findings during the training of the
Falcon2-11B which follows a multi-stage approach where the early stages are
distinguished by their context length and a final stage where we use a curated,
high-quality dataset. Additionally, we report the effect of doubling the batch
size mid-training and how training loss spikes are affected by the learning
rate. The downstream performance of the foundation model is evaluated on
established benchmarks, including multilingual and code datasets. The
foundation model shows strong generalization across all the tasks which makes
it suitable for downstream finetuning use cases. For the vision language model,
we report the performance on several benchmarks and show that our model
achieves a higher average score compared to open-source models of similar size.
The model weights and code of both Falcon2-11B and Falcon2-11B-vlm are made
available under a permissive license.


  We describe a syntactic method for taking proofs which use ultraproducts and
translating them into direct, constructive proofs.


  Learning representations of multimodal data that are both informative and
robust to missing modalities at test time remains a challenging problem due to
the inherent heterogeneity of data obtained from different channels. To address
it, we present a novel Geometric Multimodal Contrastive (GMC) representation
learning method consisting of two main components: i) a two-level architecture
consisting of modality-specific base encoders, allowing to process an arbitrary
number of modalities to an intermediate representation of fixed dimensionality,
and a shared projection head, mapping the intermediate representations to a
latent representation space; ii) a multimodal contrastive loss function that
encourages the geometric alignment of the learned representations. We
experimentally demonstrate that GMC representations are semantically rich and
achieve state-of-the-art performance with missing modality information on three
different learning problems including prediction and reinforcement learning
tasks.


  The complexity of the elliptic curve method of factorization (ECM) is proven
under the celebrated conjecture of existence of smooth numbers in short
intervals. In this work we tackle a different version of ECM which is actually
much more studied and implemented, especially because it allows us to use
ECM-friendly curves. In the case of curves with complex multiplication (CM) we
replace the heuristics by rigorous results conditional to the
Elliott-Halberstam (EH) conjecture. The proven results mirror recent theorems
concerning the number of primes p such thar p -- 1 is smooth. To each CM
elliptic curve we associate a value which measures how ECM-friendly it is. In
the general case we explore consequences of a statement which translated EH in
the case of elliptic curves.


  We report the discovery of four new hot Jupiters with the Next Generation
Transit Survey (NGTS). NGTS-15b, NGTS-16b, NGTS-17b, and NGTS-18b are
short-period ($P<5$d) planets orbiting G-type main sequence stars, with radii
and masses between $1.10-1.30$ $R_J$ and $0.41-0.76$ $M_J$. By considering the
host star luminosities and the planets' small orbital separations
($0.039-0.052$ AU), we find that all four hot Jupiters are highly irradiated
and therefore occupy a region of parameter space in which planetary inflation
mechanisms become effective. Comparison with statistical studies and a
consideration of the planets' high incident fluxes reveals that NGTS-16b,
NGTS-17b, and NGTS-18b are indeed likely inflated, although some disparities
arise upon analysis with current Bayesian inflationary models. However, the
underlying relationships which govern radius inflation remain poorly
understood. We postulate that the inclusion of additional hyperparameters to
describe latent factors such as heavy element fraction, as well as the addition
of an updated catalogue of hot Jupiters, would refine inflationary models, thus
furthering our understanding of the physical processes which give rise to
inflated planets.


  It is well known that the Klein-Gordon equation in curved spacetime is
conformally noninvariant, both with and without a mass term. We show that such
a noninvariance provides nontrivial physical insights at different levels,
first within the fully relativistic regime, then in the nonrelativistic regime
leading to the Schr\"odinger equation, and then within the de Broglie-Bohm
causal interpretation of quantum mechanics. The conformal noninvariance of the
Klein-Gordon equation coupled to a vector potential is confronted with the
conformal invariance of Maxwell's equations in the presence of a charged
current. The conformal invariance of the non-minimally coupled Klein-Gordon
equation to gravity is then examined in light of the conformal invariance of
Maxwell's equations. Finally, the consequence of the noninvariance of the
equation on the Aharonov-Bohm effect in curved spacetime is discussed.


  The radio interferometer measurement equation (RIME), especially in its 2x2
form, has provided a comprehensive matrix-based formalism for describing
classical radio interferometry and polarimetry, as shown in the previous three
papers of this series. However, recent practical and theoretical developments,
such as phased array feeds (PAFs), aperture arrays (AAs) and wide-field
polarimetry, are exposing limitations of the formalism. This paper aims to
develop a more general formalism that can be used to both clearly define the
limitations of the matrix RIME, and to describe observational scenarios that
lie outside these limitations. Some assumptions underlying the matrix RIME are
explicated and analysed in detail. To this purpose, an array correlation matrix
(ACM) formalism is explored. This proves of limited use; it is shown that
matrix algebra is simply not a sufficiently flexible tool for the job. To
overcome these limitations, a more general formalism based on tensors and the
Einstein notation is proposed and explored both theoretically, and with a view
to practical implementations. The tensor formalism elegantly yields generalized
RIMEs describing beamforming, mutual coupling, and wide-field polarimetry in
one equation. It is shown that under the explicated assumptions, tensor
equations reduce to the 2x2 RIME. From a practical point of view, some methods
for implementing tensor equations in an optimal way are proposed and analysed.
The tensor RIME is a powerful means of describing observational scenarios not
amenable to the matrix RIME. Even in cases where the latter remains applicable,
the tensor formalism can be a valuable tool for understanding the limits of
such applicability.


  The one loop UV divergences of Hilbert-Einstein gravity with a cosmological
constant and spin 0, 1/2 and 1 matter are computed making use of a covariant
derivative expansion and functional methods. For this purpose the
transformation that yields the covariant derivative is extended to include a
dynamical metric and the expansion in the fields themselves is made covariant
which is relevant for the effective action due to the non-linear character of
gravity


  We present a way to build hyperbolic spheres with conical singularities by
gluing together simple building blocks. Our construction provides good control
over the holonomy of the resulting hyperbolic cone sphere. In particular, it
can be used to realize any Deroin-Tholozan (DT) representation as the holonomy
of a hyperbolic cone sphere. Our construction is inspired by the correspondence
between DT representations and chains of triangles in the hyperbolic plane. It
gives a geometric interpretation of certain action-angle coordinates on the
space of DT representations, which come from this correspondence.


This is a short note on the log canonical inversion of adjunction.


  This paper studies the response time bound of a DAG (directed acyclic graph)
task. Recently, the idea of using multiple paths to bound the response time of
a DAG task, instead of using a single longest path in previous results, was
proposed and leads to the so-called multi-path bound. Multi-path bounds can
greatly reduce the response time bound and significantly improve the
schedulability of DAG tasks. This paper derives a new multi-path bound and
proposes an optimal algorithm to compute this bound. We further present a
systematic analysis on the dominance and the sustainability of three existing
multi-path bounds and the proposed multi-path bound. Our bound theoretically
dominates and empirically outperforms all existing multi-path bounds. What's
more, the proposed bound is the only multi-path bound that is proved to be
self-sustainable.


  We study the Kohn-Sham scheme for the calculation of the steady state linear
response to a harmonic perturbation that is turned on adiabatically. Although
in general the exact time dependent exchange-correlation potential cannot be
expressed as the functional derivative of a universal functional due to the
so-called causality paradox, we show that for a harmonic perturbation the
exchange-correlation part of the first-order Kohn-Sham potential $v_s^{(1)}(r)
\cos(\omega t)$ is given by $v_{xc}^{(1)}(r) = \delta K_{xc}^{(2)}/\delta
n^{(1)}(r)$. $K_{xc}^{(2)}$ is the exchange-correlation part of the
second-order quasienergy $K_v^{(2)}$. The Frenkel variation principle implies a
stationary principle for the second-order quasienergy. We also find an
analogous stationary principle and KS scheme in the time dependent extension of
one-matrix functional theory, in which the basic variable is the one-matrix
(one-body reduced density matrix).


  Flux jumps observed in high-$J_c$ Nb$_3$Sn conductors are urgent problems to
construct high field superconducting magnets. The low-field instabilities
usually reduce the current-carrying capability and thus cause the premature
quench of Nb$_3$Sn coils at low magnetic field. In this paper, we explore
suppressing the flux jumps by ferromagnetic (FM) layer. Firstly, we
experimentally and theoretically investigate the flux jumps of Nb$_3$Sn/FM
hybrid wires exposed to a magnetic field loop with constant sweeping rate.
Comparing with bare Nb$_3$Sn and Nb$_3$Sn/Cu wires, we reveal two underlying
mechanisms that the suppression of flux jumps is mainly attributed to the
thermal effect of FM layer for the case of lower sweeping rate, whereas both
thermal and electromagnetic effects play a crucial role for the case of higher
sweeping rate. Furthermore, we explore the flux jumps of Nb$_3$Sn/FM hybrid
wires exposed to AC magnetic fields with amplitude $B_{a0}$ and frequency
$\rm\omega$. We build up the phase diagrams of flux jumps in the plane
$\rm\omega$-$B_{a0}$ for bare Nb$_{3}$Sn wire, Nb$_{3}$Sn/Cu wire and
Nb$_{3}$Sn/FM wire, respectively. We stress that the region of flux jumps of
Nb$_{3}$Sn/FM wire is much smaller than the other two wires, which indicates
that the Nb$_{3}$Sn/FM wire has significant advantage over merely increasing
the heat capacity. The findings shed light on suppression of the flux jumps by
utilizing FM materials, which is useful for developing new type of high-$J_c$
Nb$_{3}$Sn conductors.


  Infrared and collinear events shapes are suited to directly probe properties
of hard QCD. They are traditionally used to measure the strong coupling and to
test the gauge structure of QCD. Perturbative predictions exist in several
variations all of which depend on the renormalisation scheme leading to large
theoretical uncertainties in the determination of $alpha_s$. To overcome this
dominating error more and more schemes for setting the renormalisation scale
are investigated. The application of RGI perturbation theory shows an
incredible small spread of $\alpha_s$ indicating a reduced uncertainty and
allows a measurement of the $\beta$-function directly from mean values.


  We study the long-time behavior of the Schr{\"o}dinger flow in a
heterogeneous potential $\lambda$V with small intensity 0<$\lambda$$\ll$1 (or
alternatively at high frequencies). The main new ingredient, which we introduce
in the general setting of a stationary ergodic potential, is an approximate
stationary Floquet--Bloch theory that is used to put the perturbed
Schr{\"o}dinger operator into approximate normal form. We apply this approach
to quasiperiodic potentials and establish a Nehoro{\v s}ev-type stability
result. In particular, this ensures asymptotic ballistic transport up to a
stretched exponential timescale exp($\lambda$--1/s) for some s>0. More
precisely, the approximate normal form leads to an accurate long-time
description of the Schr{\"o}dinger flow as an effective unitary correction of
the free flow. The approach is robust and generically applies to linear waves.
For classical waves, for instance, this allows to extend diffractive geometric
optics to quasiperiodically perturbed media.


  During the early part of the Covid-19 pandemic, national and local
governments introduced a number of policies to combat the spread of Covid-19.
In this paper, we propose a new approach to bound the effects of such
early-pandemic policies on Covid-19 cases and other outcomes while dealing with
complications arising from (i) limited availability of Covid-19 tests, (ii)
differential availability of Covid-19 tests across locations, and (iii)
eligibility requirements for individuals to be tested. We use our approach
study the effects of Tennessee's expansion of Covid-19 testing early in the
pandemic and find that the policy decreased Covid-19 cases.


  In visual semantic navigation, the robot navigates to a target object with
egocentric visual observations and the class label of the target is given. It
is a meaningful task inspiring a surge of relevant research. However, most of
the existing models are only effective for single-agent navigation, and a
single agent has low efficiency and poor fault tolerance when completing more
complicated tasks. Multi-agent collaboration can improve the efficiency and has
strong application potentials. In this paper, we propose the multi-agent visual
semantic navigation, in which multiple agents collaborate with others to find
multiple target objects. It is a challenging task that requires agents to learn
reasonable collaboration strategies to perform efficient exploration under the
restrictions of communication bandwidth. We develop a hierarchical decision
framework based on semantic mapping, scene prior knowledge, and communication
mechanism to solve this task. The results of testing experiments in unseen
scenes with both known objects and unknown objects illustrate the higher
accuracy and efficiency of the proposed model compared with the single-agent
model.


  We study recent data on deep inelastic e^+ p scattering at HERA to constrain
the parameters of a Randall-Sundrum-type scenario of quantum gravity with a
small extra dimension and a non-factorable geometry.


  Text simplification is the process of splitting and rephrasing a sentence to
a sequence of sentences making it easier to read and understand while
preserving the content and approximating the original meaning. Text
simplification has been exploited in NLP applications like machine translation,
summarization, semantic role labeling, and information extraction, opening a
broad avenue for its exploitation in comprehension-based question-answering
downstream tasks. In this work, we investigate the effect of text
simplification in the task of question-answering using a comprehension context.
We release Simple-SQuAD, a simplified version of the widely-used SQuAD dataset.
  Firstly, we outline each step in the dataset creation pipeline, including
style transfer, thresholding of sentences showing correct transfer, and offset
finding for each answer. Secondly, we verify the quality of the transferred
sentences through various methodologies involving both automated and human
evaluation. Thirdly, we benchmark the newly created corpus and perform an
ablation study for examining the effect of the simplification process in the
SQuAD-based question answering task. Our experiments show that simplification
leads to up to 2.04% and 1.74% increase in Exact Match and F1, respectively.
Finally, we conclude with an analysis of the transfer process, investigating
the types of edits made by the model, and the effect of sentence length on the
transfer model.


  Even though strongly correlated systems are abundant, only a few exceptional
cases admit analytical solutions. In this paper we present a large class of
solvable systems with strong correlations.. We consider a set of $N$
independent and identically distributed (i.i.d) random variables $\{X_1,\,
X_2,\ldots, X_N\}$ whose common distribution has a parameter $Y$ (or a set of
parameters) which itself is random with its own distribution. For a fixed value
of this parameter $Y$, the $X_i$ variables are independent and we call them
conditionally independent and identically distributed (c.i.i.d). However, once
integrated over the distribution of the parameter $Y$, the $X_i$ variables get
strongly correlated, yet retaining a solvable structure for various
observables, such as for the sum and the extremes of $X_i$'s. This provides a
simple procedure to generate a class of solvable strongly correlated systems.
We illustrate how this procedure works via three physical examples where $N$
particles on a line perform independent (i) Brownian motions, (ii) ballistic
motions with random initial velocities, and (iii) L\'evy flights, but they get
strongly correlated via {\it simultaneous resetting} to the origin. Our results
are verified in numerical simulations. This procedure can be used to generate
an endless variety of solvable strongly correlated systems.


  Recently, partial Bayesian neural networks (pBNNs), which only consider a
subset of the parameters to be stochastic, were shown to perform competitively
with full Bayesian neural networks. However, pBNNs are often multi-modal in the
latent variable space and thus challenging to approximate with parametric
models. To address this problem, we propose an efficient sampling-based
training strategy, wherein the training of a pBNN is formulated as simulating a
Feynman--Kac model. We then describe variations of sequential Monte Carlo
samplers that allow us to simultaneously estimate the parameters and the latent
posterior distribution of this model at a tractable computational cost. Using
various synthetic and real-world datasets we show that our proposed training
scheme outperforms the state of the art in terms of predictive performance.


  We examine in detail the cosmology based on quantal (Bohmian) trajectories as
suggested in a recent study arXiv:1404.3093[gr-qc]. We disagree with the
conclusions regarding predicting the value of the cosmological constant
$\Lambda$ and evading the big bang singularity. Furthermore, we show that the
approach of using a quantum corrected Raychaudhuri equation (QRE), as suggested
in arXiv:1404.3093[gr-qc], is unsatisfactory, because, essentially, it uses the
Raychaudhuri equation, which is a kinematical equation, in order to predict
dynamics. In addition, even within this inconsistent framework, the authors
have adopted unjustified assumptions and carried out incorrect steps leading to
doubtful conclusions.


  Charge-dependent two- and three-particle correlations measured in Xe-Xe
collisions at $\sqrt{s_{\mathrm{NN}}} = 5.44$ TeV are presented. Results are
obtained for charged particles in the pseudorapidity range $|\eta|<0.8$ and
transverse momentum interval $0.2 \le p_{\rm T}<5.0$ GeV/$c$ for different
collision centralities. The three-particle correlator ${\gamma_{\alpha\beta}}
\equiv {\langle \cos(\varphi_\alpha + \varphi_\beta - 2{\Psi_{\rm 2}})
\rangle}$, calculated for different combinations of charge sign $\alpha$ and
$\beta$, is expected to be sensitive to the presence of the Chiral Magnetic
Effect (CME). Its magnitude is similar to the one observed in Pb-Pb collisions
in contrast to a smaller CME signal in Xe-Xe collisions than in Pb-Pb
collisions predicted by Monte Carlo (MC) calculations including a magnetic
field induced by the spectator protons. These observations point to a large
non-CME contribution to the correlator. Furthermore, the charge dependence of
${\gamma_{\alpha\beta}}$ can be described by a blast wave model calculation
that incorporates background effects and by the Anomalous Viscous Fluid
Dynamics model with values of the CME signal consistent with zero. The Xe-Xe
and Pb-Pb results are combined with the expected CME signal dependence on the
system size from the MC calculations including a magnetic field to obtain the
fraction of CME contribution in ${\gamma_{\alpha\beta}}$, $f_{\rm CME}$. The
CME fraction is compatible with zero for the 30% most central events in both
systems and then becomes positive; averaging over the 0-70% centrality interval
yields an upper limit of 2% (3%) and 25% (32%) at 95% (99.7%) confidence level
for the CME signal contribution to ${\gamma_{\alpha\beta}}$ in Xe-Xe and Pb-Pb
collisions, respectively.


  In the Emergent scenario, the Universe should evolve from a non-singular
state replacing the typical singularity of General Relativity, for any initial
condition. For the scalar field model in [1] we show that only a subset of
initial conditions leads to emergence, either from a static state (an Einstein
model), or from a de Sitter state.
  Assuming a scenario based on CDM interacting with a Dark Energy fluid, we
show that in general flat and open models expand from a non-singular unstable
de Sitter state at high energies; for closed models this state is a transition
phase with a bounce. A subset of these models are qualitatively in agreement
with the observable Universe, accelerating at high energies, going through a
matter-dominated decelerated era, then accelerating toward a de Sitter phase.


  We can solve the Wheeler-DeWitt equation of the small universe enough to
metric becomes diagonal and take a Gaussian normal coordinate. Our previous
works are concerning to this paper. In this paper, we only write how to solve
the Wheeler-DeWitt equation of such universe. Our motivation is simple, that is
to solve the Wheeler-DeWitt equation. Even if the Wheeler-DeWitt equation is
solved, quantum gravity does not complete yet. However, this work may be one of
the first step to quantum gravity.


  Selected, recent results, primarily from collider experiments but including
some fixed target experiments, are presented as a survey of Quantum
Chromodynamics (QCD). The concepts of leading order and next-to-leading order
QCD are introduced. Inclusive proton-antiproton jet and dijet production and
deep inelastic electron-proton scattering at very large momentum transfer are
shown to be in good agreement with perturbative QCD (pQCD). Dijet, three-jet
and multi-jet results from proton-antiproton, electron-proton, and
electron-electron colliders at moderate Q**2 are also compared to pQCD. BFKL
searches from all three colliders are discussed. Recent measurements of
structure functions and contributions to the parton distribution functions are
presented. New measurements of the strong coupling constant are summarized, the
world average is 0.1184 with an uncertainty of 0.0031.


  The parallel mutation-selection evolutionary dynamics, in which mutation and
replication are independent events, is solved exactly in the case that the
Malthusian fitnesses associated to the genomes are described by the Random
Energy Model (REM) and by a ferromagnetic version of the REM. The solution
method uses the mapping of the evolutionary dynamics into a quantum Ising chain
in a transverse field and the Suzuki-Trotter formalism to calculate the
transition probabilities between configurations at different times. We find
that in the case of the REM landscape the dynamics can exhibit three distinct
regimes: pure diffusion or stasis for short times, depending on the fitness of
the initial configuration, and a spin-glass regime for large times. The dynamic
transition between these dynamical regimes is marked by discontinuities in the
mean-fitness as well as in the overlap with the initial reference sequence. The
relaxation to equilibrium is described by an inverse time decay. In the
ferromagnetic REM, we find in addition to these three regimes, a ferromagnetic
regime where the overlap and the mean-fitness are frozen. In this case, the
system relaxes to equilibrium in a finite time. The relevance of our results to
information processing aspects of evolution is discussed.


  The spatial distribution of elemental abundances in the disc of our Galaxy
gives insights both on its assembly process and subsequent evolution, and on
the stellar nucleogenesis of the different elements. Gradients can be traced
using several types of objects as, for instance, (young and old) stars, open
clusters, HII regions, planetary nebulae. We aim at tracing the radial
distributions of abundances of elements produced through different
nucleosynthetic channels -the alpha-elements O, Mg, Si, Ca and Ti, and the
iron-peak elements Fe, Cr, Ni and Sc - by using the Gaia-ESO idr4 results of
open clusters and young field stars. From the UVES spectra of member stars, we
determine the average composition of clusters with ages >0.1 Gyr. We derive
statistical ages and distances of field stars. We trace the abundance gradients
using the cluster and field populations and we compare them with a
chemo-dynamical Galactic evolutionary model. Results. The adopted
chemo-dynamical model, with the new generation of metallicity-dependent stellar
yields for massive stars, is able to reproduce the observed spatial
distributions of abundance ratios, in particular the abundance ratios of [O/Fe]
and [Mg/Fe] in the inner disc (5 kpc<RGC <7 kpc), with their differences, that
were usually poorly explained by chemical evolution models. Often, oxygen and
magnesium are considered as equivalent in tracing alpha-element abundances and
in deducing, e.g., the formation time-scales of different Galactic stellar
populations. In addition, often [alpha/Fe] is computed combining several
alpha-elements. Our results indicate, as expected, a complex and diverse
nucleosynthesis of the various alpha-elements, in particular in the high
metallicity regimes, pointing towards a different origin of these elements and
highlighting the risk of considering them as a single class with common
features.


  We prove the existence and uniqueness of strong solutions to the steady
isentropic compressible Navier-Stokes equations with inflow boundary conditions
for density and mixed boundary conditions for the velocity around a shear flow.
In particular, the Dirichlet boundary condition on inflow and outflow part of
the boundary while the full Navier boundary conditions on the wall $\Gamma_{0}$
for the velocity filed are considered. For our result, there are no
restrictions on the amplitude of friction coefficients $\alpha$, and the
appropriately large hypothesis for the viscous coefficients $\mu$ is enough.
One of the substantial ingredients of our proof is an elegant transformation
induced by the flow field. With the help of this transformation, we can
overcome the difficulties caused by the hyperbolicity of continuity equation,
establish the a priori estimates for a linearized system and apply the fixed
point arguments.


  We investigate the waveguiding properties of the optical interference pattern
of two counter-propagating Laguerre-Gaussian beams. The number, helicity,
radius, pitch, depth and frequencies of transverse confinement of the
waveguides are simply related to the beam parameters. Quantitative connections
to the familiar Gaussian optical lattice are made and an application to quantum
transport is suggested.


  Recently, various multimodal networks for Visually-Rich Document
Understanding(VRDU) have been proposed, showing the promotion of transformers
by integrating visual and layout information with the text embeddings. However,
most existing approaches utilize the position embeddings to incorporate the
sequence information, neglecting the noisy improper reading order obtained by
OCR tools. In this paper, we propose a robust layout-aware multimodal network
named XYLayoutLM to capture and leverage rich layout information from proper
reading orders produced by our Augmented XY Cut. Moreover, a Dilated
Conditional Position Encoding module is proposed to deal with the input
sequence of variable lengths, and it additionally extracts local layout
information from both textual and visual modalities while generating position
embeddings. Experiment results show that our XYLayoutLM achieves competitive
results on document understanding tasks.


  We consider a finite universe U (more exactly - a family U of them) and
second order quantifiers Q_K, where for each U this means quantifying over a
family of n(K)-place relations closed under permuting U. We define some natural
orders and shed some light on the classification problem of those quantifiers.


  High levels of deuterium fractionation in gas-phase molecules are usually
associated with cold regions, such as prestellar cores. Significant
fractionation ratios are also observed in hot environments such as hot cores or
hot corinos, where they are believed to be produced by the evaporation of the
icy mantles surrounding dust grains, and thus are remnants of a previous cold
(either gas-phase or grain surface) chemistry. The recent detection of DCN
towards the Orion Bar, in a clump at a characteristic temperature of 70K, has
shown that high deuterium fractionation can also be detected in PDRs. The Orion
Bar clumps thus appear as a good environment for the observational study of
deuterium fractionation in luke-warm gas, allowing to validate chemistry models
in a different temperature range, where dominating fractionation processes are
predicted to be different than in cold gas (< 20K). We aimed at studying
observationally in detail the chemistry at work in the Orion Bar PDR, to
understand if DCN is produced by ice mantle evaporation, or is the result of
warm gas-phase chemistry, involving the CH2D+ precursor ion (which survives
higher temperatures than the usual H2D+ precursor). Using the APEX and the IRAM
30m telescopes, we targetted selected deuterated species towards two clumps in
the Orion Bar. We confirmed the detection of DCN and detected two new
deuterated molecules (DCO+ and HDCO) towards one clump in the Orion Bar PDR.
Significant deuterium fractionations are found for HCN and H2CO, but a low
fractionation in HCO+. We also give upper limits for other molecules relevant
for the deuterium chemistry. (...)
  We show evidence that warm deuterium chemistry driven by CH2D+ is at work in
the clumps.


  Large scale decentralized systems, such as P2P, sensor or IoT device networks
are becoming increasingly common, and require robust protocols to address the
challenges posed by the distribution of data and the large number of peers
belonging to the network. In this paper, we deal with the problem of mining
frequent items in unstructured P2P networks. This problem, of practical
importance, has many useful applications. We design P2PSS, a fully
decentralized, gossip--based protocol for frequent items discovery, leveraging
the Space-Saving algorithm. We formally prove the correctness and theoretical
error bound. Extensive experimental results clearly show that P2PSS provides
very good accuracy and scalability, also in the presence of highly dynamic P2P
networks with churning. To the best of our knowledge, this is the first
gossip--based distributed algorithm providing strong theoretical guarantees for
both the Approximate Frequent Items Problem in Unstructured P2P Networks and
for the frequency estimation of discovered frequent items.


  In this paper, we establish BMO estimates for generalized commutators of
rough fractional maximal and integral operators on generalized weighted Morrey
spaces, respectively.


  It is important to obtain (nearly) massless localized modes for the
low-energy four-dimensional effective field theory in the brane-world scenario.
We propose a mechanism for bosonic zero modes using the field-dependent kinetic
function in the classical field theory set-up. As a particularly simple case,
we consider a domain wall in five dimensions, and show that massless states for
scalar (0-form), vector (1-form), and tensor (2-form) fields appear on a domain
wall, which may be called topological because of robustness of their existence
(insensitive to continuous deformations of parameters). The spin of localized
massless bosons is selected by the shape of the nonlinear kinetic function,
analogously to the chirality selection of fermion by the well-known
Jackiw-Rebbi mechanism. Several explicitly solvable examples are given. We
consider not only (anti)BPS domain walls in non-compact extra dimension but
also non-BPS domain walls in compact extra dimension.


  A few 85Rb atoms were trapped in a micron-size magneto-optical trap with a
high quadrupole magnetic-field gradient and the number of atoms was precisely
controlled by suppressing stochastic loading and loss events via real-time
feedback on the magnetic field gradient. The measured occupation probability of
single atom was as high as 99%. Atoms up to five were also trapped with high
occupation probabilities. The present technique could be used to make a
deterministic atom source.


  Neural architecture search (NAS) has been extensively studied in the past few
years. A popular approach is to represent each neural architecture in the
search space as a directed acyclic graph (DAG), and then search over all DAGs
by encoding the adjacency matrix and list of operations as a set of
hyperparameters. Recent work has demonstrated that even small changes to the
way each architecture is encoded can have a significant effect on the
performance of NAS algorithms.
  In this work, we present the first formal study on the effect of architecture
encodings for NAS, including a theoretical grounding and an empirical study.
First we formally define architecture encodings and give a theoretical
characterization on the scalability of the encodings we study Then we identify
the main encoding-dependent subroutines which NAS algorithms employ, running
experiments to show which encodings work best with each subroutine for many
popular algorithms. The experiments act as an ablation study for prior work,
disentangling the algorithmic and encoding-based contributions, as well as a
guideline for future work. Our results demonstrate that NAS encodings are an
important design decision which can have a significant impact on overall
performance. Our code is available at
https://github.com/naszilla/nas-encodings.


  The peculiar behaviour of the solar cycle 23 and its prolonged minima has
been one of the most studied problems over the last few years. In the present
paper, we study the asymmetries in active region magnetic flux in the northern
and southern hemispheres during complete solar cycle 23 and rising phase of
solar cycle 24. During the declining phase of solar cycle 23, we find that the
magnetic flux in the southern hemisphere is about 10 times stronger than that
in the northern hemisphere during the declining phase of the solar cycle 23 and
during the rising phase of cycle 24, however, this trend reversed. The magnetic
flux becomes about a factor of 4 stronger in the northern hemisphere to that of
southern hemisphere. Additionally, we find that there was significant delay
(about 5 months) in change of the polarity in the southern hemisphere in
comparison with the northern hemisphere. These results provide us with hints of
how the toroidal fluxes have contributed to the solar dynamo during the
prolonged minima in the solar cycle 23 and in the rising phase of the solar
cycle 24. Using a solar flux-transport dynamo model, we demonstrate that
persistently stronger sunspot cycles in one hemisphere could be caused by the
effect of greater inflows into active region belts in that hemisphere.
Observations indicate that greater inflows are associated with stronger
activity. Some other change or difference in meridional circulation between
hemispheres could cause the weaker hemisphere to become the stronger one.


  It is known that first-order logic with some counting extensions can be
efficiently evaluated on graph classes with bounded expansion, where depth-$r$
minors have constant density. More precisely, the formulas are $\exists x_1 ...
x_k \#y \varphi(x_1,...,x_k, y)>N$, where $\varphi$ is an FO-formula. If
$\varphi$ is quantifier-free, we can extend this result to nowhere dense graph
classes with an almost linear FPT run time. Lifting this result further to
slightly more general graph classes, namely almost nowhere dense classes, where
the size of depth-$r$ clique minors is subpolynomial, is impossible unless
FPT=W[1]. On the other hand, in almost nowhere dense classes we can approximate
such counting formulas with a small additive error. Note those counting
formulas are contained in FOC({<}) but not FOC1(P). In particular, it follows
that partial covering problems, such as partial dominating set, have fixed
parameter algorithms on nowhere dense graph classes with almost linear running
time.


  K fields, that is, fields with a non-standard kinetic term, allow for soliton
solutions with compact support, i.e., compactons. Compactons in 1+1 dimensions
may give rise to topological defects of the domain wall type and with finite
thickness in higher dimensions. Here we demonstrate that, for an appropriately
chosen kinetic term, propagation of linear perturbations is completely
suppressed outside the topological defect, confining the propagation of
particles inside the domain wall. On the other hand, inside the topological
defect the propagation of linear perturbations is of the standard type, in
spite of the non-standard kinetic term. Consequently, this compacton domain
wall may act like a brane of finite thickness which is embedded in a higher
dimensional space, but to which matter fields are constrained. In addition, we
find strong indications that, when gravity is taken into account, location of
gravity in the sense of Randall--Sundrum works for these compacton domain
walls. When seen from the bulk, these finite thickness branes, in fact, cannot
be distinguished from infinitely thin branes.


  In brane-world cosmologies of Randall-Sundrum type, we show that evolution of
large-scale curvature perturbations may be determined on the brane, without
solving the bulk perturbation equations. The influence of the bulk
gravitational field on the brane is felt through a projected Weyl tensor which
behaves effectively like an imperfect radiation fluid with anisotropic stress.
We define curvature perturbations on uniform density surfaces for both the
matter and Weyl fluids, and show that their evolution on large scales follows
directly from the energy conservation equations for each fluid. The total
curvature perturbation is not necessarily constant for adiabatic matter
perturbations, but can change due to the Weyl entropy perturbation. To relate
this curvature perturbation to the longitudinal gauge metric potentials
requires knowledge of the Weyl anisotropic stress which is not determined by
the equations on the brane. We discuss the implications for large-angle
anisotropies on the cosmic microwave background sky.


  We have measured solar-like oscillations in red giants using time-series
photometry from the first 34 days of science operations of the Kepler Mission.
The light curves, obtained with 30-minute sampling, reveal clear oscillations
in a large sample of G and K giants, extending in luminosity from the red clump
down to the bottom of the giant branch. We confirm a strong correlation between
the large separation of the oscillations (Delta nu) and the frequency of
maximum power (nu_max). We focus on a sample of 50 low-luminosity stars (nu_max
> 100 muHz, L <~ 30 L_sun) having high signal-to-noise ratios and showing the
unambiguous signature of solar-like oscillations. These are H-shell-burning
stars, whose oscillations should be valuable for testing models of stellar
evolution and for constraining the star-formation rate in the local disk. We
use a new technique to compare stars on a single echelle diagram by scaling
their frequencies and find well-defined ridges corresponding to radial and
non-radial oscillations, including clear evidence for modes with angular degree
l=3. Measuring the small separation between l=0 and l=2 allows us to plot the
so-called C-D diagram of delta nu_02 versus Delta nu. The small separation
delta nu_01 of l=1 from the midpoint of adjacent l=0 modes is negative,
contrary to the Sun and solar-type stars. The ridge for l=1 is notably
broadened, which we attribute to mixed modes, confirming theoretical
predictions for low-luminosity giants. Overall, the results demonstrate the
tremendous potential of Kepler data for asteroseismology of red giants.


  Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for
extracting patterns from temporal sequences. However, current RNN models are
ill-suited to process irregularly sampled data triggered by events generated in
continuous time by sensors or other neurons. Such data can occur, for example,
when the input comes from novel event-driven artificial sensors that generate
sparse, asynchronous streams of events or from multiple conventional sensors
with different update intervals. In this work, we introduce the Phased LSTM
model, which extends the LSTM unit by adding a new time gate. This gate is
controlled by a parametrized oscillation with a frequency range that produces
updates of the memory cell only during a small percentage of the cycle. Even
with the sparse updates imposed by the oscillation, the Phased LSTM network
achieves faster convergence than regular LSTMs on tasks which require learning
of long sequences. The model naturally integrates inputs from sensors of
arbitrary sampling rates, thereby opening new areas of investigation for
processing asynchronous sensory events that carry timing information. It also
greatly improves the performance of LSTMs in standard RNN applications, and
does so with an order-of-magnitude fewer computes at runtime.


  Recent years have witnessed an ever-expandingvolume of user-generated content
(UGC) videos available on the Internet. Nevertheless, progress on perceptual
quality assessmentof UGC videos still remains quite limited. There are many
distinguished characteristics of UGC videos in the complete video production
and delivery chain, and one important property closely relevant to video
quality is that there does not exist the pristine source after they are
uploaded to the hosting platform,such that they often undergo multiple
compression stages before ultimately viewed. To facilitate the UGC video
quality assessment,we created a UGC video perceptual quality assessment
database. It contains 50 source videos collected from TikTok with diverse
content, along with multiple distortion versions generated bythe compression
with different quantization levels and coding standards. Subjective quality
assessment was conducted to evaluate the video quality. Furthermore, we
benchmark the database using existing quality assessment algorithms, and
potential roomis observed to future improve the accuracy of UGC video quality
measures.


  Depression, a prominent contributor to global disability, affects a
substantial portion of the population. Efforts to detect depression from social
media texts have been prevalent, yet only a few works explored depression
detection from user-generated video content. In this work, we address this
research gap by proposing a simple and flexible multi-modal temporal model
capable of discerning non-verbal depression cues from diverse modalities in
noisy, real-world videos. We show that, for in-the-wild videos, using
additional high-level non-verbal cues is crucial to achieving good performance,
and we extracted and processed audio speech embeddings, face emotion
embeddings, face, body and hand landmarks, and gaze and blinking information.
Through extensive experiments, we show that our model achieves state-of-the-art
results on three key benchmark datasets for depression detection from video by
a substantial margin. Our code is publicly available on GitHub.


  On the basis of self-consistent Born approximation for Dirac fermions under
charged impurity scatterings in graphene, the theory for calculating the
thermo-electric power is developed by using the heat current-current
correlation function. The advantage of the present approach is its ability to
effectively treat the low doping case where the coherence process involving
carriers in both upper and lower bands becomes important. We show that the low
temperature behavior of the thermo-electric power as function of the carrier
concentration and the temperature observed by the experiments can be
successfully explained by our calculation.


  Among the phases of SiO2 are alpha and beta cristobalites, which have a long
and somewhat controversial history of proposed structural assignments and
phase-transition mechanisms. Recently, Zhang and Scott found new indications
that the higher-temperature beta phase has space group I-42d and, by assuming a
group-subgroup relationship between phases, they argued that the
lower-temperature alpha phase should have lower symmetry than that of the
widely-accepted P4_12_12 space group. With this motivation, we use
first-principles calculations to investigate the energy, structure, and local
stability of P4_12_12 and I-42d structures. We also compute the frequencies of
the zone-center phonon modes in both structures, as well as certain
zone-boundary modes in the I-42d structure, and compare with experiment. We
then argue that the various P4_12_12 and I-42d enantiomorphs can be grouped
into three clusters, each of which is identified with a three-dimensional
manifold of structures of P2_12_12_1 symmetry in which the P4_12_12 and I-42d
appear as higher-symmetry special cases. We find that there are relatively high
energy barriers between manifolds, but low barriers within a manifold.
Exploring the energy landscape within one of these manifolds, we find a
minimal-energy path connecting P4_12_12 and I-42d structures with a
surprisingly low barrier of ~5 meV per formula unit. Possible implications for
the phase-transition mechanism are discussed.


  Machine learning techniques have led to broad adoption of a statistical model
of computing. The statistical distributions natively available on quantum
processors are a superset of those available classically. Harnessing this
attribute has the potential to accelerate or otherwise improve machine learning
relative to purely classical performance. A key challenge toward that goal is
learning to hybridize classical computing resources and traditional learning
techniques with the emerging capabilities of general purpose quantum
processors. Here, we demonstrate such hybridization by training a 19-qubit gate
model processor to solve a clustering problem, a foundational challenge in
unsupervised learning. We use the quantum approximate optimization algorithm in
conjunction with a gradient-free Bayesian optimization to train the quantum
machine. This quantum/classical hybrid algorithm shows robustness to realistic
noise, and we find evidence that classical optimization can be used to train
around both coherent and incoherent imperfections.


  Due to their long-lived nature, vortex rings are highly promising for
non-contact transportation of colloidal microparticles. However, they are
complex structures, and their description using rigorous, closed-form
mathematical expressions is challenging, particularly in the presence of
strongly inhomogeneous colloidal suspensions. This study presents
straightforward analytical approximations that reveal the dynamics of vortex
rings transporting microparticles. Our results were validated using
comprehensive simulations and experimental measurements.


  This paper proposes a peer-to-peer energy trading scheme that can help the
centralized power system to reduce the total electricity demand of its
customers at the peak hour. To do so, a cooperative Stackelberg game is
formulated, in which the centralized power system acts as the leader that needs
to decide on a price at the peak demand period to incentivize prosumers to not
seeking any energy from it. The prosumers, on the other hand, act as followers
and respond to the leader's decision by forming suitable coalitions with
neighboring prosumers in order to participate in P2P energy trading to meet
their energy demand. The properties of the proposed Stackelberg game are
studied. It is shown that the game has a unique and stable Stackelberg
equilibrium, as a result of the stability of prosumers' coalitions. At the
equilibrium, the leader chooses its strategy using a derived closed-form
expression, while the prosumers choose their equilibrium coalition structure.
An algorithm is proposed that enables the centralized power system and the
prosumers to reach the equilibrium solution. Numerical case studies demonstrate
the beneficial properties of the proposed scheme.


  In this paper, we define the notions of full pro-$C^{*}$-crossed product,
respectively reduced pro-$C^{*}$-crossed product, of a pro-$C^{*}$-algebra
$A[\tau_{\Gamma}] $ by a strong bounded action $\alpha$ of a locally compact
group $G$ and investigate some their properties.


  A unitary equivalence class of endomorphisms of a unital C$^{*}$-algebra
${\cal A}$ is called a {\it sector} of ${\cal A}$. We introduced permutative
endomorphisms of the Cuntz algebra ${\cal O}_N$ in the previous work. Branching
laws of permutative representations of ${\cal O}_N$ by them are computed by
directed regular graphs. In this article, we classify sectors associated with
permutative endomorphisms of ${\cal O}_N$ by their graph invariants concretely.


  We consider hydrogen-like atoms in unstable levels of principal quantum
number n=2, confined to a finite size region in a non-homogeneous electric
field carrying handedness. The interplay between the internal degrees of
freedom of the atoms and the external ones of their c.m. motion can produce
P-odd contributions to the eigenenergies. The nominal order of such shifts is
10^-8 Hz. Typically such energy shifts depend linearly on the small P-violation
parameters delta_i similarequal 10^-12 (i=1,2), essentially the ratios of the
P-violating mixing matrix elements of the 2S and 2P states over the Lamb shift,
with i=1 (i=2) corresponding to the nuclear spin independent (dependent) term.
We show how such energy shifts can be enhanced by a factor of similarequal 10^6
in a resonance like way for special field configurations where a crossing of
unstable levels occurs, leading to P-violating effects proportional to
squareroot{delta_i}. Measurements of such effects can give information
concerning the ``spin crisis'' of the nucleons.


  We carry out first-principles calculations which demonstrate the importance
of spin-flip transitions for the intrinsic anomalous Hall conductivity of
ordered FePt alloys. We show the such transitions get enhanced by large
spin-orbit coupling of Pt atoms, becoming negligible when Pt is replaced by
lighter isoelectronic Pd. We find that spin-flip transitions in FePt originate
not only from conventional band anticrossings at the Fermi level, but also from
transitions between well-separated pairs of bands with similar dispersions. We
also predict a strong anisotropy in the anomalous Hall conductivity of FePt,
which comes from spin-flip transitions entirely, and investigate the influence
of disorder on it.


  The study of the structural properties of large random planar graphs has
become in recent years a field of intense research in computer science and
discrete mathematics. Nowadays, a random planar graph is an important and
challenging model for evaluating methods that are developed to study properties
of random graphs from classes with structural side constraints. In this paper
we focus on the structure of random biconnected planar graphs regarding the
sizes of their 3-connected building blocks, which we call cores. In fact, we
prove a general theorem regarding random biconnected graphs. If B_n is a graph
drawn uniformly at random from a class B of labeled biconnected graphs, then we
show that with probability 1-o(1) B_n belongs to exactly one of the following
categories:
  (i) Either there is a unique giant core in B_n, that is, there is a 0 < c < 1
such that the largest core contains ~ cn vertices, and every other core
contains at most n^a vertices, where 0 < a < 1; (ii) or all cores of B_n
contain O(log n) vertices.
  Moreover, we find the critical condition that determines the category to
which B_n belongs, and also provide sharp concentration results for the counts
of cores of all sizes between 1 and n. As a corollary, we obtain that a random
biconnected planar graph belongs to category (i), where in particular c =
0.765... and a = 2/3.


  The notion of fluctuation-induced forces is generalized to the cases where
the fluctuations have nonequilibrium origin. It is shown that a net force is
exerted on a single flat plate that restricts scale-free fluctuations of a
scalar field in a temperature gradient. This force tends to push the object to
the colder regions, which is a manifestation of thermophoresis or the Soret
effect. In the classic two-plate geometry, it is shown that the Casimir forces
exerted on the two plates differ from each other, and thus the Newton's third
law is violated.


  We consider the so-called Hopfield model for the electromagnetic field in a
dielectric dispersive medium in a framework in which one allows a space-time
dependence of microscopic parameters, aimed to a phenomenological description
of a space-time varying dielectric perturbation induced by means of the Kerr
effect. We discuss the analogue Hawking effect, by first analyzing the
geometrical optics for the Hopfield model, and then by introducing a simplified
model which has the bonus to avoid many difficulties which are involved in the
full Hopfield model, still keeping the same dispersion relation. Amplitude
calculations are indicated, and generalized Manley-Rowe identities are derived
in a quantum scattering theory framework. Our main result is an analytical
calculation of the spontaneous thermal emission in the single-branch case,
which is provided non perturbatively for the first time in the framework of
dielectric black holes. An universal mechanism for thermality between optical
black holes and acoustic black holes is also pointed out.


  In population synthesis applications, when considering populations with many
attributes, a fundamental problem is the estimation of rare combinations of
feature attributes. Unsurprisingly, it is notably more difficult to reliably
representthe sparser regions of such multivariate distributions and in
particular combinations of attributes which are absent from the original
sample. In the literature this is commonly known as sampling zeros for which no
systematic solution has been proposed so far. In this paper, two machine
learning algorithms, from the family of deep generative models,are proposed for
the problem of population synthesis and with particular attention to the
problem of sampling zeros. Specifically, we introduce the Wasserstein
Generative Adversarial Network (WGAN) and the Variational Autoencoder(VAE), and
adapt these algorithms for a large-scale population synthesis application. The
models are implemented on a Danish travel survey with a feature-space of more
than 60 variables. The models are validated in a cross-validation scheme and a
set of new metrics for the evaluation of the sampling-zero problem is proposed.
Results show how these models are able to recover sampling zeros while keeping
the estimation of truly impossible combinations, the structural zeros, at a
comparatively low level. Particularly, for a low dimensional experiment, the
VAE, the marginal sampler and the fully random sampler generate 5%, 21% and
26%, respectively, more structural zeros per sampling zero generated by the
WGAN, while for a high dimensional case, these figures escalate to 44%, 2217%
and 170440%, respectively. This research directly supports the development of
agent-based systems and in particular cases where detailed socio-economic or
geographical representations are required.


  The models in statistical physics such as an Ising model offer a convenient
way to characterize stationary activity of neural populations. Such stationary
activity of neurons may be expected for recordings from in vitro slices or
anesthetized animals. However, modeling activity of cortical circuitries of
awake animals has been more challenging because both spike-rates and
interactions can change according to sensory stimulation, behavior, or an
internal state of the brain. Previous approaches modeling the dynamics of
neural interactions suffer from computational cost; therefore, its application
was limited to only a dozen neurons. Here by introducing multiple analytic
approximation methods to a state-space model of neural population activity, we
make it possible to estimate dynamic pairwise interactions of up to 60 neurons.
More specifically, we applied the pseudolikelihood approximation to the
state-space model, and combined it with the Bethe or TAP mean-field
approximation to make the sequential Bayesian estimation of the model
parameters possible. The large-scale analysis allows us to investigate dynamics
of macroscopic properties of neural circuitries underlying stimulus processing
and behavior. We show that the model accurately estimates dynamics of network
properties such as sparseness, entropy, and heat capacity by simulated data,
and demonstrate utilities of these measures by analyzing activity of monkey V4
neurons as well as a simulated balanced network of spiking neurons.


  Camera calibration for estimating the intrinsic parameters and lens
distortion is a prerequisite for various monocular vision applications
including feature tracking and video stabilization. This application paper
proposes a model for estimating the parameters on the fly by fusing gyroscope
and camera data, both readily available in modern day smartphones. The model is
based on joint estimation of visual feature positions, camera parameters, and
the camera pose, the movement of which is assumed to follow the movement
predicted by the gyroscope. Our model assumes the camera movement to be free,
but continuous and differentiable, and individual features are assumed to stay
stationary. The estimation is performed online using an extended Kalman filter,
and it is shown to outperform existing methods in robustness and insensitivity
to initialization. We demonstrate the method using simulated data and empirical
data from an iPad.


  A physical program is formulated for new facilities opening in Dubna for
acceleration of heavy ions with an energy up to 5 AGeV.


  Multipolar magnetism is an emerging field of quantum materials research. The
building blocks of multipolar phenomena are magnetic ions with a non-Kramers
doublet, where the orbital and spin degrees of freedom are inextricably
intertwined, leading to unusual spin-orbital entangled states. The detection of
such subtle forms of matter has, however, been difficult due to a limited
number of appropriate experimental tools. In this work, motivated by a recent
magnetostriction experiment on Pr$_2$Zr$_2$O$_7$, we theoretically investigate
how multipolar quantum spin ice, an elusive three dimensional quantum spin
liquid, and other multipolar ordered phases in the pyrochlore materials can be
detected using magnetostriction. We provide theoretical results based on
classical and/or quantum studies of non-Kramers and Kramers magnetic ions, and
contrast the behaviors of distinct phases in both systems. Our work paves an
important avenue for future identification of exotic ground states in
multipolar systems.


  We present combined experimental and theoretical investigations on the
magnetic and magnetocaloric behavior of Nd$_2$NiMnO$_6$. The relative cooling
power (RCP) which quantifies the usefulness of a magnetocaloric (MC) material
is estimated to be $\approx 300$ J/Kg near the ferromagnetic transition at $T_C
\approx 195$ K. This RCP is comparable to the best known MC materials.
Additionally, the magnetic entropy change has a broad profile ($T_C - 50~{\rm
K} < T < T_C + 50~{\rm K}$) leading to an enhancement in the working-range of
temperatures for magnetocaloric based cooling. These features make
Nd$_2$NiMnO$_6$ a superior magnetocaloric material compared for example, to the
nonmagnetic counterpart Y$_2$NiMnO$_6$. We identify the mechanism for the
enhanced RCP which can guide search for future MC materials.


  We give a full classification of general affine connections on Galilei
manifolds in terms of independently specifiable tensor fields. This generalises
the well-known case of (torsional) Galilei connections, i.e. connections
compatible with the metric structure of the Galilei manifold. Similarly to the
well-known pseudo-Riemannian case, the additional freedom for connections that
are not metric-compatible lies in the covariant derivatives of the two tensors
defining the metric structure (the clock form and the space metric), which
however are not fully independent of each other.


  Sub-GeV dark matter particles can annihilate or decay producing e^\pm pairs
which upscatter the low-energy photon fields in the Galaxy and generate an
X-ray emission (via the Inverse Compton effect). Using X-ray data from
Xmm-Newton, Integral, NuStar and Suzaku, we derive new constraints on this
class of dark matter (DM). In the annihilation case, our new bounds are the
strongest available for DM masses above 180 MeV, reaching <sigma v> < 10^-28
cm^3/s for m_DM ~ 1 GeV. In the decay case, our bounds are the strongest to
date over a large fraction of the considered mass range, constraining tau >
10^28 s for m_DM ~ 1 GeV and improving by up to 3 orders of magnitude upon
existing limits.


  We introduce discrete multivortex solitons in a ring of nonlinear oscillators
coupled to a central site. Regular clusters of discrete vortices appear as a
result of mode collisions and we show that their stability is determined by
global symmetries rather than the stability of constituent vortices. Stable
multivortex solitons support complex vortex dynamics including charge flipping
and spiraling.


  A protocol called the "honeycomb code", or generically a "Floquet code", was
introduced by Hastings and Haah in \cite{hastings_dynamically_2021}. The
honeycomb code is a subsystem code based on the honeycomb lattice with zero
logical qubits but such that there exists a schedule for measuring two-body
gauge checks leaving enough room at all times for two protected logical qubits.
In this work we show a way to introduce boundaries to the system which
curiously presents a rotating dynamics but has constant distance and is
therefore not fault-tolerant.


  Pedestrian detection is an important component for safety of autonomous
vehicles, as well as for traffic and street surveillance. There are extensive
benchmarks on this topic and it has been shown to be a challenging problem when
applied on real use-case scenarios. In purely image-based pedestrian detection
approaches, the state-of-the-art results have been achieved with convolutional
neural networks (CNN) and surprisingly few detection frameworks have been built
upon multi-cue approaches. In this work, we develop a new pedestrian detector
for autonomous vehicles that exploits LiDAR data, in addition to visual
information. In the proposed approach, LiDAR data is utilized to generate
region proposals by processing the three dimensional point cloud that it
provides. These candidate regions are then further processed by a
state-of-the-art CNN classifier that we have fine-tuned for pedestrian
detection. We have extensively evaluated the proposed detection process on the
KITTI dataset. The experimental results show that the proposed LiDAR space
clustering approach provides a very efficient way of generating region
proposals leading to higher recall rates and fewer misses for pedestrian
detection. This indicates that LiDAR data can provide auxiliary information for
CNN-based approaches.


  A generalized description of entanglement and quantum correlation properties
constraining internal degrees of freedom of Dirac(-like) structures driven by
arbitrary Poincar\'e classes of external field potentials is proposed. The role
of (pseudo)scalar, (pseudo)vector and tensor interactions in
producing/destroying intrinsic quantum correlations for $\mbox{SU}(2) \otimes
\mbox{SU}(2)$ bi-spinor structures is discussed in terms of generic coupling
constants. By using a suitable ansatz to obtain the Dirac Hamiltonian
eigenspinor structure of time-independent solutions of the associated Liouville
equation, the quantum entanglement, via concurrence, and quantum correlations,
via geometric discord, are computed for several combinations of well-defined
Poincar\'e classes of Dirac potentials. Besides its inherent formal structure,
our results setup a framework which can be enlarged as to include localization
effects and to map quantum correlation effects into Dirac-like systems which
describe low-energy excitations of graphene and trapped ions.


  This paper introduces a new transformer-based model for the problem of travel
time estimation. The key feature of the proposed GCT-TTE architecture is the
utilization of different data modalities capturing different properties of an
input path. Along with the extensive study regarding the model configuration,
we implemented and evaluated a sufficient number of actual baselines for
path-aware and path-blind settings. The conducted computational experiments
have confirmed the viability of our pipeline, which outperformed
state-of-the-art models on both considered datasets. Additionally, GCT-TTE was
deployed as a web service accessible for further experiments with user-defined
routes.


  Low-frequency dielectric spectra of high-resistivity GaSe layered crystals
have been studied on the samples clamped between two insulating parallel plates
at frequencies up to 100 kHz. The measurements have been carried out at
different uniaxial stresses up to $2.4\times10^5$ Pa applied along the c-axis
normal to crystal layer's plane. It is revealed that the dielectric spectra of
high-resistivity GaSe layered crystals with insulating plates obey a universal
power law ${\sim}\omega^{n-1}$, where ${\omega}$ is the angular frequency and
$n\approx 0.8$, earlier observed on high-resistivity GaSe crystals with
indium-soldered contacts. The same type of spectra on the crystals with
different types of contacts (insulating and ohmic) confirms the bulk character
of the observed polarization caused by hopping charge carriers. It is shown
that the frequency-dependent dielectric constant increases linearly with the
uniaxial stress characterized by the coefficient
${\Delta}{\epsilon}/({\epsilon}{\Delta}{p})=8{\times}10^{-7}$ Pa$^{-1}$. A
slight increase of power $1-n$ with the stress is observed, that leads to a
stronger dielectric dispersion. The strong stress dependence of the
low-frequency dielectric constant in high-resistivity GaSe crystals may be
referred to the presence of the formations of elementary dipoles, rotations of
which correspond to hops of localized charge carriers.