  Double-blind conferences have engaged in debates over whether to allow
authors to post their papers online on arXiv or elsewhere during the review
process. Independently, some authors of research papers face the dilemma of
whether to put their papers on arXiv due to its pros and cons. We conduct a
study to substantiate this debate and dilemma via quantitative measurements.
Specifically, we conducted surveys of reviewers in two top-tier double-blind
computer science conferences -- ICML 2021 (5361 submissions and 4699 reviewers)
and EC 2021 (498 submissions and 190 reviewers). Our two main findings are as
follows. First, more than a third of the reviewers self-report searching online
for a paper they are assigned to review. Second, outside the review process, we
find that preprints from better-ranked affiliations see a weakly higher
visibility, with a correlation of 0.06 in ICML and 0.05 in EC. In particular,
papers associated with the top-10-ranked affiliations had a visibility of
approximately 11% in ICML and 22% in EC, whereas the remaining papers had a
visibility of 7% and 18% respectively.


  The 1T polytype of TaS2 exhibits voltage-triggered threshold switching as a
result of a phase transition from nearly commensurate to incommensurate charge
density wave states. Threshold switching, persistent above room temperature,
can be utilized in a variety of electronic devices, e.g., voltage controlled
oscillators. We evaluated the total-ionizing-dose response of thin film 1T-TaS2
at doses up to 1 Mrad(SiO2). The threshold voltage changed by less than 2%
after irradiation, with persistent self-sustained oscillations observed through
the full irradiation sequence. The radiation hardness is attributed to the high
intrinsic carrier concentration of 1T-TaS2 in both of the phases that lead to
threshold switching. These results suggest that charge density wave devices,
implemented with thin films of 1T-TaS2, are promising for applications in high
radiation environments.


  The imaging of magneto-dynamical processes has been, so far, mostly a
two-dimensional business, due to the constraints of the available experimental
techniques. In this manuscript, building on the recent developments of soft
X-ray magnetic laminography, we present an experimental setup where
magneto-dynamical processes can be resolved in all three spatial dimensions and
in time, with the possibility to freely tune the frequency of the dynamical
process. We then employ this setup to investigate the three-dimensional
dynamics of two resonant magneto-dynamical modes in a CoFeB microstructure
occurring at different frequencies, namely the fundamental vortex gyration mode
and a magnetic field-induced domain wall excitation mode. This new technique
provides much needed capabilities for the experimental investigation of the
magnetization dynamics of three-dimensional magnetic systems.


  We show how the fully resummed thermal pressure is rendered ultraviolet
finite by standard zero-temperature renormalisation. The analysis is developed
in a 6-dimensional scalar model that mimics QED and has $N$ flavours. The
$N\to\infty$ limit of the model can be calculated completely. At a critical
temperature, one of the degrees of freedom has vanishing screening mass like
the transverse gauge bosons in four-dimensional finite-temperature perturbation
theory. The renormalised nonperturbative interaction pressure of this model is
evaluated numerically.


  Understanding the history and evolution of small bodies, such as dust grains
and comets, in planet-forming disks is very important to reveal the
architectural laws responsible for the creation of planetary systems. These
small bodies in cold regions of the disks are typically considered as mixtures
of dust particles with molecular ices, where ices cover the surface of a dust
core or are actually physically mixed with dust. Whilst the first case,
ice-on-dust, has been intensively studied in the laboratory in recent decades,
the second case, ice-mixed-with-dust, present uncharted territory. This work is
the first laboratory study of the temperature-programmed desorption (TPD) of
water ice mixed with amorphous carbon and silicate grains. We show that the
kinetics of desorption of H2O ice depends strongly on the dust/ice mass ratio,
probably, due to the desorption of water molecules from a large surface of
fractal clusters composed of carbon or silicate grains. In addition, it is
shown that water ice molecules are differently bound to silicate grains in
contrast to carbon. The results provide a link between the structure and
morphology of small cosmic bodies and the kinetics of desorption of water ice
included in them.


  We utilise recent results about the transcendental solutions to Riccati
differential equations to provide a comprehensive description of the nature of
the transcendental solutions to algebraic first order differential equations of
genus zero.


  In this fluid dynamics video, we show the spontaneous random motion of thin
filaments of a shear-thickening colloidal dispersions floating on the surface
of water. The fluid is a dispersion of fumed silica nanoparticles in a low
molecular weight polypropylene glycol (PPG) solvent. No external field or force
is applied. The observed motion is driven by strong surface tension gradients
as the polypropylene glycol slowly diffuses from from the filaments into water,
resulting in the observed Marangoni flow. The motion is exaggerated by the thin
filament constructs by the attractive interactions between silica nanoparticles
and the PPG.


  We provide a fresh perspective on the problem of characterizing the DoF
region of the $K$-user MISO BC with arbitrary levels of partial CSIT. In a
previous achievability proof, Piovano and Clerckx characterized all faces
describing a polyhedral outer bound region, and then with the aid of
mathematical induction, prescribed a scheme based on rate-splitting with
flexible assignment of common DoF and power levels to achieve each such face.
We propose an alternative approach in which we deal directly with the region
achievable through rate-splitting and employ a Fourier-Motzkin procedure to
eliminate all auxiliary variables, hence reducing the achievable region to the
known outer bound. A key insight emerging from our proof is that tuning only
one power variable, as well as assigning the common DoF, is sufficient to
achieve the entire DoF region, as opposed to $K$ power variables previously
employed.


  In this paper we formulate the relationship between force-free
electrodynamics and foliations. The background metric, is considered
predetermined and electrically neutral, but otherwise arbitrary. As it turns
out, solutions to force-free electrodynamics is intimately connected to the
existence of foliations of a spacetime with prescribed properties. We also
prove a local existence and uniqueness theorem and provide a recipe for
constructing the unique solution/class of solutions when certain conditions are
met. We clarify the theorem with examples. We are also able to also prove a
singularity theorem for when non-null solutions approach the null limit. Here
too, we construct an explicit example to illustrate the singularity theorem.


  We obtain a geometric realization of a minimal 8-vertex triangulation of the
dunce hat in Euclidean 3-space. We show there is a simplicial 3-ball with 8
vertices that is collapsible, but also collapses onto the dunce hat, which is
not collapsible. This 3-ball is as small as possible, because all triangulated
3-balls with fewer vertices are extendably collapsible. As we will see, the
Alexander dual of the dunce hat is collapsible.


  In a recent paper of Wolff the optimal decay of circular L^p means of
compactly supported measures of finite energy was given for p>=2, with
application to Falconer's distance problem. The question was then raised in
that paper as to whether any non-trivial improvement to these estimates were
available for p < 2. We answer this question in the negative.


  Iron self-diffusion in nano-composite FeZr alloy has been investigated using
neutron reflectometry technique as a function of applied compressive stress. A
composite target of (Fe+Zr) and (57Fe+Zr) was alternatively sputtered to
deposit chemically homogeneous multilayer (CHM) structure,
[Fe75Zr25/57Fe75Zr25]10. The multilayers were deposited on to a bent Si wafer
using a 3-point bending device. Post-deposition, the bending of the substrate
was released which results in an applied compressive stress on to the
multilayer. In the as-deposited state, the alloy multilayer forms an amorphous
phase, which crystallizes into a nano-composite phase when heated at 373 K.
Bragg peaks due to isotopic contrast were observed from CHM, when measured by
neutron reflectivity, while x-ray reflectivity showed a pattern corresponding
to a single layer. Self-diffusion of iron was measured with the decay of the
intensities at the Bragg peaks in the neutron reflectivity pattern after
thermal annealing at different temperatures. It was found that the
self-diffusion of iron slows down with an increase in the strength of applied
compressive stress.


  Online reviews provided by consumers are a valuable asset for e-Commerce
platforms, influencing potential consumers in making purchasing decisions.
However, these reviews are of varying quality, with the useful ones buried deep
within a heap of non-informative reviews. In this work, we attempt to
automatically identify review quality in terms of its helpfulness to the end
consumers. In contrast to previous works in this domain exploiting a variety of
syntactic and community-level features, we delve deep into the semantics of
reviews as to what makes them useful, providing interpretable explanation for
the same. We identify a set of consistency and semantic factors, all from the
text, ratings, and timestamps of user-generated reviews, making our approach
generalizable across all communities and domains. We explore review semantics
in terms of several latent factors like the expertise of its author, his
judgment about the fine-grained facets of the underlying product, and his
writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet
Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii)
item facets, and (iii) review helpfulness. Large-scale experiments on five
real-world datasets from Amazon show significant improvement over
state-of-the-art baselines in predicting and ranking useful reviews.


  The presence of an extended blue horizontal branch (HB) in a stellar
population is known to affect the age inferred from spectral fitting to stellar
population synthesis models. However, most population synthesis models still
rely on theoretical isochrones which do not include realistic modelling of
extended HBs. In this work, we create detailed models for a range of old simple
stellar populations (SSPs), to create a variety of realistic HB morphologies,
from extended red clumps, to extreme blue HBs. We achieve this by utilising
stellar tracks from the BaSTI database and implementing a different mass loss
prescription for each SSP created, resulting in different HB morphologies. We
find that, for each metallicity, there is some HB morphology which maximises
Hbeta, making an underlying 14Gyr population look ~5-6Gyr old for the low and
intermediate metallicity cases, and as young as 2Gyr for a solar metallicity
SSP. We explore whether there are any spectral indices capable of breaking the
degeneracy between an old SSP with extended blue HB and a truly young or
intermediate age SSP, and find that the CaII index of Rose(1984) and the
strength of the MgII doublet at 2800A are promising candidates, in combination
with Hbeta and other metallicity indicators such as Mgb and Fe5406. We also run
Monte Carlo simulations to investigate the level of statistical fluctuations in
the spectra of typical stellar clusters. We find that fluctuations in spectral
indices are significant even for average to large globular clusters, and that
various spectral indices are affected in different ways, which has implications
for full-spectrum fitting methods. Hence we urge caution if these types of
stellar clusters are to be used as empirical calibrating objects for various
aspects of SPS models. (Abridged)


  In this paper we compare approximation properties of degree $p$ spline spaces
with different numbers of continuous derivatives. We prove that, for a given
space dimension, $\smooth {p-1}$ splines provide better a priori error bounds
for the approximation of functions in $H^{p+1}(0,1)$. Our result holds for all
practically interesting cases when comparing $\smooth {p-1}$ splines with
$\smooth {-1}$ (discontinuous) splines. When comparing $\smooth {p-1}$ splines
with $\smooth 0$ splines our proof covers almost all cases for $p\ge 3$, but we
can not conclude anything for $p=2$. The results are generalized to the
approximation of functions in $H^{q+1}(0,1)$ for $q<p$, to broken Sobolev
spaces and to tensor product spaces.


  In this note we prove a generalization of the flat extension theorem of Curto
and Fialkow for truncated moment matrices. It applies to moment matrices
indexed by an arbitrary set of monomials and its border, assuming that this set
is connected to 1. When formulated in a basis-free setting, this gives an
equivalent result for truncated Hankel operators.


  We propose to use the nuclear spin excitation in the ferromagnetic A1 phase
of the superfluid $^3$He for the axion dark matter detection. This approach is
striking in that it is sensitive to the axion-nucleon coupling, one of the most
important features of the QCD axion introduced to solve the strong CP problem.
We review a quantum mechanical description of the nuclear spin excitation and
apply it to the estimation of the axion-induced spin excitation rate. We also
describe a possible detection method of the spin excitation in detail and show
that the combination of the squeezing of the final state with the Josephson
parametric amplifier and the homodyne measurement can enhance the sensitivity.
It turns out that this approach gives good sensitivity to the axion dark matter
with the mass of $O(1) \, \mu \mathrm{eV}$ depending on the size of the
external magnetic field. We estimate the parameters of experimental setups,
e.g., the detector volume and the amplitude of squeezing, required to reach the
QCD axion parameter space.


  We compute the rate of supercollapsars by using cosmological, N-body, hydro,
chemistry simulations of structure formation, following detailed stellar
evolution according to proper yields (for He, C, N, O, Si, S, Fe, Mg, Ca, Ne,
etc.) and lifetimes for stars having different masses and metallicities, and
for different stellar populations (population III and population II-I). We find
that supercollapsars are usually associated to dense, collapsing gas with
little metal pollution and with abundances dominated by oxygen. The resulting
supercollapsar rate is about $10^{-2}\,\rm yr^{-1} sr^{-1}$ at redshift $z=0$,
and their contribution to the total rate is $ < 0.1 $ per cent, which explains
why they have never been detected so far. Expected rates at redshift $z\simeq
6$ are of the order of $\sim 10^{-3}\,\rm yr^{-1} sr^{-1}$ and decrease further
at higher $z$. Because of the strong metal enrichment by massive, short-lived
stars, only $\sim 1$ supercollapsar generation is possible in the same star
forming region. Given their sensitivity to the high-mass end of the primordial
stellar mass function, they are suitable candidates to probe pristine
population III star formation and stellar evolution at low metallicities.


  This article is motivated by the original Casson invariant regarded as an
integral lifting of the Rochlin invariant. We aim to defining an integral
lifting of the Adams e-invariant of stably framed 3-manifolds, perhaps endowed
with some additional structure. We succeed in doing so for manifolds which are
links of normal complex Gorenstein smoothable singularities. These manifolds
are naturally equipped with a canonical $\SU$-frame. To start we notice that
the set of homotopy classes of $\SU$-frames on the stable tangent bundle of
every closed oriented 3-manifold is canonically a $\mathbb Z$-torsor. Then we
define the $\widehat E$-invariant for the manifolds in question, an integer
that modulo 24 is the Adams e-invariant. The $\widehat E$-invariant for the
canonical frame equals the Milnor number plus 1, so this brings a new viewpoint
on the Milnor number of the smoothable Gorenstein surface singularities.


  We present the first provable Least-Squares Value Iteration (LSVI) algorithms
that have runtime complexity sublinear in the number of actions. We formulate
the value function estimation procedure in value iteration as an approximate
maximum inner product search problem and propose a locality sensitive hashing
(LSH) [Indyk and Motwani STOC'98, Andoni and Razenshteyn STOC'15, Andoni,
Laarhoven, Razenshteyn and Waingarten SODA'17] type data structure to solve
this problem with sublinear time complexity. Moreover, we build the connections
between the theory of approximate maximum inner product search and the regret
analysis of reinforcement learning. We prove that, with our choice of
approximation factor, our Sublinear LSVI algorithms maintain the same regret as
the original LSVI algorithms while reducing the runtime complexity to sublinear
in the number of actions. To the best of our knowledge, this is the first work
that combines LSH with reinforcement learning resulting in provable
improvements. We hope that our novel way of combining data-structures and
iterative algorithm will open the door for further study into cost reduction in
optimization.


  Studying the solar system and especially the Sun relies on the data gathered
daily from space missions. These missions are data-intensive and compressing
this data to make them efficiently transferable to the ground station is a
twofold decision to make. Stronger compression methods, by distorting the data,
can increase data throughput at the cost of accuracy which could affect
scientific analysis of the data. On the other hand, preserving subtle details
in the compressed data requires a high amount of data to be transferred,
reducing the desired gains from compression. In this work, we propose a neural
network-based lossy compression method to be used in NASA's data-intensive
imagery missions. We chose NASA's SDO mission which transmits 1.4 terabytes of
data each day as a proof of concept for the proposed algorithm. In this work,
we propose an adversarially trained neural network, equipped with local and
non-local attention modules to capture both the local and global structure of
the image resulting in a better trade-off in rate-distortion (RD) compared to
conventional hand-engineered codecs. The RD variational autoencoder used in
this work is jointly trained with a channel-dependent entropy model as a shared
prior between the analysis and synthesis transforms to make the entropy coding
of the latent code more effective. Our neural image compression algorithm
outperforms currently-in-use and state-of-the-art codecs such as JPEG and
JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet
(EUV) data. As a proof of concept for use of this algorithm in SDO data
analysis, we have performed coronal hole (CH) detection using our compressed
images, and generated consistent segmentations, even at a compression rate of
$\sim0.1$ bits per pixel (compared to 8 bits per pixel on the original data)
using EUV data from SDO.


  A joint resource allocation (RA), user association (UA), and power control
(PC) problem is addressed for proportional fairness maximization in a
cooperative multiuser downlink small cell network with limited backhaul
capacity, based on orthogonal frequency division multiplexing. Previous studies
have relaxed the per-resource-block (RB) RA and UA problem to a continuous
optimisation problem based on long-term signal-to-noise-ratio, because the
original problem is known as a combinatorial NP-hard problem. We tackle the
original per-RB RA and UA problem to obtain a near-optimal solution with
feasible complexity. We show that the conventional dual problem approach for RA
cannot find the solution satisfying the conventional KKT conditions. Inspired
by the dual problem approach, however, we derive the first order optimality
conditions for the considered RA, UA, and PC problem, and propose a sequential
optimization method for finding the solution. The overall proposed scheme can
be implemented with feasible complexity even with a large number of system
parameters. Numerical results show that the proposed scheme achieves the
proportional fairness close to its outer bound with unlimited backhaul capacity
in the low backhaul capacity regime and to that of a carefully-designed genetic
algorithm with excessive generations but without backhaul constraint in the
high backhaul capacity regime.


  CT and MRI are two of the most informative modalities in spinal diagnostics
and treatment planning. CT is useful when analysing bony structures, while MRI
gives information about the soft tissue. Thus, fusing the information of both
modalities can be very beneficial. Registration is the first step for this
fusion. While the soft tissues around the vertebra are deformable, each
vertebral body is constrained to move rigidly. We propose a weakly-supervised
deep learning framework that preserves the rigidity and the volume of each
vertebra while maximizing the accuracy of the registration. To achieve this
goal, we introduce anatomy-aware losses for training the network. We
specifically design these losses to depend only on the CT label maps since
automatic vertebra segmentation in CT gives more accurate results contrary to
MRI. We evaluate our method on an in-house dataset of 167 patients. Our results
show that adding the anatomy-aware losses increases the plausibility of the
inferred transformation while keeping the accuracy untouched.


  We prove that for particular infinite families of $L$-spaces, arising as
branched double covers, the $d$-invariants defined by Ozsv\'ath and Szab\'o are
arbitrarily large and small. As a consequence, we generalise a result by Greene
and Watson by proving, for every odd number $\Delta \geq 5$, the existence of
infinitely many non-quasi-alternating homologically thin knots with determinant
$\Delta^2$, and a result by Hoffman and Walsh concerning the existence of
hyperbolic weight $1$ manifolds that are not surgery on a knot in $S^3$.


  Atomically thin polycrystalline transition-metal dichalcogenides (TMDs) are
relevant to both fundamental science investigation and applications. TMD
thin-films present uniquely difficult challenges to effective nanoscale
crystalline characterization. Here we present a method to quickly characterize
the nanocrystalline grain structure and texture of monolayer WS2 films using
scanning nanobeam electron diffraction coupled with multivariate statistical
analysis of the resulting data. Our analysis pipeline is highly generalizable
and is a useful alternative to the time consuming, complex, and
system-dependent methodology traditionally used to analyze spatially resolved
electron diffraction measurements.


  For commutative rings, we introduce the notion of a {\em universal grading},
which can be viewed as the "largest possible grading". While not every
commutative ring (or order) has a universal grading, we prove that every {\em
reduced order} has a universal grading, and this grading is by a {\em finite}
group. Examples of graded orders are provided by group rings of finite abelian
groups over rings of integers in number fields. We generalize known properties
of nilpotents, idempotents, and roots of unity in such group rings to the case
of graded orders; this has applications to cryptography. Lattices play an
important role in this paper; a novel aspect is that our proofs use that the
additive group of any reduced order can in a natural way be equipped with a
lattice structure.


This article contains a short review of the current status of string theory.


  We develop the first steps towards an analysis of geometry on the quantum
spacetime proposed in [1]. The homogeneous elements of the universal
differential algebra are naturally identified with operators living in tensor
powers of Quantum Spacetime; this allows us to compute their spectra. In
particular, we consider operators that can be interpreted as distances, areas,
3- and 4-volumes. The Minkowski distance operator between two independent
events is shown to have pure Lebesgue spectrum with infinite multiplicity. The
Euclidean distance operator is shown to have spectrum bounded below by a
constant of the order of the Planck length. The corresponding statement is
proved also for both the space-space and space-time area operators, as well as
for the Euclidean length of the vector representing the 3-volume operators.
However, the space 3-volume operator (the time component of that vector) is
shown to have spectrum equal to the whole complex plane. All these operators
are normal, while the distance operators are also selfadjoint. The Lorentz
invariant spacetime volume operator, representing the 4- volume spanned by five
independent events, is shown to be normal. Its spectrum is pure point with a
finite distance (of the order of the fourth power of the Planck length) away
from the origin. The mathematical formalism apt to these problems is developed
and its relation to a general formulation of Gauge Theories on Quantum Spaces
is outlined. As a byproduct, a Hodge Duality between the absolute dif-
ferential and the Hochschild boundary is pointed out.


  Learning fair graph representations for downstream applications is becoming
increasingly important, but existing work has mostly focused on improving
fairness at the global level by either modifying the graph structure or
objective function without taking into account the local neighborhood of a
node. In this work, we formally introduce the notion of neighborhood fairness
and develop a computational framework for learning such locally fair
embeddings. We argue that the notion of neighborhood fairness is more
appropriate since GNN-based models operate at the local neighborhood level of a
node. Our neighborhood fairness framework has two main components that are
flexible for learning fair graph representations from arbitrary data: the first
aims to construct fair neighborhoods for any arbitrary node in a graph and the
second enables adaption of these fair neighborhoods to better capture certain
application or data-dependent constraints, such as allowing neighborhoods to be
more biased towards certain attributes or neighbors in the graph.Furthermore,
while link prediction has been extensively studied, we are the first to
investigate the graph representation learning task of fair link classification.
We demonstrate the effectiveness of the proposed neighborhood fairness
framework for a variety of graph machine learning tasks including fair link
prediction, link classification, and learning fair graph embeddings. Notably,
our approach achieves not only better fairness but also increases the accuracy
in the majority of cases across a wide variety of graphs, problem settings, and
metrics.


  The strong coupling constant $\alpha_s$ and the heavy-quark masses, $m_c$,
$m_b$, $m_t$ are extracted simultaneosly with the parton distribution functions
(PDFs) in the updated ABM12 fit including recent data from CERN-SPS, HERA,
Tevatron, and the LHC. The values of \begin{eqnarray} \nonumber
\alpha_s(M_Z)&=&0.1147\pm0.0008~({\rm exp.)},\\ \nonumber m_c(m_c)&=&1.252\pm
0.018~({\rm exp.})~{\rm GeV},\\ \nonumber m_b(m_b)&=&3.83\pm0.12~({\rm
exp.})~{\rm GeV},\\ \nonumber m_t(m_t)&=&160.9\pm1.1~({\rm exp.})~{\rm GeV}
\end{eqnarray} are obtained with the $\overline{MS}$ heavy-quark mass
definition being employed throughout the analysis.


  We identify the simplest combinations of entanglement and incompatibility
giving the maximum quantum violation for each of the 46 classes of tight Bell
inequalities for the (3,2,2) scenario, i.e., three parties, two measurements
per party, and two outcomes per measurement. This allows us to classify the
maximum quantum nonlocality according to the simplest resources needed to
achieve it. We show that entanglement and incompatibility only produce maximum
nonlocality when they are combined in specific ways. For each entanglement
class there is, in most cases, just one incompatibility class leading to
maximum nonlocality. We also identify two interesting cases. We show that the
maximum quantum violation of \'Sliwa inequality 23 only occurs when the third
party measures the identity, so nonlocality cannot increase when we add a third
party to the bipartite case. Almost quantum correlations predict that adding a
new party increases nonlocality. This points out that either almost quantum
correlations violate a fundamental principle or that there is a form of
tripartite entanglement which quantum theory cannot account for. The other
interesting case is the maximum quantum violation of \'Sliwa inequality 26,
which, like the Mermin inequality, requires maximum incompatibility for all
parties. In contrast, it requires a specific entangled state which has the same
tripartite negativity as the W state.


  We investigate the dynamics of a two-dimensional Hubbard model in a static
electric field in order to identify the conditions to reach a non-equilibrium
stationary state. For a generic electric field, the convergence to a stationary
state requires the coupling to a thermostating bath absorbing the work done by
the external force. Following the real-time dynamics of the system, we show
that a non-equilibrium stationary state is reached for essentially any value of
the coupling to the bath. We map out a phase diagram in terms of dissipation
and electric field strengths and identify the dissipation values in which
steady current is largest for a given field.


  This paper presents our solution for the ICDAR 2021 Competition on Scientific
Table Image Recognition to LaTeX. This competition has two sub-tasks: Table
Structure Reconstruction (TSR) and Table Content Reconstruction (TCR). We treat
both sub-tasks as two individual image-to-sequence recognition problems. We
leverage our previously proposed algorithm MASTER \cite{lu2019master}, which is
originally proposed for scene text recognition. We optimize the MASTER model
from several perspectives: network structure, optimizer, normalization method,
pre-trained model, resolution of input image, data augmentation, and model
ensemble. Our method achieves 0.7444 Exact Match and 0.8765 Exact Match @95\%
on the TSR task, and obtains 0.5586 Exact Match and 0.7386 Exact Match 95\% on
the TCR task.


  For prime levels $5 \le p \le 19$, sets of $\Gamma_{0}(p)$-permuted theta
quotients are constructed that generate the graded rings of modular forms of
positive integer weight for $\Gamma_{1}(p)$. An explicit formulation of the
permutation representation and several applications are given, including a new
representation for the number of $t$-core partitions. The
$\Gamma_{0}(p)$-action induces coefficient symmetries within representations
for modular forms and invariance subgroups for coupled systems of differential
equations. The symmetry for levels $p = 5,7,11$ is linked to the Kleinian
automorphism groups.


  Linear active-power-only DC power flow approximations are pervasive in the
planning and control of power systems. However, these approximations fail to
capture reactive power and voltage magnitudes, both of which are necessary in
many applications to ensure voltage stability and AC power flow feasibility.
This paper proposes linear-programming models (the LPAC models) that
incorporate reactive power and voltage magnitudes in a linear power flow
approximation. The LPAC models are built on a convex approximation of the
cosine terms in the AC equations, as well as Taylor approximations of the
remaining nonlinear terms. Experimental comparisons with AC solutions on a
variety of standard IEEE and MatPower benchmarks show that the LPAC models
produce accurate values for active and reactive power, phase angles, and
voltage magnitudes. The potential benefits of the LPAC models are illustrated
on two "proof-of-concept" studies in power restoration and capacitor placement.


  We study frustrated, two-dimensional, quantum antiferromagnets in the
vicinity of a quantum transition from a non-collinear, magnetically-ordered
ground state to a quantum disordered phase. The general scaling properties of
this transition are described. A detailed study of a particular field-theoretic
model of the transition, with bosonic spin-1/2 spinon fields, is presented.
Explicit universal scaling forms for a variety of observables are obtained and
the results are compared with numerical data on the spin-1/2 triangular
antiferromagnet. Universal properties of an alternative field-theory, with
confined spinons, are also briefly noted.


  Summarization of multimedia data becomes increasingly significant as it is
the basis for many real-world applications, such as question answering, Web
search, and so forth. Most existing multi-modal summarization works however
have used visual complementary features extracted from images rather than
videos, thereby losing abundant information. Hence, we propose a novel
multi-modal summarization task to summarize from a document and its associated
video. In this work, we also build a baseline general model with effective
strategies, i.e., bi-hop attention and improved late fusion mechanisms to
bridge the gap between different modalities, and a bi-stream summarization
strategy to employ text and video summarization simultaneously. Comprehensive
experiments show that the proposed model is beneficial for multi-modal
summarization and superior to existing methods. Moreover, we collect a novel
dataset and it provides a new resource for future study that results from
documents and videos.


  We demonstrate that incoherent photon scattering by a Bose-Einstein
condensate of non-ideal atomic gas is enhanced due to bosonic stimulation of
spontaneous emission, similarly to coherent scattering in forward direction.
Necessary initial population of non-condensate states is provided by quantum
depletion of a condensate caused by interatomic repulsion.


  We develop a comprehensive and flexible model for the connection between
satellite galaxies and dark matter subhalos in dark matter-only zoom-in
simulations of Milky Way (MW)--mass host halos. We systematically identify the
physical and numerical uncertainties in the galaxy--halo connection and
simulations underlying our method, including (i) the influence of host halo
properties; (ii) the relationship between satellite luminosities and subhalo
properties, including the effects of reionization; (iii) the relationship
between satellite and subhalo locations; (iv) the relationship between
satellite sizes and subhalo properties, including the effects of tidal
stripping; (v) satellite and subhalo disruption due to baryonic effects; and
(vi) artificial subhalo disruption and orphan satellites. To illustrate our
approach, we fit this model to the luminosity distribution of both classical MW
satellites and those discovered in the Sloan Digital Sky Survey by performing
realistic mock observations that depend on the luminosity, size, and distance
of our predicted satellites, and we infer the total satellite population that
will be probed by upcoming surveys. We argue that galaxy size and surface
brightness modeling will play a key role in interpreting current and future
observations, as the expected number of observable satellites depends
sensitively on their surface brightness distribution.


  The gravitational collapse of a complex scalar field in the harmonic map is
modeled in spherical symmetry. Previous work has shown that a change of
stability of the attracting critical solution occurs in parameter space from
the discretely self-similarity critical (DSS) solution originally found by
Choptuik to the continuously self-similar (CSS) solution found by Hirschmann
and Eardley. In the region of parameter space in which the DSS is the
attractor, a family of initial data is found which finds the CSS as its
critical solution despite the fact that it has more than one unstable mode. An
explanation of this is proposed in analogy to families that find the DSS in the
region where the CSS is the attractor.


  We report the APOGEE-2S$+$ discovery of a unique collection of
nitrogen-enhanced mildly metal-poor giant stars, peaking at [Fe/H]$\sim -0.89$
with no carbon enrichment, toward the Small and Large Magellanic Clouds (MCs),
with abundances of light- (C, N), odd-Z (Al, K) and $\alpha-$elements (O, Mg,
Si) that are typically found in Galactic globular clusters (GCs). Here we
present 44 stars in the MCs that exhibit significantly enhanced [N/Fe]
abundance ratios, well above ([N/Fe]$>+0.6$) typical Galactic levels at similar
metallicity, and a star that is very nitrogen-enhanced ([N/Fe]$> +2.45$). Our
sample consists of luminous evolved stars on the asymptotic giant branch (AGB),
eight of which are classified as bonafide semi-regular (SR) variables, as well
as low-luminosity stars similar to that of stars on the tip of the red giant
branch of stellar clusters in the MCs. It seems likely that whatever
nucleosynthetic process is responsible for these anomalous MC stars it is
similar to that which caused the common stellar populations in GCs. We
interpret these distinctive C-N patterns as the observational evidence of the
result of tidally shredded GCs in the MCs. These findings might explain some
previous conflicting results over bulge N-rich stars, and broadly help to
understand GC formation and evolution. Furthermore, the discovery of such a
large population of N-rich AGB stars in the MCs suggests that multiple stellar
populations might not only be exotic events from the past but can also form at
lower redshift.


  We use a nonequilibrium implementation of the dynamical cluster approximation
(DCA) to study the effect of short-range correlations on the dynamics of the
two-dimensional Falicov-Kimball model after an interaction quench. As in the
case of single-site dynamical mean field theory, thermalization is absent in
DCA simulations, and for quenches across the metal-insulator boundary,
nearest-neighbor charge correlations in the nonthermal steady state are found
to be larger than in the thermal state with identical energy. We investigate to
what extent it is possible to define an effective temperature of the trapped
state after a quench. Based on the ratio between the lesser and retarded
Green's function we conclude that a roughly thermal distribution is reached
within the energy intervals corresponding to the momentum-patch dependent
subbands of the spectral function. The effectively different chemical
potentials of these distributions however lead to a very hot, or even negative,
effective temperature in the energy intervals between these subbands.


  We prove that, except for a few cases, stable linearizability of finite
subgroups of the plane Cremona group implies linearizability.


  We propose a scheme to generate and detect entanglement between charge states
in superconducting nanocircuits. We discuss different procedures to
discriminate such entanglement from classical correlations. The case of
maximally entangled states of two and three coupled Josephson junctions is
discussed as example.


  5G mmWave technology can turn multipath into a friend, as multipath
components become highly resolvable in the time and angle domains. Multipath
signals have not only been used in the literature to position the user
equipment (UE) but also to create a map of the surrounding environment. Yet,
many multipath-based methods in the literature share a common assumption, which
entails that multipath signals are caused by single-bounce reflections only,
which is not usually the case. There are very few methods in the literature
that accurately filters out higher-order reflections, which renders the
exploitation of multipath signals challenging. This paper proposes an ensemble
learning-based model for classifying signal paths based on their order of
reflection using 5G channel parameters. The model is trained on a large dataset
of 3.6 million observations obtained from a quasi-real ray-tracing based 5G
simulator that utilizes 3D maps of real-world downtown environments. The
trained model had a testing accuracy of 99.5%. A single-bounce reflection-based
positioning method was used to validate the positioning error. The trained
model enabled the positioning solution to maintain sub-30cm level accuracy 97%
of the time.


  The power spectral density (PSD) function is commonly used to specify
seismometer performance. It is derived from the FFT of acceleration and
correction is made for the transfer function of the instrument that generated
the data. As with any such spectrum of density (`per Hz') type, the noise
inherent to a PSD is large. This article illustrates the value of a function
that is derived from the PSD and for which the influence of noise is
significantly reduced. Called the cumulative spectral power (CSP), it is
obtained from the PSD through the noise-reducing process of integration. The
maximum of the CSP (corresponding to the longest graphed value of the period)
provides a means for estimating the total vibrational power of the earth. The
present author has significantly simplified the process of PSD generation. Thus
routine graphing is straightforwared-of first the FFT, followed by the
generation of both a PSD and its associated CSP. The unique properties of the
CSP make it valuable for the study of a variety of earth dynamics. For example,
the strking simplicity of a CSP graph generated from a record containing a
strong teleseismic earthquake is undoubtedly important to the development and
refinement of any viable theory of earthquake dynamics.


  Rounding border effects at the escape point of open integrable billiards are
analyzed via the escape times statistics and emission angles. The model is the
rectangular billiard and the shape of the escape point is assumed to have a
semicircular form. Stickiness and self-similar structures for the escape times
and emission angles are generated inside "backgammon" like stripes of initial
conditions. These stripes are born at the boundary between two different
emission angles but same escape times. As the rounding effects increase,
backgammon stripes start to overlap and the escape times statistics obeys the
power law decay and anomalous diffusion is expected. Tiny rounded borders
(around $0.1\%$ from the whole billiard size) are shown to be sufficient to
generate the sticky motion, while borders larger than $10\%$ are enough to
produce escape times with chaotic decay.


  We outline the Minimalistic Measurement Scheme (MMS) compatible with regular
unitary evolution of a closed quantum system. Within this approach, a part of
the system becomes informationally isolated (restricted) which leads to a
natural emergence of the classical domain. This measurement scenario is a
simpler alternative to environment-induced decoherence. In its basic version,
MMS involves two ancilla qubits, $A$ and $X$, entangled with each other and
with the System $S$. Informational or thermodynamic cost of measurement is
represented by $X$-qubit being isolated, i.e. becoming unavailable for future
interactions with the rest of the system. Conditional upon this isolation,
$A$-qubit, that plays the role of an Apparatus, becomes classical and records
the outcome of the measurement. The procedure may be used to perform von
Neumann-style projective measurements or generalized ones, that corresponds to
Positive-Operator Value Measure (POVM). By repeating the same generalized
measurement multiple times with different $A$- and $X$-qubits, one
asymptotically approaches the wave function collapse in the basis determined by
the premeasurement process. We present a simple result for the total
information extracted after $N$ such weak measurements. Building upon MMS, we
propose a construction that maps a history of a quantum system onto a set of
$A$-qubits. It resembles the Consistent History (CH) formulation of Quantum
Mechanics (QM), but is distinct from it, and is built entirely within the
conventional QM. In particular, consistency postulate of CH formalism is not
automatically satisfied, but rather is an emerging property. Namely, each
measurement event corresponds to the branching of mutually exclusive classical
realities whose probabilities are additive. In a general case, however, the
superposition between different histories is determined by the history density
matrix.


  Recently, a large number of studies have been carried out on the early
signatures of sudden regime shifts in systems as diverse as ecosystems,
financial markets, population biology and complex diseases. Signatures of
regime shifts in gene expression dynamics are less systematically investigated.
In this paper, we consider sudden regime shifts in the gene expression dynamics
described by a fold-bifurcation model involving bistability and hysteresis. We
consider two alternative models, Models 1 and 2, of competence development in
the bacterial population B. subtilis and determine some early signatures of the
regime shifts between competence and noncompetence. We use both deterministic
and stochastic formalisms for the purpose of our study. The early signatures
studied include the critical slowing down as a transition point is approached,
rising variance and the lag-1 autocorrelation function, skewness and a ratio of
two mean first passage times. Some of the signatures could provide the
experimental basis for distinguishing between bistability and excitability as
the correct mechanism for the development of competence.


  In this work, we are presenting an efficient way to compute the geometric
mean of two positive definite matrices times a vector. For this purpose, we are
inspecting the application of methods based on Krylov spaces to compute the
square root of a matrix. These methods, using only matrix-vector products, are
capable of producing a good approximation of the result with a small
computational cost.


  We present Arecibo Legacy Fast ALFA (ALFALFA) and follow-up Giant Metrewave
Radio Telescope (GMRT) HI observations of the gas-rich interacting group NGC
3166/9. The sensitive ALFALFA data provide a complete census of HI-bearing
systems in the group while the high-resolution GMRT data elucidate their
origin, enabling one of the first unbiased physical studies of gas-rich dwarf
companions and the subsequent identification of second generation, tidal dwarf
galaxies in a nearby group. The ALFALFA maps reveal an extended HI envelope
around the NGC 3166/9 group core, which we mosaic at higher resolution using
six GMRT pointings spanning ~1 square degree. A thorough search of the GMRT
datacube reveals eight low-mass objects with gas masses ranging from 4x10^7 to
3x10^8 M_sol and total dynamical masses up to 1.4x10^9 M_sol. A comparison of
the HI fluxes measured from the GMRT data to those measured in the ALFALFA data
suggests that a significant fraction (~60%) of the HI is smoothly distributed
on scales greater than an arcminute (~7 kpc at the NGC 3166/9 distance). We
compute stellar masses and star formation rates for the eight low-mass GMRT
detections, using ancillary SDSS and GALEX data, and use these values to
constrain their origin. Most of the detections are likely to be either
pre-existing dwarf irregular galaxies or short-lived, tidally formed knots;
however, one candidate, AGC 208457, is clearly associated with a tidal tail
extending below NGC 3166, exhibits a dynamical to gas mass ratio close to unity
and has a stellar content and star formation rate that are broadly consistent
with both simulated as well as candidate tidal dwarf galaxies from the
literature. Our observations therefore strongly suggest that AGC 208457 is a
tidal dwarf galaxy.


  Nash equilibrium (NE) is a central concept in game theory. Here we prove
formally a published theorem on existence of an NE in two proof assistants, Coq
and Isabelle: starting from a game with finitely many outcomes, one may derive
a game by rewriting each of these outcomes with either of two basic outcomes,
namely that Player 1 wins or that Player 2 wins. If all ways of deriving such a
win/lose game lead to a game where one player has a winning strategy, the
original game also has a Nash equilibrium.
  This article makes three other contributions: first, while the original proof
invoked linear extension of strict partial orders, here we avoid it by
generalizing the relevant definition. Second, we notice that the theorem also
implies the existence of a secure equilibrium, a stronger version of NE that
was introduced for model checking. Third, we also notice that the constructive
proof of the theorem computes secure equilibria for non-zero-sum priority games
(generalizing parity games) in quasi-polynomial time.


  In this paper, we discuss a possibility to improve constraints on
spin-dependent short-range interactions in the range of 1 - 200 micrometer
significantly. For such interactions, our constraints are without competition
at the moment. They were obtained through the observation of gravitationally
bound states of ultracold neutrons. We are going to improve these constraints
by about three orders of magnitude in a dedicated experiment with polarized
neutrons using the next-generation spectrometer GRANIT.


  We have studied anomalous four-photon couplings in the $\mu^+\mu^-
\rightarrow \mu^+ \gamma\gamma \mu^-$ scattering at a future muon collider. The
collision energies of 3 TeV, 14 TeV, and 100 TeV are addressed. Both
differential and total cross sections versus invariant mass of the outgoing
photons are calculated. The best 95\% C.L. exclusion bounds on anomalous
couplings are obtained to be $g_1 = 2.23 \times 10^{-8}$ TeV$^{-4}$ and $g_2 =
4.22 \times 10^{-8}$ TeV$^{-4}$. They correspond to the muon collision energy
of 100 TeV. The partial-wave unitary constraints on $g_1$ and $g_2$ are
examined. We have demonstrated that the unitarity is not violated in a region
of the anomalous couplings obtained in the present paper.


  Accurate prediction of the natural frequency for the lateral oscillation of a
liquid drop pinned on a vertical planar surface is important to many drop
applications. The natural oscillation frequency, normalized by the capillary
frequency, is {mainly} a function of the equilibrium contact angle and the Bond
number (Bo), when the contact lines remain pinned. Parametric numerical and
experimental studies have been performed to establish a comprehensive
understanding of oscillation dynamics. {An} inviscid model has been developed
to predict the oscillation frequency for wide ranges of Bo and contact angles.
The model reveals the scaling relation between the normalized frequency and Bo,
which is validated by the numerical simulation results. For a given equilibrium
contact angle, the lateral oscillation frequency decreases with Bo, implying
that resonance frequencies will be magnified if the drop oscillations occur in
a reduced gravity environment.


  Simultaneous monitoring of ac and dc responses of the electrode-electrolyte
interface with potentiodynamic electrochemical impedance spectroscopy (PDEIS)
in silver underpotential and overpotential deposition on platinum has confirmed
the role of intrinsic Pt surface changes in the irreversibility of Ag
underpotential deposition and disclosed exceptionally high stability of Ag
monolayer on Pt. PDEIS has been demonstrated to be a convenient means for wet
surface chemistry monitoring.


  Software vulnerabilities are usually caused by design flaws or implementation
errors, which could be exploited to cause damage to the security of the system.
At present, the most commonly used method for detecting software
vulnerabilities is static analysis. Most of the related technologies work based
on rules or code similarity (source code level) and rely on manually defined
vulnerability features. However, these rules and vulnerability features are
difficult to be defined and designed accurately, which makes static analysis
face many challenges in practical applications. To alleviate this problem, some
researchers have proposed to use neural networks that have the ability of
automatic feature extraction to improve the intelligence of detection. However,
there are many types of neural networks, and different data preprocessing
methods will have a significant impact on model performance. It is a great
challenge for engineers and researchers to choose a proper neural network and
data preprocessing method for a given problem. To solve this problem, we have
conducted extensive experiments to test the performance of the two most typical
neural networks (i.e., Bi-LSTM and RVFL) with the two most classical data
preprocessing methods (i.e., the vector representation and the program
symbolization methods) on software vulnerability detection problems and
obtained a series of interesting research conclusions, which can provide
valuable guidelines for researchers and engineers. Specifically, we found that
1) the training speed of RVFL is always faster than BiLSTM, but the prediction
accuracy of Bi-LSTM model is higher than RVFL; 2) using doc2vec for vector
representation can make the model have faster training speed and generalization
ability than using word2vec; and 3) multi-level symbolization is helpful to
improve the precision of neural network models.


  We simplify a criterion (due to Ibarluc\'ia and the author) which
characterizes dynamical simplices, that is, sets $K$ of probability measures on
a Cantor space $X$ for which there exists a minimal homeomorphism of $X$ whose
set of invariant measures coincides with $K$. We then point out that this
criterion is related to Fra\"iss\'e theory, and use that connection to provide
a new proof of Downarowicz' theorem stating that any Choquet simplex is
affinely homeomorphic to a dynamical simplex. The construction enables us to
prove that there exist minimal homeomorphisms of a Cantor space which are
speedup equivalent but not orbit equivalent, answering a question of D. Ash.


  Conventional electromagnetic transient (EMT) and phasor-domain hybrid
simulation approaches presently exist for trans-mission system level studies.
Their simulation efficiency is generally constrained by the EMT simulation.
With an increasing number of distributed energy resources and non-conventional
loads being installed in distribution systems, it is imperative to extend the
hybrid simulation application to include distribution systems and integrated
transmission and distribution systems. Meanwhile, it is equally important to
improve the simulation efficiency as the modeling scope and complexity of the
detailed system in the EMT simulation increases. To meet both requirements,
this paper introduces an advanced EMT and phasor-domain hybrid simulation
approach. This approach has two main features: 1) a comprehensive phasor-domain
modeling framework which supports positive-sequence, three-sequence,
three-phase and mixed three-sequence/three-phase representations and 2) a
robust and flexible simulation mode switching scheme. The developed scheme
enables simulation switching from hybrid simulation mode back to pure
phasor-domain dynamic simulation mode to achieve significantly improved
simulation efficiency. The proposed method has been tested on integrated
transmission and distribution systems. The results show that with the developed
simulation switching feature, the total computational time is significantly
reduced compared to running the hybrid simulation for the whole simulation
period, while maintaining good simulation accuracy.


  It is well-known that quantum groups are relevant to describe the quantum
regime of 3d gravity. They encode a deformation of the gauge symmetries
parametrized by the value of the cosmological constant. They appear as a form
of regularization either through the quantization of the Chern-Simons
formulation or the state sum approach of Turaev-Viro. Such deformations are
perplexing from a continuum and classical picture since the action is defined
in terms of undeformed gauge invariance. We present here a novel way to derive
from first principle and from the classical action such quantum group
deformation. The argument relies on two main steps. First we perform a
canonical transformation, which deformed the gauge invariance and the boundary
symmetries, and makes them depend on the cosmological constant. Second we
implement a discretization procedure relying on a truncation of the degrees of
freedom from the continuum.


  The ratio of shear viscosity to entropy density in a strongly coupled CFT
plasma can be computed using the AdS/CFT correspondence either from equilibrium
correlation functions or from the Janik-Peschanski dual of the boost invariant
plasma expansion. We point out that the previously found disagreement for eta/s
at finite t' Hooft coupling is resolved once the incoming-wave boundary
condition for metric fluctuations at the horizon of the dual geometry is
properly imposed.


  We extend previous calculations of the non-local form factors of
semiclassical gravity in $4D$ to include the Einstein-Hilbert term. The
quantized fields are massive scalar, fermion and vector fields. The non-local
form factor in this case can be seen as the sum of a power series of total
derivatives, but it enables us to derive the beta function of Newton's constant
and formally evaluate the decoupling law in the new sector, which turns out to
be the standard quadratic one.


  With current hardware and software, a standard computer can now hold in RAM
an index for approximate pattern matching on about half a dozen human genomes.
Sequencing technologies have improved so quickly, however, that scientists will
soon demand indexes for thousands of genomes. Whereas most researchers who have
addressed this problem have proposed completely new kinds of indexes, we
recently described a simple technique that scales standard indexes to work on
more genomes. Our main idea was to filter the dataset with LZ77, build a
standard index for the filtered file, and then create a hybrid of that standard
index and an LZ77-based index. In this paper we describe how to our technique
to use alignments instead of LZ77, in order to simplify and speed up both
preprocessing and random access.


  Pickling behaviour of the oxide layer on hot-rolled 2205 duplex stainless
steel (DSS) was studied in H2SO4 solutions with electrolytic workstation.


  Starting from a protein solution which is metastable with respect to the
crystalline phase, the effect of adding semidilute non-adsorbing polymer is
considered. It is found to increase the chemical potential of the protein by a
few tenths of kT, which may be enough to lower the barrier to nucleation of the
crystalline phase by enough to allow crystallisation. It is also shown that
assuming that the polymer induces a pairwise additive attraction leads to
qualitatively incorrect results.


  Here we consider the possibility of preheating the Universe via the
parametric amplification of a massless, U(1) abelian gauge field. We assume
that the gauge field is coupled to the inflaton via a conformal factor with one
free parameter. We present the results of high-resolution three-dimensional
simulations of this model and show this mechanism efficiently preheats the
Universe to a radiation-dominated final state.


  A derivation of the full set of Bell inequalities involving correlation
functions, for two parties, with binary observables, and three possible local
settings. The procedure can be extended straightforwardly to multiparty
correlations.


  A search for the standard model Higgs boson decaying to a charm
quark-antiquark pair, H $\to$ $\mathrm{c\bar{c}}$, produced in association with
a leptonically decaying V (W or Z) boson is presented. The search is performed
with proton-proton collisions at $\sqrt{s}$ = 13 TeV collected by the CMS
experiment, corresponding to an integrated luminosity of 138 fb$^{-1}$. Novel
charm jet identification and analysis methods using machine learning techniques
are employed. The analysis is validated by searching for Z $\to$
$\mathrm{c\bar{c}}$ in VZ events, leading to its first observation at a hadron
collider with a significance of 5.7 standard deviations. The observed
(expected) upper limit on $\sigma$(VH)$ \mathcal{B}$(H $\to$
$\mathrm{c\bar{c}}$) is 0.94 (0.50 $^{+0.22}_{-0.15}$) pb at 95% confidence
level (CL), corresponding to 14 (7.6 $^{+3.4}_{-2.3}$) times the standard model
prediction. For the Higgs-charm Yukawa coupling modifier, $\kappa_\mathrm{c}$,
the observed (expected) 95% CL interval is 1.1 $\lt$
$\vert\kappa_\mathrm{C}\vert$ $\lt$ 5.5 ($\vert\kappa_\mathrm{c}\vert$ $\lt$
3.4), the most stringent constraint to date.


  Convolutional Neural Networks (CNNs) are a class of Artificial Neural
Networks(ANNs) that employ the method of convolving input images with
filter-kernels for object recognition and classification purposes. In this
paper, we propose a photonics circuit architecture which could consume a
fraction of energy per inference compared with state of the art electronics.


  We theoretically investigate the localization mechanism of the quantum
anomalous Hall effect (QAHE) in the presence of spin-flip disorders. We show
that the QAHE keeps quantized at weak disorders, then enters a Berry-curvature
mediated metallic phase at moderate disorders, and finally goes into the
Anderson insulating phase at strong disorders. From the phase diagram, we find
that at the charge neutrality point although the QAHE is most robust against
disorders, the corresponding metallic phase is much easier to be localized into
the Anderson insulating phase due to the \textit{interchange} of Berry
curvatures carried respectively by the conduction and valence bands. At the
end, we provide a phenomenological picture related to the topological charges
to better understand the underlying physical origin of the QAHE Anderson
localization.


  Thanks to SAURON integral-field observations we uncovered the Planetary
Nebulae (PNe) populations inhabiting the central and nuclear regions of our
galactic neighbours M32 and M31, respectively, and discuss the significant
differences between their corresponding PNe luminosity functions in light of
the properties of their parent stellar populations. In particular, we conclude
that the lack of bright PNe in the nuclear regions of M31 is likely linked to
the nearly Solar value for the stellar metallicity, consistent with previous
suggestions that a larger metallicity would bias the Horizontal-Branch (HB)
populations toward bluer colors, with fewer red HB stars capable of producing
PNe and more blue HB stars that instead could contribute to the far-UV flux
that is observed in metal-rich early-type galaxies and, incidentally, also in
the nucleus of M31.


  Multi-behavior recommendation exploits multiple types of user-item
interactions to alleviate the data sparsity problem faced by the traditional
models that often utilize only one type of interaction for recommendation. In
real scenarios, users often take a sequence of actions to interact with an
item, in order to get more information about the item and thus accurately
evaluate whether an item fits personal preference. Those interaction behaviors
often obey a certain order, and different behaviors reveal different
information or aspects of user preferences towards the target item. Most
existing multi-behavior recommendation methods take the strategy to first
extract information from different behaviors separately and then fuse them for
final prediction. However, they have not exploited the connections between
different behaviors to learn user preferences. Besides, they often introduce
complex model structures and more parameters to model multiple behaviors,
largely increasing the space and time complexity. In this work, we propose a
lightweight multi-behavior recommendation model named Cascading Residual Graph
Convolutional Network (CRGCN for short), which can explicitly exploit the
connections between different behaviors into the embedding learning process
without introducing any additional parameters. In particular, we design a
cascading residual graph convolutional network structure, which enables our
model to learn user preferences by continuously refining user embeddings across
different types of behaviors. The multi-task learning method is adopted to
jointly optimize our model based on different behaviors. Extensive experimental
results on two real-world benchmark datasets show that CRGCN can substantially
outperform state-of-the-art methods. Further studies also analyze the effects
of leveraging multi-behaviors in different numbers and orders on the final
performance.


  We study the random pinning model, in the case of a Gaussian environment
presenting power-law decaying correlations, of exponent decay a>0. We comment
on the annealed (i.e. averaged over disorder) model, which is far from being
trivial, and we discuss the influence of disorder on the critical properties of
the system. We show that the annealed critical exponent \nu^{ann} is the same
as the homogeneous one \nu^{pur}, provided that correlations are decaying fast
enough (a>2). If correlations are summable (a>1), we also show that the
disordered phase transition is at least of order 2, showing disorder relevance
if \nu^{pur}<2. If correlations are not summable (a<1), we show that the phase
transition disappears.


  When we use simulation to evaluate the performance of a stochastic system,
the simulation often contains input distributions estimated from real-world
data; therefore, there is both simulation and input uncertainty in the
performance estimates. Ignoring either source of uncertainty underestimates the
overall statistical error. Simulation uncertainty can be reduced by additional
computation (e.g., more replications). Input uncertainty can be reduced by
collecting more real-world data, when feasible. This paper proposes an approach
to quantify overall statistical uncertainty when the simulation is driven by
independent parametric input distributions; specifically, we produce a
confidence interval that accounts for both simulation and input uncertainty by
using a metamodel-assisted bootstrapping approach. The input uncertainty is
measured via bootstrapping, an equation-based stochastic kriging metamodel
propagates the input uncertainty to the output mean, and both simulation and
metamodel uncertainty are derived using properties of the metamodel. A variance
decomposition is proposed to estimate the relative contribution of input to
overall uncertainty; this information indicates whether the overall uncertainty
can be significantly reduced through additional simulation alone. Asymptotic
analysis provides theoretical support for our approach, while an empirical
study demonstrates that it has good finite-sample performance.


  We consider the allocation of Virtual Arrays (VAs) in a Heterogeneous Disk
Array (HDA). Each VA holds groups of related objects and datasets such as
files, relational tables, which has similar performance and availability
characteristics. We evaluate single-pass data allocation methods for HDA using
a synthetic stream of allocation requests, where each VA is characterized by
its RAID level, disk loads and space requirements. The goal is to maximize the
number of allocated VAs and maintain high disk bandwidth and capacity
utilization, while balancing disk loads. Although only RAID1 (basic mirroring)
and RAID5 (rotated parity arrays) are considered in the experimental study, we
develop the analysis required to estimate disk loads for other RAID levels.
Since VA loads vary significantly over time, the VA allocation is carried out
at the peak load period, while ensuring that disk bandwidth is not exceeded at
other high load periods. Experimental results with a synthetic stream of
allocation requests show that allocation methods minimizing the maximum disk
bandwidth and capacity utilization or their variance across all disks yield the
maximum number of allocated VAs. HDA saves disk bandwidth, since a single RAID
level accommodating the most stringent availability requirements for a small
subset of objects would incur an unnecessarily high overhead for updating check
blocks or data replicas for all objects. The number of allocated VAs can be
increased by adopting the clustered RAID5 paradigm, which exploits the tradeoff
between redundancy and bandwidth utilization. Since rebuild can be carried out
at the level of individual VAs, prioritizing rebuild of VAs with higher access
rates can improve overall performance.


  We consider a network of two nodes separated by a noisy channel, in which the
input and output signals have to be coordinated with the source and its
reconstruction. In the case of strictly causal encoding and non-causal
decoding, we prove inner and outer bounds for the strong coordination region
and show that the inner bound is achievable with polar codes.


  We exploit XMM-Newton archival data in a study of the extended X-ray emission
emanating from the Galactic Centre (GC) region. EPIC-pn and EPIC-MOS
observations, with a total exposure approaching 0.5 and 1 Ms respectively, were
used to create mosaiced images of a 100 pc x 100 pc region centred on Sgr A* in
four bands covering the 2-10 keV energy range. We have also constructed a set
of narrow-band images corresponding to the neutral iron fluorescence line at
6.4 keV and the K-shell lines at 6.7 keV and 6.9 keV from helium-like and
hydrogenic iron. We use a combination of spatial and spectral information to
decompose the GC emission into three distinct components. These comprise: the
emission from hard X-ray emitting unresolved point sources; the reflected
continuum and fluorescent line emission from dense molecular material; and the
soft diffuse emission from thermal plasma in the temperature range, kT ~
0.8-1.5 keV.
  We show that the unresolved-source component accounts for the bulk of the
6.7-keV and 6.9-keV line emission. We fit the observed X-ray surface brightness
distribution with an empirical 2-d model, which we then compare with a 3-d
mass-model prediction for the old stellar population in the GC. The X-ray
surface brightness falls-off more rapidly with angular offset from Sgr A* than
predicted. One interpretation is that the 2-10 keV X-ray emissivity increases
from 5 x 10^27 erg s^-1 Msun^-1 at 20' up to almost twice this value at 2'.
Alternatively, some refinement of the mass model may be required.
  The unresolved hard X-ray emitting source population, on the basis of
spectral comparisons, is most likely dominated by magnetic CVs. We use the
X-ray observations to set constraints on the number density of such sources.
Our analysis does not support the conjecture that a significant fraction of the
hard X-ray emission from the GC originates in very-hot diffuse thermal plasma.


  With the advent of Deep Learning, the field of machine learning (ML) has
surpassed human-level performance on diverse classification tasks. At the same
time, there is a stark need to characterize and quantify reliability of a
model's prediction on individual samples. This is especially true in
application of such models in safety-critical domains of industrial control and
healthcare. To address this need, we link the question of reliability of a
model's individual prediction to the epistemic uncertainty of the model's
prediction. More specifically, we extend the theory of Justified True Belief
(JTB) in epistemology, created to study the validity and limits of
human-acquired knowledge, towards characterizing the validity and limits of
knowledge in supervised classifiers. We present an analysis of neural network
classifiers linking the reliability of its prediction on an input to
characteristics of the support gathered from the input and latent spaces of the
network. We hypothesize that the JTB analysis exposes the epistemic uncertainty
(or ignorance) of a model with respect to its inference, thereby allowing for
the inference to be only as strong as the justification permits. We explore
various forms of support (for e.g., k-nearest neighbors (k-NN) and l_p-norm
based) generated for an input, using the training data to construct a
justification for the prediction with that input. Through experiments conducted
on simulated and real datasets, we demonstrate that our approach can provide
reliability for individual predictions and characterize regions where such
reliability cannot be ascertained.


  Human mesh reconstruction from a single image is challenging in the presence
of occlusion, which can be caused by self, objects, or other humans. Existing
methods either fail to separate human features accurately or lack proper
supervision for feature completion. In this paper, we propose Dense Inpainting
Human Mesh Recovery (DIMR), a two-stage method that leverages dense
correspondence maps to handle occlusion. Our method utilizes a dense
correspondence map to separate visible human features and completes human
features on a structured UV map dense human with an attention-based feature
completion module. We also design a feature inpainting training procedure that
guides the network to learn from unoccluded features. We evaluate our method on
several datasets and demonstrate its superior performance under heavily
occluded scenarios compared to other methods. Extensive experiments show that
our method obviously outperforms prior SOTA methods on heavily occluded images
and achieves comparable results on the standard benchmarks (3DPW).


  CMOS technology and its continuous scaling have made electronics and
computers accessible and affordable for almost everyone on the globe; in
addition, they have enabled the solutions of a wide range of societal problems
and applications. Today, however, both the technology and the computer
architectures are facing severe challenges/walls making them incapable of
providing the demanded computing power with tight constraints. This motivates
the need for the exploration of novel architectures based on new device
technologies; not only to sustain the financial benefit of technology scaling,
but also to develop solutions for extremely demanding emerging applications.
This paper presents two computation-in-memory based accelerators making use of
emerging memristive devices; they are Memristive Vector Processor and RRAM
Automata Processor. The preliminary results of these two accelerators show
significant improvement in terms of latency, energy and area as compared to
today's architectures and design.


  We consider banded block Toeplitz matrices $T_n$ with $n$ block rows and
columns. We show that under certain technical assumptions, the normalized
eigenvalue counting measure of $T_n$ for $n\to\infty$ weakly converges to one
component of the unique vector of measures that minimizes a certain energy
functional. In this way we generalize a recent result of Duits and Kuijlaars
for the scalar case. Along the way we also obtain an equilibrium problem
associated to an arbitrary algebraic curve, not necessarily related to a block
Toeplitz matrix.
  For banded block Toeplitz matrices, there are several new phenomena that do
not occur in the scalar case: (i) The total masses of the equilibrium measures
do not necessarily form a simple arithmetic series but in general are obtained
through a combinatorial rule; (ii) The limiting eigenvalue distribution may
contain point masses, and there may be attracting point sources in the
equilibrium problem; (iii) More seriously, there are examples where the
connection between the limiting eigenvalue distribution of $T_n$ and the
solution to the equilibrium problem breaks down. We provide sufficient
conditions guaranteeing that no such breakdown occurs; in particular we show
this if $T_n$ is a Hessenberg matrix.


  Auxiliary-field quantum Monte Carlo methods enable the calculation of thermal
and ground state properties of correlated quantum many-body systems in model
spaces that are many orders of magnitude larger than those that can be treated
by conventional diagonalization methods. We review recent developments and
applications of these methods in nuclei using the framework of the
configuration-interaction shell model.


  Third-order approximate solutions for surface gravity waves in the finite
water depth are studied in the context of potential flow theory. This solution
provides explicit expressions for the surface elevation, free-surface velocity
potential and velocity potential. The amplitude dispersion relation is also
provided. Two approaches are used to derive the third order analytical
solution, resulting in two types of approximate solutions: the perturbation
solution and the Hamiltonian solution. The perturbation solution is obtained by
classical perturbation technique in which the time variable is expanded in
multiscale to eliminate secular terms. The Hamiltonian solution is derived from
the canonical transformation in the Hamiltonian theory of water waves. By
comparing the two types of solutions, it is found that they are completely
equivalent for the first to second order solutions and the nonlinear
dispersion, but for the third order part only the sum-sum terms are the same.
Due to the canonical transformation that could completely separate the dynamic
and bound harmonics, the Hamiltonian solutions break through the difficulty
that the perturbation theory breaks down due to singularities in the transfer
functions when quartet resonance criterion is satisfied. Furthermore, it is
also found that some time-averaged quantities based on the Hamiltonian
solution, such as mean potential energy and mean kinetic energy, are equal to
those in the initial state in which sea surface is assumed to be a Gaussian
random process. This is because there are associated conserved quantities in
the Hamiltonian form. All of these show that the Hamiltonian solution is more
reasonable and accurate to describe the third order steady-state wave field.
Finally, based on the Hamiltonian solution, some statistics are given such as
the volume flux, skewness, and excess kurtosis.


  In light of prospects for measurements of $B_c \rightarrow D^{(\ast)} l \nu$
decays in the upcoming Upgrade II of the LHC, we show that by using calculated
$B_c \rightarrow D^{(\ast)}$ form factors competitive extraction of the
$|V_{ub}|$ CKM matrix element from the $B_c \to D \mu \bar{\nu}_{\mu}$ decay
might be possible. To minimize experimental and theoretical uncertainties we
provide the ratio $|V_{ub}|/|V_{cb}|$ by normalizing the $B_c \rightarrow
D^{(\ast)} \mu \bar{\nu}_{\mu}$ to $B_c \to J/\psi \mu \bar{\nu}_{\mu}$ decay.
We also briefly examine the suggestion to extract $|V_{ub}|/|V_{cs}|$ from the
theoretically interesting ratio of $B_c \rightarrow D^0 e \bar{\nu}_{e}$ and
$B_c \rightarrow B_s e \bar{\nu}_{e}$ decay rates in the zero-recoil limit.
With the present average value of $|V_{ub}|$, the predicted branching ratios
are estimated to be $BR(B_c \to D^0 \mu \bar{\nu}_{\mu}) = (2.4\pm 0.4)\cdot
10^{-5} $ and $BR(B_c \to D^{\ast} \mu \bar{\nu}_{\mu}) = (7\pm3)\cdot 10^{-5}
$, and the semileptonic ratios for testing the lepton flavour universality in
these $B_c$ decays are $R_c(D^0) =0.64 \pm 0.05$ and $R_c(D^{\ast}) = 0.55 \pm
0.05$. We also provide $q^2$ distributions and various angular observables of
$B_c \rightarrow D^{(\ast)} l \nu$ decays.


  The present paper is devoted to study local super-derivations of the super
Schr\"{o}dinger algebras. We prove that every local super-derivation on the
super Schr\"{o}dinger algebra is a super-derivation.


  The remnant of supernova explosion is widely believed to be the acceleration
site of high-energy cosmic ray particles. The acceleration timescale is,
however, typically very long. Here we report the detection of a variable
$\gamma$-ray source with the Fermi Large Area Telescope, which is positionally
and temporally consistent with a peculiar supernova, iPTF14hls. A quasi-stellar
object SDSS J092054.04+504251.5, which is probably a blazar according to the
infrared data, is found in the error circle of the $\gamma$-ray source. More
data about the $\gamma$-ray source and SDSS J092054.04+504251.5 are needed to
confirm their association. On the other hand, if the association between the
$\gamma$-ray source and the supernova is confirmed, this would be the first
time to detect high-energy $\gamma$-ray emission from a supernova, suggesting
very fast particle acceleration by supernova explosions.


  We investigate stochastic combinatorial multi-armed bandit with semi-bandit
feedback (CMAB). In CMAB, the question of the existence of an efficient policy
with an optimal asymptotic regret (up to a factor poly-logarithmic with the
action size) is still open for many families of distributions, including
mutually independent outcomes, and more generally the multivariate sub-Gaussian
family. We propose to answer the above question for these two families by
analyzing variants of the Combinatorial Thompson Sampling policy (CTS). For
mutually independent outcomes in $[0,1]$, we propose a tight analysis of CTS
using Beta priors. We then look at the more general setting of multivariate
sub-Gaussian outcomes and propose a tight analysis of CTS using Gaussian
priors. This last result gives us an alternative to the Efficient Sampling for
Combinatorial Bandit policy (ESCB), which, although optimal, is not
computationally efficient.


  Using the theory of asymptotic test ideals, we prove the prime characteristic
analogue of a characteristic $0$ result of Ein, Lazarsfeld and Smith
(arXiv:math/0202303) on uniform approximation of valuation ideals associated to
real-valued Abhyankar valuations centered on regular varieties over perfect
fields.


  In this paper, we present a general method to solve non-hermetic potentials
with PT symmetry using the definition of two $\eta$-pseudo-hermetic and
first-order operators. This generator applies to the Dirac equation which
consists of two spinor wave functions and non-hermetic potentials, with
position that mass is considered a constant value and also Hamiltonian
hierarchy method and the shape invariance property are used to perform
calculations. Furthermore, we show the correlation between the potential
parameters with transmission probabilities that $\eta$-pseudo-hermetic using
the change of focal points on Hamiltonian can be formalized based on
Schr\"{o}dinger-like equation. By using this method for some solvable
potentials such as deformed Woods Saxon's potential, it can be shown that these
real potentials can be decomposed into complex potentials consisting of
eigenvalues of a class of $\eta$-pseudo-hermitic.


  We present a case study of formal verification of full-wave rectifier for
analog and mixed signal designs. We have used the Checkmate tool from CMU [1],
which is a public domain formal verification tool for hybrid systems. Due to
the restriction imposed by Checkmate it necessitates to make the changes in the
Checkmate implementation to implement the complex and non-linear system.
Full-wave rectifier has been implemented by using the Checkmate custom blocks
and the Simulink blocks from MATLAB from Math works. After establishing the
required changes in the Checkmate implementation we are able to efficiently
verify the safety properties of the full-wave rectifier.


  We explain how the lowest-order classical gravitational radiation produced
during the inelastic scattering of two Schwarzschild black holes in General
Relativity can be obtained from a tree scattering amplitude in gauge theory
coupled to scalar fields. The gauge calculation is related to gravity through
the double copy. We remove unwanted scalar forces which can occur in the double
copy by introducing a massless scalar in the gauge theory, which is treated as
a ghost in the link to gravity. We hope these methods are a step towards a
direct application of the double copy at higher orders in classical
perturbation theory, with the potential to greatly streamline gravity
calculations for phenomenological applications.


  We present a new set of high-resolution molecular line maps of the gas
immediately surrounding various Herbig-Haro (HH) knots of the giant HH flow HH
315, from the young star PV Cephei. The observations, aimed at studying the
entrainment mechanism of the 2.6 pc-long HH 315 flow, include IRAM 30m maps of
the 12CO(2-1), 12CO(1-0), and 13CO(1-0) lines, with beam sizes of 11'', 21'',
and 22'', respectively. We compare the morphology and the kinematics of the
outflow gas, as well as the temperature and momentum distribution of the
molecular outflow with those predicted by different entrainment models. With
our detailed study we are able to conclude that jet bow shock entrainment by an
episodic stellar wind, with a time-varying axis, produces most of the
high-velocity molecular outflow observed far from the source. In addition, near
PV Cephei we find evidence for a poorly collimated, wide-angle, molecular
outflow and a collimated wiggling jet-like molecular outflow. We propose that
the poorly collimated component is entrained by a wide-angle wind, and the
collimated component is entrained by a variable jet with internal working
surfaces. If this picture is true, then a stellar wind model which allows for
the coexistence of a wide-angle component and a collimated (jet-like) stellar
wind component is needed to explain the observed properties of the PV Ceph
outflow. The wiggling axis of the redshifted molecular outflow lobe indicates
that the outflow ejection axis is changing over time. We find that the
time-scale of the axis variation shown by the molecular outflow lobe is about a
factor of 10 less than that shown by the large-scale optical HH knots.


  We contribute to micro-data model-based reinforcement learning (MBRL) by
rigorously comparing popular generative models using a fixed (random shooting)
control agent. We find that on an environment that requires multimodal
posterior predictives, mixture density nets outperform all other models by a
large margin. When multimodality is not required, our surprising finding is
that we do not need probabilistic posterior predictives: deterministic models
are on par, in fact they consistently (although non-significantly) outperform
their probabilistic counterparts. We also found that heteroscedasticity at
training time, perhaps acting as a regularizer, improves predictions at longer
horizons. At the methodological side, we design metrics and an experimental
protocol which can be used to evaluate the various models, predicting their
asymptotic performance when using them on the control problem. Using this
framework, we improve the state-of-the-art sample complexity of MBRL on Acrobot
by two to four folds, using an aggressive training schedule which is outside of
the hyperparameter interval usually considered


  Commonsense causality reasoning (CCR) aims at identifying plausible causes
and effects in natural language descriptions that are deemed reasonable by an
average person. Although being of great academic and practical interest, this
problem is still shadowed by the lack of a well-posed theoretical framework;
existing work usually relies on deep language models wholeheartedly, and is
potentially susceptible to confounding co-occurrences. Motivated by classical
causal principles, we articulate the central question of CCR and draw parallels
between human subjects in observational studies and natural languages to adopt
CCR to the potential-outcomes framework, which is the first such attempt for
commonsense tasks. We propose a novel framework, ROCK, to Reason O(A)bout
Commonsense K(C)ausality, which utilizes temporal signals as incidental
supervision, and balances confounding effects using temporal propensities that
are analogous to propensity scores. The ROCK implementation is modular and
zero-shot, and demonstrates good CCR capabilities.


  Cd3As2 is a newly booming Dirac semimetal with linear dispersion along all
three momentum directions and can be viewed as 3D analog of graphene. As
breaking of either time reversal symmetry or spatial inversion symmetry, the
Dirac semimetal is believed to transform into Weyl semimetal with exotic chiral
anomaly effect, while the experimental evidence of the chiral anomaly is still
missing in Cd3As2. Here we report the magneto-transport properties of
individual Cd3As2 nanowires. Large negative magnetoresistance (MR) with
magnitude of -63% at 60 K and -11% at 300 K are observed when the magnetic
field is parallel with the electric field direction, giving the evidence of the
chiral magnetic effect in Cd3As2 nanowires. In addition, the critical magnetic
field BC, where there is an extremum of the negative MR, increases with
increasing temperature. As the first observation of chiral anomaly induced
negative MR in Cd3As2 nanowires, it may offer valuable insights for low
dimensional physics in Dirac semimetals.


  As a means of better understanding the evolution of optically selected
galaxies we consider the distribution of galaxies within the multicolor space
$U$, $B_J$, $R_F$ and $I_N$. We find that they form an almost planar
distribution out to $B_J =22.5$ and $z<0.3$. The position of a galaxy within
this plane is dependent on its redshift, luminosity and spectral type. While in
the original $U$, $B_J$, $R_F$ and $I_N$ space these properties are highly
correlated we can define an optimal rotation of the photometric axes that makes
much of this information orthogonal. Fitting the observed spectroscopic
redshifts with a quadratic function of the four magnitudes we show that
redshifts for galaxies can be estimated to an accuracy better than $\Delta z
=0.05$. This dispersion is due to the photometric uncertainties within the
photographic data. Assuming no galaxy evolution we derive a set of simulated
galaxy fluxes in the U, J, F and N passbands. Using these data we investigate
how the redshift is encoded within the broadband magnitudes and the intrinsic
dispersion of the photometric-redshift relation. We find that the signal that
defines a galaxy's photometric redshift is not related to specific absorption
or emission lines but comes from the break in the overall shape of the galaxy
continuum at around 4000 \AA. Using high signal-to-noise photometric data we
estimate that it is possible to achieve an intrinsic dispersion of less than
$\Delta z =0.02$.


  LiDAR-camera 3D representation pretraining has shown significant promise for
3D perception tasks and related applications. However, two issues widely exist
in this framework: 1) Solely keyframes are used for training. For example, in
nuScenes, a substantial quantity of unpaired LiDAR and camera frames remain
unutilized, limiting the representation capabilities of the pretrained network.
2) The contrastive loss erroneously distances points and image regions with
identical semantics but from different frames, disturbing the semantic
consistency of the learned presentations. In this paper, we propose a novel
Vision-Foundation-Model-driven sample exploring module to meticulously select
LiDAR-Image pairs from unexplored frames, enriching the original training set.
We utilized timestamps and the semantic priors from VFMs to identify
well-synchronized training pairs and to discover samples with diverse content.
Moreover, we design a cross- and intra-modal conflict-aware contrastive loss
using the semantic mask labels of VFMs to avoid contrasting semantically
similar points and image regions. Our method consistently outperforms existing
state-of-the-art pretraining frameworks across three major public autonomous
driving datasets: nuScenes, SemanticKITTI, and Waymo on 3D semantic
segmentation by +3.0\%, +3.0\%, and +3.3\% in mIoU, respectively. Furthermore,
our approach exhibits adaptable generalization to different 3D backbones and
typical semantic masks generated by non-VFM models.


  We bring in this short report (counter -) examples on order to show a
difference between tails behavior and Grand Lebesgue Spaces norms for some
classes of random variables.


  Many Ramond-Ramond backgrounds which arise in the AdS/CFT correspondence are
described by integrable sigma-models. The equations of motion for classical
spinning strings in these backgrounds are exactly solvable by finite-gap
integration techniques. We review the finite-gap integral equations and
algebraic curves for coset sigma-models, and then apply the results to the
AdS(d+1) backgrounds with d=4,3,2,1.


  The equilibrium behavior of vortices in the classical two-dimensional (2D) XY
model with uncorrelated random phase shifts is investigated. The model
describes Josephson-Junction arrays with positional disorder, and has
ramifications in a number of other bond-disordered 2D systems. The vortex
Hamiltonian is that of a Coulomb gas in a background of quenched random
dipoles, which is capable of forming either a dielectric insulator or a plasma.
We confirm a recent suggestion by Nattermann, Scheidl, Korshunov, and Li [J.
Phys. I (France) {\bf 5}, 565 (1995)], and by Cha and Fertig [Phys. Rev. Lett.
{\bf 74}, 4867 (1995)] that, when the variance $\sigma$ of random phase shifts
is smaller than a critical value $\sigma_c$, the system is in a phase with
quasi-long-range order at low temperatures, without a reentrance transition.
This conclusion is reached through a nearly exact calculation of the
single-vortex free energy, and a Kosterlitz-type renormalization group analysis
of screening and random polarization effects from vortex-antivortex pairs. The
critical strength of disorder $\sigma_c$ is found not to be universal, but
generally lies in the range $0<\sigma_c<\pi/8$. Argument is presented to
suggest that the system at $\sigma>\sigma_c$ does not possess long-range glassy
order at any finite temperature. In the ordered phase, vortex pairs undergo a
series of spatial and angular localization processes as the temperature is
lowered. This behavior, which is common to many glass-forming systems, can be
quantified through approximate mappings to the random energy model and to the
directed polymer on the Cayley tree. Various critical properties at the
order-disorder transition are calculated.


  The angular spectrum approach (ASA)---a fast, frequency domain method for
calculation of the acoustic field---enables passive source localization and
modeling forward propagation in homogeneous media with high computational
efficiency. Here we show that, if the medium is continuously stratified, a
first-order analytical solution may be obtained for the field at arbitrary
depth. Our simulations show that the stratified ASA solution enables accurate
source localization as compared to the uncorrected ASA (error from 1.2$\pm$0.3
to 0.49$\pm$0.3 wavelengths) at scalings relevant to biomedical ($kL \sim$ 500,
where $L$ is the length of the measurement aperture), underwater ($kL \sim$
800), and atmospheric ($kL \sim$ 10) acoustic applications. Overall the total
computation was on the order milliseconds on standard hardware (225$\pm$84 ms,
compared with $78\pm63$ ms for the homogeneous ASA formulation over all cases).
Collectively, the results suggest the proposed ASA phase correction enables
efficient and accurate method for source localization in continuously
stratified environments.


  We consider the problems of deciding whether an input graph can be modified
by removing/adding at most k vertices/edges such that the result of the
modification satisfies some property definable in first-order logic. We
establish a number of sufficient and necessary conditions on the quantification
pattern of the first-order formula \phi for the problem to be fixed-parameter
tractable or to admit a polynomial kernel.


  Let $x \in [0,1)$ be a real number and denote its continued fraction
expansion by $[a_1(x),a_2(x), a_3(x),\cdots]$. The convergence exponent of
these partial quotients is defined as \[ \tau(x):= \inf\left\{s \geq 0: \sum_{n
\geq 1} a^{-s}_n(x)<\infty\right\}. \] In this paper, we investigate some
fundamental properties and multifractal analysis of the exponent $\tau(x)$.


  The critical group of an abelian network is a finite abelian group that
governs the behavior of the network on large inputs. It generalizes the
sandpile group of a graph. We show that the critical group of an irreducible
abelian network acts freely and transitively on recurrent states of the
network. We exhibit the critical group as a quotient of a free abelian group by
a subgroup containing the image of the Laplacian, with equality in the case
that the network is rectangular. We generalize Dhar's burning algorithm to
abelian networks, and estimate the running time of an abelian network on an
arbitrary input up to a constant additive error.


  A Carnot group is polarizable if it carries a homogeneous norm whose powers
are fundamental solutions for the $p$-sub-Laplacian operators for all $1<p \le
\infty$. Such groups also support a system of horizontal polar coordinates. We
prove that the converse statement is true: if a Carnot group supports a
horizontal polar coordinate system with suitable properties, then it is
polarizable.


  Adversarial attacks can readily disrupt the image classification system,
revealing the vulnerability of DNN-based recognition tasks. While existing
adversarial perturbations are primarily applied to uncompressed images or
compressed images by the traditional image compression method, i.e., JPEG,
limited studies have investigated the robustness of models for image
classification in the context of DNN-based image compression. With the rapid
evolution of advanced image compression, DNN-based learned image compression
has emerged as the promising approach for transmitting images in many
security-critical applications, such as cloud-based face recognition and
autonomous driving, due to its superior performance over traditional
compression. Therefore, there is a pressing need to fully investigate the
robustness of a classification system post-processed by learned image
compression. To bridge this research gap, we explore the adversarial attack on
a new pipeline that targets image classification models that utilize learned
image compressors as pre-processing modules. Furthermore, to enhance the
transferability of perturbations across various quality levels and
architectures of learned image compression models, we introduce a saliency
score-based sampling method to enable the fast generation of transferable
perturbation. Extensive experiments with popular attack methods demonstrate the
enhanced transferability of our proposed method when attacking images that have
been post-processed with different learned image compression models.