Abstract:
  A wide range of unconventional transport phenomena have recently been
observed in single-crystal delafossite metals. Here, we present a theoretical
framework to elucidate electron transport using a combination of
first-principles calculations and numerical modeling of the anisotropic
Boltzmann transport equation. Using PdCoO$_2$ as a model system, we study
different microscopic electron and phonon scattering mechanisms and establish
the mean free path hierarchy of quasiparticles at different temperatures. We
treat the anisotropic Fermi surface explicitly to numerically obtain
experimentally-accessible transport observables, which bridge between the
"diffusive", "ballistic", and "hydrodynamic" transport regime limits. We
illustrate that distinction between the "quasi-ballistic", and
"quasi-hydrodynamic" regimes is challenging and often needs to be quantitative
in nature. From first-principles calculations, we populate the resulting
transport regime plots, and demonstrate how the Fermi surface orientation adds
complexity to the observed transport signatures in micro-scale devices. Our
work provides key insights into microscopic interaction mechanisms on open
hexagonal Fermi surfaces and establishes their connection to the macroscopic
electron transport in finite-size channels.


Abstract:
  In the present paper we study the nature of the trivariate generating series
of weighted walks in the quarter plane. Combining the results of this paper to
previous ones, we complete the proof of the following theorem. The series
satisfies a nontrivial algebraic differential equation in one of its variable,
if and only if it satisfies a nontrivial algebraic differential equation in
each of its variables.


Abstract:
  We develop a kernel projected Wasserstein distance for the two-sample test,
an essential building block in statistics and machine learning: given two sets
of samples, to determine whether they are from the same distribution. This
method operates by finding the nonlinear mapping in the data space which
maximizes the distance between projected distributions. In contrast to existing
works about projected Wasserstein distance, the proposed method circumvents the
curse of dimensionality more efficiently. We present practical algorithms for
computing this distance function together with the non-asymptotic uncertainty
quantification of empirical estimates. Numerical examples validate our
theoretical results and demonstrate good performance of the proposed method.


Abstract:
  From a given topological hypermap $H$, we define two related hypermaps
$H^\triangle$ and $H^\nabla$ as complements of the ordinary dual hypermap $H^*$
along with the concepts of their edge hypermap quantum codes
$\mathcal{C}^\triangle$ and $\mathcal{C}^\nabla$. We then show that, when the
sets of special darts are naturally corresponded, the duality between the
ordinary hypermap quantum code $\mathcal{C}$ from $H$ and the one
$\mathcal{C}^*$ from $H^*$ can be greatly simplified to the duality between
$\mathcal{C}^\triangle$ and $\mathcal{C}^\nabla$.


Abstract:
  Deep learning models are susceptible to adversarial examples that have
imperceptible perturbations in the original input, resulting in adversarial
attacks against these models. Analysis of these attacks on the state of the art
transformers in NLP can help improve the robustness of these models against
such adversarial inputs. In this paper, we present Adv-OLM, a black-box attack
method that adapts the idea of Occlusion and Language Models (OLM) to the
current state of the art attack methods. OLM is used to rank words of a
sentence, which are later substituted using word replacement strategies. We
experimentally show that our approach outperforms other attack methods for
several text classification tasks.


Abstract:
  Event extraction (EE) has considerably benefited from pre-trained language
models (PLMs) by fine-tuning. However, existing pre-training methods have not
involved modeling event characteristics, resulting in the developed EE models
cannot take full advantage of large-scale unsupervised data. To this end, we
propose CLEVE, a contrastive pre-training framework for EE to better learn
event knowledge from large unsupervised data and their semantic structures
(e.g. AMR) obtained with automatic parsers. CLEVE contains a text encoder to
learn event semantics and a graph encoder to learn event structures
respectively. Specifically, the text encoder learns event semantic
representations by self-supervised contrastive learning to represent the words
of the same events closer than those unrelated words; the graph encoder learns
event structure representations by graph contrastive pre-training on parsed
event-related semantic structures. The two complementary representations then
work together to improve both the conventional supervised EE and the
unsupervised "liberal" EE, which requires jointly extracting events and
discovering event schemata without any annotated data. Experiments on ACE 2005
and MAVEN datasets show that CLEVE achieves significant improvements,
especially in the challenging unsupervised setting. The source code and
pre-trained checkpoints can be obtained from https://github.com/THU-KEG/CLEVE.


Abstract:
  Deep learning is effective in graph analysis. It is widely applied in many
related areas, such as link prediction, node classification, community
detection, and graph classification etc. Graph embedding, which learns
low-dimensional representations for vertices or edges in the graph, usually
employs deep models to derive the embedding vector. However, these models are
vulnerable. We envision that graph embedding methods based on deep models can
be easily attacked using adversarial examples. Thus, in this paper, we propose
Graphfool, a novel targeted label adversarial attack on graph embedding. It can
generate adversarial graph to attack graph embedding methods via classifying
boundary and gradient information in graph convolutional network (GCN).
Specifically, we perform the following steps: 1),We first estimate the
classification boundaries of different classes. 2), We calculate the minimal
perturbation matrix to misclassify the attacked vertex according to the target
classification boundary. 3), We modify the adjacency matrix according to the
maximal absolute value of the disturbance matrix. This process is implemented
iteratively. To the best of our knowledge, this is the first targeted label
attack technique. The experiments on real-world graph networks demonstrate that
Graphfool can derive better performance than state-of-art techniques. Compared
with the second best algorithm, Graphfool can achieve an average improvement of
11.44% in attack success rate.


Abstract:
  Pulsars can act as an excellent probe of the Milky Way magnetic field. The
average strength of the Galactic magnetic field component parallel to the line
of sight can be estimated as $\langle B_\parallel \rangle = 1.232 \,
\text{RM}/\text{DM}$, where $\text{RM}$ and $\text{DM}$ are the rotation and
dispersion measure of the pulsar. However, this assumes that the thermal
electron density and magnetic field of the interstellar medium are
uncorrelated. Using numerical simulations and observations, we test the
validity of this assumption. Based on magnetohydrodynamical simulations of
driven turbulence, we show that the correlation between the thermal electron
density and the small-scale magnetic field increases with increasing Mach
number of the turbulence. We find that the assumption of uncorrelated thermal
electron density and magnetic fields is valid only for subsonic and transsonic
flows, but for supersonic turbulence, the field strength can be severely
overestimated by using $1.232 \, \text{RM}/\text{DM}$. We then correlate
existing pulsar observations from the Australia Telescope National Facility
with regions of enhanced thermal electron density and magnetic fields probed by
${^{12} \mathrm {CO}}$ data of molecular clouds, magnetic fields from the
Zeeman splitting of the 21 cm line, neutral hydrogen column density, and
H$\alpha$ observations. Using these observational data, we show that the
thermal electron density and magnetic fields are largely uncorrelated over kpc
scales. Thus, we conclude that the relation $\langle B_\parallel \rangle =
1.232 \, \text{RM}/\text{DM}$ provides a good estimate of the magnetic field on
Galactic scales but might break down on sub - kpc scales.


Abstract:
  Several experimental studies have reported thermally activated behavior of
the mechanical response of solid Helium-4 which does not fit into the model of
thermally activated Frenkel pairs in an ideal crystal. The purpose of the
present work is to investigate how structural topological defects modify the
response. Using quantum Monte Carlo Worm Algorithm, we study temperature
dependence of fluctuations of the total number of particles in samples of hcp
solid Helium-4 containing several types of dislocations. Such fluctuations can
be described by the thermal activation law with the activation energy dependent
strongly on the type of the dislocation and the crystal density. The extracted
values cover a range from about 1K to 20K. It is also found that annihilation
of a jog-antijog pair can produce a local region of the solid characterized by
the orthorhombic symmetry. This serendipitous observation suggests that hcp
solid Helium-4 can undergo a displacive phase transition into the orthorhombic
crystal under uniaxial stress of about 10-15%


Abstract:
  In this work we revisit the existence, stability and dynamics of unstable
traveling solitary waves in the context of lattice dynamical systems. We
consider a nonlinear lattice of an $\alpha$-Fermi-Pasta-Ulam type with the
additional feature of all-to-all harmonic long-range interactions whose
strength decays exponentially with distance. The competition between the
nonlinear nearest-neighbor terms and the longer-range linear ones yields two
parameter regimes where the dependence of the energy $H$ of the traveling waves
on their velocity $c$ is non-monotonic and multivalued, respectively. We
examine both cases, and identify the exact (up to a prescribed numerical
tolerance) traveling waves. To investigate the stability of the obtained
solutions, we compute their Floquet multipliers, thinking of the traveling wave
problem as a periodic one modulo shifts. We show that in the general case when
the relationship between $H$ and $c$ is not single-valued, the sufficient but
not necessary criterion for stability change is $H'(s)=0$, where $s$ is the
parameter along the energy-velocity curve. Perturbing the unstable solutions
along the corresponding eigenvectors, we identify two different scenarios of
the dynamics of their transition to stable branches. In the first one, the
perturbed wave slows down after expelling a dispersive wave. The second
scenario involves an increase in the velocity of the perturbed wave accompanied
by the formation of a slower small-amplitude traveling solitary wave.


Abstract:
  Let $\mathcal{P}_r$ denote an almost-prime with at most $r$ prime factors,
counted according to multiplicity. In this paper, it is proved that for
$\alpha\in\mathbb{R}\backslash\mathbb{Q},\,\beta\in\mathbb{R}$ and
$0<\theta<10/1561$, there exist infinitely many primes $p$, such that $\|\alpha
p^2+\beta\|<p^{-\theta}$ and $p+2=\mathcal{P}_4$, which constitutes an
improvement upon the previous result.


Abstract:
  A given monoid usually admits many presentations by generators and relations
and the notion of Tietze equivalence characterizes when two presentations
describe the same monoid: it is the case when one can transform one
presentation into the other using the two families of so-called Tietze
transformations. The goal of this article is to provide an abstract and
geometrical understanding of this well-known fact, by constructing a model
structuree on the category of presentations, in which two presentations are
weakly equivalent when they present the same monoid. We show that Tietze
transformations form a pseudo-generating family of trivial cofibrations and
give a proof of the completeness of these transformations by an abstract
argument in this setting.


Abstract:
  Deconvolution is the important problem of estimating the distribution of a
quantity of interest from a sample with additive measurement error. Nearly all
methods in the literature are based on Fourier transformation because it is
mathematically a very neat solution. However, in practice these methods are
unstable, and produce bad estimates when signal-noise ratio or sample size are
low. In this paper, we develop a new deconvolution method based on maximum
likelihood with a smoothness penalty. We show that our new method has much
better performance than existing methods, particularly for small sample size or
signal-noise ratio.


Abstract:
  We identify 225 filaments from an H$_2$ column density map constructed using
simultaneous $^{12}$CO, $^{13}$CO, and C$^{18}$O (J=1-0) observations carried
out as a part of the MWISP project. We select 46 long filaments with lengths
above 1.2 pc to analyze the filament column density profiles. We divide the
selected filaments into 397 segments and calculate the column density profiles
for each segment. The symmetries of the profiles are investigated. The
proportion of intrinsically asymmetrical segments is 65.3$\%$, and that of
intrinsically symmetrical ones is 21.4$\%$. The typical full width at half
maximum (FWHM) of the intrinsically symmetrical filament segments is $\sim$
0.67 pc with the Plummer-like fitting, and $\sim$ 0.50 pc with the Gaussian
fitting, respectively. The median FWHM widths derived from the second-moment
method for intrinsically symmetrical and asymmetrical profiles are $\sim$ 0.44
and 0.46 pc, respectively. Close association exists between the filamentary
structures and the YSOs in the region.


Abstract:
  Several bacteria and bacteria strands form biofilms in different
environmental conditions, e.g. pH, temperature, nutrients, etc. Biofilm growth,
therefore, is an extremely robust process. Because of this, while biofilm
growth is a complex process affected by several variables, insights into
biofilm formation could be obtained studying simple schematic models. In this
manuscript, we describe a hybrid molecular dynamics and Monte Carlo model for
the simulation of the early stage formation of a biofilm, to explicitly
demonstrate that it is possible to account for most of the processes expected
to be relevant. The simulations account for the growth and reproduction of the
bacteria, for their interaction and motility, for the synthesis of
extracellular polymeric substances and Psl trails. We describe the effect of
these processes on the early stage formation of biofilms, in two dimensions,
and also discuss preliminary three-dimensional results.


Abstract:
  We discuss a pointwise numerical differentiation formula on multivariate
scattered data, based on the coefficients of local polynomial interpolation at
Discrete Leja Points, written in Taylor's formula monomial basis. Error bounds
for the approximation of partial derivatives of any order compatible with the
function regularity are provided, as well as sensitivity estimates to
functional perturbations, in terms of the inverse Vandermonde coefficients that
are active in the differentiation process. Several numerical tests are
presented showing the accuracy of the approximation.


Abstract:
  Quantum computing holds the promise of improving the information processing
power to levels unreachable by classical computation. Quantum walks are heading
the development of quantum algorithms for searching information on graphs more
efficiently than their classical counterparts. A quantum-walk-based algorithm
that is standing out in the literature is the lackadaisical quantum walk. The
lackadaisical quantum walk is an algorithm developed to search two-dimensional
grids whose vertices have a self-loop of weight $l$. In this paper, we address
several issues related to the application of the lackadaisical quantum walk to
successfully search for multiple solutions on grids. Firstly, we show that only
one of the two stopping conditions found in the literature is suitable for
simulations. We also demonstrate that the final success probability depends on
the space density of solutions and the relative distance between solutions.
Furthermore, this work generalizes the lackadaisical quantum walk to search for
multiple solutions on grids of arbitrary dimensions. In addition, we propose an
optimal adjustment of the self-loop weight $l$ for such scenarios of arbitrary
dimensions. It turns out the other fits of $l$ found in the literature are
particular cases. Finally, we observe a two-to-one relation between the steps
of the lackadaisical quantum walk and the ones of Grover's algorithm, which
requires modifications in the stopping condition. In conclusion, this work
deals with practical issues one should consider when applying the lackadaisical
quantum walk, besides expanding the technique to a wider range of search
problems.


Abstract:
  Practical applications of quantum computers require millions of physical
qubits and it will be challenging for individual quantum processors to reach
such qubit numbers. It is therefore timely to investigate the resource
requirements of quantum algorithms in a distributed setting, where multiple
quantum processors are interconnected by a coherent network. We introduce an
extension of the Message Passing Interface (MPI) to enable high-performance
implementations of distributed quantum algorithms. In turn, these
implementations can be used for testing, debugging, and resource estimation. In
addition to a prototype implementation of quantum MPI, we present a performance
model for distributed quantum computing, SENDQ. The model is inspired by the
classical LogP model, making it useful to inform algorithmic decisions when
programming distributed quantum computers. Specifically, we consider several
optimizations of two quantum algorithms for problems in physics and chemistry,
and we detail their effects on performance in the SENDQ model.


Abstract:
  The results from the CMS search for supersymmetric particles based on Run-2
data recorded at a center-of-mass energy of 13 TeV are summarized. Strong and
weak production of SUSY scenarios are considered. Results presented include the
searches for squarks and gluinos, direct production of charginos, neutralinos,
and sleptons. These searches involve final state objects comprising jets,
missing transverse momentum, electrons or muons, taus or photons, as well as
long-lived particles. The data in these searches are found to be consistent
with standard model predictions and no significant excess is observed. Upper
limits have been set on the masses of supersymmetric particles from a variety
of search channels.


Abstract:
  The development of deep neural networks and the emergence of pre-trained
language models such as BERT allow to increase performance on many NLP tasks.
However, these models do not meet the same popularity for tweet summarization,
which can probably be explained by the lack of existing collections for
training and evaluation. Our contribution in this paper is twofold : (1) we
introduce a large dataset for Twitter event summarization, and (2) we propose a
neural model to automatically summarize huge tweet streams. This extractive
model combines in an original way pre-trained language models and vocabulary
frequency-based representations to predict tweet salience. An additional
advantage of the model is that it automatically adapts the size of the output
summary according to the input tweet stream. We conducted experiments using two
different Twitter collections, and promising results are observed in comparison
with state-of-the-art baselines.


Abstract:
  Topology and geometry of a sphere create constraints for particles that lie
on its surface which they otherwise do not experience in Euclidean space.
Notably, the number of particles and the size of the system can be varied
separately, requiring a careful treatment of systems with one or several
characteristic length scales. All this can make it difficult to precisely
determine whether a particular system is in a disordered, fluid-like, or
crystal-like state. Here, we show how order transitions in systems of particles
interacting on the surface of a sphere can be detected by changes in two
hyperuniformity parameters, derived from spherical structure factor and cap
number variance. We demonstrate their use on two different systems -- solutions
of the thermal Thomson problem and particles interacting via an ultra-soft
potential of the generalized exponential model of order 4 -- each with a
distinct parameter regulating their degree of ordering. The hyperuniformity
parameters are not only able to detect the order transitions in both systems,
but also point out the clear differences in the ordered distributions in each
due to the nature of the interaction leading to them. Our study shows that
hyperuniformity analysis of particle distributions on the sphere provides a
powerful insight into fluid- and crystal-like order on the sphere.


Abstract:
  A shape's Heesch number is the number of layers of copies of the shape that
can be placed around it without gaps or overlaps. Experimentation and
exhaustive searching have turned up examples of shapes with finite Heesch
numbers up to six, but nothing higher. The computational problem of classifying
simple families of shapes by Heesch number can provide more experimental data
to fuel our understanding of this topic. I present a technique for computing
Heesch numbers of non-tiling polyforms using a SAT solver, and the results of
exhaustive computation of Heesch numbers up to 19-ominoes, 17-hexes, and
24-iamonds.


Abstract:
  The first epitaxial ferroelectric wurtzite film with clear
polarization-electric field hysteresis behavior is presented. The coercive
field of this epitaxial Al0.7Sc0.3N film on W/c-sapphire substrate is 0.4 +-
0.3 MV cm-1 (8 %) smaller than that of a conventional fiber textured film on a
Pt/TiOx/SiO2/Si substrate, attributed to the 0.01 +- 0.007 {\AA} smaller c-axis
lattice parameter in the epitaxial film. The strain and decrease of the
coercive field most likely originate from epitaxial strain rather than the
mismatch in thermal coefficient of expansion. These results provide an insight
for further coercive field reduction of novel wurtzite ferroelectrics using
epitaxial mismatch strain.


Abstract:
  Testing is the most direct and effective technique to ensure software
quality. However, it is a burden for developers to understand the
poorly-commented tests, which are common in industry environment projects.
Mobile applications (app) are GUI-intensive and event-driven, so test scripts
focusing on GUI interactions play a more important role in mobile app testing
besides the test cases for the source code. Therefore, more attention should be
paid to the user interactions and the corresponding user event responses.
However, test scripts are loosely linked to apps under test (AUT) based on
widget selectors, making it hard to map the operations to the functionality
code of AUT. In such a situation, code understanding algorithms may lose
efficacy if directly applied to mobile app test scripts.
  We present a novel approach, TestIntent, to infer the intent of mobile app
test scripts. TestIntent combines the GUI image understanding and code
understanding technologies. The test script is transferred into an operation
sequence model. For each operation, TestIntent extracts the operated widget
selector and link the selector to the UI layout structure, which stores the
detailed information of the widgets, including coordinates, type, etc. With
code understanding technologies, TestIntent can locate response methods in the
source code. Afterwards, NLP algorithms are adopted to understand the code and
generate descriptions. Also, TestIntent can locate widgets on the app GUI
images. Then, TestIntent can understand the widget intent with an
encoder-decoder model. With the combination of the results from GUI and code
understanding, TestIntent generates the test intents in natural language
format. We also conduct an empirical experiment, and the results prove the
outstanding performance of TestIntent. A user study also declares that
TestIntent can save developers' time to understand test scripts.


Abstract:
  We review recent interactions between mathematical theory of two-dimensional
topological order and operator algebras, particularly the Jones theory of
subfactors. The role of representation theory in terms of tensor categories is
emphasized. Connections to 2-dimensional conformal field theory are also
presented. In particular, we discuss anyon condensation, gapped domain walls
and matrix product operators in terms of operator algebras.


Abstract:
  The integration of the equations of motion of N interacting particles,
represents a classical problem in many branches of physics and chemistry. The
direct N-body problem is at the heart of simulations studying Coulomb Crystals.
We present an hand-optimized code for the latest AVX-512 set of instructions
that achieve a single core speed up of $\approx 340\%$ respect the version
optimized by the compiler. The increase performance is due a optimization on
the organization of the memory access on the inner loop on the Coulomb and,
specially, on the usage of an intrinsic function to faster compute the
$1/\sqrt{x}$. Our parallelization, which is implemented in OpenMP, achieves an
excellent scalability with the number of cores. In total, we achieve $\approx
500GFLOPS$ using a just a standard WorkStation with one Intel Skylake CPU (10
cores). It represents $\approx 75\%$ of the theoretical maximum number of
double precision FLOPS corresponding to Fused Multiplication Addition (FMA)
operations.


Abstract:
  Several methods for inversion of face recognition models were recently
presented, attempting to reconstruct a face from deep templates. Although some
of these approaches work in a black-box setup using only face embeddings,
usually, on the end-user side, only similarity scores are provided. Therefore,
these algorithms are inapplicable in such scenarios. We propose a novel
approach that allows reconstructing the face querying only similarity scores of
the black-box model. While our algorithm operates in a more general setup,
experiments show that it is query efficient and outperforms the existing
methods.


Abstract:
  Studies on online hate speech have mostly focused on the automated detection
of harmful messages. Little attention has been devoted so far to the
development of effective strategies to fight hate speech, in particular through
the creation of counter-messages. While existing manual scrutiny and
intervention strategies are time-consuming and not scalable, advances in
natural language processing have the potential to provide a systematic approach
to hatred management. In this paper, we introduce a novel ICT platform that NGO
operators can use to monitor and analyze social media data, along with a
counter-narrative suggestion tool. Our platform aims at increasing the
efficiency and effectiveness of operators' activities against islamophobia. We
test the platform with more than one hundred NGO operators in three countries
through qualitative and quantitative evaluation. Results show that NGOs favor
the platform solution with the suggestion tool, and that the time required to
produce counter-narratives significantly decreases.


Abstract:
  We utilize the experimentally known difference of the $\Lambda$ separation
energies of the mirror hypernuclei ${^4_\Lambda \rm He}$ and ${^4_\Lambda \rm
H}$ to constrain the $\Lambda$-neutron interaction. We include the leading
charge-symmetry breaking (CSB) interaction into our hyperon-nucleon interaction
derived within chiral effective field theory at next-to-leading order. In
particular, we determine the strength of the two arising CSB contact terms by a
fit to the differences of the separation energies of these hypernuclei in the
$0^+$ and $1^+$ states, respectively. By construction, the resulting
interaction describes all low energy hyperon-nucleon scattering data, the
hypertriton and the CSB in ${^4_\Lambda \rm He}$-${^4_\Lambda \rm H}$
accurately. This allows us to provide first predictions for the $\Lambda$n
scattering lengths, based solely on available hypernuclear data.


Abstract:
  The dynamics of the spread of contagions such as viruses, infectious diseases
or even rumors/opinions over contact networks (graphs) have effectively been
captured by the well known \textit{Susceptible-Infected-Susceptible} ($SIS$)
epidemic model in recent years. When it comes to competition between two such
contagions spreading on overlaid graphs, their propagation is captured by
so-called \textit{bi-virus} epidemic models. Analysis of such dynamical systems
involve the identification of equilibrium points and its convergence
properties, which determine whether either of the viruses dies out, or both
survive together. We demonstrate how the existing works are unsuccessful in
characterizing a large subset of the model parameter space, including all
parameters for which the competitiveness of the bi-virus system is significant
enough to attain coexistence of the epidemics. In this paper, we fill in this
void and obtain convergence results for the entirety of the model parameter
space; giving precise conditions (necessary and sufficient) under which the
system \textit{globally converges} to a \textit{trichotomy} of possible
outcomes: a virus-free state, a single-virus state, and to a coexistence state
-- the first such result.


Abstract:
  As more machine learning agents interact with humans, it is increasingly a
prospect that an agent trained to perform a task optimally, using only a
measure of task performance as feedback, can violate societal norms for
acceptable behavior or cause harm. Value alignment is a property of intelligent
agents wherein they solely pursue non-harmful behaviors or human-beneficial
goals. We introduce an approach to value-aligned reinforcement learning, in
which we train an agent with two reward signals: a standard task performance
reward, plus a normative behavior reward. The normative behavior reward is
derived from a value-aligned prior model previously shown to classify text as
normative or non-normative. We show how variations on a policy shaping
technique can balance these two sources of reward and produce policies that are
both effective and perceived as being more normative. We test our
value-alignment technique on three interactive text-based worlds; each world is
designed specifically to challenge agents with a task as well as provide
opportunities to deviate from the task to engage in normative and/or altruistic
behavior.


Abstract:
  In many coordination problems, independently reasoning humans are able to
discover mutually compatible policies. In contrast, independently trained
self-play policies are often mutually incompatible. Zero-shot coordination
(ZSC) has recently been proposed as a new frontier in multi-agent reinforcement
learning to address this fundamental issue. Prior work approaches the ZSC
problem by assuming players can agree on a shared learning algorithm but not on
labels for actions and observations, and proposes other-play as an optimal
solution. However, until now, this "label-free" problem has only been
informally defined. We formalize this setting as the label-free coordination
(LFC) problem by defining the label-free coordination game. We show that
other-play is not an optimal solution to the LFC problem as it fails to
consistently break ties between incompatible maximizers of the other-play
objective. We introduce an extension of the algorithm, other-play with
tie-breaking, and prove that it is optimal in the LFC problem and an
equilibrium in the LFC game. Since arbitrary tie-breaking is precisely what the
ZSC setting aims to prevent, we conclude that the LFC problem does not reflect
the aims of ZSC. To address this, we introduce an alternative informal
operationalization of ZSC as a starting point for future work.


Abstract:
  Isomers with three- and five-nucleon-hole configurations have been
established in $^{203}$Tl. These include newly identified levels with a
three-nucleon structure: {\it I}$^{\pi }$ = (15/2$^{-}$) with {\it T}$_{1/2}$ =
7.9(5) ns, and {\it I}$^{\pi }$ = (35/2$^{-}$) with {\it T}$_{1/2}$ = 4.0(5)
ns. In addition, five-quasiparticle states: {\it I}$^{\pi }$ = (39/2$^{-}$)
with {\it T}$_{1/2}$ = 1.9(2) ns, and {\it I}$^{\pi }$ = (49/2$^{+}$) with {\it
T}$_{1/2}$ = 3.4(4) ns have also been established. The previously determined
long-lived decay [{\it T}$_{1/2}$ = 6.6(3) $\mu $s from this work] is
associated with isomerism of the {\it I}$^{\pi }$ = (29/2$^{+}$) state. Levels
above this long-lived isomer have been identified through a delayed-prompt
coincidence measurement. Five-nucleon-hole states with excitation energies {\it
E}$_{x}$ $\approx $ 7 MeV have been established as well as possible octupole
excitations of the $^{208}$Pb core built on these levels. The level scheme of
$^{203}$Tl is extended up to {\it E}$_{x}$ $\approx $ 11 MeV with the inclusion
of 25 new transitions. Empirical and shell-model calculations have been
performed to aid in the description of the observed states which are found to
be predominantly of intrinsic character.


Abstract:
  In present paper a well-behaved new model of anisotropic compact star in
(3+1)-dimensional spacetime has been investigated in the background of
Einstein's general theory of relativity. The model has been developed by
choosing $g_rr$ component as Krori-Barua (KB) ansatz [Krori and Barua in J.
Phys. A, Math. Gen. 8:508, 1975]. The field equations have been solved by a
proper choice of the anisotropy factor which is physically reasonable and well
behaved inside the stellar interior. Interior spacetime has been matched
smoothly to the exterior Schwarzschild vacuum solution and it has also been
depicted graphically. Model is free from all types of singularities and is in
static equilibrium under different forces acting on the system. The stability
of the model has been tested with the help of various conditions available in
literature. The solution is compatible with observed masses and radii of a few
compact stars.


Abstract:
  The theory of symmetry indicators has enabled database searches for
topological materials in normal conducting phases, which has led to several
encyclopedic topological material databases. Here, based on recently developed
symmetry indicators for superconductors, we report our comprehensive search for
topological and nodal superconductors among nonmagnetic materials in Inorganic
Crystal Structure Database. A myriad of topological superconductors with exotic
boundary states are discovered. When materials are symmetry-enforced nodal
superconductors, positions and shapes of the nodes are also identified. These
data are aggregated at Database of Topological and Nodal Supercoductors. We
also provide a subroutine Topological Supercon, which allows users to examine
the topological nature in the superconducting phase of any material themselves
by uploading the result of first-principles calculations as an input. Our
database and subroutine, when combined with experiments, will help us
understand the unconventional pairing mechanism and facilitate realizations of
the long-sought Majorana fermions promising for topological quantum
computations.


Abstract:
  Open-source biodiversity databases contain a large amount of species
occurrence records, but these are often spatially biased, which affects the
reliability of species distribution models based on these records. Sample bias
correction techniques include data filtering at the cost of record numbers or
require considerable additional sampling effort. However, independent data are
rarely available and assessment of the correction technique must rely on
performance metrics computed with subsets of the only available (biased) data,
which may be misleading. Here we assess the extent to which an acknowledged
sample bias correction technique is likely to improve models' ability to
predict species distributions in the absence of independent data. We assessed
the variation in model predictions induced by the correction and model
stochasticity. We present an index of the effect of correction relative to
model stochasticity, the Relative Overlap Index (ROI). We tested whether the
ROI better represented the effect of correction than classic performance
metrics and absolute overlap metrics using 64 vertebrate species and 21 virtual
species with a generated sample bias. When based on absolute overlaps and
cross-validation performance metrics, we found no effect of correction, except
for cAUC. When considering its effect relative to model stochasticity, the
effect of correction depended on the site and the species. Virtual species
enabled us to verify that the correction actually improved distribution
predictions and the biological relevance of the selected variables at the sites
with a clear gradient of sample bias, and when species distribution predictors
are not correlated with sample bias patterns.


Abstract:
  We consider a model of baryogenesis that requires extending the Standard
Model by two additional multi-TeV Higgs doublets that do not break electroweak
symmetry. Adopting the ``Spontaneous Flavor Violation" framework, we can
arrange for the heavy Higgs states to have significant couplings to light
quarks. This allows for the heavy scalars to be resonantly produced at a future
100 TeV $pp$ collider and discovered in di-jet and top-pair final states up to
masses of $\mathcal{O}$(10 TeV). The same mass range can also lead to signals
in flavor experiments. Together, these measurements can play a complementary
role in probing the physics involved in this Higgs Troika baryogenesis
scenario.


Abstract:
  Automatic segmentation methods are an important advancement in medical image
analysis. Machine learning techniques, and deep neural networks in particular,
are the state-of-the-art for most medical image segmentation tasks. Issues with
class imbalance pose a significant challenge in medical datasets, with lesions
often occupying a considerably smaller volume relative to the background. Loss
functions used in the training of deep learning algorithms differ in their
robustness to class imbalance, with direct consequences for model convergence.
The most commonly used loss functions for segmentation are based on either the
cross entropy loss, Dice loss or a combination of the two. We propose the
Unified Focal loss, a new hierarchical framework that generalises Dice and
cross entropy-based losses for handling class imbalance. We evaluate our
proposed loss function on five publicly available, class imbalanced medical
imaging datasets: CVC-ClinicDB, Digital Retinal Images for Vessel Extraction
(DRIVE), Breast Ultrasound 2017 (BUS2017), Brain Tumour Segmentation 2020
(BraTS20) and Kidney Tumour Segmentation 2019 (KiTS19). We compare our loss
function performance against six Dice or cross entropy-based loss functions,
across 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating
that our proposed loss function is robust to class imbalance and consistently
outperforms the other loss functions. Source code is available at:
https://github.com/mlyg/unified-focal-loss


Abstract:
  We connect the study of pseudodeterministic algorithms to two major open
problems about the structural complexity of $\mathsf{BPTIME}$: proving
hierarchy theorems and showing the existence of complete problems. Our main
contributions can be summarised as follows.
  1. We build on techniques developed to prove hierarchy theorems for
probabilistic time with advice (Fortnow and Santhanam, FOCS 2004) to construct
the first unconditional pseudorandom generator of polynomial stretch computable
in pseudodeterministic polynomial time (with one bit of advice) that is secure
infinitely often against polynomial-time computations. As an application of
this construction, we obtain new results about the complexity of generating and
representing prime numbers.
  2. Oliveira and Santhanam (STOC 2017) established unconditionally that there
is a pseudodeterministic algorithm for the Circuit Acceptance Probability
Problem ($\mathsf{CAPP}$) that runs in sub-exponential time and is correct with
high probability over any samplable distribution on circuits on infinitely many
input lengths. We show that improving this running time or obtaining a result
that holds for every large input length would imply new time hierarchy theorems
for probabilistic time. In addition, we prove that a worst-case polynomial-time
pseudodeterministic algorithm for $\mathsf{CAPP}$ would imply that
$\mathsf{BPP}$ has complete problems.
  3. We establish an equivalence between pseudodeterministic construction of
strings of large $\mathsf{rKt}$ complexity (Oliveira, ICALP 2019) and the
existence of strong hierarchy theorems for probabilistic time.
  More generally, these results suggest new approaches for designing
pseudodeterministic algorithms for search problems and for unveiling the
structure of probabilistic time.


Abstract:
  We consider the question of sequential prediction under the log-loss in terms
of cumulative regret. Namely, given a hypothesis class of distributions,
learner sequentially predicts the (distribution of the) next letter in sequence
and its performance is compared to the baseline of the best constant predictor
from the hypothesis class. The well-specified case corresponds to an additional
assumption that the data-generating distribution belongs to the hypothesis
class as well. Here we present results in the more general misspecified case.
Due to special properties of the log-loss, the same problem arises in the
context of competitive-optimality in density estimation, and model selection.
For the $d$-dimensional Gaussian location hypothesis class, we show that
cumulative regrets in the well-specified and misspecified cases asymptotically
coincide. In other words, we provide an $o(1)$ characterization of the
distribution-free (or PAC) regret in this case -- the first such result as far
as we know. We recall that the worst-case (or individual-sequence) regret in
this case is larger by an additive constant ${d\over 2} + o(1)$. Surprisingly,
neither the traditional Bayesian estimators, nor the Shtarkov's normalized
maximum likelihood achieve the PAC regret and our estimator requires special
"robustification" against heavy-tailed data. In addition, we show two general
results for misspecified regret: the existence and uniqueness of the optimal
estimator, and the bound sandwiching the misspecified regret between
well-specified regrets with (asymptotically) close hypotheses classes.


Abstract:
  Here, we report the synthesis and physical properties of Co2FeGe (CFG)
Heusler alloy (HA) nanoparticles (NPs). The NPs of size 23 nm are prepared
using the co-precipitation method. X-ray and selected area electron diffraction
patterns have confirmed the cubic Heusler phase of the NPs with the
A2-disorder. These NPs are soft ferromagnetic, and exhibit a high saturation
magnetization (Ms) along with a very high Curie temperature (Tc) of 1060 K. The
observed Tc value matches closely with the theoretically calculated one
following a model provided by Wurmehl et al. [1]. The high Ms and Tc make the
present system a potential candidate for magnetically activated nano-devices
working at high temperatures. The near-integral value 5.9 mehoB/f.u. of Ms at
low temperatures indicates that the half-metallic ferromagnetism is preserved
even in the particles even on the 20 nm length scale. Additionally, we have
facilitated the existing HA-NP preparation method, which can be used in
synthesizing other HA-NPs. The first-principles density functional theory
computations complement the experimental results.


Abstract:
  We report on a detailed study of the optical properties of CsV$_{3}$Sb$_{5}$
at a large number of temperatures above and below the charge-density-wave (CDW)
transition. Above the CDW transition, the low-frequency optical conductivity
reveals two Drude components with distinct widths. An examination of the band
structure allows us to ascribe the narrow Drude to multiple light and Dirac
bands, and the broad Drude to the heavy bands near the $M$ points which form
saddle points near the Fermi level. Upon entering the CDW state, the opening of
the CDW gap is clearly observed. A large portion of the broad Drude is removed
by the gap, whereas the narrow Drude is not affected. Meanwhile, an absorption
peak associated with interband transitions near the saddle points shifts to
higher energy and grows in weight. These observations are consistent with the
scenario that the CDW in CsV$_{3}$Sb$_{5}$ is driven by nesting of Fermi
surfaces near the saddle points at $M$.


Abstract:
  Silicon carbide silicon carbide (SiC SiC) composites are often used in
oxidizing environments at high temperatures. Measurements of the thermal
conductance of the oxide layer provide a way to better understand the oxidation
process with high spatial resolution. We use time domain thermoreflectance
(TDTR) to map the thermal conductance of the oxide layer and the thermal
conductivity of the SiC SiC composite with a spatial resolution of 3 {\mu}m.
Heterodyne detection using a 50 kHz modulated probe beam and a 10 MHz modulated
pump suppresses the coherent pick-up and enables faster data acquisition than
what has previously been possible using sequential demodulation. By analyzing
the noise of the measured signals, we find that in the limit of small
integration time constants or low laser powers, the dominant source of noise is
the input noise of the preamplifier. The thermal conductance of the oxide that
forms on the fiber region is lower than the oxide on the matrix due to small
differences in thickness and thermal conductivity.


Abstract:
  Cold-start problems are enormous challenges in practical recommender systems.
One promising solution for this problem is cross-domain recommendation (CDR)
which leverages rich information from an auxiliary (source) domain to improve
the performance of recommender system in the target domain. In these CDR
approaches, the family of Embedding and Mapping methods for CDR (EMCDR) is very
effective, which explicitly learn a mapping function from source embeddings to
target embeddings with overlapping users. However, these approaches suffer from
one serious problem: the mapping function is only learned on limited
overlapping users, and the function would be biased to the limited overlapping
users, which leads to unsatisfying generalization ability and degrades the
performance on cold-start users in the target domain. With the advantage of
meta learning which has good generalization ability to novel tasks, we propose
a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta
stage. In the transfer (pre-training) stage, a source model and a target model
are trained on source and target domains, respectively. In the meta stage, a
task-oriented meta network is learned to implicitly transform the user
embedding in the source domain to the target feature space. In addition, the
TMCDR is a general framework that can be applied upon various base models,
e.g., MF, BPR, CML. By utilizing data from Amazon and Douban, we conduct
extensive experiments on 6 cross-domain tasks to demonstrate the superior
performance and compatibility of TMCDR.


Abstract:
  We investigate the iron-based superconductor Ba$_{1-x}$K$_x$Fe$_2$As$_2$
using intense terahertz (THz) light. In the superconducting state a THz Kerr
signal is observed and assigned to non-linear THz coupling to superconducting
degrees of freedom. The polarization dependence of the THz Kerr signal is
remarkably sensitive to the coexistence of a nematic order. In the absence of
nematic order the $C_4$ symmetric polarization dependence of the THz Kerr
signal is consistent with a coupling to the Higgs amplitude mode of the
superconducting condensate. In the coexisting nematic and superconducting state
the signal becomes purely nematic with a vanishing $C_4$ symmetric component,
signaling the emergence of a new superconducting collective mode activated by
nematicity.


Abstract:
  We present the characterization of a novel balanced homodyne detector
operating in the mid-infrared. The challenging task of revealing
non-classicality in mid-infrared light, e.~g. in quantum cascade lasers
emission, requires a high-performance detection system. Through the intensity
noise power spectral density analysis of the differential signal coming from
the incident radiation, we show that our setup is shot-noise limited. We
discuss the experimental results with a view to possible applications to
quantum technologies, such as free-space quantum communication.


Abstract:
  The universe is composed of galaxies that have diverse shapes. Once the
structure of a galaxy is determined, it is possible to obtain important
information about its formation and evolution. Morphologically classifying
galaxies means cataloging them according to their visual appearance and the
classification is linked to the physical properties of the galaxy. A
morphological classification made through visual inspection is subject to
biases introduced by subjective observations made by human volunteers. For this
reason, systematic, objective and easily reproducible classification of
galaxies has been gaining importance since the astronomer Edwin Hubble created
his famous classification method. In this work, we combine accurate visual
classifications of the Galaxy Zoo project with \emph {Deep Learning} methods.
The goal is to find an efficient technique at human performance level
classification, but in a systematic and automatic way, for classification of
elliptical and spiral galaxies. For this, a neural network model was created
through an Ensemble of four other convolutional models, allowing a greater
accuracy in the classification than what would be obtained with any one
individual. Details of the individual models and improvements made are also
described. The present work is entirely based on the analysis of images (not
parameter tables) from DR1 (www.datalab.noao.edu) of the Southern Photometric
Local Universe Survey (S-PLUS). In terms of classification, we achieved, with
the Ensemble, an accuracy of $\approx 99 \%$ in the test sample (using
pre-trained networks).


Abstract:
  Properties of the ground state and the spectrum of elementary excitations are
investigated for the low density ultracold spinor 3D Bose gas of particles with
arbitrary nonzero spin. Gross-Pitaevskii equations are derived. Within the
framework of the considering interaction Hamiltonian it is shown that the
ground state spin structure and spin part of the chemical potential is
determined by the renormalized interaction, being defined by the contribution
of the virtual large momenta. The ferromagnetic structure of the ground state,
and the equation of the phase, density, and spin dynamics are obtained from
Gross-Pitaevskii equations.


Abstract:
  We consider a singularly perturbed reaction diffusion problem as a first
order two-by-two system. Using piecewise discontinuous polynomials for the
first component and $H_{div}$-conforming elements for the second component we
provide a convergence analysis on layer adapted meshes and an optimal
convergence order in a balanced norm that is comparable with a balanced
$H^2$-norm for the second order formulation.


Abstract:
  Given an input image from a source domain and a guidance image from a target
domain, unsupervised many-to-many image-to-image (UMMI2I) translation methods
seek to generate a plausible example from the target domain that preserves
domain-invariant information of the input source image and inherits the
domain-specific information from the guidance image. For example, when
translating female faces to male faces, the generated male face should have the
same expression, pose and hair color as the input female image, and the same
facial hairstyle and other male-specific attributes as the guidance male image.
Current state-of-the art UMMI2I methods generate visually pleasing images, but,
since for most pairs of real datasets we do not know which attributes are
domain-specific and which are domain-invariant, the semantic correctness of
existing approaches has not been quantitatively evaluated yet. In this paper,
we propose a set of benchmarks and metrics for the evaluation of semantic
correctness of these methods. We provide an extensive study of existing
state-of-the-art UMMI2I translation methods, showing that all methods, to
different degrees, fail to infer which attributes are domain-specific and which
are domain-invariant from data, and mostly rely on inductive biases hard-coded
into their architectures.


Abstract:
  Inversion symmetry is a key symmetry in unconventional superconductors and
even its local breaking can have profound implications. For inversion-symmetric
systems, there is a competition on a microscopic level between the spin-orbit
coupling associated with the local lack of inversion and hybridizing terms that
`restore' inversion. Investigating a layered system with alternating
mirror-symmetry breaking, we study this competition considering the spin
response of different superconducting order parameters for the case of strong
spin-orbit coupling. We find that signatures of the local non-centrosymmetry,
such as an increased spin susceptibility in spin-singlet superconductors for
$T\rightarrow 0$, persist even into the quasi-three-dimensional regime. This
leads to a direction dependent spin response which allows to distinguish
different superconducting order parameters. Furthermore, we identify several
regimes with possible topological superconducting phases within a
symmetry-indicator analysis. Our results may have direct relevance for the
recently reported Ce-based superconductor CeRh$_2$As$_2$ and beyond.


Abstract:
  This paper's central theme is to prove the existence of an n-algebra whose
multiplication cannot be expressed employing any binary operation. Furthermore,
to prove if two algebras are not isomorphic, this property does not hold for
$3$-algebras corresponding to these two algebras. The proof drives applying
some results gotten early applying a new approach for the classification
algebras problem, introduced recently, which showed great success in solving
many classification algebras problems.


Abstract:
  Embedding quantum dots (QDs) on nanowire (NW) sidewalls allows the
integration of multi-layers of QDs into the active region of radial p-i-n
junctions to greatly enhance light emission/absorption. However, the surface
curvature makes the growth much more challenging compared with growths on
thin-films, particularly on NWs with small diameters ({\O} <100 nm). Moreover,
the {110} sidewall facets of self-catalyzed NWs favor two-dimensional growth
(2D), with the realization of three-dimensional (3D) Stranski-Krastanow growth
becoming extremely challenging. Here, we demonstrate thermally-driven formation
of Ge dots on the {110} sidewalls facets of thin self-catalyzed NWs without
using any surfactant or surface treatment. The 2D-3D transition of the
pseudomorphic Ge layer grown on GaAs NWs is driven by energy minimization under
high-temperature annealing. This method opens a new avenue to integrate QDs on
NWs without any restriction on NW diameter or elastic strain, which can allow
the formation of QDs in a wider range of materials systems where the growth of
islands by traditional mechanisms is not possible, with benefits for novel
NWQD-based optoelectronic devices.


Abstract:
  Sulfur is a volatile chemical element that plays an important role in tracing
the chemical evolution of galaxies. However, its nucleosynthesis origin and
abundance variations are still unclear. The goal of the present article is to
accurately and precisely study the S-content of large number of stars located
in the solar neighbourhood. We use the parametrisation of thousands of HR
stellar spectra provided by the AMBRE Project, and combine it with the
automated abundance determination GAUGUIN to derive LTE sulfur abundances for
1855 slow-rotating FGK-type stars. This is the largest and most precise
catalogue of S-abundances published to date. It covers a metallicity domain as
high as ~2.5dex starting at [M/H]~-2.0dex. We find that the [S/M] abundances
ratio is compatible with a plateau-like distribution in the metal-poor regime,
and then starts to decrease continuously at [M/H]~-1.0dex. This decrease
continues towards negative values for supersolar metallicity stars as recently
reported for Mg and as predicted by Galactic chemical evolution models.
Moreover, sulfur-rich stars having [M/H] in the range [-1.0,-0.5] have very
different kinematical and orbital properties with respect to more metal-rich
and sulfur-poor ones. Two disc components, associated with the thin and thick
discs, are thus seen independently in kinematics and sulfur abundances. The
sulfur radial gradients in the Galactic discs have also been estimated.
Finally, the enrichment in sulfur with respect to iron is nicely correlated
with stellar ages: older metal-poor stars have higher [S/M] ratios than younger
metal-rich ones. This work has confirmed that sulfur is an alfa-element that
could be considered to explore the Galactic populations properties.


Abstract:
  The major paradigm of applying a pre-trained language model to downstream
tasks is to fine-tune it on labeled task data, which often suffers instability
and low performance when the labeled examples are scarce.~One way to alleviate
this problem is to apply post-training on unlabeled task data before
fine-tuning, adapting the pre-trained model to target domains by contrastive
learning that considers either token-level or sequence-level similarity.
Inspired by the success of sequence masking, we argue that both token-level and
sequence-level similarities can be captured with a pair of masked
sequences.~Therefore, we propose complementary random masking (CRM) to generate
a pair of masked sequences from an input sequence for sequence-level
contrastive learning and then develop contrastive masked language modeling
(CMLM) for post-training to integrate both token-level and sequence-level
contrastive learnings.~Empirical results show that CMLM surpasses several
recent post-training methods in few-shot settings without the need for data
augmentation.


Abstract:
  The discovery of van der Waals (vdW) magnets opened up a new paradigm for
condensed matter physics and spintronic technologies. However, the operations
of active spintronic devices with vdW magnets are so far limited to cryogenic
temperatures, inhibiting its broader practical applications. Here, for the
first time, we demonstrate room temperature spin-valve devices using vdW
itinerant ferromagnet Fe5GeTe2 in heterostructures with graphene. The tunnel
spin polarization of the Fe5GeTe2/graphene vdW interface is detected to be
significantly large ~ 45 % and negative at room temperature. Lateral spin-valve
device design enables electrical control of spin signal and realization of
basic building blocks for device application such as efficient spin injection,
transport, precession, and detection functionalities. Furthermore, measurements
with different magnetic orientations provide unique insights into the magnetic
anisotropy of Fe5GeTe2 and its relation with spin polarization and dynamics in
the heterostructure. These findings open opportunities for the applications of
vdW magnet-based all-2D spintronic devices and integrated spin circuits at
ambient temperatures.


Abstract:
  The Landau-Lifshitz-Gilbert equation yields a mathematical model to describe
the evolution of the magnetization of a magnetic material, particularly in
response to an external applied magnetic field. It allows one to take into
account various physical effects, such as the exchange within the magnetic
material itself. In particular, the Landau-Lifshitz-Gilbert equation encodes
relaxation effects, i.e., it describes the time-delayed alignment of the
magnetization field with an external magnetic field. These relaxation effects
are an important aspect in magnetic particle imaging, particularly in the
calibration process. In this article, we address the data-driven modeling of
the system function in magnetic particle imaging, where the
Landau-Lifshitz-Gilbert equation serves as the basic tool to include relaxation
effects in the model. We formulate the respective parameter identification
problem both in the all-at-once and the reduced setting, present reconstruction
algorithms that yield a regularized solution and discuss numerical experiments.
Apart from that, we propose a practical numerical solver to the nonlinear
Landau-Lifshitz-Gilbert equation, not via the classical finite element method,
but through solving only linear PDEs in an inverse problem framework.


Abstract:
  In this work, we investigate an inverse problem of recovering multiple orders
in a time-fractional diffusion model from the data observed at one single point
on the boundary. We prove the unique recovery of the orders together with their
weights, which does not require a full knowledge of the domain or medium
properties, e.g., diffusion and potential coefficients, initial condition and
source in the model. The proof is based on Laplace transform and asymptotic
expansion. Further, inspired by the analysis, we propose a numerical procedure
for recovering these parameters based on a nonlinear least-squares fitting with
either fractional polynomials or rational approximations as the model function,
and provide numerical experiments to illustrate the approach for small time
$t$.


Abstract:
  Unmanned aerial vehicles (UAVs) have found widespread commercial, civilian,
and military applications. Wireless communication has always been one of the
core technologies for UAV. However, the communication capacity is becoming a
bottleneck for UAV to support more challenging application scenarios. The
heavily-occupied sub-6 GHz frequency band is not sufficient to meet the ultra
high-data-traffic requirements. The utilization of the millimeter-wave (mmWave)
frequency bands is a promising direction for UAV communications, where large
antenna arrays can be packed in a small area on the UAV to perform
three-dimensional (3D) beamforming. On the other hand, UAVs serving as aerial
access points or relays can significantly enhance the coverage and quality of
service of the terrestrial mmWave cellular networks. In this paper, we provide
a comprehensive survey on mmWave beamforming enabled UAV communications and
networking. The technical potential of and challenges for mmWave-UAV
communications are presented first. Then, we provide an overview on relevant
mmWave antenna structures and channel modeling. Subsequently, the technologies
and solutions for UAV-connected mmWave cellular networks and mmWave-UAV ad hoc
networks are reviewed, respectively. Finally, we present open issues and
promising directions for future research in mmWave beamforming enabled UAV
communications and networking.


Abstract:
  The majority of finite mixture models suffer from not allowing asymmetric
tail dependencies within components and not capturing non-elliptical clusters
in clustering applications. Since vine copulas are very flexible in capturing
these types of dependencies, we propose a novel vine copula mixture model for
continuous data. We discuss the model selection and parameter estimation
problems and further formulate a new model-based clustering algorithm. The use
of vine copulas in clustering allows for a range of shapes and dependency
structures for the clusters. Our simulation experiments illustrate a
significant gain in clustering accuracy when notably asymmetric tail
dependencies or/and non-Gaussian margins within the components exist. The
analysis of real data sets accompanies the proposed method. We show that the
model-based clustering algorithm with vine copula mixture models outperforms
the other model-based clustering techniques, especially for the non-Gaussian
multivariate data.


Abstract:
  The paper treats an agent-based model with averaging dynamics to which we
refer as the K-averaging model. Broadly speaking, our model can be added to the
growing list of dynamics exhibiting self-organization such as the well-known
Vicsek-type models [1, 2, 29]. In the $K$-averaging model, each of the $N$
particles updates their position by averaging over $K$ randomly selected
particles with additional noise. To make the $K$-averaging dynamics more
tractable, we first establish a propagation of chaos type result in the limit
of infinite particle number (i.e. $N \to \infty$) using a martingale technique.
Then, we prove the convergence of the limit equation toward a suitable Gaussian
distribution in the sense of Wasserstein distance as well as relative entropy.
We provide additional numerical simulations to illustrate both results.


Abstract:
  Ant species such as Temnothorax albipennis select a new nest site in a
distributed fashion that, if modeled correctly, can serve as useful information
for site selection algorithms for robotic swarms and other applications.
Studying and replicating the ants' house hunting behavior will also illuminate
useful distributed strategies that have evolved in nature. Many of the existing
models of househunting behaviour for T. albipennis make the assumption that all
candidate nest sites are equally distant from the ants' home nest, or that an
ant has an equal probability of finding each candidate nest site. However,
realistically this is not the case, as nests that are further away from the
home nest and nests that are difficult to access are less likely to be found,
even if they are of higher quality. We extend previous house-hunting models to
account for a pairwise distance metric between nests, compare our results to
those of real colonies, and use our results to examine the effects of house
hunting in nests of different spatial orientations. Our incorporation of
distances in the ant model appear to match empirical data in situations where a
distance-quality tradeoff between nests is relevant. Furthermore, the model
continues to be on par with previous house-hunting models in experiments where
all candidate nests are equidistant from the home nest, as is typically
assumed.


Abstract:
  We study a set of models of self-propelled particles that achieve collective
motion through similar alignment-based dynamics, considering versions with and
without repulsive interactions that do not affect the heading directions. We
explore their phase space within a broad range of values of two nondimensional
parameters (coupling strength and Peclet number), characterizing their
polarization and degree of clustering. The resulting phase diagrams display
equivalent, similarly distributed regions for all models with repulsion. The
diagrams without repulsion exhibit differences, in particular for high coupling
strengths. We compare the boundaries and representative states of all regions,
identifying various regimes that had not been previously characterized. We
analyze in detail three types of homogeneous polarized states, comparing them
to existing theoretical and numerical results by computing their velocity and
density correlations, giant number fluctuations, and local order-density
coupling. We find that they all deviate in one way or another from the
theoretical predictions, attributing these differences either to the remaining
inhomogeneities or to finite-size effects. We discuss our results in terms of
the universal or specific features of each model, their thermodynamic limit,
and the high mixing and low mixing regimes. Our study provides a broad,
overarching perspective on the multiple phases and states found in
alignment-based self-propelled particle models.


Abstract:
  We tackle the problem of visual search under resource constraints. Existing
systems use the same embedding model to compute representations (embeddings)
for the query and gallery images. Such systems inherently face a hard
accuracy-efficiency trade-off: the embedding model needs to be large enough to
ensure high accuracy, yet small enough to enable query-embedding computation on
resource-constrained platforms. This trade-off could be mitigated if gallery
embeddings are generated from a large model and query embeddings are extracted
using a compact model. The key to building such a system is to ensure
representation compatibility between the query and gallery models. In this
paper, we address two forms of compatibility: One enforced by modifying the
parameters of each model that computes the embeddings. The other by modifying
the architectures that compute the embeddings, leading to compatibility-aware
neural architecture search (CMP-NAS). We test CMP-NAS on challenging retrieval
tasks for fashion images (DeepFashion2), and face images (IJB-C). Compared to
ordinary (homogeneous) visual search using the largest embedding model
(paragon), CMP-NAS achieves 80-fold and 23-fold cost reduction while
maintaining accuracy within 0.3% and 1.6% of the paragon on DeepFashion2 and
IJB-C respectively.


Abstract:
  The task of classifying emotions within a musical track has received
widespread attention within the Music Information Retrieval (MIR) community.
Music emotion recognition has traditionally relied on the use of acoustic
features, verbal features, and metadata-based filtering. The role of musical
prosody remains under-explored despite several studies demonstrating a strong
connection between prosody and emotion. In this study, we restrict the input of
traditional machine learning algorithms to the features of musical prosody.
Furthermore, our proposed approach builds upon the prior by classifying
emotions under an expanded emotional taxonomy, using the Geneva Wheel of
Emotion. We utilize a methodology for individual data collection from
vocalists, and personal ground truth labeling by the artist themselves. We
found that traditional machine learning algorithms when limited to the features
of musical prosody (1) achieve high accuracies for a single singer, (2)
maintain high accuracy when the dataset is expanded to multiple singers, and
(3) achieve high accuracies when trained on a reduced subset of the total
features.


Abstract:
  We point out that the computation of true \emph{proof} and \emph{disproof}
numbers for proof number search in arbitrary directed acyclic graphs is
NP-hard, an important theoretical result for proof number search. The proof
requires a reduction from SAT, which demonstrates that finding true
proof/disproof number for arbitrary DAG is at least as hard as deciding if
arbitrary SAT instance is satisfiable, thus NP-hard.


Abstract:
  In this paper we provide a large family of rank-metric codes, which contains
properly the codes recently found by Longobardi and Zanella (2021) and by
Longobardi, Marino, Trombetti and Zhou (2021). These codes are
$\mathbb{F}_{q^{2t}}$-linear of dimension $2$ in the space of linearized
polynomials over $\mathbb{F}_{q^{2t}}$, where $t$ is any integer greater than
$2$, and we prove that they are maximum rank distance codes. For $t\ge 5$, we
determine their equivalence classes and these codes turn out to be inequivalent
to any other construction known so far, and hence they are really new.


Abstract:
  In Transport Mode Detection, a great diversity of methodologies exist
according to the choice made on sensors, preprocessing, model used, etc. In
this domain, the comparisons between each option are not always complete.
Experiments on a public, real-life dataset are led here to evaluate carefully
each of the choices that were made, with a specific emphasis on data fusion
methods. Our most surprising finding is that none of the methods we implemented
from the literature is better than a simple late fusion. Two important
decisions are the choice of a sensor and the choice of a representation for the
data: we found that using 2D convolutions on spectrograms with a logarithmic
axis for the frequencies was better than 1-dimensional temporal
representations.


Abstract:
  In this paper, the supervisory control of a Discrete Event System (DES)
analyses states and events to construct an autonomous package delivery system.
The delivery system includes a legged robot in order to autonomously navigate
uneven indoor terrain and a conveyor belt for transporting the package to the
legged robot.The aim of the paper is using the theory of supervisory control of
DES to supervise and control machine's state and event and ensure robots
autonomously collaborate. By applying the theory, we show the collaboration of
two individual robots to deliver goods in a multi-floor environment. The
obtained results from the theory of supervisory control are implemented and
verified in a simulation environment.


Abstract:
  The purpose of this paper is to prove that if on a commutative hypergroup an
exponential monomial has the property that the linear subspace of all sine
functions in its variety is one dimensional, then this exponential monomial is
a linear combination of generalized moment functions.


Abstract:
  This paper presents a comprehensive study of algorithms for maintaining the
number of all connected four-vertex subgraphs in a dynamic graph. Specifically,
our algorithms maintain the number of any four-vertex subgraph, which is not a
clique, in deterministic amortized update time $\mathcal{O}(m^{1/2})$, resp.,
$\mathcal{O}(m^{2/3})$. Queries can be answered in constant time. For length-3
paths, paws, 4-cycles, and diamonds these bounds match or are not far from
(conditional) lower bounds: Based on the OMv conjecture we show that any
dynamic algorithm that detects the existence of length-3 paths, 4-cycles,
diamonds, or 4-cliques takes amortized update time $\Omega(m^{1/2-\delta})$.
  Additionally, for 4-cliques and all connected induced subgraphs, we show a
lower bound of $\Omega(m^{1-\delta})$ for any small constant $\delta > 0$ for
the amortized update time, assuming the static combinatorial 4-clique
conjecture holds. This shows that the $\mathcal{O}(m)$ algorithm by Eppstein et
al. [9] for these subgraphs cannot be improved by a polynomial factor.


Abstract:
  Who joins extremist movements? Answering this question poses considerable
methodological challenges. Survey techniques are practically infeasible and
selective samples provide no counterfactual. Assigning recruits to contextual
units provides one solution, but is vulnerable to problems of ecological
inference. In this article, we take inspiration from epidemiology and the
protest literature and elaborate a technique to combine survey and ecological
approaches. The rare events, multilevel Bayesian contaminated case-control
design we propose accounts for individual-level and contextual factors, as well
as spatial autocorrelation in the incidence of recruitment. We validate our
approach by matching a sample of Islamic State (ISIS) fighters from nine
Muslim-majority countries with representative population surveys enumerated
shortly before recruits joined the movement. We find that high status
individuals in their early twenties who had university education were more
likely to join ISIS. We find more mixed evidence for relative deprivation.


Abstract:
  Range aggregate queries (RAQs) are an integral part of many real-world
applications, where, often, fast and approximate answers for the queries are
desired. Recent work has studied answering RAQs using machine learning models,
where a model of the data is learned to answer the queries. However, such
modelling choices fail to utilize any query specific information. To capture
such information, we observe that RAQs can be represented by query functions,
which are functions that take a query instance (i.e., a specific RAQ) as an
input and output its corresponding answer. Using this representation, we
formulate the problem of learning to approximate the query function, and
propose NeuroDB, a query specialized neural network framework, that answers
RAQs efficiently. NeuroDB is query-type agnostic (i.e., it does not make any
assumption about the underlying query type) and our observation that queries
can be represented by functions is not specific to RAQs. Thus, we investigate
whether NeuroDB can be used for other query types, by applying it to distance
to nearest neighbour queries. We experimentally show that NeuroDB outperforms
the state-of-the-art for this query type, often by orders of magnitude.
Moreover, the same neural network architecture as for RAQs is used, bringing to
light the possibility of using a generic framework to answer any query type
efficiently.


Abstract:
  In dense stellar environments, nuclei may become unstable against electron
captures and/or neutron emissions. These processes are of particular importance
for determining the internal constitution of white-dwarf cores and neutron-star
crusts. In this paper, the role of electron exchange and polarization effects
is studied. In particular, the instability condition for the onset of electron
captures and neutron emissions is extended so as to account for electron
exchange and polarization. Moreover, general analytical expressions for the
corresponding density and pressure are derived. The corrections to the
electron-capture threshold in white-dwarf cores are found to be very small.
Likewise, the neutron-drip density and pressure in the crusts of accreting and
nonaccreting neutron stars are only slightly shifted. Depending on the nuclear
mass model employed, electron polarization may change the composition of the
crust of nonaccreting neutron stars. On the other hand, the current
uncertainties in the masses of neutron-rich Kr and Sr isotopes are found to be
more important than electron exchange and polarization effects.


Abstract:
  Motivated by the recent progresses in the formulation of geometric theories
for the fractional quantum Hall states, we propose a novel non-relativistic
geometric model for the Laughlin states based on an extension of the
Nappi-Witten geometry. We show that the U(1) gauge sector responsible for the
fractional Hall conductance, the gravitational Chern-Simons action and Wen-Zee
term associated to the Hall viscosity can be derived from a single Chern-Simons
theory with a gauge connection that takes values in the extended Nappi-Witten
algebra. We then provide a new derivation of the chiral boson associated to the
gapless edge states from the Wess-Zumino-Witten model that is induced by the
Chern-Simons theory on the boundary.


Abstract:
  We address the Mach limit problem for the Euler equations in the analytic
spaces. We prove that, given analytic data, the solutions to the compressible
Euler equations are uniformly bounded in a suitable analytic norm and then show
that the convergence toward the incompressible Euler solution holds in the
analytic norm. We also show that the same results hold more generally for
Gevrey data with the convergence in the Gevrey norms.


Abstract:
  We propose novel triangle relations, not the well-known triangle singularity,
for better understanding of the exotic XYZ states. Nine XYZ resonances,
X(3872), Y(4230), Zc(3900), X(4012), Y(4360/4390), Zc(4020), X(4274), Y(4660),
and Zcs(3985) have been classified into triples to construct three triangles
based on the assumption that they are all tetra-quark states. We also suggest
some channels deserving search for with high priority based on this hypothesis,
as well as predictions of a few production/decay rates of these channels. We
hope further experimental studies of the XYZ states will benefit from our
results.


Abstract:
  We calculate the Gromov--Hausdorff distance between a line segment and a
circle in the Euclidean plane. To do that, we introduced a few new notions like
round spaces and nonlinearity degree of a metric space.


Abstract:
  In this paper we present a benchmark dataset generated as part of a project
for automatic identification of misogyny within online content, which focuses
in particular on memes. The benchmark here described is composed of 800 memes
collected from the most popular social media platforms, such as Facebook,
Twitter, Instagram and Reddit, and consulting websites dedicated to collection
and creation of memes. To gather misogynistic memes, specific keywords that
refer to misogynistic content have been considered as search criterion,
considering different manifestations of hatred against women, such as body
shaming, stereotyping, objectification and violence. In parallel, memes with no
misogynist content have been manually downloaded from the same web sources.
Among all the collected memes, three domain experts have selected a dataset of
800 memes equally balanced between misogynistic and non-misogynistic ones. This
dataset has been validated through a crowdsourcing platform, involving 60
subjects for the labelling process, in order to collect three evaluations for
each instance. Two further binary labels have been collected from both the
experts and the crowdsourcing platform, for memes evaluated as misogynistic,
concerning aggressiveness and irony. Finally for each meme, the text has been
manually transcribed. The dataset provided is thus composed of the 800 memes,
the labels given by the experts and those obtained by the crowdsourcing
validation, and the transcribed texts. This data can be used to approach the
problem of automatic detection of misogynistic content on the Web relying on
both textual and visual cues, facing phenomenons that are growing every day
such as cybersexism and technology-facilitated violence.


Abstract:
  We present a strangeness-neutral equation of state for QCD that exhibits
critical behavior and matches lattice QCD results for the Taylor-expanded
thermodynamic variables up to fourth-order in $\mu_B/T$. It is compatible with
the SMASH hadronic transport approach and has a range of temperatures and
baryonic chemical potentials relevant for phase II of the Beam Energy Scan at
RHIC. We provide an updated version of the software BES-EoS, which produces an
equation of state for QCD that includes a critical point in the 3D Ising model
universality class. This new version also includes isentropic trejectories and
the critical contribution to the correlation length. Since heavy-ion collisions
have zero global net-strangeness density and a fixed ratio of electric charge
to baryon number, the BES-EoS is more suitable to describe this system.
Comparison with the previous version of the EoS is thoroughly discussed.


Abstract:
  Purpose: The purpose of this work was to provide a flexible platform for
FLASH research with protons by adapting a former clinical pencil beam scanning
gantry to irradiations with ultrahigh dose rates.
  Methods: PSI Gantry 1 treated patients until December 2018. We optimized the
beamline parameters to transport the 250 MeV beam extracted from the PSI COMET
accelerator to the treatment room, maximizing the transmission of beam
intensity to the sample. We characterized a dose monitor on the gantry to
ensure good control of the dose, delivered in spot-scanning mode. We
characterized the beam for different dose rates and field sizes for
transmission irradiations. We explored scanning possibilities in order to
enable conformal irradiations or transmission irradiations of large targets
(with transverse scanning). Results: We achieved a transmission of 86 % from
the cyclotron to the treatment room. We reached a peak dose rate of 9000 Gy/s
at 3 mm water equivalent depth, along the central axis of a single pencil beam.
Field sizes of up to 5x5 mm$^{2}$ were achieved for single spot FLASH
irradiations. Fast transverse scanning allowed to cover a field of 16x1.2
cm$^{2}$. With the use of a nozzle-mounted range shifter we are able to span
depths in water ranging from 19.6 to 37.9 cm. Various dose levels were
delivered with a precision within less than 1 %. Conclusions: We have realized
a proton FLASH irradiation setup able to investigate continuously a wide dose
rate spectrum, from 1 to 9000 Gy/s in a single spot irradiation as well as in
the pencil beam scanning mode. As such, we have developed a versatile test
bench for FLASH research.


Abstract:
  We establish the most general Szasz type estimates for homogeneous Besov and
Lizorkin-Triebel spaces, and their realizations.


Abstract:
  Context. The Vista Variables in the Via Lactea (VVV) near-infrared
variability survey explores some of the most complex regions of the Milky Way
bulge and disk in terms of high extinction and high crowding. Aims. We add a
new wavelength dimension to the optical information available at the American
Association of Variable Star Observers International Variable Star Index
(VSX-AAVSO) catalogue to test the VVV survey near-infrared photometry to better
characterise these objects. Methods. We cross-matched the VVV and the VSX-AAVSO
catalogues along with Gaia Data Release 2 photometry and parallax. Results. We
present a catalogue that includes accurate individual coordinates,
near-infrared magnitudes (ZY JHKs), extinctions Aks, and distances based on
Gaia parallaxes. We also show the near-infrared CMDs and spatial distributions
for the different VSX types of variable stars, including important distance
indicators, such as RR Lyrae, Cepheids, and Miras. By analysing the photometric
flags in our catalogue, we found that about 20% of the stars with measured and
verified variability are flagged as non-stellar sources, even when they are
outside of the saturation and/or noise regimes. Additionally, we pair-matched
our sample with the VIVA catalogue and found that more than half of our sources
are missing from the VVV variability list, mostly due to observations with low
signal-to-noise ratio or photometric problems with a low percentage due to
failures in the selection process. Conclusions. Our results suggest that the
current knowledge of the variability in the Galaxy is biased to nearby stars
with low extinction. The present catalogue also provides the groundwork for
characterising the results of future large variability surveys such as the Vera
C. Rubin Observatory Legacy Survey of Space and Time in the highly crowded and
reddened regions of the Galactic plane, as well as follow-up campaigns for


Abstract:
  Virtualization enables information and communications technology industry to
better manage computing resources. In this regard, improvements in
virtualization approaches together with the need for consistent runtime
environment, lower overhead and smaller package size has led to the growing
adoption of containers. This is a technology, which packages an application,
its dependencies and Operating System (OS) to run as an isolated unit. However,
the pressing concern with the use of containers is its susceptibility to
security attacks. Consequently, a number of container scanning tools are
available for detecting container security vulnerabilities. Therefore, in this
study, we investigate the quality of existing container scanning tools by
proposing two metrics that reflects coverage and accuracy. We analyze 59
popular public container images for Java applications hosted on DockerHub using
different container scanning tools (such as Clair, Anchore, and Microscanner).
Our findings show that existing container scanning approach does not detect
application package vulnerabilities. Furthermore, existing tools do not have
high accuracy, since 34% vulnerabilities are being missed by the best
performing tool. Finally, we also demonstrate quality of Docker images for Java
applications hosted on DockerHub by assessing complete vulnerability landscape
i.e., number of vulnerabilities detected in images.


Abstract:
  We compute symplectic cohomology for Milnor fibres of certain compound Du Val
singularities that admit small resolution by using homological mirror symmetry.
Our computations suggest a new conjecture that the existence of a small
resolution has strong implications for the symplectic cohomology and
conversely. We also use our computations to give a contact invariant of the
link of the singularities and thereby distinguish many contact structures on
connected sums of $S^2 \times S^3$.


Abstract:
  Neutrino transport and neutrino-matter interactions are known to play an
important role in the evolution of neutron star mergers, and of their
post-merger remnants. Neutrinos cool remnants, drive post-merger winds, and
deposit energy in the low-density polar regions where relativistic jets may
eventually form. Neutrinos also modify the composition of the ejected material,
impacting the outcome of nucleosynthesis in merger outflows and the properties
of the optical/infrared transients that they power (kilonovae). So far, merger
simulations have largely relied on approximate treatments of the neutrinos
(leakage, moments) that simplify the equations of radiation transport in a way
that makes simulations more affordable, but also introduces unquantifiable
errors in the results. To improve on these methods, we recently published a
first simulation of neutron star mergers using a low-cost Monte-Carlo algorithm
for neutrino radiation transport. Our transport code limits costs in optically
thick regions by placing a hard ceiling on the value of the absorption opacity
of the fluid, yet all approximations made within the code are designed to
vanish in the limit of infinite numerical resolution. We provide here an
in-depth description of this algorithm, of its implementation in the SpEC
merger code, and of the expected impact of our approximations in optically
thick regions. We argue that the latter is a subdominant source of error at the
accuracy reached by current simulations, and for the interactions currently
included in our code. We also provide tests of the most important features of
this code.


Abstract:
  This paper provides theoretical and practical arguments regarding the
possibility of predicting strong and major earthquakes worldwide. Many strong
and major earthquakes can be predicted at least two to five months in advance,
based on identifying stressed areas that begin to behave abnormally before
strong events, with the size of these areas corresponding to Dobrovolsky
formula. We make predictions by combining knowledge from many different
disciplines: physics, geophysics, seismology, geology, and earth science, among
others. An integrated approach is used to identify anomalies and make
predictions, including satellite remote sensing techniques and data from
ground-based instruments. Terabytes of information are currently processed
every day with many different multi-parametric prediction systems applied
thereto. Alerts are issued if anomalies are confirmed by a few different
systems. It has been found that geophysical patterns of earthquake preparation
and stress accumulation are similar for all key seismic regions. The same
earthquake prediction methodologies and systems have been successfully applied
in global practice since 2013, with the technology successfully used to
retrospectively test against more than 700 strong and major earthquakes since
1970.


Abstract:
  A high-rate yet low-cost air-to-ground (A2G) communication backbone is
conceived for integrating the space and terrestrial network by harnessing the
opportunistic assistance of the passenger planes or high altitude platforms
(HAPs) as mobile base stations (BSs) and millimetre wave communication. The
airliners act as the network-provider for the terrestrial users while relying
on satellite backhaul. Three different beamforming techniques relying on a
large-scale planar array are used for transmission by the airliner/HAP for
achieving a high directional gain, hence minimizing the interference among the
users. Furthermore, approximate spectral efficiency (SE) and area spectral
efficiency (ASE) expressions are derived and quantified for diverse system
parameters.


Abstract:
  We present a new high-resolution, non-oscillatory semi-discrete
central-upwind scheme for one-dimensional two-layer shallow-water flows with
friction and entrainment along channels with arbitrary cross sections and
bottom topography. These flows are described by a conditionally hyperbolic
balance law with non-conservative products. A detailed description of the
properties of the model is provided, including entropy inequalities and
asymptotic approximations of the eigenvalues of the corresponding coefficient
matrix. The scheme extends existing central-upwind semi-discrete numerical
methods for hyperbolic conservation and balance laws and it satisfies two
properties crucial for the accurate simulation of shallow-water flows: it {\it
preserves the positivity} of the water depth for each layer, and it is {\it
well balanced}, i.e., the source terms arising from the geometry of the channel
are discretized so as to balance the non-linear hyperbolic flux gradients.
Along with the description of the scheme and proofs of these two properties, we
present several numerical experiments that demonstrate the robustness of the
numerical algorithm.


Abstract:
  Let ${H^p}\left( \mathbb{D} \right)$ be the Hardy space of all holomorphic
functions on the unit disk $\mathbb{D}$ with exponent $p>0$. If $D\ne
\mathbb{C}$ is a simply connected domain and $f$ is the Riemann mapping from
$\mathbb{D}$ onto $D$, then the Hardy number of $D$, introduced by Hansen, is
the supremum of all $p$ for which $f \in {H^p}\left( \mathbb{D} \right)$. Comb
domains are a well-studied class of simply connected domains that, in general,
have the form of the entire plane minus an infinite number of vertical rays. In
this paper we study the Hardy number of a class of comb domains with the aid of
the quasi-hyperbolic distance and we establish a necessary and sufficient
condition for the Hardy number of these domains to be equal to infinity.
Applying this condition, we derive several results that show how the mutual
distances and the distribution of the rays affect the finiteness of the Hardy
number. By a result of Burkholder our condition is also necessary and
sufficient for all moments of the exit time of Brownian motion from comb
domains to be infinite.


Abstract:
  Modern machine learning increasingly requires training on a large collection
of data from multiple sources, not all of which can be trusted. A particularly
concerning scenario is when a small fraction of poisoned data changes the
behavior of the trained model when triggered by an attacker-specified
watermark. Such a compromised model will be deployed unnoticed as the model is
accurate otherwise. There have been promising attempts to use the intermediate
representations of such a model to separate corrupted examples from clean ones.
However, these defenses work only when a certain spectral signature of the
poisoned examples is large enough for detection. There is a wide range of
attacks that cannot be protected against by the existing defenses. We propose a
novel defense algorithm using robust covariance estimation to amplify the
spectral signature of corrupted data. This defense provides a clean model,
completely removing the backdoor, even in regimes where previous methods have
no hope of detecting the poisoned examples. Code and pre-trained models are
available at https://github.com/SewoongLab/spectre-defense .


Abstract:
  We compute the quasinormal frequencies of $d$-dimensional spherically
symmetric black holes with leading string $\alpha'$ corrections in the eikonal
limit for tensorial gravitational perturbations and scalar test fields. We find
that, differently than in Einstein gravity, the real parts of the frequency are
no longer equal for these two cases. The corresponding imaginary parts remain
equal to the principal Lyapunov exponent corresponding to circular null
geodesics, to first order in $\alpha'$. We also compute the radius of the
shadow cast by these black holes.


Abstract:
  The set of all centralizers of elements in a finite group $G$ is denoted by
$Cent(G)$ and $G$ is called $n-$centralizer if $|Cent(G)| = n$. In this paper,
the structure of centralizers in a non-abelian finite group $G$ with this
property that $\frac{G}{Z(G)} \cong Z_{p^2} \rtimes Z_{p^2}$ is obtained. As a
consequence, it is proved that such a group has exactly $[(p+1)^2+1]$ element
centralizers and the structure of the commuting conjugacy class graph of $G$ is
completely determined.


Abstract:
  Dix formulated the inverse problem of recovering an elastic body from the
measurements of wave fronts of point scatterers. We geometrize this problem in
the framework of linear elasticity, leading to the geometrical inverse problem
of recovering a Finsler manifold from certain sphere data in a given open
subset of the manifold. We solve this problem locally along any geodesic
through the measurement set.


Abstract:
  Limited measurement availability at the distribution grid presents challenges
for state estimation and situational awareness. This paper combines the
advantages of two sparsity-based state estimation approaches (matrix completion
and compressive sensing) that have been proposed recently to address the
challenge of unobservability. The proposed approach exploits both the low rank
structure and a suitable transform domain representation to leverage the
correlation structure of the spatio-temporal data matrix while incorporating
the power-flow constraints of the distribution grid. Simulations are carried
out on three phase unbalanced IEEE 37 test system to verify the effectiveness
of the proposed approach. The performance results reveal - (1) the superiority
over traditional matrix completion and (2) very low state estimation errors for
high compression ratios representing very low observability.


Abstract:
  In this article we present some new comparisons between the Heinz and Heron
operator means, which improve some recent results known from the literature. We
derive some refinements of these inequalities for unitarily invariant norms
with the help of the contractive maps.