title:
Realization of zero-divisor graphs of finite commutative rings as
  threshold graphs

abstract:
  Let $R$ be a finite commutative ring with unity and let $G = (V, E)$ be a
simple graph. The zero-divisor graph denoted by $\Gamma(R)$ is a simple graph
with vertex set as $R$ and two vertices $x, y \in R$ are adjacent in
$\Gamma(R)$ if and only if $xy=0$. In \cite{SP}, the authors have studied the
Laplacian eigen values of the graph $\Gamma(\Z_{n})$ and for distinct proper
divisors $d_1, d_2, \cdots, d_k$ of $n$, they defined the sets as
$\mathcal{A}_{d_i} = \{x \in \Z_{n} : (x, n) = d_i\}$, where $(x, n)$ denotes
the greatest common divisor of $x$ and $n$. In this paper, we show that the
sets $\mathcal{A}_{d_i}$, where $1 \leq i \leq k$ are actually the orbits of
the group action: $Aut(\Gamma(R)) \times R \longrightarrow R$, where
$Aut(\Gamma(R))$ denotes the automorphism group of $\Gamma(R)$. We characterize
all finite commutative rings with unity of which zero-divisor graphs are not
\textit{threshold}. We study creation sequences, hyperenergeticity and
hypoenergeticity of zero-divisor graphs. We compute the Laplacian eigenvalues
of zero-divisor graphs realized by some classes of reduced and local rings. We
show that the Laplacian eigenvalues of zero-divisor graphs are the
representatives of orbits of the group action:$Aut(\Gamma(R)) \times R
\longrightarrow R$.


title:
Micromanufacturing of geometrically- and dimensionally-precise molecular
  single-crystal photonic micro-resonators via focused ion beam milling

abstract:
  Highly reproducible manufacturing of organic optical crystals with
well-defined geometry and dimension is important to realize industrially
relevant all-organic microelectronic and nanophotonic components, and photonic
integrated circuits. Here, we demonstrate programmed shape and size alteration
of perylene crystal resonators into disk and rectangular geometries using
focused-ion beam milling technique. Due to highly reproducible nature of the
employed technique, the fabricated smaller sized disk and rectangular crystal
resonators displaying shape and size dependent optical modes are suitable for
commercial nanophotonic applications.


title: Self-similar inhomogeneous stationary states under constrained dynamics

abstract:
  The dynamics of $n$ rigid objects, each having $d$ degrees of freedom, is
played out in the configuration space of dimension $nd$. Being rigid, there are
additional constraints at work that render a portion of the configuration space
inaccessible. In this paper, we make the assertion that treating the overall
dynamics as a Markov process whose states are defined by the number of contacts
made between the rigid objects provides an effective coarse grained
characterization of the otherwise complex phenomenon. This coarse graining
reduces the dimensionality of the space from $nd$ to one. We test this
assertion for a one dimensional array of curved squares each of which is
undergoing a biased diffusion in its angular orientation.


title: Astrophysical reaction rates with realistic nuclear level densities

abstract:
  Realistic nuclear level densities (NLDs) obtained within the spectral
distribution method (SDM) are employed to study nuclear processes of
astrophysical interest. The merit of SDM lies in the fact that the NLDs
corresponding to many body shell model Hamiltonian consisting of residual
interaction can be obtained for the full configurational space without recourse
to the exact diagnolization of huge matrices. We calculate NLDs and s-wave
neutron resonance spacings which agree reasonably well with the available
experimental data. By employing these NLDs, we compute reaction cross-sections
and astrophysical reaction rates for radiative neutron capture in few Fe-group
nuclei, and compare them with experimental data as well as with those obtained
with NLDs from phenomenological and microscopic mean-field models. The results
obtained for the NLDs from SDM are able to explain the experimental data quite
well. These results are of particular importance since the configuration mixing
through the residual interaction naturally accounts for the collective
excitations. In the mean-field models, the collective effects are included
through the vibrational and rotational enhancement factors and their NLDs are
further normalized at low energies with neutron resonance data.


title: How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?

abstract:
  Despite the recent success of video self-supervised learning, there is much
still to be understood about their generalization capability. In this paper, we
investigate how sensitive video self-supervised learning is to the currently
used benchmark convention and whether methods generalize beyond the canonical
evaluation setting. We do this across four different factors of sensitivity:
domain, samples, actions and task. Our comprehensive set of over 500
experiments, which encompasses 7 video datasets, 9 self-supervised methods and
6 video understanding tasks, reveals that current benchmarks in video
self-supervised learning are not a good indicator of generalization along these
sensitivity factors. Further, we find that self-supervised methods considerably
lag behind vanilla supervised pre-training, especially when domain shift is
large and the amount of available downstream samples are low. From our analysis
we distill the SEVERE-benchmark, a subset of our experiments, and discuss its
implication for evaluating the generalizability of representations obtained by
existing and future self-supervised video learning methods.


title:
Listen, Adapt, Better WER: Source-free Single-utterance Test-time
  Adaptation for Automatic Speech Recognition

abstract:
  Although deep learning-based end-to-end Automatic Speech Recognition (ASR)
has shown remarkable performance in recent years, it suffers severe performance
regression on test samples drawn from different data distributions. Test-time
Adaptation (TTA), previously explored in the computer vision area, aims to
adapt the model trained on source domains to yield better predictions for test
samples, often out-of-domain, without accessing the source data. Here, we
propose the Single-Utterance Test-time Adaptation (SUTA) framework for ASR,
which is the first TTA study in speech area to our best knowledge. The
single-utterance TTA is a more realistic setting that does not assume test data
are sampled from identical distribution and does not delay on-demand inference
due to pre-collection for the batch of adaptation data. SUTA consists of
unsupervised objectives with an efficient adaptation strategy. The empirical
results demonstrate that SUTA effectively improves the performance of the
source ASR model evaluated on multiple out-of-domain target corpora and
in-domain test samples.


title:
Causal Network Influence with Latent Homophily and Measurement Error: An
  Application to Therapeutic Community

abstract:
  The Spatial or Network Autoregressive model (SAR, NAM) is popular for
modeling the influence network connected neighbors exert on the outcome of
individuals. However, many authors have noted that the \textit{causal} network
influence or contagion cannot be identified from observational data due to the
presence of homophily. We propose a latent homophily-adjusted spatial
autoregressive model for networked responses to identify the causal contagion
and contextual effects. The latent homophily is estimated from the spectral
embedding of the network's adjacency matrix. Separately, we develop maximum
likelihood estimators for the parameters of the SAR model correcting for
measurement error when covariates are measured with error. We show that the
bias corrected MLE are consistent and derive its asymptotic limiting
distribution. We propose to estimate network influence using the bias corrected
MLE in a SAR model with the estimated latent homophily added as a covariate.
Our simulations show that the methods perform well in finite sample. We apply
our methodology to a data-set of female criminal offenders in a therapeutic
community (TC) for substance abuse and criminal behavior. We provide causal
estimates of network influence on graduation from TC and re-incarceration after
accounting for latent homophily.


title: Tests of the Z_c-like Laplace Sum Rule (LSR) results using FESR at NLO

abstract:
  In this note, we use local duality Finite Energy Sum Rule (FESR) to test the
validity of the Laplace sum rules (LSR) results truncated at the dimension-six
condensates for the estimates of the masses and couplings of the Z_c-like
ground states in Ref.1 by taking the example of the D^*D molecule
configuration. We confirm the existence of an eventual (D^*D)_1 radial
excitation with a mass around 5700 MeV and coupling of 197(25) keV to the
current which may mask the eventual Z_c(4430) radial excitation candidate
(named (D^*D)_0 in Ref.1) having a relatively small coupling
f_{(D^*D)_0}=46(56) keV. We add more explanations on the estimates in Ref.1
from LSR and comment the results in Ref.2.


title: Approximately Counting Subgraphs in Data Streams

abstract:
  Estimating the number of subgraphs in data streams is a fundamental problem
that has received great attention in the past decade. In this paper, we give
improved streaming algorithms for approximately counting the number of
occurrences of an arbitrary subgraph $H$, denoted $\# H$, when the input graph
$G$ is represented as a stream of $m$ edges. To obtain our algorithms, we
provide a generic transformation that converts constant-round sublinear-time
graph algorithms in the query access model to constant-pass sublinear-space
graph streaming algorithms. Using this transformation, we obtain the following
results. 1. We give a $3$-pass turnstile streaming algorithm for $(1\pm
\epsilon)$-approximating $\# H$ in
$\tilde{O}(\frac{m^{\rho(H)}}{\epsilon^2\cdot \# H})$ space, where $\rho(H)$ is
the fractional edge-cover of $H$. This improves upon and generalizes a result
of McGregor et al. [PODS 2016], who gave a $3$-pass insertion-only streaming
algorithm for $(1\pm \epsilon)$-approximating the number $\# T$ of triangles in
$\tilde{O}(\frac{m^{3/2}}{\epsilon^2\cdot \# T})$ space if the algorithm is
given additional oracle access to the degrees. 2. We provide a constant-pass
streaming algorithm for $(1\pm \epsilon)$-approximating $\# K_r$ in
$\tilde{O}(\frac{m\lambda^{r-2}}{\epsilon^2\cdot \# K_r})$ space for any $r\geq
3$, in a graph $G$ with degeneracy $\lambda$, where $K_r$ is a clique on $r$
vertices. This resolves a conjecture by Bera and Seshadhri [PODS 2020]. More
generally, our reduction relates the adaptivity of a query algorithm to the
pass complexity of a corresponding streaming algorithm, and it is applicable to
all algorithms in standard sublinear-time graph query models, e.g., the
(augmented) general model.


title:
$n$-Lie conformal algebras and its associated infinite-dimensional
  $n$-Lie algebras

abstract:
  In this paper, we introduce a $\{\lambda_{1\to n-1}\}$-bracket and a
distribution notion of an $n$-Lie conformal algebra. For any $n$-Lie conformal
algebra $R$, there exists a series of associated infinite-dimensional linearly
compact $n$-Lie algebras $\{(\mathscr{L}ie_p\mbox{ }R)_\_\}_{(p\ge1)}$. We show
that torsionless finite $n$-Lie conformal algebras $R$ and $S$ are isomorphic
if and only if $(\mathscr{L}ie_p\mbox{ }R)_\_\simeq (\mathscr{L}ie_p\mbox{
}S)_\_$ as linearly compact $n$-Lie algebras with $\partial_{t_i}$-action for
any $p\ge1$. Moreover, the representation and cohomology theory of $n$-Lie
conformal algebras are established. In particular, the complex of $R$ is
isomorphic to a subcomplex of $n$-Lie algebra $(\mathscr{L}ie_p\mbox{ }R)_\_$.


title: OneLabeler: A Flexible System for Building Data Labeling Tools

abstract:
  Labeled datasets are essential for supervised machine learning. Various data
labeling tools have been built to collect labels in different usage scenarios.
However, developing labeling tools is time-consuming, costly, and
expertise-demanding on software development. In this paper, we propose a
conceptual framework for data labeling and OneLabeler based on the conceptual
framework to support easy building of labeling tools for diverse usage
scenarios. The framework consists of common modules and states in labeling
tools summarized through coding of existing tools. OneLabeler supports
configuration and composition of common software modules through visual
programming to build data labeling tools. A module can be a human, machine, or
mixed computation procedure in data labeling. We demonstrate the expressiveness
and utility of the system through ten example labeling tools built with
OneLabeler. A user study with developers provides evidence that OneLabeler
supports efficient building of diverse data labeling tools.


title:
The study of 13C({\alpha},n)16O reactions in the Majorana Demonstrator
  calibration data

abstract:
  Neutron captures and delayed decays of reaction products are common sources
of backgrounds in ultra-rare event searches. In this work, we studied
$^{13}$C($\alpha,n)^{16}$O reactions induced by $\alpha$-particles emitted
within the calibration sources of the \textsc{Majorana Demonstrator}. These
sources are thorium-based calibration standards enclosed in carbon-rich
materials. The reaction rate was estimated by using the 6129-keV $\gamma$-rays
emitted from the excited $^{16}$O states that are populated when the incoming
$\alpha$-particles exceed the reaction Q-value. Thanks to the excellent energy
performance of the \textsc{Demonstrator}'s germanium detectors, these
characteristic photons can be clearly observed in the calibration data.
Facilitated by \textsc{Geant4} simulations, a comparison between the observed
6129-keV photon rates and predictions by a TALYS-based software was performed.
The measurements and predictions were found to be consistent, albeit with large
statistical uncertainties. This agreement provides support for background
projections from ($\alpha,n$)-reactions in future double-beta decay search
efforts.


title: CMB two-point angular correlation function in the Ellipsoidal Universe

abstract:
  We suggest that the Ellipsoidal Universe cosmological model, proposed several
years ago to account for the low quadrupole temperature-temperature correlation
of the Cosmic Microwave Background, can also provide temperature-temperature
two-point angular correlation function in reasonable agreement with Planck
observations.


title: DC Quantum Magnetometry Below the Ramsey Limit

abstract:
  We demonstrate quantum sensing of dc magnetic fields that exceeds the
sensitivity of conventional $T_2^\ast$-limited dc magnetometry by more than an
order of magnitude. We used nitrogen-vacancy centers in a diamond rotating at
periods comparable to the spin coherence time, and characterize the dependence
of magnetic sensitivity on measurement time and rotation speed. Our method
up-converts only the dc field of interest and preserves the quantum coherence
of the sensor. These results definitively improve the sensitivity of a quantum
magnetometer to dc fields, an important and useful addition to the quantum
sensing toolbox.


title:
Characterizations for the existence of traces of first-order Sobolev
  spaces on hyperbolic fillings

abstract:
  In this paper, we study the existence of traces for Sobolev spaces on the
hyperbolic filling $X$ of a compact metric space $Z$ equipped with a doubling
measure. Given a suitable metric on $X$, we can regard $Z$ as the boundary of
$X$. After equipping $X$ with a weighted measure $\mu$ via the measure on $Z$
and the Euclidean arc length, we give characterizations for the existence of
traces for first-order Sobolev spaces.


title: Leveraging Search History for Improving Person-Job Fit

abstract:
  As the core technique of online recruitment platforms, person-job fit can
improve hiring efficiency by accurately matching job positions with qualified
candidates. However, existing studies mainly focus on the recommendation
scenario, while neglecting another important channel for linking positions with
job seekers, i.e. search. Intuitively, search history contains rich user
behavior in job seeking, reflecting important evidence for job intention of
users. In this paper, we present a novel Search History enhanced Person-Job Fit
model, named as SHPJF. To utilize both text content from jobs/resumes and
search histories from users, we propose two components with different purposes.
For text matching component, we design a BERT-based text encoder for capturing
the semantic interaction between resumes and job descriptions. For intention
modeling component, we design two kinds of intention modeling approaches based
on the Transformer architecture, either based on the click sequence or query
text sequence. To capture underlying job intentions, we further propose an
intention clustering technique to identify and summarize the major intentions
from search logs. Extensive experiments on a large real-world recruitment
dataset have demonstrated the effectiveness of our approach.


title: Multi-phase image segmentation by the Allen--Cahn Chan--Vese model

abstract:
  This paper proposes an Allen-Cahn Chan-Vese model to settle the multi-phase
image segmentation. We first integrate the Allen--Cahn term and the Chan--Vese
fitting energy term to establish an energy functional, whose minimum locates
the segmentation contour. The subsequent minimization process can be attributed
to variational calculation on fitting intensities and the solution
approximation of several Allen-Cahn equations, wherein $n$ Allen-Cahn equations
are enough to partition $m = 2^n$ segments. The derived Allen-Cahn equations
are solved by efficient numerical solvers with exponential time integrations
and finite difference space discretization. The discrete maximum bound
principle and energy stability of the proposed numerical schemes are proved.
Finally, the capability of our segmentation method is verified in various
experiments for different types of images.


title: Making a case for femto- phono- magnetism with FePt

abstract:
  In the field of femtomagnetism magnetic matter is controlled by ultrafast
laser pulses; here we show that coupling phonon excitations of the nuclei to
spin and charge leads to femto-phono-magnetism, a powerful route to control
magnetic order at ultrafast times. With state-of-the-art theoretical
simulations of coupled spin-, charge-, and lattice-dynamics we identify strong
non-adiabatic spin-phonon coupled modes that dominate early time spin dynamics.
Activating these phonon modes we show leads to an additional (up to 40\% extra)
loss of moment in FePt occurring within 40 femtoseconds of the pump laser
pulse. Underpinning this enhanced ultrafast loss of spin moment we identify a
physical mechanism in which minority spin-current drives an enhanced inter-site
minority charge transfer, in turn promoting increased on-site spin flips. Our
finding demonstrates that the nuclear system, often assumed to play only the
role of an energy sink aiding long time re-magnetisation of the spin system,
can play a profound role in controlling femtosecond spin-dynamics in materials.


title:
On the structure of singular points of a solution to Newton's least
  resistance problem

abstract:
  We consider the following problem stated in 1993 by Buttazzo and Kawohl:
minimize the functional $\int\!\!\int_\Omega (1 + |\nabla u(x,y)|^2)^{-1} dx\,
dy$ in the class of concave functions $u: \Omega \to [0,M]$, where $\Omega
\subset \mathbb{R}^2$ is a convex domain and $M > 0$. It generalizes the
classical minimization problem, which was initially stated by I. Newton in 1687
in the more restricted class of radial functions. The problem is not solved
until now; there is even nothing known about the structure of singular points
of a solution.
  In this paper we, first, solve a family of auxiliary 2D least resistance
problems and, second, apply the obtained results to study singular points of a
solution to our original problem. More precisely, we derive a necessary
condition for a point being a ridge singular point of a solution and prove, in
particular, that all ridge singular points with horizontal edge lie on the top
level and zero level sets.


title:
Determining the number of factors in a large-dimensional generalised
  factor model

abstract:
  This paper proposes new estimators of the number of factors for a generalised
factor model with more relaxed assumptions than the strict factor model. Under
the framework of large cross-sections $N$ and large time dimensions $T$, we
first derive the bias-corrected estimator $\hat \sigma^2_*$ of the noise
variance in a generalised factor model by random matrix theory. Then we
construct three information criteria based on $\hat \sigma^2_*$, further
propose the consistent estimators of the number of factors. Finally,
simulations and real data analysis illustrate that our proposed estimations are
more accurate and avoid the overestimation in some existing works.


title: Causality Inspired Representation Learning for Domain Generalization

abstract:
  Domain generalization (DG) is essentially an out-of-distribution problem,
aiming to generalize the knowledge learned from multiple source domains to an
unseen target domain. The mainstream is to leverage statistical models to model
the dependence between data and labels, intending to learn representations
independent of domain. Nevertheless, the statistical models are superficial
descriptions of reality since they are only required to model dependence
instead of the intrinsic causal mechanism. When the dependence changes with the
target distribution, the statistic models may fail to generalize. In this
regard, we introduce a general structural causal model to formalize the DG
problem. Specifically, we assume that each input is constructed from a mix of
causal factors (whose relationship with the label is invariant across domains)
and non-causal factors (category-independent), and only the former cause the
classification judgments. Our goal is to extract the causal factors from inputs
and then reconstruct the invariant causal mechanisms. However, the theoretical
idea is far from practical of DG since the required causal/non-causal factors
are unobserved. We highlight that ideal causal factors should meet three basic
properties: separated from the non-causal ones, jointly independent, and
causally sufficient for the classification. Based on that, we propose a
Causality Inspired Representation Learning (CIRL) algorithm that enforces the
representations to satisfy the above properties and then uses them to simulate
the causal factors, which yields improved generalization ability. Extensive
experimental results on several widely used datasets verify the effectiveness
of our approach.


title:
Bifurcation theory captures band formation in the Vicsek model of flock
  formation

abstract:
  Collective behavior occurs ubiquitously in nature and it plays a key role in
bacterial colonies, mammalian cells or flocks of birds. Here, we examine the
average density and velocity of self-propelled particles, which are described
by a system of partial differential equations near the flocking transition of
the Vicsek model. This agent-based model illustrates the trend towards flock
formation of animals that align their velocities to an average of those of
their neighbors. Near the flocking transition, particle density and velocity
obey partial differential equations that include a parameter $\epsilon$
measuring the distance to the bifurcation point. We have obtained analytically
the Riemann invariants in one and two spatial dimensions for the hyperbolic
($\epsilon=0$) and parabolic ($\epsilon\neq 0$) system and, under periodic
initial-boundary value conditions, we show that the solutions include wave
trains. Additionally, we have found wave trains having oscillation frequencies
that agree with those predicted by a linearization approximation and that may
propagate at angles depending on the initial condition. The wave amplitudes
increase with time for the hyperbolic system but are stabilized to finite
values for the parabolic system. To integrate the partial differential
equations, we design a basic numerical scheme which is first order in time and
space. To mitigate numerical dissipation and ensure good resolution of the wave
features, we also use a high order accurate WENO5 reconstruction procedure in
space and a third order accurate Runge-Kutta scheme in time. Comparisons with
direct simulations of the Vicsek model confirm these predictions.

categories: cond-mat.stat-mech cond-mat.soft nlin.AO nlin.PS physics.bio-ph


title:
Wake asymmetry weakening in viscoelastic fluids: A numerical discovery
  and mechanism understanding

abstract:
  Viscoelasticity weakens the asymmetry of laminar shedding flow behind a blunt
body in a free domain. In the present study, this finding is confirmed by four
unsteady viscoelastic flows with asymmetric configuration against each blunt
body, i.e., flow over an inclined flat plate with various angles of incidence,
flow over a rotating circular cylinder, flow over a circular cylinder with
asymmetrical slip boundary distribution, and flow over eight equal side-by-side
circular cylinders placed inclined closely through direct numerical simulation
(DNS) combined with the Peterlin approximation of the finitely extensible
nonlinear elastic (FENE-P) model. At high Weissenberg number ($We$), an arc
shape region with high elastic stress, which is similar to shock wave, forms in
the frontal area of each blunt body. This region acts as a stationary shield to
divide the flow into different regimes. Thus, the free stream resembles to pass
this shield instead of the original blunt body. As this shield has symmetric
feature, the wake flow restores symmetry.


title: Audio-Adaptive Activity Recognition Across Video Domains

abstract:
  This paper strives for activity recognition under domain shift, for example
caused by change of scenery or camera viewpoint. The leading approaches reduce
the shift in activity appearance by adversarial training and self-supervised
learning. Different from these vision-focused works we leverage activity sounds
for domain adaptation as they have less variance across domains and can
reliably indicate which activities are not happening. We propose an
audio-adaptive encoder and associated learning methods that discriminatively
adjust the visual feature representation as well as addressing shifts in the
semantic distribution. To further eliminate domain-specific features and
include domain-invariant activity sounds for recognition, an audio-infused
recognizer is proposed, which effectively models the cross-modal interaction
across domains. We also introduce the new task of actor shift, with a
corresponding audio-visual dataset, to challenge our method with situations
where the activity appearance changes dramatically. Experiments on this
dataset, EPIC-Kitchens and CharadesEgo show the effectiveness of our approach.


title:
Influence-aware Task Assignment in Spatial Crowdsourcing (Technical
  Report)

abstract:
  With the widespread diffusion of smartphones, Spatial Crowdsourcing (SC),
which aims to assign spatial tasks to mobile workers, has drawn increasing
attention in both academia and industry. One of the major issues is how to best
assign tasks to workers. Given a worker and a task, the worker will choose to
accept the task based on her affinity towards the task, and the worker can
propagate the information of the task to attract more workers to perform it.
These factors can be measured as worker-task influence. Since workers'
affinities towards tasks are different and task issuers may ask workers who
performed tasks to propagate the information of tasks to attract more workers
to perform them, it is important to analyze worker-task influence when making
assignments. We propose and solve a novel influence-aware task assignment
problem in SC, where tasks are assigned to workers in a manner that achieves
high worker-task influence. In particular, we aim to maximize the number of
assigned tasks and worker-task influence. To solve the problem, we first
determine workers' affinities towards tasks by identifying workers' historical
task-performing patterns. Next, a Historical Acceptance approach is developed
to measure workers' willingness of performing a task, i.e., the probability of
workers visiting the location of the task when they are informed. Next, we
propose a Random reverse reachable-based Propagation Optimization algorithm
that exploits reverse reachable sets to calculate the probability of workers
being informed about tasks in a social network. Based on worker-task influence
derived from the above three factors, we propose three influence-aware task
assignment algorithms that aim to maximize the number of assigned tasks and
worker-task influence. Extensive experiments on two real-world datasets offer
detailed insight into the effectiveness of our solutions.


title:
"This is Fake News": Characterizing the Spontaneous Debunking from
  Twitter Users to COVID-19 False Information

abstract:
  False information spreads on social media, and fact-checking is a potential
countermeasure. However, there is a severe shortage of resources of
fact-checkers, and an efficient way to scale fact-checking is desperately
needed, especially in pandemics like COVID-19. In this study, we focus on
spontaneous debunking by social media users, which has been missed in existing
research despite its indicated usefulness for fact-checking and countering
false information. Specifically, we characterize the fake tweets that tend to
be debunked and Twitter users who often debunk fake tweets. For this analysis,
we create a comprehensive dataset of responses to tweets with false
information, annotate a subset of them, and build a classification model for
detecting debunking behaviors. We find that most fake tweets are left
undebunked and spontaneous debunking is slower than other forms of responses,
and exhibits partisanship in political topics. These results provide actionable
insights into utilizing spontaneous debunking to scale conventional
fact-checking, thereby supplementing existing research from a new perspective.


title: A polynomial approximation result for free Herglotz functions

abstract:
  In this paper, we prove a noncommutative (nc for short) analog of Schwarz
lemma for the nc Schur-Agler class and prove that the regular nc Schur-Agler
class and the regular free Herglotz class are homeomorphic. Moreover, we give a
characterization of regular free Herglotz functions. As an application, we will
show that any regular free Herglotz functions can uniformly be approximated by
regular Herglotz free polynomials.


title: Classically Replaceable Operation

abstract:
  Quantum information science provides powerful technologies beyond the scope
of classical physics. In practice, accurate control of quantum operations is a
challenging task with current quantum devices. The implementation of high
fidelity and multi-qubit quantum operations consumes massive resources and
requires complicated hardware design to fight against noise. An approach to
improving the performance and efficiency is to replace some quantum operations
with tractable classical processing. Despite the common practice of this
approach, rigorous criteria to determine whether a given quantum operation is
replaceable "classically" are still missing. In this work, we define the
classically replaceable operations in four general scenarios. In each scenario,
we provide their necessary and sufficient criteria and point out the
corresponding classical processing. For a practically favourable case of
unitary classically replaceable operations, we show that the replaced classical
processing is deterministic. Beyond that, we regard the irreplaceability with
classical processing as a quantum resource and relate it to the performance of
a channel in a non-local game, as manifested in a robustness measure.


title: Universal Dynamic Scaling and Contact Dynamics in Quenched Quantum Gases

abstract:
  Recently universal dynamic scaling is observed in several systems, which
exhibit a spatiotemporal self-similar scaling behavior, analogous to the
spatial scaling near phase transitions. The latter arises from the emergent
continuous scaling symmetry due to the divergent correlation length. Motivated
by this, we investigate the relation between the scaling dynamics and
continuous scaling symmetry. We derive a theorem that the scaling invariance of
the quenched Hamiltonian and the initial density matrix can lead to the
universal dynamic scaling in quench dynamics. It is demonstrated both in the
two-body problem analytically and in the many-body problem numerically. For the
latter one, we calculate the dynamics of quantum gases quenched from
noninteracting to finite interaction in the framework of non-equilibrium
high-temperature virial expansion. A dynamic scaling of the momentum
distribution appears in certain momentum-time windows at unitarity as well as
in the weak interacting limit. Remarkably, this universal scaling dynamics
persists approximately with smaller scaling exponents even if the scaling
symmetry is fairly broken. Our findings may offer a new perspective to
interpret the related experiments. We also calculate the Contact dynamics in
the BEC-BCS crossover. Surprisingly, the half-way time displays a maximum near
unitarity while some damping oscillations occur on the BEC side due to the
dimer state, which can be used to detect possible two-body bound states in
experiments.


title:
Markov partitions for the geodesic flow on compact Riemann surfaces of
  constant negative curvature

abstract:
  It is well-known that hyperbolic flows admit Markov partitions of arbitrarily
small size. However, the constructions of Markov partitions for general
hyperbolic flows are very abstract and not easy to understand. To establish a
more detailed understanding of Markov partitions, in this paper we consider the
geodesic flow on Riemann surfaces of constant negative curvature. We provide a
rigorous construction of Markov partitions for this hyperbolic flow with
explicit forms of rectangles and local cross sections. The local product
structure is also calculated in detail.


title: Neutrino Masses and Mass Hierarchy: Evidence for the Normal Hierarchy

abstract:
  The latest cosmological constraints on the sum of neutrino masses, in
combination with the latest laboratory measurements on oscillations, provide a
"decisive" Bayesian evidence for the normal neutrino mass hierarchy. We show
that this result is robust to the choice of prior by exploring two extremes on
the range of prior choices. For Majorana neutrinos this has important
implications for the upper limit of the neutrino-less double beta decay half
life and thus for the technology and resources needed for future double beta
decay experiments.


title:
Invariance principle and CLT for the spiked eigenvalues of
  large-dimensional Fisher matrices and applications

abstract:
  This paper aims to derive asymptotical distributions of the spiked
eigenvalues of the large-dimensional spiked Fisher matrices without Gaussian
assumption and the restrictive assumptions on covariance matrices. We first
establish invariance principle for the spiked eigenvalues of the Fisher matrix.
That is, we show that the limiting distributions of the spiked eigenvalues are
invariant over a large class of population distributions satisfying certain
conditions. Using the invariance principle, we further established a central
limit theorem (CLT) for the spiked eigenvalues. As some interesting
applications, we use the CLT to derive the power functions of Roy Maximum root
test for linear hypothesis in linear models and the test in signal detection.
We conduct some Monte Carlo simulation to compare the proposed test with
existing ones.


title: Evaporation of Black Holes in Anti-de Sitter Space-time

abstract:
  We study the thermodynamics of an uncharged, non-rotating BTZ black hole. In
addition to the thermal properties, we are interested in the phase transition
between two locally thermodynamic stable phases which may arise from different
areas of validity for the ads/CFT correspondence. We report on the calculation
of entanglement entropies for quantum field theory defined on two dimensional
Minkowski space by using its geometric realization as the hyperbolic plane. For
free quantum fields in one or more spatial dimensions, we show that a geometric
approach reproduces some of the recent results found by studying explicit field
correlators. Using this technique, we further explore the geometrical behavior
when gravity is turned on and study how it affects thermalization properties
such as decoherence times and out-of-equilibrium phase transitions. We show
that the microstates for both phases can be described by three macroscopic
quantities, thereby allowing us to derive a first order equation for these
variables, whereupon they solve this equation at extremality. Superluminal
signalling is ruled out if and only if one assumes that no superradiance occurs
during evaporation; but it is allowed within the thin membrane paradigm already
introduced earlier in this context. Our result holds independent on how long it
takes for radiation to escape from near extremal configurations; while there
are reasonable expectations regarding this time scale our derivation is
completely model-independent.


title: End-to-End Active Speaker Detection

abstract:
  Recent advances in the Active Speaker Detection (ASD) problem build upon a
two-stage process: feature extraction and spatio-temporal context aggregation.
In this paper, we propose an end-to-end ASD workflow where feature learning and
contextual predictions are jointly learned. Our end-to-end trainable network
simultaneously learns multi-modal embeddings and aggregates spatio-temporal
context. This results in more suitable feature representations and improved
performance in the ASD task. We also introduce interleaved graph neural network
(iGNN) blocks, which split the message passing according to the main sources of
context in the ASD problem.
  Experiments show that the aggregated features from the iGNN blocks are more
suitable for ASD, resulting in state-of-the art performance.
  Finally, we design a weakly-supervised strategy, which demonstrates that the
ASD problem can also be approached by utilizing audiovisual data but relying
exclusively on audio annotations. We achieve this by modelling the direct
relationship between the audio signal and the possible sound sources
(speakers), as well as introducing a contrastive loss.


title: Group pattern detection of longitudinal data using functional statistics

abstract:
  Estimations and evaluations of the main patterns of time series data in
groups benefit large amounts of applications in various fields. Different from
the classical auto-correlation time series analysis and the modern neural
networks techniques, in this paper we propose a combination of functional
analysis of variance (FANOVA) and permutation tests in a more intuitive manner
for a limited sample size. First, FANOVA is applied in order to separate the
common information and to dig out the additional categorical influence through
paired group comparison, the results of which are secondly analyzed through
permutation tests to identify the time zones where the means of the different
groups differ significantly. Normalized kernel functions of different groups
are able to reflect remarkable mean characteristics in grouped unities, also
meaningful for deeper interpretation and group-wise classification. In order to
learn whether and when the proposed method of FANOVA and permutation F-test
works precisely and efficiently, we compare the estimated kernel results with
the ground truth on simulated data. After the confirmation of the model's
efficiency from simulation, we apply it also to the RAVDESS facial dataset to
extract the emotional behaviors of humans based on facial muscles contractions
(so-called action units (AU) technically in computer graphics), by comparing
the neutral performances with emotional ones.


title: Electron liquid state in the spin-1/2 anisotropic Kondo lattice

abstract:
  In the framework of the mean field approach, we provide analytical and
numerical solution of the spin-1/2 anisotropic Kondo lattice for arbitrary
dimension at half filling. Nontrivial solution for the amplitude of the field
opens a gap in the fermion spectrum of an electron liquid in which local
moments on the lattice sites are realized. The ground state in the insulator
state is determined by a static Z_2 field of local moments, which forms the
lattice with a double cell, conduction electrons move in this field. Due to
hybridization between electron states a large Fermi surface is formed in the
Kondo lattice. A gap in the quasi-particle spectrum is calculated depending on
the magnitudes of exchange integrals for the simple lattices with different
dimension. The proposed approach is also valid for describing the Kondo lattice
with weak anisotropy of the exchange interaction, which makes it possible to
study the behavior of the spin-1/2 Kondo lattice with an isotropic exchange
interaction.


title: Understanding Currencies in Video Games: A Review

abstract:
  This paper presents a review of the status of currencies in video games. The
business of video games is a multibillion-dollar industry, and its internal
economy design is an important field to investigate. In this study, we have
distinguished virtual currencies in terms of game mechanics and virtual
currency schema, and we have examined 11 games that have used virtual
currencies in a significant way and have provided insight for game designers on
the internal game economy by showing tangible examples of game mechanics
presented in our model


title: A Network-Based Explanation of Inequality Perceptions

abstract:
  Across income groups and countries, individual citizens perceive economic
inequality spectacularly wrong. These misperceptions have far-reaching
consequences, as it is perceived inequality, not actualinequality informing
redistributive preferences. The prevalence of this phenomenon is independent of
social class and welfare regime, which suggests the existence of a common
mechanism behind public perceptions. The literature has identified several
stylised facts on how individual perceptions respond to actual inequality and
how these biases vary systematically along the income distribution. We propose
a network-based explanation of perceived inequality building on recent advances
in random geometric graph theory. The generating mechanism can replicate all of
forementioned stylised facts simultaneously. It also produces social networks
that exhibit salient features of real-world networks; namely, they cannot be
statistically distinguished from small-world networks, testifying to the
robustness of our approach. Our results, therefore, suggest that homophilic
segregation is a promising candidate to explain inequality perceptions with
strong implications for theories of consumption and voting behaviour.


title:
Are Instrumental Variables Really That Instrumental? Endogeneity
  Resolution in Regression Models for Comparative Studies

abstract:
  We provide a justification for why, and when, endogeneity will not cause bias
in the interpretation of the coefficients in a regression model. This technique
can be a viable alternative to, or even used alongside, the instrumental
variable method. We show that when performing any comparative study, it is
possible to measure the true change in the coefficients under a broad set of
conditions. Our results hold, as long as the product of the covariance
structure between the explanatory variables and the covariance between the
error term and the explanatory variables are equal, within the same system at
different time periods or across multiple systems at the same point in time.

categories: econ.GN cs.NA math.NA q-fin.EC stat.AP stat.ME


title:
Determining the seismic age of the young open cluster $\alpha$ Per using
  $\delta$ Scuti stars

abstract:
  In this work we aim at constraining the age of the young open cluster Melotte
20, known as $\alpha$ Per, using seismic indices. The method consists of the
following steps: 1) Extract the frequency content of a sample of stars in the
field of an open cluster. 2) Search for possible regularities in the frequency
spectra of $\delta$ Scuti candidates, using different techniques, such as the
Fourier transform, the autocorrelation function, the histogram of frequency
differences and the \'echelle diagram. 3) Constrain the age of the selected
stars by both the physical parameters and seismic indices by comparing them
with a grid of asteroseismic models representative of $\delta$ Scuti stars. 4)
Find possible common ages between these stars to determine the age of the
cluster. We performed the pulsation analysis with MultiModes, a rapid, accurate
and powerful open-source code, which is presented in this paper. The result is
that the age of $\alpha$ Per could be between 96 and 100 Myr. This is an
improvement over different techniques in the past. We therefore show that space
astroseismology is capable of taking important steps in the dating of young
open clusters.


title:
BARCOR: Towards A Unified Framework for Conversational Recommendation
  Systems

abstract:
  Recommendation systems focus on helping users find items of interest in the
situations of information overload, where users' preferences are typically
estimated by the past observed behaviors. In contrast, conversational
recommendation systems (CRS) aim to understand users' preferences via
interactions in conversation flows. CRS is a complex problem that consists of
two main tasks: (1) recommendation and (2) response generation. Previous work
often tried to solve the problem in a modular manner, where recommenders and
response generators are separate neural models. Such modular architectures
often come with a complicated and unintuitive connection between the modules,
leading to inefficient learning and other issues. In this work, we propose a
unified framework based on BART for conversational recommendation, which
tackles two tasks in a single model. Furthermore, we also design and collect a
lightweight knowledge graph for CRS in the movie domain. The experimental
results show that the proposed methods achieve the state-of-the-art performance
in terms of both automatic and human evaluation.


title:
Image quality assessment for machine learning tasks using
  meta-reinforcement learning

abstract:
  In this paper, we consider image quality assessment (IQA) as a measure of how
images are amenable with respect to a given downstream task, or task
amenability. When the task is performed using machine learning algorithms, such
as a neural-network-based task predictor for image classification or
segmentation, the performance of the task predictor provides an objective
estimate of task amenability. In this work, we use an IQA controller to predict
the task amenability which, itself being parameterised by neural networks, can
be trained simultaneously with the task predictor. We further develop a
meta-reinforcement learning framework to improve the adaptability for both IQA
controllers and task predictors, such that they can be fine-tuned efficiently
on new datasets or meta-tasks. We demonstrate the efficacy of the proposed
task-specific, adaptable IQA approach, using two clinical applications for
ultrasound-guided prostate intervention and pneumonia detection on X-ray
images.


title: A Network Approach to Consumption

abstract:
  The nexus between debt and inequality has attracted considerable scholarly
attention in the wake of the global financial crisis. One prominent candidate
to explain the striking co-evolution of income inequality and private debt in
this period has been the theory of upward-looking consumption externalities
leading to expenditure cascades. We propose a parsimonious model of
upward-looking consumption at the micro level mediated by perception networks
with empirically plausible topologies. This allows us to make sense of the
ambiguous empirical literature on the relevance of this channel. Up to our
knowledge, our approach is the first to make the reference group to which
conspicuous consumption relates explicit. Our model, based purely on current
income, replicates the major stylised facts regarding micro consumption
behaviour and is thus observationally equivalent to the workhorse permanent
income hypothesis, without facing its dual problem of `excess smoothness' and
`excess sensitivity'. We also demonstrate that the network topology and
segregation has a significant effect on consumption patterns which has so far
been neglected.


title:
Unsupervised Vision-Language Parsing: Seamlessly Bridging Visual Scene
  Graphs with Language Structures via Dependency Relationships

abstract:
  Understanding realistic visual scene images together with language
descriptions is a fundamental task towards generic visual understanding.
Previous works have shown compelling comprehensive results by building
hierarchical structures for visual scenes (e.g., scene graphs) and natural
languages (e.g., dependency trees), individually. However, how to construct a
joint vision-language (VL) structure has barely been investigated. More
challenging but worthwhile, we introduce a new task that targets on inducing
such a joint VL structure in an unsupervised manner. Our goal is to bridge the
visual scene graphs and linguistic dependency trees seamlessly. Due to the lack
of VL structural data, we start by building a new dataset VLParse. Rather than
using labor-intensive labeling from scratch, we propose an automatic alignment
procedure to produce coarse structures followed by human refinement to produce
high-quality ones. Moreover, we benchmark our dataset by proposing a
contrastive learning (CL)-based framework VLGAE, short for Vision-Language
Graph Autoencoder. Our model obtains superior performance on two derived tasks,
i.e., language grammar induction and VL phrase grounding. Ablations show the
effectiveness of both visual cues and dependency relationships on fine-grained
VL structure construction.


title: The Lattice-Theoretic Essence of PropertyDirected Reachability Analysis

abstract:
  We present LT-PDR, a lattice-theoretic generalization of Bradley's property
directed reachability analysis (PDR) algorithm. LT-PDR identifies the essence
of PDR to be an ingenious combination of verification and refutation attempts
based on the Knaster-Tarski and Kleene theorems. We introduce four concrete
instances of LT-PDR, derive their implementation from a generic Haskell
implementation of LT-PDR, and experimentally evaluate them. We also present a
categorical structural theory that derives these instances.


title: On Mackey Decomposition for locally profinite groups

abstract:
  To study induced representation of some class of groups, Mackey's theory is
very useful. In this paper, we consider some generalization of Mackey's theory
for locally profinite groups. In particular, we give conditions on groups under
which we have the Mackey decomposition and some examples such that we do not
have the Mackey decomposition in some sense.


title: A General Survey on Attention Mechanisms in Deep Learning

abstract:
  Attention is an important mechanism that can be employed for a variety of
deep learning models across many different domains and tasks. This survey
provides an overview of the most important attention mechanisms proposed in the
literature. The various attention mechanisms are explained by means of a
framework consisting of a general attention model, uniform notation, and a
comprehensive taxonomy of attention mechanisms. Furthermore, the various
measures for evaluating attention models are reviewed, and methods to
characterize the structure of attention models based on the proposed framework
are discussed. Last, future work in the field of attention models is
considered.


title: Pattern-Division Multiplexing for Continuous-Aperture MIMO

abstract:
  In recent years, continuous-aperture multiple-input multiple-output
(CAP-MIMO) is reinvestigated to achieve improved communication performance with
limited antenna apertures. Unlike the classical MIMO composed of discrete
antennas, CAP-MIMO has a continuous antenna surface, which is expected to
generate any current distribution (i.e., pattern) and induce controllable
spatial electromagnetic waves. In this way, the information can be modulated on
the electromagnetic waves, which makes it promising to approach the ultimate
capacity of finite apertures. The pattern design for CAP-MIMO is the key factor
to determine the communication performance, but it has not been well studied in
the literature. In this paper, we propose the pattern-division multiplexing to
design the patterns for CAP-MIMO. Specifically, we first derive the system
model of a typical multi-user CAP-MIMO system, which allows us to formulate the
sum-rate maximization problem. Then, we propose a general pattern-division
multiplexing technique to transform the design of continuous pattern functions
to the design of their projection lengths on finite orthogonal bases. Based on
this technique, we further propose a pattern design scheme to solve the
formulated sum-rate maximization problem. Simulation results show that, the
sum-rate achieved by the proposed scheme is about 260% higher than that
achieved by the benchmark scheme.


title: A Unified Study of Machine Learning Explanation Evaluation Metrics

abstract:
  The growing need for trustworthy machine learning has led to the blossom of
interpretability research. Numerous explanation methods have been developed to
serve this purpose. However, these methods are deficiently and inappropriately
evaluated. Many existing metrics for explanations are introduced by researchers
as by-products of their proposed explanation techniques to demonstrate the
advantages of their methods. Although widely used, they are more or less
accused of problems. We claim that the lack of acknowledged and justified
metrics results in chaos in benchmarking these explanation methods -- Do we
really have good/bad explanation when a metric gives a high/low score? We split
existing metrics into two categories and demonstrate that they are insufficient
to properly evaluate explanations for multiple reasons. We propose guidelines
in dealing with the problems in evaluating machine learning explanation and
encourage researchers to carefully deal with these problems when developing
explanation techniques and metrics.


title: A Survey on Aspect-Based Sentiment Classification

abstract:
  With the constantly growing number of reviews and other sentiment-bearing
texts on the Web, the demand for automatic sentiment analysis algorithms
continues to expand. Aspect-based sentiment classification (ABSC) allows for
the automatic extraction of highly fine-grained sentiment information from text
documents or sentences. In this survey, the rapidly evolving state of the
research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the
ABSC models into three major categories: knowledge-based, machine learning, and
hybrid models. This taxonomy is accompanied with summarizing overviews of the
reported model performances, and both technical and intuitive explanations of
the various ABSC models. State-of-the-art ABSC models are discussed, such as
models based on the transformer model, and hybrid deep learning models that
incorporate knowledge bases. Additionally, various techniques for representing
the model inputs and evaluating the model outputs are reviewed. Furthermore,
trends in the research on ABSC are identified and a discussion is provided on
the ways in which the field of ABSC can be advanced in the future.


title:
bitsa_nlp@LT-EDI-ACL2022: Leveraging Pretrained Language Models for
  Detecting Homophobia and Transphobia in Social Media Comments

abstract:
  Online social networks are ubiquitous and user-friendly. Nevertheless, it is
vital to detect and moderate offensive content to maintain decency and empathy.
However, mining social media texts is a complex task since users don't adhere
to any fixed patterns. Comments can be written in any combination of languages
and many of them may be low-resource.
  In this paper, we present our system for the LT-EDI shared task on detecting
homophobia and transphobia in social media comments. We experiment with a
number of monolingual and multilingual transformer based models such as mBERT
along with a data augmentation technique for tackling class imbalance. Such
pretrained large models have recently shown tremendous success on a variety of
benchmark tasks in natural language processing. We observe their performance on
a carefully annotated, real life dataset of YouTube comments in English as well
as Tamil.
  Our submission achieved ranks 9, 6 and 3 with a macro-averaged F1-score of
0.42, 0.64 and 0.58 in the English, Tamil and Tamil-English subtasks
respectively. The code for the system has been open sourced.


title:
First-Principles Theory of the Rate of Magnetic Reconnection in
  Magnetospheric and Solar Plasmas

abstract:
  The rate of magnetic reconnection is of the utmost importance in a variety of
processes because it controls, for example, the rate energy is released in
solar flares, the speed of the Dungey convection cycle in Earth's
magnetosphere, and the energy release rate in harmful geomagnetic substorms. It
is known from numerical simulations and satellite observations that the rate is
approximately 0.1 in normalized units, but despite years of effort, a full
theoretical prediction has not been obtained. Here, we present a
first-principles theory for the reconnection rate in non-relativistic
electron-ion collisionless plasmas, and show that the same prediction explains
why Sweet-Parker reconnection is considerably slower. The key consideration of
this analysis is the pressure at the reconnection site (i.e., the x-line). We
show that the Hall electromagnetic fields in antiparallel reconnection cause an
energy void, equivalently a pressure depletion, at the x-line, so the
reconnection exhaust opens out, enabling the fast rate of 0.1. If the energy
can reach the x-line to replenish the pressure, the exhaust does not open out.
In addition to heliospheric applications, these results are expected to impact
reconnection studies in planetary magnetospheres, magnetically confined fusion
devices, and astrophysical plasmas.

categories: physics.plasm-ph astro-ph.EP astro-ph.HE astro-ph.SR physics.space-ph


title:
Safe Hierarchical Model Predictive Control and Planning for Autonomous
  Systems

abstract:
  Planning and control for autonomous vehicles usually are hierarchical
separated. However, increasing performance demands and operating in highly
dynamic environments requires an frequent re-evaluation of the planning and
tight integration of control and planning to guarantee safety. We propose an
integrated hierarchical predictive control and planning approach to tackle this
challenge. Planner and controller are based on the repeated solution of moving
horizon optimal control problems. The planner can choose different low-layer
controller modes for increased flexibility and performance instead of using a
single controller with a large safety margin for collision avoidance under
uncertainty. Planning is based on simplified system dynamics and safety, yet
flexible operation is ensured by constraint tightening based on a mixed-integer
linear programming formulation. A cyclic horizon tube-based model predictive
controller guarantees constraint satisfaction for different control modes and
disturbances. Examples of such modes are a slow-speed movement with high
precision and fast-speed movements with large uncertainty bounds. Allowing for
different control modes reduces the conservatism, while the hierarchical
decomposition of the problem reduces the computational cost and enables
real-time implementation. We derive conditions for recursive feasibility to
ensure constraint satisfaction and obstacle avoidance to guarantee safety and
ensure compatibility between the layers and modes. Simulation results
illustrate the efficiency and applicability of the proposed hierarchical
strategy.


title: Trace Embeddings from Zero Surgery Homeomorphisms

abstract:
  Manolescu and Piccirillo recently initiated a program to construct an exotic
$S^4$ or $\# n \mathbb{CP}^2$ by using zero surgery homeomorphisms and
Rasmussen's $s$-invariant. They find five knots that if any were slice, one
could construct an exotic $S^4$ and disprove the Smooth $4$-dimensional
Poincar\'e conjecture. We rule out this exciting possibility and show that
these knots are not slice. To do this, we use a zero surgery homeomorphism to
relate slice properties of two knots \textit{stably} after a connected sum with
some $4$-manifold. Furthermore, we show that our techniques will extend to the
entire infinite family of zero surgery homeomorphisms constructed by Manolescu
and Piccirillo. However, our methods do not completely rule out the possibility
of constructing an exotic $S^4$ or $\# n \mathbb{CP}^2$ as Manolescu and
Piccirillo proposed. We explain the limits of these methods hoping this will
inform and invite new attempts to construct an exotic $S^4$ or $\# n
\mathbb{CP}^2$. We also show a family of homotopy spheres constructed by
Manolescu and Piccirillo using annulus twists of a ribbon knot are all
standard.


title: Optimizing Airborne Wind Energy with Reinforcement Learning

abstract:
  Airborne Wind Energy is a lightweight technology that allows power extraction
from the wind using airborne devices such as kites and gliders, where the
airfoil orientation can be dynamically controlled in order to maximize
performance. The dynamical complexity of turbulent aerodynamics makes this
optimization problem unapproachable by conventional methods such as classical
control theory, which rely on accurate and tractable analytical models of the
dynamical system at hand. Here we propose to attack this problem through
Reinforcement Learning, a technique that -- by repeated trial-and-error
interactions with the environment -- learns to associate observations with
profitable actions without requiring prior knowledge of the system. We show
that in a simulated environment Reinforcement Learning finds an efficient way
to control a kite so that it can tow a vehicle for long distances. The
algorithm we use is based on a small set of intuitive observations and its
physically transparent interpretation allows to describe the approximately
optimal strategy as a simple list of manoeuvring instructions.


title:
Discovering Human-Object Interaction Concepts via Self-Compositional
  Learning

abstract:
  A comprehensive understanding of human-object interaction (HOI) requires
detecting not only a small portion of predefined HOI concepts (or categories)
but also other reasonable HOI concepts, while current approaches usually fail
to explore a huge portion of unknown HOI concepts (i.e., unknown but reasonable
combinations of verbs and objects). In this paper, 1) we introduce a novel and
challenging task for a comprehensive HOI understanding, which is termed as HOI
Concept Discovery; and 2) we devise a self-compositional learning framework (or
SCL) for HOI concept discovery. Specifically, we maintain an online updated
concept confidence matrix during training: 1) we assign pseudo-labels for all
composite HOI instances according to the concept confidence matrix for
self-training; and 2) we update the concept confidence matrix using the
predictions of all composite HOI instances. Therefore, the proposed method
enables the learning on both known and unknown HOI concepts. We perform
extensive experiments on several popular HOI datasets to demonstrate the
effectiveness of the proposed method for HOI concept discovery, object
affordance recognition and HOI detection. For example, the proposed
self-compositional learning framework significantly improves the performance of
1) HOI concept discovery by over 10% on HICO-DET and over 3% on V-COCO,
respectively; 2) object affordance recognition by over 9% mAP on MS-COCO and
HICO-DET; and 3) rare-first and non-rare-first unknown HOI detection relatively
over 30% and 20%, respectively. Code and models will be made publicly available
at https://github.com/zhihou7/HOI-CL.


title:
Non-linear PDEs approach to statistical mechanics of Dense Associative
  Memories

abstract:
  Dense associative memories (DAM), are widespread models in artificial
intelligence used for pattern recognition tasks; computationally, they have
been proven to be robust against adversarial input and theoretically,
leveraging their analogy with spin-glass systems, they are usually treated by
means of statistical-mechanics tools. Here we develop analytical methods, based
on nonlinear PDEs, to investigate their functioning. In particular, we prove
differential identities involving DAM partition function and macroscopic
observables useful for a qualitative and quantitative analysis of the system.
These results allow for a deeper comprehension of the mechanisms underlying
DAMs and provide interdisciplinary tools for their study.

categories: cond-mat.dis-nn cond-mat.stat-mech math-ph math.MP


title:
Dynamic programming principle for a controlled FBSDE system and
  associated extended HJB equation

abstract:
  This paper investigates the dynamic programming principle for a general
stochastic control problem in which the state processes are described by a
forward-backward stochastic differential equation (FBSDE). Using the method of
S-topology, we show that there exists an optimal control for the value
function. Then a dynamic programming principle is established. As a
consequence, an extended Hamilton-Jacobi-Bellman (HJB) equation is derived. The
existence and uniqueness of both smooth solution and a new type of viscosity
solution are investigated for this extended HJB equation. Compared with the
extant researches on stochastic maximum principle, this paper is the first
normal work on partial differential equation (PDE) method for a controlled
FBSDE system.


title:
Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural
  Network and LightGBM

abstract:
  The Coronavirus was detected in Wuhan, China in late 2019 and then led to a
pandemic with a rapid worldwide outbreak. The number of infected people has
been swiftly increasing since then. Therefore, in this study, an attempt was
made to propose a new and efficient method for automatic diagnosis of Corona
disease from X-ray images using Deep Neural Networks (DNNs). In the proposed
method, the DensNet169 was used to extract the features of the patients' Chest
X-Ray (CXR) images. The extracted features were given to a feature selection
algorithm (i.e., ANOVA) to select a number of them. Finally, the selected
features were classified by LightGBM algorithm. The proposed approach was
evaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies
in the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e.,
COVID-19, Pneumonia, and No-findings) classification problems, respectively.


title: Example-based Hypernetworks for Out-of-Distribution Generalization

abstract:
  While Natural Language Processing (NLP) algorithms keep reaching
unprecedented milestones, out-of-distribution generalization is still
challenging. In this paper we address the problem of multi-source adaptation to
unknown domains: Given labeled data from multiple source domains, we aim to
generalize to data drawn from target domains that are unknown to the algorithm
at training time. We present an algorithmic framework based on example-based
Hypernetwork adaptation: Given an input example, a T5 encoder-decoder first
generates a unique signature which embeds this example in the semantic space of
the source domains, and this signature is then fed into a Hypernetwork which
generates the weights of the task classifier. In an advanced version of our
model, the learned signature also serves for improving the representation of
the input example. In experiments with two tasks, sentiment classification and
natural language inference, across 29 adaptation settings, our algorithms
substantially outperform existing algorithms for this adaptation setup. To the
best of our knowledge, this is the first time Hypernetworks are applied to
domain adaptation or in example-based manner in NLP.


title: UAST: Unicode Aware Sanskrit Transliteration

abstract:
  Devanagari is the writing system that is adapted by various languages like
Sanskrit. International Alphabet of Sanskrit Transliteration (IAST) is a
transliteration scheme for the romanization of the Sanskrit language. IAST
makes use of diacritics to represent various characters. On a computer, these
are represented using Unicode standard which differs from how the Sanskrit
language behaves at a very fundamental level. This results in an issue that is
encountered while designing typesetting software for Devanagari and IAST. We
hereby discuss the problems and provide a solution that solves the issue of
incompatibilities between various transliteration and encoding schemes.
Implementation and source code available at https://github.com/dhruvildave/uast


title: StruBERT: Structure-aware BERT for Table Search and Matching

abstract:
  A large amount of information is stored in data tables. Users can search for
data tables using a keyword-based query. A table is composed primarily of data
values that are organized in rows and columns providing implicit structural
information. A table is usually accompanied by secondary information such as
the caption, page title, etc., that form the textual information. Understanding
the connection between the textual and structural information is an important
yet neglected aspect in table retrieval as previous methods treat each source
of information independently. In addition, users can search for data tables
that are similar to an existing table, and this setting can be seen as a
content-based table retrieval. In this paper, we propose StruBERT, a
structure-aware BERT model that fuses the textual and structural information of
a data table to produce context-aware representations for both textual and
tabular content of a data table. StruBERT features are integrated in a new
end-to-end neural ranking model to solve three table-related downstream tasks:
keyword- and content-based table retrieval, and table similarity. We evaluate
our approach using three datasets, and we demonstrate substantial improvements
in terms of retrieval and classification metrics over state-of-the-art methods.


title: Yang-Mills connections and sigma-models on quantum Heisenberg manifolds

abstract:
  We construct a spectral triple on a quantum Heisenberg manifold, which
generalizes the results of Chakraborty and Shinha, and associate to it an
energy functional on the set of projections, following the approach of
Mathai-Rosenberg to non-linear sigma models. The spectral triples that we
construct extend the We derive a lower bound for this energy functional that is
linked on the topological charge of the projection which depends on the
curvature of a compatible connection. A detailed study of this lower bound is
given for the Kang projection in quantum Heisenberg manifolds. These results
display an intriguing interplay between non-linear sigma models and Yang-Mills
theory on quantum Heisenberg manifolds, unlike in the well-studied case of
noncommutative tori.


title:
High-Performance All-Optical Modulator Based on Graphene-hBN
  Heterostructures

abstract:
  Graphene has emerged as an ultrafast photonic material for on-chip
all-optical modulation. However, its atomic thickness limits its interaction
with guided optical modes, which results in a high switching energy per bit or
low modulation efficiencies. Nonetheless, it is possible to enhance the
interaction of guided light with graphene by nanophotonic means. Herein, we
present a practical design of an all-optical modulator that is based on
graphene and hexagonal boron nitride (hBN) heterostructures that are hybrid
integrated into silicon slot waveguides. Using this device, a high extinction
ratio (ER) of 7.3 dB, an ultralow insertion loss (IL) of <0.6 dB, and
energy-efficient switching (<0.33 pJ/bit) are attainable for a 20{\mu}m long
modulator with double layer graphene. In addition, the device performs
ultrafast switching with a recovery time of <600 fs, and could potentially be
employed as a high-performance all-optical modulator with an ultra-high
bandwidth in the hundreds of GHz. Moreover, the modulation efficiency of the
device is further enhanced by stacking additional layers of graphene-hBN
heterostructures, while theoretically maintaining an ultrafast response. The
proposed device exhibits highly promising performance metrics, which are
expected to serve the needs of next-generation photonic computing systems.


title: Variational quantum simulation of long-range interacting systems

abstract:
  Current quantum simulators suffer from multiple limitations such as short
coherence time, noisy operations, faulty readout and restricted qubit
connectivity. Variational quantum algorithms are the most promising approach in
near-term quantum simulation to achieve quantum advantage over classical
computers. Here, we explore variational quantum algorithms, with different
levels of qubit connectivity, for digital simulation of the ground state of
long-range interacting systems. We find that as the interaction becomes more
long-ranged, the variational algorithms become less efficient, achieving lower
fidelity and demanding more optimization iterations. In particular, when the
system is near its criticality the efficiency is even lower. Increasing the
connectivity between distant qubits improves the results, even with less
quantum and classical resources. Our results show that by mixing circuit layers
with different levels of connectivity one can sensibly improve the results.
Interestingly, the order of layers becomes very important and grouping the
layers with long-distance connectivity at the beginning of the circuit
outperforms other permutations. The same design of circuits can also be used to
variationally produce spin squeezed states, as a resource for quantum
metrology. The quantum variational method indeed outperforms the ground state
and quench dynamics approach for creating spin squeezing.


title:
Racial Sentencing Disparities and Differential Progression Through the
  Criminal Justice System: Evidence From Linked Federal and State Court Data

abstract:
  Several key actors -- police, prosecutors, judges -- can alter the course of
individuals passing through the multi-staged criminal justice system. I use
linked arrest-sentencing data for federal courts from 1994-2010 to examine the
role that earlier stages play when estimating Black-white sentencing gaps. I
find no evidence of sample selection at play in the federal setting, suggesting
federal judges are largely responsible for racial sentencing disparities. In
contrast, I document substantial sample selection bias in two different state
courts systems. Estimates of racial and ethnic sentencing gaps that ignore
selection underestimate the true disparities by 15% and 13% respectively.


title:
Star-forming S0 Galaxies in SDSS-MaNGA: fading spirals or rejuvenated
  S0s?

abstract:
  We investigate the origin of rare star-formation in an otherwise red-and-dead
population of S0 galaxies using spatially resolved spectroscopy. Our sample
consists of $120$ low redshift ($z<0.1$) star-forming S0 (SF-S0) galaxies from
the SDSS-IV MaNGA DR15. We have selected this sample after a visual inspection
of deep images from the DESI Legacy Imaging Surveys DR9 and the Subaru/HSC-SSP
survey PDR3, to remove contamination from spiral galaxies. We also construct
two control samples of star-forming spirals (SF-Sps) and quenched S0s (Q-S0s)
to explore their evolutionary link with the star-forming S0s. To study
star-formation at resolved scales, we use dust-corrected $H_\alpha$ luminosity
and stellar density ($\Sigma_\star$) maps to construct radial profiles of
star-formation rate (SFR) surface density ($\Sigma_{SFR}$) and specific SFR
(sSFR). Examining these radial profiles, we find that star-formation in SF-S0s
is centrally dominated as opposed to disc dominated star-formation in spirals.
We also compared various global (size-mass relation, bulge-to-total luminosity
ratio) and local (central stellar velocity dispersion) properties of SF-S0s to
those of the control sample galaxies. We find that SF-S0s are structurally
similar to the quenched S0s and are different from star-forming spirals. We
infer that SF-S0s are unlikely to be fading spirals. Inspecting stellar and gas
velocity maps, we find that more than $50\%$ of the SF-S0 sample shows signs of
recent galaxy interactions such as kinematic misalignment, counter-rotation,
and unsettled kinematics. Based on these results, we conclude that in our
sample of SF-S0s, star-formation has been rejuvenated, with minor mergers
likely to be a major driver.


title:
Privacy-preserving record linkage using local sensitive hash and private
  set intersection

abstract:
  The amount of data stored in data repositories increases every year. This
makes it challenging to link records between different datasets across
companies and even internally, while adhering to privacy regulations. Address
or name changes, and even different spelling used for entity data, can prevent
companies from using private deduplication or record-linking solutions such as
private set intersection (PSI). To this end, we propose a new and efficient
privacy-preserving record linkage (PPRL) protocol that combines PSI and local
sensitive hash (LSH) functions, and runs in linear time. We explain the privacy
guarantees that our protocol provides and demonstrate its practicality by
executing the protocol over two datasets with $2^{20}$ records each, in $11-45$
minutes, depending on network settings.


title: HELoC: Hierarchical Contrastive Learning of Source Code Representation

abstract:
  Abstract syntax trees (ASTs) play a crucial role in source code
representation. However, due to the large number of nodes in an AST and the
typically deep AST hierarchy, it is challenging to learn the hierarchical
structure of an AST effectively. In this paper, we propose HELoC, a
hierarchical contrastive learning model for source code representation. To
effectively learn the AST hierarchy, we use contrastive learning to allow the
network to predict the AST node level and learn the hierarchical relationships
between nodes in a self-supervised manner, which makes the representation
vectors of nodes with greater differences in AST levels farther apart in the
embedding space. By using such vectors, the structural similarities between
code snippets can be measured more precisely. In the learning process, a novel
GNN (called Residual Self-attention Graph Neural Network, RSGNN) is designed,
which enables HELoC to focus on embedding the local structure of an AST while
capturing its overall structure. HELoC is self-supervised and can be applied to
many source code related downstream tasks such as code classification, code
clone detection, and code clustering after pre-training. Our extensive
experiments demonstrate that HELoC outperforms the state-of-the-art source code
representation models.


title: Valley control by linearly polarized laser pulses

abstract:
  Underpinning the field of "valleytronics" is the coupling of the helicity of
circularly polarized light to the valley degree of freedom, and this remains
the only known lightform to exhibit this remarkable effect. Here we show that
on femtosecond time scales valley coupling is a much more general effect. We
find that two time separated linearly polarized pulses allow almost complete
control over valley excitation, with the pulse time difference and polarization
vectors emerging as key parameters for valley control. In contrast to the Berry
curvature that underpins the effect for circularly polarized light, we
demonstrate that a different phase structure drives this effect, with
excitations during each linear pulse acquiring a valley discriminating phase
involving the polarisation angle of linear light. Unimportant in a single
linear pulse, for pairs of pulses these can constructively and destructively
interfere. Employing state-of-the-art time dependent density function theory,
we show that the effect is robust to the complexities of charge dynamics in a
real material with the example of a transitional metal dichalcogenide.


title:
A predictive model for planning emergency events rescue during COVID-19
  in Lombardy, Italy

abstract:
  Italy, particularly the Lombardy region, was among the first countries
outside of Asia to report cases of COVID-19. The emergency medical service
called Regional Emergency Agency (AREU) coordinates the intra- and
inter-regional non-hospital emergency network and the European emergency number
service in Lombardy. AREU must deal with daily and seasonal variations of call
volume. The number and type of emergency calls changed dramatically during the
COVID-19 pandemic. A model to predict incoming calls and how many of these turn
into events, i.e., dispatch of transport and equipment until the rescue is
completed, was developed to address the emergency period. We used the
generalized additive model with a negative binomial family to predict the
number of events one, two, five, and seven days ahead. The over-dispersion of
the data was tackled by using the negative binomial family and the nonlinear
relationship between the number of events and covariates (e.g., seasonal
effects) by smoothing splines. The model coefficients show the effect of
variables, e.g., the day of the week, on the number of events and how these
effects change during the pre-COVID-19 period. The proposed model returns
reasonable mean absolute errors for most of the 2020-2021 period.


title:
Creating quantum spin chains through edge reconstruction in pure
  graphene armchair nanoribbons towards ballistic spin transport

abstract:
  It is well-known that ferromagnetism can be realized along the zigzag
graphene nanoribbon edges, but the armchair graphene nanoribbon edges (AGNEs)
are nonmagnetic. Here, we achieve Heisenberg antiferromagnetic spin chains
through edge reconstruction along the AGNEs. The reconstructed edge consists of
pentagonal carbon rings or a hybrid of pentagonal and hexagonal carbon rings.
The resultant nanoribbons are narrow-gap semiconductors and the band edge
states are either spin-degenerate edge states or nonmagnetic bulk states. The
spin is located on the outermost carbon of the pentagonal ring, and the
inter-spin exchange is the nearest-neighbor antiferromagnetic interaction. For
finite chain lengthes or nonzero magnetization, there are nonzero spin Drude
weights and thus ballistic quantum spin transport can be achieved along the
reconstructed edges, These could be used for quantum spin information transfer
and spintronic applications.


title: An Introduction to Multiparameter Persistence

abstract:
  In topological data analysis (TDA), one often studies the shape of data by
constructing a filtered topological space, whose structure is then examined
using persistent homology. However, a single filtered space often does not
adequately capture the structure of interest in the data, and one is led to
consider multiparameter persistence, which associates to the data a space
equipped with a multiparameter filtration. Multiparameter persistence has
become one of the most active areas of research within TDA, with exciting
progress on several fronts. In this article, we introduce multiparameter
persistence and survey some of this recent progress, with a focus on ideas
likely to lead to practical applications in the near future.


title: Novel ensemble collaboration method for dynamic scheduling problems

abstract:
  Dynamic scheduling problems are important optimisation problems with many
real-world applications. Since in dynamic scheduling not all information is
available at the start, such problems are usually solved by dispatching rules
(DRs), which create the schedule as the system executes. Recently, DRs have
been successfully developed using genetic programming. However, a single DR may
not efficiently solve different problem instances. Therefore, much research has
focused on using DRs collaboratively by forming ensembles. In this paper, a
novel ensemble collaboration method for dynamic scheduling is proposed. In this
method, DRs are applied independently at each decision point to create a
simulation of the schedule for all currently released jobs. Based on these
simulations, it is determined which DR makes the best decision and that
decision is applied. The results show that the ensembles easily outperform
individual DRs for different ensemble sizes. Moreover, the results suggest that
it is relatively easy to create good ensembles from a set of independently
evolved DRs.


title: Video Polyp Segmentation: A Deep Learning Perspective

abstract:
  In the deep learning era, we present the first comprehensive video polyp
segmentation (VPS) study. Over the years, developments in VPS are not moving
forward with ease due to the lack of large-scale fine-grained segmentation
annotations. To tackle this issue, we first introduce a high-quality per-frame
annotated VPS dataset, named SUN-SEG, which includes 158,690 frames from the
famous SUN dataset. We provide additional annotations with diverse types, i.e.,
attribute, object mask, boundary, scribble, and polygon. Second, we design a
simple but efficient baseline, dubbed PNS+, consisting of a global encoder, a
local encoder, and normalized self-attention (NS) blocks. The global and local
encoders receive an anchor frame and multiple successive frames to extract
long-term and short-term feature representations, which are then progressively
updated by two NS blocks. Extensive experiments show that PNS+ achieves the
best performance and real-time inference speed (170fps), making it a promising
solution for the VPS task. Third, we extensively evaluate 13 representative
polyp/object segmentation models on our SUN-SEG dataset and provide
attribute-based comparisons. Benchmark results are available at https:
//github.com/GewelsJI/VPS.


title: On unsuperstable theories in GDST

abstract:
  We study the $\kappa$-Borel-reducibility of isomorphism relations of complete
first order theories by using coloured trees. Under some cardinality
assumptions, we show the following: For all theories T and T', if T is
classifiable and T' is unsuperstable, then the isomorphism of models of T' is
strictly above the isomorphism of models of T with respect to
$\kappa$-Borel-reducibility.


title:
3D Uplink Channel Modeling of UAV-based mmWave Fronthaul Links for
  Future Small Cell Networks

abstract:
  In this study, we consider an unmanned aerial vehicle (UAV)-assisted
heterogeneous network that is offered as a cost-effective and easy to deploy
solution to solve the problem related to transferring traffic of distributed
small cells to the core network. For any given distribution of small cell base
stations (SBSs), we first characterize an accurate millimeter wave (mmWave)
channel model for SBS to networked flying platform (NFP) by taking into
consideration real parameters such as UAV's vibrations, distribution of SBSs,
position of UAVs in the sky, real three-dimensional (3D) antenna pattern model
provided by 3GPP along with interference caused by antenna side lobes and
frequency reuse. Then, for the characterized channel, we derive an analytical
closed-form expression for the end-to-end signal-to-noise plus interference
ratio (SINR). Based on that, we derive the closed-form expressions for the
outage probability and channel capacity of the considered UAV-based mmWave
uplinks. The accuracy of the derived analytical expressions is verified by
Monte Carlo simulations. Finally, we investigate the effects of different
channel parameters such as antenna pattern gain, strength of UAV's vibrations,
UAVs' positions in the sky, distribution of SBSs, and frequency reuse on the
performance of the considered UAV-based uplink channel in terms of average
capacity and outage probability.


title:
Stability of a cascade system with two stations and its extension for
  multiple stations

abstract:
  We consider a two station cascade system in which waiting or externally
arriving customers at station $1$ move to the station $2$ if the queue size of
station $1$ including a customer being served is greater than a given threshold
level $C_{1} \ge 1$ and if station $2$ is empty. Assuming that external
arrivals are subject to independent renewal processes satisfying certain
regularity conditions and service times are $i.i.d.$ at each station, we derive
necessary and sufficient conditions for a Markov process describing this system
to be positive recurrent in the sense of Harris. This result is extended to the
cascade system with a general number $k$ of stations in series. This extension
requires the actual traffic intensities of stations $2,3,\ldots, k-1$ for $k
\ge 3$. We finally note that the modeling assumptions on the renewal arrivals
and $i.i.d.$ service times are not essential if the notion of the stability is
replaced by a certain sample path condition. This stability notion is identical
with the standard stability if the whole system is described by the Markov
process which is a Harris irreducible $T$-process.


title:
Simulating open quantum many-body systems using optimised circuits in
  digital quantum simulation

abstract:
  Digital quantum computers are potentially an ideal platform for simulating
open quantum many-body systems beyond the digital classical computers. Many
studies have focused on obtaining the ground state by simulating time dynamics
or variational approaches of closed quantum systems. However, dynamics of open
quantum systems has not been given much attention with a reason being their
non-unitary dynamics not natural to simulate on a set of unitary gate
operations in quantum computing. Here we study prototypical models in open
quantum systems with Trotterisations for the modified stochastic
Schr{\"o}dinger equation (MSSE). Minimising the leading error in MSSE enables
to optimise the quantum circuits, and we run the optimised circuits with the
noiseless \textit{quantum assembly language (QASM) simulator} and the noisy IBM
Quantum devices. The \textit{QASM simulator} enables to study the reachable
system size that is comparable to the limits of classical computers. The
results show that the nonequilibrium critical phenomena in open quantum systems
are successfully obtained with high precision. Furthermore, we run the
algorithm on the IBM Quantum devices, showing that the current machine is
challenging, to give quantitatively accurate time dynamics due to the noise.
Despite errors, the results by IBM devices qualitatively follow the trend of
critical behaviour and include a possibility to demonstrate quantum advantage
when the noise is reduced. We discuss how much noise should be reduced for a
certain fidelity using the noise model, which will be crucial to demonstrate
quantum advantage from future quantum devices.


title:
StrokeSight: A Novel EEG-Based Diagnostic System for Strokes Using
  Spectral Analysis and Deep Learning

abstract:
  A stroke is defined as a neurologic deficit arising from an interruption in
blood supply to the brain. According to the World Health Organization, over 15
million people suffer from strokes annually, of which almost 70% die or are
permanently disabled. Effective treatment must be administered within one hour
to prevent irreversible brain damage. Unfortunately, the current gold standards
for diagnosis, CT and MRI, are time-consuming, expensive, and immobile.
Electroencephalograms reveal biomarkers of strokes while being inexpensive and
available for remote use, but no system exists that utilizes them for this
purpose. To address this issue, we created StrokeSight, a novel, open-source
web application that automatically provides a full diagnosis and visualization
of ischemic and hemorrhagic strokes in under 50 seconds using 60-second
electroencephalograms. We first calculated the averaged power spectral
densities for 132, 60-second electroencephalogram readings, which we then used
to train three deep neural networks that respectively predict a stroke type
(control/ischemic/hemorrhagic), location (left/right hemisphere), and severity
(small/large) with accuracies of 97.5%, 94.4%, and 100%. StrokeSight also
implements a novel process to visualize spectral abnormalities caused by
strokes. Azimuthal equidistant projection and multivariate spline interpolation
are used to reshape 3D electrodes onto a head-shaped 2D plane and then a
contour map of each frequency band power is created, allowing neurologists to
quickly and accurately interpret electroencephalogram data. StrokeSight could
act as a revolutionary solution for stroke care that drastically improves the
speed, cost efficiency, and accessibility of stroke diagnosis while allowing
for personalized treatment and interpretation.


title: Learning Graph Regularisation for Guided Super-Resolution

abstract:
  We introduce a novel formulation for guided super-resolution. Its core is a
differentiable optimisation layer that operates on a learned affinity graph.
The learned graph potentials make it possible to leverage rich contextual
information from the guide image, while the explicit graph optimisation within
the architecture guarantees rigorous fidelity of the high-resolution target to
the low-resolution source. With the decision to employ the source as a
constraint rather than only as an input to the prediction, our method differs
from state-of-the-art deep architectures for guided super-resolution, which
produce targets that, when downsampled, will only approximately reproduce the
source. This is not only theoretically appealing, but also produces crisper,
more natural-looking images. A key property of our method is that, although the
graph connectivity is restricted to the pixel lattice, the associated edge
potentials are learned with a deep feature extractor and can encode rich
context information over large receptive fields. By taking advantage of the
sparse graph connectivity, it becomes possible to propagate gradients through
the optimisation layer and learn the edge potentials from data. We extensively
evaluate our method on several datasets, and consistently outperform recent
baselines in terms of quantitative reconstruction errors, while also delivering
visually sharper outputs. Moreover, we demonstrate that our method generalises
particularly well to new datasets not seen during training.


title: Benchmarking Algorithms for Automatic License Plate Recognition

abstract:
  We evaluated a lightweight Convolutional Neural Network (CNN) called LPRNet
[1] for automatic License Plate Recognition (LPR). We evaluated the algorithm
on two datasets, one composed of real license plate images and the other of
synthetic license plate images. In addition, we compared its performance
against Tesseract [2], an Optical Character Recognition engine. We measured
performance based on recognition accuracy and Levenshtein Distance. LPRNet is
an end-to-end framework and demonstrated robust performance on both datasets,
delivering 90 and 89 percent recognition accuracy on test sets of 1000 real and
synthetic license plate images, respectively. Tesseract was not trained using
real license plate images and performed well only on the synthetic dataset
after pre-processing steps delivering 93 percent recognition accuracy. Finally,
Pareto analysis for frequency analysis of misclassified characters allowed us
to find in detail which characters were the most conflicting ones according to
the percentage of accumulated error. Depending on the region, license plate
images possess particular characteristics. Once properly trained, LPRNet can be
used to recognize characters from a specific region and dataset. Future work
can focus on applying transfer learning to utilize the features learned by
LPRNet and fine-tune it given a smaller, newer dataset of license plates.


title:
Adversarial Representation Sharing: A Quantitative and Secure
  Collaborative Learning Framework

abstract:
  The performance of deep learning models highly depends on the amount of
training data. It is common practice for today's data holders to merge their
datasets and train models collaboratively, which yet poses a threat to data
privacy. Different from existing methods such as secure multi-party computation
(MPC) and federated learning (FL), we find representation learning has unique
advantages in collaborative learning due to the lower communication overhead
and task-independency. However, data representations face the threat of model
inversion attacks. In this article, we formally define the collaborative
learning scenario, and quantify data utility and privacy. Then we present ARS,
a collaborative learning framework wherein users share representations of data
to train models, and add imperceptible adversarial noise to data
representations against reconstruction or attribute extraction attacks. By
evaluating ARS in different contexts, we demonstrate that our mechanism is
effective against model inversion attacks, and achieves a balance between
privacy and utility. The ARS framework has wide applicability. First, ARS is
valid for various data types, not limited to images. Second, data
representations shared by users can be utilized in different tasks. Third, the
framework can be easily extended to the vertical data partitioning scenario.


title: Overview of the LAMOST survey in the first decade

abstract:
  The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), also
known as the Guoshoujing Telescope, is a major national scientific facility for
astronomical research located in Xinglong, China. Beginning with a pilot survey
in 2011, LAMOST has been surveying the night sky for more than 10 years. The
LAMOST survey covers various objects in the Universe, from normal stars to
peculiar ones, from the Milky Way to other galaxies, and from stellar black
holes and their companions to quasars that ignite ancient galaxies. Until the
latest data release 8, the LAMOST survey has released spectra for more than 10
million stars, ~220,000 galaxies, and ~71,000 quasars. With this largest
celestial spectra database ever constructed, LAMOST has helped astronomers to
deepen their understanding of the Universe, especially for our Milky Way galaxy
and the millions of stars within it. In this article, we briefly review the
characteristics, observations, and scientific achievements of LAMOST. In
particular, we show how astrophysical knowledge about the Milky Way has been
improved by LAMOST data.

categories: astro-ph.GA astro-ph.EP astro-ph.HE astro-ph.IM astro-ph.SR