title: Belief identification with state-dependent utilities

abstract:
  It is well known that individual beliefs cannot be identified using
traditional choice data, unless we impose the practically restrictive and
conceptually awkward assumption that utilities are state-independent. In this
paper, we propose a novel methodology that solves this long-standing
identification problem in a simple way. Our method relies on the concept of
influential actions. These are actions that are controlled by the analyst and
lead the agent to change her beliefs. Notably, the analyst does not need to
have any idea on how the agent's beliefs will change in response to an
influential action. Then, instead of eliciting directly the agent's beliefs
about the state space, we elicit her subjective probabilities about the
influential action having been undertaken conditional on each state
realization. The latter can be easily done with existing elicitation tools. It
turns out that this is enough to uniquely identify her beliefs about the state
space irrespective of her utility function, thus solving the identification
problem. We discuss that this method can be used in most applications of
interest. As an example, we show how it can provide a new useful tool for
identifying motivated beliefs on an individual level.


title: Attention Aided CSI Wireless Localization

abstract:
  Deep neural networks (DNNs) have become a popular approach for wireless
localization based on channel state information (CSI). A common practice is to
use the raw CSI in the input and allow the network to learn relevant channel
representations for mapping to location information. However, various works
show that raw CSI can be very sensitive to system impairments and small changes
in the environment. On the contrary, hand-designing features may hinder the
limits of channel representation learning of the DNN. In this work, we propose
attention-based CSI for robust feature learning. We evaluate the performance of
attended features in centralized and distributed massive MIMO systems for
ray-tracing channels in two non-stationary railway track environments. By
comparison to a base DNN, our approach provides exceptional performance.


title:
Soft-CP: A Credible and Effective Data Augmentation for Semantic
  Segmentation of Medical Lesions

abstract:
  The medical datasets are usually faced with the problem of scarcity and data
imbalance. Moreover, annotating large datasets for semantic segmentation of
medical lesions is domain-knowledge and time-consuming. In this paper, we
propose a new object-blend method(short in soft-CP) that combines the
Copy-Paste augmentation method for semantic segmentation of medical lesions
offline, ensuring the correct edge information around the lession to solve the
issue above-mentioned. We proved the method's validity with several datasets in
different imaging modalities. In our experiments on the KiTS19[2] dataset,
Soft-CP outperforms existing medical lesions synthesis approaches. The Soft-CP
augementation provides gains of +26.5% DSC in the low data regime(10% of data)
and +10.2% DSC in the high data regime(all of data), In offline training data,
the ratio of real images to synthetic images is 3:1.


title:
Latent class mixed modelling for phenotypic stratification of primary
  biliary cholangitis patients on first line treatment

abstract:
  In patients with primary biliary cholangitis (PBC), the serum liver
biochemistry measured during treatment with ursodeoxycholic acid (the UDCA
response) accurately predicts long-term outcome. In this study we sought to use
liver biochemistry, and in particular alkaline phosphatase (ALP), as a
surrogate marker of disease activity, for phenotypic stratification in PBC
using a computational modelling approach. Our aim here was to identify distinct
disease subgroups of patients with distinct disease trajectories. Methods: We
used longitudinal ALP results from 1,601 PBC patients on first line treatment
with UDCA, and applied latent class mixed modelling (LCMM), to identify
distinct phenotypic subgroups, each with distinct disease trajectories, and
risks of end stage liver disease (ESLD). Results: We identified four well
discriminated phenotypic subgroups within our PBC cohort, each with distinct
disease trajectories.


title: Stability Of Matrix Polynomials In One And Several Variables

abstract:
  The paper presents methods of eigenvalue localisation of regular matrix
polynomials, in particular, stability of matrix polynomials is investigated.
For this aim a stronger notion of hyperstability is introduced and widely
discussed. Matrix versions of the Gauss-Lucas theorem and Sz\'asz inequality
are shown. Further, tools for investigating (hyper)stability by multivariate
complex analysis methods are provided. Several second- and third-order matrix
polynomials with particular semi-definiteness assumptions on coefficients are
shown to be stable.


title: Weak Seasonality on Temperate Exoplanets Around Low-mass Stars

abstract:
  Planets with non-zero obliquity and/or orbital eccentricity experience
seasonal variations of stellar irradiation at local latitudes. The extent of
the atmospheric response can be crudely estimated by the ratio between the
orbital timescale and the atmospheric radiative timescale. Given a set of
atmospheric parameters, we show that this ratio depends mostly on the stellar
properties and is independent of orbital distance and planetary equilibrium
temperature. For Jupiter-like atmospheres, this ratio is $\ll1$ for planets
around very-low-mass M dwarfs and $\gtrsim1$ when the stellar mass is greater
than about 0.6 solar mass. Complications can arise from various factors,
including varying atmospheric metallicity, clouds, and atmospheric dynamics.
Given the eccentricity and obliquity, the seasonal response is expected to be
systematically weaker for gaseous exoplanets around low-mass stars and stronger
for those around more massive stars. The amplitude and phase lag of atmospheric
seasonal variations as a function of host stellar mass are quantified by
idealized analytic models. At the infrared emission level in the photosphere,
the relative amplitudes of thermal flux and temperature perturbations are
negligible, and their phase lags are closed to $-90^{\circ}$ for Jupiter-like
planets around very-low-mass stars. The relative amplitudes and phase lags
increase gradually with increasing stellar mass. With a particular stellar
mass, the relative amplitude and phase lag decrease from low to high infrared
optical depth. We also present numerical calculations for a better illustration
of the seasonal behaviors. Lastly, we discuss implications for the atmospheric
circulation and future atmospheric characterization of exoplanets in systems
with different stellar masses.


title: High-Pressure NMR Enabled by Diamond Nitrogen-Vacancy Centers

abstract:
  The integration of NMR and high pressure technique brings unique
opportunities to study electronic, structural and dynamical properties under
extreme conditions. Despite a great degree of success has been achieved using
coil-based schemes, the contradictory requirement on sample volume of these two
techniques remains an outstanding challenge. In this letter, we introduce
diamond nitrogen-vacancy (NV) centers, as the source and probe of in-situ
nuclear spin polarization, to address the sample volume issue. We demonstrate
hyperpolarization and coherent control of $^{14}$N nuclear spins under high
pressures. NMR spectra of a micro-diamond are measured up to 16.6 GPa, and
unexpected pressure shift of the $^{14}$N nuclear quadrupole and hyperfine
coupling terms are observed. Our work contributes to quantum sensing enhanced
spectrometry under extreme conditions.


title:
Equivalence of mean-field avalanches and branching diffusions: From the
  Brownian force model to the super-Brownian motion

abstract:
  We point out that the mean-field theory of avalanches in the dynamics of
elastic interfaces, the so-called Brownian force model (BFM) developed recently
in non-equilibrium statistical physics, is equivalent to the so-called
super-Brownian motion (SBM) developed in probability theory, a continuum limit
of branching processes related to {\it space-embedded} Galton-Watson trees. In
particular the exact solvability property recently (re-)discovered from the
field theory in mean-field avalanches (the "instanton equation") maps onto the
so-called Dawson-Watanabe 1968 duality property. In the light of this
correspondence we compare the results obtained independently in the two fields,
and transport some of them from one field to the other. In particular, we
discuss a scaling limit of the branching Brownian motion which maps onto the
continuum field theory of mean-field avalanches

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


title: Nonintegrability of the SEIR epidemic model

abstract:
  We prove the nonintegrability of the susceptible-exposed-infected-removed
(SEIR) epidemic model in the Bogoyavlenskij sense. This property of the SEIR
model is different from the more fundamental susceptible-infected-removed (SIR)
model, which is Bogoyavlenskij-nonintegrable. Our basic tool for the proof is
an extension of the Morales-Ramis theory due to Ayoul and Zung. Moreover, we
extend the system to a six-dimensional system to treat transcendental first
integrals and commutative vector fields. We also use the fact that the
incomplete gamma function $\Gamma(\alpha,x)$ is not elementary for
$\alpha\not\in\mathbb{N}$, of which a proof is included.


title:
Dynamic doping and Cottrell atmosphere optimize the thermoelectric
  performance of n-type PbTe

abstract:
  High thermoelectric energy conversion efficiency requires a large
figure-of-merit, zT, over a broad temperature range. To achieve this, we
optimize the carrier concentrations of n-type PbTe from room up to hot-end
temperatures by co-doping Bi and Ag. Bi is an efficient n-type dopant in PbTe,
often leading to excessive carrier concentration at room temperature. As
revealed by density functional theory calculations, the formation of Bi and Ag
defect complexes is exploited to optimize the room temperature carrier
concentration. At elevated temperatures, we demonstrate the dynamic dissolution
of Ag2Te precipitates in PbTe in situ by heating in a scanning transmission
electron microscope. The release of n-type Ag interstitials with increasing
temperature fulfills the requirement of higher carrier concentrations at the
hot end. Moreover, as characterized by atom probe tomography, Ag atoms
aggregate along parallel dislocation arrays to form Cottrell atmospheres. This
results in enhanced phonon scattering and leads to a low lattice thermal
conductivity. As a result of the synergy of dynamic doping and phonon
scattering at decorated dislocations, an average zT of 1.0 is achieved in
n-type Bi/Ag-codoped PbTe between 400 and 825 K. Introducing dopants with
temperature-dependent solubility and strong interaction with dislocation cores
enables simultaneous optimization of the average power factor and thermal
conductivity, providing a new concept to exploit in the field of
thermoelectrics.


title:
An Adaptive and Scalable ANN-based Model-Order-Reduction Method for
  Large-Scale TO Designs

abstract:
  Topology Optimization (TO) provides a systematic approach for obtaining
structure design with optimum performance of interest. However, the process
requires numerical evaluation of objective function and constraints at each
iteration, which is computational expensive especially for large-scale design.
Deep learning-based models have been developed to accelerate the process either
by acting as surrogate models replacing the simulation process, or completely
replacing the optimization process. However, most of them require a large set
of labelled training data, which are generated mostly through simulations. The
data generation time scales rapidly with the design domain size, decreasing the
efficiency of the method itself. Another major issue is the weak
generalizability of most deep learning models. Most models are trained to work
with the design problem similar to that used for data generation and require
retraining if the design problem changes. In this work a scalable deep
learning-based model-order-reduction method is proposed to accelerate
large-scale TO process, by utilizing MapNet, a neural network which maps the
field of interest from coarse-scale to fine-scale. The proposed method allows
for each simulation of the TO process to be performed at a coarser mesh,
thereby greatly reducing the total computational time. Moreover, by using
domain fragmentation, the transferability of the MapNet is largely improved.
Specifically, it has been demonstrated that the MapNet trained using data from
one cantilever beam design with a specific loading condition can be directly
applied to other structure design problems with different domain shapes, sizes,
boundary and loading conditions.


title: Skew Dyck paths without up--down--left

abstract:
  Skew Dyck paths without up-down-left are enumerated. In a second step, the
number of contiguous subwords 'up-down-left' are counted. This explains and
extends results that were posted in the Encyclopedia of Integer Sequences.


title:
Learning Whole Heart Mesh Generation From Patient Images For
  Computational Simulations

abstract:
  Patient-specific cardiac modeling combines geometries of the heart derived
from medical images and biophysical simulations to predict various aspects of
cardiac function. However, generating simulation-suitable models of the heart
from patient image data often requires complicated procedures and significant
human effort. We present a fast and automated deep-learning method to construct
simulation-suitable models of the heart from medical images. The approach
constructs meshes from 3D patient images by learning to deform a small set of
deformation handles on a whole heart template. For both 3D CT and MR data, this
method achieves promising accuracy for whole heart reconstruction, consistently
outperforming prior methods in constructing simulation-suitable meshes of the
heart. When evaluated on time-series CT data, this method produced more
anatomically and temporally consistent geometries than prior methods, and was
able to produce geometries that better satisfy modeling requirements for
cardiac flow simulations. Our source code will be available on GitHub.


title:
Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot
  Interactions

abstract:
  In this work, we propose a framework for allowing autonomous robots deployed
for extended periods of time in public spaces to adapt their own behaviour
online from user interactions. The robot behaviour planning is embedded in a
Reinforcement Learning (RL) framework, where the objective is maximising the
level of overall user engagement during the interactions. We use the
Upper-Confidence-Bound Value-Iteration (UCBVI) algorithm, which gives a helpful
way of managing the exploration-exploitation trade-off for real-time
interactions. An engagement model trained end-to-end generates the reward
function in real-time during policy execution. We test this approach in a
public museum in Lincoln (UK), where the robot is deployed as a tour guide for
the visitors. Results show that after a couple of months of exploration, the
robot policy learned to maintain the engagement of users for longer, with an
increase of 22.8% over the initial static policy in the number of items visited
during the tour and a 30% increase in the probability of completing the tour.
This work is a promising step toward behavioural adaptation in long-term
scenarios for robotics applications in social settings.


title:
Reinforcement learning reward function in unmanned aerial vehicle
  control tasks

abstract:
  This paper presents a new reward function that can be used for deep
reinforcement learning in unmanned aerial vehicle (UAV) control and navigation
problems. The reward function is based on the construction and estimation of
the time of simplified trajectories to the target, which are third-order Bezier
curves. This reward function can be applied unchanged to solve problems in both
two-dimensional and three-dimensional virtual environments. The effectiveness
of the reward function was tested in a newly developed virtual environment,
namely, a simplified two-dimensional environment describing the dynamics of UAV
control and flight, taking into account the forces of thrust, inertia, gravity,
and aerodynamic drag. In this formulation, three tasks of UAV control and
navigation were successfully solved: UAV flight to a given point in space,
avoidance of interception by another UAV, and organization of interception of
one UAV by another. The three most relevant modern deep reinforcement learning
algorithms, Soft actor-critic, Deep Deterministic Policy Gradient, and Twin
Delayed Deep Deterministic Policy Gradient were used. All three algorithms
performed well, indicating the effectiveness of the selected reward function.


title:
Thermal Tides in the Martian Atmosphere near Northern Summer Solstice
  Observed by ACS/TIRVIM onboard TGO

abstract:
  Thermal tides in the Martian atmosphere are analyzed using temperature
profiles retrieved from nadir observations obtained by the TIRVIM
Fourier-spectrometer, part of the Atmospheric Chemistry Suite (ACS) onboard the
ExoMars Trace Gas Orbiter (TGO). The data is selected near the northern summer
solstice at solar longitude (LS) 75{\deg}-105{\deg} of Martian Year (MY) 35.
The observations have a full local time coverage, which enables analyses of
daily temperature anomalies. The observed zonal mean temperature is lower by
4-6K at ~100Pa, but higher towards the summer pole, compared to the LMD Mars
General Circulation Model (GCM). Wave mode decomposition shows dominant diurnal
tide and important semi-diurnal tide and diurnal Kelvin wave, with maximal
amplitudes of 5K, 3K, and 2.5K, respectively, from tens to hundreds of Pa. The
results generally agree well with the LMD Mars GCM, but with noticeable earlier
phases of diurnal (~1h) and semi-diurnal (~3h) tides.


title:
Variational Hierarchical Directed Bounding Box Construction for Solid
  Mesh Models

abstract:
  Object oriented bounding box tree (OBB-Tree for short) has many applications
in collision detection, real-time rendering, etc. It has a wide range of
applications. The construction of the hierarchical directed bounding box of the
solid mesh model is studied, and a new optimization solution method is
proposed. But this part of the external space volume that does not belong to
the solid mesh model is used as the error, and an error calculation method
based on hardware acceleration is given. Secondly, the hierarchical bounding
box construction problem is transformed into a variational approximation
problem, and the optimal hierarchical directed bounding box is obtained by
solving the global error minimum. In the optimization calculation, we propose
that combining Lloyd clustering iteration in the same layer and MultiGrid-like
reciprocating iteration between layers. Compared with previous results, this
method can generate aired original solid mesh models are more tightly packed
with hierarchical directed bounding box approximation. In the practical
application of collision detection, the results constructed using this method
can reduce the computational time of collision detection and improve detection
efficiency.


title:
Elastic Full Procrustes Analysis of Plane Curves via Hermitian
  Covariance Smoothing

abstract:
  Determining the mean shape of a collection of curves is not a trivial task,
in particular when curves are only irregularly/sparsely sampled at discrete
points. We propose an elastic full Procrustes mean of shapes of (oriented)
plane curves, which are considered equivalence classes of parameterized curves
with respect to translation, rotation, scale, and re-parameterization
(warping), based on the square-root-velocity framework. Identifying the real
plane with the complex numbers, we establish a connection to covariance
estimation in irregular/sparse functional data analysis and propose Hermitian
covariance smoothing for (in)elastic full Procrustes mean estimation. We
demonstrate the performance of the approach in a phonetic study on tongue
shapes and in different realistic simulation settings, inter alia based on
handwriting data.


title:
Jet Streams and Tracer Mixing in the Atmospheres of Brown Dwarfs and
  Isolated Young Giant Planets

abstract:
  Observations of brown dwarfs and relatively isolated young extrasolar giant
planets have provided unprecedented details to probe atmospheric dynamics in a
new regime. Questions about mechanisms governing global circulation remain to
be addressed. Previous studies have shown that small-scale, randomly varying
thermal perturbations resulting from interactions between convection and the
overlying stratified layers can drive zonal jet streams, waves, and turbulence.
Here, we improve upon our previous general circulation model by using a
two-stream grey radiative transfer scheme to represent more realistic heating
and cooling rates. We examine the formation of zonal jets and their time
evolution, and vertical mixing of passive tracers including clouds and chemical
species. Under relatively weak radiative and frictional dissipation, robust
zonal jets with speeds up to a few hundred $\rm m\;s^{-1}$ are typical
outcomes. The off-equatorial jets tend to be pressure-independent while the
equatorial jets exhibit significant vertical wind shear. Models with strong
dissipation inhibit jet formation and have isotropic turbulence in
off-equatorial regions. Quasi-periodic oscillations of the equatorial flow with
periods ranging from tens of days to months are prevalent at relatively low
atmospheric temperatures. Sub-micron cloud particles can be transported to
several scale heights above the condensation level, while larger particles form
thinner layers. Cloud decks are inhomogeneous near their cloud tops. Chemical
tracers with chemical timescales $>10^5$ s can be driven out of equilibrium.
The equivalent vertical diffusion coefficients, $K_{\mathrm{zz}}$, for the
global-mean tracer, are diagnosed from our models and are typically on the
order of $1\sim10^2\rm m^2\;s^{-1}$. Finally, we derive an analytic estimation
of $K_{\mathrm{zz}}$ for different types of tracers under relevant conditions.


title:
Like-charge attraction at the nanoscale: ground-state correlations and
  water destructuring

abstract:
  Like-charge attraction, driven by ionic correlations, challenges our
understanding of electrostatics both in soft and hard matter. For two charged
planar surfaces confining counterions and water, we prove that even at
relatively low correlation strength, the relevant physics is the ground-state
one, oblivious of fluctuations. Based on this, we derive a simple and accurate
interaction pressure, that fulfills known exact requirements and can be used as
an effective potential. We test this equation against implicit-solvent Monte
Carlo simulations and against explicit-solvent simulations of cement and
several types of clays. We argue that water destructuring under nanometric
confinement drastically reduces dielectric screening, enhancing ionic
correlations. Our equation of state at reduced permittivity therefore explains
the exotic attractive regime reported for these materials, even in absence of
multivalent counterions.

categories: cond-mat.soft cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.stat-mech physics.chem-ph


title:
Recognising the importance of preference change: A call for a
  coordinated multidisciplinary research effort in the age of AI

abstract:
  As artificial intelligence becomes more powerful and a ubiquitous presence in
daily life, it is imperative to understand and manage the impact of AI systems
on our lives and decisions. Modern ML systems often change user behavior (e.g.
personalized recommender systems learn user preferences to deliver
recommendations that change online behavior). An externality of behavior change
is preference change. This article argues for the establishment of a
multidisciplinary endeavor focused on understanding how AI systems change
preference: Preference Science. We operationalize preference to incorporate
concepts from various disciplines, outlining the importance of meta-preferences
and preference-change preferences, and proposing a preliminary framework for
how preferences change. We draw a distinction between preference change,
permissible preference change, and outright preference manipulation. A
diversity of disciplines contribute unique insights to this framework.


title:
Hankel Determinant and Orthogonal Polynomials for a Perturbed Gaussian
  Weight: from Finite $n$ to Large $n$ Asymptotics

abstract:
  We study the monic polynomials orthogonal with respect to a symmetric
perturbed Gaussian weight $$ w(x;t):=\mathrm{e}^{-x^2}\left(1+t\:
x^2\right)^\lambda,\qquad x\in \mathbb{R}, $$ where $t> 0,\;\lambda\in
\mathbb{R}$. This weight is related to the single-user MIMO systems in
information theory. It is shown that the recurrence coefficient $\beta_n(t)$ is
related to a particular Painlev\'{e} V transcendent, and the sub-leading
coefficient $\mathrm{p}(n,t)$ satisfies the Jimbo-Miwa-Okamoto $\sigma$-form of
the Painlev\'{e} V equation. Furthermore, we derive the second-order difference
equations satisfied by $\beta_n(t)$ and $\mathrm{p}(n,t)$, respectively. This
enables us to obtain the large $n$ full asymptotic expansions for $\beta_n(t)$
and $\mathrm{p}(n,t)$ with the aid of Dyson's Coulomb fluid approach. We also
consider the Hankel determinant $D_n(t)$, generated by the perturbed Gaussian
weight. It is found that $H_n(t)$, a quantity allied to the logarithmic
derivative of $D_n(t)$, can be expressed in terms of $\beta_n(t)$ and
$\mathrm{p}(n,t)$. Based on this result, we obtain the large $n$ asymptotic
expansion of $H_n(t)$ and then that of the Hankel determinant $D_n(t)$.


title:
Estimation for the reaction term in semi-linear SPDEs under small
  diffusivity

abstract:
  We consider the estimation of a non-linear reaction term in the stochastic
heat or more generally in a semi-linear stochastic partial differential
equation (SPDE). Consistent inference is achieved by studying a small
diffusivity level, which is realistic in applications. Our main result is a
central limit theorem for the estimation error of a parametric estimator, from
which confidence intervals can be constructed. Statistical efficiency is
demonstrated by establishing local asymptotic normality. The estimation method
is extended to local observations in time and space, which allows for
non-parametric estimation of a reaction intensity varying in time and space.
Furthermore, discrete observations in time and space can be handled. The
statistical analysis requires advanced tools from stochastic analysis like
Malliavin calculus for SPDEs, the infinite-dimensional Gaussian Poincar\'e
inequality and regularity results for SPDEs in $L^p$-interpolation spaces.


title: Stochastic Video Prediction with Structure and Motion

abstract:
  While stochastic video prediction models enable future prediction under
uncertainty, they mostly fail to model the complex dynamics of real-world
scenes. For example, they cannot provide reliable predictions for scenes with a
moving camera and independently moving foreground objects in driving scenarios.
The existing methods fail to fully capture the dynamics of the structured world
by only focusing on changes in pixels. In this paper, we assume that there is
an underlying process creating observations in a video and propose to factorize
it into static and dynamic components. We model the static part based on the
scene structure and the ego-motion of the vehicle, and the dynamic part based
on the remaining motion of the dynamic objects. By learning separate
distributions of changes in foreground and background, we can decompose the
scene into static and dynamic parts and separately model the change in each.
Our experiments demonstrate that disentangling structure and motion helps
stochastic video prediction, leading to better future predictions in complex
driving scenarios on two real-world driving datasets, KITTI and Cityscapes.


title:
On the rigorous mathematical derivation for the viscous primitive
  equations with density stratification

abstract:
  In this paper, we rigorously derive the governed equations describing the
motion of stable stratified fluid, from the mathematical point of view.
Specially, we prove that the scaled Boussinesq equations strongly converge to
the viscous primitive equations with density stratification as the aspect
ration parameter goes to zero, and the rate of convergence is of the same order
as the aspect ratio parameter. Moreover, in order to obtain this convergence
result, we also establish the global well-posedness of strong solutions to the
viscous primitive equations with density stratification.


title: Rational points of rigid-analytic sets: a Pila-Wilkie type theorem

abstract:
  We establish a rigid-analytic analog of the Pila-Wilkie counting theorem,
giving sub-polynomial upper bounds for the number of rational points in the
transcendental part of a $\mathbb{Q}_p$-analytic set, and the number of
rational functions in a $\mathbb{F}_q((t))$-analytic set. For
$\mathbb{Z}[[t]]$-analytic sets we prove such bounds uniformly for the
specialization to every non-archimedean local field.


title:
Approximation and Interpolation of Singular Measures by Trigonometric
  Polynomials

abstract:
  Complex signed measures of finite total variation are a powerful signal model
in many applications. Restricting to the $d$-dimensional torus, finitely
supported measures allow for exact recovery if the trigonometric moments up to
some order are known. Here, we consider the approximation of general measures,
e.g., supported on a curve, by trigonometric polynomials of fixed degree with
respect to the Wasserstein-1 distance. We prove sharp lower bounds for their
best approximation and (almost) matching upper bounds for effectively
computable approximations when the trigonometric moments of the measure are
known. A second class of sum of squares polynomials is shown to interpolate the
characteristic function on the support of the measure and to converge to zero
outside.


title:
Logarithmic convexity and impulsive controllability for the 1-D heat
  equation with dynamic boundary conditions

abstract:
  In this paper, we prove a logarithmic convexity that reflects an
observability estimate at a single point of time for 1-D heat equation with
dynamic boundary conditions. Consequently, we establish the impulse approximate
controllability for the impulsive heat equation with dynamic boundary
conditions. Moreover, we obtain an explicit upper bound of the cost of impulse
control. At the end, we give a constructive algorithm for computing the
impulsive control of minimal $L^2$-norm. We also present some numerical tests
to validate the theoretical results and show the efficiency of the designed
algorithm.


title:
Strategic Analysis to defend against Griefing Attack in Lightning
  Network

abstract:
  Payments routed in Lightning Network are susceptible to a \emph{griefing
attack}. In this attack, the channels get blocked, and the affected parties
cannot process any payment request. Our work is the first to analyze griefing
attacks in Hashed Timelock Contract or \emph{HTLC}, from a game-theoretic point
of view. Using the same model, we analyze another payment protocol Hashed
Timelock Contract with Griefing-Penalty or \emph{HTLC-GP}, which was proposed
to counter griefing attacks. We find that \emph{HTLC-GP} is \emph{weakly
effective} in disincentivizing the attacker. To further increase the cost of
attack, we introduce the concept of \emph{guaranteed minimum compensation} and
integrate it into \emph{HTLC-GP}. This modified payment protocol is termed
$\textrm{HTLC-GP}^{\zeta}$ and unlike \emph{HTLC-GP}, the protocol considers
the participants to act rationally. By experimenting on several instances of
Lightning Network, we show that the capacity locked drops to $40\%$ in the case
of \emph{HTLC-GP} when the rate of griefing-penalty is set to $4.5\times
10^{-5}$, and $28\%$ in the case of $\textrm{HTLC-GP}^{\zeta}$ when guaranteed
minimum compensation is $2.5\%$ of the transaction amount. These results
justify our claim that $\textrm{HTLC-GP}^{\zeta}$ is better than \emph{HTLC-GP}
to counter griefing attacks.


title:
Synergy between 6G and AI: Open Future Horizons and Impending Security
  Risks

abstract:
  This paper investigates the synergy between 6G and AI. It argues that they
can unlock future horizons, by discussing how they can address future
challenges in healthcare, transportation, virtual reality, education, resource
management, robotics, in addition to public safety and warfare. However, these
great opportunities come also with greater risk. Therefore, the paper provides
an overview of the security risks and challenges, along with possible
mitigation techniques.


title: Finite GK-dimensional Nichols Algebras over the Infinite Dihedral Group

abstract:
  We contribute to the classification of Hopf algebras with finite
Gelfand-Kirillov dimension, GK-dimension for short, through the study of
Nichols algebras over $\mathbb{D}_{\infty}$, the infinite dihedral group. We
find all the irreducible Yetter-Drinfeld modules $V$ over
$\mathbb{D}_{\infty}$, and determine which Nichols algebras $\mathcal{B}(V)$ of
$V$ are finite GK-dimensional.


title:
MIDAS: Multi-sensorial Immersive Dynamic Autonomous System Improves
  Motivation of Stroke Affected Patients for Hand Rehabilitation

abstract:
  Majority of stroke survivors are left with poorly functioning paretic hands.
Current rehabilitation devices have failed to motivate the patients enough to
continue rehabilitation exercises. The objective of this project, MIDAS
(Multi-sensorial Immersive Dynamic Autonomous System) is a proof of concept by
using an immersive system to improve motivation of stroke patients for hand
rehabilitation. MIDAS is intended for stroke patients who suffer from light to
mild stroke. MIDAS is lightweight and portable. It consists of a hand
exoskeleton subsystem, a Virtual Reality (VR) subsystem, and an olfactory
subsystem. Altogether, MIDAS engages four out of five senses during
rehabilitation. To evaluate the efficacy of MIDAS a pilot study consisting of
three sessions is carried out on five stroke affected patients. Subsystems of
MIDAS are added progressively in each session. The game environment, sonic
effects, and scent released is carefully chosen to enhance the immersive
experience. 60% of the scores of user experience are above 40 (out of 56). 96%
Self Rehabilitation Motivation Scale (SRMS) rating shows that the participants
are motivated to use MIDAS and 87% rating shows that MIDAS is exciting for
rehabilitation. Participants experienced elevated motivation to continue stroke
rehabilitation using MIDAS and no undesired side effects were reported.


title:
Iwin: Human-Object Interaction Detection via Transformer with Irregular
  Windows

abstract:
  This paper presents a new vision Transformer, named Iwin Transformer, which
is specifically designed for human-object interaction (HOI) detection, a
detailed scene understanding task involving a sequential process of
human/object detection and interaction recognition. Iwin Transformer is a
hierarchical Transformer which progressively performs token representation
learning and token agglomeration within irregular windows. The irregular
windows, achieved by augmenting regular grid locations with learned offsets, 1)
eliminate redundancy in token representation learning, which leads to efficient
human/object detection, and 2) enable the agglomerated tokens to align with
humans/objects with different shapes, which facilitates the acquisition of
highly-abstracted visual semantics for interaction recognition. The
effectiveness and efficiency of Iwin Transformer are verified on the two
standard HOI detection benchmark datasets, HICO-DET and V-COCO. Results show
our method outperforms existing Transformers-based methods by large margins
(3.7 mAP gain on HICO-DET and 2.0 mAP gain on V-COCO) with fewer training
epochs ($0.5 \times$).


title:
One-loop electroweak radiative corrections to polarized M{\o}ller
  scattering

abstract:
  This work is devoted to a theoretical description of polarized M{\o}ller
scattering. Complete one-loop electroweak radiative corrections are calculated
in the helicity amplitude approach with allowance for the exact dependence on
the muon mass. Numerical results are presented for integrated unpolarized and
polarized cross sections as well as angular differential distributions.
Calculations are performed using ReneSANCe Monte Carlo generator and MCSANC
Monte Carlo integrator.


title: End-to-End Video Text Spotting with Transformer

abstract:
  Recent video text spotting methods usually require the three-staged pipeline,
i.e., detecting text in individual images, recognizing localized text, tracking
text streams with post-processing to generate final results. These methods
typically follow the tracking-by-match paradigm and develop sophisticated
pipelines. In this paper, rooted in Transformer sequence modeling, we propose a
simple, but effective end-to-end video text DEtection, Tracking, and
Recognition framework (TransDETR). TransDETR mainly includes two advantages: 1)
Different from the explicit match paradigm in the adjacent frame, TransDETR
tracks and recognizes each text implicitly by the different query termed text
query over long-range temporal sequence (more than 7 frames). 2) TransDETR is
the first end-to-end trainable video text spotting framework, which
simultaneously addresses the three sub-tasks (e.g., text detection, tracking,
recognition). Extensive experiments in four video text datasets (i.e.,ICDAR2013
Video, ICDAR2015 Video, Minetto, and YouTube Video Text) are conducted to
demonstrate that TransDETR achieves state-of-the-art performance with up to
around 8.0% improvements on video text spotting tasks. The code of TransDETR
can be found at https://github.com/weijiawu/TransDETR.


title:
Multi-Agent Terraforming: Efficient Multi-Agent Path Finding via
  Environment Manipulation

abstract:
  Multi-agent pathfinding (MAPF) is concerned with planning collision-free
paths for a team of agents from their start to goal locations in an environment
cluttered with obstacles. Typical approaches for MAPF consider the locations of
obstacles as being fixed, which limits their effectiveness in automated
warehouses, where obstacles (representing pods or shelves) can be moved out of
the way by agents (representing robots) to relieve bottlenecks and introduce
shorter routes. In this work we initiate the study of MAPF with movable
obstacles. In particular, we introduce a new extension of MAPF, which we call
Terraforming MAPF (tMAPF), where some agents are responsible for moving
obstacles to clear the way for other agents. Solving tMAPF is extremely
challenging as it requires reasoning not only about collisions between agents,
but also where and when obstacles should be moved. We present extensions of two
state-of-the-art algorithms, CBS and PBS, in order to tackle tMAPF, and
demonstrate that they can consistently outperform the best solution possible
under a static-obstacle setting.


title: Unsupervised Domain Adaptation for Nighttime Aerial Tracking

abstract:
  Previous advances in object tracking mostly reported on favorable
illumination circumstances while neglecting performance at nighttime, which
significantly impeded the development of related aerial robot applications.
This work instead develops a novel unsupervised domain adaptation framework for
nighttime aerial tracking (named UDAT). Specifically, a unique object discovery
approach is provided to generate training patches from raw nighttime tracking
videos. To tackle the domain discrepancy, we employ a Transformer-based
bridging layer post to the feature extractor to align image features from both
domains. With a Transformer day/night feature discriminator, the daytime
tracking model is adversarially trained to track at night. Moreover, we
construct a pioneering benchmark namely NAT2021 for unsupervised domain
adaptive nighttime tracking, which comprises a test set of 180 manually
annotated tracking sequences and a train set of over 276k unlabelled nighttime
tracking frames. Exhaustive experiments demonstrate the robustness and domain
adaptability of the proposed framework in nighttime aerial tracking. The code
and benchmark are available at https://github.com/vision4robotics/UDAT.


title:
Robust Kagome Electronic Structure in Topological Quantum Magnets
  XMn6Sn6 (X = Dy, Tb, Gd, Y)

abstract:
  Crystal geometry can greatly influence the emergent properties of quantum
materials. As an example, the kagome lattice is an ideal platform to study the
rich interplay between topology, magnetism, and electronic correlation. In this
work, combining high-resolution angle-resolved photoemission spectroscopy and
ab-initio calculation, we systematically investigate the electronic structure
of XMn6Sn6 (X = Dy, Tb, Gd, Y) family compounds. We observe the Dirac fermion
and the flat band arising from the magnetic kagome lattice of Mn atoms.
Interestingly, the flat band locates in the same energy region in all compounds
studied, regardless of their different magnetic ground states and 4f electronic
configurations. These observations suggest a robust Mn magnetic kagome lattice
across the XMn6Sn6 family, thus providing an ideal platform for the search and
investigation on new emergent phenomena in magnetic topological materials.


title: Document Dewarping with Control Points

abstract:
  Document images are now widely captured by handheld devices such as mobile
phones. The OCR performance on these images are largely affected due to
geometric distortion of the document paper, diverse camera positions and
complex backgrounds. In this paper, we propose a simple yet effective approach
to rectify distorted document image by estimating control points and reference
points. After that, we use interpolation method between control points and
reference points to convert sparse mappings to backward mapping, and remap the
original distorted document image to the rectified image. Furthermore, control
points are controllable to facilitate interaction or subsequent adjustment. We
can flexibly select post-processing methods and the number of vertices
according to different application scenarios. Experiments show that our
approach can rectify document images with various distortion types, and yield
state-of-the-art performance on real-world dataset. This paper also provides a
training dataset based on control points for document dewarping. Both the code
and the dataset are released at
https://github.com/gwxie/Document-Dewarping-with-Control-Points.


title:
More on the Brownian force model: avalanche shapes, tip driven, higher
  $d$

abstract:
  The Brownian force model (BFM) is the mean-field model for the avalanches of
an elastic interface slowly driven in a random medium. It describes the
spatio-temporal statistics of the velocity field, and, to some extent is
analytically tractable. We extend our previous studies to obtain several
observables for the BFM with short range elasticity, related to the local jump
sizes $S(x)$ and to the avalanche spatial extension in $d=1$, or the avalanche
span in $d>1$. In $d=1$ we consider both driving (i) by an imposed force (ii)
by an imposed displacement "at the tip" and obtain in each case the mean
spatial shape $\langle S(x) \rangle$ at fixed extension, or at fixed seed to
edge distance. We find that near an edge $x_e$, $S(x) \simeq \sigma |x-x_e|^3$
where $\sigma$ has a universal distribution that we obtain. We also obtain the
spatiotemporal shape near the edge. In $d>1$ we obtain (i) the mean shape
$\langle S(x_1,x_\perp) \rangle$ for a fixed span, which exhibits a non-trivial
dependence in the transverse distance to the seed $x_\perp$ (ii) the mean shape
around a point which has not moved, $\langle S(x) \rangle_{S(0)=0}$, which
vanishes at the center as $|x|^{b_d}$ with non trivial exponents, $b_1=4$,
$b_2=2 \sqrt{2}$ and $b_3=\frac{1}{2} (\sqrt{17}-1)$. We obtain the probability
distributions in any $d$ for the maximal radius of an avalanche and the minimal
distance of approach to a given point, as well as the probability of not
hitting a cone in $d=2$. These results equivalently apply to the continuum
limit of some branching diffusions, as detailed in a companion paper.


title: Parallel Instance Query Network for Named Entity Recognition

abstract:
  Named entity recognition (NER) is a fundamental task in natural language
processing. Recent works treat named entity recognition as a reading
comprehension task, constructing type-specific queries manually to extract
entities. This paradigm suffers from three issues. First, type-specific queries
can only extract one type of entities per inference, which is inefficient.
Second, the extraction for different types of entities is isolated, ignoring
the dependencies between them. Third, query construction relies on external
knowledge and is difficult to apply to realistic scenarios with hundreds of
entity types. To deal with them, we propose Parallel Instance Query Network
(PIQN), which sets up global and learnable instance queries to extract entities
from a sentence in a parallel manner. Each instance query predicts one entity,
and by feeding all instance queries simultaneously, we can query all entities
in parallel. Instead of being constructed from external knowledge, instance
queries can learn their different query semantics during training. For training
the model, we treat label assignment as a one-to-many Linear Assignment Problem
(LAP) and dynamically assign gold entities to instance queries with minimal
assignment cost. Experiments on both nested and flat NER datasets demonstrate
that our proposed method outperforms previous state-of-the-art models.


title: Towards 3D Scene Understanding by Referring Synthetic Models

abstract:
  Promising performance has been achieved for visual perception on the point
cloud. However, the current methods typically rely on labour-extensive
annotations on the scene scans. In this paper, we explore how synthetic models
alleviate the real scene annotation burden, i.e., taking the labelled 3D
synthetic models as reference for supervision, the neural network aims to
recognize specific categories of objects on a real scene scan (without scene
annotation for supervision). The problem studies how to transfer knowledge from
synthetic 3D models to real 3D scenes and is named Referring Transfer Learning
(RTL). The main challenge is solving the model-to-scene (from a single model to
the scene) and synthetic-to-real (from synthetic model to real scene's object)
gap between the synthetic model and the real scene. To this end, we propose a
simple yet effective framework to perform two alignment operations. First,
physical data alignment aims to make the synthetic models cover the diversity
of the scene's objects with data processing techniques. Then a novel
\textbf{convex-hull regularized feature alignment} introduces learnable
prototypes to project the point features of both synthetic models and real
scenes to a unified feature space, which alleviates the domain gap. These
operations ease the model-to-scene and synthetic-to-real difficulty for a
network to recognize the target objects on a real unseen scene. Experiments
show that our method achieves the average mAP of 46.08\% and 55.49\% on the
ScanNet and S3DIS datasets by learning the synthetic models from the ModelNet
dataset. Code will be publicly available.


title:
Spectroscopic evidence of flat bands in breathing kagome semiconductor
  Nb3I8

abstract:
  Kagome materials have become solid grounds to study the interplay among
geometry, topology, correlation, and magnetism. Recently, semiconductors
Nb3X8(X = Cl, Br, I) have been predicted to be two-dimensional (2D) magnets and
importantly these materials possess breathing kagome geometry. Electronic
structure study of these promising materials is still lacking. Here, we report
the spectroscopic evidence of at and weakly dispersing bands in
breathing-kagome semiconductor Nb3I8 around 500 meV binding energy, which is
well supported by our first-principles calculations. These bands originate from
the breathing kagome lattice of Niobium atoms and have Nb d character. They are
found to be sensitive to polarization of the incident photon beam. Our study
provides insight into the electronic structure and at band topology in an
exfoliable kagome semiconductor thereby providing an important platform to
understand the interaction of geometry and electron correlations in 2D
material.


title: Macroscopic charge segregation in driven polyelectrolyte solutions

abstract:
  Understanding the behavior of charged complex fluids is crucial for a
plethora of important industrial, technological, and medical applications.
Using coarse-grained molecular dynamics simulations, here we investigate the
properties of a polyelectrolyte solution, with explicit counterions and
implicit solvent, that is driven by a steady electric field. By properly tuning
the interplay between interparticle electrostatics and the applied electric
field, we uncover two nonequilibrium continuous phase transitions as a function
of the driving field. The first transition occurs from a homogeneously mixed
phase to a macroscopically charge segregated phase, in which the
polyelectrolyte solution self-organizes to form two lanes of like-charges,
parallel to the applied field. We show that the fundamental underlying factor
responsible for the emergence of this charge segregation in the presence of
electric field is the excluded volume interactions of the drifting
polyelectrolyte chains. As the drive is increased further, a re-entrant
transition is observed from a charge segregated phase to a homogeneous phase.
The re-entrance is signaled by the decrease in mobility of the monomers and
counterions, as the electric field is increased. Furthermore, with multivalent
counterions, a counterintuitive regime of negative differential mobility is
observed, in which the charges move progressively slower as the driving field
is increased. We show that all these features can be consistently explained by
an intuitive trapping mechanism that operates between the oppositely moving
charges, and present numerical evidence to support our claims. Parameter
dependencies and phase diagrams are studied to better understand charge
segregation in such driven polyelectrolyte solutions.


title: On the stable equivalences between finite tensor categories

abstract:
  We aim to study Morita theory for tensor triangulated categories. For two
finite tensor categories having no projective simple objects, we prove that
their stable equivalence induced by an exact $\Bbbk$-linear monoidal functor
can be lifted to a tensor equivalence under some certain conditions.


title:
Absorption Variability of the Highly Obscured Active Galactic Nucleus
  NGC 4507

abstract:
  We present a detailed study of the highly obscured active galaxy NGC 4507,
performed using four Nuclear Spectroscopic Telescope Array (NuSTAR)
observations carried out between May and August in 2015 (~ 130 ks in total).
Using various phenomenological and physically motivated torus models, we
explore the properties of the X-ray source and those of the obscuring material.
The primary X-ray emission is found to be non-variable, indicating a stable
accretion during the period of the observations. We find the equatorial column
density of the obscuring materials to be ~ 2 x 10^24 cm^-2 while the line of
sight column density to be ~ 7 - 8 x 10^23 cm^-22. The source is found to be
deeply buried with the torus covering factor ~ 0.85. We observe variability in
the line-of-sight column density on a timescale of < 35 days. The covering
factor of the Compton-Thick material is found to be ~ 0.35, in agreement with
the results of recent X-ray surveys. From the variability of the line-of-sight
column density, we estimate that the variable absorbing material is likely
located either in the BLR or in the torus.


title: Human Values Violations in Stack Overflow: An Exploratory Study

abstract:
  A growing number of software-intensive systems are being accused of violating
or ignoring human values (e.g., privacy, inclusion, and social responsibility),
and this poses great difficulties to individuals and society. Such violations
often occur due to the solutions employed and decisions made by developers of
such systems that are misaligned with user values. Stack Overflow is the most
popular QA website among developers to share their issues, solutions (e.g.,
code snippets), and decisions during software development. We conducted an
exploratory study to investigate the occurrence of human values violations in
Stack Overflow posts. As comments under posts are often used to point out the
possible issues and weaknesses of the posts, we analyzed 2000 Stack Overflow
comments and their corresponding posts (1980 unique questions or answers) to
identify the types of human values violations and the reactions of Stack
Overflow users to such violations. Our study finds that 315 out of 2000
comments contain concerns indicating their associated posts (313 unique posts)
violate human values. Leveraging Schwartz's theory of basic human values as the
most widely used values model, we show that hedonism and benevolence are the
most violated value categories. We also find the reaction of Stack Overflow
commenters to perceived human values violations is very quick, yet the majority
of posts (76.35%) accused of human values violation do not get downvoted at
all. Finally, we find that the original posters rarely react to the concerns of
potential human values violations by editing their posts. At the same time,
they usually are receptive when responding to these comments in follow-up
comments of their own.


title:
A Learning Convolutional Neural Network Approach for Network Robustness
  Prediction

abstract:
  Network robustness is critical for various societal and industrial networks
again malicious attacks. In particular, connectivity robustness and
controllability robustness reflect how well a networked system can maintain its
connectedness and controllability against destructive attacks, which can be
quantified by a sequence of values that record the remaining connectivity and
controllability of the network after a sequence of node- or edge-removal
attacks. Traditionally, robustness is determined by attack simulations, which
are computationally very time-consuming or even practically infeasible. In this
paper, an improved method for network robustness prediction is developed based
on learning feature representation using convolutional neural network
(LFR-CNN). In this scheme, higher-dimensional network data are compressed to
lower-dimensional representations, and then passed to a CNN to perform
robustness prediction. Extensive experimental studies on both synthetic and
real-world networks, both directed and undirected, demonstrate that 1) the
proposed LFR-CNN performs better than other two state-of-the-art prediction
methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive
to the variation of the network size, which significantly extends its
applicability; 3) although LFR-CNN needs more time to perform feature learning,
it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN
not only can accurately predict network robustness, but also provides a good
indicator for connectivity robustness, better than the classical spectral
measures.


title:
FaceOri: Tracking Head Position and Orientation Using Ultrasonic Ranging
  on Earphones

abstract:
  Face orientation can often indicate users' intended interaction target. In
this paper, we propose FaceOri, a novel face tracking technique based on
acoustic ranging using earphones. FaceOri can leverage the speaker on a
commodity device to emit an ultrasonic chirp, which is picked up by the set of
microphones on the user's earphone, and then processed to calculate the
distance from each microphone to the device. These measurements are used to
derive the user's face orientation and distance with respect to the device. We
conduct a ground truth comparison and user study to evaluate FaceOri's
performance. The results show that the system can determine whether the user
orients to the device at a 93.5% accuracy within a 1.5 meters range.
Furthermore, FaceOri can continuously track the user's head orientation with a
median absolute error of 10.9 mm in the distance, 3.7 degrees in yaw, and 5.8
degrees in pitch. FaceOri can allow for convenient hands-free control of
devices and produce more intelligent context-aware interaction.


title: 3D Human Pose Estimation Using M\"obius Graph Convolutional Networks

abstract:
  3D human pose estimation is fundamental to understanding human behavior.
Recently, promising results have been achieved by graph convolutional networks
(GCNs), which achieve state-of-the-art performance and provide rather
light-weight architectures. However, a major limitation of GCNs is their
inability to encode all the transformations between joints explicitly. To
address this issue, we propose a novel spectral GCN using the M\"obius
transformation (M\"obiusGCN). In particular, this allows us to directly and
explicitly encode the transformation between joints, resulting in a
significantly more compact representation. Compared to even the lightest
architectures so far, our novel approach requires 90-98% fewer parameters, i.e.
our lightest M\"obiusGCN uses only 0.042M trainable parameters. Besides the
drastic parameter reduction, explicitly encoding the transformation of joints
also enables us to achieve state-of-the-art results. We evaluate our approach
on the two challenging pose estimation benchmarks, Human3.6M and MPI-INF-3DHP,
demonstrating both state-of-the-art results and the generalization capabilities
of M\"obiusGCN.


title:
An Empirical Investigation on the Challenges Faced by Women in the
  Software Industry: A Case Study

abstract:
  Addressing women's under-representation in the software industry, a widely
recognized concern, requires attracting as well as retaining more women.
Hearing from women practitioners, particularly those positioned in
multi-cultural settings, about their challenges and and adopting their lived
experienced solutions can support the design of programs to resolve the
under-representation issue.
  Goal: We investigated the challenges women face in global software
development teams, particularly what motivates women to leave their company;
how those challenges might break down according to demographics; and strategies
to mitigate the identified challenges.
  Method: To achieve this goal, we conducted an exploratory case study in
Ericsson, a global technology company. We surveyed 94 women and employed
mixed-methods to analyze the data.
  Results: Our findings reveal that women face socio-cultural challenges,
including work-life balance issues, benevolent and hostile sexism, lack of
recognition and peer parity, impostor syndrome, glass ceiling bias effects, the
prove-it-again phenomenon, and the maternal wall. The participants of our
research provided different suggestions to address/mitigate the reported
challenges, including sabbatical policies, flexibility of location and time,
parenthood support, soft skills training for managers, equality of payment and
opportunities between genders, mentoring and role models to support career
growth, directives to hire more women, inclusive groups and events, women's
empowerment, and recognition for women's success. The framework of challenges
and suggestions can inspire further initiatives both in academia and industry
to onboard and retain women.


title:
Enhanced phase noise reduction in localized two-way optical frequency
  comparison

abstract:
  High-stability optical frequency comparison over fiber link enables the
establishment of ultrastable optical clock networks, having the potential to
promote a series of applications, including metrology, geodesy, and astronomy.
In this article, we theoretically analyze and experimentally demonstrate a
timedelayed local two-way (TD-LTW) optical frequency comparison scheme with
improved comparison stability, showing that the fractional instability of
optical frequency comparison over a 50- km transfer link can be reduced from
$1.30\times10^{-15}$ to $5.25\times10^{-16}$ at the 1 s integration time with
an improvement factor of 2.48. Additionally, we also for the first time model
and experimentally verify the effect of the inhomogeneous phase noise along the
fiber link on the system performance. We believe that the theory and technique
proposed here will be helpful in developing the high-stability optical clock
networks over large-area fiber links.


title: A Neural-Symbolic Approach to Natural Language Understanding

abstract:
  Deep neural networks, empowered by pre-trained language models, have achieved
remarkable results in natural language understanding (NLU) tasks. However,
their performances can deteriorate drastically when logical reasoning is needed
in the process. This is because, ideally, NLU needs to depend on not only
analogical reasoning, which deep neural networks are good at, but also logical
reasoning. According to the dual-process theory, analogical reasoning and
logical reasoning are respectively carried out by System 1 and System 2 in the
human brain. Inspired by the theory, we present a novel framework for NLU
called Neural-Symbolic Processor (NSP), which performs analogical reasoning
based on neural processing and performs logical reasoning based on both neural
and symbolic processing. As a case study, we conduct experiments on two NLU
tasks, question answering (QA) and natural language inference (NLI), when
numerical reasoning (a type of logical reasoning) is necessary. The
experimental results show that our method significantly outperforms
state-of-the-art methods in both tasks.


title: Revealing Intrinsic Flat $\Lambda$CDM Biases with Standardizable Candles

abstract:
  Emerging high redshift cosmological probes, in particular quasars (QSOs),
show a preference for larger matter densities, $\Omega_{m} \approx 1$, within
the flat $\Lambda$CDM framework. Here, using the Risaliti-Lusso relation for
standardizable QSOs, we demonstrate that the QSOs recover the \textit{same}
Planck-$\Lambda$CDM Universe as Type Ia supernovae (SN), $\Omega_m \approx 0.3$
at lower redshifts $ 0 < z \lesssim 0.7$, before transitioning to an
Einstein-de Sitter Universe ($\Omega_m =1$) at higher redshifts $z \gtrsim 1$.
We illustrate the same trend, namely increasing $\Omega_{m}$ and decreasing
$H_0$ with redshift, in SN but poor statistics prevent a definitive statement.
We explain physically why the trend is expected in the flat $\Lambda$CDM
cosmology, illustrate the intrinsic bias and non-Gaussian tails with mock
Pantheon data, and identify a similar trend in BAO below $z=1$. Our results
highlight an intrinsic bias in the flat $\Lambda$CDM Universe, whereby
$\Omega_m$ increases, $H_0$ decreases and $S_8$ increases with effective
redshift, thus providing a new perspective on $\Lambda$CDM tensions; even in a
Planck-$\Lambda$CDM Universe the current tensions might have been expected.


title: Envelopes of Bisection Lines of Polygons

abstract:
  A bisecting line divides a polygon in two parts of equal areas and the
envelope of bisecting lines $\mathcal{H}$ is a concatenation of hyperbolic
arcs. Some concatenation points are cusps, while other are not. In this paper
we consider two closely related sets, the mid-point locus $\mathcal{M}$ and the
discrete envelope $\mathcal{E}$, that help in characterizing the cusp points.
Moreover, these sets allow us to give a natural solution to the inverse
problem, i.e., the problem of finding polygons with a given envelope of
bisecting lines.


title:
Who will share Fake-News on Twitter? Psycholinguistic cues in online
  post histories discriminate Between actors in the misinformation ecosystem

abstract:
  The spread of misinformation or fake-news is a global concern that undermines
progress on issues such as protecting democracy and public health. Past
research aiming to combat its spread has largely focused on identifying its
semantic content and media outlets publishing such news. In contrast, we aim to
identify individuals who are more likely to share fake-news by studying the
language of actors in the fake-news ecosystem (such as fake-news sharers,
fact-check sharers and random twitter users), and creating a linguistic profile
of them. Fake-news sharers and fact-check sharers use significantly more
high-arousal negative emotions in their language, but fake-news sharers express
more existentially-based needs than other actors. Incorporating
psycholinguistic cues as inferred from their tweets into a model of
socio-demographic predictors considerably improves classification accuracy of
fake-news sharers. The finding that fake-news sharers differ in important ways
from other actors in the fake-news ecosystem (such as in their existential
needs), but are also similar to them in other ways (such as in their anger
levels), highlights the importance of studying the entire fake-news ecosystem
to increase accuracy in identification and prediction. Our approach can help
mitigate fake-news sharing by enabling platforms to pre-emptively screen
potential fake-news sharers' posts.


title:
Robust analyses for longitudinal clinical trials with missing and
  non-normal continuous outcomes

abstract:
  Missing data is unavoidable in longitudinal clinical trials, and outcomes are
not always normally distributed. In the presence of outliers or heavy-tailed
distributions, the conventional multiple imputation with the mixed model with
repeated measures analysis of the average treatment effect (ATE) based on the
multivariate normal assumption may produce bias and power loss. Control-based
imputation (CBI) is an approach for evaluating the treatment effect under the
assumption that participants in both the test and control groups with missing
outcome data have a similar outcome profile as those with an identical history
in the control group. We develop a general robust framework to handle
non-normal outcomes under CBI without imposing any parametric modeling
assumptions. Under the proposed framework, sequential weighted robust
regressions are applied to protect the constructed imputation model against
non-normality in both the covariates and the response variables. Accompanied by
the subsequent mean imputation and robust model analysis, the resulting ATE
estimator has good theoretical properties in terms of consistency and
asymptotic normality. Moreover, our proposed method guarantees the analysis
model robustness of the ATE estimation, in the sense that its asymptotic
results remain intact even when the analysis model is misspecified. The
superiority of the proposed robust method is demonstrated by comprehensive
simulation studies and an AIDS clinical trial data application.


title: CRISPnet: Color Rendition ISP Net

abstract:
  Image signal processors (ISPs) are historically grown legacy software systems
for reconstructing color images from noisy raw sensor measurements. They are
usually composited of many heuristic blocks for denoising, demosaicking, and
color restoration. Color reproduction in this context is of particular
importance, since the raw colors are often severely distorted, and each smart
phone manufacturer has developed their own characteristic heuristics for
improving the color rendition, for example of skin tones and other visually
important colors.
  In recent years there has been strong interest in replacing the historically
grown ISP systems with deep learned pipelines. Much progress has been made in
approximating legacy ISPs with such learned models. However, so far the focus
of these efforts has been on reproducing the structural features of the images,
with less attention paid to color rendition.
  Here we present CRISPnet, the first learned ISP model to specifically target
color rendition accuracy relative to a complex, legacy smart phone ISP. We
achieve this by utilizing both image metadata (like a legacy ISP would), as
well as by learning simple global semantics based on image classification --
similar to what a legacy ISP does to determine the scene type. We also
contribute a new ISP image dataset consisting of both high dynamic range
monitor data, as well as real-world data, both captured with an actual cell
phone ISP pipeline under a variety of lighting conditions, exposure times, and
gain settings.


title:
Application of de-shape synchrosqueezing to estimate gait cadence from a
  single-sensor accelerometer placed in different body locations

abstract:
  Objective: Commercial and research-grade wearable devices have become
increasingly popular over the past decade. Information extracted from devices
using accelerometers is frequently summarized as "number of steps" or "activity
counts". Raw accelerometry data that can be easily extracted from
accelerometers used in research, for instance ActiGraph GT3X+, are frequently
discarded. Approach: In our work, we use the raw data recorded from a single
sensor installed in different body locations to extract gait cadence and other
gait characteristics via an innovative use of the de-shape synchrosqueezing
algorithm. The proposed methodology is tested on data collected in a
semi-controlled experiment with 32 participants walking on a one-kilometer
predefined course. Walking was executed on a flat surface as well as on the
stairs (up and down). Results: With the leave-one-subject-out cross validation,
the accuracy and F1 of determining if a subject is walking from the single
sensor installed on a wrist (hip, left ankle, right ankle respectively) was
found to be 86% and 79% (92% and 87%, 94% and 90%, and 94% and 90%,
respectively). The cadences of walking on a flat surface, ascending stairs, and
descending stairs, determined from the wrist (hip, left ankle, and right ankle,
respectively) sensor, were 1.98+-0.15 Hz, 1.99+-0.26 Hz, and 2.03+-0.26 Hz
respectively (1.98+-0.14 Hz, 1.97+-0.25 Hz, and 2.02+-0.23 Hz, respectively;
1.98+-0.14 Hz, 1.93+-0.22 Hz and 2.06+-0.24 Hz, respectively, and 1.98+-0.14
Hz, 1.97+-0.22 Hz, and 2.04+-0.24 Hz, respectively), which indicates that the
cadence is fastest while descending stairs and slowest when ascending stairs.
The larger standard deviation observed on the wrist sensor is in line with our
expectations. Conclusion: We show that our method can detect walking bouts and
extract the cadence with high accuracy, even when the sensor is placed on the
wrist.


title:
White Paper on High Temperature Superconducting Bi-2212 Magnets for
  Energy Frontier Circular Colliders

abstract:
  As the only high-temperature superconducting (HTS) material available as an
isotropic, twisted, multifilamentary round wire, Bi-2212 is very promising for
expanding the high-field superconducting accelerator magnet toolbox beyond the
round-wire, isotropic Nb-Ti and Nb3Sn conductors used by HEP so far. In this
paper, we describe the important roles that Bi-2212 might play for future high
energy circular colliders including both high energy proton and muon colliders.
We describe its present technology status (conductor development, magnet design
concepts, prototype magnet status) and then provide a ten-year plan for >15 T
accelerator magnets and >25 T solenoid magnets within an integrated strategy to
engage industry and the entire US and international scientific enterprise
interested in HTS magnet applications.


title: LEReg: Empower Graph Neural Networks with Local Energy Regularization

abstract:
  Researches on analyzing graphs with Graph Neural Networks (GNNs) have been
receiving more and more attention because of the great expressive power of
graphs. GNNs map the adjacency matrix and node features to node representations
by message passing through edges on each convolution layer. However, the
message passed through GNNs is not always beneficial for all parts in a graph.
Specifically, as the data distribution is different over the graph, the
receptive field (the farthest nodes that a node can obtain information from)
needed to gather information is also different. Existing GNNs treat all parts
of the graph uniformly, which makes it difficult to adaptively pass the most
informative message for each unique part. To solve this problem, we propose two
regularization terms that consider message passing locally: (1) Intra-Energy
Reg and (2) Inter-Energy Reg. Through experiments and theoretical discussion,
we first show that the speed of smoothing of different parts varies enormously
and the topology of each part affects the way of smoothing. With Intra-Energy
Reg, we strengthen the message passing within each part, which is beneficial
for getting more useful information. With Inter-Energy Reg, we improve the
ability of GNNs to distinguish different nodes. With the proposed two
regularization terms, GNNs are able to filter the most useful information
adaptively, learn more robustly and gain higher expressiveness. Moreover, the
proposed LEReg can be easily applied to other GNN models with plug-and-play
characteristics. Extensive experiments on several benchmarks verify that GNNs
with LEReg outperform or match the state-of-the-art methods. The effectiveness
and efficiency are also empirically visualized with elaborate experiments.


title:
Annealing temperature dependence of thermo-spin and
  magneto-thermoelectric conversion in Co$_2$MnGa films on Y$_3$Fe$_5$O$_{12}$
  and Gd$_3$Ga$_5$O$_{12}$

abstract:
  We have characterized Co$_2$MnGa (CMG) Heusler alloy films grown on
Y$_3$Fe$_5$O$_{12}$ (YIG) and Gd$_3$Ga$_5$O$_{12}$ (GGG) substrates at
different annealing temperatures and investigated thermo-spin and
magneto-thermoelectric conversion properties by means of a lock-in thermography
technique. X-ray diffraction, magnetization, and electrical transport
measurements show that the annealing process induces the crystallized
structures of CMG while the electrical conduction is lost due to large
roughness above 500 {\deg}C (550 {\deg}C) for the CMG films on YIG (GGG),
highlighting the difficulty of fabricating highly ordered continuous CMG films
on garnet structures. Our lock-in thermography measurements show that the
annealing process results in increase of the current-induced temperature change
for CMG/GGG and decrease of that for CMG/YIG. The former indicates the
enhancement of the anomalous Ettingshausen effect in CMG through
crystallization. The latter can be explained by the superposition of the
anomalous Ettingshausen effect and the spin Peltier effect induced by the
positive (negative) charge-to-spin conversion for the amorphous (crystallized)
CMG films. These results provide a hint to construct spin-caloritronic devices
based on Heusler alloys.


title: Improved Semi-Quantum Key Distribution with Two Almost-Classical Users

abstract:
  Semi-quantum key distribution (SQKD) protocols attempt to establish a shared
secret key between users, secure against computationally unbounded adversaries.
Unlike standard quantum key distribution protocols, SQKD protocols contain at
least one user who is limited in their quantum abilities and is almost
"classical" in nature. In this paper, we revisit a mediated semi-quantum key
distribution protocol, introduced by Massa et al., in 2019, where users need
only the ability to detect a qubit, or reflect a qubit; they do not need to
perform any other basis measurement; nor do they need to prepare quantum
signals. Users require the services of a quantum server which may be controlled
by the adversary. In this paper, we show how this protocol may be extended to
improve its efficiency and also its noise tolerance. We discuss an extension
which allows more communication rounds to be directly usable; we analyze the
key-rate of this extension in the asymptotic scenario for a particular class of
attacks and compare with prior work. Finally, we evaluate the protocol's
performance in a variety of lossy and noisy channels.


title:
Accelerating Integrated Task and Motion Planning with Neural Feasibility
  Checking

abstract:
  As robots play an increasingly important role in the industrial, the
expectations about their applications for everyday living tasks are getting
higher. Robots need to perform long-horizon tasks that consist of several
sub-tasks that need to be accomplished. Task and Motion Planning (TAMP)
provides a hierarchical framework to handle the sequential nature of
manipulation tasks by interleaving a symbolic task planner that generates a
possible action sequence, with a motion planner that checks the kinematic
feasibility in the geometric world, generating robot trajectories if several
constraints are satisfied, e.g., a collision-free trajectory from one state to
another. Hence, the reasoning about the task plan's geometric grounding is
taken over by the motion planner. However, motion planning is computationally
intense and is usability as feasibility checker casts TAMP methods inapplicable
to real-world scenarios. In this paper, we introduce neural feasibility
classifier (NFC), a simple yet effective visual heuristic for classifying the
feasibility of proposed actions in TAMP. Namely, NFC will identify infeasible
actions of the task planner without the need for costly motion planning, hence
reducing planning time in multi-step manipulation tasks. NFC encodes the image
of the robot's workspace into a feature map thanks to convolutional neural
network (CNN). We train NFC using simulated data from TAMP problems and label
the instances based on IK feasibility checking. Our empirical results in
different simulated manipulation tasks show that our NFC generalizes to the
entire robot workspace and has high prediction accuracy even in scenes with
multiple obstructions. When combined with state-of-the-art integrated TAMP, our
NFC enhances its performance while reducing its planning time.


title:
Self-supervised Point Cloud Completion on Real Traffic Scenes via
  Scene-concerned Bottom-up Mechanism

abstract:
  Real scans always miss partial geometries of objects due to the
self-occlusions, external-occlusions, and limited sensor resolutions. Point
cloud completion aims to refer the complete shapes for incomplete 3D scans of
objects. Current deep learning-based approaches rely on large-scale complete
shapes in the training process, which are usually obtained from synthetic
datasets. It is not applicable for real-world scans due to the domain gap. In
this paper, we propose a self-supervised point cloud completion method (TraPCC)
for vehicles in real traffic scenes without any complete data. Based on the
symmetry and similarity of vehicles, we make use of consecutive point cloud
frames to construct vehicle memory bank as reference. We design a bottom-up
mechanism to focus on both local geometry details and global shape features of
inputs. In addition, we design a scene-graph in the network to pay attention to
the missing parts by the aid of neighboring vehicles. Experiments show that
TraPCC achieve good performance for real-scan completion on KITTI and nuScenes
traffic datasets even without any complete data in training. We also show a
downstream application of 3D detection, which benefits from our completion
approach.


title: Preservation of superamalgamation by expansions

abstract:
  Under a completion assumption, we show that the superamalgamation property
for some class of ordered structures implies amalgamation for classes with
added operations, including isotone, idempotent, extensive, antitone and
closure operations. The result applies, for example, to partially ordered sets,
semilattices, lattices and Boolean algebras.


title:
Distributionally robust risk evaluation with causality constraint and
  structural information

abstract:
  This work studies distributionally robust evaluation of expected function
values over temporal data. A set of alternative measures is characterized by
the causal optimal transport. We prove the strong duality and recast the
causality constraint as minimization over an infinite-dimensional test function
space. We approximate test functions by neural networks and prove the sample
complexity with Rademacher complexity. Moreover, when structural information is
available to further restrict the ambiguity set, we prove the dual formulation
and provide efficient optimization methods. Simulation on stochastic volatility
and empirical analysis on stock indices demonstrate that our framework offers
an attractive alternative to the classic optimal transport formulation.


title: Smooth Knot Limit Sets of the Complex Hyperbolic Plane

abstract:
  It is shown that if a regular knot of class C2 is embedded in the boundary of
the complex hyperbolic plane as the limit set of a discrete subgroup of PU(2,
1) then it is either a chain or an R-circle.


title:
On the long-time asymptotics of the modified Camassa-Holm equation with
  step-like initial data

abstract:
  We study the long time asymptotic behavior for the Cauchy problem of the
modified Camassa-Holm (mCH) equation with step-like initial data \begin{align}
&m_{t}+\left(m\left(u^{2}-u_{x}^{2}\right)\right)_{x}=0, \quad m=u-u_{xx},
\nonumber \\ &u(x,0)=u_0(x)\to \left\{ \begin{array}{ll}
  A_1, &\ x\to+\infty,\\[5pt]
  A_2, &\ x\to-\infty,
  \end{array}\right.\nonumber \end{align} where $A_1$ and $A_2$ are two
positive constants. Our main technical tool is the representation of the Cauchy
problem with an associated matrix Riemann-Hilbert (RH) problem and the
consequent asymptotic analysis of this RH problem. Based on the spectral
analysis of the Lax pair associated with the mCH equation and scattering
matrix, the solution of the step-like initial problem is characterized via the
solution of a RH problem in the new scale $(y,t)$. We adopt double coordinates
$(\xi, c)$ to divide the half-plane $\{ (\xi,c): \xi \in \mathbb{R}, \ c> 0, \
\xi=y/t\}$ into four asymptotic regions. Further using the Deift-Zhou steepest
descent method, we derive different long time asymptotic expansion of the
solution $u(y,t)$ in different space-time regions by the different choice of
g-function. The corresponding leading asymptotic approximations are given with
the slow/fast decay step-like background wave in genus-0 regions and elliptic
waves in genus-2 regions. The second term of the asymptotics is characterized
by Airy function or parabolic cylinder model. Their residual error order is
$\mathcal{O}(t^{-1})$ or $\mathcal{O}(t^{-2})$ respectively.


title: Hybrid classifiers of pairwise Markov models

abstract:
  The article studies segmentation problem (also known as classification
problem) with pairwise Markov models (PMMs). A PMM is a process where the
observation process and underlying state sequence form a two-dimensional Markov
chain, it is a natural generalization of a hidden Markov model. To demonstrate
the richness of the class of PMMs, we examine closer a few examples of rather
different types of PMMs: a model for two related Markov chains, a model that
allows to model an inhomogeneous Markov chain as a homogeneous one and a
semi-Markov model. The segmentation problem assumes that one of the marginal
processes is observed and the other one is not, the problem is to estimate the
unobserved state path given the observations. The standard state path
estimators often used are the so-called Viterbi path (a sequence with maximum
state path probability given the observations) or the pointwise maximum a
posteriori (PMAP) path (a sequence that maximizes the conditional state
probability for given observations pointwise). Both these estimators have their
limitations, therefore we derive formulas for calculating the so-called hybrid
path estimators which interpolate between the PMAP and Viterbi path. We apply
the introduced algorithms to the studied models in order to demonstrate the
properties of different segmentation methods, and to illustrate large variation
in behaviour of different segmentation methods in different PMMs. The studied
examples show that a segmentation method should always be chosen with care by
taking into account the particular model of interest.


title:
Nonlinear propagation of solitonic pulses in mismatched dual-core highly
  nonlinear fibers

abstract:
  We investigate experimentally and theoretically the effect of dual-core
propagation constant mismatch on the nonlinear switching mechanism in a
dual-core high index-contrast soft glass optical fiber. We focus femtosecond
pulses on one core only to identify transitions among inter-core oscillations,
self-trapping in the cross core and retaining of the pulse in the straight
(excited) core, as governed by the pulse energy. A model based on the system of
coupled nonlinear Schrodinger equations reveals the effect of the mismatch
parameter and pulse duration on the full picture of the energy dependence in
the solitonic propagation scheme. Optimal values of the mismatch and pulse
width are selected to ensure stable conditions for effective nonlinear
switching performance. The theoretical predictions are in rather good agreement
with the experimental results.


title: Multi-view Multi-behavior Contrastive Learning in Recommendation

abstract:
  Multi-behavior recommendation (MBR) aims to jointly consider multiple
behaviors to improve the target behavior's performance. We argue that MBR
models should: (1) model the coarse-grained commonalities between different
behaviors of a user, (2) consider both individual sequence view and global
graph view in multi-behavior modeling, and (3) capture the fine-grained
differences between multiple behaviors of a user. In this work, we propose a
novel Multi-behavior Multi-view Contrastive Learning Recommendation (MMCLR)
framework, including three new CL tasks to solve the above challenges,
respectively. The multi-behavior CL aims to make different user single-behavior
representations of the same user in each view to be similar. The multi-view CL
attempts to bridge the gap between a user's sequence-view and graph-view
representations. The behavior distinction CL focuses on modeling fine-grained
differences of different behaviors. In experiments, we conduct extensive
evaluations and ablation tests to verify the effectiveness of MMCLR and various
CL tasks on two real-world datasets, achieving SOTA performance over existing
baselines. Our code will be available on
\url{https://github.com/wyqing20/MMCLR}


title:
A Statistical Analysis for the Neutrinoless Double-Beta Decay Matrix
  element of 48Ca

abstract:
  Neutrinoless double beta decay ($0\nu\beta\beta$) nuclear matrix elements
(NME) are the object of many theoretical calculation methods, and are very
important for analysis and guidance of a large number of experimental efforts.
However, there are large discrepancies between the NME values provided by
different methods. In this paper we propose a statistical analysis of the
$^{48}$Ca $0\nu\beta\beta$ NME using the interacting shell model, emphasizing
the range of the NME probable values and its correlations with observables that
can be obtained from the existing nuclear data. Based on this statistical
analysis with three independent effective Hamiltonians we propose a common
probability distribution function for the $0\nu\beta\beta$ NME, which has a
range of (0.45 - 0.95) at 90\% confidence level of, and a mean value of 0.68.


title: Probing the parameters of the intergalactic medium using quasars

abstract:
  We continue our series of papers on intergalactic medium (IGM) tracers using
quasi-stellar objects (QSOs), having examined gamma-ray bursts (GRBs) and
blazars in earlier studies. We have estimated the IGM properties of hydrogen
column density (Nhxigm), temperature and metallicity using XMM-Newton QSO
spectra over a large redshift range, with a collisional ionisation equilibrium
(CIE) model for the ionised plasma. The Nhxigm parameter results were robust
with respect to intrinsic power laws, spectral counts, reflection hump and soft
excess features. There is scope for a luminosity bias given both luminosity and
Nhxigm scale with redshift, but we find this unlikely given the consistent IGM
parameter results across the other tracer types reviewed. The impact of
intervening high column density absorbers was found to be minimal. The Nhxigm
from the QSO sample scales as (1 + z)^1.5+/-0.2. The mean hydrogen density at z
= 0 is n0 = (2.8 +/- 0.3) x 10^-7 cm^-3, the mean IGM temperature over the full
redshift range is log(T/K) = 6.5+/-0.1, and the mean metallicity is [X/H] =
-1.3+/-0.1(Z 0.05). Aggregating with our previous GRB and blazar tracers, we
conclude that we have provided evidence of the IGM contributing substantially
and consistently to the total X-ray absorption seen in the spectra. These
results are based on the necessarily simplistic slab model used for the IGM,
due to the inability of current X-ray data to constrain the IGM redshift
distribution


title: Small Batch Sizes Improve Training of Low-Resource Neural MT

abstract:
  We study the role of an essential hyper-parameter that governs the training
of Transformers for neural machine translation in a low-resource setting: the
batch size. Using theoretical insights and experimental evidence, we argue
against the widespread belief that batch size should be set as large as allowed
by the memory of the GPUs. We show that in a low-resource setting, a smaller
batch size leads to higher scores in a shorter training time, and argue that
this is due to better regularization of the gradients during training.


title:
Prospects for slepton pair production in the future $e^-e^+$ Higgs
  factories

abstract:
  The Circular Electron Positron Collider (CEPC) with a center-of-mass energy
$\sqrt{s}$ = 240 GeV is proposed to serve as a Higgs factory, while it can also
provide good opportunity for new physics searches at lower energy, which are
difficult in hadron colliders but well-motivated by some theory models such as
dark matter. This paper presents the sensitivity study of direct stau / smuon
production at CEPC using full Monte Carlo (MC) simulation. With the assumption
of flat 5% systematic uncertainty, the CEPC has the potential to discover the
production of combined left-handed and right-handed stau up to 116 GeV if
exists, or up to 113 GeV for the production of pure left-handed / right-handed
stau; the discovery potential of direct smuon reaches up to 117 GeV with the
same assumption. Due to the conserved assumption of systematic uncertainty and
limited reliance on the reconstruction and detector geometry in this study, the
results can be used as reference for similar searches in other electron
positron colliders at a center-of-mass energy close to 240 GeV, such as Future
Circular Collider $e^{+}e^{-}$ (FCC-ee) and the International Linear Collider
(ILC).


title: Cluster & Tune: Boost Cold Start Performance in Text Classification

abstract:
  In real-world scenarios, a text classification task often begins with a cold
start, when labeled data is scarce. In such cases, the common practice of
fine-tuning pre-trained models, such as BERT, for a target classification task,
is prone to produce poor performance. We suggest a method to boost the
performance of such models by adding an intermediate unsupervised
classification task, between the pre-training and fine-tuning phases. As such
an intermediate task, we perform clustering and train the pre-trained model on
predicting the cluster labels. We test this hypothesis on various data sets,
and show that this additional classification phase can significantly improve
performance, mainly for topical classification tasks, when the number of
labeled instances available for fine-tuning is only a couple of dozen to a few
hundred.


title:
Neuro-physical dynamic load modeling using differentiable parametric
  optimization

abstract:
  In this work, we investigate a data-driven approach for obtaining a reduced
equivalent load model of distribution systems for electromechanical transient
stability analysis. The proposed reduced equivalent is a neuro-physical model
comprising of a traditional ZIP load model augmented with a neural network.
This neuro-physical model is trained through differentiable programming. We
discuss the formulation, modeling details, and training of the proposed model
set up as a differential parametric program. The performance and accuracy of
this neurophysical ZIP load model is presented on a medium-scale 350-bus
transmission-distribution network.


title:
Should Users Trust Their Android Devices? An Analysis and Scoring System
  for Pre-Installed Applications

abstract:
  Most users have no idea that their Android devices are equipped with many
pre-installed applications which have the capability of tracking and monitoring
them. Although applications coming pre-installed pose a great danger to user
security and privacy, they have received little attention so far among
researchers in the field. In this study, we collect a dataset comprising such
applications and make it publicly available. Using this dataset, we analyze
tracker SDKs, manifest files and the use of cloud services and report our
results. We also conduct a user survey to understand concerns and perceptions
of users. Last but not least, we present a scoring system which assigns scores
for smart phones consolidating our findings based on certain criteria. Users
could give their own trust decisions based on the available concise information
about the security and privacy impacts of applications pre-installed on their
devices.


title: Point3D: tracking actions as moving points with 3D CNNs

abstract:
  Spatio-temporal action recognition has been a challenging task that involves
detecting where and when actions occur. Current state-of-the-art action
detectors are mostly anchor-based, requiring sensitive anchor designs and huge
computations due to calculating large numbers of anchor boxes. Motivated by
nascent anchor-free approaches, we propose Point3D, a flexible and
computationally efficient network with high precision for spatio-temporal
action recognition. Our Point3D consists of a Point Head for action
localization and a 3D Head for action classification. Firstly, Point Head is
used to track center points and knot key points of humans to localize the
bounding box of an action. These location features are then piped into a
time-wise attention to learn long-range dependencies across frames. The 3D Head
is later deployed for the final action classification. Our Point3D achieves
state-of-the-art performance on the JHMDB, UCF101-24, and AVA benchmarks in
terms of frame-mAP and video-mAP. Comprehensive ablation studies also
demonstrate the effectiveness of each module proposed in our Point3D.


title:
A Dual-Arm Robot that Manipulates Heavy Plates Cooperatively with a
  Vacuum Lifter

abstract:
  A vacuum lifter is widely used to hold and pick up large, heavy, and flat
objects. Conventionally, when using a vacuum lifter, a human worker watches the
state of a running vacuum lifter and adjusts the object's pose to maintain
balance. In this work, we propose using a dual-arm robot to replace the human
workers and develop planning and control methods for a dual-arm robot to raise
a heavy plate with the help of a vacuum lifter. The methods help the robot
determine its actions by considering the vacuum lifer's suction position and
suction force limits. The essence of the methods is two-fold. First, we build a
Manipulation State Graph (MSG) to store the weighted logical relations of
various plate contact states and robot/vacuum lifter configurations, and search
the graph to plan efficient and low-cost robot manipulation sequences. Second,
we develop a velocity-based impedance controller to coordinate the robot and
the vacuum lifter when lifting an object. With its help, a robot can follow the
vacuum lifter's motion and realize compliant robot-vacuum lifter collaboration.
The proposed planning and control methods are investigated using real-world
experiments. The results show that a robot can effectively and flexibly work
together with a vacuum lifter to manipulate large and heavy plate-like objects
with the methods' support.


title:
Data sharing practices across knowledge domains: a dynamic examination
  of data availability statements in PLOS ONE publications

abstract:
  As the importance of research data gradually grows in sciences, data sharing
has come to be encouraged and even mandated by journals and funders in recent
years. Following this trend, the data availability statement has been
increasingly embraced by academic communities as a means of sharing research
data as part of research articles. This paper presents a quantitative study of
which mechanisms and repositories are used to share research data in PLOS ONE
articles. We offer a dynamic examination of this topic from the disciplinary
and temporal perspectives based on all statements in English-language research
articles published between 2014 and 2020 in the journal. We find a slow yet
steady growth in the use of data repositories to share data over time, as
opposed to sharing data in the paper or supplementary materials; this indicates
improved compliance with the journal's data sharing policies. We also find that
multidisciplinary data repositories have been increasingly used over time,
whereas some disciplinary repositories show a decreasing trend. Our findings
can help academic publishers and funders to improve their data sharing policies
and serve as an important baseline dataset for future studies on data sharing
activities.


title:
Exascale Grid Optimization (ExaGO) toolkit: An open-source
  high-performance package for solving large-scale grid optimization problems

abstract:
  This paper introduces the Exascale Grid Optimization (ExaGO) toolkit, a
library for solving large-scale alternating current optimal power flow (ACOPF)
problems including stochastic effects, security constraints and multi-period
constraints. ExaGO can run on parallel distributed memory platforms, including
massively parallel hardware accelerators such as graphical processing units
(GPUs). We present the details of the ExaGO library including its architecture,
formulations, modeling details, and its performance for several optimization
applications.