title:
The Impact of Standard Ablation Strategies for Atrial Fibrillation on
  Cardiovascular Performance in a Four-chamber Heart Model

abstract:
  Atrial fibrillation is one of the most frequent cardiac arrhythmias in the
industrialized world and ablation therapy is the method of choice for many
patients. However, ablation scars alter the electrophysiological activation and
the mechanical behavior of the affected atria. Different ablation strategies
with the aim to terminate atrial fibrillation and prevent its recurrence exist
but their impact on the hemodynamic performance of the heart has not been
investigated thoroughly. In this work, we present a simulation study analyzing
five commonly used ablation scar patterns and their combinations in the left
atrium regarding their impact on the pumping function of the heart using an
electromechanical whole-heart model. We analyzed how the altered atrial
activation and increased stiffness due to the ablation scar affect atrial as
well as ventricular contraction and relaxation. We found that systolic and
diastolic function of the left atrium is impaired by ablation scars and that
the reduction of atrial stroke volume of up to 11.43% depends linearly on the
amount of inactivated tissue. Consequently, the end-diastolic volume of the
left ventricle, and thus stroke volume, was reduced by up to 1.4% and 1.8%,
respectively. During ventricular systole, left atrial pressure was increased by
up to 20% due to changes in the atrial activation sequence and the stiffening
of scar tissue. This study provides biomechanical evidence that atrial ablation
has acute effects not only on atrial contraction but also on ventricular
pumping function. Our results have the potential to help tailoring ablation
strategies towards minimal global hemodynamic impairment.


title: Active Few-Shot Learning with FASL

abstract:
  Recent advances in natural language processing (NLP) have led to strong text
classification models for many tasks. However, still often thousands of
examples are needed to train models with good quality. This makes it
challenging to quickly develop and deploy new models for real world problems
and business needs. Few-shot learning and active learning are two lines of
research, aimed at tackling this problem. In this work, we combine both lines
into FASL, a platform that allows training text classification models using an
iterative and fast process. We investigate which active learning methods work
best in our few-shot setup. Additionally, we develop a model to predict when to
stop annotating. This is relevant as in a few-shot setup we do not have access
to a large validation set.


title: Paving Springer fibers for E7

abstract:
  The purpose of this paper is to show that, for each unipotent element in type
E7, the corresponding Springer fiber can be paved by affine spaces.


title:
Long Term X-Ray Spectral Variation of the Wolf-Rayet Binary WR 102-1 in
  the Galactic bulge: evidence for wind distortion in the binary

abstract:
  WR~102-1 was detected by Suzaku as a conspicuous point source in the 6.7 keV
intensity map of the central region of the Milky Way. The source was suggested
as a possible Wolf-Rayet binary based on its X-ray and infrared spectral
characteristics. The iron line emission is expected to originate in the
Wolf-Rayet star's dynamic stellar-wind when colliding the companion's mild
stellar wind. Here, we report the result of a long-term X-ray monitoring of
WR~102-1 since 1998 using archival data of ASCA, XMM-Newton, Chandra, Suzaku,
and Swift to reveal variations of the iron K-emission line and the
circumstellar absorption. Consequently, we have detected significant redshifts
of the iron K-emission line from the XMM-Newton observation in March 2003 and
the Suzaku observation in September 2006. Furthermore, when the red-shift was
observed, which suggests that the Wolf-Rayet star was in front of the companion
star, the circumstellar absorption values were smaller than other periods.
These results appear contrary to the expectation if the Wolf-Rayet's stellar
wind is spherically symmetric, but may be understood if the Wolf-Rayet star's
stellar wind is significantly distorted due to the rapid orbital motion near
the periastron.


title:
Placement and Resource Allocation of Wireless-Powered Multiantenna UAV
  for Energy-Efficient Multiuser NOMA

abstract:
  This paper investigates a new downlink nonorthogonal multiple access (NOMA)
system, where a multiantenna unmanned aerial vehicle (UAV) is powered by
wireless power transfer (WPT) and serves as the base station for multiple pairs
of ground users (GUs) running NOMA in each pair. An energy efficiency (EE)
maximization problem is formulated to jointly optimize the WPT time and the
placement for the UAV, and the allocation of the UAV's transmit power between
different NOMA user pairs and within each pair. To efficiently solve this
nonconvex problem, we decompose the problem into three subproblems using block
coordinate descent. For the subproblem of intra-pair power allocation within
each NOMA user pair, we construct a supermodular game with confirmed
convergence to a Nash equilibrium. Given the intra-pair power allocation,
successive convex approximation is applied to convexify and solve the
subproblem of WPT time allocation and inter-pair power allocation between the
user pairs. Finally, we solve the subproblem of UAV placement by using the
Lagrange multiplier method. Simulations show that our approach can
substantially outperform its alternatives that do not use NOMA and WPT
techniques or that do not optimize the UAV location.


title:
Sub-Riemannian Landmark Matching and its interpretation as residual
  neural networks

abstract:
  The problem of finding a time-dependent vector field which warps an initial
set of points to a target set is common in shape analysis. It is an example of
a problem in the diffeomorphic shape matching regime and can be thought of as a
type of spatial discretization of the more general LDDMM framework as it
transforms into a finite-dimensional Hamiltonian system. In this paper, we
consider landmark matching modified by restricting the set of available vector
fields in the sense that vector fields are parametrized by a set of controls.
We determine the geometric setting of the problem, referred to as
sub-Riemannian landmark matching, and derive equations of motions for the
controls. We provide two computational algorithms and demonstrate them in
numerical examples. In particular, the experiments highlight the importance of
the regularization term. The reason why this is of interest is that
sub-Riemannian landmark matching is demonstrated to have connections with
neural networks, in particular the interpretation of residual neural networks
as time discretizations of continuous control problems. It allows shape
analysis practitioners to think about neural networks in terms of shape
analysis, thereby providing a bridge between the two fields.


title: Differentiable Collision Avoidance Using Collision Primitives

abstract:
  A central aspect of robotic motion planning is collision avoidance, where a
multitude of different approaches are currently in use. Optimization-based
motion planning is one method, that often heavily relies on distance
computations between robots and obstacles. These computations can easily become
a bottleneck, as they do not scale well with the complexity of the robots or
the environment. To improve performance, many different methods suggested to
use collision primitives, i.e. simple shapes that approximate the more complex
rigid bodies, and that are simpler to compute distances to and from. However,
each pair of primitives requires its own specialized code, and certain pairs
are known to suffer from numerical issues. In this paper, we propose an
easy-to-use, unified treatment of a wide variety of primitives. We formulate
distance computation as a minimization problem, which we solve iteratively. We
show how to take derivatives of this minimization problem, allowing it to be
seamlessly integrated into a trajectory optimization method. Our experiments
show that our method performs favourably, both in terms of timing and the
quality of the trajectory. The source code of our implementation will be
released upon acceptance.


title:
Analyzing the Impact of Undersampling on the Benchmarking and
  Configuration of Evolutionary Algorithms

abstract:
  The stochastic nature of iterative optimization heuristics leads to
inherently noisy performance measurements. Since these measurements are often
gathered once and then used repeatedly, the number of collected samples will
have a significant impact on the reliability of algorithm comparisons. We show
that care should be taken when making decisions based on limited data.
Particularly, we show that the number of runs used in many benchmarking
studies, e.g., the default value of 15 suggested by the COCO environment, can
be insufficient to reliably rank algorithms on well-known numerical
optimization benchmarks.
  Additionally, methods for automated algorithm configuration are sensitive to
insufficient sample sizes. This may result in the configurator choosing a
`lucky' but poor-performing configuration despite exploring better ones. We
show that relying on mean performance values, as many configurators do, can
require a large number of runs to provide accurate comparisons between the
considered configurations. Common statistical tests can greatly improve the
situation in most cases but not always. We show examples of performance losses
of more than 20%, even when using statistical races to dynamically adjust the
number of runs, as done by irace. Our results underline the importance of
appropriately considering the statistical distribution of performance values.


title:
Concatenation of dishonest Feller processes, exit laws, and limit
  theorems on graphs

abstract:
  We provide a rather explicit formula for the resolvent of a~concatenation of
$N$ processes in terms of their exit laws and certain probability measures
characterizing the way the processes are concatenated. As an application, we
prove an averaging principle saying that by concatenating asymptotically
splittable processes one can approximate Markov chains.


title:
Non-Geometric Cospectral Mates of Line Graphs with a Linear
  Representation

abstract:
  For an incidence geometry $\mathcal{G} = (\mathcal{P}, \mathcal{L},
\text{I})$ with a linear representation $\mathcal{T}_n^*(\mathcal{K})$, we
apply WQH switching to construct a non-geometric graph $\Gamma'$ cospectral
with the line graph $\Gamma$ of $\mathcal{G}$.
  As an application, we show that for $h \geq 2$ and $0 < m < h$, there are
strongly regular graphs with parameters $(v, k, \lambda, \mu) = (2^{2h}
(2^{m+h}+2^m-2^h), 2^h (2^h+1)(2^m-1), 2^h (2^{m+1}-3), 2^h (2^m-1))$ which are
not point graphs of partial geometries of order $(s,t,\alpha) =
((2^h+1)(2^m-1), 2^h-1, 2^m-1)$.


title:
Third Powers of Quadratics are generically Identifiable up to quadratic
  Rank

abstract:
  We consider the inverse problem for the map $$
  (S^2 (\mathbb C^n))^{m} \to S^6(\mathbb C^n), (q_{1},\ldots, q_{m}) \mapsto
\sum_{i=1}^m q_{i}^3, \qquad (n, m \in \mathbb N) $$ which captures the moment
problem for mixtures of centered Gaussians in the smallest interesting degree.
We show that for any $ n\in \mathbb N $, this map is generically one-to-one (up
to permutations of $ q_1,\ldots, q_m $) as long as $ m\le {n\choose 2} + 1 $,
thus proving generic identifiability for mixtures of centered Gaussians from
their (exact) moments of degree at most $ 6 $ up to rank $ {n\choose 2} + 1 $.
We rely on the study of tangent spaces of secant varieties and the contact
locus.


title: Random monotone factorisations of the cycle

abstract:
  In this article we study decreasing and increasing factorisations of the
cycle, which are decompositions of the cycle $(1~2\dots n)$ into a product of
$n-1$ transpositions satisfying monotonicity conditions. We explicit a
bijection between such factorisations and plane trees with $n$ vertices. This
will allow us to study some of their combinatorial properties, as well as a
geometric representation in terms of laminations, which are non-crossing line
segments in the unit disk.


title:
Generative or Contrastive? Phrase Reconstruction for Better Sentence
  Representation Learning

abstract:
  Though offering amazing contextualized token-level representations, current
pre-trained language models actually take less attention on acquiring
sentence-level representation during its self-supervised pre-training. If
self-supervised learning can be distinguished into two subcategories,
generative and contrastive, then most existing studies show that sentence
representation learning may more benefit from the contrastive methods but not
the generative methods. However, contrastive learning cannot be well compatible
with the common token-level generative self-supervised learning, and does not
guarantee good performance on downstream semantic retrieval tasks. Thus, to
alleviate such obvious inconveniences, we instead propose a novel generative
self-supervised learning objective based on phrase reconstruction. Empirical
studies show that our generative learning may yield powerful enough sentence
representation and achieve performance in Sentence Textual Similarity (STS)
tasks on par with contrastive learning. Further, in terms of unsupervised
setting, our generative method outperforms previous state-of-the-art SimCSE on
the benchmark of downstream semantic retrieval tasks.


title: On the implementation of Adaptive and Filtered MHE

abstract:
  Optimisation-based algorithms known as Moving Horizon Estimator (MHE) have
been developed through the years. This paper illustrates the implementation of
the policy introduced in the companion paper submitted to the 18th IFAC
Workshop on Control Applications of Optimization [Oliva and Carnevale, 2022],
in which we propose two techniques to reduce the computational cost of MHEs.
These solutions mainly rely on output filtering and adaptive sampling. The use
of filters reduces the total amount of data used by MHE, shortening the length
of the moving window (buffer) and consequently decreasing the time consumption
for plant dynamics integration. Meanwhile, the proposed adaptive sampling
policy discards those sampled data that do not allow a sensible improvement of
the estimation error. Algorithms and numerical simulations are provided to show
the effectiveness of the proposed strategies.


title:
Multi-Component Imaging of the Fermi Gamma-ray Sky in the
  Spatio-spectral Domain

abstract:
  We perform two distinct spatio-spectral reconstructions of the gamma-ray sky
in the range of 0.56-316 GeV based on Fermi Large Area Telescope (LAT) data.
Both describe the sky brightness to be composed of a diffuse-emission and a
point-source component. The first model requires minimal assumptions and
provides a template-free reconstruction as a reference. It makes use of spatial
and spectral correlations to distinguish between the different components. The
second model is physics-informed and further differentiates between diffuse
emission of hadronic and leptonic origin. For this, we assume parametric, but
spatially varying energy spectra to distinguish between the processes and use
thermal Galactic dust observations to indicate the preferred sites of hadronic
interactions. To account for instrumental effects we model the point-spread,
the energy dispersion, and the exposure of the telescope throughout the
observation. The reconstruction problem is formulated as a Bayesian inference
task, that is solved by variational inference. We show decompositions of the
Gamma-ray flux into diffuse and point-like emissions, and of the diffuse
emissions into multiple physically motivated components. The diffuse
decomposition provides an unprecedented view of the Galactic leptonic diffuse
emission. It shows the Fermi bubbles and their spectral variations in high
fidelity and other areas exhibiting strong cosmic ray electron contents, such
as a thick disk in the inner Galaxy and outflow regions. Furthermore, we report
a hard spectrum gamma ray arc in the northern outer bubble co-spatial with the
reported X-ray arc by the eROSITA collaboration. All our spatio-spectral sky
reconstructions and their uncertainty quantification are publicly available.


title:
Lefschetz properties for jacobian rings of cubic fourfolds and other
  Artinian algebras

abstract:
  In this paper, we exploit some geometric-differential techniques to prove the
strong Lefschetz property in degree $1$ for a complete intersection standard
Artinian Gorenstein algebra of codimension $6$ presented by quadrics. We prove
also some strong Lefschetz properties for the same kind of Artinian algebras in
higher codimensions. Moreover, we analyze some loci that come naturally into
the picture of "special" Artinian algebras: for them, we give some geometric
descriptions and show a connection between the non emptiness of the so-called
non-Lefschetz locus in degree $1$ and the "lifting" of a weak Lefschetz
property to an algebra from one of its quotients.


title:
Wind power predictions from nowcasts to 4-hour forecasts: a learning
  approach with variable selection

abstract:
  We study the prediction of short term wind speed and wind power (every 10
minutes up to 4 hours ahead). Accurate forecasts for those quantities are
crucial to mitigate the negative effects of wind farms' intermittent production
on energy systems and markets. For those time scales, outputs of numerical
weather prediction models are usually overlooked even though they should
provide valuable information on higher scales dynamics. In this work, we
combine those outputs with local observations using machine learning. So as to
make the results usable for practitioners, we focus on simple and well known
methods which can handle a high volume of data. We study first variable
selection through two simple techniques, a linear one and a nonlinear one. Then
we exploit those results to forecast wind speed and wind power still with an
emphasis on linear models versus nonlinear ones. For the wind power prediction,
we also compare the indirect approach (wind speed predictions passed through a
power curve) and the indirect one (directly predict wind power).


title: Aritm\'etica, Grupos y An\'alisis (AGRA II, Cusco, Per\'u)

abstract:
  These are lecture notes for the AGRA II school, which took place in August
2015 at Universidad de San Antonio Abad del Cusco (Per\'u). They are geared
towards graduate students and young researchers.
  I. Modular forms and Shimura curves (R. J. Miatello, G. Tornar\'ia, A.
Pacetti, M. Harris)
  II. Additive combinatorics (J. Ru\'e, J. Wolf, J. Cilleruelo, P. Candela)
  III. Introduction to elliptic curves (M. Rebolledo and M. Hindry)
  IV. Growth in groups and expanders (H. A. Helfgott, M. Belolipetsky)
  V. Analysis and geometry in groups (A. Zuk)
  VI. Equidistribution and Diophantine analysis (R. Menares)


title:
High resolution X-ray study of supernova remnant J0453.6$-$6829 with
  unusually high forbidden-to-resonance ratio

abstract:
  Recent high-resolution X-ray spectroscopy has revealed that several supernova
remnants (SNRs) in the Large Magellanic Cloud (LMC) show unusually high
forbidden-to-resonance ($f/r$) line ratios. While their origin is still
uncertain and debated, most of these SNRs have asymmetric morphology and/or
show evidence of interaction with dense material, which may hint at the true
nature of the anomalous $f/r$ ratios. Here we report on a detailed spectral
analysis of an LMC SNR J0453.6$-$6829 with the Reflection Grating Spectrometer
(RGS) onboard XMM-Newton. We find that the $f/r$ ratio of O$~$VII
($=1.06^{+0.09}_{-0.10}$) is significantly higher than expected from the
previously-reported thermal model. The spectrum is fairly explained by taking
into account a charge exchange (CX) emission in addition to the thermal
component. Analyzing archival ATCA & Parkes radio data, we also reveal that
H$~$I cloud is possibly interacting with J0453.6$-$6829. These results support
the presence of CX in J0453.6$-$6829, as the origin of the obtained high $f/r$
ratio. Although a contribution of the resonance scattering (RS) cannot be ruled
out at this time, we conclude that CX seems more likely than RS considering the
relatively symmetric morphology of this remnant.


title: A construction for variable dimension strong non-overlapping matrices

abstract:
  We propose a method for the construction of sets of variable dimension strong
non-overlapping matrices basing on any strong non-overlapping set of strings.


title: Analyzing the Intensity of Complaints on Social Media

abstract:
  Complaining is a speech act that expresses a negative inconsistency between
reality and human expectations. While prior studies mostly focus on identifying
the existence or the type of complaints, in this work, we present the first
study in computational linguistics of measuring the intensity of complaints
from text. Analyzing complaints from such perspective is particularly useful,
as complaints of certain degrees may cause severe consequences for companies or
organizations. We create the first Chinese dataset containing 3,103 posts about
complaints from Weibo, a popular Chinese social media platform. These posts are
then annotated with complaints intensity scores using Best-Worst Scaling (BWS)
method. We show that complaints intensity can be accurately estimated by
computational models with the best mean square error achieving 0.11.
Furthermore, we conduct a comprehensive linguistic analysis around complaints,
including the connections between complaints and sentiment, and a cross-lingual
comparison for complaints expressions used by Chinese and English speakers. We
finally show that our complaints intensity scores can be incorporated for
better estimating the popularity of posts on social media.


title:
Gravitational waveform model based on photon motion for spinning black
  holes

abstract:
  The waveforms from binary black hole mergers include inspiral, merger and
ringdown parts. Usually, the inspiral waveform can be obtained by calibrating
from post-Newtonian approximation; The merger and ringdown ones can be gotten
from the quasi-normal modes (QNMs) with black hole perturbation theory.
However, for more general black holes, the calculation of the quasi-normal
modes is not trivial. In this paper we use the photon sphere to get the
quasi-normal modes of spinning black hole. Then we connect the ringdown wave
with the inspiral part to get full waveforms and compare with the ones from
numerical relativity(NR). We find that they match with each other very well. In
principle this method can be extended to arbitrary black holes. As an example,
the ringdown waveforms from parametrized axisymmetric black hole are obtained.
We also use this method to get ringdown signals of the accelerating final black
hole due to gravitational recoil during the merger. Even for the extreme cases,
the acceleration due to the recoil can not produce detectable effects.


title:
BugListener: Identifying and Synthesizing Bug Reports from Collaborative
  Live Chats

abstract:
  In community-based software development, developers frequently rely on
live-chatting to discuss emergent bugs/errors they encounter in daily
development tasks. However, it remains a challenging task to accurately record
such knowledge due to the noisy nature of interleaved dialogs in live chat
data. In this paper, we first formulate the task of identifying and
synthesizing bug reports from community live chats, and propose a novel
approach, named BugListener, to address the challenges. Specifically,
BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive
chat logs by using a Feed-Forward neural network; 2) Identify the bug-report
dialogs from separated dialogs by modeling the original dialog to the
graph-structured dialog and leveraging the graph neural network to learn the
contextual information; 3) Synthesize the bug reports by utilizing the TextCNN
model and Transfer Learning network to classify the sentences into three
groups: observed behaviors (OB), expected behaviors (EB), and steps to
reproduce the bug (SR). BugListener is evaluated on six open source projects.
The results show that: for bug report identification, BugListener achieves the
average F1 of 74.21%, improving the best baseline by 10.37%; and for bug report
synthesis task, BugListener could classify the OB, EB, and SR sentences with
the F1 of 67.37%, 87.14%, and 65.03%, improving the best baselines by 7.21%,
7.38%, 5.30%, respectively. A human evaluation also confirms the effectiveness
of BugListener in generating relevant and accurate bug reports. These
demonstrate the significant potential of applying BugListener in
community-based software development, for promoting bug discovery and quality
improvement.


title:
A Variational Autoencoder for Heterogeneous Temporal and Longitudinal
  Data

abstract:
  The variational autoencoder (VAE) is a popular deep latent variable model
used to analyse high-dimensional datasets by learning a low-dimensional latent
representation of the data. It simultaneously learns a generative model and an
inference network to perform approximate posterior inference. Recently proposed
extensions to VAEs that can handle temporal and longitudinal data have
applications in healthcare, behavioural modelling, and predictive maintenance.
However, these extensions do not account for heterogeneous data (i.e., data
comprising of continuous and discrete attributes), which is common in many
real-life applications. In this work, we propose the heterogeneous longitudinal
VAE (HL-VAE) that extends the existing temporal and longitudinal VAEs to
heterogeneous data. HL-VAE provides efficient inference for high-dimensional
datasets and includes likelihood models for continuous, count, categorical, and
ordinal data while accounting for missing observations. We demonstrate our
model's efficacy through simulated as well as clinical datasets, and show that
our proposed model achieves competitive performance in missing value imputation
and predictive accuracy.


title: Multi-Level Interaction Reranking with User Behavior History

abstract:
  As the final stage of the multi-stage recommender system (MRS), reranking
directly affects users' experience and satisfaction, thus playing a critical
role in MRS. Despite the improvement achieved in the existing work, three
issues are yet to be solved. First, users' historical behaviors contain rich
preference information, such as users' long and short-term interests, but are
not fully exploited in reranking. Previous work typically treats items in
history equally important, neglecting the dynamic interaction between the
history and candidate items. Second, existing reranking models focus on
learning interactions at the item level while ignoring the fine-grained
feature-level interactions. Lastly, estimating the reranking score on the
ordered initial list before reranking may lead to the early scoring problem,
thereby yielding suboptimal reranking performance. To address the above issues,
we propose a framework named Multi-level Interaction Reranking (MIR). MIR
combines low-level cross-item interaction and high-level set-to-list
interaction, where we view the candidate items to be reranked as a set and the
users' behavior history in chronological order as a list. We design a novel
SLAttention structure for modeling the set-to-list interactions with
personalized long-short term interests. Moreover, feature-level interactions
are incorporated to capture the fine-grained influence among items. We design
MIR in such a way that any permutation of the input items would not change the
output ranking, and we theoretically prove it. Extensive experiments on three
public and proprietary datasets show that MIR significantly outperforms the
state-of-the-art models using various ranking and utility metrics.


title:
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in
  Text Classification

abstract:
  Incorrect labels in training data occur when human annotators make mistakes
or when the data is generated via weak or distant supervision. It has been
shown that complex noise-handling techniques - by modeling, cleaning or
filtering the noisy instances - are required to prevent models from fitting
this label noise. However, we show in this work that, for text classification
tasks with modern NLP models like BERT, over a variety of noise types, existing
noisehandling methods do not always improve its performance, and may even
deteriorate it, suggesting the need for further investigation. We also back our
observations with a comprehensive analysis.


title: Constructions of Polyphase Golay Complementary Arrays

abstract:
  Golay complementary matrices (GCM) have recently drawn considerable
attentions owing to its potential applications in omnidirectional precoding. In
this paper we generalize the GCM to multi-dimensional Golay complementary
arrays (GCA) and propose new constructions of GCA pairs and GCA quads. These
constructions are facilitated by introducing a set of identities over a
commutative ring. We prove that a quaternary GCA pair is feasible if the
product of the array sizes in all dimensions is a quaternary Golay number with
an additional constraint on the factorization of the product. For the binary
GCM quads, we conjecture that the feasible sizes are arbitrary, and verify for
sizes within 78 $\times$ 78 and other less densely distributed sizes. For the
quaternary GCM quads, all the positive integers within 1000 can be covered for
the size in one dimension.


title:
On Lipschitz functions on groups equipped with conjugation-invariant
  norms

abstract:
  We observe that a function on a group equipped with a bi-invariant word
metric is Lipschitz if and only if it is a partial quasimorphism bounded on the
generating set. We also show that an undistorted element is always detected by
a homogeneous partial quasimorphisms. We provide a general homogenisation
procedure for Lipschitz functions and relate partial quasimorphisms on a group
to ones on its asymptotic cones.


title:
Structural Polymorphism Kinetics Promoted by Charged Oxygen Vacancy in
  HfO$_2$

abstract:
  Defects such as oxygen vacancy are widely considered to be critical for the
performance of HfO2-based devices, and yet atomistic mechanisms underlying
various exotic effects such as wake-up and fluid imprint remain elusive. Here,
guided by a lattice-mode-matching criterion, we systematically study the phase
transitions between different polymorphs of hafnia under the influences of
neutral and positively charged oxygen vacancies by mapping out the minimum
energy pathways using a first-principles-based variable-cell nudged elastic
band technique. We find that the positively charged oxygen vacancy can
substantially promote the transition of various nonpolar phases to the polar
phase kinetically, enabled by a transient high-energy tetragonal phase and
extreme charge-carrier-inert ferroelectricity of the polar $Pca2_1$ phase. The
intricate coupling between structural polymorphism kinetics and the charge
state of the oxygen vacancy has important implications for the origin of
ferroelectricity in HfO$_2$-based thin films as well as wake-up, fluid imprint,
and inertial switching.


title: Nonlinear shallow-water waves with vertical odd viscosity

abstract:
  The breaking of detailed balance in fluids through Coriolis forces or
odd-viscous stresses has profound effects on the dynamics of surface waves.
Here we explore both weakly and strongly non-linear waves in a
three-dimensional fluid with vertical odd viscosity. Our model describes the
free surface of a shallow fluid composed of nearly vertical vortex filaments,
which all stand perpendicular to the surface. We find that the odd viscosity in
this configuration induces previously unexplored non-linear effects in
shallow-water waves, arising from both stresses on the surface and stress
gradients in the bulk. By assuming weak nonlinearity, we find reduced equations
including Korteweg-de Vries (KdV), Ostrovsky, and Kadomtsev-Petviashvilli (KP)
equations with modified coefficients. At sufficiently large odd viscosity, the
dispersion changes sign, allowing for compact two-dimensional solitary waves.
We show that odd viscosity and surface tension have the same effect on the free
surface, but distinct signatures in the fluid flow. Our results describe the
collective dynamics of many-vortex systems, which can also occur in oceanic and
atmospheric geophysics.


title: W boson mass in Singlet-Triplet Scotogenic dark matter model

abstract:
  The recent high precision measurement of $W$ boson mass by CDF-II
collaboration points to the contribution(s) of new physics beyond the Standard
Model. One of the minimalistic ways to account for the anomalous $W$ boson mass
is by introducing a hyperchargeless real $SU(2)_L$ triplet scalar whose vacuum
expectation value explicitly contributes to the $W$ boson mass at the tree
level while the $Z$ boson mass remains the same. Such a triplet can be
naturally embedded in a singlet-triplet scotogenic model for one loop neutrino
mass generated by dark sector particles running in the loop. We discuss the
detailed phenomenology of the model, obtaining the parameter space consistent
with the CDF-II $W$ boson mass measurements. The dark matter as well as the
constraints comings from $S$, $T$, $U$ parameters are also analyzed.


title: Circular Polarimetry of Suspect Wind-accreting Magnetic pre-Polars

abstract:
  We present results from a circular polarimetric survey of candidate detached
magnetic white dwarf - M dwarf binaries obtained using the Nordic Optical
Telescope, La Palma. We obtained phase resolved spectropolarimetry and imaging
polarimetry of seven systems, five of which show clearly variable circular
polarisation. The data indicate that these targets have white dwarfs with
magnetic field strengths > 80 MG. Our study reveals that cyclotron emission can
dominate the optical luminosity at wavelengths corresponding to the cyclotron
emission harmonics, even in systems where the white dwarfs are only
wind-accreting. This implies that a very significant fraction of the the
stellar wind of the companion star is captured by the magnetic white dwarf
reducing the magnetic braking in pre-CVs. Furthermore, the polarimetric
confirmation of several detached, wind-accreting magnetic systems provides
observational constraints on the models of magnetic CV evolution and white
dwarf magnetic field generation. We also find that the white dwarf magnetic
field configuration in at least two of these systems appears to be very
complex.


title: Biocosmology: Towards the birth of a new science

abstract:
  Cosmologists wish to explain how our Universe, in all its complexity, could
ever have come about. For that, we assess the number of states in our Universe
now. This plays the role of entropy in thermodynamics of the Universe, and
reveals the magnitude of the problem of initial conditions to be solved. The
usual budgeting accounts for gravity, thermal motions, and finally the vacuum
energy whose entropy, given by the Bekenstein bound, dominates the entropy
budget today. There is however one number which we have not accounted for: the
number of states in our complex biosphere. What is the entropy of life and is
it sizeable enough to need to be accounted for at the Big Bang? Building on
emerging ideas within theoretical biology, we show that the configuration space
of living systems, unlike that of their fundamental physics counterparts, can
grow rapidly in response to emerging biological complexity. A model for this
expansion is provided through combinatorial innovation by the Theory of the
Adjacent Possible (TAP) and its corresponding TAP equation, whose solutions we
investigate, confirming the possibility of rapid state-pace growth. While the
results of this work remain far from being firmly established, the evidence we
provide is many-fold and strong. The implications are far-reaching, and open a
variety of lines for future investigation, a new scientific field we term
biocosmology. In particular the relationship between the information content in
life and the information content in the Universe may need to be rebuilt from
scratch.


title: Biocosmology: Biology from a cosmological perspective

abstract:
  The Universe contains everything that exists, including life. And all that
exists, including life, obeys universal physical laws. Do those laws then give
adequate foundations for a complete explanation of biological phenomena? We
discuss whether and how cosmology and physics must be modified to be able to
address certain questions which arise at their intersection with biology. We
show that a universe that contains life, in the form it has on Earth, is in a
certain sense radically non-ergodic, in that the vast majority of possible
organisms will never be realized. We argue from this that complete explanations
in cosmology require a mixture of reductionist and functional explanations.


title: Explicit Solutions for Safety Problems Using Control Barrier Functions

abstract:
  The control Barrier function approach has been widely used for safe
controller synthesis. By solving an online convex quadratic programming
problem, an optimal safe controller can be synthesized implicitly in
state-space. Since the solution is unique, the mapping from state-space to
control inputs is injective, thus enabling us to evaluate the underlying
relationship. In this paper we aim at explicitly synthesizing a safe control
law as a function of the state for nonlinear control-affine systems with
limited control ability. We propose to transform the online quadratic
programming problem into an offline parameterized optimisation problem which
considers states as parameters. The obtained explicit safe controller is shown
to be a piece-wise Lipschitz continuous function over the partitioned state
space if the program is feasible. We address the infeasible cases by solving a
parameterized adaptive control Barrier function-based quadratic programming
problem. Extensive simulation results show the state-space partition and the
controller properties.


title:
Exploration strategies for articulatory synthesis of complex syllable
  onsets

abstract:
  High-quality articulatory speech synthesis has many potential applications in
speech science and technology. However, developing appropriate mappings from
linguistic specification to articulatory gestures is difficult and time
consuming. In this paper we construct an optimisation-based framework as a
first step towards learning these mappings without manual intervention. We
demonstrate the production of syllables with complex onsets and discuss the
quality of the articulatory gestures with reference to coarticulation.


title: Quantum walks of two correlated photons in a 2D synthetic lattice

abstract:
  Quantum walks represent paradigmatic quantum evolutions, enabling powerful
applications in the context of topological physics and quantum computation.
They have been implemented in diverse photonic architectures, but the
realization of a two-particle dynamics on a multi-dimensional lattice has
hitherto been limited to continuous-time evolutions. To fully exploit the
computational capabilities of quantum interference it is crucial to develop
platforms handling multiple photons that propagate across multi-dimensional
lattices. Here, we report a discrete-time quantum walk of two correlated
photons in a two-dimensional lattice, synthetically engineered by manipulating
a set of optical modes carrying quantized amounts of transverse momentum.
Mode-couplings are introduced via the polarization-controlled diffractive
action of thin geometric-phase optical elements. The entire platform is
compact, efficient, scalable, and represents a versatile tool to simulate
quantum evolutions on complex lattices. We expect that it will have a strong
impact on diverse fields such as quantum state engineering, topological quantum
photonics, and Boson Sampling.


title: Real-Time Optical Time Interpolation Using Spectral Interferometry

abstract:
  A simple scheme for all-optical time interpolation using spectral
interferometry is put forward that is in principle capable of single-shot
measurements. In this method, the arrival time of optical timing pulses is
encoded into the spectrum of a time-stretched supercontinuum via cross-phase
modulation. The proof-of-concept test setup points toward femtosecond-level
absolute timing capabilities with only minor additions to modern optical
clockwork.


title:
Theoretical study of the process $D^+_s \to \pi^+ K^0_S K^0_S$ and the
  isovector partner of $f_0(1710)$

abstract:
  We present a theoretical study of $a_0(1710)$, the isovector partner of
$f_0(1710)$, in the process $D^+_s \to \pi^+ K^0_SK^0_S$. The weak interaction
part proceeds through the charm quark decay process: $c(\bar{s}) \to (s +
\bar{d} + u)(\bar{s})$, while the hadronization part takes place in two
mechanisms, differing in how the quarks from the weak decay combines into $\pi
K^*$ with a quark-antiquark pair $q\bar{q}$ with the vacuum quantum numbers. In
addition to the contribution from the tree diagram of the $K^{*+} \to \pi^+
K^0_S$, we have also considered the $K^*\bar{K}^*$ final-state interactions
within the chiral unitary approach to generate the intermediate state
$a_0(1710)$, then it decays into the final states $K^0_SK^0_S$. We find that
the recent experimental measurements on the $K^0_SK^0_S$ and $\pi^+K^0_S$
invariant mass distributions can be well reproduced, and the proposed mechanism
can provide valuable information on the nature of scalar $f_0(1710)$ and its
isovector partner $a_0(1710)$ resonances.


title:
Electrostatic Dust Ejection From Asteroid (3200) Phaethon With the Aid
  of Mobile Alkali Ions at Perihelion

abstract:
  The asteroid (3200) Phaethon is known to be the parent body of the Geminids,
although meteor showers are commonly associated with the activity of periodic
comets. What is most peculiar to the asteroid is its comet-like activity in the
ejection of micrometer-sized dust particles at every perihelion passage, while
the activity of the asteroid has never been identified outside the
near-perihelion zone at $0.14~\mathrm{au}$ from the Sun. From the theoretical
point of view, we argue that the activity of the asteroid is well explained by
the electrostatic lofting of micrometer-sized dust particles with the aid of
mobile alkali ions at high temperatures. The mass-loss rates of
micrometer-sized particles from the asteroid in our model is entirely
consistent with the values inferred from visible observations of Phaethon's
dust tail. For millimeter-sized particles, we predict three orders of
magnitudes higher mass-loss rates, which could also account for the total mass
of the Geminid meteoroid stream by the electrostatic lofting mechanism.


title:
Safety Verification and Controller Synthesis for Systems with Input
  Constraints

abstract:
  In this paper we consider the safety verification and safe controller
synthesis problems for nonlinear control systems. The Control Barrier
Certificates (CBC) approach is proposed as an extension to the Barrier
certificates approach. Our approach can be used to characterize the control
invariance of a given set in terms of safety of a general nonlinear control
system subject to input constraints. From the point of view of controller
design, the proposed method provides an approach to synthesize a safe control
law that guarantees that the trajectories of the system starting from a given
initial set do not enter an unsafe set. Unlike the related control Barrier
functions approach, our formulation only considers the vector field within the
tangent cone of the zero level set defined by the certificates, and is shown to
be less conservative by means of numerical evidence. For polynomial systems
with semi-algebraic initial and safe sets, CBCs and safe control laws can be
synthesized using sum-of-squares decomposition and semi-definite programming.
Examples demonstrate our method.


title:
Attentive Dual Stream Siamese U-net for Flood Detection on
  Multi-temporal Sentinel-1 Data

abstract:
  Due to climate and land-use change, natural disasters such as flooding have
been increasing in recent years. Timely and reliable flood detection and
mapping can help emergency response and disaster management. In this work, we
propose a flood detection network using bi-temporal SAR acquisitions. The
proposed segmentation network has an encoder-decoder architecture with two
Siamese encoders for pre and post-flood images. The network's feature maps are
fused and enhanced using attention blocks to achieve more accurate detection of
the flooded areas. Our proposed network is evaluated on publicly available
Sen1Flood11 benchmark dataset. The network outperformed the existing
state-of-the-art (uni-temporal) flood detection method by 6\% IOU. The
experiments highlight that the combination of bi-temporal SAR data with an
effective network architecture achieves more accurate flood detection than
uni-temporal methods.


title: Runtime Prevention of Deserialization Attacks

abstract:
  Untrusted deserialization exploits, where a serialised object graph is used
to achieve denial-of-service or arbitrary code execution, have become so
prominent that they were introduced in the 2017 OWASP Top 10. In this paper, we
present a novel and lightweight approach for runtime prevention of
deserialization attacks using Markov chains. The intuition behind our work is
that the features and ordering of classes in malicious object graphs make them
distinguishable from benign ones. Preliminary results indeed show that our
approach achieves an F1-score of 0.94 on a dataset of 264 serialised payloads,
collected from an industrial Java EE application server and a repository of
deserialization exploits.


title: Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation

abstract:
  Deep neural networks are highly susceptible to learning biases in visual
data. While various methods have been proposed to mitigate such bias, the
majority require explicit knowledge of the biases present in the training data
in order to mitigate. We argue the relevance of exploring methods which are
completely ignorant of the presence of any bias, but are capable of identifying
and mitigating them. Furthermore, we propose using Bayesian neural networks
with an epistemic uncertainty-weighted loss function to dynamically identify
potential bias in individual training samples and to weight them during
training. We find a positive correlation between samples subject to bias and
higher epistemic uncertainties. Finally, we show the method has potential to
mitigate visual bias on a bias benchmark dataset and on a real-world face
detection problem, and we consider the merits and weaknesses of our approach.


title: Essential spectrum and Feller type properties

abstract:
  We give necessary and sufficient conditions for a regular semi-Dirichlet form
to enjoy a new Feller type property, which we call \emph{weak Feller property}.
Our characterization involves potential theoretic as well as probabilistic
aspects and seems to be new even in the symmetric case. As a consequence, in
the symmetric case, we obtain a new variant of a decomposition principle of the
essential spectrum for (the self-adjoint operators induced by) regular
symmetric Dirichlet forms and a Persson type theorem, which applies e.g. to
Cheeger forms on $\mathsf{RCD^*}$ spaces.


title:
You Are What You Write: Preserving Privacy in the Era of Large Language
  Models

abstract:
  Large scale adoption of large language models has introduced a new era of
convenient knowledge transfer for a slew of natural language processing tasks.
However, these models also run the risk of undermining user trust by exposing
unwanted information about the data subjects, which may be extracted by a
malicious party, e.g. through adversarial attacks. We present an empirical
investigation into the extent of the personal information encoded into
pre-trained representations by a range of popular models, and we show a
positive correlation between the complexity of a model, the amount of data used
in pre-training, and data leakage. In this paper, we present the first wide
coverage evaluation and comparison of some of the most popular
privacy-preserving algorithms, on a large, multi-lingual dataset on sentiment
analysis annotated with demographic information (location, age and gender). The
results show since larger and more complex models are more prone to leaking
private information, use of privacy-preserving methods is highly desirable. We
also find that highly privacy-preserving technologies like differential privacy
(DP) can have serious model utility effects, which can be ameliorated using
hybrid or metric-DP techniques.


title: Suppression of Quasiperiodicity in Circle Maps with Quenched Disorder

abstract:
  We show that introducing quenched disorder into a circle map leads to the
suppression of quasiperiodic behavior in the limit of large system sizes.
Specifically, for most parameters the fraction of disorder realizations showing
quasiperiodicity decreases with the system size and eventually vanishes in the
limit of infinite size, where almost all realizations show mode-locking.
Consequently, in this limit, and in strong contrast to standard circle maps,
almost the whole parameter space corresponding to invertible dynamics consists
of Arnold tongues.


title:
Design of Cryogenic Fully Differential Gain Boosting-OTA by the
  $g_{m}/I_{d}$ methodology used for a 14 bit Pipelined-SAR ADC

abstract:
  Quantum computing (QC) requires cryogenic electronic circuits as control and
readout sub-systems of quantum chips to meet the qubit scale-up challenges.At
this temperature,MOSFETs transistors exhibition many changes such as higher
threshold voltage,higher mobility,and steeper substhreshold slope.We present a
cryogenic fully differential gain boosting-OTA used for a 14 bit Pipelined-SAR
ADC operating at 4.2K as the readout circuit for semiconductor-based quantum
computing system.Using $g_{m}/I_{d}$ methodology to get pre-computed lookup
tables based on the cryogenic 110nm BSIM4 model.The proposed OTA achieves very
high unity-gain frequency@1.23GHz and open-loop low frequency gain@101dB.The
total power consumption is 2.66mW at 4.2K,and a setting accuracy better than
0.01\% with $f_{-3dB}$ of 37MHz in a closed-loop application.


title: A group theoretic description of the $\kappa$-Poincar\'e Hopf algebra

abstract:
  It is well known in the literature that the momentum space associated to the
$\kappa$-Poincar\'e algebra is described by the Lie group
$\mathsf{A}\mathsf{N}(3)$. In this letter we show that the full
$\kappa$-Poincar\'e Hopf algebra structure can be obtained from rather
straightforward group-theoretic manipulations starting from the Iwasawa
decomposition of the of the $\mathsf{SO(1,4)}$ group.


title:
Adjusting Thermal Stability in Double-Barrier MTJ for Energy Improvement
  in Cryogenic STT-MRAMs

abstract:
  This paper investigates the impact of thermal stability relaxation in
double-barrier magnetic tunnel junctions (DMTJs) for energy-efficient
spin-transfer torque magnetic random access memories (STT-MRAMs) operating at
the liquid nitrogen boiling point (77K). Our study is carried out through a
macrospin-based Verilog-A compact model of DMTJ, along with a 65nm commercial
process design kit (PDK) calibrated down to 77K under silicon measurements.
Comprehensive bitcell-level electrical characterization is used to estimate the
energy/latency per operation and leakage power at the memory
architecture-level. As a main result of our analysis, we show that
energy-efficient small-to-large embedded memories can be obtained by
significantly relaxing the non-volatility requirement of DMTJ devices at room
temperature (i.e., by reducing the cross-section area), while maintaining the
typical 10-years retention time at cryogenic temperatures. This makes
DMTJ-based STT-MRAM operating at 77K more energy-efficient than six-transistors
static random-access memory (6T-SRAM) under both read and write accesses (-56%
and -37% on average, respectively). Obtained results thus prove that DMTJ-based
STT-MRAM with relaxed retention time is a promising alternative for the
realization of reliable and energy-efficient embedded memories operating at
cryogenic temperatures.


title: Representation of Squares by Nonsingular Cubic Forms

abstract:
  We prove an asymptotic formula for the number of representations of squares
by nonsingular cubic forms in six or more variables. The main ingredients of
the proof are Heath-Brown's form of the Circle Method and various exponential
sum results. The depth of the exponential sum results is comparable to Hooley's
work on cubic forms in nine variables, in particular we prove an analogue of
Katz' bound.


title: Adversarial Scratches: Deployable Attacks to CNN Classifiers

abstract:
  A growing body of work has shown that deep neural networks are susceptible to
adversarial examples. These take the form of small perturbations applied to the
model's input which lead to incorrect predictions. Unfortunately, most
literature focuses on visually imperceivable perturbations to be applied to
digital images that often are, by design, impossible to be deployed to physical
targets. We present Adversarial Scratches: a novel L0 black-box attack, which
takes the form of scratches in images, and which possesses much greater
deployability than other state-of-the-art attacks. Adversarial Scratches
leverage B\'ezier Curves to reduce the dimension of the search space and
possibly constrain the attack to a specific location. We test Adversarial
Scratches in several scenarios, including a publicly available API and images
of traffic signs. Results show that, often, our attack achieves higher fooling
rate than other deployable state-of-the-art methods, while requiring
significantly fewer queries and modifying very few pixels.


title: Case-Aware Adversarial Training

abstract:
  The neural network (NN) becomes one of the most heated type of models in
various signal processing applications. However, NNs are extremely vulnerable
to adversarial examples (AEs). To defend AEs, adversarial training (AT) is
believed to be the most effective method while due to the intensive
computation, AT is limited to be applied in most applications. In this paper,
to resolve the problem, we design a generic and efficient AT improvement
scheme, namely case-aware adversarial training (CAT). Specifically, the
intuition stems from the fact that a very limited part of informative samples
can contribute to most of model performance. Alternatively, if only the most
informative AEs are used in AT, we can lower the computation complexity of AT
significantly as maintaining the defense effect. To achieve this, CAT achieves
two breakthroughs. First, a method to estimate the information degree of
adversarial examples is proposed for AE filtering. Second, to further enrich
the information that the NN can obtain from AEs, CAT involves a weight
estimation and class-level balancing based sampling strategy to increase the
diversity of AT at each iteration. Extensive experiments show that CAT is
faster than vanilla AT by up to 3x while achieving competitive defense effect.


title:
Balanced Wireless Crowd Charging with Mobility Prediction and Social
  Awareness

abstract:
  The advancements in peer-to-peer wireless power transfer (P2P-WPT) have
empowered the portable and mobile devices to wirelessly replenish their battery
by directly interacting with other nearby devices. The existing works
unrealistically assume the users to exchange energy with any of the users and
at every such opportunity. However, due to the users' mobility, the inter-node
meetings in such opportunistic mobile networks vary, and P2P energy exchange in
such scenarios remains uncertain. Additionally, the social interests and
interactions of the users influence their mobility as well as the energy
exchange between them. The existing P2P-WPT methods did not consider the joint
problem for energy exchange due to user's inevitable mobility, and the
influence of sociality on the latter. As a result of computing with imprecise
information, the energy balance achieved by these works at a slower rate as
well as impaired by energy loss for the crowd. Motivated by this problem
scenario, in this work, we present a wireless crowd charging method, namely
MoSaBa, which leverages mobility prediction and social information for improved
energy balancing. MoSaBa incorporates two dimensions of social information,
namely social context and social relationships, as additional features for
predicting contact opportunities. In this method, we explore the different
pairs of peers such that the energy balancing is achieved at a faster rate as
well as the energy balance quality improves in terms of maintaining low energy
loss for the crowd. We justify the peer selection method in MoSaBa by detailed
performance evaluation. Compared to the existing state-of-the-art, the proposed
method achieves better performance trade-offs between energy-efficiency, energy
balance quality and convergence time.


title:
Loop correction to the scalar Casimir energy density and generation of
  topological mass due to a helix boundary condition in a scenario with Lorentz
  violation

abstract:
  In this paper the effective potential approach in quantum field theory is
used in order to investigate self-interaction loop correction to the Casimir
energy density and generation of topological mass for both massless and massive
real scalar fields. It is assumed that the scalar field obeys a helix boundary
condition. In addition, it is also considered a CPT-even aether-type violation
of the Lorentz symmetry. In the absence of the Lorentz violation we obtain
analytical expressions for the loop correction to the Casimir energy density
and to the mass of the scalar field. The same expressions are also obtained
assuming the Lorentz violation in each of the spacetime directions. We also
show some graphs that exhibit how the loop correction and the Lorentz violation
affect the the Casimir energy density and the mass of the scalar field.


title:
Subsonic accretion and dynamical friction for a black hole moving
  through a self-interacting scalar dark matter cloud

abstract:
  We investigate the flow around a black hole moving through a cloud of
self-interacting scalar dark matter. We focus on the large scalar mass limit,
with quartic self-interactions, and on the subsonic regime. We show how the
scalar field behaves as a perfect gas of adiabatic index $\gamma_{\rm ad}=2$ at
large radii while the accretion rate is governed by the relativistic regime
close to the Schwarzschild radius. We obtain analytical results thanks to
large-radius expansions, which are also related to the small-scale relativistic
accretion rate. We find that the accretion rate is greater than for
collisionless particles, by a factor $c/c_s \gg 1$, but smaller than for a
perfect gas, by a factor $c_s/c \ll 1$, where $c_s$ is the speed of sound. The
dynamical friction is smaller than for a perfect gas, by the same factor $c_s/c
\ll 1$, and also smaller than Chandrasekhar's result for collisionless
particles, by a factor $c_s/(cC)$, where $C$ is the Coulomb logarithm. It is
also smaller than for fuzzy dark matter, by a factor $v_0/c \ll 1$.


title:
Euclid detectability of pair instability supernovae in binary population
  synthesis models consistent with merging binary black holes

abstract:
  We infer the expected detection number of pair instability supernovae (PISNe)
during the operation of the Euclid space telescope, based on two binary
population models that are consistent with binary black holes (BHs) observed by
gravitational waves. The two models consider different PISN criteria depending
on the $^{12}$C$(\alpha, \gamma)^{16}$O reaction rate. The fiducial and
$3\sigma$ models adopt the standard and $3\sigma$-smaller $^{12}$C$(\alpha,
\gamma)^{16}$O reaction rate, which predicts that stars with helium core masses
$65-135 M_\odot$ and $90-180 M_\odot$ cause PISNe, respectively. Our fiducial
model predicts that Euclid detects several Type I or hydrogen-poor PISNe. For
the $3\sigma$ model, detection of $\sim 1$ Type I PISN by Euclid is expected if
the stellar mass distribution extends to $M_{\max} \sim 600 M_\odot$, but the
expected number becomes significantly smaller if $M_{\max} \sim 300 M_\odot$.
Thus, we may be able to prove or distinguish the fiducial and $3\sigma$ models
by the observed PISN rate. This will help us to constrain the origin of binary
BHs and the $^{12}$C$(\alpha, \gamma)^{16}$O reaction rate. PISN ejecta mass
estimates from light curves and spectra obtained by follow-up observations
would also be important to constrain the $^{12}$C$(\alpha, \gamma)^{16}$O
reaction rate.

categories: astro-ph.HE astro-ph.CO astro-ph.GA astro-ph.SR


title: Congruences for sums of powers of an integer

abstract:
  For coprime positive integers $q$ and $e$, let $m(q,e)$ denote the least
positive integer $t$ such that there exists a sum of $t$ powers of $q$ which is
divisible by $e$. We prove an upper bound for $m(q.e)$ and investigate the case
where $m(q,e)$ is "large". We also pay special attention to the situation where
$e$ is a prime power.


title: A Probabilistic Time-Evolving Approach to Scanpath Prediction

abstract:
  Human visual attention is a complex phenomenon that has been studied for
decades. Within it, the particular problem of scanpath prediction poses a
challenge, particularly due to the inter- and intra-observer variability, among
other reasons. Besides, most existing approaches to scanpath prediction have
focused on optimizing the prediction of a gaze point given the previous ones.
In this work, we present a probabilistic time-evolving approach to scanpath
prediction, based on Bayesian deep learning. We optimize our model using a
novel spatio-temporal loss function based on a combination of Kullback-Leibler
divergence and dynamic time warping, jointly considering the spatial and
temporal dimensions of scanpaths. Our scanpath prediction framework yields
results that outperform those of current state-of-the-art approaches, and are
almost on par with the human baseline, suggesting that our model is able to
generate scanpaths whose behavior closely resembles those of the real ones.


title: Deep subspace encoders for continuous-time state-space identification

abstract:
  Continuous-time (CT) models have shown an improved sample efficiency during
learning and enable ODE analysis methods for enhanced interpretability compared
to discrete-time (DT) models. Even with numerous recent developments, the
multifaceted CT state-space model identification problem remains to be solved
in full, considering common experimental aspects such as the presence of
external inputs, measurement noise, and latent states. This paper presents a
novel estimation method that includes these aspects and that is able to obtain
state-of-the-art results on multiple benchmarks where a small fully connected
neural network describes the CT dynamics. The novel estimation method called
the subspace encoder approach ascertains these results by altering the
well-known simulation loss to include short subsections instead, by using an
encoder function and a state-derivative normalization term to obtain a
computationally feasible and stable optimization problem. This encoder function
estimates the initial states of each considered subsection. We prove that the
existence of the encoder function has the necessary condition of a Lipschitz
continuous state-derivative utilizing established properties of ODEs.


title:
Polarization effects in the total rate of biharmonic $\omega + 3\omega$
  ionization of atoms

abstract:
  Total ionization rate of biharmonic ($\omega + 3\omega$) ionization are
studied within the independent particle approximation and third order
perturbation theory. Particular attention is payed to how the polarization
affects the total rate. The ratio of the biharmonic ionization is analyzed for
linearly and circularly polarized beams as well as for co- and counterrotating
elliptically polarized beams are analyzed and their dependence on beam
parameters, such as the photon frequency or the phase between the $\omega$ and
$3\omega$ light beams, is explained. We show that the interference of the
biharmonic ionization amplitudes determines the dominance of a particular beam
polarization over another and that it can be controlled by an appropriate
choice of beam parameters. We demonstrate our findings for the ionization of
neon $L$ shell electrons.


title:
Color-Kinematics Duality for Sudakov Form Factor in Non-Supersymmetric
  Pure Yang-Mills Theory

abstract:
  We study the duality between color and kinematics for the Sudakov form
factors of ${\rm tr}(F^2)$ in non-supersymmetric pure Yang-Mills theory. We
construct the integrands that manifest the color-kinematics duality up to two
loops. The resulting numerators are given in terms of Lorentz products of
momenta and polarization vectors, which have same powers of loop momenta as
that from the Feynman rules. The integrands are checked by $d$-dimensional
unitarity cuts and are valid in any dimension. We find that massless-bubble and
tadpole topologies are needed at two loops to realize the color-kinematics
duality. Interestingly, the two-loop solution contains a large number of free
parameters suggesting the duality may hold at higher loop orders.


title:
Curvilinear parallelogram identity and mean-value property for a
  semilinear hyperbolic equation of second-order

abstract:
  In this paper, we discuss some of the important qualitative properties of
solutions of second-order hyperbolic equations, whose coefficients of the terms
involving the second-order derivatives are independent of desired function and
its derivatives. Solutions of these equations have a special property called
curvilinear parallelogram identity (or mean-value property), which can be used
to solve some initial-boundary value problems.


title: Video Moment Retrieval from Text Queries via Single Frame Annotation

abstract:
  Video moment retrieval aims at finding the start and end timestamps of a
moment (part of a video) described by a given natural language query. Fully
supervised methods need complete temporal boundary annotations to achieve
promising results, which is costly since the annotator needs to watch the whole
moment. Weakly supervised methods only rely on the paired video and query, but
the performance is relatively poor. In this paper, we look closer into the
annotation process and propose a new paradigm called "glance annotation". This
paradigm requires the timestamp of only one single random frame, which we refer
to as a "glance", within the temporal boundary of the fully supervised
counterpart. We argue this is beneficial because comparing to weak supervision,
trivial cost is added yet more potential in performance is provided. Under the
glance annotation setting, we propose a method named as Video moment retrieval
via Glance Annotation (ViGA) based on contrastive learning. ViGA cuts the input
video into clips and contrasts between clips and queries, in which glance
guided Gaussian distributed weights are assigned to all clips. Our extensive
experiments indicate that ViGA achieves better results than the
state-of-the-art weakly supervised methods by a large margin, even comparable
to fully supervised methods in some cases.


title: Generating 3D Molecules for Target Protein Binding

abstract:
  A fundamental problem in drug discovery is to design molecules that bind to
specific proteins. To tackle this problem using machine learning methods, here
we propose a novel and effective framework, known as GraphBP, to generate 3D
molecules that bind to given proteins by placing atoms of specific types and
locations to the given binding site one by one. In particular, at each step, we
first employ a 3D graph neural network to obtain geometry-aware and chemically
informative representations from the intermediate contextual information. Such
context includes the given binding site and atoms placed in the previous steps.
Second, to preserve the desirable equivariance property, we select a local
reference atom according to the designed auxiliary classifiers and then
construct a local spherical coordinate system. Finally, to place a new atom, we
generate its atom type and relative location w.r.t. the constructed local
coordinate system via a flow model. We also consider generating the variables
of interest sequentially to capture the underlying dependencies among them.
Experiments demonstrate that our GraphBP is effective to generate 3D molecules
with binding ability to target protein binding sites. Our implementation is
available at https://github.com/divelab/GraphBP.


title:
Two Low-complexity DOA Estimators for Massive/Ultra-massive MIMO Receive
  Array

abstract:
  Eigen-decomposition-based direction finding methods of using
large-scale/ultra-large-scale fully-digital receive antenna arrays leads to a
high or ultra-high complexity. To address the complexity dilemma, in this
paper, two low-complexity estimators are proposed: partitioned subarray
combining (PSAC) and power iteration max correlation successive convex
approximation (PI-Max-CSCA). Compared with the conventional no-partitioned
direction finding method like root multiple signal classification (Root-MUSIC),
in PSAC method, the total set of antennas are equally partitioned into subsets
of antennas, called subarrays, each subarray performs independent DOA
estimation, and finally all DOA estimates are coherently combined to give the
final estimate. In PI-Max-CSCA method, using a fraction of all subarrays to
make an initial coarse direction measurement (ICDM), the power iterative method
is adopted to compute the more precise steering vector (SV) by exploiting the
total array, and a more accurate DOA value is found using ICDM and SV through
the maximum correlation method solved by successive convex approximation.


title: Strong convexity of affine phase retrieval

abstract:
  The recovery of a signal from the intensity measurements with some entries
being known in advance is termed as {\em affine phase retrieval}. In this
paper, we prove that a natural least squares formulation for the affine phase
retrieval is strongly convex on the entire space under some mild conditions,
provided the measurements are complex Gaussian random vecotrs and the
measurement number $m \gtrsim d \log d$ where $d$ is the dimension of signals.
Based on the result, we prove that the simple gradient descent method for the
affine phase retrieval converges linearly to the target solution with high
probability from an arbitrary initial point. These results show an essential
difference between the affine phase retrieval and the classical phase
retrieval, where the least squares formulations for the classical phase
retrieval are non-convex.


title:
Study of the $h_c$($1^1P_1$) meson via $\psi(2S) \rightarrow \pi^0 h_c$
  decays at BESIII

abstract:
  Using 448 million $\psi(2S)$ events, the spin-singlet $P$-wave charmonium
state $h_c$(1$^1P_1$) is studied via the $\psi(2S) \rightarrow \pi^0 h_c$ decay
followed by the $h_c \to \gamma \eta_c$ transition. The branching fractions are
measured to be $\mathcal{B}_{\rm Inc}(\psi(2S) \rightarrow \pi^0h_c) \times
\mathcal{B}_{\rm Tag}(h_c \rightarrow \gamma \eta_c) = (4.17 ^{+0.27}_{-0.25}
\pm 0.19) \times 10^{-4}$ , $\mathcal{B}_{\rm Inc}(\psi(2S) \rightarrow
\pi^0h_c) = (7.23 \pm 0.33 \pm 0.38) \times 10^{-4}$, and $\mathcal{B}_{\rm
Tag}(h_c \rightarrow \gamma \eta_c) = (57.66 ^{+3.62}_{-3.50} \pm 0.58)\%$,
where the uncertainties are statistical and systematic, respectively. The
$h_c$(1$^1P_1$) mass and width are determined to be $M = (3525.32 \pm 0.06 \pm
0.15)~\rm{MeV}/c^{\rm{2}}$ and ${\mathit \Gamma} = (0.78 ^{+0.27}_{-0.24} \pm
0.12)~\rm{MeV}$. Using the center of gravity mass of the three $\chi_{cJ}$(1$^3
P_J$) mesons ($M(\rm c.o.g.)$), the $1P$ hyperfine mass splitting is estimated
to be $\Delta_{\rm hyp} = M(h_c) - M(\rm c.o.g.) = (0.03 \pm 0.06 \pm 0.15) ~
\rm{MeV}/c^{\rm{2}}$, which is consistent with the expectation that the $1P$
hyperfine splitting is zero at the lowest-order.


title:
Distance and age of the massive stellar cluster Westerlund 1. I.
  Parallax method using Gaia-EDR3

abstract:
  Westerlund 1 (Wd 1) is one of the most massive young star clusters in the
Milky Way. Although relevant for star formation and evolution, its fundamental
parameters are not yet very well constrained. Our goal is to derive an accurate
distance and provide constraints on the cluster age. We used the photometric
and astrometric information available in the Gaia Early Data Release 3
(Gaia-EDR3) to infer its distance of 4.06$^{+0.36}_{-0.34}$ kpc. Modelling of
the eclipsing binary system W36 reported in Paper II led to the distance of
4.34$\pm$0.25 kpc, in agreement with the Gaia-EDR3 distance and, therefore,
validating the parallax zero-point correction approach appropriate for red
objects. By taking advantage of another two recent distance determinations
using the Gaia-EDR3, we obtained a weighted mean distance for the cluster as
d$_{\rm wd1}$=4.23$^{+0.15}_{-0.13}$ kpc ($m-M$=13.13$^{+0.08}_{-0.07}$ mag),
which has an unprecedented accuracy of 4\%. We adopted recent Geneva
evolutionary tracks for supra-solar metallicity objects to infer the age of the
faintest RSG source from Wd 1, leading to a cluster age of 11.0$\pm$0.5 Myr, in
excellent agreement with recent work by Beasor \& Davies (10.4$^{+1.3}_{-1.2}$
Myr) based on MIST evolutionary models. The age of W36 was reported to be
3.5$\pm$0.5 Myr in Paper II, supporting recent claims of a temporal spread of
several Myr for the star-forming process within Wd 1 instead of a monolithic
starburst scenario.


title: Electric field in spatially inhomogeneous non-neutral plasma

abstract:
  We present a general expression for the probability distribution function of
electric field in a plasma cloud formed by the impact of a laser pulse on a gas
or a solid body. We also present the results of numerical calculation of this
function for the case of non-interacting particles depending on the plasma
cloud size. It takes into account the ionic microfield and the macrofield
arising from the charge imbalance. As the charge imbalance increases, the
effect of a sharp increase in the distribution function for large field values
is observed. Good agreement between the calculation of the shift of the
spectral line and the experiment is obtained. The results obtained are of
crucial importance for diagnosing plasma in various applications.


title:
Nearly optimal bounds for the global geometric landscape of phase
  retrieval

abstract:
  The phase retrieval problem is concerned with recovering an unknown signal
$\bf{x} \in \mathbb{R}^n$ from a set of magnitude-only measurements
$y_j=|\langle \bf{a}_j,\bf{x} \rangle|, \; j=1,\ldots,m$. A natural least
squares formulation can be used to solve this problem efficiently even with
random initialization, despite its non-convexity of the loss function. One way
to explain this surprising phenomenon is the benign geometric landscape: (1)
all local minimizers are global; and (2) the objective function has a negative
curvature around each saddle point and local maximizer. In this paper, we show
that $m=O(n \log n)$ Gaussian random measurements are sufficient to guarantee
the loss function of a commonly used estimator has such benign geometric
landscape with high probability. This is a step toward answering the open
problem given by Sun-Qu-Wright, in which the authors suggest that $O(n \log n)$
or even $O(n)$ is enough to guarantee the favorable geometric property.


title: Flux-tube-dependent propagation of Alfv\'en waves in the solar corona

abstract:
  Alfv\'en-wave turbulence has emerged as an important heating mechanism to
accelerate the solar wind. The generation of this turbulent heating is
dependent on the presence and subsequent interaction of counter-propagating
alfv\'en waves. This requires us to understand the propagation and evolution of
alfv\'en waves in the solar wind in order to develop an understanding of the
relationship between turbulent heating and solar-wind parameters. We aim to
study the response of the solar wind upon injecting monochromatic
single-frequency alfv\'en waves at the base of the corona for various magnetic
flux-tube geometries. We used an ideal magnetohydrodynamic (MHD) model using an
adiabatic equation of state. An Alfv\'en pump wave was injected into the quiet
solar wind by perturbing the transverse magnetic field and velocity components.
Alfv\'en waves were found to be reflected due to the development of the
parametric decay instability (PDI). Further investigation revealed that the PDI
was suppressed both by efficient reflections at low frequencies as well as
magnetic flux-tube geometries.


title:
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent
  Reinforcement Learning

abstract:
  Recently, model-based agents have achieved better performance compared with
model-free ones using the same computational budget and training time in
single-agent environments. However, due to the complexity of multi-agent
systems, it is very difficult to learn the model of the environment. When
model-based methods are applied to multi-agent tasks, the significant
compounding error may hinder the learning process. In this paper, we propose an
implicit model-based multi-agent reinforcement learning method based on value
decomposition methods. Under this method, agents can interact with the learned
virtual environment and evaluate the current state value according to imagined
future states, which makes agents have foresight. Our method can be applied to
any multi-agent value decomposition method. The experimental results show that
our method improves the sample efficiency in partially observable Markov
decision process domains.


title: A Mid-infrared Study of Superluminous Supernovae

abstract:
  We present the mid-infrared (MIR) light curves (LC) of 10 superluminous
supernovae (SLSNe) at $z<0.12$ based on WISE data at 3.4 and 4.6 $\mu$m. Three
of them, including PS15br, SN 2017ens, and SN 2017err show rebrightening which
started at 200--400 days and ended at 600--1000 days, indicating the presence
of dust. In four of the left seven SLSNe, dust emission was detected with
monochromatic luminosities of $10^7\sim10^8\ L_\odot$ at epochs of 100--500
days based on MIR colors $W1-W2\sim1$. Among the three SLSNe which show
rebrightening, we further analysed PS15br and SN 2017ens. We modeled the SEDs
at 500--700 days, which gives dust temperatures of 600--1100 K, dust masses of
$\gtrsim 10^{-2}\ M_\odot$, and luminosities of $10^8\sim10^9$ $L_\odot$.
Considering the time delay and the huge amount of energy released, the emitting
dust can hardly be pre-existing dust heated whether collisionally by shocks or
radiatively by peak SLSN luminosity or shock emission. Instead, it can be newly
formed dust additionally heated by the interaction of circum-stellar medium,
indicated by features in their spectra and slowly declining bolometric LCs. The
dust masses appear to be ten times greater than those formed in normal
core-collapse supernovae at similar epochs. Combining with the analysis of SN
2018bsz by Chen et al. (2022), we suggest that SLSNe have higher dust formation
efficiency, although future observations are required to reach a final
conclusion.


title:
Ferromagnetic-antiferromagnetic coexisting ground states and exchange
  bias effects in $\bf{MnBi_4Te_7}$ and $\bf{MnBi_6Te_{10}}$

abstract:
  Natural superlattice structures $\rm{(MnBi_2Te_4)(Bi_2Te_3)}$$_n$ ($n$ = 1,
2,...), in which magnetic $\rm{MnBi_2Te_4}$ layers are separated by nonmagnetic
$\rm{Bi_2Te_3}$ layers, hold band topology, magnetism and reduced interlayer
coupling, providing a promising platform for the realization of exotic
topological quantum states. However, their magnetism in the two-dimensional
limit, which is crucial for further exploration of quantum phenomena, remains
elusive. Here, complex ferromagnetic (FM)-antiferromagnetic (AFM) coexisting
ground states that persist up to the 2-septuple layers (SLs) limit are observed
and comprehensively investigated in $\rm{MnBi_4Te_7}$ ($n$ = 1) and
$\rm{MnBi_6Te_{10}}$ ($n$ = 2). The ubiquitous Mn-Bi site mixing modifies or
even changes the sign of the subtle inter-SL magnetic interactions, yielding a
spatially inhomogeneous interlayer coupling. Further, a tunable exchange bias
effect is observed in $\rm{(MnBi_2Te_4)(Bi_2Te_3)}$$_n$ ($n$ = 1, 2), arising
from the coupling between the FM and AFM components in the ground state. Our
work highlights a new approach toward the fine-tuning of magnetism and paves
the way for further study of quantum phenomena in
$\rm{(MnBi_2Te_4)(Bi_2Te_3)}$$_n$ ($n$ = 1, 2,...) as well as their magnetic
applications.


title: Sheaf semantics of termination-insensitive noninterference

abstract:
  We propose a new sheaf semantics for secure information flow over a space of
abstract behaviors, based on synthetic domain theory: security classes are
open/closed partitions, types are sheaves, and redaction of sensitive
information corresponds to restricting a sheaf to a closed subspace. Our
security-aware computational model satisfies termination-insensitive
noninterference automatically, and therefore constitutes an intrinsic
alternative to state of the art extrinsic/relational models of noninterference.
Our semantics is the latest application of Sterling and Harper's recent
re-interpretation of phase distinctions and noninterference in programming
languages in terms of Artin gluing and topos-theoretic open/closed modalities.
Prior applications include parametricity for ML modules, the proof of
normalization for cubical type theory by Sterling and Angiuli, and the
cost-aware logical framework of Niu et al. In this paper we employ the phase
distinction perspective twice: first to reconstruct the syntax and semantics of
secure information flow as a lattice of phase distinctions between "higher" and
"lower" security, and second to verify the computational adequacy of our sheaf
semantics vis-\`a-vis an extension of Abadi et al.'s dependency core calculus
with a construct for declassifying termination channels.


title:
Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag
  Recommendation

abstract:
  Recommending appropriate tags to items can facilitate content organization,
retrieval, consumption and other applications, where hybrid tag recommender
systems have been utilized to integrate collaborative information and content
information for better recommendations. In this paper, we propose a
multi-auxiliary augmented collaborative variational auto-encoder (MA-CVAE) for
tag recommendation, which couples item collaborative information and item
multi-auxiliary information, i.e., content and social graph, by defining a
generative process. Specifically, the model learns deep latent embeddings from
different item auxiliary information using variational auto-encoders (VAE),
which could form a generative distribution over each auxiliary information by
introducing a latent variable parameterized by deep neural network. Moreover,
to recommend tags for new items, item multi-auxiliary latent embeddings are
utilized as a surrogate through the item decoder for predicting recommendation
probabilities of each tag, where reconstruction losses are added in the
training phase to constrict the generation for feedback predictions via
different auxiliary embeddings. In addition, an inductive variational graph
auto-encoder is designed where new item nodes could be inferred in the test
phase, such that item social embeddings could be exploited for new items.
Extensive experiments on MovieLens and citeulike datasets demonstrate the
effectiveness of our method.


title: Cost Minimization of Cloud Services for On-Demand Video Streaming

abstract:
  Cloud Technology is adopted to process video streams because of the great
features provided to video stream providers such as the high flexibility of
using virtual machines and storage servers at low rates. Video stream providers
prepare several formats of the same video to satisfy all users' devices'
specifications. Video streams in the cloud are either transcoded or stored.
However, storing all formats of videos is still costly. In this research, we
develop an approach that optimizes cloud storage. Particularly, we propose a
method that decides which video in which cloud storage should be stored to
minimize the overall cost of cloud services. The results of the proposed
approach are promising, it shows effectiveness when the number of frequently
accessed video grow in a repository, and when the views of videos increases.
The proposed method decreases the cost of using cloud services by up to 22%.


title:
SAAC: Safe Reinforcement Learning as an Adversarial Game of
  Actor-Critics

abstract:
  Although Reinforcement Learning (RL) is effective for sequential
decision-making problems under uncertainty, it still fails to thrive in
real-world systems where risk or safety is a binding constraint. In this paper,
we formulate the RL problem with safety constraints as a non-zero-sum game.
While deployed with maximum entropy RL, this formulation leads to a safe
adversarially guided soft actor-critic framework, called SAAC. In SAAC, the
adversary aims to break the safety constraint while the RL agent aims to
maximize the constrained value function given the adversary's policy. The
safety constraint on the agent's value function manifests only as a repulsion
term between the agent's and the adversary's policies. Unlike previous
approaches, SAAC can address different safety criteria such as safe
exploration, mean-variance risk sensitivity, and CVaR-like coherent risk
sensitivity. We illustrate the design of the adversary for these constraints.
Then, in each of these variations, we show the agent differentiates itself from
the adversary's unsafe actions in addition to learning to solve the task.
Finally, for challenging continuous control tasks, we demonstrate that SAAC
achieves faster convergence, better efficiency, and fewer failures to satisfy
the safety constraints than risk-averse distributional RL and risk-neutral soft
actor-critic algorithms.


title:
6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target
  Generation

abstract:
  IPv6 scanning has always been a challenge for researchers in the field of
network measurement. Due to the considerable IPv6 address space, while recent
network speed and computational power have been improved, using a brute-force
approach to probe the entire network space of IPv6 is almost impossible.
Systems are required an algorithmic approach to generate more possible active
target candidate sets to probe. In this paper, we first try to use deep
learning to design such IPv6 target generation algorithms. The model
effectively learns the address structure by stacking the gated convolutional
layer to construct Variational Autoencoder (VAE). We also introduce two address
classification methods to improve the model effect of the target generation.
Experiments indicate that our approach 6GCVAE outperformed the conventional VAE
models and the state-of-the-art target generation algorithm in two active
address datasets.


title:
Stable distributions and pseudo-processes related to fractional Airy
  functions

abstract:
  In this paper we study pseudo-processes related to odd-order heat-type
equations composed with L\'evy stable subordinators. The aim of the article is
twofold. We first show that the pseudo-density of the subordinated
pseudo-process can be represented as an expectation of damped oscillations with
generalized gamma distributed parameters. This stochastic representation also
arises as the solution to a fractional diffusion equation, involving a
higher-order Riesz-Feller operator, which generalizes the odd-order heat-type
equation. We then prove that, if the stable subordinator has a suitable
exponent, the time-changed pseudo-process becomes a genuine L\'evy stable
process. This result permits us to obtain a power series representation for the
probability density function of an arbitrary asymmetric stable process of
exponent $\nu>1$ and skewness parameter $\beta$, with $0<|\beta|<1$. The
methods we use in order to carry out our analysis are based on the study of a
fractional Airy function which arises in the study of the higher-order
Riesz-Feller operator.


title:
Concentration of invariant means and dynamics of chain stabilizers in
  continuous geometries

abstract:
  We prove a concentration inequality for invariant means on topological
groups, namely for such adapted to a chain of amenable topological subgroups.
The result is based on an application of Azuma's martingale inequality and
provides a method for establishing extreme amenability. Building on this
technique, we exhibit new examples of extremely amenable groups arising from
von Neumann's continuous geometries. Along the way, we also answer a question
by Pestov on dynamical concentration in direct products of amenable topological
groups.


title:
Nonlinear stability of planar viscous shock wave to three-dimensional
  compressible Navier-Stokes equations

abstract:
  We prove the nonlinear stability of the planar viscous shock up to a
time-dependent shift for the three-dimensional (3D) compressible Navier-Stokes
equations under the generic perturbations, in particular, without zero mass
conditions. Moreover, the time-dependent shift function keeps the shock profile
shape time-asymptotically. Our stability result is unconditional for the weak
planar Navier-Stokes shock. Our proof is motivated by the $a$-contraction
method (a kind of weighted $L^2$-relative entropy method) with time-dependent
shift introduced in [10,11,13] for the stability of viscous shock in
one-dimensional (1D) case. Instead of the classical anti-derivative techniques,
we perform the stability analysis of planar Navier-Stokes shock in original
$H^2$-perturbation framework and therefore zero mass conditions are not
necessarily needed, which, in turn, brings out the essential difficulties due
to the compressibility of viscous shock. Furthermore, compared with 1D case,
there are additional difficulties coming from the wave propagation along the
multi-dimensional transverse directions and their interactions with the viscous
shock. To overcome these difficulties, a multi-dimensional version sharp
weighted Poincar${\rm \acute{e}}$ inequality (see Lemma 3.1), $a$-contraction
techniques with time-dependent shift, and some essential physical structures of
the multi-dimensional Navier-Stokes system are fully used.


title:
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using
  Knowledge Distillation

abstract:
  Real-time 6D object pose estimation is essential for many real-world
applications, such as robotic grasping and augmented reality. To achieve an
accurate object pose estimation from RGB images in real-time, we propose an
effective and lightweight model, namely High-Resolution 6D Pose Estimation
Network (HRPose). We adopt the efficient and small HRNetV2-W18 as a feature
extractor to reduce computational burdens while generating accurate 6D poses.
With only 33\% of the model size and lower computational costs, our HRPose
achieves comparable performance compared with state-of-the-art models.
Moreover, by transferring knowledge from a large model to our proposed HRPose
through output and feature-similarity distillations, the performance of our
HRPose is improved in effectiveness and efficiency. Numerical experiments on
the widely-used benchmark LINEMOD demonstrate the superiority of our proposed
HRPose against state-of-the-art methods.


title:
GRB 210121A: Observation of photospheric emissions from different
  regimes and the evolution of the outflow

abstract:
  GRB 210121A was observed by Insight-HXMT, Gravitational wave high-energy
Electromagnetic Counterpart All-sky Monitor (GECAM), Fermi Gamma-ray Burst
Monitor (Fermi/GBM) on Jan 21st 2021. In this work, photospheric emission from
a structured jet is preferred to interpret the prompt emission phase of GRB
210121A and emissions from different regimes are observed on-axis.
Particularly, the emission from the intermediate photosphere is first observed
in the first 1.3 s of the prompt emission, while those from the other part are
dominant by the saturated regime, and offers an alternative explanation
compared with the previous work. Moreover, the emissions with considering the
intermediate photosphere can well interpret the changes on low-energy photon
index $\alpha$ during the pulses. Besides, the evolution of the outflow is
extracted from time-resolved analysis, and a correlation of $\Gamma_0 \propto
L^{0.24\pm0.04}_0$ is obtained, which implies that the jet may be mainly
launched by neutrino annihilation in a hyper-accretion disk.


title:
First Light And Reionisation Epoch Simulations (FLARES) V: The redshift
  frontier

abstract:
  The James Webb Space Telescope (JWST) is set to transform many areas of
astronomy, one of the most exciting is the expansion of the redshift frontier
to $z>10$. In its first year alone JWST should discover hundreds of galaxies,
dwarfing the handful currently known. To prepare for these powerful
observational constraints, we use the First Light And Reionisation Epoch
(FLARES) simulations to predict the physical and observational properties of
the $z>10$ population of galaxies accessible to JWST. This is the first time
such predictions have been made using a hydrodynamical model validated at low
redshift. Our predictions at $z=10$ are broadly in agreement with current
observational constraints on the far-UV luminosity function and UV continuum
slope $\beta$, though the observational uncertainties are large. We note
tension with recent constraints $z\sim 13$ from Harikane et al. 2022 - compared
to these constraints, FLARES predicts objects with the same space density
should have an order of magnitude lower luminosity, though this is mitigated
slightly if dust attenuation is negligible in these systems. Our predictions
suggest that in JWST's first cycle alone, around $600$ galaxies should be
identified at $z>10$, with the first small samples available at $z>13$.


title: A Mobile Food Recognition System for Dietary Assessment

abstract:
  Food recognition is an important task for a variety of applications,
including managing health conditions and assisting visually impaired people.
Several food recognition studies have focused on generic types of food or
specific cuisines, however, food recognition with respect to Middle Eastern
cuisines has remained unexplored. Therefore, in this paper we focus on
developing a mobile friendly, Middle Eastern cuisine focused food recognition
application for assisted living purposes. In order to enable a low-latency,
high-accuracy food classification system, we opted to utilize the Mobilenet-v2
deep learning model. As some of the foods are more popular than the others, the
number of samples per class in the used Middle Eastern food dataset is
relatively imbalanced. To compensate for this problem, data augmentation
methods are applied on the underrepresented classes. Experimental results show
that using Mobilenet-v2 architecture for this task is beneficial in terms of
both accuracy and the memory usage. With the model achieving 94% accuracy on 23
food classes, the developed mobile application has potential to serve the
visually impaired in automatic food recognition via images.


title: PP-Matting: High-Accuracy Natural Image Matting

abstract:
  Natural image matting is a fundamental and challenging computer vision task.
It has many applications in image editing and composition. Recently, deep
learning-based approaches have achieved great improvements in image matting.
However, most of them require a user-supplied trimap as an auxiliary input,
which limits the matting applications in the real world. Although some
trimap-free approaches have been proposed, the matting quality is still
unsatisfactory compared to trimap-based ones. Without the trimap guidance, the
matting models suffer from foreground-background ambiguity easily, and also
generate blurry details in the transition area. In this work, we propose
PP-Matting, a trimap-free architecture that can achieve high-accuracy natural
image matting. Our method applies a high-resolution detail branch (HRDB) that
extracts fine-grained details of the foreground with keeping feature resolution
unchanged. Also, we propose a semantic context branch (SCB) that adopts a
semantic segmentation subtask. It prevents the detail prediction from local
ambiguity caused by semantic context missing. In addition, we conduct extensive
experiments on two well-known benchmarks: Composition-1k and Distinctions-646.
The results demonstrate the superiority of PP-Matting over previous methods.
Furthermore, we provide a qualitative evaluation of our method on human matting
which shows its outstanding performance in the practical application. The code
and pre-trained models will be available at PaddleSeg:
https://github.com/PaddlePaddle/PaddleSeg.


title: FenceNet: Fine-grained Footwork Recognition in Fencing

abstract:
  Current data analysis for the Canadian Olympic fencing team is primarily done
manually by coaches and analysts. Due to the highly repetitive, yet dynamic and
subtle movements in fencing, manual data analysis can be inefficient and
inaccurate. We propose FenceNet as a novel architecture to automate the
classification of fine-grained footwork techniques in fencing. FenceNet takes
2D pose data as input and classifies actions using a skeleton-based action
recognition approach that incorporates temporal convolutional networks to
capture temporal information. We train and evaluate FenceNet on the Fencing
Footwork Dataset (FFD), which contains 10 fencers performing 6 different
footwork actions for 10-11 repetitions each (652 total videos). FenceNet
achieves 85.4% accuracy under 10-fold cross-validation, where each fencer is
left out as the test set. This accuracy is within 1% of the current
state-of-the-art method, JLJA (86.3%), which selects and fuses features
engineered from skeleton data, depth videos, and inertial measurement units.
BiFenceNet, a variant of FenceNet that captures the "bidirectionality" of human
movement through two separate networks, achieves 87.6% accuracy, outperforming
JLJA. Since neither FenceNet nor BiFenceNet requires data from wearable
sensors, unlike JLJA, they could be directly applied to most fencing videos,
using 2D pose data as input extracted from off-the-shelf 2D human pose
estimators. In comparison to JLJA, our methods are also simpler as they do not
require manual feature engineering, selection, or fusion.