title:
Black-Box Min--Max Continuous Optimization Using CMA-ES with Worst-case
  Ranking Approximation

abstract:
  In this study, we investigate the problem of min-max continuous optimization
in a black-box setting $\min_{x} \max_{y}f(x,y)$. A popular approach updates
$x$ and $y$ simultaneously or alternatingly. However, two major limitations
have been reported in existing approaches. (I) As the influence of the
interaction term between $x$ and $y$ (e.g., $x^\mathrm{T} B y$) on the
Lipschitz smooth and strongly convex-concave function $f$ increases, the
approaches converge to an optimal solution at a slower rate. (II) The
approaches fail to converge if $f$ is not Lipschitz smooth and strongly
convex-concave around the optimal solution. To address these difficulties, we
propose minimizing the worst-case objective function $F(x)=\max_{y}f(x,y)$
directly using the covariance matrix adaptation evolution strategy, in which
the rankings of solution candidates are approximated by our proposed worst-case
ranking approximation (WRA) mechanism. Compared with existing approaches,
numerical experiments show two important findings regarding our proposed
method. (1) The proposed approach is efficient in terms of $f$-calls on a
Lipschitz smooth and strongly convex-concave function with a large interaction
term. (2) The proposed approach can converge on functions that are not
Lipschitz smooth and strongly convex-concave around the optimal solution,
whereas existing approaches fail.


title: Accelerated quantum adiabatic transfer in superconducting qubits

abstract:
  Quantum adiabatic transfer is widely used in quantum computation and quantum
simulation. However, the transfer speed is limited by the quantum adiabatic
approximation condition, which hinders its application in quantum systems with
a short decoherence time. Here we demonstrate quantum adiabatic state transfers
that jump along geodesics in one-qubit and two-qubit superconducting transmons.
This approach possesses the advantages of speed, robustness, and high fidelity
compared with the usual adiabatic process. Our protocol provides feasible
strategies for improving state manipulation and gate operation in
superconducting quantum circuits.


title: Stochastic Volterra equations with H\"older diffusion coefficients

abstract:
  The existence of strong solutions and pathwise uniqueness are established for
stochastic Volterra equations with H\"older continuous diffusion coefficients
and sufficiently regular kernels. Moreover, we study the sample path
regularity, the integrability and the semimartingale property of solutions to
stochastic Volterra equations.


title: An updated review of the new hadron states

abstract:
  The past decades witnessed the golden era of hadron physics. Many excited
open heavy flavor mesons and baryons have been observed since 2017. We shall
provide an updated review of the recent experimental and theoretical progresses
in this active field. Besides the conventional heavy hadrons, we shall also
review the recently observed open heavy flavor tetraquark states $X(2900)$ and
$T_{cc}^+(3875)$ as well as the hidden heavy flavor multiquark states
$X(6900)$, $P_{cs}(4459)^0$, $Z_{cs}(3985)^-$, $Z_{cs}(4000)^+$, and
$Z_{cs}(4220)^+$. We will also cover the recent progresses on the glueballs and
light hybrid mesons, which are the direct manifestations of the non-Abelian
$SU(3)$ gauge interaction of the Quantum Chromodynamics in the low-energy
region.


title:
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow
  Prediction

abstract:
  Urban metro flow prediction is of great value for metro operation scheduling,
passenger flow management and personal travel planning. However, it faces two
main challenges. First, different metro stations, e.g. transfer stations and
non-transfer stations, have unique traffic patterns. Second, it is challenging
to model complex spatio-temporal dynamic relation of metro stations. To address
these challenges, we develop a spatio-temporal dynamic graph relational
learning model (STDGRL) to predict urban metro station flow. First, we propose
a spatio-temporal node embedding representation module to capture the traffic
patterns of different stations. Second, we employ a dynamic graph relationship
learning module to learn dynamic spatial relationships between metro stations
without a predefined graph adjacency matrix. Finally, we provide a
transformer-based long-term relationship prediction module for long-term metro
flow prediction. Extensive experiments are conducted based on metro data in
Beijing, Shanghai, Chongqing and Hangzhou. Experimental results show the
advantages of our method beyond 11 baselines for urban metro flow prediction.


title:
Scheduling Coflows for Minimizing the Total Weighted Completion Time in
  Identical Parallel Networks

abstract:
  Coflow is a recently proposed network abstraction to capture communication
patterns in data centers. The coflow scheduling problem in large data centers
is one of the most important $NP$-hard problems. Previous research on coflow
scheduling focused mainly on the single-switch model. However, with recent
technological developments, this single-core model is no longer sufficient.
This paper considers the coflow scheduling problem in identical parallel
networks. The identical parallel network is an architecture based on multiple
network cores running in parallel. Coflow can be considered as divisible or
indivisible. Different flows in a divisible coflow can be transmitted through
different network cores. Considering the divisible coflow scheduling problem,
we propose a $(6-\frac{2}{m})$-approximation algorithm with arbitrary release
times, and a $(5-\frac{2}{m})$-approximation without release time, where $m$ is
the number of network cores. On the other hand, when coflow is indivisible, we
propose a $(7-\frac{2}{m})$-approximation algorithm with arbitrary release
times, and a $(6-\frac{2}{m})$-approximation without release time.


title: Quasi-groupes de Frobenius dimensionnels

abstract:
  We are interested in a class of groups, quasi-Frobenius groups (with
involutions), whose internal structure generalizes that of the classical groups
GA1(C), PGL 2(C) and SO3(R) : a subgroup and its conjugates, of finite index in
their normalizer and trivial mutual intersection, cover "generically" the
ambient group. From the perspective of model theory, we work with the
hypothesis of the existence of a good notion of dimension on definable sets (we
must distinguish between the o-minimal case and the ranked case). We pay
special attention to the ranked case. By studying the geometry of incidence
induced by involutions, we sketch a classification of quasi-Frobenius groups
and thus determine under which conditionsclassical groups can be identified in
a dimensional framework -- --
  Nous nous int\'eressons \`a une classe de groupes, les quasi-groupes de
Frobenius (avec involutions), dont la structure interne g\'en\'eralise celle
des groupes classiques GA1(C), PGL2(C) et SO3(R) : un sous-groupe et ses
conjugu\'es, d'indice fini dans leur normalisateur et d'intersection mutuelle
triviale, recouvrent "g\'en\'eriquement" le groupe ambiant. Dans la perspective
de la th\'eorie des mod\`eles, nous travaillons avec l'hypoth\`ese de
l'existence d'une bonne notion dimension sur les ensembles d\'efinissables (il
faut distinguer le cas o-minimal et le cas rang\'e). Nous accordons une
attention particuli\`ere au cas rang\'e. En \'etudiant la g\'eom\'etrie
d'incidence induite par les involutions, nous esquissons une classification des
quasi-groupes de Frobenius et nous d\'eterminons ainsi sous quelles conditions
des groupes classiques peuvent \^etre identifi\'es dans un cadre dimensionnel.


title:
Using Synthetic Data for Conversational Response Generation in
  Low-resource Settings

abstract:
  Response generation is a task in natural language processing (NLP) where a
model is trained to respond to human statements. Conversational response
generators take this one step further with the ability to respond within the
context of previous responses. While there are existing techniques for training
such models, they all require an abundance of conversational data which are not
always available for low-resource languages. In this research, we make three
contributions. First, we released the first Filipino conversational dataset
collected from a popular Philippine online forum, which we named the PEx
Conversations Dataset. Second, we introduce a data augmentation (DA)
methodology for Filipino data by employing a Tagalog RoBERTa model to increase
the size of the existing corpora. Lastly, we published the first Filipino
conversational response generator capable of generating responses related to
the previous 3 responses. With the supplementary synthetic data, we were able
to improve the performance of the response generator by up to 12.2% in
BERTScore, 10.7% in perplexity, and 11.7% in content word usage as compared to
training with zero synthetic data.


title:
Adversarial Analysis of the Differentially-Private Federated Learning in
  Cyber-Physical Critical Infrastructures

abstract:
  Differential privacy (DP) is considered to be an effective
privacy-preservation method to secure the promising distributed machine
learning (ML) paradigm-federated learning (FL) from privacy attacks (e.g.,
membership inference attack). Nevertheless, while the DP mechanism greatly
alleviates privacy concerns, recent studies have shown that it can be exploited
to conduct security attacks (e.g., false data injection attacks). To address
such attacks on FL-based applications in critical infrastructures, in this
paper, we perform the first systematic study on the DP-exploited poisoning
attacks from an adversarial point of view. We demonstrate that the DP method,
despite providing a level of privacy guarantee, can effectively open a new
poisoning attack vector for the adversary. Our theoretical analysis and
empirical evaluation of a smart grid dataset show the FL performance
degradation (sub-optimal model generation) scenario due to the differential
noise-exploited selective model poisoning attacks. As a countermeasure, we
propose a reinforcement learning-based differential privacy level selection
(rDP) process. The rDP process utilizes the differential privacy parameters
(privacy loss, information leakage probability, etc.) and the losses to
intelligently generate an optimal privacy level for the nodes. The evaluation
shows the accumulated reward and errors of the proposed technique converge to
an optimal privacy policy.


title:
Location-assisted precoding in 5G LEO systems: architectures and
  performances

abstract:
  Satellite communication systems are a fundamental component in support of
Europe's ambition to deploy smart and sustainable networks and services for the
success of its digital economy. To cope with the 5G and beyond ever increasing
demand for larger throughput, aggressive frequency reuse schemes (i.e., full
frequency reuse), with the implementation of precoding/beamforming to cope with
the massive co-channel interference, are recognised as one of the key
technologies. While the best performance can be obtained with the knowledge of
the Channel State Information (CSI) at the transmitter, this also poses some
technical challenges related to signalling and synchronisation. In this paper,
we focus on precoding solutions that only needs the knowledge of the users'
positions at the transmitter side, namely the recently introduced Switchable
Multi-Beam (MB) and Spatially Sampled MMSE (SS-MMSE) precoding. Compared to the
vast majority of the studies in the literature, we take into account both the
users' and the satellite movement in a Low Earth Orbit (LEO)
mega-constellation, also proposing two system architectures. The extensive
numerical assessment provides a valuable insight on the performance of these
two precoding schemes compared to the optimal MMSE solution.


title: CHIEF: Clustering with Higher-order Motifs in Big Networks

abstract:
  Clustering a group of vertices in networks facilitates applications across
different domains, such as social computing and Internet of Things. However,
challenges arises for clustering networks with increased scale. This paper
proposes a solution which consists of two motif clustering techniques: standard
acceleration CHIEF-ST and approximate acceleration CHIEF-AP. Both algorithms
first find the maximal k-edge-connected subgraphs within the target networks to
lower the network scale, then employ higher-order motifs in clustering. In the
first procedure, we propose to lower the network scale by optimizing the
network structure with maximal k-edge-connected subgraphs. For CHIEF-ST, we
illustrate that all target motifs will be kept after this procedure when the
minimum node degree of the target motif is equal or greater than k. For
CHIEF-AP, we prove that the eigenvalues of the adjacency matrix and the
Laplacian matrix are relatively stable after this step. That is, CHIEF-ST has
no influence on motif clustering, whereas CHIEF-AP introduces limited yet
acceptable impact. In the second procedure, we employ higher-order motifs,
i.e., heterogeneous four-node motifs clustering in higher-order dense networks.
The contributions of CHIEF are two-fold: (1) improved efficiency of motif
clustering for big networks; (2) verification of higher-order motif
significance. The proposed solutions are found to outperform baseline
approaches according to experiments on real and synthetic networks, which
demonstrates CHIEF's strength in large network analysis. Meanwhile,
higher-order motifs are proved to perform better than traditional triangle
motifs in clustering.


title:
Calibrated regression estimation using empirical likelihood under data
  fusion

abstract:
  Data analysis based on information from several sources is common in economic
and biomedical studies. This setting is often referred to as the data fusion
problem, which differs from traditional missing data problems since no complete
data is observed for any subject. We consider a regression analysis when the
outcome variable and some covariates are collected from two different sources.
By leveraging the common variables observed in both data sets, doubly robust
estimation procedures are proposed in the literature to protect against
possible model misspecifications. However, they employ only a single propensity
score model for the data fusion process and a single imputation model for the
covariates available in one data set. It may be questionable to assume that
either model is correctly specified in practice. We therefore propose an
approach that calibrates multiple propensity score and imputation models to
gain more protection based on empirical likelihood methods. The resulting
estimator is consistent when any one of those models is correctly specified and
is robust against extreme values of the fitted propensity scores. We also
establish its asymptotic normality property and discuss the semiparametric
estimation efficiency. Simulation studies show that the proposed estimator has
substantial advantages over existing doubly robust estimators, and an assembled
U.S. household expenditure data example is used for illustration.


title:
Yunshan Cup 2020: Overview of the Part-of-Speech Tagging Task for
  Low-resourced Languages

abstract:
  The Yunshan Cup 2020 track focused on creating a framework for evaluating
different methods of part-of-speech (POS). There were two tasks for this track:
(1) POS tagging for the Indonesian language, and (2) POS tagging for the Lao
tagging. The Indonesian dataset is comprised of 10000 sentences from Indonesian
news within 29 tags. And the Lao dataset consists of 8000 sentences within 27
tags. 25 teams registered for the task. The methods of participants ranged from
feature-based to neural networks using either classical machine learning
techniques or ensemble methods. The best performing results achieve an accuracy
of 95.82% for Indonesian and 93.03%, showing that neural sequence labeling
models significantly outperform classic feature-based methods and rule-based
methods.


title: ConvSearch: A Open-Domain Conversational Search Behavior Dataset

abstract:
  Conversational Search has been paid much attention recently with the
increasing popularity of intelligent user interfaces. However, compared with
the endeavour in designing effective conversational search algorithms,
relatively much fewer researchers have focused on the construction of benchmark
datasets. For most existing datasets, the information needs are defined by
researchers and search requests are not proposed by actual users. Meanwhile,
these datasets usually focus on the conversations between users and agents
(systems), while largely ignores the search behaviors of agents before they
return response to users. To overcome these problems, we construct a Chinese
Open-Domain Conversational Search Behavior Dataset (ConvSearch) based on
Wizard-of-Oz paradigm in the field study scenario. We develop a novel
conversational search platform to collect dialogue contents, annotate dialogue
quality and candidate search results and record agent search behaviors. 25
search agents and 51 users are recruited for the field study that lasts about
45 days. The ConvSearch dataset contains 1,131 dialogues together with
annotated search results and corresponding search behaviors. We also provide
the intent labels of each search behavior iteration to support intent
understanding related researches. The dataset is already open to public for
academic usage.


title:
Almost sure well-posedness for incompressible Navier-Stokes equations
  with arbitrary regularity

abstract:
  In this paper, we study the random data problem for incompressible
Navier-Stokes equations in Euclidean space. We prove that for any $s\in
\mathbb{R}$, the almost sure local well-posedness holds in $H^s(\mathbb{R}^d)$
when $d\geq2$, and the almost sure global well-posedness holds in
$H^s(\mathbb{R}^2)$. Our results have no regularity restriction, and thus can
cover arbitrary rough data.


title:
CAIPI in Practice: Towards Explainable Interactive Medical Image
  Classification

abstract:
  Would you trust physicians if they cannot explain their decisions to you?
Medical diagnostics using machine learning gained enormously in importance
within the last decade. However, without further enhancements many
state-of-the-art machine learning methods are not suitable for medical
application. The most important reasons are insufficient data set quality and
the black-box behavior of machine learning algorithms such as Deep Learning
models. Consequently, end-users cannot correct the model's decisions and the
corresponding explanations. The latter is crucial for the trustworthiness of
machine learning in the medical domain. The research field explainable
interactive machine learning searches for methods that address both
shortcomings. This paper extends the explainable and interactive CAIPI
algorithm and provides an interface to simplify human-in-the-loop approaches
for image classification. The interface enables the end-user (1) to investigate
and (2) to correct the model's prediction and explanation, and (3) to influence
the data set quality. After CAIPI optimization with only a single
counterexample per iteration, the model achieves an accuracy of $97.48\%$ on
the Medical MNIST and $95.02\%$ on the Fashion MNIST. This accuracy is
approximately equal to state-of-the-art Deep Learning optimization procedures.
Besides, CAIPI reduces the labeling effort by approximately $80\%$.


title:
Accelerating Backward Aggregation in GCN Training with Execution Path
  Preparing on GPUs

abstract:
  The emerging Graph Convolutional Network (GCN) has now been widely used in
many domains, and it is challenging to improve the efficiencies of applications
by accelerating the GCN trainings. For the sparsity nature and exploding scales
of input real-world graphs, state-of-the-art GCN training systems (e.g.,
GNNAdvisor) employ graph processing techniques to accelerate the message
exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these
systems treat both the aggregation stages of forward and backward propagation
phases as all-active graph processing procedures that indiscriminately conduct
computation on all vertices of an input graph.
  In this paper, we first point out that in a GCN training problem with a given
training set, the aggregation stages of its backward propagation phase (called
as backward aggregations in this paper) can be converted to partially-active
graph processing procedures, which conduct computation on only partial vertices
of the input graph. By leveraging such a finding, we propose an execution path
preparing method that collects and coalesces the data used during backward
propagations of GCN training conducted on GPUs. The experimental results show
that compared with GNNAdvisor, our approach improves the performance of the
backward aggregation of GCN trainings on typical real-world graphs by
1.48x~5.65x. Moreover, the execution path preparing can be conducted either
before the training (during preprocessing) or on-the-fly with the training.
When used during preprocessing, our approach improves the overall GCN training
by 1.05x~1.37x. And when used on-the-fly, our approach improves the overall GCN
training by 1.03x~1.35x.


title: Towards An End-to-End Framework for Flow-Guided Video Inpainting

abstract:
  Optical flow, which captures motion information across frames, is exploited
in recent video inpainting methods through propagating pixels along its
trajectories. However, the hand-crafted flow-based processes in these methods
are applied separately to form the whole inpainting pipeline. Thus, these
methods are less efficient and rely heavily on the intermediate results from
earlier stages. In this paper, we propose an End-to-End framework for
Flow-Guided Video Inpainting (E$^2$FGVI) through elaborately designed three
trainable modules, namely, flow completion, feature propagation, and content
hallucination modules. The three modules correspond with the three stages of
previous flow-based methods but can be jointly optimized, leading to a more
efficient and effective inpainting process. Experimental results demonstrate
that the proposed method outperforms state-of-the-art methods both
qualitatively and quantitatively and shows promising efficiency. The code is
available at https://github.com/MCG-NKU/E2FGVI.


title: An Asymmetrical Model for High Energy Radiation of Cassiopeia A

abstract:
  Cassiopeia A (Cas A) supernova remnant shows strong radiation from radio to
gamma-ray bands. The mechanism of gamma-ray radiation in Cas A and its possible
contribution to PeV cosmic rays are still under debate. The X-ray imaging
reveals an asymmetric profile of Cas A, suggesting the existence of a jet-like
structure. In this work, we propose an asymmetrical model for Cas A, consisting
of a fast moving jet-like structure and a slowly expanding isotropic shell.
This model can account for the multi-wavelength spectra of Cas A, especially
for the power-law hard X-ray spectrum from $\sim$ 60 to 220 keV. The GeV to TeV
emission from Cas A should be contributed by both hadronic and leptonic
processes. Moreover, the jet-like structure may produce a gamma-ray flux of
$\sim 10^{-13}\rm erg\ cm^{-2}\ s^{-1}$ at $\sim 100$ TeV, to be examined by
LHAASO and CTA.


title:
Microjoule-level mid-infrared femtosecond pulse generation in
  hollow-core fibres

abstract:
  We demonstrate a fibre-based approach that generates mid-infrared femtosecond
pulses in the 3-4 {\mu}m spectral region with microjoule-level single pulse
energy. This is realised in a piece of gas-filled antiresonant hollow-core
fibre that is pumped by a two-micron light source. A rapid variation of the
dispersion near a structural resonance of the fibre creates a phase-matching
point in the mid-infrared, which mediates the frequency-down conversion. We
generate femtosecond pulses centred at 3.16 {\mu}m wavelength with the pulse
energy of more than 1 {\mu}J, achieving the conversion efficiency as high as
9.4%. The wavelength of the radiation is determined solely by the dielectric
wall thickness of the cladding elements, while the yield is subject to other
experimental parameters. This, combined with high power-handling capability of
hollow-core fibres, makes it possible to power scale the mid-infrared output by
either increasing the pulse energy or repetition rate of the pump. The
technique presents a new pathway to build an all-fibre-based mid-infrared
supercontinuum source, which promises to be a powerful new tool for ultrahigh
sensitivity molecular spectroscopy.


title:
An existence result for $p$-Laplace equation with gradient nonlinearity
  in $\mathbb{R}^N$

abstract:
  We prove the existence of a weak solution to the problem \begin{equation*}
\begin{split} -\Delta_{p}u+V(x)|u|^{p-2}u & =f(u,|\nabla u|^{p-2}\nabla u), \ \
\ \\ u(x) & >0\ \ \forall x\in\mathbb{R}^{N}, \end{split} \end{equation*} where
$\Delta_{p}u=\hbox{div}(|\nabla u|^{p-2}\nabla u)$ is the $p$-Laplace operator,
$1<p<N$ and the nonlinearity
$f:\mathbb{R}\times\mathbb{R}^{N}\rightarrow\mathbb{R}$ is continuous and it
depends on gradient of the solution. We use an iterative technique based on the
Mountain pass theorem to prove our existence result.


title:
Familiarity-based Collaborative Team Recognition in Academic Social
  Networks

abstract:
  Collaborative teamwork is key to major scientific discoveries. However, the
prevalence of collaboration among researchers makes team recognition
increasingly challenging. Previous studies have demonstrated that people are
more likely to collaborate with individuals they are familiar with. In this
work, we employ the definition of familiarity and then propose MOTO
(faMiliarity-based cOllaborative Team recOgnition algorithm) to recognize
collaborative teams. MOTO calculates the shortest distance matrix within the
global collaboration network and the local density of each node. Central team
members are initially recognized based on local density. Then MOTO recognizes
the remaining team members by using the familiarity metric and shortest
distance matrix. Extensive experiments have been conducted upon a large-scale
data set. The experimental results show that compared with baseline methods,
MOTO can recognize the largest number of teams. The teams recognized by MOTO
possess more cohesive team structures and lower team communication costs
compared with other methods. MOTO utilizes familiarity in team recognition to
identify cohesive academic teams. The recognized teams are in line with
real-world collaborative teamwork patterns. Based on team recognition using
MOTO, the research team structure and performance are further analyzed for
given time periods. The number of teams that consist of members from different
institutions increases gradually. Such teams are found to perform better in
comparison with those whose members are from the same institution.


title: Disentangling the Computational Complexity of Network Untangling

abstract:
  We study the network untangling problem introduced by Rozenshtein, Tatti, and
Gionis [DMKD 2021], which is a variant of Vertex Cover on temporal graphs --
graphs whose edge set changes over discrete time steps. They introduce two
problem variants. The goal is to select at most $k$ time intervals for each
vertex such that all time-edges are covered and (depending on the problem
variant) either the maximum interval length or the total sum of interval
lengths is minimized. This problem has data mining applications in finding
activity timelines that explain the interactions of entities in complex
networks.
  Both variants of the problem are NP-hard. In this paper, we initiate a
multivariate complexity analysis involving the following parameters: number of
vertices, lifetime of the temporal graph, number of intervals per vertex, and
the interval length bound. For both problem versions, we (almost) completely
settle the parameterized complexity for all combinations of those four
parameters, thereby delineating the border of fixed-parameter tractability.


title:
Deep learning-based single-shot computational spectrometer using
  multilayer thin films

abstract:
  Computational spectrometers have mobile application potential, such as
on-site detection and self-diagnosis, by offering compact size, fast operation
time, high resolution, wide working range, and low-cost production. Although
these spectrometers have been extensively studied, demonstrations are confined
to a few examples of straightforward spectra. This study demonstrates deep
learning (DL)-based single-shot computational spectrometer for narrow and broad
spectra using a multilayer thin-film filter array. For measuring light
intensities, the device was built by attaching the filter array, fabricated
using a wafer-level stencil lithography process, to a complementary
metal-oxide-semiconductor image sensor. All the intensities were extracted from
a monochrome image captured with a single exposure. A DL architecture
comprising a dense layer and a U-Net backbone with residual connections was
employed for spectrum reconstruction. The measured intensities were fed into
the DL architecture for reconstruction as spectra. We reconstructed 323
continuous spectra with an average root mean squared error of 0.0288 in a
500-850 nm wavelength range with 1-nm spacing. Our computational spectrometer
achieved a compact size, fast measuring time, high resolution, and wide working
range.


title: MDS and AMDS symbol-pair codes are constructed from repeated-root codes

abstract:
  Symbol-pair codes introduced by Cassuto and Blaum in 2010 are designed to
protect against the pair errors in symbol-pair read channels. One of the
central themes in symbol-error correction is the construction of maximal
distance separable (MDS) symbol-pair codes that possess the largest possible
pair-error correcting performance. In this paper, we construct more general
generator polynomials for two classes of MDS symbol-pair codes with code length
$lp$. Based on repeated-root cyclic codes, we derive all MDS symbol-pair codes
of length $3p$, when the degree of the generator polynomials is no more than
10. We also give two new classes of (almost maximal distance separable) AMDS
symbol-pair codes with the length $lp$ or $4p$ by virtue of repeated-root
cyclic codes. For length $3p$, we derive all AMDS symbol-pair codes, when the
degree of the generator polynomials is less than 10.
  The main results are obtained by determining the solutions of certain
equations over finite fields.


title: Behavioral uncertainty quantification for data-driven control

abstract:
  This paper explores the problem of uncertainty quantification in the
behavioral setting for data-driven control. Building on classical ideas from
robust control, the problem is regarded as that of selecting a metric which is
best suited to a data-based description of uncertainties. Leveraging on
Willems' fundamental lemma, restricted behaviors are viewed as subspaces of
fixed dimension, which may be represented by data matrices. Consequently,
metrics between restricted behaviors are defined as distances between points on
the Grassmannian, i.e., the set of all subspaces of equal dimension in a given
vector space. A new metric is defined on the set of restricted behaviors as a
direct finite-time counterpart of the classical gap metric. The metric is shown
to capture parametric uncertainty for the class of autoregressive (AR) models.
Numerical simulations illustrate the value of the new metric with a data-driven
mode recognition and control case study.


title:
Convergence rates for energies of interacting particles whose
  distribution spreads out as their number increases

abstract:
  We consider a class of particle systems which appear in various applications
such as approximation theory, plasticity, potential theory and space-filling
designs. The positions of the particles on the real line are described as a
global minimum of an interaction energy, which consists of a nonlocal,
repulsive interaction part and a confining part. Motivated by the applications,
we cover non-standard scenarios in which the confining potential weakens as the
number of particles increases. This results in a large area over which the
particles spread out. Our aim is to approximate the particle interaction energy
by a corresponding continuum interacting energy. Our main results are bounds on
the corresponding energy difference and on the difference between the related
potential values. We demonstrate that these bounds are useful to problems in
approximation theory and plasticity. The proof of these bounds relies on
convexity assumptions on the interaction and confining potentials. It combines
recent advances in the literature with a new upper bound on the minimizer of
the continuum interaction energy.


title:
First principles investigation of anionic redox in bisulfate lithium
  battery cathodes

abstract:
  The search for an alternative high-voltage polyanionic cathode material for
Li-ion batteries is vital to improve the energy densities beyond the
state-of-the-art, where sulfate frameworks form an important class of
high-voltage cathode materials due to the strong inductive effect of the
S$^{6+}$ ion. Here, we have investigated the mechanism of cationic and/or
anionic redox in Li$_x$M(SO$_4$)$_2$ frameworks (M = Mn, Fe, Co, and Ni and 0
$\leq$ x $\leq$ 2) using density functional calculations. Specifically, we have
used a combination of Hubbard $U$ corrected strongly constrained and
appropriately normed (SCAN+$U$) and generalized gradient approximation
(GGA+$U$) functionals to explore the thermodynamic (polymorph stability),
electrochemical (intercalation voltage), geometric (bond lengths), and
electronic (band gaps, magnetic moments, charge populations, etc.) properties
of the bisulfate frameworks considered. Importantly, we find that the anionic
(cationic) redox process is dominant throughout delithiation in the Ni (Mn)
bisulfate, as verified using our calculated projected density of states, bond
lengths, and on-site magnetic moments. On the other hand, in Fe and Co
bisulfates, cationic redox dominates the initial delithiation (1 $\leq$ x
$\leq$ 2), while anionic redox dominates subsequent delithiation (0 $\leq$ x
$\leq$ 2). In addition, evaluation of the crystal overlap Hamilton population
reveals insignificant bonding between oxidizing O atoms throughout the
delithiation process in the Ni bisulfate, indicating robust battery performance
that is resistant to irreversible oxygen evolution. Finally, we observe both
GGA+$U$ and SCAN+$U$ predictions are in qualitative agreement for the various
properties predicted. Our work should open new avenues for exploring lattice
oxygen redox in novel high voltage polyanionic cathodes, especially using the
SCAN+$U$ functional.


title:
Faster-TAD: Towards Temporal Action Detection with Proposal Generation
  and Classification in a Unified Network

abstract:
  Temporal action detection (TAD) aims to detect the semantic labels and
boundaries of action instances in untrimmed videos. Current mainstream
approaches are multi-step solutions, which fall short in efficiency and
flexibility. In this paper, we propose a unified network for TAD, termed
Faster-TAD, by re-purposing a Faster-RCNN like architecture. To tackle the
unique difficulty in TAD, we make important improvements over the original
framework. We propose a new Context-Adaptive Proposal Module and an innovative
Fake-Proposal Generation Block. What's more, we use atomic action features to
improve the performance. Faster-TAD simplifies the pipeline of TAD and gets
remarkable performance on lots of benchmarks, i.e., ActivityNet-1.3 (40.01%
mAP), HACS Segments (38.39% mAP), SoccerNet-Action Spotting (54.09% mAP). It
outperforms existing single-network detector by a large margin.


title:
Rolling Colors: Adversarial Laser Exploits against Traffic Light
  Recognition

abstract:
  Traffic light recognition is essential for fully autonomous driving in urban
areas. In this paper, we investigate the feasibility of fooling traffic light
recognition mechanisms by shedding laser interference on the camera. By
exploiting the rolling shutter of CMOS sensors, we manage to inject a color
stripe overlapped on the traffic light in the image, which can cause a red
light to be recognized as a green light or vice versa. To increase the success
rate, we design an optimization method to search for effective laser parameters
based on empirical models of laser interference. Our evaluation in emulated and
real-world setups on 2 state-of-the-art recognition systems and 5 cameras
reports a maximum success rate of 30% and 86.25% for Red-to-Green and
Green-to-Red attacks. We observe that the attack is effective in continuous
frames from more than 40 meters away against a moving vehicle, which may cause
end-to-end impacts on self-driving such as running a red light or emergency
stop. To mitigate the threat, we propose redesigning the rolling shutter
mechanism.


title:
Detecting Outlier Patterns with Query-based Artificially Generated
  Searching Conditions

abstract:
  In the age of social computing, finding interesting network patterns or
motifs is significant and critical for various areas such as decision
intelligence, intrusion detection, medical diagnosis, social network analysis,
fake news identification, national security, etc. However, sub-graph matching
remains a computationally challenging problem, let alone identifying special
motifs among them. This is especially the case in large heterogeneous
real-world networks. In this work, we propose an efficient solution for
discovering and ranking human behavior patterns based on network motifs by
exploring a user's query in an intelligent way. Our method takes advantage of
the semantics provided by a user's query, which in turn provides the
mathematical constraint that is crucial for faster detection. We propose an
approach to generate query conditions based on the user's query. In particular,
we use meta paths between nodes to define target patterns as well as their
similarities, leading to efficient motif discovery and ranking at the same
time. The proposed method is examined on a real-world academic network, using
different similarity measures between the nodes. The experiment result
demonstrates that our method can identify interesting motifs, and is robust to
the choice of similarity measures.


title: Flat trace estimates for Anosov flows

abstract:
  We prove a high energy flat trace estimate for the modified resolvent of the
generator of an Anosov flow. This fills a gap in the proof of the local trace
formula in [Jin-Zworski '17] and is a by-product of the authors' ongoing
project of its generalization to Axiom A flows.


title:
Double Descent in Random Feature Models: Precise Asymptotic Analysis for
  General Convex Regularization

abstract:
  We prove rigorous results on the double descent phenomenon in random features
(RF) model by employing the powerful Convex Gaussian Min-Max Theorem (CGMT) in
a novel multi-level manner. Using this technique, we provide precise asymptotic
expressions for the generalization of RF regression under a broad class of
convex regularization terms including arbitrary separable functions. We further
compute our results for the combination of $\ell_1$ and $\ell_2$ regularization
case, known as elastic net, and present numerical studies about it. We
numerically demonstrate the predictive capacity of our framework, and show
experimentally that the predicted test error is accurate even in the
non-asymptotic regime.


title:
Poisson Equations with locally-Lipschitz coefficients and Uniform in
  Time Averaging for Stochastic Differential Equations via Strong Exponential
  Stability

abstract:
  We study Poisson equations and averaging for Stochastic Differential
Equations (SDEs). Poisson equations are essential tools in both probability
theory and partial differential equations (PDEs). Their vast range of
applications includes the study of the asymptotic behaviour of solutions of
parabolic PDEs, the treatment of multi-scale and homogenization problems as
well as the theoretical analysis of approximations of solution of Stochastic
Differential Equations (SDEs). The study of Poisson equations in non-compact
state space is notoriously difficult. Results exists, but only for the case
when the coefficients of the PDE are either bounded or satisfy linear growth
assumptions (and even the latter case has only recently been achieved). In this
paper we treat Poisson equations on non-compact state spaces for coefficients
that can grow super-linearly. This is one of the two building blocks towards
the second (and main) result of the paper, namely in this paper we succeed in
obtaining a {\em uniform in time} (UiT) averaging result (with a rate) for SDE
models with super-linearly growing coefficients. This seems to be the first UiT
averaging result for slow-fast systems of SDEs (and indeed we are not aware of
any other UiT multiscale theorems in the PDE or ODE literature either). Key to
obtaining both our UiT averaging result and to enable dealing with the
super-linear growth of the coefficients (both of the slow-fast system and of
the associated Poisson equation) is conquering exponential decay in time of the
space-derivatives of appropriate Markov semigroups. We refer to semigroups
which enjoy this property as being Strongly Exponentially Stable.


title: Relevance of Wrong-Way Risk in Funding Valuation Adjustments

abstract:
  March 2020, the world was thrown into a period of financial distress. This
manifested through increased uncertainty in the financial markets. Many
interest rates collapsed and funding spreads surged significantly, which
increased due to the market turmoil. In light of these events, it is key to
understand and model Wrong-Way Risk (WWR) in a Funding Valuation Adjustment
(FVA) context. WWR might currently not be incorporated in FVA calculations in
banks' Valuation Adjustment (xVA) engines. However, we demonstrate that WWR
effects are non-negligible in FVA modeling from a risk-management perspective.
We look at the impact of various modeling choices such as including the default
times of the relevant parties and we consider different choices of funding
spread. A case study is presented for interest rate derivatives.


title: PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model

abstract:
  Real-world applications have high demands for semantic segmentation methods.
Although semantic segmentation has made remarkable leap-forwards with deep
learning, the performance of real-time methods is not satisfactory. In this
work, we propose PP-LiteSeg, a novel lightweight model for the real-time
semantic segmentation task. Specifically, we present a Flexible and Lightweight
Decoder (FLD) to reduce computation overhead of previous decoder. To strengthen
feature representations, we propose a Unified Attention Fusion Module (UAFM),
which takes advantage of spatial and channel attention to produce a weight and
then fuses the input features with the weight. Moreover, a Simple Pyramid
Pooling Module (SPPM) is proposed to aggregate global context with low
computation cost. Extensive evaluations demonstrate that PP-LiteSeg achieves a
superior trade-off between accuracy and speed compared to other methods. On the
Cityscapes test set, PP-LiteSeg achieves 72.0% mIoU/273.6 FPS and 77.5%
mIoU/102.6 FPS on NVIDIA GTX 1080Ti. Source code and models are available at
PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.


title: Bridging the Gap: Decoding the Intrinsic Nature of Time in Market Data

abstract:
  Intrinsic time is an example of an event-based conception of time, used to
analyze financial time series. Here, for the first time, we reveal the
connection between intrinsic time and physical time. In detail, we present an
analytic relationship which links the two different time paradigms. Central to
this discovery are the emergence of scaling laws. Indeed, a novel empirical
scaling law is presented, relating to the variability of what is know as
overshoots in the intrinsic time framework. To evaluate the validity of the
theoretically derived expressions, three time series are analyzed; in detail,
Brownian motion and two tick-by-tick empirical currency market data sets (one
crypto and one fiat). Finally, the time series analyzed in physical time can be
decomposed into their liquidity and volatility components, both only visible in
intrinsic time, further highlighting the utility of this temporal kinship.


title:
Beyond Separability: Analyzing the Linear Transferability of Contrastive
  Representations to Related Subpopulations

abstract:
  Contrastive learning is a highly effective method which uses unlabeled data
to produce representations which are linearly separable for downstream
classification tasks. Recent works have shown that contrastive representations
are not only useful when data come from a single domain, but are also effective
for transferring across domains. Concretely, when contrastive representations
are trained on data from two domains (a source and target) and a linear
classification head is trained to predict labels using only the labeled source
data, the resulting classifier also exhibits good transfer to the target
domain. In this work, we analyze this linear transferability phenomenon,
building upon the framework proposed by HaoChen et al (2021) which relates
contrastive learning to spectral clustering of a positive-pair graph on the
data. We prove that contrastive representations capture relationships between
subpopulations in the positive-pair graph: linear transferability can occur
when data from the same class in different domains (e.g., photo dogs and
cartoon dogs) are connected in the graph. Our analysis allows the source and
target classes to have unbounded density ratios and be mapped to distant
representations. Our proof is also built upon technical improvements over the
main results of HaoChen et al (2021), which may be of independent interest.


title: Domain-Agnostic Prior for Transfer Semantic Segmentation

abstract:
  Unsupervised domain adaptation (UDA) is an important topic in the computer
vision community. The key difficulty lies in defining a common property between
the source and target domains so that the source-domain features can align with
the target-domain semantics. In this paper, we present a simple and effective
mechanism that regularizes cross-domain representation learning with a
domain-agnostic prior (DAP) that constrains the features extracted from source
and target domains to align with a domain-agnostic space. In practice, this is
easily implemented as an extra loss term that requires a little extra costs. In
the standard evaluation protocol of transferring synthesized data to real data,
we validate the effectiveness of different types of DAP, especially that
borrowed from a text embedding model that shows favorable performance beyond
the state-of-the-art UDA approaches in terms of segmentation accuracy. Our
research reveals that UDA benefits much from better proxies, possibly from
other data modalities.


title: Language Model for Text Analytic in Cybersecurity

abstract:
  NLP is a form of artificial intelligence and machine learning concerned with
a computer or machine's ability to understand and interpret human language.
Language models are crucial in text analytics and NLP since they allow
computers to interpret qualitative input and convert it to quantitative data
that they can use in other tasks. In essence, in the context of transfer
learning, language models are typically trained on a large generic corpus,
referred to as the pre-training stage, and then fine-tuned to a specific
underlying task. As a result, pre-trained language models are mostly used as a
baseline model that incorporates a broad grasp of the context and may be
further customized to be used in a new NLP task.
  The majority of pre-trained models are trained on corpora from general
domains, such as Twitter, newswire, Wikipedia, and Web. Such off-the-shelf NLP
models trained on general text may be inefficient and inaccurate in specialized
fields. In this paper, we propose a cybersecurity language model called
SecureBERT, which is able to capture the text connotations in the cybersecurity
domain, and therefore could further be used in automation for many important
cybersecurity tasks that would otherwise rely on human expertise and tedious
manual efforts. SecureBERT is trained on a large corpus of cybersecurity text
collected and preprocessed by us from a variety of sources in cybersecurity and
the general computing domain. Using our proposed methods for tokenization and
model weights adjustment, SecureBERT is not only able to preserve the
understanding of general English as most pre-trained language models can do,
but also effective when applied to text that has cybersecurity implications.


title: The loss value of multilinear regression

abstract:
  A determinant formula for the euclidean distance between a point and a linear
subspace is presented. As a consequence determinant formulas are derived for: a
positive semidefinite, hermitian matrix; the loss value of multilinear
regression; the multiple linear regression coefficient.


title: Learning to Adapt Clinical Sequences with Residual Mixture of Experts

abstract:
  Clinical event sequences in Electronic Health Records (EHRs) record detailed
information about the patient condition and patient care as they occur in time.
Recent years have witnessed increased interest of machine learning community in
developing machine learning models solving different types of problems defined
upon information in EHRs. More recently, neural sequential models, such as RNN
and LSTM, became popular and widely applied models for representing patient
sequence data and for predicting future events or outcomes based on such data.
However, a single neural sequential model may not properly represent complex
dynamics of all patients and the differences in their behaviors. In this work,
we aim to alleviate this limitation by refining a one-fits-all model using a
Mixture-of-Experts (MoE) architecture. The architecture consists of multiple
(expert) RNN models covering patient sub-populations and refining the
predictions of the base model. That is, instead of training expert RNN models
from scratch we define them on the residual signal that attempts to model the
differences from the population-wide model. The heterogeneity of various
patient sequences is modeled through multiple experts that consist of RNN.
Particularly, instead of directly training MoE from scratch, we augment MoE
based on the prediction signal from pretrained base GRU model. With this way,
the mixture of experts can provide flexible adaptation to the (limited)
predictive power of the single base RNN model. We experiment with the newly
proposed model on real-world EHRs data and the multivariate clinical event
prediction task. We implement RNN using Gated Recurrent Units (GRU). We show
4.1% gain on AUPRC statistics compared to a single RNN prediction.


title:
SEAL: A Large-scale Video Dataset of Multi-grained Spatio-temporally
  Action Localization

abstract:
  In spite of many dataset efforts for human action recognition, current
computer vision algorithms are still limited to coarse-grained spatial and
temporal annotations among human daily life. In this paper, we introduce a
novel large-scale video dataset dubbed SEAL for multi-grained Spatio-tEmporal
Action Localization. SEAL consists of two kinds of annotations, SEAL Tubes and
SEAL Clips. We observe that atomic actions can be combined into many complex
activities. SEAL Tubes provide both atomic action and complex activity
annotations in tubelet level, producing 49.6k atomic actions spanning 172
action categories and 17.7k complex activities spanning 200 activity
categories. SEAL Clips localizes atomic actions in space during two-second
clips, producing 510.4k action labels with multiple labels per person.
Extensive experimental results show that SEAL significantly helps to advance
video understanding.


title:
A conjecture of Akbari, Cameron, and Khosrovshahi on the existence of a
  $(0,1)$-vector in the row space of every adjacency matrix

abstract:
  We look for a non-zero $(0, 1)$-vector in the row space of the adjacency
matrix $A(\Gamma)$ of a graph $\Gamma,$ provided $\Gamma$ has at least one
edge. Akbari, Cameron, and Khosrovshahi conjectured that there exists a
non-zero $(0,1)$-vector in the row space of $A(\Gamma)$ (over the real numbers)
which does not occur as a row of $A(\Gamma).$ This conjecture can be easily
verified for graphs having diameter is equal to $1$ (complete graphs). In this
article, we affirmatively prove this conjecture for any graph whose diameter is
$\geq 4.$ Furthermore, in the remaining two cases that is, for graphs with
diameter is equal to $2$ or $3,$ we report some progress in support of the
conjecture.


title:
A Hessian inversion-free exact second order method for distributed
  consensus optimization

abstract:
  We consider a standard distributed consensus optimization problem where a set
of agents connected over an undirected network minimize the sum of their
individual local strongly convex costs. Alternating Direction Method of
Multipliers ADMM and Proximal Method of Multipliers PMM have been proved to be
effective frameworks for design of exact distributed second order methods
involving calculation of local cost Hessians. However, existing methods involve
explicit calculation of local Hessian inverses at each iteration that may be
very costly when the dimension of the optimization variable is large. In this
paper we develop a novel method termed INDO Inexact Newton method for
Distributed Optimization that alleviates the need for Hessian inverse
calculation. INDO follows the PMM framework but unlike existing work
approximates the Newton direction through a generic fixed point method, e.g.,
Jacobi Overrelaxation, that does not involve Hessian inverses. We prove exact
global linear convergence of INDO and provide analytical studies on how the
degree of inexactness in the Newton direction calculation affects the overall
methods convergence factor. Numerical experiments on several real data sets
demonstrate that INDOs speed is on par or better as state of the art methods
iterationwise hence having a comparable communication cost. At the same time,
for sufficiently large optimization problem dimensions n (even at n on the
order of couple of hundreds), INDO achieves savings in computational cost by at
least an order of magnitude.


title:
Scalable implementation of $(d+1)$ mutually unbiased bases for
  $d$-dimensional quantum key distribution

abstract:
  A high-dimensional quantum key distribution (QKD) can improve error rate
tolerance and the secret key rate. Many $d$-dimensional QKDs have used two
mutually unbiased bases (MUBs), while $(d+1)$ MUBs enable a more robust QKD.
However, a scalable implementation has not been achieved because the setups
have required $d$ devices even for two MUBs or a flexible convertor for a
specific optical mode. Here, we propose a scalable and general implementation
of $(d+1)$ MUBs using $\log_p d$ interferometers in prime power dimensions
$d=p^N$. We implemented the setup for time-bin states and observed an average
error rate of 3.8% for phase bases, which is lower than the 23.17% required for
a secure QKD against collective attack in $d=4$.


title: Integrability of supersymmetric Calogero-Moser models

abstract:
  We analyze the integrability of the ${\cal N}$-extended supersymmetric
Calogero-Moser model. We explicitly construct the Lax pair $\{L,A\}$ for this
system, which properly reproduces all equations of motion. After adding a
supersymmetric oscillator potential we reduce the latter to solving
$\dot{U}\,{=}\,A\,U$ for the time evolution operator $U(t)$. The bosonic
variables, however, evolve independently of $U$ on closed trajectories, as is
required for superintegrability. To visualize the structure of the conserved
currents we derive the complete set of Liouville charges up to the fifth power
in the momenta, for the ${\cal N}{=}\,2$ supersymmetric model. The additional,
non-involutive, conserved charges needed for a maximal superintegrability of
this model are also found.


title:
Exploration with Global Consistency Using Real-Time Re-integration and
  Active Loop Closure

abstract:
  Despite recent progress of robotic exploration, most methods assume that
drift-free localization is available, which is problematic in reality and
causes severe distortion of the reconstructed map. In this work, we present a
systematic exploration mapping and planning framework that deals with drifted
localization, allowing efficient and globally consistent reconstruction. A
real-time re-integration-based mapping approach along with a frame pruning
mechanism is proposed, which rectifies map distortion effectively when drifted
localization is corrected upon detecting loop-closure. Besides, an exploration
planning method considering historical viewpoints is presented to enable active
loop closing, which promotes a higher opportunity to correct localization
errors and further improves the mapping quality. We evaluate both the mapping
and planning methods as well as the entire system comprehensively in simulation
and real-world experiments, showing their effectiveness in practice. The
implementation of the proposed method will be made open-source for the benefit
of the robotics community.


title:
Customizable End-to-end Optimization of Online Neural Network-supported
  Dereverberation for Hearing Devices

abstract:
  This work focuses on online dereverberation for hearing devices using the
weighted prediction error (WPE) algorithm. WPE filtering requires an estimate
of the target speech power spectral density (PSD). Recently deep neural
networks (DNNs) have been used for this task. However, these approaches
optimize the PSD estimate which only indirectly affects the WPE output, thus
potentially resulting in limited dereverberation. In this paper, we propose an
end-to-end approach specialized for online processing, that directly optimizes
the dereverberated output signal. In addition, we propose to adapt it to the
needs of different types of hearing-device users by modifying the optimization
target as well as the WPE algorithm characteristics used in training. We show
that the proposed end-to-end approach outperforms the traditional and
conventional DNN-supported WPEs on a noise-free version of the WHAMR! dataset.


title:
The specific angular momentum of disc galaxies and its connection with
  galaxy morphology, bar structure and disc gravitational instability

abstract:
  The specific angular momenta ($j\equiv J/M$) of stars ($j_{\star}$), gas
($j_{\mathrm{gas}}$), baryons as a whole ($j_{\mathrm{b}}$) and dark matter
haloes ($j_{\mathrm{h}}$) contain clues of vital importance about how galaxies
form and evolve. Using one of the largest samples of disc galaxies (S0-BCD)
with high-quality rotation curves and near-infrared surface photometry, we
perform a detailed comparative analysis of $j$ that stretches across a variety
of galaxy properties. Our analysis imposes tight constraints on the "retained"
fractions of specific angular momentum ($j_{\star}/j_{\mathrm{h}}$,
$j_{\mathrm{HI}}/j_{\mathrm{h}}$ and $j_{\mathrm{b}}/j_{\mathrm{h}}$), as well
as on their systematic trends with mass fraction and galaxy morphology, thus on
how well specific angular momentum is conserved in the process of disc galaxy
formation and evolution. Besides, our analysis demonstrates how challenging it
is to characterize barred galaxies from a gravitational instability point of
view. This is true not only for the popular Efstathiou, Lake & Negroponte
(1982) bar instability criterion, which fails to separate barred from
non-barred galaxies in about 55% of the cases, but also for the mass-weighted
Toomre (1964) parameter of atomic gas, $\langle Q_{\mathrm{HI}}\rangle$, which
succeeds in separating barred from non-barred galaxies, but only in a
statistical sense.


title: Wick-type stochastic parabolic equations with random potentials

abstract:
  The stochastic parabolic equations with random potentials, driving forces and
initial conditions are considered. The Wick product is used to give sense to
the product of two generalized stochastic processes, and the existence and
uniqueness of solutions are proved via the chaos expansion method from white
noise analysis. The estimates on coefficients in the chaos expansion form of
the solutions are provided.


title:
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance
  Evaluated on Non-stationary Seismic Signal Time Series

abstract:
  One of the latest self-supervised learning (SSL) methods, VICReg, showed a
great performance both in the linear evaluation and the fine-tuning evaluation.
However, VICReg is proposed in computer vision and it learns by pulling
representations of random crops of an image while maintaining the
representation space by the variance and covariance loss. However, VICReg would
be ineffective on non-stationary time series where different parts/crops of
input should be differently encoded to consider the non-stationarity. Another
recent SSL proposal, Temporal Neighborhood Coding (TNC) is effective for
encoding non-stationary time series. This study shows that a combination of a
VICReg-style method and TNC is very effective for SSL on non-stationary time
series, where a non-stationary seismic signal time series is used as an
evaluation dataset.


title:
A computational study of accelerating, steady and fading negative
  streamers in ambient air

abstract:
  We study negative streamers in ambient air using a 2D axisymmetric fluid
model. Depending on the background electric field, we observe accelerating,
steady and fading negative streamers. Fading occurs in low background fields,
in which negative streamers undergo a streamer-to-avalanche transition: their
field enhancement keeps decreasing and the streamer velocity becomes comparable
to the maximal electron drift velocity. Our focus is on the steady propagation
mode, during which streamer properties like radius and velocity hardly change.
However, this mode is unstable, in the sense that a small change in conditions
leads to acceleration or deceleration. We observe steady negative streamers in
background fields ranging from 9.19 kV/cm to 15.75 kV/cm, indicating that there
is no unique steady propagation field (or stability field). Another finding is
that steady negative streamers are able to keep propagating over tens of
centimeters, with only a finite conductive length behind their heads, similar
to steady positive streamers. Approximately linear relationships are observed
between the optical diameter and properties like the streamer velocity and the
streamer head potential. From these linear relations, we obtain rough lower
bounds of about 0.27 mm to 0.35 mm for the minimal optical diameter of steady
negative streamers. The lowest background field in which a steady negative
streamer could be obtained is 9.19 kV/cm. In contrast, steady positive
streamers have recently been obtained in a background field as low as 4.07
kV/cm. We find that the properties of steady negative and positive streamers
differ significantly. For example, for steady negative streamers the ratio
between streamer velocity and maximal electron drift velocity ranges from about
2 to 4.5, whereas for steady positive streamers this ratio ranges from about
0.05 to 0.26.


title: Dynamical state of galaxy clusters evaluated from X-ray images

abstract:
  X-ray images of galaxy clusters often show disturbed structures that are
indication of cluster mergers. As a complementary to our previous work on
dynamical state of 964 clusters observed by the Chandra, we process the X-ray
images for 1308 clusters from XMM-Newton archival data, together with the
images of 22 clusters newly released by the Chandra, and evaluate their
dynamical state from these X-ray images. The concentration index $c$, the
centroid shift $\omega$, the power ratio $P_3/P_0$ are calculated in circular
regions with a certain radius of 500 kpc, and the morphology index $\delta$ is
estimated within elliptical regions adaptive to the cluster size and shape. In
addition, the dynamical parameters for 42 clusters previously estimated from
Chandra images are upgraded based on the newly available redshifts. Good
consistence is found between dynamical parameters derived from XMM-Newton and
Chandra images for the overlapped sample of clusters in the two datasets. The
dependence of mass scaling relations on dynamical state is shown by using data
of 388 clusters.


title: Polynomial growth and subgroups of $\mathrm{Out}(F_{\tt n})$

abstract:
  This paper, which is the last of a series of three papers, studies dynamical
properties of elements of $\mathrm{Out}(F_{\tt n})$, the outer automorphism
group of a nonabelian free group $F_{\tt n}$. We prove that, for every subgroup
$H$ of $\mathrm{Out}(F_{\tt n})$, there exists an element $\phi \in H$ such
that, for every element $g$ of $F_{\tt n}$, the conjugacy class $[g]$ has
polynomial growth under iteration of $\phi$ if and only if $[g]$ has polynomial
growth under iteration of every element of $H$.


title: Aesthetic Text Logo Synthesis via Content-aware Layout Inferring

abstract:
  Text logo design heavily relies on the creativity and expertise of
professional designers, in which arranging element layouts is one of the most
important procedures. However, few attention has been paid to this task which
needs to take many factors (e.g., fonts, linguistics, topics, etc.) into
consideration. In this paper, we propose a content-aware layout generation
network which takes glyph images and their corresponding text as input and
synthesizes aesthetic layouts for them automatically. Specifically, we develop
a dual-discriminator module, including a sequence discriminator and an image
discriminator, to evaluate both the character placing trajectories and rendered
shapes of synthesized text logos, respectively. Furthermore, we fuse the
information of linguistics from texts and visual semantics from glyphs to guide
layout prediction, which both play important roles in professional layout
design. To train and evaluate our approach, we construct a dataset named as
TextLogo3K, consisting of about 3,500 text logo images and their pixel-level
annotations. Experimental studies on this dataset demonstrate the effectiveness
of our approach for synthesizing visually-pleasing text logos and verify its
superiority against the state of the art.


title: A special case of a conjecture of Hellerstein, Shen and Williamson

abstract:
  The paper proves a special case of a conjecture of Hellerstein, Shen and
Williamson concerning non-real zeros of derivatives of real meromorphic
functions.


title: A New Optical Model for Photomultiplier Tubes

abstract:
  It is critical to construct an accurate optical model of photomultiplier
tubes (PMTs) in many applications to describe the angular and spectral
responses of the photon detection efficiency (PDE) of the PMTs in their working
media. In this study, we propose a new PMT optical model to describe both light
interactions with the PMT window and optical processes inside PMTs with
reasonable accuracy based on the optics theory and a GEANT4-based simulation
toolkit. The proposed model builds a relationship between the PDE and the
underlying processes that the PDE relies on. This model also provides a tool to
transform the PDE measured in one working medium (like air) to the PDE in other
media (like water, liquid scintillator, etc). Using two 20" MCP-PMTs and one
20" dynode PMT, we demonstrate a complete procedure to obtain the key
parameters used in the model from experimental data, such as the optical
properties of the antireflective coating and photocathode of the three PMTs.
The proposed model can effectively reproduce the angular responses of the
quantum efficiency of PMTs, even though an ideally uniform photocathode is
assumed in the model. Interestingly, the proposed model predicts a similar
level ($20\%\sim30\%$) of light yield excess observed in the experimental data
of many liquid scintillator-based neutrino detectors, compared with that
predicted at the stage of detector design. However, this excess has never been
explained, and the proposed PMT model provides a good explanation for it, which
highlights the imperfections of PMT models used in their detector simulations.


title: Fundamental limits to learning closed-form mathematical models from data

abstract:
  Given a finite and noisy dataset generated with a closed-form mathematical
model, when is it possible to learn the true generating model from the data
alone? This is the question we investigate here. We show that this
model-learning problem displays a transition from a low-noise phase in which
the true model can be learned, to a phase in which the observation noise is too
high for the true model to be learned by any method. Both in the low-noise
phase and in the high-noise phase, probabilistic model selection leads to
optimal generalization to unseen data. This is in contrast to standard machine
learning approaches, including artificial neural networks, which are limited,
in the low-noise phase, by their ability to interpolate. In the transition
region between the learnable and unlearnable phases, generalization is hard for
all approaches including probabilistic model selection.

categories: cs.LG cond-mat.dis-nn cond-mat.stat-mech physics.comp-ph physics.data-an


title: Partition genericity and pigeonhole basis theorems

abstract:
  There exist two notions of typicality in computability theory, namely,
genericity and randomness. In this article, we introduce a new notion of
genericity, called partition genericity, which is at the intersection of these
two notions of typicality, and show that many basis theorems apply to partition
genericity. More precisely, we prove that every co-hyperimmune set and every
Kurtz random is partition generic, and that every partition generic set admits
weak infinite subsets. In particular, we answer a question of Kjos-Hanssen and
Liu by showing that every Kurtz random admits an infinite subset which does not
compute any set of positive Hausdorff dimension. Partition genericty is a
partition regular notion, so these results imply many existing pigeonhole basis
theorems.


title:
Weyl Tensors, Strongly Regular Graphs, Multiplicative Characters, and a
  Quadratic Matrix Equation

abstract:
  Let $S$ be a real symmetric $n\times n$-matrix with zeros on the diagonal and
let $\theta$ be a real number. We show that the set of quadratic equations
\[\sum_{k}S_{i,k}S_{k,j}+S_{i,j}^2=\theta S_{i,j},\text { for }i<j,\] has
solutions $(S,\theta)$, with $S\neq0$, in all dimensions $n\geq 4$. % The
equations originate from a question about the Riemannian curvature tensor. Our
solutions relate the equations to strongly regular graphs, to group rings, and
to multiplicative characters of finite fields.


title:
Joint species distribution models with imperfect detection for
  high-dimensional spatial data

abstract:
  Determining spatial distributions of species and communities are key
objectives of ecology and conservation. Joint species distribution models use
multi-species detection-nondetection data to estimate species and community
distributions. The analysis of such data is complicated by residual
correlations between species, imperfect detection, and spatial autocorrelation.
While methods exist to accommodate each of these complexities, there are few
examples in the literature that address and explore all three complexities
simultaneously. Here we developed a spatial factor multi-species occupancy
model to explicitly account for species correlations, imperfect detection, and
spatial autocorrelation. The proposed model uses a spatial factor dimension
reduction approach and Nearest Neighbor Gaussian Processes to ensure
computational efficiency for data sets with both a large number of species
(e.g., > 100) and spatial locations (e.g., 100,000). We compare the proposed
model performance to five candidate models, each addressing a subset of the
three complexities. We implemented the proposed and competing models in the
spOccupancy software, designed to facilitate application via an accessible,
well-documented, and open-source R package. Using simulations, we found
ignoring the three complexities when present leads to inferior model predictive
performance. Using a case study on 98 bird species across the continental US,
the spatial factor multi-species occupancy model had the highest predictive
performance among the candidate models. Our proposed framework, together with
its implementation in spOccupancy, serves as a user-friendly tool to understand
spatial variation in species distributions and biodiversity metrics while
addressing common complexities in multi-species detection-nondetection data.


title:
Exact WKB-like Formulae for the Energies by means of the Quantum
  Hamilton-Jacobi Equation

abstract:
  It is shown that by means of the approach based on the Quantum
Hamilton-Jacobi equation, it is possible to modify the WKB expressions for the
energy levels of quantum systems, when incorrect, obtaining exact WKB-like
formulae. This extends the results found in previous papers, where it was
demonstrated that the QHJ method provides exact WKB-like expressions for the
wave functions.


title: Evolutionary Diversity Optimisation for The Traveling Thief Problem

abstract:
  There has been a growing interest in the evolutionary computation community
to compute a diverse set of high-quality solutions for a given optimisation
problem. This can provide the practitioners with invaluable information about
the solution space and robustness against imperfect modelling and minor
problems' changes. It also enables the decision-makers to involve their
interests and choose between various solutions. In this study, we investigate
for the first time a prominent multi-component optimisation problem, namely the
Traveling Thief Problem (TTP), in the context of evolutionary diversity
optimisation. We introduce a bi-level evolutionary algorithm to maximise the
structural diversity of the set of solutions. Moreover, we examine the
inter-dependency among the components of the problem in terms of structural
diversity and empirically determine the best method to obtain diversity. We
also conduct a comprehensive experimental investigation to examine the
introduced algorithm and compare the results to another recently introduced
framework based on the use of Quality Diversity (QD). Our experimental results
show a significant improvement of the QD approach in terms of structural
diversity for most TTP benchmark instances.


title:
Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture
  Embeddings

abstract:
  Embedding-based approaches for dialog response retrieval embed the
context-response pairs as points in the embedding space. These approaches are
scalable, but fail to account for the complex, many-to-many relationships that
exist between context-response pairs. On the other end of the spectrum, there
are approaches that feed the context-response pairs jointly through multiple
layers of neural networks. These approaches can model the complex relationships
between context-response pairs, but fail to scale when the set of responses is
moderately large (>100). In this paper, we combine the best of both worlds by
proposing a scalable model that can learn complex relationships between
context-response pairs. Specifically, the model maps the contexts as well as
responses to probability distributions over the embedding space. We train the
models by optimizing the Kullback-Leibler divergence between the distributions
induced by context-response pairs in the training data. We show that the
resultant model achieves better performance as compared to other
embedding-based approaches on publicly available conversation data.


title: On (almost) realizable subsequences of linearly recurrent sequences

abstract:
  In this note we show that if $(u_n)_{n\geqslant 1}$ is a simple linearly
recurrent sequence of integers whose minimal recurrence of order $k$ involves
only positive coefficients that has positive initial terms, then
$(Mu_{n^s})_{n\geqslant 1}$ is the sequence of periodic point counts for some
map for a suitable positive integer $M$ and $s$ any sufficiently large multiple
of $k!$. This extends a result of Moss and Ward [The Fibonacci Quarterly 60
(2022), 40-47] who proved the result for the Fibonacci sequence.


title:
A New Dataset for Topic-Based Paragraph Classification in
  Genocide-Related Court Transcripts

abstract:
  Recent progress in natural language processing has been impressive in many
different areas with transformer-based approaches setting new benchmarks for a
wide range of applications. This development has also lowered the barriers for
people outside the NLP community to tap into the tools and resources applied to
a variety of domain-specific applications. The bottleneck however still remains
the lack of annotated gold-standard collections as soon as one's research or
professional interest falls outside the scope of what is readily available. One
such area is genocide-related research (also including the work of experts who
have a professional interest in accessing, exploring and searching large-scale
document collections on the topic, such as lawyers). We present GTC (Genocide
Transcript Corpus), the first annotated corpus of genocide-related court
transcripts which serves three purposes: (1) to provide a first reference
corpus for the community, (2) to establish benchmark performances (using
state-of-the-art transformer-based approaches) for the new classification task
of paragraph identification of violence-related witness statements, (3) to
explore first steps towards transfer learning within the domain. We consider
our contribution to be addressing in particular this year's hot topic on
Language Technology for All.


title: Nonlinear dissipation induced photon blockade

abstract:
  We theoretically propose a scheme for photon blockade in a cavity quantum
electrodynamical system consisting of an N-type atomic medium interacting with
a single-mode Fabry-Perot cavity. In contrast to inefficient
nonlinear-dispersion-induced photon blockade suppressed by a large detuning,
the photon blockade in our scheme is induced by a large nonlinear dissipation
of the cavity created by the N-type atomic system. A deep photon blockade is
manifested with a vanishing equal-time second-order correlation function within
the cavity linewidth. This work provides an efficient photon blockade because
it work in the near-resonance case.


title:
There is no "Weil-"cohomology theory with real coefficients for
  arithmetic curves

abstract:
  A well known argument by Serre shows that there is no Weil cohomology theory
with real coefficients for smooth projective varieties over
$\bar{\mathbb{F}}_p$. In this note we explain why no "Weil-"cohomology theory
with real coefficients can exist for arithmetic schemes over spec $\mathbb{Z}$,
even for spectra of number rings.


title:
Strongly interacting multi-solitons for generalized Benjamin-Ono
  equations

abstract:
  We consider the generalized Benjamin-Ono equation:
$$\partial_tu+\partial_x(-|D|u+|u|^{p-1}u)=0,$$ with $L^2$-supercritical power
$p>3$ or $L^2$-subcritical power $2<p<3$. We will construct strongly
interacting multi-solitary wave of the form: $\sum_{i=1}^nQ(\cdot-t-x_i(t))$,
where $n\geq 2$, and the parameters $x_i(t)$ satisfying
$x_{i}(t)-x_{i+1}(t)\sim \alpha_k \sqrt{t}$ as $t\rightarrow +\infty$, for some
universal positive constants $\alpha_k$. We will also prove the uniqueness of
such solutions in the case of $n=2$ and $p>3$.


title:
A Novel Cloud-Based Framework for Standardised Simulations in the Latin
  American Giant Observatory (LAGO)

abstract:
  LAGO, the Latin American Giant Observatory, is an extended cosmic ray
observatory, consisting of a wide network of water Cherenkov detectors located
in 10 countries. With different altitudes and geomagnetic rigidity cutoffs,
their geographic distribution, combined with the new electronics for control,
atmospheric sensing and data acquisition, allows the realisation of diverse
astrophysics studies at a regional scale. It is an observatory designed, built
and operated by the LAGO Collaboration, a non-centralised alliance of 30
institutions from 11 countries.
  While LAGO has access to different computational frameworks, it lacks
standardised computational mechanisms to fully grasp its cooperative approach.
The European Commission is fostering initiatives aligned to LAGO objectives,
especially to enable Open Science and its long-term sustainability. This work
introduces the adaptation of LAGO to this paradigm within the EOSC-Synergy
project, focusing on the simulations of the expected astrophysical signatures
at detectors deployed at the LAGO sites around the World.


title: Uniform Anderson Localization in One-Dimensional Floquet Maps

abstract:
  Anderson localization in discrete-time quantum map dynamics shows unique
features and provides significant numerical benefits. We propose a minimal
single-particle map in one dimension with nearest-neighbor hopping only and a
gapless spectrum. We demonstrate that strong disorder in a local phase field
leads to the universality of Anderson localization within the spectrum, such
that for every possible value of quasi-energy, localization happens in the same
way. We build an exact theory for the calculation of the localization length,
$1/L_\text{loc}=\left|\ln\left(|\sin(\theta)|\right)\right|$, that is tunable
between zero and infinity by variation of a control parameter $\theta$ related
to hopping.


title:
Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News
  Dataset

abstract:
  Fake news provokes many societal problems; therefore, there has been
extensive research on fake news detection tasks to counter it. Many fake news
datasets were constructed as resources to facilitate this task. Contemporary
research focuses almost exclusively on the factuality aspect of the news.
However, this aspect alone is insufficient to explain "fake news," which is a
complex phenomenon that involves a wide range of issues. To fully understand
the nature of each instance of fake news, it is important to observe it from
various perspectives, such as the intention of the false news disseminator, the
harmfulness of the news to our society, and the target of the news. We propose
a novel annotation scheme with fine-grained labeling based on detailed
investigations of existing fake news datasets to capture these various aspects
of fake news. Using the annotation scheme, we construct and publish the first
Japanese fake news dataset. The annotation scheme is expected to provide an
in-depth understanding of fake news. We plan to build datasets for both
Japanese and other languages using our scheme. Our Japanese dataset is
published at https://hkefka385.github.io/dataset/fakenews-japanese/.


title:
A study of the F-giant star $\theta$ Scorpii A: a post-merger rapid
  rotator?

abstract:
  We report high-precision observations of the linear polarization of the
F1$\,$III star $\theta$ Scorpii. The polarization has a wavelength dependence
of the form expected for a rapid rotator, but with an amplitude several times
larger than seen in otherwise similar main-sequence stars. This confirms the
expectation that lower-gravity stars should have stronger
rotational-polarization signatures as a consequence of the density dependence
of the ratio of scattering to absorption opacities. By modelling the
polarization, together with additional observational constraints (incorporating
a revised analysis of Hipparcos astrometry, which clarifies the system's binary
status), we determine a set of precise stellar parameters, including a rotation
rate $\omega\, (= \Omega/\Omega_{\rm c})\ge 0.94$, polar gravity $\log{g_p} =
2.091 ^{+0.042}_{-0.039}$ (dex cgs), mass $3.10 ^{+0.37}_{-0.32}$ solar masses,
and luminosity $\log(L/Lsun) =3.149^{+0.041}_{-0.028}$. These values are
incompatible with evolutionary models of single rotating stars, with the star
rotating too rapidly for its evolutionary stage, and being undermassive for its
luminosity. We conclude that $\theta$ Sco A is most probably the product of a
binary merger.


title: Efficient attack sequences in m-eternal domination

abstract:
  We study the m-eternal domination problem from the perspective of the
attacker. For many graph classes, the minimum required number of guards to
defend eternally is known. By definition, if the defender has less than the
required number of guards, then there exists a sequence of attacks that ensures
the attacker's victory. Little is known about such sequences of attacks, in
particular, no bound on its length is known.
  We show that if the game is played on a tree $T$ on $n$ vertices and the
defender has less than the necessary number of guards, then the attacker can
win in at most $n$ turns. Furthermore, we present an efficient procedure that
produces such an attacking strategy.


title:
Large Eddy Simulations of Magnetized Mergers of Neutron Stars with
  Neutrinos

abstract:
  Neutron star mergers are very violent events involving extreme physical
processes: dynamic, strong-field gravity, large magnetic field, very hot, dense
matter, and the copious production of neutrinos. Accurate modeling of such a
system and its associated multi-messenger signals, such as gravitational waves,
short gamma ray burst, and kilonova, requires the inclusion of all these
processes, and is increasingly important in light of advancements in
multi-messenger astronomy generally, and in gravitational wave astronomy in
particular (such as the development of third-generation detectors). Several
general relativistic codes have been incorporating some of these elements with
different levels of realism. Here, we extend our code MHDuet, which can perform
large eddy simulations of magnetohydrodynamics to help capture the magnetic
field amplification during the merger, and to allow for realistic equations of
state and neutrino cooling via a leakage scheme. We perform several tests
involving isolated and binary neutron stars demonstrating the accuracy of the
code.


title:
Symmetric Bidirectional Quantum Teleportation using a Six-Qubit Cluster
  State as a Quantum Channel

abstract:
  Bidirectional quantum teleportation is a fundamental protocol for exchanging
quantum information between two quantum nodes. All bidirectional quantum
teleportation protocols till now have achieved a maximum efficiency of $40\%$.
Here, we propose a new scheme for symmetric bidirectional quantum teleportation
using a six-qubit cluster state as the quantum channel, for symmetric
($3\leftrightarrow3$) qubit bidirectional quantum teleportation of a special
three-qubit entangled state. The novelty of our scheme lies in its
generalization for ($N\leftrightarrow N$) qubit bidirectional quantum
teleportation employing a $2N$-qubit cluster state. The efficiency of the
proposed protocol is remarkably increased to $50\%$ which is highest till now.
Interestingly, only GHZ-state measurements and four Toffoli-gate operations are
necessary which is independent of the number $(N)$ of qubits to be teleported.


title: Baryogenesis in non-extensive Tsallis Cosmology

abstract:
  Non-extensive Tsallis thermostatistics is a widespread paradigm to describe
large-scale gravitational systems. In this work we use Tsallis Cosmology to
study thermodynamic gravity and derive modified Friedmann equations. We show
that corrections induced by non-extensivity affect the Hubble function
evolution during the radiation-dominated epoch. In turn, this leads to
non-trivial modifications of the mass density and pressure content of the
Universe, which provide a viable mechanism allowing for baryogenesis, even in
the presence of the standard interaction between the Ricci scalar and baryon
current. By demanding consistency with current observational bounds on
baryogenesis, we constrain Tsallis $\delta$ parameter to be $|\delta-1| \simeq
10^{-3}$. Based on the recently established connection between Tsallis
thermostatistics and the quantum gravitational generalization of the
uncertainty principle at Planck scale (GUP), we finally show that this bound is
in agreement with the estimation of the GUP parameter predicted by many quantum
gravity models.


title:
High-dimensional time series segmentation via factor-adjusted vector
  autoregressive modelling

abstract:
  Piecewise stationarity is a widely adopted assumption for modelling
non-stationary time series. However fitting piecewise stationary vector
autoregressive (VAR) models to high-dimensional data is challenging as the
number of parameters increases as a quadratic of the dimension. Recent
approaches to address this have imposed sparsity assumptions on the parameters
of the VAR model, but such assumptions have been shown to be inadequate when
datasets exhibit strong (auto)correlations. We propose a piecewise stationary
time series model that accounts for pervasive serial and cross-sectional
correlations through a factor structure, and only assumes that any remaining
idiosyncratic dependence between variables can be modelled by a sparse VAR
model. We propose an accompanying two-stage change point detection methodology
which fully addresses the challenges arising from not observing either the
factors or the idiosyncratic VAR process directly. Its consistency in
estimating both the total number and the locations of the change points in the
latent components, is established under conditions considerably more general
than those in the existing literature. We demonstrate the competitive
performance of the proposed methodology on simulated datasets and an
application to US blue chip stocks data.


title:
Improving Multi-task Generalization Ability for Neural Text Matching via
  Prompt Learning

abstract:
  Text matching is a fundamental technique in both information retrieval and
natural language processing. Text matching tasks share the same paradigm that
determines the relationship between two given texts. Evidently, the
relationships vary from task to task, e.g. relevance in document retrieval,
semantic alignment in paraphrase identification and answerable judgment in
question answering. However, the essential signals for text matching remain in
a finite scope, i.e. exact matching, semantic matching, and inference matching.
Recent state-of-the-art neural text matching models, e.g. pre-trained language
models (PLMs), are hard to generalize to different tasks. It is because the
end-to-end supervised learning on task-specific dataset makes model
overemphasize the data sample bias and task-specific signals instead of the
essential matching signals, which ruins the generalization of model to
different tasks. To overcome this problem, we adopt a
specialization-generalization training strategy and refer to it as
Match-Prompt. In specialization stage, descriptions of different matching tasks
are mapped to only a few prompt tokens. In generalization stage, text matching
model explores the essential matching signals by being trained on diverse
multiple matching tasks. High diverse matching tasks avoid model fitting the
data sample bias on a specific task, so that model can focus on learning the
essential matching signals. Meanwhile, the prompt tokens obtained in the first
step are added to the corresponding tasks to help the model distinguish
different task-specific matching signals. Experimental results on eighteen
public datasets show that Match-Prompt can significantly improve multi-task
generalization capability of PLMs in text matching, and yield better in-domain
multi-task, out-of-domain multi-task and new task adaptation performance than
task-specific model.


title:
Adaptive warped kernel estimation for nonparametric regression with
  circular responses

abstract:
  In this paper, we deal with nonparametric regression for circular data,
meaning that observations are represented by points lying on the unit circle.
We propose a kernel estimation procedure with data-driven selection of the
bandwidth parameter. For this purpose, we use a warping strategy combined with
a Goldenshluger-Lepski type estimator. To study optimality of our methodology,
we consider the minimax setting and prove, by establishing upper and lower
bounds, that our procedure is nearly optimal on anisotropic Holder classes of
functions for pointwise estimation. The obtained rates also reveal the specific
nature of regression for circular responses. Finally, a numerical study is
conducted, illustrating the good performances of our approach.


title: On Strong A-statistical Convergence in Probabilistic Metric Spaces

abstract:
  In this paper we study some basic properties of strong A-statistical
convergence and strong A-statistical Cauchyness of sequences in probabilistic
metric spaces not done earlier. We also study some basic properties of strong
A-statistical limit points and strong A-statistical cluster points of a
sequence in a probabilistic metric space. Further we also introduce the notion
of strong statistically A-summable sequence in a probabilistic metric space and
study its relationship with strong A-statistical convergence.


title:
Co-constructing Shared Values and Ethical Practice for the Next
  Generation: Lessons Learned from a Curriculum on Information Ethics

abstract:
  We present the motivation, design, outline, and lessons learned from an
online course in scientific integrity, research ethics, and information ethics
provided to over 2000 doctoral and engineering students in STEM fields, first
at the University Paris-Saclay, and now expanded to an online MOOC available to
students across the world, in English. Unlike a course in scientific domains,
meant to provide students with methods, tools, and concepts they can apply in
their future career, the goal of such a training is not so much to equip them,
but to make them aware of the impact of their work on society, care about the
responsibilities that befall on them, and make them realize not all share the
same opinions on how should technology imprint society. While we provide
conceptual tools, this is more to sustain interest and engage students. We want
them to debate on concrete ethical issues and realize the difficulty of
reconciling positions on contemporary dilemma such as dematerialized
intellectual property, freedom of expression online and its counterparts, the
protection of our digital selves, the management of algorithmic decision, the
control of autonomous systems, and the resolution of the digital divide. As a
bold shortcut, our course is about introducing and motivating Hegelian
dialectics in STEM curricula, usually more bent on an Aristotelian perspective.


title:
A contribution to the mathematical theory of diffraction. Part I: A note
  on double Fourier integrals

abstract:
  We consider a large class of physical fields $u$ written as double inverse
Fourier transforms of some spectral functions $F$ of two complex variables.
Such integrals occur very often in practice, especially in diffraction theory.
Our aim is to provide a closed-form far-field asymptotic expansion of $u$. In
order to do so, we need to generalise the well-established complex analysis
notion of contour indentation to integrals of functions of two complex
variables. It is done by introducing the so-called bridge and arrow notation.
Thanks to another integration surface deformation, we show that, to achieve our
aim, we only need to study a finite number of real points in the Fourier space:
the contributing points. This result is called the locality principle. We
provide an extensive set of results allowing one to decide whether a point is
contributing or not. Moreover, to each contributing point, we associate an
explicit closed-form far-field asymptotic component of $u$. We conclude the
article by validating this theory against full numerical computations for two
specific examples.


title: Breaking reciprocity by designed loss

abstract:
  In this paper we show how designed loss in open quantum systems can break
reciprocity of the state propagation, and how the non-reciprocal and even
unidirectional propagation can be achieved for different kinds of designed
loss, both linear and nonlinear ones. In particular, we show how a
unidirectional propagation can be achieved for states of certain symmetry in
linear schemes, demonstrate possibility of building a single-mode optical
insulator by combining two kinds of nonlinear designed losses, and the way to
build non-reciprocal circulator with a planar structure of dissipatively
coupled waveguides. We discuss feasibility of the considered schemes and
suggest possible realizations.


title:
Revealing directed effective connectivity of cortical neuronal networks
  from measurements

abstract:
  In the study of biological networks, one of the major challenges is to
understand the relationships between network structure and dynamics. In this
paper, we model in vitro cortical neuronal cultures as stochastic dynamical
systems and apply a method that reconstructs directed networks from dynamics
[Ching and Tam, Phys. Rev. E 95, 010301(R), 2017] to reveal directed effective
connectivity, namely the directed links and synaptic weights, of the neuronal
cultures from voltage measurements recorded by a multielectrode array. The
effective connectivity so obtained reproduces several features of cortical
regions in rats and monkeys and has similar network properties as the synaptic
network of the nematode C. elegans, the only organism whose entire nervous
system has been mapped out as of today. The distribution of the incoming degree
is bimodal and the distributions of the average incoming and outgoing synaptic
strength are non-Gaussian with long tails. The effective connectivity captures
different information from the commonly studied functional connectivity,
estimated using statistical correlation between spiking activities. The average
synaptic strengths of excitatory incoming and outgoing links are found to
increase with the spiking activity in the estimated effective connectivity but
not in the functional connectivity estimated using the same sets of voltage
measurements. These results thus demonstrate that the reconstructed effective
connectivity can capture the general properties of synaptic connections and
better reveal relationships between network structure and dynamics.


title: Good point sequencings of Steiner triple systems

abstract:
  An l-good sequencing of a Steiner triple system of order v, STS(v), is a
permutation of the points of the system such that no l consecutive points in
the permutation contains a block. It is known that every STS(v) with v > 3 has
a 3-good sequencing. It is proved that every STS(v) with v >= 13 has a 4-good
sequencing and every 3-chromatic STS(v) with v >= 15 has a 5-good sequencing.
Computational results for Steiner triple systems of small order are also given.