title:
MVP-Human Dataset for 3D Human Avatar Reconstruction from Unconstrained
  Frames

abstract:
  In this paper, we consider a novel problem of reconstructing a 3D human
avatar from multiple unconstrained frames, independent of assumptions on camera
calibration, capture space, and constrained actions. The problem should be
addressed by a framework that takes multiple unconstrained images as inputs,
and generates a shape-with-skinning avatar in the canonical space, finished in
one feed-forward pass. To this end, we present 3D Avatar Reconstruction in the
wild (ARwild), which first reconstructs the implicit skinning fields in a
multi-level manner, by which the image features from multiple images are
aligned and integrated to estimate a pixel-aligned implicit function that
represents the clothed shape. To enable the training and testing of the new
framework, we contribute a large-scale dataset, MVP-Human (Multi-View and
multi-Pose 3D Human), which contains 400 subjects, each of which has 15 scans
in different poses and 8-view images for each pose, providing 6,000 3D scans
and 48,000 images in total. Overall, benefits from the specific network
architecture and the diverse data, the trained model enables 3D avatar
reconstruction from unconstrained frames and achieves state-of-the-art
performance.


title:
Design and analysis of computer experiments with both numeral and
  distribution inputs

abstract:
  Nowadays stochastic computer simulations with both numeral and distribution
inputs are widely used to mimic complex systems which contain a great deal of
uncertainty. This paper studies the design and analysis issues of such computer
experiments. First, we provide preliminary results concerning the Wasserstein
distance in probability measure spaces. To handle the product space of the
Euclidean space and the probability measure space, we prove that, through the
mapping from a point in the Euclidean space to the mass probability measure at
this point, the Euclidean space can be isomorphic to the subset of the
probability measure space, which consists of all the mass measures, with
respect to the Wasserstein distance. Therefore, the product space can be viewed
as a product probability measure space. We derive formulas of the Wasserstein
distance between two components of this product probability measure space.
Second, we use the above results to construct Wasserstein distance-based
space-filling criteria in the product space of the Euclidean space and the
probability measure space. A class of optimal Latin hypercube-type designs in
this product space are proposed. Third, we present a Wasserstein distance-based
Gaussian process model to analyze data from computer experiments with both
numeral and distribution inputs. Numerical examples and real applications to a
metro simulation are presented to show the effectiveness of our methods.


title: Ground-state phase diagram and thermodynamics of coupled trimer chains

abstract:
  The density matrix renormalization group and quantum Monte Carlo method are
used to describe coupled trimer chains in a magnetic field h. The Hamiltonian
contains exchange terms involving the intra-trimer coupling J1 (taken as the
unit of energy) and the inter-trimer coupling J2, plus the Zeeman interaction
for a magnetic field h along the z direction. Results for the magnetization per
trimer m are calculated in regimes of positive and negative values of the ratio
J = J2 / J1, from which the rich field-induced ground state phase diagram h vs.
J is derived, with the presence of Luttinger liquid, the 1/3-plateau (m = 1/2),
and the one of fully polarized magnetization (m = 3/2). Also, the zero-field
Lanczos calculation of spin-wave dispersion from the 1/3-plateau for $S^z = 1$
is shown at the previous regimes of J values. In addition, we also report on
the decay of correlation functions of trimers along open chains, as well as the
average two-magnon distribution. The ground state is ferrimagnetic for $0<J\leq
1$, and is a singlet for $-1\leq J<0$. In the singlet phase, the spin
correlation functions along the legs present an antiferromagnetic power-law
decay, similar to the spin-1/2 linear chain, thus suggesting that the ground
state is made of three coupled antiferromagnetically oriented chains. In the
singlet phase, the dimensionless thermal magnetic susceptibility per site
normalized by 1/|J| gets closer to $1/\pi^2$ as the temperature $T\rightarrow
0$. For the ferrimagnetic phase, we fit the susceptibility to the experimental
data for the compound Pb$_3$Cu$_3$(PO$_4$)$_4$ and estimate the model exchange
couplings: J1=74.8 K and J=0.4. These values imply a range of energies for the
magnon excitations that are in accord with the data from neutron scattering
experiments on Pb$_3$Cu$_3$(PO$_4$)$_4$ for two excitation modes. The
1/3-plateau closes only at 1/|J|=0 with J<0.


title: The Integral of Secant and Stereographic Projections of Conic Sections

abstract:
  We show that the four best-known substitutions used to integrate secant rely
on different rational parametrizations of conic sections, coming from
stereographic projections. We also investigate other possible explanations for
the four substitutions, as well as connections between the buildups leading to
them.


title: M2N: Mesh Movement Networks for PDE Solvers

abstract:
  Mainstream numerical Partial Differential Equation (PDE) solvers require
discretizing the physical domain using a mesh. Mesh movement methods aim to
improve the accuracy of the numerical solution by increasing mesh resolution
where the solution is not well-resolved, whilst reducing unnecessary resolution
elsewhere. However, mesh movement methods, such as the Monge-Ampere method,
require the solution of auxiliary equations, which can be extremely expensive
especially when the mesh is adapted frequently. In this paper, we propose to
our best knowledge the first learning-based end-to-end mesh movement framework
for PDE solvers. Key requirements of learning-based mesh movement methods are
alleviating mesh tangling, boundary consistency, and generalization to mesh
with different resolutions. To achieve these goals, we introduce the neural
spline model and the graph attention network (GAT) into our models
respectively. While the Neural-Spline based model provides more flexibility for
large deformation, the GAT based model can handle domains with more complicated
shapes and is better at performing delicate local deformation. We validate our
methods on stationary and time-dependent, linear and non-linear equations, as
well as regularly and irregularly shaped domains. Compared to the traditional
Monge-Ampere method, our approach can greatly accelerate the mesh adaptation
process, whilst achieving comparable numerical error reduction.


title:
PUERT: Probabilistic Under-sampling and Explicable Reconstruction
  Network for CS-MRI

abstract:
  Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from
sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two
basic issues, i.e., where to sample and how to reconstruct. To deal with both
problems simultaneously, we propose a novel end-to-end Probabilistic
Under-sampling and Explicable Reconstruction neTwork, dubbed PUERT, to jointly
optimize the sampling pattern and the reconstruction network. Instead of
learning a deterministic mask, the proposed sampling subnet explores an optimal
probabilistic sub-sampling pattern, which describes independent Bernoulli
random variables at each possible sampling point, thus retaining robustness and
stochastics for a more reliable CS reconstruction. A dynamic gradient
estimation strategy is further introduced to gradually approximate the
binarization function in backward propagation, which efficiently preserves the
gradient information and further improves the reconstruction quality. Moreover,
in our reconstruction subnet, we adopt a model-based network design scheme with
high efficiency and interpretability, which is shown to assist in further
exploitation for the sampling subnet. Extensive experiments on two widely used
MRI datasets demonstrate that our proposed PUERT not only achieves
state-of-the-art results in terms of both quantitative metrics and visual
quality but also yields a sub-sampling pattern and a reconstruction model that
are both customized to training data.


title: Embedding Knowledge for Document Summarization: A Survey

abstract:
  Knowledge-aware methods have boosted a range of Natural Language Processing
applications over the last decades. With the gathered momentum, knowledge
recently has been pumped into enormous attention in document summarization
research. Previous works proved that knowledge-embedded document summarizers
excel at generating superior digests, especially in terms of informativeness,
coherence, and fact consistency. This paper pursues to present the first
systematic survey for the state-of-the-art methodologies that embed knowledge
into document summarizers. Particularly, we propose novel taxonomies to
recapitulate knowledge and knowledge embeddings under the document
summarization view. We further explore how embeddings are generated in learning
architectures of document summarization models, especially in deep learning
models. At last, we discuss the challenges of this topic and future directions.


title: An Empirical Study of Delayed Games on Steam

abstract:
  The gaming industry is rapidly expanding. With over 2.7 billion players
worldwide, game development has become increasingly challenging. To meet the
ever-changing demands and expectations of players and due to unforeseen
hindrances in the development process, game developers may require to delay the
release of their game. We conducted an empirical study of 23,485 games on the
Steam platform to analyze how often, and which games delayed their release
date. We find that delaying a release is common: 48% of the studied games had a
delayed initial release. Games delayed their release by a median of 14 days.
Games for which a release date range (e.g., "Q1 2019") was specified, rather
than a specific date were more likely to release within that range. Across
different game genres, the percentage of games that delay release is similar
(ranging from 48% to 52%). Finally, games with a delayed release are rated
lower than games that release on time, but the difference is negligible.


title:
RedMulE: A Compact FP16 Matrix-Multiplication Accelerator for Adaptive
  Deep Learning on RISC-V-Based Ultra-Low-Power SoCs

abstract:
  The fast proliferation of extreme-edge applications using Deep Learning (DL)
based algorithms required dedicated hardware to satisfy extreme-edge
applications' latency, throughput, and precision requirements. While inference
is achievable in practical cases, online finetuning and adaptation of general
DL models are still highly challenging. One of the key stumbling stones is the
need for parallel floating-point operations, which are considered unaffordable
on sub-100 mW extreme-edge SoCs. We tackle this problem with RedMulE
(Reduced-precision matrix Multiplication Engine), a parametric low-power
hardware accelerator for FP16 matrix multiplications - the main kernel of DL
training and inference - conceived for tight integration within a cluster of
tiny RISC-V cores based on the PULP (Parallel Ultra-Low-Power) architecture. In
22 nm technology, a 32-FMA RedMulE instance occupies just 0.07 mm^2 (14% of an
8-core RISC-V cluster) and achieves up to 666 MHz maximum operating frequency,
for a throughput of 31.6 MAC/cycle (98.8% utilization). We reach a
cluster-level power consumption of 43.5 mW and a full-cluster energy efficiency
of 688 16-bit GFLOPS/W. Overall, RedMulE features up to 4.65x higher energy
efficiency and 22x speedup over SW execution on 8 RISC-V cores.


title: Exploring Security Practices of Smart Contract Developers

abstract:
  Smart contracts are self-executing programs that run on blockchains (e.g.,
Ethereum). 680 million US dollars worth of digital assets controlled by smart
contracts have been hacked or stolen due to various security vulnerabilities in
2021. Although security is a fundamental concern for smart contracts, it is
unclear how smart contract developers approach security. To help fill this
research gap, we conducted an exploratory qualitative study consisting of a
semi-structured interview and a code review task with 29 smart contract
developers with diverse backgrounds, including 10 early stage (less than one
year of experience) and 19 experienced (2-5 years of experience) smart contract
developers.
  Our findings show a wide range of smart contract security perceptions and
practices including various tools and resources they used. Our early-stage
developer participants had a much lower success rate (15%) of identifying
security vulnerabilities in the code review task than their experienced
counterparts (55%). Our hierarchical task analysis of their code reviews
implies that just by accessing standard documentation, reference
implementations and security tools is not sufficient. Many developers checked
those materials or used a security tool but still failed to identify the
security issues. In addition, several participants pointed out shortcomings of
current smart contract security tooling such as its usability. We discuss how
future education and tools could better support developers in ensuring smart
contract security.


title: Co-citation and Co-authorship Networks of Statisticians

abstract:
  We collected and cleaned a large data set on publications in statistics. The
data set consists of the coauthor relationships and citation relationships of
83, 331 papers published in 36 representative journals in statistics,
probability, and machine learning, spanning 41 years. The data set allows us to
construct many different networks, and motivates a number of research problems
about the research patterns and trends, research impacts, and network topology
of the statistics community. In this paper we focus on (i) using the citation
relationships to estimate the research interests of authors, and (ii) using the
coauthor relationships to study the network topology.
  Using co-citation networks we constructed, we discover a "statistics
triangle", reminiscent of the statistical philosophy triangle (Efron, 1998). We
propose new approaches to constructing the "research map" of statisticians, as
well as the "research trajectory" for a given author to visualize his/her
research interest evolvement. Using co-authorship networks we constructed, we
discover a multi-layer community tree and produce a Sankey diagram to visualize
the author migrations in different sub-areas. We also propose several new
metrics for research diversity of individual authors.
  We find that "Bayes", "Biostatistics", and "Nonparametric" are three primary
areas in statistics. We also identify 15 sub-areas, each of which can be viewed
as a weighted average of the primary areas, and identify several underlying
reasons for the formation of co-authorship communities. We also find that the
research interests of statisticians have evolved significantly in the 41-year
time window we studied: some areas (e.g., biostatistics, high-dimensional data
analysis, etc.) have become increasingly more popular.


title:
Nontrivial electrophoresis of silica micro and nanorods in a nematic
  liquid crystal

abstract:
  We study DC and AC electrophoresis of silica micro and nanorods in a thin
film of a nematic liquid crystal. These particles induce virtual topological
defects and also demonstrate nontrivial electrophoresis in a nematic liquid
crystal. We measure several nonlinear electrophoretic mobility coefficients and
compare with those calculated theoretically. We demonstrate a competing effect
of the elastic and electrostatic torques that arises due to tilting of the rods
with respect to the liquid crystal director. A basic theory describing this
effect allows us to measure the effective polarizability of the rods. Our
approach is simple and applicable to a wide variety of asymmetric and
polarizable particles.


title: Failure Mode Analysis in Microsecond UV Laser Annealing of Cu Thin Films

abstract:
  The need of surface-localized thermal processing is strongly increasing
especially w.r.t three-dimensionally (3D) integrated electrical devices. UV
laser annealing (UV-LA) technology well addresses this challenge. Particularly
UV-LA can reduce resistivity by enlarging metallic grains in lines or thin
films, irradiating only the interconnects for short timescales. However, the
risk of failure in electrical performance must be correctly managed, and that
of UV-LA has not been deeply studied yet. In this work microsecond-scale UV-LA
is applied on a stack comparable to an interconnect structure
(dielectric/Cu/Ta/SiO2/Si) in either melt or sub-melt regime for grain growth.
The failure modes such as (i) Cu diffusion into SiO2, (ii) O incorporation into
Cu, and (iii) intermixing between Cu and Ta are investigated.


title:
A recursive algorithm for an efficient and accurate computation of
  incomplete Bessel functions

abstract:
  In a previous work, we developed an algorithm for the computation of
incomplete Bessel functions, which pose as a numerical challenge, based on the
$G_{n}^{(1)}$ transformation and Slevinsky-Safouhi formula for differentiation.
In the present contribution, we improve this existing algorithm for incomplete
Bessel functions by developing a recurrence relation for the numerator sequence
and the denominator sequence whose ratio forms the sequence of approximations.
By finding this recurrence relation, we reduce the complexity from ${\cal
O}(n^4)$ to ${\cal O}(n)$. We plot relative error showing that the algorithm is
capable of extremely high accuracy for incomplete Bessel functions.


title:
Density functional study of thermodynamic properties, thermal expansion
  and lattice thermal conductivity of Fe$_{2}$VAl at high temperature region

abstract:
  Here, we present the phonon calculations for thermodynamic properties,
thermal expansion and lattice thermal conductivity of Fe$_{2}$VAl in the
temperature range of $300-800$ K and compared with existing experiment. Phonon
dispersion is computed using finite displacement method and supercell approach.
The positive frequencies of all the phonon modes indicate the mechanical
stability of the compound. The specific heat at constant volume and Helmholtz
free energy are calculated under harmonic approximation, while calculation of
thermal expansion is done under quasi-harmonic approximation. Lattice thermal
conductivity ($\kappa_{L}$) is calculated using first-principle anharmonic
lattice dynamics calculations. The zero-point energy and Debye temperature are
computed as $\sim$21 kJ/mol and 638 K, respectively. The calculated thermal
expansions are found to be $\sim$6.3 $\times$ 10$^{-6}$ K$^{-1}$ and $\sim$7.2
$\times$ 10$^{-6}$ K$^{-1}$ at 300 and 800 K, respectively. A significant
deviation between calculated ($\sim$48.6 W/m-K) and experimental ($\sim$22.8
W/m-K) values of $\kappa_{L}$ are observed at 300 K. But, as the temperature
increases, the calculated and experimental $\kappa_{L}$ come closer with the
corresponding values of $\sim$18.2 W/m-K and $\sim$11.0 W/m-K at 800. The
possible reasons for the deviation of $\kappa_{L}$ are addressed. The
temperature dependent of phonon lifetime is computed in order to understand the
feature of $\kappa_{L}$. Present study suggests that DFT based phononic
calculations provide reasonably good explanations of available experimental
phonon related properties of Fe$_{2}$VAl in the high temperature range of
$300-800$ K.


title:
Kirchberg algebras with the same homotopy groups of their automorphism
  groups

abstract:
  We determine when two unital UCT Kirchberg algebras with finitely generated
K-groups have the same homotopy groups of their automorphism groups and reveal
a kind of duality between two algebras given by the Spanier--Whitehead duality
for $KK$-theory.


title: Quantum Theory of Weyl Invariant Scalar-tensor Gravity

abstract:
  We perform a manifestly covariant quantization of a Weyl invariant, i.e., a
locally scale invariant, scalar-tensor gravity in the extended de Donder gauge
condition (or harmonic gauge condition) for general coordinate invariance and a
new scalar gauge for Weyl invariance within the framework of BRST formalism. It
is shown that choral symmetry, which is a Poincar${\rm{\acute{e}}}$-like
$\IOSp(8|8)$ supersymmetry in case of Einstein gravity, is extended to a
Poincar${\rm{\acute{e}}}$-like $\IOSp(10|10)$ supersymmetry. We point out that
there is a gravitational conformal symmetry in quantum gravity and account for
how conventional conformal symmetry in a flat Minkowski space-time is related
to the gravitational conformal symmetry. Moreover, we examine the mechanism of
spontaneous symmetry breakdown of the choral symmetry, and show that the
gravitational conformal symmetry is spontaneously broken to the Poincar\'e
symmetry and the corresponding massless Nambu-Goldstone bosons are the graviton
and the dilaton. We also prove the unitarity of the physical S-matrix on the
basis of the BRST quartet mechanism.


title:
A slow blow up solution for the four dimensional energy critical semi
  linear heat equation

abstract:
  We consider the energy critical four dimensional semi-linear heat equation \[
\partial_{t}v-\Delta v-v^{3}=0, \quad(t,x)\in \mathbb{R}\times \mathbb{R}^4. \]
Formal computation of Filippas et al. (R. Soc. Lond. Proc. 2000) conjectures
the existence of a sequence of type II blow-up solutions with various blow-up
rates \[ \|v(t)\|_{L^\infty(\mathbb{R}^4)}\approx
\frac{|\log(T-t)|^{\frac{2L}{2L-1}}}{(T-t)^L} ,\quad L=1,2,\cdots.\] Schweyer
(J. Funct. Anal. 2012) rigorously constructs a type II blow-up solution for the
case $L=1$. In this paper, we show the existence of type II blow-up solution
for $L=2$. The method here could be generalized to deal with all the cases
$L\geq 2$.


title:
2D LiDAR and Camera Fusion Using Motion Cues for Indoor Layout
  Estimation

abstract:
  This paper presents a novel indoor layout estimation system based on the
fusion of 2D LiDAR and intensity camera data. A ground robot explores an indoor
space with a single floor and vertical walls, and collects a sequence of
intensity images and 2D LiDAR datasets. The LiDAR provides accurate depth
information, while the camera captures high-resolution data for semantic
interpretation. The alignment of sensor outputs and image segmentation are
computed jointly by aligning LiDAR points, as samples of the room contour, to
ground-wall boundaries in the images. The alignment problem is decoupled into a
top-down view projection and a 2D similarity transformation estimation, which
can be solved according to the vertical vanishing point and motion of two
sensors. The recursive random sample consensus algorithm is implemented to
generate, evaluate and optimize multiple hypotheses with the sequential
measurements. The system allows jointly analyzing the geometric interpretation
from different sensors without offline calibration. The ambiguity in images for
ground-wall boundary extraction is removed with the assistance of LiDAR
observations, which improves the accuracy of semantic segmentation. The
localization and mapping is refined using the fused data, which enables the
system to work reliably in scenes with low texture or low geometric features.


title:
Quantum Bohmian Inspired Potential to Model Non-Gaussian Events and the
  Application in Financial Markets

abstract:
  We have implemented quantum modeling mainly based on Bohmian Mechanics to
study time series that contain strong coupling between their events. We firstly
propose how compared to normal densities, our target time series seem to be
associated with a higher number of rare events, and Gaussian statistics tend to
underestimate these events' frequency drastically. To this end, we suggest that
by imposing Gaussian densities to the natural processes, one will seriously
neglect the existence of extreme events in many circumstances. The central
question of our study concerns the consideration of the effects of these rare
events in the corresponding probability densities and studying their role from
the point of view of quantum measurements. To model the non-Gaussian behavior
of these time-series, we utilize the multifractal random walk (MRW) approach
and control the non-Gaussianity parameter $\lambda$ accordingly. Using the
framework of quantum mechanics, we then examine the role of $\lambda$ in
quantum potentials derived for these time series. Our Bohmian quantum analysis
shows that the derived potential takes some negative values in high frequencies
(its mean values), then substantially increases, and the value drops again for
the rare events. We thus conclude that these events could generate a potential
barrier that the system, lingering in a non-Gaussian high-frequency region,
encounters, and their role becomes more prominent when it comes to transversing
this barrier. In this study, as an example of the application of quantum
potential outside of the micro-world, we compute the quantum potentials for the
S\&P financial market time series to verify the presence of rare events in the
non-Gaussian densities for this real data and remark the deviation from the
Gaussian case.


title:
Nematic fluctuations in the non-superconducting iron pnictide
  BaFe$_{1.9-x}$Ni$_{0.1}$Cr$_{x}$As$_{2}$

abstract:
  The main driven force of the electronic nematic phase in iron-based
superconductors is still under debate. Here, we report a comprehensive study on
the nematic fluctuations in a non-superconducting iron pnictide system
BaFe$_{1.9-x}$Ni$_{0.1}$Cr$_{x}$As$_{2}$ by electronic transport,
angle-resolved photoemission spectroscopy (ARPES) and inelastic neutron
scattering (INS) measurements. Previous neutron diffraction and transport
measurements suggested that the collinear antiferromagnetism persists to
$x=0.8$, with similar N\'{e}el temperature $T_N$ and structural transition
temperature $T_s$ around 32 K, but the charge carriers change from electron
type to hole type around $x=$ 0.5. In this study, we have found that the
in-plane resistivity anisotropy also highly depends on the Cr dopings and the
type of charge carriers. While ARPES measurements suggest possibly weak orbital
anisotropy onset near $T_s$ for both $x=0.05$ and $x=0.5$ compounds, INS
experiments reveal clearly different onset temperatures of low-energy spin
excitation anisotropy, which is likely related to the energy scale of spin
nematicity. These results suggest that the interplay between the local spins on
Fe atoms and the itinerant electrons on Fermi surfaces is crucial to the
nematic fluctuations of iron pnictides, where the orbital degree of freedom may
behave differently from the spin degree of freedom, and the transport
properties are intimately related to the spin dynamics.


title:
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance
  Text Classification

abstract:
  Recent works have empirically shown the effectiveness of data augmentation
(DA) in NLP tasks, especially for those suffering from data scarcity.
Intuitively, given the size of generated data, their diversity and quality are
crucial to the performance of targeted tasks. However, to the best of our
knowledge, most existing methods consider only either the diversity or the
quality of augmented data, thus cannot fully mine the potential of DA for NLP.
In this paper, we present an easy and plug-in data augmentation framework EPiDA
to support effective text classification. EPiDA employs two mechanisms:
relative entropy maximization (REM) and conditional entropy minimization (CEM)
to control data generation, where REM is designed to enhance the diversity of
augmented data while CEM is exploited to ensure their semantic consistency.
EPiDA can support efficient and continuous data generation for effective
classifier training. Extensive experiments show that EPiDA outperforms existing
SOTA methods in most cases, though not using any agent networks or pre-trained
generation networks, and it works well with various DA algorithms and
classification models. Code is available at
https://github.com/zhaominyiz/EPiDA.


title: Bounding the Effects of Continuous Treatments for Hidden Confounders

abstract:
  Causal inference involves the disentanglement of effects due to a treatment
variable from those of confounders, observed as covariates or not. Since one
outcome is ever observed at a time, the problem turns into one of predicting
counterfactuals on every individual in the dataset. Observational studies
complicate this endeavor by permitting dependencies between the treatment and
other variables in the sample. If the covariates influence the propensity of
treatment, then one suffers from covariate shift. Should the outcome and the
treatment be affected by another variable even after accounting for the
covariates, there is also hidden confounding. That is immeasurable by
definition. Rather, one must study the worst possible consequences of bounded
levels of hidden confounding on downstream decision-making. We explore this
problem in the case of continuous treatments. We develop a framework to compute
ignorance intervals on the partially identified dose-response curves, which
enable us to quantify the susceptibility of our inference to hidden
confounders. Our method is supported by simulations as well as empirical tests
based on two observational studies.


title: Earthquake Predicting and Tectonic Stress-Reducing Possibilities

abstract:
  The paper describes a theoretical model of the generation of electromagnetic
emissions detected prior to the earthquake, a scheme of the earthquake
prediction methodology, the possible methods, which are capable of simultaneous
determination of all three characteristic parameters necessary for incoming
earthquake prediction. We have suggested the possible arrangements of the
VLF/LF mobile network and offered a completely different view of seismic risk
assessing and reducing the accumulated tectonic stress in the earthquake focus
as well.


title:
Constructions of near MDS codes which are optimal locally recoverable
  codes

abstract:
  A linear code with parameters $[n,k,n-k]$ is said to be almost maximum
distance separable (AMDS for short). An AMDS code whose dual is also AMDS is
referred to as an near maximum distance separable (NMDS for short) code. NMDS
codes have nice applications in finite geometry, combinatorics, cryptography
and data storage. In this paper, we first present several constructions of NMDS
codes and determine their weight enumerators. In particular, some constructions
produce NMDS codes with the same parameters but different weight enumerators.
Then we determine the locality of the NMDS codes and obtain many families of
distance-optimal and dimension-optimal locally repairable codes.


title: Locality Sensitive Hashing for Structured Data: A Survey

abstract:
  Data similarity (or distance) computation is a fundamental research topic
which fosters a variety of similarity-based machine learning and data mining
applications. In big data analytics, it is impractical to compute the exact
similarity of data instances due to high computational cost. To this end, the
Locality Sensitive Hashing (LSH) technique has been proposed to provide
accurate estimators for various similarity measures between sets or vectors in
an efficient manner without the learning process. Structured data (e.g.,
sequences, trees and graphs), which are composed of elements and relations
between the elements, are commonly seen in the real world, but the traditional
LSH algorithms cannot preserve the structure information represented as
relations between elements. In order to conquer the issue, researchers have
been devoted to the family of the hierarchical LSH algorithms. In this paper,
we explore the present progress of the research into hierarchical LSH from the
following perspectives: 1) Data structures, where we review various
hierarchical LSH algorithms for three typical data structures and uncover their
inherent connections; 2) Applications, where we review the hierarchical LSH
algorithms in multiple application scenarios; 3) Challenges, where we discuss
some potential challenges as future directions.


title:
COVID-Net Biochem: An Explainability-driven Framework to Building
  Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19
  Patients from Clinical and Biochemistry Data

abstract:
  Ever since the declaration of COVID-19 as a pandemic by the World Health
Organization in 2020, the world has continued to struggle in controlling and
containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus.
This has been especially challenging with the rise of the Omicron variant and
its subvariants and recombinants, which has led to a significant increase in
patients seeking treatment and has put a tremendous burden on hospitals and
healthcare systems. A major challenge faced during the pandemic has been the
prediction of survival and the risk for additional injuries in individual
patients, which requires significant clinical expertise and additional
resources to avoid further complications. In this study we propose COVID-Net
Biochem, an explainability-driven framework for building machine learning
models to predict patient survival and the chance of developing kidney injury
during hospitalization from clinical and biochemistry data in a transparent and
systematic manner. In the first "clinician-guided initial design" phase, we
prepared a benchmark dataset of carefully selected clinical and biochemistry
data based on clinician assessment, which were curated from a patient cohort of
1366 patients at Stony Brook University. A collection of different machine
learning models with a diversity of gradient based boosting tree architectures
and deep transformer architectures was designed and trained specifically for
survival and kidney injury prediction based on the carefully selected clinical
and biochemical markers.


title:
Oriented Hamiltonian Cycles in Tournaments: a Proof of Rosenfeld's
  Conjecture

abstract:
  Rosenfeld in 1974 conjectured that there is an integer N > 8 such that every
tournament of order n > N contains every non-directed cycle of order n. We
prove that, with exactly 35 exceptions, every tournament of order n > 2
contains each non-directed cycle of order m, 2 < m < n+1.


title: Progressive Learning for Image Retrieval with Hybrid-Modality Queries

abstract:
  Image retrieval with hybrid-modality queries, also known as composing text
and image for image retrieval (CTI-IR), is a retrieval task where the search
intention is expressed in a more complex query format, involving both vision
and text modalities. For example, a target product image is searched using a
reference product image along with text about changing certain attributes of
the reference image as the query. It is a more challenging image retrieval task
that requires both semantic space learning and cross-modal fusion. Previous
approaches that attempt to deal with both aspects achieve unsatisfactory
performance. In this paper, we decompose the CTI-IR task into a three-stage
learning problem to progressively learn the complex knowledge for image
retrieval with hybrid-modality queries. We first leverage the semantic
embedding space for open-domain image-text retrieval, and then transfer the
learned knowledge to the fashion-domain with fashion-related pre-training
tasks. Finally, we enhance the pre-trained model from single-query to
hybrid-modality query for the CTI-IR task. Furthermore, as the contribution of
individual modality in the hybrid-modality query varies for different retrieval
scenarios, we propose a self-supervised adaptive weighting strategy to
dynamically determine the importance of image and text in the hybrid-modality
query for better retrieval. Extensive experiments show that our proposed model
significantly outperforms state-of-the-art methods in the mean of Recall@K by
24.9% and 9.5% on the Fashion-IQ and Shoes benchmark datasets respectively.


title:
String Rearrangement Inequalities and a Total Order Between Primitive
  Words

abstract:
  We study the following rearrangement problem: Given $n$ words, rearrange and
concatenate them so that the obtained string is lexicographically smallest (or
largest, respectively). We show that this problem reduces to sorting the given
words so that their repeating strings are non-decreasing (or non-increasing,
respectively), where the repeating string of a word $A$ refers to the infinite
string $AAA\ldots$. Moreover, for fixed size alphabet $\Sigma$, we design an
$O(L)$ time sorting algorithm of the words (in the mentioned orders), where $L$
denotes the total length of the input words. Hence we obtain an $O(L)$ time
algorithm for the rearrangement problem. Finally, we point out that comparing
primitive words via comparing their repeating strings leads to a total order,
which can further be extended to a total order on the finite words (or all
words).


title: Factorization de la cohomologie \'etale p-adique de la tour de Drinfeld

abstract:
  For a finite extension $F$ of ${\mathbf Q}_p$, Drinfeld defined a tower of
coverings of ${\mathbb P}^1\setminus {\mathbb P}^1(F)$ (the Drinfeld
half-plane). For $F = {\mathbf Q}_p$, we describe a decomposition of the
$p$-adic geometric \'etale cohomology of this tower analogous to Emerton's
decomposition of completed cohomology of the tower of modular curves. A crucial
ingredient is a finitness theorem for the arithmetic \'etale cohomology modulo
$p$ which is shown by first proving, via a computation of nearby cycles, that
this cohomology has finite presentation. This last result holds for all $F$;
for $F\neq {\mathbf Q}_p$, it implies that the representations of ${\rm
GL}_2(F)$ obtained from the cohomology of the Drinfeld tower are not admissible
contrary to the case $F = {\mathbf Q}_p$.


title:
Clusters of galaxies up to z=1.5 identified from photometric data of the
  Dark Energy Survey and unWISE

abstract:
  Using photometric data from the Dark Energy Survey and the Wide-field
Infrared Survey Explorer, we estimate photometric redshifts for 105 million
galaxies using the nearest-neighbour algorithm. From such a large database,
151,244 clusters of galaxies are identified in the redshift range of 0.1<z<1.5
based on the overdensity of the total stellar mass of galaxies within a given
photometric redshift slice, among which 76,826 clusters are newly identified
and 30,477 clusters have a redshift z>1. We cross-match these clusters with
those in the catalogues identified from the X-ray surveys and the
Sunyaev-Zel'dovich (SZ) effect by the Planck, South Pole Telescope and Atacama
Cosmology Telescope surveys, and get the redshifts for 45 X-ray clusters and 56
SZ clusters. More than 95% SZ clusters in the sky region have counterparts in
our catalogue. We find multiple optical clusters in the line of sight towards
about 15% of SZ clusters.


title:
RealNet: Combining Optimized Object Detection with Information Fusion
  Depth Estimation Co-Design Method on IoT

abstract:
  Depth Estimation and Object Detection Recognition play an important role in
autonomous driving technology under the guidance of deep learning artificial
intelligence. We propose a hybrid structure called RealNet: a co-design method
combining the model-streamlined recognition algorithm, the depth estimation
algorithm with information fusion, and deploying them on the Jetson-Nano for
unmanned vehicles with monocular vision sensors. We use ROS for experiment. The
method proposed in this paper is suitable for mobile platforms with high
real-time request. Innovation of our method is using information fusion to
compensate the problem of insufficient frame rate of output image, and improve
the robustness of target detection and depth estimation under monocular
vision.Object Detection is based on YOLO-v5. We have simplified the network
structure of its DarkNet53 and realized a prediction speed up to 0.01s. Depth
Estimation is based on the VNL Depth Estimation, which considers multiple
geometric constraints in 3D global space. It calculates the loss function by
calculating the deviation of the virtual normal vector VN and the label, which
can obtain deeper depth information. We use PnP fusion algorithm to solve the
problem of insufficient frame rate of depth map output. It solves the motion
estimation depth from three-dimensional target to two-dimensional point based
on corner feature matching, which is faster than VNL calculation. We
interpolate VNL output and PnP output to achieve information fusion.
Experiments show that this can effectively eliminate the jitter of depth
information and improve robustness. At the control end, this method combines
the results of target detection and depth estimation to calculate the target
position, and uses a pure tracking control algorithm to track it.


title: Correspondance de Langlands locale $p$-adique et anneaux de Kisin

abstract:
  We use a ${\mathcal B}$-adic completion and the $p$-adic local Langlands
correspondence for ${\mathrm {GL}}_2({\mathbf Q}_p )$ to give a construction of
Kisin's rings and the attached universal Galois representations (in dimension 2
and for ${\mathbf Q}_p$) directly from the classical Langlands correspondence.
This gives, in particular, a uniform proof of the geometric Breuil-M\'ezard
conjecture in the supercuspidal case.


title:
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask
  Training

abstract:
  Recent studies on the lottery ticket hypothesis (LTH) show that pre-trained
language models (PLMs) like BERT contain matching subnetworks that have similar
transfer learning performance as the original PLM. These subnetworks are found
using magnitude-based pruning. In this paper, we find that the BERT subnetworks
have even more potential than these studies have shown. Firstly, we discover
that the success of magnitude pruning can be attributed to the preserved
pre-training performance, which correlates with the downstream transferability.
Inspired by this, we propose to directly optimize the subnetwork structure
towards the pre-training objectives, which can better preserve the pre-training
performance. Specifically, we train binary masks over model weights on the
pre-training tasks, with the aim of preserving the universal transferability of
the subnetwork, which is agnostic to any specific downstream tasks. We then
fine-tune the subnetworks on the GLUE benchmark and the SQuAD dataset. The
results show that, compared with magnitude pruning, mask training can
effectively find BERT subnetworks with improved overall performance on
downstream tasks. Moreover, our method is also more efficient in searching
subnetworks and more advantageous when fine-tuning within a certain range of
data scarcity. Our code is available at https://github.com/llyx97/TAMT.


title:
A three-dimensional dynamic mode decomposition analysis of wind farm
  flow aerodynamics

abstract:
  High-fidelity large-eddy simulations are suitable to obtain insight into the
complex flow dynamics in extended wind farms. In order to better understand
these flow dynamics, we use dynamic mode decomposition (DMD) to analyze and
reconstruct the flow field in large-scale numerically simulated wind farms by
large-eddy simulations (LES). Different wind farm layouts are considered, and
we find that a combination of horizontal and vertical staggering leads to
improved wind farm performance compared to traditional horizontal staggering.
We analyze the wind farm flows using the amplitude selection (AP) and
sparsity-promoting (SP method) DMD approach. We find that the AP method tends
to select modes with a small length scale and a high frequency, while the SP
method selects large coherent structures with low frequency. The latter are
somewhat reminiscent of modes obtained using proper orthogonal decomposition
(POD). We find that a relatively limited number of SP-DMD modes is sufficient
to accurately reconstruct the flow field in the entire wind farm, whereas the
AP-DMD method requires more modes to achieve an accurate reconstruction. Thus,
the SP-DMD method has a smaller performance loss compared to the AP-DMD method
in terms of the reconstruction of the flow field.


title: Graph Neural Network-based Early Bearing Fault Detection

abstract:
  Early detection of faults is of importance to avoid catastrophic accidents
and ensure safe operation of machinery. A novel graph neural network-based
fault detection method is proposed to build a bridge between AI and real-world
running mechanical systems. First, the vibration signals, which are Euclidean
structured data, are converted into graph (non-Euclidean structured data), so
that the vibration signals, which are originally independent of each other, are
correlated with each other. Second, inputs the dataset together with its
corresponding graph into the GNN for training, which contains graphs in each
hidden layer of the network, enabling the graph neural network to learn the
feature values of itself and its neighbors, and the obtained early features
have stronger discriminability. Finally, determines the top-n objects that are
difficult to reconstruct in the output layer of the GNN as fault objects. A
public datasets of bearings have been used to verify the effectiveness of the
proposed method. We find that the proposed method can successfully detect
faulty objects that are mixed in the normal object region.


title:
Accurately measuring phase profiles of structured light in optical
  manipulation

abstract:
  Accurate phase measurement is highly desirable in optical manipulation driven
by structured light, since the phase profiles have a close connection with
optical force distribution, strongly affecting the capability of optical traps.
Here, we report a novel interferometric phase measurement technique for
accurately measuring the phase profiles of structured light in holographic
optical trapping system. The related experiments are performed, in which the
obtained phase profiles agree well with the designed phase profiles. It reveals
that the phase profiles of structured light can be accurately obtained.
Importantly, the fast switch between phase measurement and optical manipulation
experiment can be easily realized, which are strongly preferable in optical
trapping system. This study offers a promising way for accurately obtaining
phase profiles of structure light, which is very helpful for realizing optical
trapping with high performance.


title:
Phase dependent evolution within large luminosity range of 1A 0535+262
  observed by Insight-HXMT during 2020 giant outburst

abstract:
  We have performed phase-resolved spectral analysis of the accreting pulsar
1A~0535+262 based on observations of Insight-HXMT during the 2020 Type-II
outburst of the source. We focus on the two-dimensional dependence of the
cyclotron resonance scattering features (CRSFs) along the outburst time and at
different phases. The fundamental CRSF line (f-CRSF) shows different time- and
phase-dependent behaviors. At higher luminosity, the phase profile of the
f-CRSF energy changes from a single peak to double peaks, with the transition
occurring at MJD 59185. On the contrary, the first harmonic CRSF (1-st CRSF) at
$\sim$ 100 keV is only detected within a narrow phase range (0.8$-$1.0)
accompanied by a shallow f-CRSF line. Based on these results, we speculate that
when the source enters the supercritical regime, the higher accretion column
can significantly enhance the harmonic line at a narrow phase through an
"anti-pencil" beam at a higher energy band. At the same time, it will also
affect the behavior of the fundamental line.


title:
High-throughput characterization of transition metal dichalcogenide
  alloys: Thermodynamic stability and electronic band alignment

abstract:
  Alloying offers a way to tune many of the properties of the transition metal
dichalcogenide (TMD) monolayers. While these systems in many cases have been
thoroughly investigated previously, the fundamental understanding of critical
temperatures, phase diagrams and band edge alignment is still incomplete. Based
on first principles calculations and alloy cluster expansions we compute the
phase diagrams 72 TMD monolayer alloys and classify the mixing behavior. We
show that ordered phases in general are absent at room temperature but that
there exists some alloys, which have a stable Janus phase at room temperature.
Furthermore, for a subset of these alloys, we quantify the band edge bowing and
show that the band edge positions for the mixing alloys can be continuously
tuned in the range set by the boundary phases.


title:
Optimizing Task Placement and Online Scheduling for Distributed GNN
  Training Acceleration

abstract:
  Training Graph Neural Networks (GNN) on large graphs is resource-intensive
and time-consuming, mainly due to the large graph data that cannot be fit into
the memory of a single machine, but have to be fetched from distributed graph
storage and processed on the go. Unlike distributed deep neural network (DNN)
training, the bottleneck in distributed GNN training lies largely in large
graph data transmission for constructing mini-batches of training samples.
Existing solutions often advocate data-computation colocation, and do not work
well with limited resources where the colocation is infeasible. The potentials
of strategical task placement and optimal scheduling of data transmission and
task execution have not been well explored. This paper designs an efficient
algorithm framework for task placement and execution scheduling of distributed
GNN training, to better resource utilization, improve execution pipelining, and
expediting training completion. Our framework consists of two modules: (i) an
online scheduling algorithm that schedules the execution of training tasks, and
the data transmission plan; and (ii) an exploratory task placement scheme that
decides the placement of each training task. We conduct thorough theoretical
analysis, testbed experiments and simulation studies, and observe up to 67%
training speed-up with our algorithm as compared to representative baselines.


title:
Numerical methods that preserve a Lyapunov function for Ordinary
  Differential Equations

abstract:
  The paper studies numerical methods that preserve a Lyapunov function of a
dynamical system, i.e. numerical approximations whose energy decreases, just
like in the original differential equation. With this aim, a discrete gradient
method is implemented for numerical integration of a system of ordinary
differential equations. In principle, this procedure yields first order
methods, but the analysis paves the way to the design of higher-order methods.
As a case in point, the proposed method is applied to the Duffing equation
without external forcing, considering that in this case, preserving the
Lyapunov function is more important than accuracy of particular trajectories.
Results are validated by means of numerical experiments, where the discrete
gradient method is compared to standard Runge-Kutta methods. As predicted by
the theory, discrete gradient methods preserve the Lyapunov function, whereas
conventional methods fail to do so, since either periodic solutions appear or
the energy does not decrease. Besides, the discrete gradient method outperforms
conventional schemes when these do preserve the Lyapunov function, in terms of
computational cost, thus the proposed method is promising.


title:
Nevanlinna counting functions and pull-back measures on maximal ideal
  space of $H^\infty$

abstract:
  In this paper we give precise characterizations of the relation between the
Nevanlinna counting function and pull-back measure of an analytic self-map of
the unit disk near the boundary. We show that it is quite worth considering
these two concepts on the maximal ideal space of the bounded analytic
functions.


title: Lesion Localization in OCT by Semi-Supervised Object Detection

abstract:
  Over 300 million people worldwide are affected by various retinal diseases.
By noninvasive Optical Coherence Tomography (OCT) scans, a number of abnormal
structural changes in the retina, namely retinal lesions, can be identified.
Automated lesion localization in OCT is thus important for detecting retinal
diseases at their early stage. To conquer the lack of manual annotation for
deep supervised learning, this paper presents a first study on utilizing
semi-supervised object detection (SSOD) for lesion localization in OCT images.
To that end, we develop a taxonomy to provide a unified and structured
viewpoint of the current SSOD methods, and consequently identify key modules in
these methods. To evaluate the influence of these modules in the new task, we
build OCT-SS, a new dataset consisting of over 1k expert-labeled OCT B-scan
images and over 13k unlabeled B-scans. Extensive experiments on OCT-SS identify
Unbiased Teacher (UnT) as the best current SSOD method for lesion localization.
Moreover, we improve over this strong baseline, with mAP increased from 49.34
to 50.86.


title: Occupation time of a renewal process coupled to a discrete Markov chain

abstract:
  A semi-Markov process is one that changes states in accordance with a Markov
chain but takes a random amount of time between changes. We consider the
generalisation to semi-Markov processes of the classical Lamperti law for the
occupation time of a two-state Markov process. We provide an explicit
expression in Laplace space for the distribution of an arbitrary linear
combination of the occupation times in the various states of the process. We
discuss several consequences of this result. In particular, we infer the
limiting distribution of this quantity rescaled by time in the long-time
scaling regime, as well as the finite-time corrections to its moments.


title:
Bi-objective Optimization of Information Rate and Harvested Power in
  RIS-aided SWIPT Systems

abstract:
  The problem of simultaneously optimizing the information rate and the
harvested power in a reconfigurable intelligent surface (RIS)-aided
multiple-input single-output downlink wireless network with simultaneous
wireless information and power transfer (SWIPT) is addressed. The beamforming
vectors, RIS reflection coefficients, and power split ratios are jointly
optimized subject to maximum power constraints, minimum harvested power
constraints, and realistic constraints on the RIS reflection coefficients. A
practical algorithm is developed through an interplay of alternating
optimization, sequential optimization, and pricing-based methods. Numerical
results show that the deployment of RISs can significantly improve the
information rate and the amount of harvested power.


title:
Experimental Platform for Boundary Control of Mechanical
  Frenkel-Kontorova Model

abstract:
  In this paper, we present a laboratory mechatronic platform for experimental
verification and demonstration of various dynamical and control system
phenomena exhibited by the Frenkel-Kontorova (FK) model -- a spatially
discretized version of the sine-Gordon equation. The platform consists of an
array of torsionally coupled pendulums pivoting around a single shaft that can
be controlled through the motors at boundaries while all angles are read
electronically. We first introduce and describe the platform, providing details
of its mechatronic design and software architecture. All the files are freely
shared with the research community under an open-source license through a
public repository. The motivation for this sharing is to help reproducibility
or research -- the platform can be useful for others as a testbed for control
algorithms for this class of dynamical systems, for instance, distributed
control or control of flexible structures. In the second part of the paper, we
then showcase the platform using two control problem formulations tailored to
the FK model -- we discuss the practical motivation for studying these
problems, propose (some) methods for solving them and demonstrate the
functionality using experiments. In particular, the first control formulation
deals with non-collocated control/regulation of a single pendulum through one
boundary of the array in the presence of a disturbance sent from the other
boundary; the second control problem consists in synchronizing pendulums'
angular speeds. Some other motivated problems can certainly be formulated,
solved, and demonstrated using the proposed platform.


title:
Piecewise-Linear Activations or Analytic Activation Functions: Which
  Produce More Expressive Neural Networks?

abstract:
  Many currently available universal approximation theorems affirm that deep
feedforward networks defined using any suitable activation function can
approximate any integrable function locally in $L^1$-norm. Though different
approximation rates are available for deep neural networks defined using other
classes of activation functions, there is little explanation for the
empirically confirmed advantage that ReLU networks exhibit over their classical
(e.g. sigmoidal) counterparts. Our main result demonstrates that deep networks
with piecewise linear activation (e.g. ReLU or PReLU) are fundamentally more
expressive than deep feedforward networks with analytic (e.g. sigmoid, Swish,
GeLU, or Softplus). More specifically, we construct a strict refinement of the
topology on the space $L^1_{\operatorname{loc}}(\mathbb{R}^d,\mathbb{R}^D)$ of
locally Lebesgue-integrable functions, in which the set of deep ReLU networks
with (bilinear) pooling $\operatorname{NN}^{\operatorname{ReLU} +
\operatorname{Pool}}$ is dense (i.e. universal) but the set of deep feedforward
networks defined using any combination of analytic activation functions with
(or without) pooling layers $\operatorname{NN}^{\omega+\operatorname{Pool}}$ is
not dense (i.e. not universal). Our main result is further explained by
\textit{quantitatively} demonstrating that this "separation phenomenon" between
the networks in $\operatorname{NN}^{\operatorname{ReLU}+\operatorname{Pool}}$
and those in $\operatorname{NN}^{\omega+\operatorname{Pool}}$ by showing that
the networks in $\operatorname{NN}^{\operatorname{ReLU}}$ are capable of
approximate any compactly supported Lipschitz function while
\textit{simultaneously} approximating its essential support; whereas, the
networks in $\operatorname{NN}^{\omega+\operatorname{pool}}$ cannot.


title:
Improving the Naturalness of Simulated Conversations for End-to-End
  Neural Diarization

abstract:
  This paper investigates a method for simulating natural conversation in the
model training of end-to-end neural diarization (EEND). Due to the lack of any
annotated real conversational dataset, EEND is usually pretrained on a
large-scale simulated conversational dataset first and then adapted to the
target real dataset. Simulated datasets play an essential role in the training
of EEND, but as yet there has been insufficient investigation into an optimal
simulation method. We thus propose a method to simulate natural conversational
speech. In contrast to conventional methods, which simply combine the speech of
multiple speakers, our method takes turn-taking into account. We define four
types of speaker transition and sequentially arrange them to simulate natural
conversations. The dataset simulated using our method was found to be
statistically similar to the real dataset in terms of the silence and overlap
ratios. The experimental results on two-speaker diarization using the CALLHOME
and CSJ datasets showed that the simulated dataset contributes to improving the
performance of EEND.


title:
Naming the Identified Feature Implementation Blocks from Software Source
  Code

abstract:
  Identifying software identifiers that implement a particular feature of a
software product is known as feature identification. Feature identification is
one of the most critical and popular processes performed by software engineers
during software maintenance activity. However, a meaningful name must be
assigned to the Identified Feature Implementation Block (IFIB) to complete the
feature identification process. The feature naming process remains a
challenging task, where the majority of existing approaches manually assign the
name of the IFIB. In this paper, the approach called FeatureClouds was
proposed, which can be exploited by software developers to name the IFIBs from
software code. FeatureClouds approach incorporates word clouds visualization
technique to name Feature Blocks (FBs) by using the most frequent words across
these blocks. FeatureClouds had evaluated by assessing its added benefit to the
current approaches in the literature, where limited tool support was supplied
to software developers to distinguish feature names of the IFIBs. For validity,
FeatureClouds had applied to draw shapes and ArgoUML software. The findings
showed that the proposed approach achieved promising results according to
well-known metrics in terms of Precision and Recall.


title: Invariants of multi-linkoids

abstract:
  In this paper, we extend the definition of a knotoid that was introduced by
Turaev, to multi-linkoids that consist of a number of knot and knotoid
components. We study invariants of multi-linkoids that lie in a closed
orientable surface, namely the Kauffman bracket polynomial, ordered bracket
polynomial, the Kauffman skein module, and the $T$-invariant in relation with
generalized $\Theta$-graphs.


title: Continuous rational functions are deterministic regular

abstract:
  A word-to-word function is rational if it can be realized by a
non-deterministic one-way transducer. Over finite words, it is a classical
result that any rational function is regular, i.e. it can be computed by a
deterministic two-way transducer, or equivalently, by a deterministic streaming
string transducer (a one-way automaton which manipulates string registers).
  This result no longer holds for infinite words, since a non-deterministic
one-way transducer can guess, and check along its run, properties such as
infinitely many occurrences of some pattern, which is impossible for a
deterministic machine. In this paper, we identify the class of rational
functions over infinite words which are also computable by a deterministic
two-way transducer. It coincides with the class of rational functions which are
continuous, and this property can thus be decided. This solves an open question
raised in a previous paper of Dave et al.


title: Quantifying quantum computational complexity via information scrambling

abstract:
  The advent of quantum technologies brought forward much attention to the
theoretical characterization of the computational resources they provide. One
outstanding challenge to such characterization is the mathematical complexity
that their evaluation possesses. A method to quantify quantum computational
complexity is to use a class of functions called magic monotones, which are,
however, notoriously hard and impractical to evaluate. In this work, we provide
a new perspective on calculating magic monotones by connecting them to the
concept of information scrambling. Specifically, we establish a connection
between information scrambling in random quantum circuits and the magic these
circuits generate. This connection allows us to establish a novel,
experimentally scalable way to approximate magic monotones in an arbitrary
Hilbert space dimension and therefore evaluate the amount of quantum resources
using out-of-time-order correlator measurements. Furthermore, we exploit our
result connecting scrambling and magic to formulate a simple criterion to
determine chaoticity of a given Hamiltonian.


title: Asymptotic Normality of the Coefficients of the Morgan-Voyce Polynomials

abstract:
  We study arithmetic and asymptotic properties of polynomials provided by
$Q_n(x):= x \sum_{k=1}^n k \, Q_{n-k}(x)$ with initial value $Q_0(x)=1$. The
coefficients satisfy a central limit theorem and a local limit theorem
involving Fibonacci numbers. We apply methods of Berry and Esseen, Harper,
Bender, and Canfield.


title:
An ab initio approach to understand the structural, thermophysical,
  electronic, and optical properties of binary silicide SrSi2: A double Weyl
  semimetal

abstract:
  A large number of hitherto unexplored elastic, thermophysical, acoustic, and
optoelectronic properties of a double Weyl semimetal SrSi2 have been
investigated in this study. Density functional theory (DFT) based methodology
has been employed. Analyses of computed elastic parameters reveal that SrSi2 is
a mechanically stable, ductile, moderately machinable, and relatively soft
material. The compound is predicted to be dynamically stable and possesses
significant metallic bonding. Study of thermophysical properties, namely, Debye
temperature, Gr\"uneisen parameter, acoustic parameters, melting temperature,
heat capacity, thermal expansion coefficient, and dominant phonon mode is also
indicative of soft nature of SrSi2. The electronic band structure calculations
without and with spin-orbit coupling disclose semimetallic character with clear
Weyl nodes close to the Fermi level. The electronic dispersion is anisotropic
characterized by nearly flat and linear regions within the Brillouin zone.
Optical parameters at different photon energies are investigated. SrSi2 shows
excellent nonselective reflection spectrum across an extended range of energy
encompassing the visible region implying that the compound under study has
significant potential to be used as an efficient solar energy reflector. SrSi2
absorbs ultraviolet light quite efficiently. The compound also possesses high
refractive index in the low energy. All these optical features can be useful in
optoelectronic device applications.


title:
Open-domain Dialogue Generation Grounded with Dynamic Multi-form
  Knowledge Fusion

abstract:
  Open-domain multi-turn conversations normally face the challenges of how to
enrich and expand the content of the conversation. Recently, many approaches
based on external knowledge are proposed to generate rich semantic and
information conversation. Two types of knowledge have been studied for
knowledge-aware open-domain dialogue generation: structured triples from
knowledge graphs and unstructured texts from documents. To take both advantages
of abundant unstructured latent knowledge in the documents and the information
expansion capabilities of the structured knowledge graph, this paper presents a
new dialogue generation model, Dynamic Multi-form Knowledge Fusion based
Open-domain Chatt-ing Machine (DMKCM).In particular, DMKCM applies an indexed
text (a virtual Knowledge Base) to locate relevant documents as 1st hop and
then expands the content of the dialogue and its 1st hop using a commonsense
knowledge graph to get apposite triples as 2nd hop. To merge these two forms of
knowledge into the dialogue effectively, we design a dynamic virtual knowledge
selector and a controller that help to enrich and expand knowledge space.
Moreover, DMKCM adopts a novel dynamic knowledge memory module that effectively
uses historical reasoning knowledge to generate better responses. Experimental
results indicate the effectiveness of our method in terms of dialogue coherence
and informativeness.


title:
On Stute's representation for a class of smooth, possibly data-adaptive
  empirical copula processes

abstract:
  Given a random sample from a continuous multivariate distribution, Stute's
representation is obtained for empirical copula processes constructed from a
broad class of smooth, possibly data-adaptive nonparametric copula estimators.
The latter class contains for instance empirical Bernstein copulas introduced
by Sancetta and Satchell and thus the empirical beta copula proposed by Segers,
Sibuya and Tsukahara. The almost sure rate in Stute's representation is
expressed in terms of a parameter controlling the speed at which the spread of
the smoothing region decreases as the sample size increases.


title:
Post Processing Recommender Systems with Knowledge Graphs for Recency,
  Popularity, and Diversity of Explanations

abstract:
  Existing explainable recommender systems have mainly modeled relationships
between recommended and already experienced products, and shaped explanation
types accordingly (e.g., movie "x" starred by actress "y" recommended to a user
because that user watched other movies with "y" as an actress). However, none
of these systems has investigated the extent to which properties of a single
explanation (e.g., the recency of interaction with that actress) and of a group
of explanations for a recommended list (e.g., the diversity of the explanation
types) can influence the perceived explaination quality. In this paper, we
conceptualized three novel properties that model the quality of the
explanations (linking interaction recency, shared entity popularity, and
explanation type diversity) and proposed re-ranking approaches able to optimize
for these properties. Experiments on two public data sets showed that our
approaches can increase explanation quality according to the proposed
properties, fairly across demographic groups, while preserving recommendation
utility. The source code and data are available at
https://github.com/giacoballoccu/explanation-quality-recsys.


title:
Parameter and $q$ asymptotics of $\mathfrak{L}_{q}$-norms of
  hypergeometric orthogonal polynomials

abstract:
  The three canonical families of the hypergeometric orthogonal polynomials
(Hermite, Laguerre and Jacobi) control the physical wavefunctions of the bound
stationary states of a great deal of quantum systems. The algebraic
$\mathfrak{L}_{q}$-norms of these polynomials describe many physical, chemical
and information-theoretical properties of these systems, such as e.g. the
kinetic and Weizs\"acker energies, the position and momentum expectation
values, the R\'enyi and Shannon entropies and the Cram\'er-Rao, the
Fisher-Shannon and LMC measures of complexity. In this work we examine,
partially review and solve the $q$-asymptotics and the parameter asymptotics
(i.e., when the weight function's parameter tends towards infinity) of the
unweighted and weighted $\mathfrak{L}_{q}$-norms for these orthogonal
polynomials. This study has been motivated by the application of these
algebraic norms to the energetic, entropic and complexity-like properties of
the highly-excited Rydberg and high-dimensional pseudo-classical states of
harmonic (oscillator-like) and Coulomb (hydrogenic) systems, and other quantum
systems subject to central potentials of anharmonic type.


title: Regulating Group Exposure for Item Providers in Recommendation

abstract:
  Engaging all content providers, including newcomers or minority demographic
groups, is crucial for online platforms to keep growing and working. Hence,
while building recommendation services, the interests of those providers should
be valued. In this paper, we consider providers as grouped based on a common
characteristic in settings in which certain provider groups have low
representation of items in the catalog and, thus, in the user interactions.
Then, we envision a scenario wherein platform owners seek to control the degree
of exposure to such groups in the recommendation process. To support this
scenario, we rely on disparate exposure measures that characterize the gap
between the share of recommendations given to groups and the target level of
exposure pursued by the platform owners. We then propose a re-ranking procedure
that ensures desired levels of exposure are met. Experiments show that, while
supporting certain groups of providers by rendering them with the target
exposure, beyond-accuracy objectives experience significant gains with
negligible impact in recommendation utility.


title:
Log Calabi-Yau structure of projective threefolds admitting polarized
  endomorphisms

abstract:
  Let $X$ be a normal projective variety admitting a polarized endomorphism
$f$, i.e., $f^*H\sim qH$ for some ample divisor $H$ and integer $q>1$. It was
conjectured by Broustet and Gongyo that $X$ is of Calabi-Yau type, i.e.,
$(X,\Delta)$ is lc for some effective $\mathbb{Q}$-divisor such that
$K_X+\Delta\sim_{\mathbb{Q}} 0$. In this paper, we establish a general
guideline based on the equivariant minimal model program and the canonical
bundle formula. In this way, we prove the conjecture when $X$ is a smooth
projective threefold.


title: Semi-Integrated-Sensing-and-Communication (Semi-ISaC): From OMA to NOMA

abstract:
  The new concept of semi-integrated-sensing-and-communication (Semi-ISaC) is
proposed for next-generation cellular networks. We propose a novel Semi-ISaC
framework which provides more freedom as it allows that a portion of the
bandwidth is exclusively used for either wireless communication or for radar
detection, while the rest is for ISaC transmission. To enhance the bandwidth
efficiency (BE), we investigate the evolution of Semi-ISaC networks from
orthogonal multiple access (OMA) to non-orthogonal multiple access (NOMA).
First, we evaluate the performance of an OMA-based Semi-ISaC network. As for
the communication signals, we investigate both the outage probability (OP) and
the ergodic rate. As for the radar echoes, we characterize the ergodic radar
estimation information rate (REIR). Based on these metrics, we derive the
analytical and asymptotic expressions of the ergodic RIER. Then, we investigate
the performance of a NOMA-based Semi-ISaC network. More specifically, we derive
the analytical expressions of both the OP and of the ergodic rate for the
communication signals. The asymptotic expressions of OP are also derived for
quantifying the diversity gains of the communication signals. As for the radar
echoes, we derive the analytical and asymptotic ergodic REIR. The high
signal-to-noise ratio (SNR) slopes are also evaluated. The analytical results
indicate that: 1) Under a two-user NOMA Semi-ISaC scenario, the diversity order
of the near-user equals to the coefficient of the Nakagami-m fading channels
($m$), while that of the far-user is zero; and 2) The high-SNR slope for the
ergodic REIR is based on the ratio of the radar signal's duty cycle and the
pulse duration. Our simulation results show that: 1) Semi-ISaC has better
channel capacity than conventional ISaC; and 2) NOMA-based Semi-ISaC has better
channel capacity than OMA-based Semi-ISaC.


title:
Modeling Gas Flow Directions as State Variables: Does it Provide More
  Flexibility to Power Systems?

abstract:
  As a common practice, the direction of natural gas flow in every pipeline is
determined ex-ante for simplification purposes, and treated as a given
parameter within the scheduling problem. However, in integrated gas and
electric power networks with a large share of intermittent renewable power
supply, it is no longer straightforward to optimally predetermine the gas flow
directions. A wrong predetermination of gas flow directions may result in
feasible but not necessarily optimal schedules. We propose a mixed-integer
linear optimization model to determine the optimal gas flow directions while
scheduling the system. This unlocks additional flexibility to power systems,
provided that a tight coordination between power and gas systems exists. The
increased flexibility, although it comes at the cost of increased computational
complexity, is quantified by comparing the total operational cost of the entire
system with bidirectional gas flows as opposed to unidirectional gas flows. We
numerically show that modeling gas flow directions as state variables may bring
added value not only in the meshed but also in the radial gas networks.


title:
"It Feels Like Being Locked in A Cage": Understanding Blind or Low
  Vision Streamers' Perceptions of Content Curation Algorithms

abstract:
  Blind or low vision (BLV) people were recently reported to be live streamers
on the online platforms that employed content curation algorithms. Recent
research uncovered algorithm biases suppressing the content created by
marginalized populations. However, little is known about the effects of the
algorithms adopted by live streaming platforms on BLV streamers and how they,
as a marginalized population, perceive the effects of the algorithms. We
interviewed BLV streamers (N=19) of Douyin -- a popular live stream platform in
China -- to understand their perceptions of algorithms, perceived challenges,
and mitigation strategies. Our findings show the perceived factors contributing
to disadvantages under algorithmic evaluation of BLV streamers' content (e.g.,
issues with filming and timely interaction with viewers) and perceived
algorithmic suppression (e.g., content not amplified to sighted users but
suppressed within the BLV community). Their mitigation strategies (e.g., not
watching other BLV streamers' shows) tended to be passive. We discuss design
considerations to design a more inclusive and fair live streaming platform.


title:
Classical vs quantum intensity-field correlations of scattered light
  from extended cold atomic clouds

abstract:
  We calculate the intensity-field correlations in the light scattered by N
cold atoms driven by a quasi-resonant laser field. Fundamental differences
occur if the atomic state is an entangled single-excitation state or a coherent
factorized state. We provide analytic expressions for the two-time field and
intensity correlation functions for the timed Dicke state and the quasi-Bloch
state. The comparison with multi-atom simulations shows good agreement between
numerical and analytic solutions.


title:
The Generalized Degrees-of-Freedom of the Asymmetric Interference
  Channel with Delayed CSIT

abstract:
  In this paper, we investigate the generalized degrees-of-freedom (GDoF) of
the asymmetric interference channel with delayed channel state information at
the transmitter (CSIT), where each transmitter has two antennas, each receiver
has one antenna, and the strength for each interfering link can vary. The
optimal sum-GDoF is characterized by matched converse and achievability proof.
Through our results, we also reveal that in our antenna setting, the symmetric
GDoF lower bound in [Mohanty et. al, TIT 2019] can be elevated, and the
symmetric GDoF upper bound in [Mohanty et. al, TIT 2019] is tight in fact.


title: Kernel of Arithmetic Jet Spaces

abstract:
  Fix a Dedekind domain $\mathcal{O}$ and a non-zero prime $\mathfrak{p}$ in it
along with a uniformizer $\pi$. In the first part of the paper, we construct
$m$-shifted $\pi$-typical Witt vectors $W_{mn}(B)$ for any $\mathcal{O}$
algebra $B$ of length $m+n+1$. They are a generalization of the usual
$\pi$-typical Witt vectors. Along with it we construct a lift of Frobenius,
called the lateral Frobenius $\tilde{F}: W_{mn}(B) \rightarrow W_{m(n-1)}(B)$
and show that it satisfies a natural identity with the usual Frobenius map. Now
given a group scheme $G$ defined over $\mathrm{Spec}~ R$, where $R$ is an
$\mathcal{O}$-algebra with a fixed $\pi$-derivation $\delta$ on it, one
naturally considers the $n$-th arithmetic jet space $J^nG$ whose points are the
Witt ring valued points of $G$. This leads to a natural projection map of group
schemes $u: J^{m+n}G \rightarrow J^mG$. Let $N^{mn}G$ denote the kernel of $u$.
  One of our main results then prove that for $n \geq 1$, $N^{mn}G$ is
naturally isomorphic to $J^{n-1}(N^{m1}G)$ as group schemes. Hence this implies
that for any $\pi$-formal group scheme $\hat{G}$ over $\mathrm{Spf}~ R$,
$N^{mn}\hat{G}$ is isomorphic to $J^{n-1}(N^{m1}G)$. As an application, if
$\hat{G}$ is a smooth commutative $\pi$-formal group scheme of dimension $d$
and $R$ is of characteristic 0 whose ramification is bounded above by $p-2$,
then our result implies that $J^nG$ is a canonical extension of $\hat{G}$ by
$(\mathbb{W}_{n-1})^d$ where $\mathbb{W}_{n-1}$ is the $\pi$-formal group
scheme $\hat{\mathbb{A}}^n$ endowed with the group law of addition of Witt
vectors. Our results also give a geometric characterization of $G(\pi^{n+1}R)$
which is the subgroup of points of $G(R)$ that reduces to identity under the
modulo $\pi^{n+1}$ map.


title: One-Shot Domain-Adaptive Imitation Learning via Progressive Learning

abstract:
  Traditional deep learning-based visual imitation learning techniques require
a large amount of demonstration data for model training, and the pre-trained
models are difficult to adapt to new scenarios. To address these limitations,
we propose a unified framework using a novel progressive learning approach
comprised of three phases: i) a coarse learning phase for concept
representation, ii) a fine learning phase for action generation, and iii) an
imaginary learning phase for domain adaptation. Overall, this approach leads to
a one-shot domain-adaptive imitation learning framework. We use robotic pouring
task as an example to evaluate its effectiveness. Our results show that the
method has several advantages over contemporary end-to-end imitation learning
approaches, including an improved success rate for task execution and more
efficient training for deep imitation learning. In addition, the
generalizability to new domains is improved, as demonstrated here with novel
background, target container and granule combinations. We believe that the
proposed method can be broadly applicable to different industrial or domestic
applications that involve deep imitation learning for robotic manipulation,
where the target scenarios have high diversity while the human demonstration
data is limited.


title:
Wide sampling and efficient updating Monte Carlo algorithms for dimer
  models

abstract:
  Quantum dimer model is a low-energy and efficient model to study quantum spin
systems and strong-correlated physics. As a foreseeing step and without loss of
generality, we study the classical dimers on square lattice by means of Monte
Carlo method. For efficient states updating in dimer model, we introduce a
highly-efficient loop updating algorithm directed by energy criterion called
energy directed loop algorithm and improve the pocket algorithm to compare them
with the traditional directed loop algorithm. By comparisons, our energy
directed loop algorithm increases the convergent speed of Monte Carlo and
shorten the auto-correlated time in classical hard-core dimer model. Both the
improved pocket algorithm and energy path algorithm can be used in varietal
dimer models and succeed in traversing the topological sections rapidly.


title:
Critical orbits in Schwarzschild black hole with quintessence and string
  cloud background space-times

abstract:
  In this work, we have investigated the geodesic structure of the spacetime
surrounding Schwarzschild black holes with quintessence field parameter $q$ and
string cloud parameter $a$. Two horizon structures exist due to the
quintessence field. Coordinate time $t$ and proper time $\tau$ show some
interesting variations due to the parameters. we also look at orbits around
these black holes, escape velocities and the existence of critical orbits for
different values of the parameters.


title: On Finite-Time Mutual Information

abstract:
  Shannon-Hartley theorem can accurately calculate the channel capacity when
the signal observation time is infinite. However, the calculation of
finite-time mutual information, which remains unknown, is essential for guiding
the design of practical communication systems. In this paper, we investigate
the mutual information between two correlated Gaussian processes within a
finite-time observation window. We first derive the finite-time mutual
information by providing a limit expression. Then we numerically compute the
mutual information within a single finite-time window. We reveal that the
number of bits transmitted per second within the finite-time window can exceed
the mutual information averaged over the entire time axis, which is called the
exceed-average phenomenon. Furthermore, we derive a finite-time mutual
information formula under a typical signal autocorrelation case by utilizing
the Mercer expansion of trace class operators, and reveal the connection
between the finite-time mutual information problem and the operator theory.
Finally, we analytically prove the existence of the exceed-average phenomenon
in this typical case, and demonstrate its compatibility with the Shannon
capacity.


title:
The influence of phonon harmonicity on spectrally pure resonant Stokes
  fields

abstract:
  Thanks to their highly coherent emission and compact form factor, single
axial mode diamond Raman lasers have been identified as a valuable asset for
applications including integrated quantum technology, high resolution
spectroscopy or coherent optical communications. While the fundamental emission
linewidth of these lasers can be Fourier limited, their thermo-optic
characteristics lead to drifts in their carrier frequency, posing important
challenges for applications requiring ultra-stable emission. We propose here a
method for measuring accurately the temperature-dependent index of refraction
of diamond by employing standing Stokes waves produced in a monolithic
Fabry-Perot (FP) diamond Raman resonator. Our approach takes into account the
influence of the temperature on the first-order phonon line and the average
lattice phonon frequency under intense stimulated Raman scattering (SRS)
conditions. We further utilize this model to calculate the
temperature-dependent thermo-optic coefficient and the Gruneisen parameter of
diamond in the visible spectral range. The theory is accompanied by the
demonstration of tunable Fourier-limited Stokes nanosecond pulses with a
stabilized center frequency deviation of less than <4 MHz.


title:
Vortex shedding from a microsphere oscillating in superfluid ^4He at mK
  temperatures and from a laser beam moving in a Bose-Einstein condensate

abstract:
  Turbulent drag of an oscillating microsphere, that is levitating in
superfluid $^4$He at mK temperatures, is unstable slightly above a critical
velocity amplitude $v_c$. The lifetime $\tau$ of the turbulent state is
determined by the number $n$ of vortices shed per half-period. It is found that
this number is identical to the superfluid Reynolds number. The possibility of
moving a levitating sphere through superfluid $^3$He at microkelvin
temperatures is considered. A laser beam moving through a Bose-Einstein
condensate (BEC) (as observed by other authors) also produces vortices in the
BEC. In particular, in either case a linear dependence of the shedding
frequency $f_v$ on $\Delta v = v - v_c$ is observed, where $v$ is the velocity
amplitude of the sphere or the constant velocity of the laser beam above $v_c$
for the onset of turbulent flow: $f_v = a \Delta v$, where the coefficient $a$
is proportional to the oscillation frequency $ \omega $ above some
characteristic frequency $\omega_k$ and assumes a finite value for steady
motion $\omega \rightarrow 0$. A relation between the superfluid Reynolds
number and the superfluid Strouhal number is presented that is different from
classical turbulence.


title: Source-Free Domain Adaptation via Distribution Estimation

abstract:
  Domain Adaptation aims to transfer the knowledge learned from a labeled
source domain to an unlabeled target domain whose data distributions are
different. However, the training data in source domain required by most of the
existing methods is usually unavailable in real-world applications due to
privacy preserving policies. Recently, Source-Free Domain Adaptation (SFDA) has
drawn much attention, which tries to tackle domain adaptation problem without
using source data. In this work, we propose a novel framework called SFDA-DE to
address SFDA task via source Distribution Estimation. Firstly, we produce
robust pseudo-labels for target data with spherical k-means clustering, whose
initial class centers are the weight vectors (anchors) learned by the
classifier of pretrained model. Furthermore, we propose to estimate the
class-conditioned feature distribution of source domain by exploiting target
data and corresponding anchors. Finally, we sample surrogate features from the
estimated distribution, which are then utilized to align two domains by
minimizing a contrastive adaptation loss function. Extensive experiments show
that the proposed method achieves state-of-the-art performance on multiple DA
benchmarks, and even outperforms traditional DA methods which require plenty of
source data.


title: RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on

abstract:
  Virtual try-on(VTON) aims at fitting target clothes to reference person
images, which is widely adopted in e-commerce.Existing VTON approaches can be
narrowly categorized into Parser-Based(PB) and Parser-Free(PF) by whether
relying on the parser information to mask the persons' clothes and synthesize
try-on images. Although abandoning parser information has improved the
applicability of PF methods, the ability of detail synthesizing has also been
sacrificed. As a result, the distraction from original cloth may persistin
synthesized images, especially in complicated postures and high resolution
applications. To address the aforementioned issue, we propose a novel PF method
named Regional Mask Guided Network(RMGN). More specifically, a regional mask is
proposed to explicitly fuse the features of target clothes and reference
persons so that the persisted distraction can be eliminated. A posture
awareness loss and a multi-level feature extractor are further proposed to
handle the complicated postures and synthesize high resolution images.
Extensive experiments demonstrate that our proposed RMGN outperforms both
state-of-the-art PB and PF methods.Ablation studies further verify the
effectiveness ofmodules in RMGN.


title:
Direct Visualization and Manipulation of Tunable Quantum Well State in
  Semiconducting Nb2SiTe4

abstract:
  Quantum well states (QWSs) can form at the surface or interfaces of materials
with confinement potential. They have broad applications in electronic and
optical devices such as high mobility electron transistor, photodetector and
quantum well laser. The properties of the QWSs are usually the key factors for
the performance of the devices. However, direct visualization and manipulation
of such states are in general challenging. In this work, by using
angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling
microscopy/spectroscopy (STM/STS), we directly probe the QWSs generated on the
vacuum interface of a narrow band gap semiconductor Nb2SiTe4. Interestingly,
the position and splitting of QWSs could be easily manipulated via potassium
(K) dosage onto the sample surface. Our results suggest Nb2SiTe4 to be an
intriguing semiconductor system to study and engineer the QWSs, which has great
potential in device applications.


title:
Evolution of phase transition in the finite-fugacity extended dimer
  model

abstract:
  We investigate the evolution of phase transition of the classical fully
compact dimer model on the bipartite square lattice with second nearest bonds
at finite temperatures. We use the numeric Monte Carlo method with the
directed-loop algorithm to simulate the model. Our results show that the order
of the phase transition depends on the fugacity of the second nearest bonds. We
find that the phase transition reduces from the Kosterlitz-Thouless transition
to unconventional high-order phase transitions which feature the coexistence of
properties Kosterliz-Thouless transition and the first-order phases transition
simultaneously. As the fugacity increases further, phase transition evolves to
the first-order phase transition. In addition, our results of dimer-dimer
correlation functions and their corresponding structure factor functions
computed by us show the evolution of decay correlation for different fugacity.


title:
On The large Time Asymptotics of Klein-Gordon type equations with
  General Data-I

abstract:
  We study the Klein-Gordon equation with general interaction term, which may
be linear or nonlinear, and space-time dependent. The initial data is general,
large and non-radial. We prove that global solutions are asymptotically given
by a free wave and a weakly localized part. The proof is based on constructing
in a new way the Free Channel Wave Operator, and further tools from the recent
works \cite{Liu-Sof1,Liu-Sof2,SW2020,SW2022}. This work generalizes the results
of the first part of \cite{Liu-Sof1,Liu-Sof2} on the Schr\"odinger equation to
arbitrary dimension, and non-radial data.


title: The ringing of quantum corrections Schwarzschild black hole with GUP

abstract:
  Schwarzschild black holes with quantum corrections are studied under scalar
field perturbations and electromagnetic field perturbations to analyze the
effect of the correction term on the potential function and quasinormal mode
(QNM). In classical general relativity, spacetime is continuous and there is no
existence of the so-called minimal length. The introduction of the correction
items of the generalized uncertainty principle (GUP), i.e., the parameter
$\beta$, can change the singularity structure of the hole-degree gauge and may
lead to discretization in time and space. We apply the sixth-order WKB method
to approximate the QNM of Schwarzschild black holes with quantum corrections
and perform numerical analysis to derive the results of the method. Also, we
find that the effective potential and QNM in scalar fields are larger than
those in electromagnetic fields.


title:
Observation of Dimension-Crossover of a Tunable 1D Dirac Fermion in
  Topological Semimetal NbSi$_x$Te$_2$

abstract:
  Condensed matter systems in low dimensions exhibit emergent physics that does
not exist in three dimensions. When electrons are confined to one dimension
(1D), some significant electronic states appear, such as charge density wave,
spin-charge separations and Su-Schrieffer-Heeger (SSH) topological state.
However, a clear understanding of how the 1D electronic properties connects
with topology is currently lacking. Here we systematically investigated the
characteristic 1D Dirac fermion electronic structure originated from the
metallic NbTe$_2$ chains on the surface of the composition-tunable layered
compound NbSi$_x$Te$_2$ ($x$ = 0.40 and 0.43) using angle-resolved
photoemission spectroscopy. We found the Dirac fermion forms a Dirac nodal line
structure protected by the combined $\widetilde{\mathcal{M}}{\rm_y}$ and
time-reversal symmetry T and proves the NbSi$_x$Te$_2$ system as a topological
semimetal, in consistent with the ab-initio calculations. As $x$ decreases, the
interaction between adjacent NbTe2 chains increases and Dirac fermion goes
through a dimension-crossover from 1D to 2D, as evidenced by the variation of
its Fermi surface and Fermi velocity across the Brillouin zone in consistence
with a Dirac SSH model. Our findings demonstrate a tunable 1D Dirac electron
system, which offers a versatile platform for the exploration of intriguing 1D
physics and device applications.


title: Design of DIRK Schemes with High Weak Stage Order

abstract:
  Runge-Kutta (RK) methods may exhibit order reduction when applied to certain
stiff problems. While fully implicit RK schemes exist that avoid order
reduction via high-stage order, DIRK (diagonally implicit Runge-Kutta) schemes
are practically important due to their structural simplicity; however, these
cannot possess high stage order. The concept of weak stage order (WSO) can also
overcome order reduction, and it is compatible with the DIRK structure. DIRK
schemes of WSO up to 3 have been proposed in the past, however, based on a
simplified framework that cannot be extended beyond WSO 3. In this work a
general theory of WSO is employed to overcome the prior WSO barrier and to
construct practically useful high-order DIRK schemes with WSO 4 and above. The
resulting DIRK schemes are stiffly accurate, L-stable, have optimized error
coefficients, and are demonstrated to perform well on a portfolio of relevant
ODE and PDE test problems.


title: Large Hall electron mobilities in head-to-head BaTiO$_3$-domain walls

abstract:
  Strongly charged head-to-head (H2H) domain walls (DWs) that are purposely
engineered along the [110] crystallographic orientation into ferroelectric
BaTiO$_3$ single crystals have been proposed as novel 2-dimensional electron
gases (2DEGs) due to their significant domain wall conductivity (DWC). Here, we
quantify these 2DEG properties through dedicated Hall-transport measurements in
van-der-Pauw 4-point geometry at room temperature, finding the electron
mobility to reach around 400~cm$^2$(Vs)$^{-1}$, while the 2-dimensional charge
density amounts to ~7$\times$10$^3$cm$^{-2}$. We underline the necessity to
take account of thermal and geometrical-misalignment offset voltages by
evaluating the Hall resistance under magnetic-field sweeps, since otherwise
dramatic errors of several hundred percent in the derived mobility and charge
density values can occur. Apart from the specific characterization of the
conducting BaTiO$_3$ DW, we propose the method as an easy and fast way to
quantitatively characterize ferroic conducting DWs, complementary to previously
proposed scanning-probe-based Hall-potential analyses.


title: A method for solving discontinuous systems with sliding modes

abstract:
  The paper deals with systems of ordinary differential equations containing in
the right-hand side controls which are discontinuous in phase variables. These
controls cause the occurrence of sliding modes. If one uses one of the
well-known definitions of the solution of discontinuous systems, then the
motion of an object in a sliding mode can be described in terms of differential
inclusions. With the help of the previously developed apparatus for solving
differential inclusions, a method is constructed for finding the trajectories
of a system moving in a sliding mode. Since some of frequently used
discontinuous controls contain nonsmooth functions of phase variables, the
paper pays special attention to study the differential properties of such
systems. At the end of the paper controls of a slightly different, in contrast
to the classical, type are considered which have useful differential
properties, and a method is constructed for solving systems with such controls
considered both before hitting the discontinuity surface and moving in its
vicinity.


title: Curious congruences for cyclotomic polynomials

abstract:
  Let $\Phi_n^{(k)}(x)$ be the $k$-th derivative of $n$-th cyclotomic
polynomial. Extending a work of D.~H.~Lehmer, we show some curious congruences:
$2\Phi^{(3)}_n(1)$ is divisible by $\phi(n)-2$ and $\Phi^{(2k+1)}_n(1)$ is
divisible by $\phi(n)-2k$ for $k\ge 2$.