title: PCB Component Detection using Computer Vision for Hardware Assurance

abstract:
  Printed Circuit Board (PCB) assurance in the optical domain is a crucial
field of study. Though there are many existing PCB assurance methods using
image processing, computer vision (CV), and machine learning (ML), the PCB
field is complex and increasingly evolving so new techniques are required to
overcome the emerging problems. Existing ML-based methods outperform
traditional CV methods, however they often require more data, have low
explainability, and can be difficult to adapt when a new technology arises. To
overcome these challenges, CV methods can be used in tandem with ML methods. In
particular, human-interpretable CV algorithms such as those that extract color,
shape, and texture features increase PCB assurance explainability. This allows
for incorporation of prior knowledge, which effectively reduce the number of
trainable ML parameters and thus, the amount of data needed to achieve high
accuracy when training or retraining an ML model. Hence, this study explores
the benefits and limitations of a variety of common computer vision-based
features for the task of PCB component detection using semantic data. Results
of this study indicate that color features demonstrate promising performance
for PCB component detection. The purpose of this paper is to facilitate
collaboration between the hardware assurance, computer vision, and machine
learning communities.


title:
TraSeTR: Track-to-Segment Transformer with Contrastive Query for
  Instance-level Instrument Segmentation in Robotic Surgery

abstract:
  Surgical instrument segmentation -- in general a pixel classification task --
is fundamentally crucial for promoting cognitive intelligence in robot-assisted
surgery (RAS). However, previous methods are struggling with discriminating
instrument types and instances. To address the above issues, we explore a mask
classification paradigm that produces per-segment predictions. We propose
TraSeTR, a novel Track-to-Segment Transformer that wisely exploits tracking
cues to assist surgical instrument segmentation. TraSeTR jointly reasons about
the instrument type, location, and identity with instance-level predictions
i.e., a set of class-bbox-mask pairs, by decoding query embeddings.
Specifically, we introduce the prior query that encoded with previous temporal
knowledge, to transfer tracking signals to current instances via identity
matching. A contrastive query learning strategy is further applied to reshape
the query feature space, which greatly alleviates the tracking difficulty
caused by large temporal variations. The effectiveness of our method is
demonstrated with state-of-the-art instrument type segmentation results on
three public datasets, including two RAS benchmarks from EndoVis Challenges and
one cataract surgery dataset CaDIS.


title:
Consecutive topological phase transitions and colossal magnetoresistance
  in a magnetic topological semimetal

abstract:
  The combination of magnetic symmetries and electronic band topology provides
a promising route for realizing topologically nontrivial quasiparticles, and
the manipulation of magnetic structures may enable the switching between
topological phases, with the potential for achieving functional physical
properties. Here, we report measurements of the electrical resistivity of
EuCd$_2$As$_2$ under pressure, which show an intriguing insulating dome at
pressures between $p_{\rm c1}\sim1.0$~GPa and $p_{\rm c2}\sim2.0$~GPa, situated
between two regimes with metallic transport. The insulating state can be fully
suppressed by a small magnetic field, leading to a colossal negative
magnetoresistance on the order of $10^5$\%, accessible via a modest field of
$\sim0.2$~T. First-principles calculations reveal that the dramatic evolution
of the resistivity under pressure is due to consecutive transitions of
EuCd$_2$As$_2$ from a magnetic topological insulator to a trivial insulator,
and then to a Weyl semimetal, with the latter resulting from a pressure-induced
change in the magnetic ground state. Similarly, the colossal magnetoresistance
results from a field-induced polarization of the magnetic moments, transforming
EuCd$_2$As$_2$ from a trivial insulator to a Weyl semimetal. These findings
underscore weak magnetic exchange couplings and spin anisotropy as ingredients
for discovering tunable magnetic topological materials with desirable
functionalities.


title: Transformer for Graphs: An Overview from Architecture Perspective

abstract:
  Recently, Transformer model, which has achieved great success in many
artificial intelligence fields, has demonstrated its great potential in
modeling graph-structured data. Till now, a great variety of Transformers has
been proposed to adapt to the graph-structured data. However, a comprehensive
literature review and systematical evaluation of these Transformer variants for
graphs are still unavailable. It's imperative to sort out the existing
Transformer models for graphs and systematically investigate their
effectiveness on various graph tasks. In this survey, we provide a
comprehensive review of various Graph Transformer models from the architectural
design perspective. We first disassemble the existing models and conclude three
typical ways to incorporate the graph information into the vanilla Transformer:
1) GNNs as Auxiliary Modules, 2) Improved Positional Embedding from Graphs, and
3) Improved Attention Matrix from Graphs. Furthermore, we implement the
representative components in three groups and conduct a comprehensive
comparison on various kinds of famous graph data benchmarks to investigate the
real performance gain of each component. Our experiments confirm the benefits
of current graph-specific modules on Transformer and reveal their advantages on
different kinds of graph tasks.


title:
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech
  recognition

abstract:
  We propose multi-layer perceptron (MLP)-based architectures suitable for
variable length input. MLP-based architectures, recently proposed for image
classification, can only be used for inputs of a fixed, pre-defined size.
However, many types of data are naturally variable in length, for example,
acoustic signals. We propose three approaches to extend MLP-based architectures
for use with sequences of arbitrary length. The first one uses a circular
convolution applied in the Fourier domain, the second applies a depthwise
convolution, and the final relies on a shift operation. We evaluate the
proposed architectures on an automatic speech recognition task with the
Librispeech and Tedlium2 corpora. The best proposed MLP-based architectures
improves WER by 1.0 / 0.9%, 0.9 / 0.5% on Librispeech dev-clean/dev-other,
test-clean/test-other set, and 0.8 / 1.1% on Tedlium2 dev/test set using 86.4%
the size of self-attention-based architecture.


title:
Approximation of solutions to parabolic Lam\'e type operators in
  cylinder domains and Carleman's formulas for them

abstract:
  Let $s \in {\mathbb N}$, $T_1,T_2 \in {\mathbb R}$, $T_1<T_2$, and let
$\Omega, \omega $ be bounded domains in ${\mathbb R}^n$, $n \geq 1$ such that
$\omega \subset \Omega$ and the complement $\Omega \setminus \omega$ have no
non-empty compact components in
  $\Omega$. We investigate the problem of approximation of solutions to
parabolic Lam\'e type system from the Lebesgue class
  $L^2(\omega \times (T_1,T_2))$ in a cylinder domain
  $\omega \times (T_1,T_2) \subset {\mathbb R}^{n+1}$ by more regular solutions
in a bigger domain $\Omega \times (T_1,T_2)$. As an application of the obtained
approximation theorems we construct Carleman's formulas for recovering
solutions to these parabolic operators from the Sobolev class $H^{2s,s}(\Omega
\times (T_1,T_2))$ via values the solutions on a part of the lateral surface of
the cylinder and the corresponding them stress tensors.


title:
Wearable SELD dataset: Dataset for sound event localization and
  detection using wearable devices around head

abstract:
  Sound event localization and detection (SELD) is a combined task of
identifying the sound event and its direction. Deep neural networks (DNNs) are
utilized to associate them with the sound signals observed by a microphone
array. Although ambisonic microphones are popular in the literature of SELD,
they might limit the range of applications due to their predetermined geometry.
Some applications (including those for pedestrians that perform SELD while
walking) require a wearable microphone array whose geometry can be designed to
suit the task. In this paper, for the development of such a wearable SELD, we
propose a dataset named Wearable SELD dataset. It consists of data recorded by
24 microphones placed on a head and torso simulators (HATS) with some
accessories mimicking wearable devices (glasses, earphones, and headphones). We
also provide experimental results of SELD using the proposed dataset and
SELDNet to investigate the effect of microphone configuration.


title: Warm Dark Matter from Higher-Dimensional Gauge Theories

abstract:
  Warm dark matter particles with masses in the keV range have been linked with
the large group representations in gauge theories through a high number of
species at decoupling. In this paper, we address WDM fermionic degrees of
freedom from such representations. Bridging higher-dimensional particle physics
theories with cosmology studies and astrophysical observations, our approach is
two-folded, i.e., it includes realistic models from higher-dimensional
representations and constraints from simulations tested against observations.
Starting with superalgebras in exceptional periodicity theories, we discuss
several symmetry reductions and we consider several representations that
accommodate a high number of degrees of freedom. We isolate a model that
naturally accommodates both the standard model representation and the fermionic
dark matter in agreement with both large and small-scale constraints. This
model considers an intersection of branes in $D=27+3$ in a manner that provides
the degrees of freedom for the standard model on one hand and 2048 fermionic
degrees of freedom for dark matter, corresponding to a $\sim$2 keV particle
mass, on the other. In this context, we discuss the theoretical implications
and the observable predictions.


title: Dressed black holes in the new tensor-vector-scalar theory

abstract:
  As incarnations of gravity in its prime, black holes are arguably the best
target for us to demystify gravity. Keeping in mind the prominent role black
holes play in gravitational wave astronomy, it becomes a must for a theory to
possess black hole solutions with only measurable departures from their general
relativity counterparts. In this paper, we present black holes in a
tensor-vector-scalar representation of relativistic modified Newtonian
dynamics. We find that the theory allows Schwarzschild and nearly-Schwarzschild
black holes as solutions, while the nontrivial scalar and vector fields
generally diverge at the event horizon. Whether this is a physical pathology or
not poses a challenge for these solutions, and by extension, the model.
However, even if it is, this pathology could be overcome when the black hole
hair vanishes.


title: The Gene of Scientific Success

abstract:
  This paper elaborates how to identify and evaluate causal factors to improve
scientific impact. Currently, analyzing scientific impact can be beneficial to
various academic activities including funding application, mentor
recommendation, and discovering potential cooperators etc. It is universally
acknowledged that high-impact scholars often have more opportunities to receive
awards as an encouragement for their hard working. Therefore, scholars spend
great efforts in making scientific achievements and improving scientific impact
during their academic life. However, what are the determinate factors that
control scholars' academic success? The answer to this question can help
scholars conduct their research more efficiently. Under this consideration, our
paper presents and analyzes the causal factors that are crucial for scholars'
academic success. We first propose five major factors including
article-centered factors, author-centered factors, venue-centered factors,
institution-centered factors, and temporal factors. Then, we apply recent
advanced machine learning algorithms and jackknife method to assess the
importance of each causal factor. Our empirical results show that
author-centered and article-centered factors have the highest relevancy to
scholars' future success in the computer science area. Additionally, we
discover an interesting phenomenon that the h-index of scholars within the same
institution or university are actually very close to each other.


title: BCFT and Islands in Two Dimensions

abstract:
  By combining the AdS/BCFT correspondence and the brane world holography, we
expect an equivalence relation between a boundary conformal field theory (BCFT)
and a gravitational system coupled to a CFT. However, it still remains unclear
how the boundary condition of the BCFT is translated in the gravitational
system. We examine this duality relation in a two-dimensional setup by looking
at the computation of entanglement entropy and energy flux conservation. We
also identify the two-dimensional gravity which is dual to the boundary
dynamics of a BCFT. Moreover, we show that by considering a gravity solution
with scalar fields turned on, we can reproduce one point functions correctly in
the AdS/BCFT.


title: A Creativity Survey of Parallel Sorting Algorithm

abstract:
  Sorting is one of the most fundamental problems in the field of computer
science. With the rapid development of manycore processors, it shows great
importance to design efficient parallel sort algorithm on manycore
architecture. This paper studies the parallel memory sorting method on modern
hardware, and summarizes its research status and progress. Classify the
research problems, research methods and measurement methods of the target
papers and references. In the end, we summarize all the researches and list the
directions not researched and innovative places. Keywords: Sorting Algorithm,
Parallel Algorithm, Parallel Optimization, CPU, GPU, Memory Hierarchy


title:
Normal Cones Intersection Rule and Optimality Analysis for Low-Rank
  Matrix Optimization with Affine Manifolds

abstract:
  The low-rank matrix optimization with affine manifold (rank-MOA) aims to
minimize a continuously differentiable function over a low-rank set
intersecting with an affine manifold. This paper is devoted to the optimality
analysis for rank-MOA. As a cornerstone, the intersection rule of the
Fr\'{e}chet normal cone to the feasible set of the rank-MOA is established
under some mild linear independence assumptions. Aided with the resulting
explicit formulae of the underlying normal cone, the so-called F-stationary
point and the \alpha-stationary point of rank-MOA are investigated and the
relationship with local/global minimizers are then revealed in terms of
first-order optimality conditions. Furthermore, the second-order optimality
analysis, including the necessary and the sufficient conditions, is proposed
based on the second-order differentiation information of the model. All these
results will enrich the theory of low-rank matrix optimization and give
potential clues to designing efficient numerical algorithms for seeking low
rank solutions. Meanwhile, two specific applications of the rank-MOA are
discussed to illustrate our proposed optimality analysis.


title:
End-to-End Training of Both Translation Models in the Back-Translation
  Framework

abstract:
  Semi-supervised learning algorithms in neural machine translation (NMT) have
significantly improved translation quality compared to the supervised learning
algorithms by using additional monolingual corpora. Among them,
back-translation is a theoretically well-structured and cutting-edge method.
Given two pre-trained NMT models between source and target languages, one
translates a monolingual sentence as a latent sentence, and the other
reconstructs the monolingual input sentence given the latent sentence.
Therefore, previous works tried to apply the variational auto-encoder's (VAE)
training framework to the back-translation framework. However, the discrete
property of the latent sentence made it impossible to use backpropagation in
the framework. This paper proposes a categorical reparameterization trick that
generates a differentiable sentence, with which we practically implement the
VAE's training framework for the back-translation and train it by end-to-end
backpropagation. In addition, we propose several regularization techniques that
are especially advantageous to this framework. In our experiments, we
demonstrate that our method makes backpropagation available through the latent
sentences and improves the BLEU scores on the datasets of the WMT18 translation
task.


title: Insightful Mining Equilibria

abstract:
  The selfish mining attack, arguably the most famous game-theoretic attack in
blockchain, indicates that the Bitcoin protocol is not incentive-compatible.
Most subsequent works mainly focus on strengthening the selfish mining
strategy, thus enabling a single strategic agent more likely to deviate. In
sharp contrast, little attention has been paid to the resistant behavior
against the selfish mining attack, let alone further equilibrium analysis for
miners and mining pools in the blockchain as a multi-agent system.
  In this paper, first, we propose a strategy called insightful mining to
counteract selfish mining. By infiltrating an undercover miner into the selfish
pool, the insightful pool could acquire the number of its hidden blocks. We
prove that, with this extra insight, the utility of the insightful pool could
be strictly greater than the selfish pool's when they have the same mining
power. Then we investigate the mining game where all pools can either choose to
be honest or take the insightful mining strategy. We characterize the Nash
equilibrium of this mining game, and derive three corollaries: (a) each mining
game has a pure Nash equilibrium; (b) honest mining is a Nash equilibrium if
the largest mining pool has a fraction of mining power no more than 1/3; (c)
there are at most two insightful pools under equilibrium no matter how the
mining power is distributed.


title:
Absolute and "upstream" convective instabilities in plane
  Couette-Poiseuille flow

abstract:
  Here we report some interesting new features of the spatio-temporal
instability of the incompressible plane Couette-Poiseuille flow (CPF). First of
all, this flow represents the first instance of a "non-inflectional" absolute
instability, within constant-viscosity formulation, which is triggered when one
of the plates moves opposite to the bulk motion. More strikingly, with further
increase in the negative plate motion, the absolute instability ($\textrm{AI}$)
transitions to an "upstream" convective instability ($\textrm{CI}^-$), wherein
an unstable wave packet moves opposite to the direction of the bulk flow. Thus,
the CPF exhibits a unique $\textrm{CI}^+ \to \textrm{AI} \to \textrm{CI}^-$
transition, for a given Reynolds number ($Re$), where $\textrm{CI}^+$ denotes
the commonly-observed case of a "downstream" convective instability. This type
of transition has not been reported for other known examples of absolutely
unstable flows. We compute the leading and trailing edge velocities for an
amplifying wave packet and find that, for the plane Poiseuille flow, both these
velocities approach zero as $Re \to \infty$. As a result, at high $Re$, even
the slightest of negative plate motions is sufficient to trigger $\textrm{AI}$
and subsequently $\textrm{CI}^-$, as observed for the CPF. The wave-packet
dispersion first increases with $Re$, followed by a decrease, which points to a
peculiar "dual" role of viscosity in sustaining $\textrm{AI}$ in the CPF,
namely, viscosity promotes sustenance of $\textrm{AI}$ at moderate Reynolds
numbers but suppresses it at low and high Reynolds numbers. These results can
be well understood within the Ginzburg-Landau framework, and therefore can be
expected to have a wider applicability.


title:
Nonadiabatic dissociation of molecular condensates: competition between
  multi-mode reaction channels

abstract:
  We formulate and solve a model describing a nonadiabatic dissociation process
of molecular condensates. The dynamical process includes multi-mode reaction
channels, and the competition between channel parameters leads to a non-trivial
dependence on critical components such as path interference and symmetries. We
find an analytical solution under $CPT$ symmetry, where $C$ is charge
conjugation, $P$ is parity, and $T$ is time-reversal symmetry. Our solution
predicts a population imbalance between atomic modes that is exponentially
sensitive to channel parameters. However, a weakly broken symmetry alters the
population at each atomic mode and can reverse the population imbalance. Our
solution also demonstrates a strong quantum correlation between atomic modes
that leads to the spontaneous production of atoms in a multi-mode squeezed
state. Moreover, our results are applicable in solving time-dependent
PT-symmetric quantum mechanics, which naturally manifests in the Heisenberg
picture.


title:
An analysis of citing and referencing habits across all scholarly
  disciplines: approaches and trends in bibliographic metadata errors

abstract:
  Considering citing and referencing as facilitators of scientific
communication and information retrieving; as providers of metadata which
support searches in bibliographic catalogs and, as facets of descriptive
representation, the discussion expands previous approaches concerning errors in
bibliographic metadata, based on the analysis of reference elements, i.e.
bibliographic references, mentions, quotations, and respective in-text
reference pointers, from current articles of journals across the 27 subject
areas composing SCImago Journal & Country Rank. The findings pointed that
bibliographic errors have perpetuated for decades and, insofar as information
supports other than printed documents become more popular among the scientific
community, bibliographic errors and their possible causes have increased,
despite the encouraged use of technological facilities, i.e. the reference
managers. The results also suggested a trend of distancing among representative
description facets, i.e., citing, referencing and cataloging, since the
revision being carried within the representative description, which culminated
on the publication of the conceptual models FRBR and IFLA-lRM, do not consider
citing and referencing matters.


title: Acoustic Event Detection with Classifier Chains

abstract:
  This paper proposes acoustic event detection (AED) with classifier chains, a
new classifier based on the probabilistic chain rule. The proposed AED with
classifier chains consists of a gated recurrent unit and performs iterative
binary detection of each event one by one. In each iteration, the event's
activity is estimated and used to condition the next output based on the
probabilistic chain rule to form classifier chains. Therefore, the proposed
method can handle the interdependence among events upon classification, while
the conventional AED methods with multiple binary classifiers with a linear
layer and sigmoid function have placed an assumption of conditional
independence. In the experiments with a real-recording dataset, the proposed
method demonstrates its superior AED performance to a relative 14.80%
improvement compared to a convolutional recurrent neural network baseline
system with the multiple binary classifiers.


title:
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth
  Completion and Grasping

abstract:
  Transparent objects are common in our daily life and frequently handled in
the automated production line. Robust vision-based robotic grasping and
manipulation for these objects would be beneficial for automation. However, the
majority of current grasping algorithms would fail in this case since they
heavily rely on the depth image, while ordinary depth sensors usually fail to
produce accurate depth information for transparent objects owing to the
reflection and refraction of light. In this work, we address this issue by
contributing a large-scale real-world dataset for transparent object depth
completion, which contains 57,715 RGB-D images from 130 different scenes. Our
dataset is the first large-scale real-world dataset and provides the most
comprehensive annotation. Cross-domain experiments show that our dataset has a
great generalization ability. Moreover, we propose an end-to-end depth
completion network, which takes the RGB image and the inaccurate depth map as
inputs and outputs a refined depth map. Experiments demonstrate superior
efficacy, efficiency and robustness of our method over previous works, and it
is able to process images of high resolutions under limited hardware resources.
Real robot experiment shows that our method can also be applied to novel object
grasping robustly. The full dataset and our method are publicly available at
www.graspnet.net/transcg.


title: Full-Span Log-Linear Model and Fast Learning Algorithm

abstract:
  The full-span log-linear(FSLL) model introduced in this paper is considered
an $n$-th order Boltzmann machine, where $n$ is the number of all variables in
the target system. Let $X=(X_0,...,X_{n-1})$ be finite discrete random
variables that can take $|X|=|X_0|...|X_{n-1}|$ different values. The FSLL
model has $|X|-1$ parameters and can represent arbitrary positive distributions
of $X$. The FSLL model is a "highest-order" Boltzmann machine; nevertheless, we
can compute the dual parameters of the model distribution, which plays
important roles in exponential families, in $O(|X|\log|X|)$ time. Furthermore,
using properties of the dual parameters of the FSLL model, we can construct an
efficient learning algorithm. The FSLL model is limited to small probabilistic
models up to $|X|\approx2^{25}$; however, in this problem domain, the FSLL
model flexibly fits various true distributions underlying the training data
without any hyperparameter tuning. The experiments presented that the FSLL
successfully learned six training datasets such that $|X|=2^{20}$ within one
minute with a laptop PC.


title:
A comprehensive study on how to construct local bases in deep
  variational quantum eigensolver for molecular systems

abstract:
  Current quantum computers are limited in the number of qubits and coherence
time, constraining the algorithms executable with sufficient fidelity.
Variational quantum eigensolver (VQE) is an algorithm to find an approximate
ground state of a quantum system and expected to work on even such a device.
The deep VQE [K. Fujii, et al., arXiv:2007.10917] is an extension of the
original VQE algorithm, which takes a divide-and-conquer approach to relax the
hardware requirement. While the deep VQE is successfully applied for spin
models and periodic material, its validity on a molecule, where the Hamiltonian
is highly non-local in the qubit basis, is still unexplored. Here, we discuss
the performance of the deep VQE algorithm applied to quantum chemistry
problems. Specifically, we examine different subspace forming methods and
compare their accuracy and complexity on a ten H-atom tree-like molecule as
well as a 13 H-atom version. Additionally, we propose multiple methods to lower
the number of qubits required to calculate the ground state of a molecule. We
find that the deep VQE can simulate the electron-correlation energy of the
ground-state to an error of below 1%, thus helping us to reach chemical
accuracy in some cases. The accuracy differences and qubits reduction
highlights the basis creation method's impact on the deep VQE.


title: Non-Autoregressive ASR with Self-Conditioned Folded Encoders

abstract:
  This paper proposes CTC-based non-autoregressive ASR with self-conditioned
folded encoders. The proposed method realizes non-autoregressive ASR with fewer
parameters by folding the conventional stack of encoders into only two blocks;
base encoders and folded encoders. The base encoders convert the input audio
features into a neural representation suitable for recognition. This is
followed by the folded encoders applied repeatedly for further refinement.
Applying the CTC loss to the outputs of all encoders enforces the consistency
of the input-output relationship. Thus, folded encoders learn to perform the
same operations as an encoder with deeper distinct layers. In experiments, we
investigate how to set the number of layers and the number of iterations for
the base and folded encoders. The results show that the proposed method
achieves a performance comparable to that of the conventional method using only
38% as many parameters. Furthermore, it outperforms the conventional method
when increasing the number of iterations.


title: Selected Topics in Analytic Conformal Bootstrap: A Guided Journey

abstract:
  This review aims to offer a pedagogical introduction to the analytic
conformal bootstrap program via a journey through selected topics. We review
analytic methods which include the large spin perturbation theory, Mellin space
methods and the Lorentzian inversion formula. These techniques are applied to a
variety of topics ranging from large-N theories, to the epsilon expansion and
holographic superconformal correlators, and are demonstrated in a large number
of explicit examples.


title:
High Precision QED Initial State Corrections for $e^+ e^- \rightarrow
  \gamma^*/Z^*$ Annihilation

abstract:
  The precise knowledge of the QED initial state corrections is of instrumental
importance in studying high luminosity measurements in $e^+e^-$ annihilation at
facilities like LEP, the International Linear Collider ILC, CLIC, a Giga-$Z$
facility, and the planned FCC\_ee. Logarithmic corrections of up to $O(\alpha^6
L^5)$ are necessary with various subleading terms taken into account. This
applies to both the inclusive measurement of processes like $e^+ e^-
\rightarrow \gamma^*/Z^*$ and also the forward-backward asymmetry. As has been
shown recently, techniques from massive QCD, such as the computation of massive
on-shell operator matrix elements, can be used for these calculations. We give
an introduction to this topic and present both the calculation methods and the
numerical corrections having been reached so far.


title: A Method for Decrypting Data Infected with Hive Ransomware

abstract:
  Among the many types of malicious codes, ransomware poses a major threat.
Ransomware encrypts data and demands a ransom in exchange for decryption. As
data recovery is impossible if the encryption key is not obtained, some
companies suffer from considerable damage, such as the payment of huge amounts
of money or the loss of important data. In this paper, we analyzed Hive
ransomware, which appeared in June 2021. Hive ransomware has caused immense
harm, leading the FBI to issue an alert about it. To minimize the damage caused
by Hive Ransomware and to help victims recover their files, we analyzed Hive
Ransomware and studied recovery methods. By analyzing the encryption process of
Hive ransomware, we confirmed that vulnerabilities exist by using their own
encryption algorithm. We have recovered the master key for generating the file
encryption key partially, to enable the decryption of data encrypted by Hive
ransomware. We recovered 95% of the master key without the attacker's RSA
private key and decrypted the actual infected data. To the best of our
knowledge, this is the first successful attempt at decrypting Hive ransomware.
It is expected that our method can be used to reduce the damage caused by Hive
ransomware.


title:
Recurrent X-ray flares of the black hole candidate in the globular
  cluster RZ 2109 in NGC 4472

abstract:
  We report on the systematic analysis of the X-ray observations of the
ultra-luminous X-ray source XMMU J122939.7+075333 located in the globular
cluster RZ 2109 in the Virgo galaxy NGC 4472. The inclusion of observations and
time intervals ignored in previous works and the careful selection of
extraction regions and energy bands have allowed us to identify new flaring
episodes, in addition to the ones that made it one of the best black hole
candidates in globular clusters. Although most observations are too short and
sparse to recognize a regular pattern, the spacing of the three most recent
X-ray flares is compatible with a ~34 hours recurrence time. If confirmed by
future observations, such behavior, together with the soft spectrum of the
X-ray flares, would be strikingly similar to the quasi-periodic eruptions
recently discovered in galactic nuclei. Following one of the possible
interpretations of these systems and of a peculiar class of extra-galactic
X-ray transients, we explore the possibility that XMMU J122939.7+075333 might
be powered by the partial disruption of a white dwarf by an intermediate mass
(M~700 Msun) black hole.


title: Revisiting the Evaluation Metrics of Paraphrase Generation

abstract:
  Paraphrase generation is an important NLP task that has achieved significant
progress recently. However, one crucial problem is overlooked, `how to evaluate
the quality of paraphrase?'. Most existing paraphrase generation models use
reference-based metrics (e.g., BLEU) from neural machine translation (NMT) to
evaluate their generated paraphrase. Such metrics' reliability is hardly
evaluated, and they are only plausible when there exists a standard reference.
Therefore, this paper first answers one fundamental question, `Are existing
metrics reliable for paraphrase generation?'. We present two conclusions that
disobey conventional wisdom in paraphrasing generation: (1) existing metrics
poorly align with human annotation in system-level and segment-level paraphrase
evaluation. (2) reference-free metrics outperform reference-based metrics,
indicating that the standard references are unnecessary to evaluate the
paraphrase's quality. Such empirical findings expose a lack of reliable
automatic evaluation metrics. Therefore, this paper proposes BBScore, a
reference-free metric that can reflect the generated paraphrase's quality.
BBScore consists of two sub-metrics: S3C score and SelfBLEU, which correspond
to two criteria for paraphrase evaluation: semantic preservation and diversity.
By connecting two sub-metrics, BBScore significantly outperforms existing
paraphrase evaluation metrics.


title:
Structural and Semantic Contrastive Learning for Self-supervised Node
  Representation Learning

abstract:
  Graph Contrastive Learning (GCL) recently has drawn much research interest
for learning generalizable, transferable, and robust node representations in a
self-supervised fashion. In general, the contrastive learning process in GCL is
performed on top of the representations learned by a graph neural network (GNN)
backbone, which transforms and propagates the node contextual information based
on its local neighborhoods. However, existing GCL efforts have severe
limitations in terms of both encoding architecture, augmentation, and
contrastive objective, making them commonly inefficient and ineffective to use
in different datasets. In this work, we go beyond the existing unsupervised GCL
counterparts and address their limitations by proposing a simple yet effective
framework S$^3$-CL. Specifically, by virtue of the proposed structural and
semantic contrastive learning, even a simple neural network is able to learn
expressive node representations that preserve valuable structural and semantic
patterns. Our experiments demonstrate that the node representations learned by
S$^3$-CL achieve superior performance on different downstream tasks compared to
the state-of-the-art GCL methods.


title: Second-order topological insulator in periodically driven lattice

abstract:
  The higher-order topological insulator (HOTI) is a new type of topological
system which has special bulkedge correspondence compared with conventional
topological insulators. In this work, we propose a scheme to realize Floquet
HOTI in ultracold atom systems. With the combination of periodically
spin-dependent driving of the superlattices and a next-next-nearest-neighbor
d-wave-like anisotropic coupling term between different spin components, a
Floquet second-order topological insulator with four zero-energy corner states
emerges, whose Wannier bands are gapless and exhibit interesting bulk topology.
Furthermore, the anisotropic coupling with nearest-neighbor form will also
induce some intriguing topological phenomena, e.g. non-topologically protected
corner states and topological semimetal for two different types of lattice
structures respectively. Our scheme may give insight into the construction of
different types of higher-order topological insulators in synthetic systems. It
also provides an experimentally feasible platform to research the relations
between different types of topological states and may have a wide range of
applications in future.


title:
Online Scheduling of Time-Critical Tasks to Minimize the Number of
  Calibrations

abstract:
  We study the online scheduling problem where the machines need to be
calibrated before processing any jobs. To calibrate a machine, it will take
$\lambda$ time steps as the activation time, and then the machine will remain
calibrated status for $T$ time steps. The job can only be processed by the
machine that is in calibrated status. Given a set of jobs arriving online, each
of the jobs is characterized by a release time, a processing time, and a
deadline. We assume that there is an infinite number of machines for usage. The
objective is to minimize the total number of calibrations while feasibly
scheduling all jobs.
  For the case that all jobs have unit processing times, we propose an
$\mathcal{O}(\lambda)$-competitive algorithm, which is asymptotically optimal.
When $\lambda=0$, the problem is degraded to rent minimization, where our
algorithm achieves a competitive ratio of $3e+7(\approx 15.16)$ which improves
upon the previous results for such problems.


title:
Doping control of magnetic anisotropy for stable antiskyrmion formation
  in schreibersite (Fe,Ni)$_3$P with $S_4$ symmetry

abstract:
  Magnetic skyrmions, vortex-like topological spin textures, have attracted
much interest in a wide range of research fields from fundamental physics to
spintronics applications. Recently, growing attention has also been paid to
antiskyrmions emerging in opposite topological charge in non-centrosymmetric
magnets with $D_{2\mathrm{d}}$ or $S_4$ symmetry. In these magnets, complex
interplay among anisotropic Dzyaloshinskii-Moriya interaction, uniaxial
magnetic anisotropy, and magnetic dipolar interactions generates a variety of
magnetic structures. However, the relation between the stability of
antiskyrmions and these magnetic interactions remains to be elucidated. In this
work, we control the uniaxial magnetic anisotropy of schreibersite (Fe,Ni)$_3$P
with $S_4$ symmetry by doping and investigate its impact on the stability of
antiskyrmions. Our magnetometry study, supported by ferromagnetic resonance
spectroscopy, shows that the variation of the Ni content and slight doping with
4$d$ transition metals considerably change the magnetic anisotropy. In
particular, doping with Pd induces easy-axis anisotropy, giving rise to
formation of antiskyrmions, while a temperature-induced spin reorientation is
observed in a Rh-doped compound. In combination with Lorentz transmission
electron microscopy and micromagnetic simulations, we quantitatively analyze
the stability of antiskyrmion as functions of uniaxial anisotropy and
demagnetization energy, and demonstrate that subtle balance between them is
necessary to stabilize the antiskyrmions.


title: Interior ideal in regular and intra regular semigroup

abstract:
  Following G.Szasz [2] a subsemigroup I of semigroup S is called an interior
ideal if SIS \subset I. In this paper we explore the classes of regular
semigroup and its different subclasses by their interior ideals. Furthermore,
we introduce the strongly prime, prime, semiprime, strongly irreducible, and
irreducible interior ideals of semigroups and also characterize those
semigroups for which each interior ideal is strongly prime. Some important
interplay between the classes of all interior ideals and other ideals are given
here. In addition to this, we present different characterizations of semigroups
by their minimal interior ideals.


title: Multi-Objective Model Selection for Time Series Forecasting

abstract:
  Research on time series forecasting has predominantly focused on developing
methods that improve accuracy. However, other criteria such as training time or
latency are critical in many real-world applications. We therefore address the
question of how to choose an appropriate forecasting model for a given dataset
among the plethora of available forecasting methods when accuracy is only one
of many criteria. For this, our contributions are two-fold. First, we present a
comprehensive benchmark, evaluating 7 classical and 6 deep learning forecasting
methods on 44 heterogeneous, publicly available datasets. The benchmark code is
open-sourced along with evaluations and forecasts for all methods. These
evaluations enable us to answer open questions such as the amount of data
required for deep learning models to outperform classical ones. Second, we
leverage the benchmark evaluations to learn good defaults that consider
multiple objectives such as accuracy and latency. By learning a mapping from
forecasting models to performance metrics, we show that our method PARETOSELECT
is able to accurately select models from the Pareto front -- alleviating the
need to train or evaluate many forecasting models for model selection. To the
best of our knowledge, PARETOSELECT constitutes the first method to learn
default models in a multi-objective setting.


title:
Energy-Efficient UAV Communications: A Generalised Propulsion Energy
  Consumption Model

abstract:
  This paper proposes a generalised propulsion energy consumption model (PECM)
for rotary-wing ummanned aerial vehicles (UAVs) under the consideration of the
practical thrust-to-weight ratio (TWR) with respect to the velocity,
acceleration and direction change of the UAVs. To verify the effectiveness of
the proposed PECM, we consider a UAV-enabled communication system, where a
rotary-wing UAV serves multiple ground users as an aerial base station. We aim
to maximize the energy efficiency (EE) of the UAV by jointly optimizing the
user scheduling and UAV trajectory variables. However, the formulated problem
is a non-convex fractional integer programming problem, which is challenging to
obtain its optimal solution. To tackle this, we propose an efficient iterative
algorithm by decomposing the original problem into two sub-problems to obtain a
suboptimal solution based on the successive convex approximation technique.
Simulation results show that the optimized UAV trajectory by applying the
proposed PECM are smoother and the corresponding EE has significant improvement
as compared to other benchmark schemes.


title: LiDAR-Inertial 3D SLAM with Plane Constraint for Multi-story Building

abstract:
  The ubiquitous planes and structural consistency are the most apparent
features of indoor multi-story Buildings compared with outdoor environments. In
this paper, we propose a tightly coupled LiDAR-Inertial 3D SLAM framework with
plane features for the multi-story building. The framework we proposed is
mainly composed of three parts: tightly coupled LiDAR-Inertial odometry,
extraction of representative planes of the structure, and factor graph
optimization. By building a local map and inertial measurement unit (IMU)
pre-integration, we get LiDAR scan-to-local-map matching and IMU measurements,
respectively. Minimize the joint cost function to obtain the LiDAR-Inertial
odometry information. Once a new keyframe is added to the graph, all the planes
of this keyframe that can represent structural features are extracted to find
the constraint between different poses and stories. A keyframe-based factor
graph is conducted with the constraint of planes, and LiDAR-Inertial odometry
for keyframe poses refinement. The experimental results show that our algorithm
has outstanding performance in accuracy compared with the state-of-the-art
algorithms.


title:
Adaptive Model Refinement Approach for Bayesian Uncertainty
  Quantification in Turbulence Model

abstract:
  The Bayesian uncertainty quantification technique has become well established
in turbulence modeling over the past few years. However, it is computationally
expensive to construct a globally accurate surrogate model for Bayesian
inference in a high-dimensional design space, which limits uncertainty
quantification for complex flow configurations. Borrowing ideas from stratified
sampling and inherited sampling, an adaptive model refinement approach is
proposed in this work, which concentrates on asymptotically improving the local
accuracy of the surrogate model in the high-posterior-density region by
adaptively appending model evaluation points. To achieve this goal, a
modification of inherited Latin hypercube sampling is proposed and then
integrated into the Bayesian framework. The effectiveness and efficiency of the
proposed approach are demonstrated through a two-dimensional heat source
inversion problem and its extension to a high-dimensional design space.
Compared with the prior-based method, the adaptive model refinement approach
has the ability to obtain more reliable inference results using fewer
evaluation points. Finally, the approach is applied to parametric uncertainty
quantification of the Menter shear-stress transport turbulence model for an
axisymmetric transonic bump flow and provides convincing numerical results.


title: A Faster Interior-Point Method for Sum-of-Squares Optimization

abstract:
  We present a faster interior-point method for optimizing sum-of-squares (SOS)
polynomials, which are a central tool in polynomial optimization and capture
convex programming in the Lasserre hierarchy. Let $p = \sum_i q^2_i$ be an
$n$-variate SOS polynomial of degree $2d$. Denoting by $L := \binom{n+d}{d}$
and $U := \binom{n+2d}{2d}$ the dimensions of the vector spaces in which
$q_i$'s and $p$ live respectively, our algorithm runs in time
$\tilde{O}(LU^{1.87})$. This is polynomially faster than state-of-art SOS and
semidefinite programming solvers, which achieve runtime
$\tilde{O}(L^{0.5}\min\{U^{2.37}, L^{4.24}\})$.
  The centerpiece of our algorithm is a dynamic data structure for maintaining
the inverse of the Hessian of the SOS barrier function under the polynomial
interpolant basis, which efficiently extends to multivariate SOS optimization,
and requires maintaining spectral approximations to low-rank perturbations of
elementwise (Hadamard) products. This is the main challenge and departure from
recent IPM breakthroughs using inverse-maintenance, where low-rank updates to
the slack matrix readily imply the same for the Hessian matrix.


title:
Dynamic Object Comprehension: A Framework For Evaluating Artificial
  Visual Perception

abstract:
  Augmented and Mixed Reality are emerging as likely successors to the mobile
internet. However, many technical challenges remain. One of the key
requirements of these systems is the ability to create a continuity between
physical and virtual worlds, with the user's visual perception as the primary
interface medium. Building this continuity requires the system to develop a
visual understanding of the physical world. While there has been significant
recent progress in computer vision and AI techniques such as image
classification and object detection, success in these areas has not yet led to
the visual perception required for these critical MR and AR applications. A
significant issue is that current evaluation criteria are insufficient for
these applications. To motivate and evaluate progress in this emerging area,
there is a need for new metrics. In this paper we outline limitations of
current evaluation criteria and propose new criteria.


title: A Three-component Model for Cosmic-ray Spectrum and Dipole Anisotropy

abstract:
  Using a three-component, multi-scale diffusion model, we show that the
cosmic-ray (CR) proton and helium spectra and the dipole anisotropy can be
explained with reasonable parameters. The model includes a nearby source
associated with the supernova remnant (SNR) that gave rise to the Geminga
pulsar, a source at the Galactic center, and a component associated with the
Galactic disk. The CR flux below TeV is dominated by the disk component. The
center source with a continuous injection of CRs starting about 18 Myr ago is
needed to explain the anisotropy above 100 TeV. With the assumption of
universal CR spectra injected by all SNRs, the nearby source can produce a TeV
spectral bump observed at Earth via slow diffusion across the interstellar
magnetic field, which needs to have an angle $ \theta \approx 5^{\circ} $
between the field line and the line of sight toward the source, and have weak
magnetic turbulence with the Alfv\'{e}n Mach number $ M_{\text{A}}\approx 0.1
$. Considering the modulation of the Galactic-scale anisotropy by this magnetic
field, in a quasi-local approach the field may be directed at a right ascension
about $ -90^{\circ} $ and a declination about $ -7.4^{\circ} $ in the
equatorial coordinate system.


title:
Feels Bad Man: Dissecting Automated Hateful Meme Detection Through the
  Lens of Facebook's Challenge

abstract:
  Internet memes have become a dominant method of communication; at the same
time, however, they are also increasingly being used to advocate extremism and
foster derogatory beliefs. Nonetheless, we do not have a firm understanding as
to which perceptual aspects of memes cause this phenomenon. In this work, we
assess the efficacy of current state-of-the-art multimodal machine learning
models toward hateful meme detection, and in particular with respect to their
generalizability across platforms. We use two benchmark datasets comprising
12,140 and 10,567 images from 4chan's "Politically Incorrect" board (/pol/) and
Facebook's Hateful Memes Challenge dataset to train the competition's
top-ranking machine learning models for the discovery of the most prominent
features that distinguish viral hateful memes from benign ones. We conduct
three experiments to determine the importance of multimodality on
classification performance, the influential capacity of fringe Web communities
on mainstream social platforms and vice versa, and the models' learning
transferability on 4chan memes. Our experiments show that memes' image
characteristics provide a greater wealth of information than its textual
content. We also find that current systems developed for online detection of
hate speech in memes necessitate further concentration on its visual elements
to improve their interpretation of underlying cultural connotations, implying
that multimodal models fail to adequately grasp the intricacies of hate speech
in memes and generalize across social media platforms.


title:
Half-Quantized Hall Effect and Power Law Decay of Edge Current
  Distribution

abstract:
  The half-quantized Hall conductance is characteristic of quantum systems with
parity anomaly. Here we investigate topological and transport properties of a
class of parity anomalous semimetals, in which massive Dirac fermions coexist
with massless Dirac fermions in momentum space or real space, and uncovered a
distinct bulk-edge correspondence that the half-quantized Hall effect is
realized via the bulk massless Dirac fermions while the nontrivial Berry
curvature is provided by the massive Dirac fermions. The spatial distribution
of the edge current decays away from the boundary in a power law instead of an
exponential law in integer quantum Hall effect. We further address physical
relevance of parity anomalous semimetal to three-dimensional semi-magnetic
topological insulators and two-dimensional photonic crystals.


title: Learning continuous models for continuous physics

abstract:
  Dynamical systems that evolve continuously over time are ubiquitous
throughout science and engineering. Machine learning (ML) provides data-driven
approaches to model and predict the dynamics of such systems. A core issue with
this approach is that ML models are typically trained on discrete data, using
ML methodologies that are not aware of underlying continuity properties, which
results in models that often do not capture the underlying continuous dynamics
of a system of interest. As a result, these ML models are of limited use for
for many scientific and engineering applications. To address this challenge, we
develop a convergence test based on numerical analysis theory. Our test
verifies whether a model has learned a function that accurately approximates a
system's underlying continuous dynamics. Models that fail this test fail to
capture relevant dynamics, rendering them of limited utility for many
scientific prediction tasks; while models that pass this test enable both
better interpolation and better extrapolation in multiple ways. Our results
illustrate how principled numerical analysis methods can be coupled with
existing ML training/testing methodologies to validate models for science and
engineering applications.


title:
Predict the Rover Mobility over Soft Terrain using Articulated Wheeled
  Bevameter

abstract:
  Robot mobility is critical for mission success, especially in soft or
deformable terrains, where the complex wheel-soil interaction mechanics often
leads to excessive wheel slip and sinkage, causing the eventual mission
failure. To improve the success rate, online mobility prediction using vision,
infrared imaging, or model-based stochastic methods have been used in the
literature. This paper proposes an on-board mobility prediction approach using
an articulated wheeled bevameter that consists of a force-controlled arm and an
instrumented bevameter (with force and vision sensors) as its end-effector. The
proposed bevameter, which emulates the traditional terramechanics tests such as
pressure-sinkage and shear experiments, can measure contact parameters ahead of
the rover's body in real-time, and predict the slip and sinkage of supporting
wheels over the probed region. Based on the predicted mobility, the rover can
select a safer path in order to avoid dangerous regions such as those covered
with quicksand. Compared to the literature, our proposed method can avoid the
complicated terramechanics modeling and time-consuming stochastic prediction;
it can also mitigate the inaccuracy issues arising in non-contact vision-based
methods. We also conduct multiple experiments to validate the proposed
approach.


title:
A fine-grained, versatile index of remoteness to characterize
  place-level rurality

abstract:
  Rural-urban classifications are essential for analyzing geographic,
demographic, environmental, or socioeconomic processes across the rural-urban
continuum. However, existing county-level classifications may ignore the
within-county variations of rurality, which can be problematic if the scale of
interest is at the place-level or finer. Moreover, existing rural-urban
classification are often inconsistent over time and thus, impede the long-term
analysis of rural-urban dynamics. We developed a distance-based method to
generate place-level remoteness estimates based on simple input data. We create
our remoteness index based on place-level population data for the U.S. from
1980 to 2010. The proposed index is generalizable to data-scarce environments
and earlier time periods and is based on the distances of a given place to the
nearest places of different population sizes, and allows for fine-grained,
temporally consistent analyses of rural-urban processes.


title: The Development and Prospect of Code Clone

abstract:
  The application of code clone technology accelerates code search, improves
code reuse efficiency, and assists in software quality assessment and code
vulnerability detection. However, the application of code clones also
introduces software quality issues and increases the cost of software
maintenance. As an important research field in software engineering, code clone
has been extensively explored and studied by researchers, and related studies
on various sub-research fields have emerged, including code clone detection,
code clone evolution, code clone analysis, etc. However, there lacks a
comprehensive exploration of the entire field of code clone, as well as an
analysis of the trend of each sub-research field. This paper collects related
work of code clones in the past ten years. In summary, the contributions of
this paper mainly include: (1) summarize and classify the sub-research fields
of code clone, and explore the relative popularity and relation of these
sub-research fields; (2) analyze the overall research trend of code clone and
each sub-research field; (3) compare and analyze the difference between academy
and industry regarding code clone research; (4) construct a network of
researchers, and excavate the major contributors in code clone research field;
(5) The list of popular conferences and journals was statistically analyzed.
The popular research directions in the future include clone visualization,
clone management, etc. For the clone detection technique, researchers can
optimize the scalability and execution efficiency of the method, targeting
particular clone detection tasks and contextual environments, or apply the
technology to other related research fields continuously.


title:
Mirror-Yolo: An attention-based instance segmentation and detection
  model for mirrors

abstract:
  Mirrors can degrade the performance of computer vision models, however to
accurately detect mirrors in images remains challenging. YOLOv4 achieves
phenomenal results both in object detection accuracy and speed, nevertheless
the model often fails in detecting mirrors. In this paper, a novel mirror
detection method `Mirror-YOLO' is proposed, which mainly targets on mirror
detection. Based on YOLOv4, the proposed model embeds an attention mechanism
for better feature acquisition, and a hypercolumn-stairstep approach for
feature map fusion. Mirror-YOLO can also produce accurate bounding polygons for
instance segmentation. The effectiveness of our proposed model is demonstrated
by our experiments, compared to the existing mirror detection methods, the
proposed Mirror-YOLO achieves better performance in detection accuracy on the
mirror image dataset.


title: The Complexity of SPEs in Mean-payoff Games

abstract:
  We establish that the subgame perfect equilibrium (SPE) threshold problem for
mean-payoff games is NP-complete. While the SPE threshold problem was recently
shown to be decidable (in doubly exponential time) and NP-hard, its exact worst
case complexity was left open.


title: Causal inference with recurrent and competing events

abstract:
  Many research questions concern treatment effects on outcomes that can recur
several times in the same individual. For example, medical researchers are
interested in treatment effects on hospitalizations in heart failure patients
and sports injuries in athletes. Competing events, such as death, complicate
causal inference in studies of recurrent events because once a competing event
occurs, an individual cannot have more recurrent events. Several statistical
estimands have been studied in recurrent event settings, with and without
competing events. However, the causal interpretations of these estimands, and
the conditions that are required to identify these estimands from observed
data, have yet to be formalized. Here we use a counterfactual framework for
causal inference to formulate several causal estimands in recurrent event
settings, with and without competing events. When competing events exist, we
clarify when commonly used classical statistical estimands can be interpreted
as causal quantities from the causal mediation literature, such as (controlled)
direct effects and total effects. Furthermore, we show that recent results on
interventionist mediation estimands allow us to define new causal estimands
with recurrent and competing events that may be of particular clinical
relevance in many subject matter settings. We use causal directed acyclic
graphs and single world intervention graphs to illustrate how to reason about
identification conditions for the various causal estimands using subject matter
knowledge. Furthermore, using results on counting processes, we show how our
causal estimands and their identification conditions, which are articulated in
discrete time, converge to classical continuous-time counterparts in the limit
of fine discretizations of time. Finally, we propose several estimators and
establish their consistency for the various identifying functionals.


title:
Tracking the Mn diffusion in the carbon-supported nanoparticles through
  the collaborative analysis of atom probe and evaporation simulation

abstract:
  Carbon-supported nanoparticles have been used widely as efficient catalysts
due to their enhanced surface-to-volume ratio. To investigate their
structure-property relationships, acquiring 3D elemental distribution is highly
required. Here, the carbon-supported Pt, PtMn alloy, and ordered Pt3Mn
nanoparticles are synthesized and analyzed with atom probe tomography as model
systems. The significant difference of Mn distribution after the heat-treatment
was found. Finally, the field evaporation behavior of the carbon support was
discussed and each acquired reconstruction was compared with computational
results from the evaporation simulation. This paper provides a guideline for
studies using atom probe tomography on the heterogeneous carbon-nanoparticle
system that leads to insights toward to a wide application in carbon-supported
nano-catalysts.


title: CLS: Cross Labeling Supervision for Semi-Supervised Learning

abstract:
  It is well known that the success of deep neural networks is greatly
attributed to large-scale labeled datasets. However, it can be extremely
time-consuming and laborious to collect sufficient high-quality labeled data in
most practical applications. Semi-supervised learning (SSL) provides an
effective solution to reduce the cost of labeling by simultaneously leveraging
both labeled and unlabeled data. In this work, we present Cross Labeling
Supervision (CLS), a framework that generalizes the typical pseudo-labeling
process. Based on FixMatch, where a pseudo label is generated from a
weakly-augmented sample to teach the prediction on a strong augmentation of the
same input sample, CLS allows the creation of both pseudo and complementary
labels to support both positive and negative learning. To mitigate the
confirmation bias of self-labeling and boost the tolerance to false labels, two
different initialized networks with the same structure are trained
simultaneously. Each network utilizes high-confidence labels from the other
network as additional supervision signals. During the label generation phase,
adaptive sample weights are assigned to artificial labels according to their
prediction confidence. The sample weight plays two roles: quantify the
generated labels' quality and reduce the disruption of inaccurate labels on
network training. Experimental results on the semi-supervised classification
task show that our framework outperforms existing approaches by large margins
on the CIFAR-10 and CIFAR-100 datasets.


title:
Early exclusion leads to cyclical cooperation in repeated group
  interactions

abstract:
  Explaining the emergence and maintenance of cooperation among selfish
individuals from an evolutionary perspective remains a grand challenge in
biology, economy, and social sciences. Social exclusion is believed to be an
answer to this conundrum. However, previously related studies often assume
one-shot interactions and ignore how free-riding is identified, which seem to
be too idealistic. In this work, we consider repeated interactions where
excluders need to pay a monitoring cost to identify free-riders for exclusion
and free-riders cannot participate in the following possible game interactions
once they are identified and excluded by excluders in the repeated interaction
process. We reveal that the introduction of such exclusion can prevent the
breakdown of cooperation in repeated group interactions. In particular, we
demonstrate that an evolutionary oscillation among cooperators, defectors, and
excluders can appear in infinitely large populations when early exclusion is
implemented. In addition, we find that the population spends most of the time
in states where cooperators dominate for early exclusion when stochastic
mutation-selection is considered in finite populations. Our results highlight
that early exclusion is successful in solving the mentioned enigma of
cooperation in repeated group interactions.


title:
Finding Representative Sampling Subsets in Sensor Graphs using Time
  Series Similarities

abstract:
  With the increasing use of IoT-enabled sensors, it is important to have
effective methods for querying the sensors. For example, in a dense network of
battery-driven temperature sensors, it is often possible to query (sample) just
a subset of the sensors at any given time, since the values of the non-sampled
sensors can be estimated from the sampled values. If we can divide the set of
sensors into disjoint so-called representative sampling subsets that each
represent the other sensors sufficiently well, we can alternate the sampling
between the sampling subsets and thus, increase battery life significantly. In
this paper, we formulate the problem of finding representative sampling subsets
as a graph problem on a so-called sensor graph with the sensors as nodes. Our
proposed solution, SubGraphSample, consists of two phases. In Phase-I, we
create edges in the sensor graph based on the similarities between the time
series of sensor values, analyzing six different techniques based on proven
time series similarity metrics. In Phase-II, we propose two new techniques and
extend four existing ones to find the maximal number of representative sampling
subsets. Finally, we propose AutoSubGraphSample which auto-selects the best
technique for Phase-I and Phase-II for a given dataset. Our extensive
experimental evaluation shows that our approach can yield significant battery
life improvements within realistic error bounds.


title:
Schedule-based Analysis of Transmission Risk in Public Transportation
  Systems

abstract:
  Airborne diseases, including COVID-19, raise the question of transmission
risk in public transportation systems. However, quantitative analysis of the
effectiveness of transmission risk mitigation methods in public transportation
is lacking. The paper develops a transmission risk modeling framework based on
the Wells-Riley model using as inputs transit operating characteristics,
schedule, Origin-Destination (OD) demand, and virus characteristics. The model
is sensitive to various factors that operators can control, as well as external
factors that may be subject of broader policy decisions (e.g. mask wearing).
The model is utilized to assess transmission risk as a function of OD flows,
planned operations, and factors such as mask-wearing, ventilation, and
infection rates. Using actual data from the Massachusetts Bay Transportation
Authority (MBTA) Red Line, the paper explores the transmission risk under
different infection rate scenarios, both in magnitude and spatial
characteristics. The paper assesses the combined impact from viral load related
factors and passenger load factors. Increasing frequency can mitigate
transmission risk, but cannot fully compensate for increases in infection
rates. Imbalanced passenger distribution on different cars of a train is shown
to increase the overall system-wide infection probability. Spatial infection
rate patterns should also be taken into account during policymaking as it is
shown to impact transmission risk. For lines with branches, demand distribution
among the branches is important and headway allocation adjustment among
branches to balance the load on trains to different branches can help reduce
risk.

categories: cs.SI cs.SY eess.SY physics.soc-ph stat.AP


title:
CSCNet: Contextual Semantic Consistency Network for Trajectory
  Prediction in Crowded Spaces

abstract:
  Trajectory prediction aims to predict the movement trend of the agents like
pedestrians, bikers, vehicles. It is helpful to analyze and understand human
activities in crowded spaces and widely applied in many areas such as
surveillance video analysis and autonomous driving systems. Thanks to the
success of deep learning, trajectory prediction has made significant progress.
The current methods are dedicated to studying the agents' future trajectories
under the social interaction and the sceneries' physical constraints. Moreover,
how to deal with these factors still catches researchers' attention. However,
they ignore the \textbf{Semantic Shift Phenomenon} when modeling these
interactions in various prediction sceneries. There exist several kinds of
semantic deviations inner or between social and physical interactions, which we
call the "\textbf{Gap}". In this paper, we propose a \textbf{C}ontextual
\textbf{S}emantic \textbf{C}onsistency \textbf{Net}work (\textbf{CSCNet}) to
predict agents' future activities with powerful and efficient context
constraints. We utilize a well-designed context-aware transfer to obtain the
intermediate representations from the scene images and trajectories. Then we
eliminate the differences between social and physical interactions by aligning
activity semantics and scene semantics to cross the Gap. Experiments
demonstrate that CSCNet performs better than most of the current methods
quantitatively and qualitatively.


title: Soliton asymptotics for the KdV shock problem of low regularity

abstract:
  We revisit the asymptotic analysis of the KdV shock problem in the soliton
region. Our approach is based on the analysis of the associated Riemann-Hilbert
problem and we extend the domain of validity of the asymptotic formulas while
at the same time requiring less decay and smoothness for the initial data.


title:
Dirac dark matter, dark radiation and type-II seesaw in alternative
  $U(1)_X$ standard model

abstract:
  We propose an extra $U(1)_X$ model with an alternative charge assignment for
right-handed (RH) neutrinos. The type-II seesaw mechanism by a triplet Higgs
field is promising for neutrino mass generation because of the alternative
charge assignment. The small vacuum expectation value (VEV) of an additional
Higgs doublet naturally leads to a very small VEV of the triplet Higgs field,
and as a result, the smallness of neutrino mass can be understood. With the
minimal Higgs field for $U(1)_X$ with the charge $1$, RH neutrinos are
candidates for Dirac dark matter (DM) and dark radiation (DR). We have derived
and imposed the LHC bound, the DR constraint and the bound from DM direct
searches in the wide range of parameter space. Among various $U(1)_X$ choices,
the DM direct search bound is found to be weakest for $U(1)_R$ where the
constraints from thermal DM and non-negligible DR can be compatible. Such a
number of the effective neutrino species would be interesting from the
viewpoint of the so-called Hubble tension.


title:
A Study of Designing Compact Audio-Visual Wake Word Spotting System
  Based on Iterative Fine-Tuning in Neural Network Pruning

abstract:
  Audio-only-based wake word spotting (WWS) is challenging under noisy
conditions due to environmental interference in signal transmission. In this
paper, we investigate on designing a compact audio-visual WWS system by
utilizing visual information to alleviate the degradation. Specifically, in
order to use visual information, we first encode the detected lips to
fixed-size vectors with MobileNet and concatenate them with acoustic features
followed by the fusion network for WWS. However, the audio-visual model based
on neural networks requires a large footprint and a high computational
complexity. To meet the application requirements, we introduce a neural network
pruning strategy via the lottery ticket hypothesis in an iterative fine-tuning
manner (LTH-IF), to the single-modal and multi-modal models, respectively.
Tested on our in-house corpus for audio-visual WWS in a home TV scene, the
proposed audio-visual system achieves significant performance improvements over
the single-modality (audio-only or video-only) system under different noisy
conditions. Moreover, LTH-IF pruning can largely reduce the network parameters
and computations with no degradation of WWS performance, leading to a potential
product solution for the TV wake-up scenario.


title:
A hybrid 2-stage vision transformer for artificial intelligence-assisted
  5 class pathologic diagnosis of gastric endoscopic biopsies: a diagnostic
  tool for guiding gastric cancer treatment

abstract:
  Gastric endoscopic screening is an effective way to decide appropriate
gastric cancer (GC) treatment at an early stage, reducing GC-associated
mortality rate. Although artificial intelligence (AI) has brought a great
promise to assist pathologist to screen digitalized whole slide images,
automatic classification systems for guiding proper GC treatment based on
clinical guideline are still lacking. We propose an AI system classifying 5
classes of GC histology, which can be perfectly matched to general GC treatment
guidance. The AI system was designed to mimic the way pathologist understand
slides through multi-scale self-attention mechanism using a 2-stage Vision
Transformer network. The AI system performance was evaluated on 876 internal
endoscopic slides and 336 external endoscopic slides from clinical cohort. We
further evaluated practical usability of the AI system on observation of
AI-assisted 6 pathologist performance. The AI system demonstrates clinical
capability by achieving class-average diagnostic sensitivity of above 85% for
both internal and external cohort analysis. Furthermore, AI-assisted
pathologists showed significantly improved diagnostic sensitivity by 10% within
18% saved screening time compared to human pathologists (p-values of 0.006 and
0.030, respectively). The reliable performance of the AI system in multi-center
cohort testing and its clinical applicability demonstrate that AI-assisted
endoscopic CG screening would help reduce the workload of limited pathologists.
Furthermore, the AI system has a great potential for providing presumptive
pathologic opinion for deciding proper treatment for early GC patients.


title: Scheduling Complexity of Interleaving Search

abstract:
  miniKanren is a lightweight embedded language for logic and relational
programming. Many of its useful features come from a distinctive search
strategy, called interleaving search. However, with interleaving search
conventional ways of reasoning about the complexity and performance of logical
programs become irrelevant. We identify an important key component --
scheduling -- which makes the reasoning for miniKanren so different, and
present a semi-automatic technique to estimate the scheduling impact via
symbolic execution for a reasonably wide class of programs.


title: Visual Ground Truth Construction as Faceted Classification

abstract:
  Recent work in Machine Learning and Computer Vision has provided evidence of
systematic design flaws in the development of major object recognition
benchmark datasets. One such example is ImageNet, wherein, for several
categories of images, there are incongruences between the objects they
represent and the labels used to annotate them. The consequences of this
problem are major, in particular considering the large number of machine
learning applications, not least those based on Deep Neural Networks, that have
been trained on these datasets. In this paper we posit the problem to be the
lack of a knowledge representation (KR) methodology providing the foundations
for the construction of these ground truth benchmark datasets. Accordingly, we
propose a solution articulated in three main steps: (i) deconstructing the
object recognition process in four ordered stages grounded in the philosophical
theory of teleosemantics; (ii) based on such stratification, proposing a novel
four-phased methodology for organizing objects in classification hierarchies
according to their visual properties; and (iii) performing such classification
according to the faceted classification paradigm. The key novelty of our
approach lies in the fact that we construct the classification hierarchies from
visual properties exploiting visual genus-differentiae, and not from
linguistically grounded properties. The proposed approach is validated by a set
of experiments on the ImageNet hierarchy of musical experiments.


title:
On the energy of Schwarzschild spacetime with the post-Newtonian
  approximation

abstract:
  With the post-Newtonian approxination, the energy of Schwarzschild spacetime
in the Weinberg prescription is obtained. The energy for the first
post-Newtonian approximation $E^{(1)} = m$ gives the Newtonian treatment of
Schwarzschild spacetime. However, for the second post-Newtonian approximation,
the erergy is shown that $E^{(1)}$ adds extra terms $E^{(2)}$ which consist of
the energy stored in the configuration $E_{\rm config}$, in the gravitational
field $E_{\rm field}$. and a term of surface integral. These extra terms gives
post-Newtonian corrections to the Newtonian treatment.


title:
Survey on Self-supervised Representation Learning Using Image
  Transformations

abstract:
  Deep neural networks need huge amount of training data, while in real world
there is a scarcity of data available for training purposes. To resolve these
issues, self-supervised learning (SSL) methods are used. SSL using geometric
transformations (GT) is a simple yet powerful technique used in unsupervised
representation learning. Although multiple survey papers have reviewed SSL
techniques, there is none that only focuses on those that use geometric
transformations. Furthermore, such methods have not been covered in depth in
papers where they are reviewed. Our motivation to present this work is that
geometric transformations have shown to be powerful supervisory signals in
unsupervised representation learning. Moreover, many such works have found
tremendous success, but have not gained much attention. We present a concise
survey of SSL approaches that use geometric transformations. We shortlist six
representative models that use image transformations including those based on
predicting and autoencoding transformations. We review their architecture as
well as learning methodologies. We also compare the performance of these models
in the object recognition task on CIFAR-10 and ImageNet datasets. Our analysis
indicates the AETv2 performs the best in most settings. Rotation with feature
decoupling also performed well in some settings. We then derive insights from
the observed results. Finally, we conclude with a summary of the results and
insights as well as highlighting open problems to be addressed and indicating
various future directions.


title:
Performance of Hybrid THz and Multi-Antenna RF System with Diversity
  Combining

abstract:
  Recent studies investigate single-antenna radio frequency (RF) systems mixed
with terahertz (THz) wireless communications. This paper considers a two-tier
system of THz for backhaul and multiple-antenna assisted RF for the access
network. We analyze the system performance by employing both selection
combining (SC) and maximal ratio combining (MRC) receivers for the RF link
integrated with the THz using the fixed-gain amplify and forward (AF) protocol.
We develop the probability density function (PDF) and cumulative distribution
function (CDF) of the end-to-end signal-to-noise (SNR) of the dual-hop system
over independent and non-identically distributed (i.ni.d.) $\alpha$-$\mu$
fading channels with a statistical model for misalignment errors in the THz
wireless link. We use the derived statistical results to develop analytical
expressions of the outage probability, average bit error rate (BER), and
ergodic capacity for the performance assessment of the considered system. We
develop diversity order of the system using asymptotic analysis in the high SNR
region, demonstrating the scaling of system performance with the number of
antennas. We use computer simulations to show the effect of system and channel
parameters on the performance of the hybrid THz-RF link with multi-antenna
diversity schemes.


title: SAITS: Self-Attention-based Imputation for Time Series

abstract:
  Missing data in time series is a pervasive problem that puts obstacles in the
way of pattern recognition, especially in real-world applications. A popular
solution is imputation, where the fundamental challenge is to determine what
values should be filled in. This paper proposes SAITS, a novel method based on
the self-attention mechanism for missing value imputation in multivariate time
series. Trained by a joint-optimization approach, SAITS learns missing values
from a weighted combination of two diagonally-masked self-attention (DMSA)
blocks. DMSA explicitly captures both the temporal dependencies and feature
correlations between time steps, which improves imputation accuracy and
training speed. Meanwhile, the weighted-combination design enables SAITS to
dynamically assign weights to the learned representations from two DMSA blocks
according to the attention map and the missingness information. Extensive
experiments demonstrate that SAITS outperforms the state-of-the-art methods on
the time-series imputation task efficiently and reveal SAITS' potential to
improve the learning performance of pattern recognition models on incomplete
time-series data from the real world.


title: TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting

abstract:
  In this paper, we propose a three-stream adaptive fusion network named
TAFNet, which uses paired RGB and thermal images for crowd counting.
Specifically, TAFNet is divided into one main stream and two auxiliary streams.
We combine a pair of RGB and thermal images to constitute the input of main
stream. Two auxiliary streams respectively exploit RGB image and thermal image
to extract modality-specific features. Besides, we propose an Information
Improvement Module (IIM) to fuse the modality-specific features into the main
stream adaptively. Experiment results on RGBT-CC dataset show that our method
achieves more than 20% improvement on mean average error and root mean squared
error compared with state-of-the-art method. The source code will be publicly
available at https://github.com/TANGHAIHAN/TAFNet.


title: Metrics and quasimetrics induced by point pair function

abstract:
  We study the point pair function in subdomains $G$ of $\mathbb{R}^n$. We
prove that, for every domain $G\subsetneq\mathbb{R}^n$, the this function is a
quasi-metric with the constant less than or equal to $\sqrt{5}\slash2$.
Moreover, we show that it is a metric in the domain
$G=\mathbb{R}^n\setminus\{0\}$ with $n\geq1$. We also consider generalized
versions of the point pair function, depending on an arbitrary constant
$\alpha>0$, and show that in some domains these generalizations are metrics if
and only if $\alpha\leq12$.


title:
DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections
  for Object Classification

abstract:
  Automated vehicles need to detect and classify objects and traffic
participants accurately. Reliable object classification using automotive radar
sensors has proved to be challenging. We propose a method that combines
classical radar signal processing and Deep Learning algorithms. The
range-azimuth information on the radar reflection level is used to extract a
sparse region of interest from the range-Doppler spectrum. This is used as
input to a neural network (NN) that classifies different types of stationary
and moving objects. We present a hybrid model (DeepHybrid) that receives both
radar spectra and reflection attributes as inputs, e.g. radar cross-section.
Experiments show that this improves the classification performance compared to
models using only spectra. Moreover, a neural architecture search (NAS)
algorithm is applied to find a resource-efficient and high-performing NN. NAS
yields an almost one order of magnitude smaller NN than the manually-designed
one while preserving the accuracy. The proposed method can be used for example
to improve automatic emergency braking or collision avoidance systems.


title:
End-to-end Music Remastering System Using Self-supervised and
  Adversarial Training

abstract:
  Mastering is an essential step in music production, but it is also a
challenging task that has to go through the hands of experienced audio
engineers, where they adjust tone, space, and volume of a song. Remastering
follows the same technical process, in which the context lies in mastering a
song for the times. As these tasks have high entry barriers, we aim to lower
the barriers by proposing an end-to-end music remastering system that
transforms the mastering style of input audio to that of the target. The system
is trained in a self-supervised manner, in which released pop songs were used
for training. We also anticipated the model to generate realistic audio
reflecting the reference's mastering style by applying a pre-trained encoder
and a projection discriminator. We validate our results with quantitative
metrics and a subjective listening test and show that the model generated
samples of mastering style similar to the target.


title: A formation mechanism for the large plumes in the prominence

abstract:
  To understand the formation mechanism of large plumes in solar prominences,
we investigate the formation process of two such phenomena. We studied the
dynamic and thermal properties of two large plumes using observations from New
Vacuum Solar Telescope, the Solar Dynamic Observatory, and the Solar
Terrestrial Relations Observatory-Ahead. We find that two large plumes observed
with high-resolution data are quite different from previously studied
small-scale plumes. They are born at the top of a prominence bubble with a
large projected area of 10-20 Mm^2 . Before the occurrence of each large plume,
the bubble expands and takes on a quasi-semicircular appearance. Meanwhile, the
emission intensity of extreme-ultra-violet (EUV) bands increases in the bubble.
A small-scale filament is found to erupt in the bubble during the second large
plume. At the point at which the height of the bubble is comparable with half
the width of the bubble, the bubble becomes unstable and generates the plumes.
During the formation of plumes, two side edges of the top of the bubble, which
are dominated by opposite Doppler signals, approach each other. The large plume
then emerges and keeps rising up with a constant speed of about 13-15 km/s.
These two large plumes have temperatures of 1.3 x 10^6 Kelvin and densities of
2.0 x 10^9 cm^-3, two orders hotter and one order less dense than the typical
prominence. We also find that the bubble is a hot, low-density volume instead
of a void region beneath the cold and dense prominence. Therefore, we conclude
that these two large plumes are the result of the breakup of the prominence
bubble triggered by an enhancement of thermal pressure; they separate from the
bubble, most likely by magnetic reconnection.


title:
Recovering Unbalanced Communities in the Stochastic Block Model With
  Application to Clustering with a Faulty Oracle

abstract:
  The stochastic block model (SBM) is a fundamental model for studying graph
clustering or community detection in networks. It has received great attention
in the last decade and the balanced case, i.e., assuming all clusters have
large size, has been well studied. However, our understanding of SBM with
unbalanced communities (arguably, more relevant in practice) is still very
limited. In this paper, we provide a simple SVD-based algorithm for recovering
the communities in the SBM with communities of varying sizes. Under the
KS-threshold conjecture, the tradeoff between the parameters in our algorithm
is nearly optimal up to polylogarithmic factors for a wide range of regimes. As
a byproduct, we obtain a time-efficient algorithm with improved query
complexity for a clustering problem with a faulty oracle, which improves upon a
number of previous work (Mazumdarand Saha [NIPS 2017], Larsen, Mitzenmacher and
Tsourakakis [WWW 2020], Peng and Zhang[COLT 2021]). Under the KS-threshold
conjecture, the query complexity of our algorithm is nearly optimal up to
polylogarithmic factors.


title: Contrastive Meta Learning with Behavior Multiplicity for Recommendation

abstract:
  A well-informed recommendation framework could not only help users identify
their interested items, but also benefit the revenue of various online
platforms (e.g., e-commerce, social media). Traditional recommendation models
usually assume that only a single type of interaction exists between user and
item, and fail to model the multiplex user-item relationships from multi-typed
user behavior data, such as page view, add-to-favourite and purchase. While
some recent studies propose to capture the dependencies across different types
of behaviors, two important challenges have been less explored: i) Dealing with
the sparse supervision signal under target behaviors (e.g., purchase). ii)
Capturing the personalized multi-behavior patterns with customized dependency
modeling. To tackle the above challenges, we devise a new model CML,
Contrastive Meta Learning (CML), to maintain dedicated cross-type behavior
dependency for different users. In particular, we propose a multi-behavior
contrastive learning framework to distill transferable knowledge across
different types of behaviors via the constructed contrastive loss. In addition,
to capture the diverse multi-behavior patterns, we design a contrastive meta
network to encode the customized behavior heterogeneity for different users.
Extensive experiments on three real-world datasets indicate that our method
consistently outperforms various state-of-the-art recommendation methods. Our
empirical studies further suggest that the contrastive meta learning paradigm
offers great potential for capturing the behavior multiplicity in
recommendation. We release our model implementation at:
https://github.com/weiwei1206/CML.git.


title:
A Collection and Categorization of Open-Source Wind and Wind Power
  Datasets

abstract:
  Wind power and other forms of renewable energy sources play an ever more
important role in the energy supply of today's power grids. Forecasting
renewable energy sources has therefore become essential in balancing the power
grid. While a lot of focus is placed on new forecasting methods, little
attention is given on how to compare, reproduce and transfer the methods to
other use cases and data. One reason for this lack of attention is the limited
availability of open-source datasets, as many currently used datasets are
non-disclosed and make reproducibility of research impossible. This
unavailability of open-source datasets is especially prevalent in commercially
interesting fields such as wind power forecasting. However, with this paper we
want to enable researchers to compare their methods on publicly available
datasets by providing the, to our knowledge, largest up-to-date overview of
existing open-source wind power datasets, and a categorization into different
groups of datasets that can be used for wind power forecasting. We show that
there are publicly available datasets sufficient for wind power forecasting
tasks and discuss the different data groups properties to enable researchers to
choose appropriate open-source datasets and compare their methods on them.


title:
Pressure-induced superconductivity in iron-based spin-ladder compound
  BaFe2+{\delta}(S1-Sex)3

abstract:
  The iron-based superconductors had a significant impact on condensed matter
physics. They have a common structural motif of a two-dimensional square iron
lattice and exhibit fruitful physical properties as a strongly correlated
electron system. During the extensive investigations, quasi-one-dimensional
iron-based spin-ladder compounds attracted much attention as a platform for
studying the interplay between magnetic and orbital ordering. In these
compounds, BaFe2S3 and BaFe2Se3 were found to exhibit superconductivity under
high pressure, having a different crystal and magnetic structure at low
temperature. We report a brief review of the iron-based spin-ladder compound
and recent studies for BaFe2+d(S1-xSex)3. BaFe2(S0.75 Se0.25)3 is in the
vicinity of the boundary of two different magnetic phases and it is intriguing
to perform high pressure experiments for studying superconductivity, since
effects of large magnetic fluctuations on superconductivity are expected. The
effect of iron stoichiometry on the interplay between magnetism and
superconductivity is also studied by changing the iron concentration in
BaFe2+dSe3.


title: Point Cloud Generation with Continuous Conditioning

abstract:
  Generative models can be used to synthesize 3D objects of high quality and
diversity. However, there is typically no control over the properties of the
generated object.This paper proposes a novel generative adversarial network
(GAN) setup that generates 3D point cloud shapes conditioned on a continuous
parameter. In an exemplary application, we use this to guide the generative
process to create a 3D object with a custom-fit shape. We formulate this
generation process in a multi-task setting by using the concept of auxiliary
classifier GANs. Further, we propose to sample the generator label input for
training from a kernel density estimation (KDE) of the dataset. Our ablations
show that this leads to significant performance increase in regions with few
samples. Extensive quantitative and qualitative experiments show that we gain
explicit control over the object dimensions while maintaining good generation
quality and diversity.


title: On the four-term relation on Khovanov homology

abstract:
  The goal of this paper is to prove a categorified analogue of Kontsevich's
$4T$ relation on Vassiliev derivatives of Khovanov homology.


title:
Hyperspherical approach to atom--dimer collision with the Jacobi
  boundary condition

abstract:
  In this study, we investigate atom--dimer scattering within the framework of
the hyperspherical method. The coupled channel Schr\"odinger equation is solved
using the R-matrix propagation technique combined with the smooth variable
discretization method. In the matching procedure, the asymptotic wave functions
are expressed in the rotated Jacobi coordinates. We apply this approach to the
elastic scattering $^{3}$He(T$\uparrow$) + $^{4}$He$_{2}$ and H$\uparrow$ +
H$\uparrow$Li processes. The convergence of the scattering length as a function
of the propagation distance is studied. We find that the method is reliable and
can provide considerable savings over previous propagators.


title:
Centrality-dependent chemical potentials of light hadrons and quarks
  based on transverse momentum spectra and particle yield ratios in Au-Au
  collisions

abstract:
  We describe the transverse momentum spectra of $\pi^\pm$, $K^\pm$, $p$, and
$\bar{p}$ produced in different centralities gold-gold (Au-Au) collisions at
different collision energies range from 7.7 to 62.4 GeV by a two-component
Erlang distribution. The fitting results are consistent with the experimental
data, and the centrality- and energy-dependent yield ratios of negative to
positive particles are obtained from the normalization constants. Based on the
yield ratios, the energy- and centrality-dependent chemical potentials of light
hadrons and quarks are extracted. The study shows that the dependences of the
three types of particle yield ratios on centrality are not significant,
especially for $\pi$. The logarithms of the three yield ratios show obvious
linear dependence on $1/\sqrt{s_{NN}}$ over a range from 7.7 to 62.4 GeV. The
extracted chemical potentials show obvious dependence on energy, and decrease
with the increase of energy. The dependences of the energy-dependent chemical
potentials of light hadrons and quarks on centrality are relatively more
obvious in low energy region. The derived curves of chemical potentials for all
centralities, from the linear fits of the logarithms of yield ratios vs energy,
have the extremum at the same energy of 3.526 GeV, which possibly is the
critical energy of phase transition from a liquid-like hadron state to a
gas-like quark state in the collision system. With the increase of energy, all
types of chemical potentials become small and tend to zero at very high energy,
which indicates that with the increase of energy, the hadronic interactions
gradually fade and the partonic interactions gradually become greater.


title: Complete minors and average degree -- a short proof

abstract:
  We provide a short and self-contained proof of the classical result of
Kostochka and of Thomason, ensuring that every graph of average degree $d$ has
a complete minor of order $d/\sqrt{\log d}$.


title: Potassium abundance in the Earth and Borexino data

abstract:
  An independent analysis of Borexino single event energy spectrum of recoil
electrons and alphas was carried out. We compared two sets of single event
sources. The first set is similar to the one used in Borexino Collaboration
analysis. The second set additionally includes the scattering of
$^{40}$K-geo-($\bar{\nu} + \nu$) on scintillator electrons. We found two
equivalent minima for $\chi^2$ for second set. The one is for total counting
rates $R(^{40}$K-geo-$(\bar{\nu} + \nu)) = 0.0$ and $R(^{210}$Bi) = 10
cpd/100t. The other one is for $R(^{40}$K-geo-$(\bar{\nu} + \nu)) = 7.05$
cpd/100t and $R(^{210}$Bi$) = 6$ cpd/100t. We performed MC pseudo-experiments
and found that we do not have enough statistics and need to know the bismuth
concentration in the scintillator for definite measurement of potassium
abundance in the Earth. The possibility of building a next-generation detector
for looking for the $^{40}$K-geo-($\bar{\nu} + \nu$) flux is being considered.