title: An Adaptive Gradient Method with Energy and Momentum

abstract:
  We introduce a novel algorithm for gradient-based optimization of stochastic
objective functions. The method may be seen as a variant of SGD with momentum
equipped with an adaptive learning rate automatically adjusted by an 'energy'
variable. The method is simple to implement, computationally efficient, and
well suited for large-scale machine learning problems. The method exhibits
unconditional energy stability for any size of the base learning rate. We
provide a regret bound on the convergence rate under the online convex
optimization framework. We also establish the energy-dependent convergence rate
of the algorithm to a stationary point in the stochastic non-convex setting. In
addition, a sufficient condition is provided to guarantee a positive lower
threshold for the energy variable. Our experiments demonstrate that the
algorithm converges fast while generalizing better than or as well as SGD with
momentum in training deep neural networks, and compares also favorably to Adam.


title: Learning to Censor by Noisy Sampling

abstract:
  Point clouds are an increasingly ubiquitous input modality and the raw signal
can be efficiently processed with recent progress in deep learning. This signal
may, often inadvertently, capture sensitive information that can leak semantic
and geometric properties of the scene which the data owner does not want to
share. The goal of this work is to protect sensitive information when learning
from point clouds; by censoring the sensitive information before the point
cloud is released for downstream tasks. Specifically, we focus on preserving
utility for perception tasks while mitigating attribute leakage attacks. The
key motivating insight is to leverage the localized saliency of perception
tasks on point clouds to provide good privacy-utility trade-offs. We realize
this through a mechanism called Censoring by Noisy Sampling (CBNS), which is
composed of two modules: i) Invariant Sampler: a differentiable point-cloud
sampler which learns to remove points invariant to utility and ii) Noisy
Distorter: which learns to distort sampled points to decouple the sensitive
information from utility, and mitigate privacy leakage. We validate the
effectiveness of CBNS through extensive comparisons with state-of-the-art
baselines and sensitivity analyses of key design choices. Results show that
CBNS achieves superior privacy-utility trade-offs on multiple datasets.


title:
Self-Supervised Robust Scene Flow Estimation via the Alignment of
  Probability Density Functions

abstract:
  In this paper, we present a new self-supervised scene flow estimation
approach for a pair of consecutive point clouds. The key idea of our approach
is to represent discrete point clouds as continuous probability density
functions using Gaussian mixture models. Scene flow estimation is therefore
converted into the problem of recovering motion from the alignment of
probability density functions, which we achieve using a closed-form expression
of the classic Cauchy-Schwarz divergence. Unlike existing
nearest-neighbor-based approaches that use hard pairwise correspondences, our
proposed approach establishes soft and implicit point correspondences between
point clouds and generates more robust and accurate scene flow in the presence
of missing correspondences and outliers. Comprehensive experiments show that
our method makes noticeable gains over the Chamfer Distance and the Earth
Mover's Distance in real-world environments and achieves state-of-the-art
performance among self-supervised learning methods on FlyingThings3D and KITTI,
even outperforming some supervised methods with ground truth annotations.


title:
Topological defects and other properties of multicomponent
  superconductors

abstract:
  In recent years, there were a number of experimental developments and
discoveries of novel superconducting materials which exhibit multicomponent,
many-body degrees of freedom. These superconductors, that are described by
several superconducting condensates, feature many new interesting phenomena
that are absent in their single-component counterparts. Several of these new
aspects of multicomponent superconductivity are addressed in this report.


title: A Computationally Efficient Approach to Fully Bayesian Benchmarking

abstract:
  In small area estimation, it is often necessary to resort to model-based
methods in order to produce estimates in areas with little or no data. In many
settings, we require that some aggregate of small area estimates agree with a
national level estimate that may be considered more reliable, for internal
consistency purposes. The process of enforcing this agreement is referred to as
benchmarking, and while methods currently exist to perform benchmarking in many
settings, few are ideal for applications with non-normal outcomes and many are
computationally inefficient. Fully Bayesian benchmarking is a theoretically
appealing approach insofar as we can obtain posterior distributions conditional
on a benchmarking constraint. However, existing implementations are often
computationally prohibitive. In this paper, we summarize existing benchmarking
methods and their shortcomings in the setting of small area estimation with
binary outcomes, and propose an approach in which an unbenchmarked method that
produces samples can be combined with a rejection sampler to produce fully
Bayesian benchmarked estimates in a computationally efficient way. To
illustrate our approach, we provide comparisons of various benchmarking methods
in applications to HIV prevalence and under-5 mortality estimation. Code
implementing our methodology is available in the R package simultBench.


title:
Safe and Efficient Model Predictive Control Using Neural Networks: An
  Interior Point Approach

abstract:
  Model predictive control (MPC) provides a useful means for controlling
systems with constraints, but suffers from the computational burden of
repeatedly solving an optimization problem in real time. Offline (explicit)
solutions for MPC attempt to alleviate real time computational challenges using
either multiparametric programming or machine learning. The multiparametric
approaches are typically applied to linear or quadratic MPC problems, while
learning-based approaches can be more flexible and are less memory-intensive.
Existing learning-based approaches offer significant speedups, but the
challenge becomes ensuring constraint satisfaction while maintaining good
performance.
  In this paper, we provide a neural network parameterization of MPC policies
that explicitly encodes the constraints of the problem. Therefore, the
constraints are satisfied by design. By exploring the interior of the MPC
feasible set in an unsupervised learning paradigm, the neural network finds
better policies faster than projection-based methods and exhibits substantially
faster solve times. We use the proposed policy to solve both a robust
(tube-based) and a scenario-based MPC problem, and demonstrate the performance
and computational gains on two standard test systems.


title:
Biceph-Net: A robust and lightweight framework for the diagnosis of
  Alzheimer's disease using 2D-MRI scans and deep similarity learning

abstract:
  Alzheimer's Disease (AD) is a neurodegenerative disease that is one of the
significant causes of death in the elderly population. Many deep learning
techniques have been proposed to diagnose AD using Magnetic Resonance Imaging
(MRI) scans. Predicting AD using 2D slices extracted from 3D MRI scans is
challenging as the inter-slice information gets lost. To this end, we propose a
novel and lightweight framework termed 'Biceph-Net' for AD diagnosis using 2D
MRI scans that model both the intra-slice and inter-slice information.
Biceph-Net has been experimentally shown to perform similar to other
Spatio-temporal neural networks while being computationally more efficient.
Biceph-Net is also superior in performance compared to vanilla 2D convolutional
neural networks (CNN) for AD diagnosis using 2D MRI slices. Biceph-Net also has
an inbuilt neighbourhood-based model interpretation feature that can be
exploited to understand the classification decision taken by the network.
Biceph-Net experimentally achieves a test accuracy of 100% in the
classification of Cognitively Normal (CN) vs AD, 98.16% for Mild Cognitive
Impairment (MCI) vs AD, and 97.80% for CN vs MCI vs AD.


title: Deep Frequency Filtering for Domain Generalization

abstract:
  Improving the generalization capability of Deep Neural Networks (DNNs) is
critical for their practical uses, which has been a longstanding challenge.
Some theoretical studies have revealed that DNNs have preferences to different
frequency components in the learning process and indicated that this may affect
the robustness of learned features. In this paper, we propose Deep Frequency
Filtering (DFF) for learning domain-generalizable features, which is the first
endeavour to explicitly modulate frequency components of different transfer
difficulties across domains during training. To achieve this, we perform Fast
Fourier Transform (FFT) on feature maps at different layers, then adopt a
light-weight module to learn the attention masks from frequency representations
after FFT to enhance transferable frequency components while suppressing the
components not conductive to generalization. Further, we empirically compare
different types of attention for implementing our conceptualized DFF. Extensive
experiments demonstrate the effectiveness of the proposed DFF and show that
applying DFF on a plain baseline outperforms the state-of-the-art methods on
different domain generalization tasks, including close-set classification and
open-set retrieval.


title: Dynamic behavior for a gradient algorithm with energy and momentum

abstract:
  This paper investigates a novel gradient algorithm, using both energy and
momentum (called AGEM), for solving general non-convex optimization problems.
The solution properties of the AGEM algorithm, including uniformly boundedness
and convergence to critical points, are examined. The dynamic behavior is
studied through analysis of a high-resolution ODE system. Such ODE system is
nonlinear and obtained by taking the limit of the discrete scheme while keeping
the momentum effect through a rescale of the momentum parameter. In particular,
we show global well-posedness of the ODE system, time-asymptotic convergence of
solution trajectories, and further establish a linear convergence rate for
objective functions satisfying the Polyak-Lojasiewicz condition.


title:
Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A
  Multi-level Deep Learning Approach

abstract:
  Recommender systems have been successfully used in many domains with the help
of machine learning algorithms. However, such applications tend to use
multi-dimensional user data, which has raised widespread concerns about the
breach of users privacy. Meanwhile, wearable technologies have enabled users to
collect fitness-related data through embedded sensors to monitor their
conditions or achieve personalized fitness goals. In this paper, we propose a
novel privacy-aware personalized fitness recommender system. We introduce a
multi-level deep learning framework that learns important features from a
large-scale real fitness dataset that is collected from wearable IoT devices to
derive intelligent fitness recommendations. Unlike most existing approaches,
our approach achieves personalization by inferring the fitness characteristics
of users from sensory data and thus minimizing the need for explicitly
collecting user identity or biometric information, such as name, age, height,
weight. In particular, our proposed models and algorithms predict (a)
personalized exercise distance recommendations to help users to achieve target
calories, (b) personalized speed sequence recommendations to adjust exercise
speed given the nature of the exercise and the chosen route, and (c)
personalized heart rate sequence to guide the user of the potential health
status for future exercises. Our experimental evaluation on a real-world Fitbit
dataset demonstrated high accuracy in predicting exercise distance, speed
sequence, and heart rate sequence compared to similar studies. Furthermore, our
approach is novel compared to existing studies as it does not require
collecting and using users sensitive information, and thus it preserves the
users privacy.


title:
Towards Expressive Speaking Style Modelling with Hierarchical Context
  Information for Mandarin Speech Synthesis

abstract:
  Previous works on expressive speech synthesis mainly focus on current
sentence. The context in adjacent sentences is neglected, resulting in
inflexible speaking style for the same text, which lacks speech variations. In
this paper, we propose a hierarchical framework to model speaking style from
context. A hierarchical context encoder is proposed to explore a wider range of
contextual information considering structural relationship in context,
including inter-phrase and inter-sentence relations. Moreover, to encourage
this encoder to learn style representation better, we introduce a novel
training strategy with knowledge distillation, which provides the target for
encoder training. Both objective and subjective evaluations on a Mandarin
lecture dataset demonstrate that the proposed method can significantly improve
the naturalness and expressiveness of the synthesized speech.


title:
Hybrid integration of deterministic quantum dots-based single-photon
  sources with CMOS-compatible silicon carbide photonics

abstract:
  Thin film 4H-silicon carbide (4H-SiC) is emerging as a contender for
realizing large-scale optical quantum circuits due to its high CMOS technology
compatibility and large optical nonlinearities. Though, challenges remain in
producing wafer-scale 4H-SiC thin film on insulator (4H-SiCOI) for dense
integration of photonic circuits, and in efficient coupling of deterministic
quantum emitters that are essential for scalable quantum photonics. Here we
demonstrate hybrid integration of self-assembled InGaAs quantum dots (QDs)
based single-photon sources (SPSs) with wafer-scale 4H-SiC photonic chips
prepared by ion slicing technique. By designing a bilayer vertical coupler, we
realize generation and highly efficient routing of single-photon emission in
the hybrid quantum photonic chip. Furthermore, we realize a chip-integrated
beamsplitter operation for triggered single photons through fabricating a 1x2
multi-mode interferometer (MMI) with a symmetric power splitting ratio of
50:50. The successful demonstration of heterogeneously integrating QDs-based
SPSs on 4H-SiC photonic chip prepared by ion slicing technique constitutes an
important step toward CMOS-compatible, fast reconfigurable quantum photonic
circuits with deterministic SPSs.


title:
Hyper-Dilaton-Weyl Multiplet of 4D, ${\mathcal{N}}=2$ Conformal
  Supergravity

abstract:
  We define a new dilaton-Weyl multiplet of ${\mathcal{N}}=2$ conformal
supergravity in four dimensions. This is constructed by reinterpreting the
equations of motion of an on-shell hypermultiplet as constraints that render
some of the fields of the standard-Weyl multiplet composite. The independent
bosonic components include four scalar fields and a triplet of gauge two-forms.
The resulting, so-called, hyper-dilaton-Weyl multiplet defines a $24+24$
off-shell representation of the local ${\mathcal{N}}=2$ superconformal algebra.
By coupling the hyper-dilaton-Weyl multiplet to an off-shell vector multiplet
compensator, we obtain one of the two minimal $32+32$ off-shell multiplets of
${\mathcal{N}}=2$ Poincar\'e supergravity constructed by M\"uller in 1986.
On-shell, this contains the minimal ${\mathcal{N}}=2$ Poincar\'e supergravity
multiplet together with a hypermultiplet where one of its physical scalars
plays the role of a dilaton, while its three other scalars are dualised to a
triplet of real gauge two-forms. Interestingly, a $BF$-coupling induces a
scalar potential for the dilaton without a standard gauging.


title:
Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With
  Self-supervised Learning

abstract:
  Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis
rate. Factors influencing recurrence and metastasis are currently unknown and
there are no distinct histopathological or morphological features indicating
the risks of recurrence and metastasis in LSCC. Our study focuses on the
recurrence prediction of LSCC based on H&E-stained histopathological
whole-slide images (WSI). Due to the small size of LSCC cohorts in terms of
patients with available recurrence information, standard end-to-end learning
with various convolutional neural networks for this task tends to overfit.
Also, the predictions made by these models are hard to interpret.
Histopathology WSIs are typically very large and are therefore processed as a
set of smaller tiles. In this work, we propose a novel conditional
self-supervised learning (SSL) method to learn representations of WSI at the
tile level first, and leverage clustering algorithms to identify the tiles with
similar histopathological representations. The resulting representations and
clusters from self-supervision are used as features of a survival model for
recurrence prediction at the patient level. Using two publicly available
datasets from TCGA and CPTAC, we show that our LSCC recurrence prediction
survival model outperforms both LSCC pathological stage-based approach and
machine learning baselines such as multiple instance learning. The proposed
method also enables us to explain the recurrence histopathological risk factors
via the derived clusters. This can help pathologists derive new hypotheses
regarding morphological features associated with LSCC recurrence.


title: Categorical and topological entropies on symplectic manifolds

abstract:
  In this paper, we prove that if a symplectic automorphism is of a certain
type, that we call "Penner type", then its categorical entropy bounds its
topological entropy from below. Along the way, we show that its categorical
entropy on the compact Fukaya category agrees with that on the wrapped Fukaya
category. Also, we prove that for any compactly supported symplectic
automorphism, its categorical entropy on the wrapped Fukaya category also
agrees with that on any partially wrapped Fukaya category. This is a corollary
of our result for general triangulated categories, which gives inequalities
between the categorical entropy of functors on categories and their
localisations.


title: Domination and independence number of large $2$-crossing-critical graphs

abstract:
  After $2$-crossing-critical graphs were characterized in 2016, their most
general subfamily, large $3$-connected $2$-crossing-critical graphs, has
attracted separate attention. This paper presents sharp upper and lower bounds
for their domination and independence number.


title:
TPPD: Targeted Pseudo Partitioning based Defence for Cross-Core Covert
  Channel Attacks

abstract:
  Contemporary computing employs cache hierarchy to fill the speed gap between
processors and main memories. In order to optimise system performance, Last
Level Caches(LLC) are shared among all the cores. Cache sharing has made them
an attractive surface for cross-core timing channel attacks. In these attacks,
an attacker running on another core can exploit the access timing of the victim
process to infiltrate the secret information. One such attack is called
cross-core Covert Channel Attack (CCA). Timely detection and then prevention of
cross-core CCA is critical for maintaining the integrity and security of users,
especially in a shared computing environment. In this work, we have proposed an
efficient cross-core CCA mitigation technique. We propose a way-wise cache
partitioning on targeted sets, only for the processes suspected to be
attackers. In this way, the performance impact on the entire LLC is minimised,
and benign applications can utilise the LLC to its full capacity. We have used
a cycle-accurate simulator (gem5) to analyse the per-formance of the proposed
method and its security effectiveness. It has been successful in abolishing the
cross-core covert timing channel attack with no significant performance impact
on benign applications. It causes 23% less cache misses in comparison to
existing partitioning based solutions while requiring 0.26% storage overhead.


title:
Self-supervised Learning of Adversarial Example: Towards Good
  Generalizations for Deepfake Detection

abstract:
  Recent studies in deepfake detection have yielded promising results when the
training and testing face forgeries are from the same dataset. However, the
problem remains challenging when one tries to generalize the detector to
forgeries created by unseen methods in the training dataset. This work
addresses the generalizable deepfake detection from a simple principle: a
generalizable representation should be sensitive to diverse types of forgeries.
Following this principle, we propose to enrich the "diversity" of forgeries by
synthesizing augmented forgeries with a pool of forgery configurations and
strengthen the "sensitivity" to the forgeries by enforcing the model to predict
the forgery configurations. To effectively explore the large forgery
augmentation space, we further propose to use the adversarial training strategy
to dynamically synthesize the most challenging forgeries to the current model.
Through extensive experiments, we show that the proposed strategies are
surprisingly effective (see Figure 1), and they could achieve superior
performance than the current state-of-the-art methods. Code is available at
\url{https://github.com/liangchen527/SLADD}.


title: Dynamics of fast ions produced from laser produced aluminum plasma

abstract:
  In this work, dynamics of multi-charged ions emitted from an aluminum plasma
produced by Q switched Nd: Yag laser is studied using time of flight (TOF)
measurements from Langmuir Probe (LP) and spectroscopy (STOF) under Ar ambient
of 0.02 mbar. The temporal evolution of multi-charged ions, background neutrals
and ions is systematically studied for varying laser intensities. The temporal
evolution shows all the species have dual peak structure for all the laser
energies considered in the study. The fast peak is sharp whereas the slow peak
is broad similar to that observed in previous studies. Moreover, higher charged
ions have higher velocity, indicating acceleration from the transient electric
field produced at the very initial temporal stages of expansion. Interestingly,
the fast peak gets delayed, whereas the slow peak advances in time with
increased laser intensity, which has not been reported in earlier studies. The
observations point towards the possible role of ambipolar electric fields in
the unexpected observed behavior of the TOF profiles.


title:
Integrating Vectorized Lexical Constraints for Neural Machine
  Translation

abstract:
  Lexically constrained neural machine translation (NMT), which controls the
generation of NMT models with pre-specified constraints, is important in many
practical scenarios. Due to the representation gap between discrete constraints
and continuous vectors in NMT models, most existing works choose to construct
synthetic data or modify the decoding algorithm to impose lexical constraints,
treating the NMT model as a black box. In this work, we propose to open this
black box by directly integrating the constraints into NMT models.
Specifically, we vectorize source and target constraints into continuous keys
and values, which can be utilized by the attention modules of NMT models. The
proposed integration method is based on the assumption that the correspondence
between keys and values in attention modules is naturally suitable for modeling
constraint pairs. Experimental results show that our method consistently
outperforms several representative baselines on four language pairs,
demonstrating the superiority of integrating vectorized lexical constraints.


title: Tree Dimension and the Sauer-Shelah Dichotomy

abstract:
  We introduce tree dimension and its leveled variant in order to to measure
the complexity of leaf sets in binary trees. We then provide a tight upper
bound on the size of such sets using leveled tree dimension. This, in turn,
implies both the famous Sauer-Shelah Lemma for VC dimension and Bhaskar's
version for Littlestone dimension, giving clearer insight into why these
results place the exact same upper bound on their respective shatter functions.
We also classify the isomorphism types of maximal leaf sets by tree dimension.
Finally, we generalize this analysis to higher-arity trees.


title: Supporting Developers in Addressing Human-centric Issues in Mobile Apps

abstract:
  Failure to consider the characteristics, limitations, and abilities of
diverse end-users during mobile apps development may lead to problems for
end-users such as accessibility and usability issues. We refer to this class of
problems as human-centric issues. Despite their importance, there is a limited
understanding of the types of human-centric issues that are encountered by
end-users and taken into account by the developers of mobile apps. In this
paper, we examine what human-centric issues end-users report through Google App
Store reviews, which human-centric issues are a topic of discussion for
developers on GitHub, and whether end-users and developers discuss the same
human-centric issues. We then investigate whether an automated tool might help
detect such human-centric issues and whether developers would find such a tool
useful. To do this, we conducted an empirical study by extracting and manually
analysing a random sample of 1,200 app reviews and 1,200 issue comments from 12
diverse projects that exist on both Google App Store and GitHub. Our analysis
led to a taxonomy of human-centric issues that categorises human-centric issues
into three-high levels: App Usage, Inclusiveness, and User Reaction. We then
developed machine learning and deep learning models that are promising in
automatically identifying and classifying human-centric issues from app reviews
and developer discussions. A survey of mobile app developers shows that the
automated detection of human-centric issues has practical applications. Guided
by our findings, we highlight some implications and possible future work to
further understand and incorporate human-centric issues in mobile apps
development.


title: Perturbation in Warm Chromo-Natural Inflation

abstract:
  We examined the chromo-natural inflation in the context of warm inflation.
The dynamical equations of this model are obtained. We studied the cosmological
perturbation theory in this model. The sources of density fluctuations in this
model are mainly the thermal fluctuations of the inflaton field like general
warm inflationary model. Finally, cosmological observables, namely, the
spectral index and tensor to scalar ratio are calculated. It is found that the
cosmological observables are consistent with observational data and the tensor
to scalar ratio is smaller than that in the chromo-natural inflation.


title:
Efficient Pairing in Unknown Environments: Minimal Observations and
  TSP-based Optimization

abstract:
  Generating paired sequences with maximal compatibility from a given set is
one of the most important challenges in various applications, including
information and communication technologies. However, the number of possible
pairings explodes in a double factorial order as a function of the number of
entities, manifesting the difficulties of finding the optimal pairing that
maximizes the overall reward. In the meantime, in real-world systems, such as
user pairing in non-orthogonal multiple access (NOMA), pairing often needs to
be conducted at high speed in dynamically changing environments; hence,
efficient recognition of the environment and finding high reward pairings are
highly demanded. In this paper, we demonstrate an efficient pairing algorithm
to recognize compatibilities among elements as well as to find a pairing that
yields a high total compatibility. The proposed pairing strategy consists of
two phases. The first is the observation phase, where compatibility information
among elements is obtained by only observing the sum of rewards. We show an
efficient strategy that allows obtaining all compatibility information with
minimal observations. The minimum number of observations under these conditions
is also discussed, along with its mathematical proof. The second is the
combination phase, by which a pairing with a large total reward is determined
heuristically. We transform the pairing problem into a traveling salesman
problem (TSP) in a three-layer graph structure, which we call Pairing-TSP. We
demonstrate heuristic algorithms in solving the Pairing-TSP efficiently. This
research is expected to be utilized in real-world applications such as NOMA,
social networks, among others.


title:
Physics-Driven Deep Learning for Computational Magnetic Resonance
  Imaging

abstract:
  Physics-driven deep learning methods have emerged as a powerful tool for
computational magnetic resonance imaging (MRI) problems, pushing reconstruction
performance to new limits. This article provides an overview of the recent
developments in incorporating physics information into learning-based MRI
reconstruction. We consider inverse problems with both linear and non-linear
forward models for computational MRI, and review the classical approaches for
solving these. We then focus on physics-driven deep learning approaches,
covering physics-driven loss functions, plug-and-play methods, generative
models, and unrolled networks. We highlight domain-specific challenges such as
real- and complex-valued building blocks of neural networks, and translational
applications in MRI with linear and non-linear forward models. Finally, we
discuss common issues and open challenges, and draw connections to the
importance of physics-driven learning when combined with other downstream tasks
in the medical imaging pipeline.

categories: eess.IV cs.CV cs.LG eess.SP physics.med-ph


title:
Minimizing Age-upon-Decisions in Bufferless System: Service Scheduling
  and Decision Interval

abstract:
  In Internet of Things (IoT), the decision timeliness of time-sensitive
applications is jointly affected by the statistics of update process and
decision process. This work considers an update-and-decision system with a
Poisson-arrival bufferless queue, where updates are delivered and processed for
making decisions with exponential or periodic intervals. We use
age-upon-decisions (AuD) to characterize timeliness of updates at decision
moments, and the missing probability to specify whether updates are useful for
decision-making. Our theoretical analyses 1) present the average AuDs and the
missing probabilities for bufferless systems with exponential or deterministic
decision intervals under different service time distributions; 2) show that for
service scheduling, the deterministic service time achieves a lower average AuD
and a smaller missing probability than the uniformly distributed and the
negative exponentially distributed service time; 3) prove that the average AuD
of periodical decision system is larger than and will eventually drop to that
of Poisson decision system along with the increase of decision rate; however,
the missing probability in periodical decision system is smaller than that of
Poisson decision system. The numerical results and simulations verify the
correctness of our analyses, and demonstrate that the bufferless systems
outperform the systems applying infinite buffer length.


title: Training-free Transformer Architecture Search

abstract:
  Recently, Vision Transformer (ViT) has achieved remarkable success in several
computer vision tasks. The progresses are highly relevant to the architecture
design, then it is worthwhile to propose Transformer Architecture Search (TAS)
to search for better ViTs automatically. However, current TAS methods are
time-consuming and existing zero-cost proxies in CNN do not generalize well to
the ViT search space according to our experimental observations. In this paper,
for the first time, we investigate how to conduct TAS in a training-free manner
and devise an effective training-free TAS (TF-TAS) scheme. Firstly, we observe
that the properties of multi-head self-attention (MSA) and multi-layer
perceptron (MLP) in ViTs are quite different and that the synaptic diversity of
MSA affects the performance notably. Secondly, based on the observation, we
devise a modular strategy in TF-TAS that evaluates and ranks ViT architectures
from two theoretical perspectives: synaptic diversity and synaptic saliency,
termed as DSS-indicator. With DSS-indicator, evaluation results are strongly
correlated with the test accuracies of ViT models. Experimental results
demonstrate that our TF-TAS achieves a competitive performance against the
state-of-the-art manually or automatically design ViT architectures, and it
promotes the searching efficiency in ViT search space greatly: from about $24$
GPU days to less than $0.5$ GPU days. Moreover, the proposed DSS-indicator
outperforms the existing cutting-edge zero-cost approaches (e.g., TE-score and
NASWOT).


title:
A First-Principles Study on the Adsorption of Small Molecules on
  Arsenene: Comparison of Oxidation Kinetics in Arsenene, Antimonene,
  Phosphorene and InSe

abstract:
  Arsenene, a new group V two-dimensional (2D) semiconducting material beyond
phosphorene and antimonene, has recently gained an increasing attention owning
to its various interesting properties which can be altered or intentionally
functionalized by chemical reactions with various molecules. This work provides
a systematic study on the interactions of arsenene with the small molecules,
including H2, NH3, O2, H2O, NO, and NO2. It is predicted that O2, H2O, NO, and
NO2 are strong acceptors, while NH3 serves as a donor. Importantly, it is shown
a negligible charge transfer between H2 and arsenene which is ten times lower
than that between H2 and phosphorene and about thousand times lower than that
between H2 and InSe and antimonene. The calculated energy barrier for O2
splitting on arsenene is found to be as low as 0.67 eV. Thus, pristine arsenene
may easily oxidize in ambient conditions as other group V 2D materials. On the
other hand, the acceptor role of H2O on arsenene, similarly to the cases of
antimonene and InSe, may help to prevent the proton transfer between H2O and O
species by forming acids, which suppresses further structural degradation of
arsenene. The structural decomposition of the 2D layers upon interaction with
the environment may be avoided due to the acceptor role of H2O molecules as the
study predicts from the comparison of common group V 2D materials. However, the
protection for arsenene is still required due to its strong interaction with
other small environmental molecules. The present work renders the possible ways
to protect arsenene from structure degradation and to modulate its electronic
properties, which is useful for the material synthesis, storage and
applications.


title:
New nonlocal constants and first integrals for nonlinear Jacobi-type
  equations

abstract:
  We prove a set of general theorems that provide new nonlocal constants and
first integrals for nonlinear Jacobi-type ordinary differential equations.
Applications include equations of the Painleve-Gambier classification.


title: Spectral analysis of a mixed method for linear elasticity

abstract:
  The purpose of this paper is to analyze a mixed method for linear elasticity
eigenvalue problem, which approximates numerically the stress, displacement,
and rotation, by piecewise $(k+1)$, $k$ and $(k+1)$-th degree polynomial
functions ($k\geq 1$), respectively. The numerical eigenfunction of stress is
symmetric. By the discrete $H^1$-stability of numerical displacement, we prove
an $O(h^{k+2})$ approximation to the $L^{2}$-orthogonal projection of the
eigenspace of exact displacement for the eigenvalue problem with proper
regularity assumption. Thus via postprocessing, we obtain a better
approximation to the exact displacement of the eigenproblem than conventional
methods. We also prove that numerical approximation to the eigenfunction of
stress is locking free with respect to Poisson ratio. We introduce a
hybridization to reduce the mixed method to a condensed eigenproblem and prove
an $O(h^2)$ initial approximation (independent of the inverse of the elasticity
operator) of the eigenvalue for the nonlinear eigenvalue problems by using the
discrete $H^1$-stability of numerical displacement, while only $O(h)$
approximation can be obtained if we use the traditional inf-sup condition.


title:
Modality Competition: What Makes Joint Training of Multi-modal Network
  Fail in Deep Learning? (Provably)

abstract:
  Despite the remarkable success of deep multi-modal learning in practice, it
has not been well-explained in theory. Recently, it has been observed that the
best uni-modal network outperforms the jointly trained multi-modal network,
which is counter-intuitive since multiple signals generally bring more
information. This work provides a theoretical explanation for the emergence of
such performance gap in neural networks for the prevalent joint training
framework. Based on a simplified data distribution that captures the realistic
property of multi-modal data, we prove that for the multi-modal late-fusion
network with (smoothed) ReLU activation trained jointly by gradient descent,
different modalities will compete with each other. The encoder networks will
learn only a subset of modalities. We refer to this phenomenon as modality
competition. The losing modalities, which fail to be discovered, are the
origins where the sub-optimality of joint training comes from. Experimentally,
we illustrate that modality competition matches the intrinsic behavior of
late-fusion joint training.


title:
The Harmony Index: a Utilitarian Metric for Measuring Effectiveness in
  Mixed-Skill Teams

abstract:
  As teamwork becomes ever-more important in a new age of remote work, it is
critical to develop metrics to quantitatively evaluate how effective teams are.
This is especially true with mixed-modality teams, such as those that include a
human and an agent or human and robot. We propose a novel utilitarian metric,
the Harmony Index, which quantifies the effectiveness of team members by
classifying them into four sub-types based on the result of their teaming on
overall effectiveness. This index is evaluated using a real-world dataset of
over 1 million interactions, and potential future uses of this index are
explored in the realm of team science.


title: Hybrid Relay-Reflecting Intelligent Surface-Aided Covert Communications

abstract:
  In this paper, we propose to use a hybrid relay-reflecting intelligent
surface (HR-RIS) to enhance the performance of a covert communication system.
Specifically, the HR-RIS consists of passive reflecting elements and active
relay elements to manipulate the wireless signals from a transmitter to a
desired receiver while ensuring the covertness of the transmission via avoiding
such signals being detected by a warden. To fully explore the benefits offered
by the HR-RIS, we first formulate the joint design of the transmit power and
relay/reflection coefficients of the HR-RIS as an optimization problem to
maximize the covert rate subject to a covertness constraint. To tackle the
solution to this optimization problem, we then derive a closed-form expression
for an upper bound on covert rate, based on which we develop an alternate
algorithm to solve the formulated optimization problem. Our examination shows
that the HR-RIS outperforms the traditional RIS in term of achieving a higher
covert rate. Interestingly, we also observe the major part of the performance
gain brought by the HR-RIS can be obtained by a small number of active relay
elements (e.g., 5) and further increasing this number does not improve the
covert communication performance.


title: Efficient Few-Shot Object Detection via Knowledge Inheritance

abstract:
  Few-shot object detection (FSOD), which aims at learning a generic detector
that can adapt to unseen tasks with scarce training samples, has witnessed
consistent improvement recently. However, most existing methods ignore the
efficiency issues, e.g., high computational complexity and slow adaptation
speed. Notably, efficiency has become an increasingly important evaluation
metric for few-shot techniques due to an emerging trend toward embedded AI. To
this end, we present an efficient pretrain-transfer framework (PTF) baseline
with no computational increment, which achieves comparable results with
previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an
initializer named knowledge inheritance (KI) to reliably initialize the novel
weights for the box classifier, which effectively facilitates the knowledge
transfer process and boosts the adaptation speed. Within the KI initializer, we
propose an adaptive length re-scaling (ALR) strategy to alleviate the vector
length inconsistency between the predicted novel weights and the pretrained
base weights. Finally, our approach not only achieves the SOTA results across
three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits
high efficiency with 1.8-9.0x faster adaptation speed against the other methods
on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is
the first work to consider the efficiency problem in FSOD. We hope to motivate
a trend toward powerful yet efficient few-shot technique development. The codes
are publicly available at https://github.com/Ze-Yang/Efficient-FSOD.


title: Transverse Spectral Instabilities in Konopelchenko-Dubrovsky Equation

abstract:
  We study the transverse spectral stability of the one-dimensional
small-amplitude periodic traveling wave solutions of the (2+1)-dimensional
Konopelchenko-Dubrovsky (KD) equation. We show that these waves are
transversely unstable with respect to two-dimensional perturbations that are
periodic in both directions with long wavelength in the transverse direction.
We also show that these waves are transversely stable with respect to
perturbations which are either mean-zero periodic or square-integrable in the
direction of the propagation of the wave and periodic in the transverse
direction with finite or short wavelength. We discuss the implications of these
results for special cases of the KD equation - namely, KP-II and mKP-II
equations.


title:
Second-order accurate numerical scheme with graded meshes for the
  nonlinear partial integrodifferential equation arising from viscoelasticity

abstract:
  This paper establishes and analyzes a second-order accurate numerical scheme
for the nonlinear partial integrodifferential equation with a weakly singular
kernel. In the time direction, we apply the Crank-Nicolson method for the time
derivative, and the product-integration (PI) rule is employed to deal with
Riemann-Liouville fractional integral. From which, the non-uniform meshes are
utilized to compensate for the singular behavior of the exact solution at $t=0$
so that our method can reach second-order convergence for time. In order to
formulate a fully discrete implicit difference scheme, we employ a standard
centered difference formula for the second-order spatial derivative, and the
Galerkin method based on piecewise linear test functions is used to approximate
the nonlinear convection term. Then we derive the existence and uniqueness of
numerical solutions for the proposed implicit difference scheme. Meanwhile,
stability and convergence are proved by means of the discrete energy method.
Furthermore, to demonstrate the effectiveness of the proposed method, we
utilize a fixed point iterative algorithm to calculate the discrete scheme.
Finally, numerical experiments illustrate the feasibility and efficiency of the
proposed scheme, in which numerical results are consistent with our theoretical
analysis.


title:
Super-Eddington accretion of the first Galactic Ultra-luminous X-ray
  pulsar Swift J0243.6+6124

abstract:
  We present a detailed timing study of the pulse profile of Swift J0243.6+6124
with HXMT and Fermi/GBM data during its 2017 giant outburst. The double-peak
profile at luminosity above $5\times10^{38}$erg\,s$^{-1}$ is found to be 0.25
phase offset from that below $1.5\times10^{38}$erg\,s$^{-1}$, which strongly
supports for a transition from a pencil beam to a fan beam, and thus for the
formation of shock dominated accretion column. During the rising stage of the
high double-peak regime, the faint peak got saturated in 10-100 keV band above
a luminosity of $L_t\sim1.3\times10^{39}$erg\,s$^{-1}$, which is coincident
with sudden spectral changes of both the main and faint peaks. They imply a
sudden change of emission pattern around $L_t$. The spin-up rate ($\dot{\nu}$)
is linearly correlated with luminosity ($L$) below $L_t$, consistent with the
prediction of a radiation pressure dominated (RPD) disk. The $\dot{\nu}-L$
relation flattens above $L_t$, indicating a less efficient transfer of angular
momentum and a change of accretion disk geometry above $L_t$. It is likely due
to irradiation of the disk by the central accretion column and indicates
significant radiation feedback before the inner disk radius reaching the
spherization radius.


title: Bivariate Distribution Regression with Application to Insurance Data

abstract:
  This article introduces an estimation method for the conditional joint
distribution of bivariate outcomes, based on the distribution regression
approach and the factorization method. The proposed method can apply for
discrete, continuous or mixed distribution outcomes. It is semiparametric in
that both marginal and joint distributions are left unspecified, conditional on
covariates. Unlike the existing parametric approaches, our method is simple yet
flexible to encapsulate distributional dependence structures of bivariate
outcomes and covariates. Various simulation results confirm that our method can
perform similarly or better in finite samples compared to the alternative
methods. In an application to the study of a motor third-part liability
insurance portfolio, the proposed method effectively captures key
distributional features in the data, especially the value at risks conditional
on covariates. This result suggests that this semiparametric approach can serve
as an alternative in insurance risk management.


title: Observation of the double Dalitz decay $\eta'\to e^+e^-e^+e^-$

abstract:
  Based on $(10087 \pm 44)$ million $J/\psi$ events collected with the BESIII
detector at BEPCII, the double Dalitz decay $\eta'\to e^+e^-e^+e^-$ is observed
for the first time via the $J/\psi\to\gamma\eta'$ decay process. The
significance is found to be 5.7$\sigma$ with systematic uncertainties taken
into consideration. Its branching fraction is determined to be
$\mathcal{B}(\eta'\to e^+ e^- e^+ e^-)
=(4.5\pm1.0(\mathrm{stat.})\pm0.5(\mathrm{sys.})) \times 10^{-6}$.


title:
Negative Selection by Clustering for Contrastive Learning in Human
  Activity Recognition

abstract:
  Contrastive learning has been applied to Human Activity Recognition (HAR)
based on sensor data owing to its ability to achieve performance comparable to
supervised learning with a large amount of unlabeled data and a small amount of
labeled data. The pre-training task for contrastive learning is generally
instance discrimination, which specifies that each instance belongs to a single
class, but this will consider the same class of samples as negative examples.
Such a pre-training task is not conducive to human activity recognition tasks,
which are mainly classification tasks. To address this problem, we follow
SimCLR to propose a new contrastive learning framework that negative selection
by clustering in HAR, which is called ClusterCLHAR. Compared with SimCLR, it
redefines the negative pairs in the contrastive loss function by using
unsupervised clustering methods to generate soft labels that mask other samples
of the same cluster to avoid regarding them as negative samples. We evaluate
ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR,
using mean F1-score as the evaluation metric. The experiment results show that
it outperforms all the state-of-the-art methods applied to HAR in
self-supervised learning and semi-supervised learning.


title:
Koopman and Perron-Frobenius Operators on reproducing kernel Banach
  spaces

abstract:
  Koopman and Perron-Frobenius operators for dynamical systems have been
getting popular in a number of fields in science these days. Properties of the
Koopman operator essentially depend on the choice of function spaces where it
acts. Particularly the case of reproducing kernel Hilbert spaces (RKHSs) draws
more and more attention in data science. In this paper, we give a general
framework for Koopman and Perron-Frobenius operators on reproducing kernel
Banach spaces (RKBSs). More precisely, we extend basic known properties of
these operators from RKHSs to RKBSs and state new results, including symmetry
and sparsity concepts, on these operators on RKBS for discrete and continuous
time systems.


title:
Enhanced Contour Tracking: a Time-Varying Internal Model Principle-Based
  Approach

abstract:
  We propose a time-varying internal model principle-based contouring control
(TV-IMCC) methodology to enhance the contour tracking performance with both
axial and contour error reduction. The proposed TV-IMCC is composed of two main
parts: an extended position domain framework with master-slave structures for
contour regulation, and the time-varying internal model controllers for axial
tracking precision improvement. The important features of the TV-IMCC lie in:
1) it is of an asymptotical stability for irregular contour tracking compared
to the cross coupled control (CCC); 2) it does not lead to system nonlinearity
and hence is well-suited for tracking multi-axis contours compared to the task
coordinate frame (TCF) control; 3) it reduces the tracking error and extends
the class of contours can be tracked compared to the position domain control
(PDC). Furthermore, the stability analysis of the closed-loop system with the
proposed TV-IMCC methodology is provided. And various simulation and
experimental results validate the TV-IMCC with enhanced contour tracking
performance. A major potential of the proposed TV-IMCC is in the field of
multi-axis ultra-precision contour tracking. Moreover, the TV-IMCC does not
require strict precision of the master axis, and hence it can be easily applied
to macro-micro motion systems, such as galvanometer scanner-stage synchronized
systems.


title: On the spectrum of the spectrum of the periodic anderson-bernoulli model

abstract:
  We analyze the spectrum of a discrete Schrodinger operator with a potential
given by a periodic variant of the Anderson Model. In order to do so, we study
the uniform hyperbolicity of a Schrodinger cocycle generated by the SL(2,R)
transfer matrices. In the specific case of the potential generated by an
alternating sequence of random values we show that the almost sure spectrum
consists of at most 4 intervals.


title: Electrical and magneto transport in 2D semiconducting MXene Ti2CO2

abstract:
  The Hall scattering factor is formulated using Rode's iterative approach to
solving the Boltzmann transport equation in such a way that it may be easily
computed within the scope of ab-inito calculations. Using this method in
conjunction with density functional theory based calculations, we demonstrate
that the Hall scattering factor in electron-doped Ti2CO2 varies greatly with
temperature and concentration, ranging from 0.2 to around 1.3 for weak magnetic
fields. The electrical transport was modelled primarily using three scattering
mechanisms: piezoelectric scattering, acoustic scattering, and polar optical
phonons. Even though the mobility in this material is primarily limited by
acoustic phonons, piezoelectric scattering also plays an important role which
was not highlighted earlier.


title: Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions

abstract:
  The syntactic categories of categorial grammar formalisms are structured
units made of smaller, indivisible primitives, bound together by the underlying
grammar's category formation rules. In the trending approach of constructive
supertagging, neural models are increasingly made aware of the internal
category structure, which in turn enables them to more reliably predict rare
and out-of-vocabulary categories, with significant implications for grammars
previously deemed too complex to find practical use. In this work, we revisit
constructive supertagging from a graph-theoretic perspective, and propose a
framework based on heterogeneous dynamic graph convolutions aimed at exploiting
the distinctive structure of a supertagger's output space. We test our approach
on a number of categorial grammar datasets spanning different languages and
grammar formalisms, achieving substantial improvements over previous state of
the art scores. Code will be made available at
https://github.com/konstantinosKokos/dynamic-graph-supertagging


title:
A Multi-Characteristic Learning Method with Micro-Doppler Signatures for
  Pedestrian Identification

abstract:
  The identification of pedestrians using radar micro-Doppler signatures has
become a hot topic in recent years. In this paper, we propose a
multi-characteristic learning (MCL) model with clusters to jointly learn
discrepant pedestrian micro-Doppler signatures and fuse the knowledge learned
from each cluster into final decisions. Time-Doppler spectrogram (TDS) and
signal statistical features extracted from FMCW radar, as two categories of
micro-Doppler signatures, are used in MCL to learn the micro-motion information
inside pedestrians' free walking patterns. The experimental results show that
our model achieves a higher accuracy rate and is more stable for pedestrian
identification than other studies, which make our model more practical.


title:
Disassortative mixing of boundedly-rational players in socio-ecological
  systems

abstract:
  Bounded rationality refers to the non-optimal rationality of players in
non-cooperative games. In a networked game, the bounded rationality of players
may be heterogeneous and spatially distributed. It has been shown that the
`system rationality', which indicates the overall rationality of a network of
players, may play a key role in the emergence of scale-free or core-periphery
topologies in real-world networks. On the other hand, scalar-assortativity is a
metric used to quantify the assortative mixing of nodes with respect to a given
scalar attribute. In this work, we observe the effect of node rationality-based
scalar-assortativity, on the system rationality of a network. Based on
simulation results, we show that irrespective of the placement of nodes with
higher rationality, it is the disassortative mixing of node rationality that
helps to maximize system rationality in a population. The findings may have
useful interpretations and applications in socio-economic systems in maximizing
the utility of interactions in a population of strategic players.


title: Sylvester sums on the Frobenius set in arithmetic progression

abstract:
  Let $a_1,a_2,\dots,a_k$ be positive integers with
$\gcd(a_1,a_2,\dots,a_k)=1$. The concept of the weighted sum $\sum_{n\in{\rm
NR}}\lambda^{n}$ is introduced in \cite{KZ0,KZ}, where ${\rm NR}={\rm
NR}(a_1,a_2,\dots,a_k)$ denotes the set of positive integers nonrepresentable
in terms of $a_1,a_2,\dots,a_k$. When $\lambda=1$, such a sum is often called
Sylvester sum. The main purpose of this paper is to give explicit expressions
of the Sylvester sum ($\lambda=1$) and the weighed sum ($\lambda\ne 1$), where
$a_1,a_2,\dots,a_k$ forms arithmetic progressions. As applications, various
other cases are also considered, including weighted sums, almost arithmetic
sequences, arithmetic sequences with an additional term, and geometric-like
sequences. Several examples illustrate and confirm our results.


title:
Comparative Analysis of GPGPU based ACO and PSO Algorithm for Employee
  Scheduling Problems

abstract:
  Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are
simple, easy to implement, their robustness to control parameters, and their
computational efficiency when compared with mathematical algorithms and other
heuristic optimization techniques. The calculation in PSO and ACO is very
simple. Compared with the other developing calculations, it occupies the bigger
optimization ability and it can be completed easily. It is used to solve many
NP-Hard problems. Employee Scheduling is a real-life NP-Hard problem faced by
many organizations. Self-scheduling in all situations is not always practical
and possible. Nurse Rostering is related to highly constrained resource
allocation problem into slots in a legal shift Earlier the problem was solved
using different heuristic algorithms. In this dissertation, we have proposed,
GPGPU based parallelization of PSO and ACO to solve Employee scheduling
problems. To parallelize both algorithms, a master-slave approach is used. The
BCV 8.13.1 data set is used for experimentation purposes. Analysis of results
is done based on mean, standard deviation, standard mean error. Keywords:
Employee Scheduling, Parallelization, PSO, GPGPU.


title: Nonlinear Fano resonance without a continuum

abstract:
  A new mechanism of Fano resonance induced by distant discrete levels in atoms
or molecules is proposed and experimentally verified by Doppler-free Lamb-dip
spectroscopy of vibration-rotational transitions of CO$_2$. The observed
spectrum has an asymmetric profile and its amplitude increases quadratically
with the probe laser power. Our results appeal to further investigations of
Fano resonances and facilitate a broad range of topics based on narrow
transitions.


title:
A Method of Data Augmentation to Train a Small Area Fingerprint
  Recognition Deep Neural Network with a Normal Fingerprint Database

abstract:
  Fingerprints are popular among the biometric based systems due to ease of
acquisition, uniqueness and availability. Nowadays it is used in smart phone
security, digital payment and digital locker. The traditional fingerprint
matching methods based on minutiae are mainly applicable for large-area
fingerprint and the accuracy rate would reduce significantly when dealing with
small-area fingerprint from smart phone. There are many attempts to using deep
learning for small-area fingerprint recognition, and there are many successes.
But training deep neural network needs a lot of datasets for training. There is
no well-known dataset for small-area, so we have to make datasets ourselves. In
this paper, we propose a method of data augmentation to train a small-area
fingerprint recognition deep neural network with a normal fingerprint database
(such as FVC2002) and verify it via tests. The experimental results showed the
efficiency of our method.


title:
Particle-In-Cell observations of Brillouin scattering for laser
  interacting with magnetized overdense plasma

abstract:
  One dimensional Particle-in-cell simulations using OSIRIS-4.0 has been
conducted to study the interaction of a laser electromagnetic pulse with an
overdense magnetized plasma target. The external magnetic field has been chosen
to be directed along the laser propagation direction. This geometry supports
the propagation of right (R) and left (L) circularly polarised electromagnetic
waves in the plasma. The laser pulse is allowed to propagate inside the plasma
when its frequency falls in the pass band of the dispersion curves of L and/or
R waves. The strength of the applied external magnetic field is chosen as a
parameter to ensure that the laser frequency lies in the appropriate pass band.
It is demonstrated that for all possible polarization of the incident laser,
parametric process involving a scattered Electromagnetic wave and an
electrostatic mode occur. The parametric process has been identified as that
due to the Brillouin back scattering process.


title:
Deuteron analyzing powers $A_y$, $A_{yy}$ and $A_{xx}$ in dp-elastic
  scattering at large transverse momenta

abstract:
  The results on the the vector $A_y$, $A_{yy}$ and $A_{xx}$ analyzing powers
in deuteron-proton elastic scattering at large scattering angles are presented.
These data were obtained at internal target at JINR Nuclotron in the energy
range 400-1800 MeV using polarized deuteron beam from new polarized ion source.
New data on the deuteron analyzing powers in the wide energy range demonstrate
the sensitivity to the short-range spin structure of the isoscalar
nucleon-nucleon correlations.


title: Scale-Equivalent Distillation for Semi-Supervised Object Detection

abstract:
  Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on
self-training, i.e., generating hard pseudo-labels by a teacher model on
unlabeled data as supervisory signals. Although they achieved certain success,
the limited labeled data in semi-supervised learning scales up the challenges
of object detection. We analyze the challenges these methods meet with the
empirical experiment results. We find that the massive False Negative samples
and inferior localization precision lack consideration. Besides, the large
variance of object sizes and class imbalance (i.e., the extreme ratio between
background and object) hinder the performance of prior arts. Further, we
overcome these challenges by introducing a novel approach, Scale-Equivalent
Distillation (SED), which is a simple yet effective end-to-end knowledge
distillation framework robust to large object size variance and class
imbalance. SED has several appealing benefits compared to the previous works.
(1) SED imposes a consistency regularization to handle the large scale variance
problem. (2) SED alleviates the noise problem from the False Negative samples
and inferior localization precision. (3) A re-weighting strategy can implicitly
screen the potential foreground regions of the unlabeled data to reduce the
effect of class imbalance. Extensive experiments show that SED consistently
outperforms the recent state-of-the-art methods on different datasets with
significant margins. For example, it surpasses the supervised counterpart by
more than 10 mAP when using 5% and 10% labeled data on MS-COCO.


title:
Quantitative Evaluation Approach for Translation of Perceptual
  Soundscape Attributes: Initial Application to the Thai Language

abstract:
  Translation of perceptual soundscape attributes from one language to another
remains a challenging task that requires a high degree of fidelity in both
psychoacoustic and psycholinguistic senses across the target population. Due to
the inherently subjective nature of human perception, translating soundscape
attributes using only small focus group discussion or expert panels could lead
to translations with psycholinguistic meanings that, in a non-expert setting,
deviate or distort from that of the source language. In this work, we present a
quantitative evaluation method based on the circumplex model of soundscape
perception to assess the overall translation quality across a set of criteria.
As an initial application domain, we demonstrated the use of the quantitative
evaluation framework in the context of an English-to-Thai translation of
soundscape attributes.


title: New Distinguishers for Negation-Limited Weak Pseudorandom Functions

abstract:
  We show how to distinguish circuits with $\log k$ negations (a.k.a
$k$-monotone functions) from uniformly random functions in
$\exp\left(\tilde{O}\left(n^{1/3}k^{2/3}\right)\right)$ time using random
samples. The previous best distinguisher, due to the learning algorithm by
Blais, Cannone, Oliveira, Servedio, and Tan (RANDOM'15), requires
$\exp\big(\tilde{O}(n^{1/2} k)\big)$ time.
  Our distinguishers are based on Fourier analysis on \emph{slices of the
Boolean cube}. We show that some "middle" slices of negation-limited circuits
have strong low-degree Fourier concentration and then we apply a variation of
the classic Linial, Mansour, and Nisan "Low-Degree algorithm" (JACM'93) on
slices. Our techniques also lead to a slightly improved weak learner for
negation limited circuits under the uniform distribution.


title: Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition

abstract:
  We introduce Ev-TTA, a simple, effective test-time adaptation algorithm for
event-based object recognition. While event cameras are proposed to provide
measurements of scenes with fast motions or drastic illumination changes, many
existing event-based recognition algorithms suffer from performance
deterioration under extreme conditions due to significant domain shifts. Ev-TTA
mitigates the severe domain gaps by fine-tuning the pre-trained classifiers
during the test phase using loss functions inspired by the spatio-temporal
characteristics of events. Since the event data is a temporal stream of
measurements, our loss function enforces similar predictions for adjacent
events to quickly adapt to the changed environment online. Also, we utilize the
spatial correlations between two polarities of events to handle noise under
extreme illumination, where different polarities of events exhibit distinctive
noise distributions. Ev-TTA demonstrates a large amount of performance gain on
a wide range of event-based object recognition tasks without extensive
additional training. Our formulation can be successfully applied regardless of
input representations and further extended into regression tasks. We expect
Ev-TTA to provide the key technique to deploy event-based vision algorithms in
challenging real-world applications where significant domain shift is
inevitable.


title: Proper conflict-free list-coloring, subdivisions, and layered treewidth

abstract:
  Proper conflict-free coloring is an intermediate notion between proper
coloring of a graph and proper coloring of its square. Typical examples of
graphs with large proper conflict-free chromatic number include $(\leq
1)$-subdivisions of graphs with large chromatic number. In this paper, we prove
that a rough converse statement is true even for the list-coloring setting: for
every graph $H$, there exists an integer $c_H$ such that every graph with no
subdivision of $H$ is (properly) conflict-free $c_H$-choosable. This gives a
(partial) answer of a question of Caro, Petru\v{s}evski and \v{S}krekovski. We
also prove quantitatively better bounds for minor-closed families, implying
some known results about (non-list) proper conflict-free coloring and odd
coloring in the literature. Moreover, we prove that every graph with layered
treewidth at most $w$ is (properly) conflict-free $(8w-1)$-choosable. This
result applies to the class of $(g,k)$-planar graphs, which are graph classes
whose coloring problems attract attentions recently.


title:
Distort to Detect, not Affect: Detecting Stealthy Sensor Attacks with
  Micro-distortion

abstract:
  In this paper, we propose an effective and easily deployable approach to
detect the presence of stealthy sensor attacks in industrial control systems,
where (legacy) control devices critically rely on accurate (and usually
non-encrypted) sensor readings. Specifically, we focus on stealthy attacks that
crash a sensor and then immediately impersonate that sensor by sending out fake
readings. We consider attackers who aim to stay hidden in the system for a
prolonged period. To detect such attacks, our approach relies on continuous
injection of "micro distortion" to the original sensor's readings. In
particular, the injected distortion should be kept strictly within a small
magnitude (e.g., $0.5\%$ of the possible operating value range), to ensure it
does not affect the normal functioning of the ICS. Our approach uses a
pre-shared secret sequence between a sensor and the defender to generate the
micro-distortions. One key challenge is that the micro-distortions injected are
often much lower than the sensor's actual readings, hence can be easily
overwhelmed by the latter. To overcome this, we leverage the observation that
sensor readings in many ICS (and power grid in particular) often change
gradually in a significant fraction of time (i.e., with small difference
between consecutive time slots). We devise a simple yet effective algorithm
that can detect stealthy attackers in a highly accurate and fast (i.e., using
less than 100 samples) manner. We demonstrate the effectiveness of our defense
using real-world sensor reading traces from two different smart grid systems.


title: Local Statistics of Random Permutations from Free Products

abstract:
  Let $\Gamma=G_1*\ldots*G_k$ be a free product of groups where each of
$G_1,\ldots,G_k$ is either finite, finitely generated free, or an orientable
hyperbolic surface group. For a fixed element $\gamma\in\Gamma$, a
$\gamma$-random permutation in the symmetric group $S_N$ is the image of
$\gamma$ through a uniformly random homomorphism $\Gamma\to S_{N}$. In this
paper we study local statistics of $\gamma$-random permutations and their
asymptotics as $N$ grows. We first consider
$\mathbb{E}\left[\mathrm{fix}_{\gamma}\left(N\right)\right]$, the expected
number of fixed points in a $\gamma$-random permutation in $S_{N}$. We show
that unless $\gamma$ has finite order, the limit of
$\mathbb{E}\left[\mathrm{fix}_{\gamma}\left(N\right)\right]$ as $N\to\infty$ is
an integer, and is equal to the number of subgroups $H\le\Gamma$ containing
$\gamma$ such that $H\cong\mathbb{Z}$ or $H\cong C_{2}*C_{2}$. Equivalently,
this is the number of subgroups $H\le\Gamma$ containing $\gamma$ and having
(rational) Euler characteristic zero. We also prove there is an asymptotic
expansion for $\mathbb{E}\left[\mathrm{fix}_{\gamma}\left(N\right)\right]$ and
determine the limit distribution of the number of fixed points as $N\to\infty$.
These results are then generalized to all statistics of cycles of fixed
lengths.


title: Packing metric mean dimension of sets of generic points

abstract:
  We first establish a mean version of Katok's entropy formula in this paper,
which is an analogue of the classical Katok's entropy formula. Then we
establish two new variational principles for Bowen upper metric mean dimension
and packing upper metric mean dimension on subsets in terms of two
measure-theoretic quantities related to Katok's entropy defined by
Carath$\bar{e}$odory-Pesin structures. Finally, we obtain a new formula of
packing metric mean dimension of generic points of ergodic measures without
additional conditions compared with our previous work \cite{ycz22}.
Additionally, we obtain inverse variational principles for packing metric mean
dimension.


title: Few-shot Named Entity Recognition with Self-describing Networks

abstract:
  Few-shot NER needs to effectively capture information from limited instances
and transfer useful knowledge from external resources. In this paper, we
propose a self-describing mechanism for few-shot NER, which can effectively
leverage illustrative instances and precisely transfer knowledge from external
resources by describing both entity types and mentions using a universal
concept set. Specifically, we design Self-describing Networks (SDNet), a
Seq2Seq generation model which can universally describe mentions using
concepts, automatically map novel entity types to concepts, and adaptively
recognize entities on-demand. We pre-train SDNet with large-scale corpus, and
conduct experiments on 8 benchmarks from different domains. Experiments show
that SDNet achieves competitive performances on all benchmarks and achieves the
new state-of-the-art on 6 benchmarks, which demonstrates its effectiveness and
robustness.


title: Inquisitive Logic as an Epistemic Logic of Knowing How

abstract:
  In this paper, we present an alternative interpretation of propositional
inquisitive logic as an epistemic logic of knowing how. In our setting, an
inquisitive logic formula $\alpha$ being supported by a state is formalized as
"knowing how to resolve $\alpha$" (more colloquially, "knowing how $\alpha$ is
true") holds on the S5 epistemic model corresponding to the state. Based on
this epistemic interpretation, we use a dynamic epistemic logic with both
know-how and know-that operators to capture the epistemic information behind
the innocent-looking connectives in inquisitive logic. We show that the set of
valid know-how formulas corresponds precisely to the inquisitive logic. The
main result is a complete axiomatization with intuitive axioms using the full
dynamic epistemic language. Moreover, we show that the know-how operator and
the dynamic operator can both be eliminated without changing the expressivity
over models, which is consistent with the modal translation of inquisitive
logic existing in the literature. We hope our framework can give an intuitive
alternative interpretation of various concepts and technical results in
inquisitive logic, and also provide a powerful and flexible tool to do
inquisitive reasoning in an epistemic context.


title: Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis

abstract:
  Many studies on dialog emotion analysis focus on utterance-level emotion
only. These models hence are not optimized for dialog-level emotion detection,
i.e. to predict the emotion category of a dialog as a whole. More importantly,
these models cannot benefit from the context provided by the whole dialog. In
real-world applications, annotations to dialog could fine-grained, including
both utterance-level tags (e.g. speaker type, intent category, and emotion
category), and dialog-level tags (e.g. user satisfaction, and emotion curve
category). In this paper, we propose a Context-based Hierarchical Attention
Capsule~(Chat-Capsule) model, which models both utterance-level and
dialog-level emotions and their interrelations. On a dialog dataset collected
from customer support of an e-commerce platform, our model is also able to
predict user satisfaction and emotion curve category. Emotion curve refers to
the change of emotions along the development of a conversation. Experiments
show that the proposed Chat-Capsule outperform state-of-the-art baselines on
both benchmark dataset and proprietary dataset. Source code will be released
upon acceptance.


title:
ATOMS: ALMA Three-millimeter Observations of Massive Star-forming
  regions -- X. Chemical differentiation among the massive cores in G9.62+0.19

abstract:
  Investigating the physical and chemical structures of massive star-forming
regions is critical for understanding the formation and the early evolution of
massive stars. We performed a detailed line survey toward six dense cores named
as MM1, MM4, MM6, MM7, MM8, and MM11 in G9.62+0.19 star-forming region resolved
in ALMA band 3 observations. Toward these cores, about 172 transitions have
been identified and attributed to 16 species including organic Oxygen-,
Nitrogen-, Sulfur-bearing molecules and their isotopologues. Four dense cores
MM7, MM8, MM4, and MM11 are line rich sources. Modeling of these spectral lines
reveals the rotational temperature in a range of 72$-$115~K, 100$-$163~K,
102$-$204~K, and 84$-$123~K for the MM7, MM8, MM4, and MM11, respectively. The
molecular column densities are 1.6 $\times$ 10$^{15}$ $-$ 9.2 $\times$
10$^{17}$~cm$^{-2}$ toward the four cores. The cores MM8 and MM4 show chemical
difference between Oxygen- and Nitrogen-bearing species, i.e., MM4 is rich in
oxygen-bearing molecules while nitrogen-bearing molecules especially
vibrationally excited HC$_{3}$N lines are mainly observed in MM8. The distinct
initial temperature at accretion phase may lead to this N/O differentiation.
Through analyzing column densities and spatial distributions of O-bearing
Complex Organic Molecules (COMs), we found that C$_{2}$H$_{5}$OH and
CH$_{3}$OCH$_{3}$ might have a common precursor, CH$_{3}$OH. CH$_{3}$OCHO and
CH$_{3}$OCH$_{3}$ are likely chemically linked. In addition, the observed
variation in HC$_{3}$N and HC$_{5}$N emission may indicate that their different
formation mechanism at hot and cold regions.


title:
Hybrid Angle Control and Almost Global Stability of Non-synchronous
  Hybrid AC/DC Power Grids

abstract:
  This paper explores the stability of nonsynchronous hybrid ac/dc power grids
under the grid-forming hybrid angle control strategy. We formulate dynamical
models for the ac grids and transmission lines, interlinking converters, and dc
generations and interconnections. Next, we establish the existence and
uniqueness of the closed-loop equilibria for the overall system. Subsequently,
we demonstrate global attractivity of the equilibria, local asymptotic
stability of the desired equilibrium point, and instability and
zero-Lebesgue-measure region of attraction for other equilibria. The theoretic
results are derived under mild, parametric, and unified stability/instability
conditions. Finally, relying on the intermediate results, we conclude the
almost global asymptotic stability of the hybrid ac/dc power grids with
interlinking converters that are equipped with hybrid angle control. Last, we
present several remarks on the practical and theoretical aspects of the problem
under investigation.


title:
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument
  Mining Tasks

abstract:
  Traditionally, a debate usually requires a manual preparation process,
including reading plenty of articles, selecting the claims, identifying the
stances of the claims, seeking the evidence for the claims, etc. As the AI
debate attracts more attention these years, it is worth exploring the methods
to automate the tedious process involved in the debating system. In this work,
we introduce a comprehensive and large dataset named IAM, which can be applied
to a series of argument mining tasks, including claim extraction, stance
classification, evidence extraction, etc. Our dataset is collected from over 1k
articles related to 123 topics. Near 70k sentences in the dataset are fully
annotated based on their argument properties (e.g., claims, stances, evidence,
etc.). We further propose two new integrated argument mining tasks associated
with the debate preparation process: (1) claim extraction with stance
classification (CESC) and (2) claim-evidence pair extraction (CEPE). We adopt a
pipeline approach and an end-to-end method for each integrated task separately.
Promising experimental results are reported to show the values and challenges
of our proposed tasks, and motivate future research on argument mining.


title:
Can Prompt Probe Pretrained Language Models? Understanding the Invisible
  Risks from a Causal View

abstract:
  Prompt-based probing has been widely used in evaluating the abilities of
pretrained language models (PLMs). Unfortunately, recent studies have
discovered such an evaluation may be inaccurate, inconsistent and unreliable.
Furthermore, the lack of understanding its inner workings, combined with its
wide applicability, has the potential to lead to unforeseen risks for
evaluating and applying PLMs in real-world applications. To discover,
understand and quantify the risks, this paper investigates the prompt-based
probing from a causal view, highlights three critical biases which could induce
biased results and conclusions, and proposes to conduct debiasing via causal
intervention. This paper provides valuable insights for the design of unbiased
datasets, better probing frameworks and more reliable evaluations of pretrained
language models. Furthermore, our conclusions also echo that we need to rethink
the criteria for identifying better pretrained language models. We openly
released the source code and data at https://github.com/c-box/causalEval.


title: Limits of Mahler measures in multiple variables

abstract:
  We prove that certain sequences of Laurent polynomials, obtained from a fixed
Laurent polynomial P by monomial substitutions, give rise to sequences of
Mahler measures which converge to the Mahler measure of P. This generalizes
previous work of Boyd and Lawton, who considered univariate monomial
substitutions. We provide moreover an explicit upper bound for the error term
in this convergence, generalizing work of Dimitrov and Habegger, and a full
asymptotic expansion for a family of 2-variable polynomials, whose Mahler
measures were studied independently by the third author.


title:
Reproduction of initial distributions from the first hitting time
  distribution for birth-and-death processes

abstract:
  For birth-and-death processes, we show that every initial distribution is
reproduced from the first hitting time distribution. The reproduction is done
by applying to the distribution function a differential operator defined
through the eigenfunction of the generator. Using the spectral theory for
generalized second-order differential operators, we study asymmetric random
walks.


title:
Lattice instability and Raman spectra of BaO under high pressure: A
  first principles study

abstract:
  Alkaline-earth metal oxides, in particular MgO and CaO dominate Earths lower
mantle, therefore, exploring high pressure behavior of this class of compounds
is of significant geophysical research interest. Among all these compounds, BaO
exhibits rich polymorphism in the pressure range of 0-1.5 Mbar. Static enthalpy
calculations revealed that BaO undergoes a pressure induced structural phase
transition from NaCl-type (B1) $\rightarrow$ NiAs-type (B8) $\rightarrow$
distorted CsCl-type (d-B2) $\rightarrow$ CsCl-type (B2) at 5.1, 19.5, 120 GPa
respectively. B1 $\rightarrow$ B8 $\&$ B8 $\rightarrow$ d-B2 transitions are
found to be first order in nature whereas d-B2$\rightarrow$ B2 is a second
order or weak first order phase transition. Interestingly, d-B2 phase shows
stability over a wide pressure range, $\sim$19.5-113 GPa. Mechanical and
dynamical stabilities of ambient and high pressure phases are demonstrated
through computed elastic constants and phonon dispersion curves, respectively.
Under high pressure, significant phonon softening and soft phonon mode along
M-direction are observed for B8, d-B2 and B2 phases, respectively. Pressure
dependent Raman spectra suggest a phase transition from d-B2 to Raman inactive
phase under pressure. Overall, the present study provides a comprehensive
understanding of underlying mechanisms behind pressure-induced structural phase
transitions in BaO.


title:
Multi-Mosaics: Corpus Summarizing and Exploration using multiple
  Concordance Mosaic Visualisations

abstract:
  Researchers working in areas such as lexicography, translation studies, and
computational linguistics, use a combination of automated and semi-automated
tools to analyze the content of text corpora. Keywords, named entities, and
events are often extracted automatically as the first step in the analysis.
Concordancing -- or the arranging of passages of a textual corpus in
alphabetical order according to user-defined keywords -- is one of the oldest
and still most widely used forms of text analysis. This paper describes
Multi-Mosaics, a tool for corpus analysis using multiple implicitly linked
Concordance Mosaic visualisations. Multi-Mosaics supports examining linguistic
relationships within the context windows surrounding extracted keywords.


title:
Effects of a single impurity in a Luttinger liquid with spin-orbit
  coupling

abstract:
  In quasi-1D conducting nanowires spin-orbit coupling destructs spin-charge
separation, intrinsic to Tomonaga-Luttinger liquid (TLL). We study
renormalization of a single scattering impurity in a such liquid. Performing
bosonization of low-energy excitations and exploiting perturbative
renormalization analysis we extend the phase portrait in $K_s$ - $K_c$ space,
obtained previously for TLL with decoupled spin-charge channels.


title: ECO v1: Towards Event-Centric Opinion Mining

abstract:
  Events are considered as the fundamental building blocks of the world. Mining
event-centric opinions can benefit decision making, people communication, and
social good. Unfortunately, there is little literature addressing event-centric
opinion mining, although which significantly diverges from the well-studied
entity-centric opinion mining in connotation, structure, and expression. In
this paper, we propose and formulate the task of event-centric opinion mining
based on event-argument structure and expression categorizing theory. We also
benchmark this task by constructing a pioneer corpus and designing a two-step
benchmark framework. Experiment results show that event-centric opinion mining
is feasible and challenging, and the proposed task, dataset, and baselines are
beneficial for future studies.


title:
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual
  Information Maximization

abstract:
  The key towards learning informative node representations in graphs lies in
how to gain contextual information from the neighbourhood. In this work, we
present a simple-yet-effective self-supervised node representation learning
strategy via directly maximizing the mutual information between the hidden
representations of nodes and their neighbourhood, which can be theoretically
justified by its link to graph smoothing. Following InfoNCE, our framework is
optimized via a surrogate contrastive loss, where the positive selection
underpins the quality and efficiency of representation learning. To this end,
we propose a topology-aware positive sampling strategy, which samples positives
from the neighbourhood by considering the structural dependencies between nodes
and thus enables positive selection upfront. In the extreme case when only one
positive is sampled, we fully avoid expensive neighbourhood aggregation. Our
methods achieve promising performance on various node classification datasets.
It is also worth mentioning by applying our loss function to MLP based node
encoders, our methods can be orders of faster than existing solutions. Our
codes and supplementary materials are available at
https://github.com/dongwei156/n2n.


title: Chebyshev's Bias against Splitting and Principal Primes in Global Fields

abstract:
  Reasons for the emergence of Chebyshev's bias were investigated. The Deep
Riemann Hypothesis (DRH) enables us to reveal that the bias is a natural
phenomenon for achieving a well-balanced disposition of the whole sequence of
primes, in the sense that the Euler product converges at the center. By means
of a weighted counting function of primes, the authors succeed in expressing
magnitudes of the deflection by a certain asymptotic formula under the
assumption of DRH, which provides a new formulation of Chebyshev's bias.
  For any Galois extension of global fields and for any element $\sigma$ in the
Galois group, we have established a criterion of the bias of primes whose
Frobenius elements are equal to $\sigma$ under the assumption of DRH. As an
application we have obtained a bias toward non-splitting and non-principle
primes in abelian extensions under DRH. In positive characteristic cases, DRH
is known, and all these results hold unconditionally.


title:
PEAR: Personalized Re-ranking with Contextualized Transformer for
  Recommendation

abstract:
  The goal of recommender systems is to provide ordered item lists to users
that best match their interests. As a critical task in the recommendation
pipeline, re-ranking has received increasing attention in recent years. In
contrast to conventional ranking models that score each item individually,
re-ranking aims to explicitly model the mutual influences among items to
further refine the ordering of items given an initial ranking list. In this
paper, we present a personalized re-ranking model (dubbed PEAR) based on
contextualized transformer. PEAR makes several major improvements over the
existing methods. Specifically, PEAR not only captures feature-level and
item-level interactions, but also models item contexts from both the initial
ranking list and the historical clicked item list. In addition to item-level
ranking score prediction, we also augment the training of PEAR with a
list-level classification task to assess users' satisfaction on the whole
ranking list. Experimental results on both public and production datasets have
shown the superior effectiveness of PEAR compared to the previous re-ranking
models.


title:
Chiplet Actuary: A Quantitative Cost Model and Multi-Chiplet
  Architecture Exploration

abstract:
  Multi-chip integration is widely recognized as the extension of Moore's Law.
Cost-saving is a frequently mentioned advantage, but previous works rarely
present quantitative demonstrations on the cost superiority of multi-chip
integration over monolithic SoC. In this paper, we build a quantitative cost
model and put forward an analytical method for multi-chip systems based on
three typical multi-chip integration technologies to analyze the cost benefits
from yield improvement, chiplet and package reuse, and heterogeneity. We
re-examine the actual cost of multi-chip systems from various perspectives and
show how to reduce the total cost of the VLSI system through appropriate
multi-chiplet architecture.


title:
Unveiling the emergent traits of chiral spin textures in magnetic
  multilayers

abstract:
  Magnetic skyrmions are topologically wound nanoscale textures of spins whose
ambient stability and electrical manipulation in multilayer films have led to
an explosion of research activities. While past efforts focused predominantly
on isolated skyrmions, recently ensembles of chiral spin textures, consisting
of skyrmions and magnetic stripes, were shown to possess rich interactions with
potential for device applications. However, several fundamental aspects of
chiral spin texture phenomenology remain to be elucidated, including their
domain wall structure, thermodynamic stability, and morphological transitions.
Here we unveil the evolution of these textural characteristics on a tunable
multilayer platform - wherein chiral interactions governing spin texture
energetics can be widely varied - using a combination of full-field electron
and soft X-ray microscopies with numerical simulations. With increasing chiral
interactions, we demonstrate the emergence of N\'eel helicity, followed by a
marked reduction in domain compressibility, and finally a transformation in the
skyrmion formation mechanism. Together with an analytical model, these
experiments establish a comprehensive microscopic framework for investigating
and tailoring chiral spin texture character in multilayer films.


title: Event-Based Dense Reconstruction Pipeline

abstract:
  Event cameras are a new type of sensors that are different from traditional
cameras. Each pixel is triggered asynchronously by event. The trigger event is
the change of the brightness irradiated on the pixel. If the increment or
decrement of brightness is higher than a certain threshold, an event is output.
Compared with traditional cameras, event cameras have the advantages of high
dynamic range and no motion blur. Since events are caused by the apparent
motion of intensity edges, the majority of 3D reconstructed maps consist only
of scene edges, i.e., semi-dense maps, which is not enough for some
applications. In this paper, we propose a pipeline to realize event-based dense
reconstruction. First, deep learning is used to reconstruct intensity images
from events. And then, structure from motion (SfM) is used to estimate camera
intrinsic, extrinsic and sparse point cloud. Finally, multi-view stereo (MVS)
is used to complete dense reconstruction.


title: A class of invariant diffusion processes in one dimension

abstract:
  This paper relies on a simple test to decide whether or not nontrivial
symmetries of a large class of inhomogeneous diffusion processes on the real
line exist. When these symmetries are confirmed a priori by just picking
coefficients including physically meaningful diffusion and drift terms, the
transformation to canonical forms with four- and six-dimensional symmetry
groups and the full list of their infinitesimal generators are then immediately
at ones's disposable without any cumbersome calculations. The results are
applied to several models previously treated in a less systematic way in the
literature to demonstrate the efficiency of the current approach.


title:
Momentum distribution functions and pair correlation functions of
  unpolarized uniform electron gas in warm dense matter regime

abstract:
  In this paper we continued our research of the uniform electron gas, using
the single--momentum path integral Monte Carlo method, and studied the momentum
distribution functions and the pair correlation functions in the warm dense
matter regime. We discovered that the single--particle momentum distribution
function deviates from the Fermi distribution and forms so-called "quantum
tails" at high momenta, if non-ideality is strong enough in both degenerate and
non-degenerate cases. This effect is always followed by the appearance of the
short--range order on the pair distribution functions and can be explained via
the tunneling through the effective potential wells surrounding the electrons.
Also we calculated the average kinetic and potential energies in the wide range
of states, expanding our previous results significantly.


title:
DAN: a Segmentation-free Document Attention Network for Handwritten
  Document Recognition

abstract:
  Unconstrained handwritten text recognition is a challenging computer vision
task. It is traditionally handled by a two-step approach combining line
segmentation followed by text line recognition. For the first time, we propose
an end-to-end segmentation-free architecture for the task of handwritten
document recognition: the Document Attention Network. In addition to the text
recognition, the model is trained to label text parts using begin and end tags
in an XML-like fashion. This model is made up of an FCN encoder for feature
extraction and a stack of transformer decoder layers for a recurrent
token-by-token prediction process. It takes whole text documents as input and
sequentially outputs characters, as well as logical layout tokens. Contrary to
the existing segmentation-based approaches, the model is trained without using
any segmentation label. We achieve competitive results on the READ 2016 dataset
at page level, as well as double-page level with a CER of 3.53% and 3.69%,
respectively. We also provide results for the RIMES 2009 dataset at page level,
reaching 4.54% of CER.
  We provide all source code and pre-trained model weights at
https://github.com/FactoDeepLearning/DAN.


title: Pre-training to Match for Unified Low-shot Relation Extraction

abstract:
  Low-shot relation extraction~(RE) aims to recognize novel relations with very
few or even no samples, which is critical in real scenario application.
Few-shot and zero-shot RE are two representative low-shot RE tasks, which seem
to be with similar target but require totally different underlying abilities.
In this paper, we propose Multi-Choice Matching Networks to unify low-shot
relation extraction. To fill in the gap between zero-shot and few-shot RE, we
propose the triplet-paraphrase meta-training, which leverages triplet
paraphrase to pre-train zero-shot label matching ability and uses meta-learning
paradigm to learn few-shot instance summarizing ability. Experimental results
on three different low-shot RE tasks show that the proposed method outperforms
strong baselines by a large margin, and achieve the best performance on
few-shot RE leaderboard.


title: Certified Symmetry and Dominance Breaking for Combinatorial Optimisation

abstract:
  Symmetry and dominance breaking can be crucial for solving hard combinatorial
search and optimisation problems, but the correctness of these techniques
sometimes relies on subtle arguments. For this reason, it is desirable to
produce efficient, machine-verifiable certificates that solutions have been
computed correctly. Building on the cutting planes proof system, we develop a
certification method for optimisation problems in which symmetry and dominance
breaking are easily expressible. Our experimental evaluation demonstrates that
we can efficiently verify fully general symmetry breaking in Boolean
satisfiability (SAT) solving, thus providing, for the first time, a unified
method to certify a range of advanced SAT techniques that also includes XOR and
cardinality reasoning. In addition, we apply our method to maximum clique
solving and constraint programming as a proof of concept that the approach
applies to a wider range of combinatorial problems.


title:
ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through
  Regularized Self-Attention

abstract:
  Sparse Transformer has recently attracted a lot of attention since the
ability for reducing the quadratic dependency on the sequence length. We argue
that two factors, information bottleneck sensitivity and inconsistency between
different attention topologies, could affect the performance of the Sparse
Transformer. This paper proposes a well-designed model named ERNIE-Sparse. It
consists of two distinctive parts: (i) Hierarchical Sparse Transformer (HST) to
sequentially unify local and global information. (ii) Self-Attention
Regularization (SAR) method, a novel regularization designed to minimize the
distance for transformers with different attention topologies. To evaluate the
effectiveness of ERNIE-Sparse, we perform extensive evaluations. Firstly, we
perform experiments on a multi-modal long sequence modeling task benchmark,
Long Range Arena (LRA). Experimental results demonstrate that ERNIE-Sparse
significantly outperforms a variety of strong baseline methods including the
dense attention and other efficient sparse attention methods and achieves
improvements by 2.77% (57.78% vs. 55.01%). Secondly, to further show the
effectiveness of our method, we pretrain ERNIE-Sparse and verified it on 3 text
classification and 2 QA downstream tasks, achieve improvements on
classification benchmark by 0.83% (92.46% vs. 91.63%), on QA benchmark by 3.24%
(74.67% vs. 71.43%). Experimental results continue to demonstrate its superior
performance.


title: Unified Structure Generation for Universal Information Extraction

abstract:
  Information extraction suffers from its varying targets, heterogeneous
structures, and demand-specific schemas. In this paper, we propose a unified
text-to-structure generation framework, namely UIE, which can universally model
different IE tasks, adaptively generate targeted structures, and
collaboratively learn general IE abilities from different knowledge sources.
Specifically, UIE uniformly encodes different extraction structures via a
structured extraction language, adaptively generates target extractions via a
schema-based prompt mechanism - structural schema instructor, and captures the
common IE abilities via a large-scale pre-trained text-to-structure model.
Experiments show that UIE achieved the state-of-the-art performance on 4 IE
tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings
for a wide range of entity, relation, event and sentiment extraction tasks and
their unification. These results verified the effectiveness, universality, and
transferability of UIE.