May 2022 arXiv papers — page 157

Everton J. Silva, Elizabeth W. Karas, Lucelina B. Santos

In this work, we propose integral global optimality conditions for multiobjective problems not necessarily differentiable. The integral characterization, already known for single objective problems, are extended to multiobjective problems by weighted sum and Chebyshev weighted scalarizations. Using this last scalarization, we propose an algorithm for obtaini

Alexander Berkovich, Ali Kemal Uncu

We refine Schmidt's problem and a partition identity related to 2-color partitions which we will refer to as Uncu-Andrews-Paule theorem. We will approach the problem using Boulet-Stanley weights and a formula on Rogers-Szegő polynomials by Berkovich-Warnaar, and present various Schmidt's problem alike theorems and their refinements. Our new Schmidt t

Achille Lambrecht, Carlo Massobrio, Mauro Boero, Guido Ori

First-principles molecular dynamics is employed to describe the atomic structure of amorphous SiN, a non-stoichiometric compound belonging to the Si$_x$N$_{y}$ family. To produce the amorphous state via the cooling of the liquid, both the Car-Parrinello and the Born-Oppenheimer approaches are exploited to obtain a system featuring sizeable atomic mobility. A

Jagdish C. Joshi, Shuta J. Tanaka, Luis Salvador Miranda, Soebur Razzaque

Recently, the Large High Altitude Air Shower Observatory (LHAASO) reported the discovery of 12 ultrahigh-energy (UHE; $\mathrm{\varepsilon} \ge 100$ TeV) gamma-ray sources located in the Galactic plane. A few of these UHE gamma-ray emitting regions are in spatial coincidence with pulsar wind nebulae (PWNe). We consider a sample of five sources; two of them a

Ilya Tyuryukanov, Marjan Popov, Jorrit A. Bos, Mart A. M. M. van der Meijden

With the growing number of severe system disturbances and blackouts around the world, controlled system separation is becoming an increasingly important system integrity protection scheme (SIPS) to save the electric power system from a complete or partial disintegration. A successful controlled splitting approach should at least tackle the following two well

Wenbin Song, Di Wu, Weiming Shen, Benoit Boulet

Early fault detection (EFD) of rotating machines is important to decrease the maintenance cost and improve the mechanical system stability. One of the key points of EFD is developing a generic model to extract robust and discriminative features from different equipment for early fault detection. Most existing EFD methods focus on learning fault representatio

Ganchao Wei, Ian H. Stevenson

In many areas of the brain, neural spiking activity covaries with features of the external world, such as sensory stimuli or an animal's movement. Experimental findings suggest that the variability of neural activity changes over time and may provide information about the external world beyond the information provided by the average neural activity. To f

Dingding Hu, Meng Yuan, Tao Yu, Pengfei Li

The receiver operating characteristic (ROC) curve is a powerful statistical tool and has been widely applied in medical research. In the ROC curve estimation, a commonly used assumption is that larger the biomarker value, greater severity the disease. In this paper, we mathematically interpret ``greater severity of the disease" as ``larger probability of

Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun

Many instances of algorithmic bias are caused by distributional shifts. For example, machine learning (ML) models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we leverage this connection between algorithmic fairness and distribution shifts to show that algorithmic fairness interventions can help ML

Alex Doak, Ricardo Barros, Paul A Milewski

In this paper, we investigate mode-2 solitary waves in a three-layer stratified flow model. Localised travelling wave solutions to both the fully nonlinear problem (Euler equations), and the three-layer Miyata-Choi-Camassa equations are found numerically and compared. Mode-2 solitary waves with speeds slower than the linear mode-1 long-wave speed are typical

Shiang Tang

Let $G$ be a split reductive group with $\dim Z(G) \leq 1$. We show that for any prime $p$ that is large enough relative to $G$, there is a finitely ramified Galois representation $ρ\colon Γ_{\mathbb Q} \to G(\mathbb Z_p)$ with open image. We also show that for any given integer $e$, if the index of irregularity of $p$ is at most $e$ and if $p$ is large enou

Jiaju Lin, Qin Chen, Jie Zhou, Jian Jin

Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document. Most previous work focuses on learning the direct relations between arguments and the given trigger, while the implicit relations with long-range dependency are not well studied. Moreover, recent neural network based approaches rely on a large amount of

Nils A. Nilsson

We study the effects of explicit spacetime-symmetry breaking on primordial tensor fluctuations using an effective-field theory for Lorentz/CPT violation. We find that the graviton is still massless, but that the propagation speed of tensor modes is modified, and we obtain a constraint on the coefficient determining the symmetry breaking on the order of $10^{

Francesco Cordoni, Fabrizio Lillo

A large body of empirical literature has shown that market impact of financial prices is transient. However, from a theoretical standpoint, the origin of this temporary nature is still unclear. We show that an implied transient impact arises from the Nash equilibrium between a directional trader and one arbitrageur in a market impact game with fixed and perm

Gu Bowen, Li Dong, Xu Yongjun, Li Chunguo

In this paper, we investigate and analyze full-duplex-based backscatter communications with multiple backscatter devices (BDs). Different from previous works where only the energy from the energy source is harvested, BDs are also allowed to harvest energy from previous BDs by recycling the backscattering energy. Our objective is to maximize the total energy

Baozhi Li, Hongyan Sun, Hua-Ming Wang

In this paper, we consider certain linear-fractional branching processes with immigration in varying environments. For $n\ge0,$ let $Z_n$ counts the number of individuals of the $n$-th generation, which excludes the immigrant which enters into the system at time $n.$ We call $n$ a regeneration time if $Z_n=0.$ We give first a criterion for the finiteness or

Rustem Takhanov

The classical Mercer's theorem claims that a continuous positive definite kernel $K({\mathbf x}, {\mathbf y})$ on a compact set can be represented as $\sum_{i=1}^\infty λ_iϕ_i({\mathbf x})ϕ_i({\mathbf y})$ where $\{(λ_i,ϕ_i)\}$ are eigenvalue-eigenvector pairs of the corresponding integral operator. This infinite representation is known to converge unifo

Bert van Geemen

Recent papers by Markman and O'Grady give, besides their main results on the Hodge conjecture and on hyperkaehler varieties, surprising and explicit descriptions of families of abelian fourfolds of Weil type with trivial discriminant. They also provide a new perspective on the well-known fact that these abelian varieties are Kuga Satake varieties for cer

Paolo Lisca, Andrea Parma

We show that every smooth, closed, orientable 4-manifold X admits a special kind of handlebody decomposition that we call horizontal. We classify the closed 4-manifolds with the simplest horizontal decompositions and we describe all such decompositions of CP^2, showing that they give rise to infinitely many of the known embeddings of rational homology balls

Guangwen Li, Xiao Yu

It is known belief propagation decoding variants of LDPC codes can be unrolled easily as neural networks after assigning differed weights to message passing edges flexibly. In this paper we focus on how to determine these weights, in the form of trainable paramters, within a framework of deep learning. Firstly, a new method is proposed to generate high-quali

Yang Zhou, Wee Sun Lee

Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express any pre-defined relation and are labeled as "none" or "no relation". For good document-level RE performanc

Andrei Constantinescu, Roger Wattenhofer

We introduce two-crossing elections as a generalization of single-crossing elections, showing a number of new results. First, we show that two-crossing elections can be recognized in polynomial time, by reduction to the well-studied consecutive ones problem. We also conjecture that recognizing $k$-crossing elections is NP-complete in general, providing evide

Xiuxian Li, Lihua Xie, Na Li

Distributed online optimization and game have been increasingly researched in the last decade, mostly motivated by its wide applications in sensor networks, robotics (e.g., distributed target tracking and formation control), smart grids, deep learning, and so forth. In these problems, there is a network of agents who may be cooperative (i.e., distributed onl

Takuma Aihara, Qi Wang

We investigate symmetry of the silting quiver of a given algebra which is induced by an anti-automorphism of the algebra. In particular, one shows that if there is a primitive idempotent fixed by the anti-automorphism, then the 2-silting quiver ($=$ the support $τ$-tilting quiver) has a bisection. Consequently, in that case, we obtain that the cardinality of

Sheng Xu, Wenqi Fan, Wangxia Wu, Wei Wang

The interaction between planar shock waves and droplets, involved the evolution of high transient unsteady wave structures and the induced cavitation process, occurs widely in nature and industry. In this paper, a combination of theoretical analysis and high-resolution numerical simulation is employed to study the inherent characteristics of the interaction.

Konstantin D. Pandl, Florian Leiser, Scott Thiebes, Ali Sunyaev

Federated learning (FL) has received high interest from researchers and practitioners to train machine learning (ML) models for healthcare. Ensuring the trustworthiness of these models is essential. Especially bias, defined as a disparity in the model's predictive performance across different subgroups, may cause unfairness against specific subgroups, wh

Hideki Matsumura

A quadrature formula is a formula computing a definite integration by evaluation at finite points. The existence of certain quadrature formulas for orthogonal polynomials is related to interesting problems such as Waring's problem in number theory and spherical designs in algebraic combinatorics. Sawa and Uchida proved the existence and the non-existence

Qiaoqiao Ding, Hui Ji, Yuhui Quan, Xiaoqun Zhang

Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation. In recent years, supervised deep learning (DL) has been extensively studied for LDCT image reconstruction, which trains a network over a dataset containing many pairs of normal-dose and low-dose images. However, the challenge on col

Yixin Cheng, Bernardo Pereira Nunes

Semantic technologies are evolving and being applied in several research areas, including the education domain. This paper presents the outcomes of a systematic review carried out to provide an overview of the application of semantic technologies in the context of the Computer Science curriculum and discuss the limitations in this field whilst offering insig

Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang

Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs due to their non-differentiability. Most existing methods either suffer from high latency (i.e., long simulation time steps), or cannot achieve as high performance as Artificial Neural Networks

Yusuke Ide, Takashi Komatsu, Norio Konno, Iwao Sato

We present an explicit formula for the determinant on the Metzler matrix of a digraph $D$. Furthermore, we introduce a walk-type zeta function with respect to this Metzler matrix of the symmetric digraph of a finite torus, and express its limit formula by using the integral expression.

Dennis J. N. J. Soemers, Éric Piette, Matthew Stephenson, Cameron Browne

There are several different game description languages (GDLs), each intended to allow wide ranges of arbitrary games (i.e., general games) to be described in a single higher-level language than general-purpose programming languages. Games described in such formats can subsequently be presented as challenges for automated general game playing agents, which ar

Rick K. Acosta-Vega, Encarnación Algaba, Joaquín Sánchez-Soriano

In this paper, we analyze the problem of how to adapt the concept of priority to situations where several perfectly divisible resources have to be allocated among certain set of agents that have exactly one claim which is used for all resources. In particular, we introduce constrained sequential priority rules and two constrained random arrival rules, which

Jaime Carracedo-Cosme, Carlos Romero-Muñiz, Pablo Pou, Rubén Pérez

Despite being the main tool to visualize molecules at the atomic scale, AFM with CO-functionalized metal tips is unable to chemically identify the observed molecules. Here we present a strategy to address this challenging task using deep learning techniques. Instead of identifying a finite number of molecules following a traditional classification approach,

Pedro Zattoni Scroccaro, Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani

In the past few years, Online Convex Optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this paper, we propose new step-size rules and OCO algorithms that simultaneously exploit gradient predictions, function predictions and dynamics, features particularl

Giovanni Gaiffi, Oscar Papini, Viola Siconolfi

In this paper we find monomial bases for the integer cohomology rings of compact wonderful models of toric arrangements. In the description of the monomials various combinatorial objects come into play: building sets, nested sets, and the fan of a suitable toric variety. We provide some examples computed via a SageMath program and then we focus on the case o

Arnab Maiti, Palash Dey

In the classical Binary Networked Public Goods (BNPG) game, a player can either invest in a public project or decide not to invest. Based on the decisions of all the players, each player receives a reward as per his/her utility function. However, classical models of BNPG game do not consider altruism which players often exhibit and can significantly affect e

Héber H. Arcolezi, Jean-François Couchot, Denis Renaud, Bechara Al Bouna

This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-of-the-art formal notion, has been used as the privacy guarantee in two different and independent steps when training deep learning models. On one hand, we considered \textit{gradie

Lilan Dai, Yunnan Li

In this paper, we introduce a method computing the primitive decomposition of idempotents of any semisimple finite group algebra based on its matrix representations and Wedderburn decomposition. Particularly, we use this method to calculate the examples of the dihedral group algebras $\mathbb{C}[D_{2n}]$ and generalized quaternion group algebras $\mathbb{C}[

Gui-Lei Zhu, Jing Liu, Ying Wu, Xin-You Lü

An experimentally feasible magnetometer based on a dual-coupling optomechanical system is proposed, where the radiation-pressure coupling transduces the magnetic signal to the optical phase, and the quadratic optomechanical interaction induces a periodic squeezing effect. The latter not only amplifies the signal to be measured, but also accelerates the signa

Ruonan Li, Yutao Tang, Shurong Li

This paper investigates a robust positive consensus problem for a class of heterogeneous high-order multi-agent systems subject to external inputs. Compared with existing multi-agent consensus results, the most distinct feature of the formulated problem is that the state variables of all heterogeneous agents are confined in the positive orthant. To solve thi

Elisa Prato

Toric quasifolds are highly singular spaces that were first introduced in order to address, from the symplectic viewpoint, the longstanding open problem of extending the classical constructions of toric geometry to those simple convex polytopes that are not rational. We illustrate toric quasifolds, and their atlases, by describing some notable examples. We c

Lorenzo Miretti, Renato L. G. Cavalcante, Slawomir Stanczak, Martin Schubert

Many common instances of power control problems for cellular and cell-free massive MIMO networks can be interpreted as max-min utility optimization problems involving affine interference mappings and polyhedral constraints. We show that these problems admit a closed-form solution which depends on the spectral radius of known matrices. In contrast, previous s

Jian Wang, Shipeng Wang, Weihua Yang

Let $F$ be an $(r+1)$-color critical graph with $r\geq 2$, that is, $χ(F)=r+1$ and there is an edge $e$ in $F$ such that $χ(F-e)=r$. Gerbner recently conjectured that every $n$-vertex maximal $F$-free graph with at least $(1-\frac{1}{r})\frac{n^2}{2}- o(n^{\frac{r+1}{r}})$ edges contains an induced complete $r$-partite graph on $n-o(n)$ vertices. Let $F_{s,k

Tieguang Zi, Ziqi Zhou, Hai-Tian Wang, Peng-Cheng Li

We derive the approximate, ``analytic-kludge'' (AK) waveforms for the inspiral of a charged stellar-mass compact object (CO) into a charged massive Kerr-Newman (KN) black hole (BH). The modifications of the inspiral orbit due to the charges in this system can be attributed to three sources: the electric force between the CO and the MBH, the energy fl

Cheng Chen, Yudong Zhu, Zhenshan Tan, Qingrong Cheng

Visual Dialog aims to answer multi-round, interactive questions based on the dialog history and image content. Existing methods either consider answer ranking and generating individually or only weakly capture the relation across the two tasks implicitly by two separate models. The research on a universal framework that jointly learns to rank and generate an

Ting Sun, Qian Zhao, Kun Xue, Zhi-Wei Lu

High-energy spin-polarized electron, positron, and $γ$-photon beams have many significant applications in the study of material properties, nuclear structure, particle physics, and high-energy astrophysics. Thus,efficient production of such polarized beams attracts a broad spectrum of research interests. This is driven mainly by the rapid advancements in ult

Hyeon Jeon, Hyung-Kwon Ko, Soohyun Lee, Jaemin Jo

We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of high-dimensional data more accurately. In UMATO, optimization is divided into two phases so that the resulting embeddings can depict the global structure reliably while preserving the

Yifat Moadim-Lesimcha, Michael M. Schein

We compute the local pro-isomorphic zeta functions at all but finitely many primes for a certain family of class-two-nilpotent Lie lattices of even rank, parametrized by irreducible non-linear polynomials $f(x) \in \mathbb{Z} [x]$, that corresponds to a family of groups introduced by Grunewald and Segal. The result is expressed in terms of a combinatorially

Fiammetta Battaglia, Elisa Prato

First, we examine the notion of nonrational convex polytope and nonrational fan in the context of toric geometry. We then discuss and interrelate some recent developments in the subject.

Kamiel Janssens, Guillaume Boileau, Marie-Anne Bizouard, Nelson Christensen

Several proposed gravitational wave interferometers have a triangular configuration, such as the Einstein Telescope and the Laser Interferometer Space Antenna. For such a configuration one can construct a unique null channel insensitive to gravitational waves from all directions. We expand on earlier work and describe how to use the null channel formalism to

Mihir Parmar, Swaroop Mishra, Mor Geva, Chitta Baral

In recent years, progress in NLU has been driven by benchmarks. These benchmarks are typically collected by crowdsourcing, where annotators write examples based on annotation instructions crafted by dataset creators. In this work, we hypothesize that annotators pick up on patterns in the crowdsourcing instructions, which bias them to write many similar examp

Chen-Te Ma, Yiwen Pan, Hui Zhang

Spontaneous symmetry breaking occurs when the underlying laws of a physical system are symmetric, but the vacuum state chosen by the system is not. The (3+1)d $\phi^4$ theory is relatively simple compared to other more complex theories, making it a good starting point for investigating the origin of non-trivial vacua. The adaptive perturbation method is a te

Igor P. Ivanov

Vortex states of photons, electrons, and other particles are non--plane-wave solutions of the corresponding wave equation with helicoidal wave fronts. These states possess an intrinsic orbital angular momentum with respect to the average propagation direction, which represents a new degree of freedom, previously unexplored in particle or nuclear collisions.

Youlin Li, Yuhe Zhang

In this paper, we determine the Euler characteristics and signatures of the exact symplectic fillings of the contact double, 3-fold or 4-fold cyclic covers of the standard contact 3-sphere branched over certain transverse quasi-positive links. These links include all quasi-positive knots with crossing numbers smaller than 11 and all quasi-positive links with

Xiaohu Zheng, Ke Jia, Junhai Ren, Chongli Yang

The layered Mott insulator $α$-RuCl${_3}$ has been extensively studied as a potential Kitaev quantum spin liquid candidate. Here, by constructing heterostructures with graphite, we employed electron tunneling measurements on few-layer $α$-RuCl${_3}$ using a scanning tunneling microscopy/spectroscopy. Characteristic tunneling spectra were detected on $α$-RuCl

Igor G. Korepanov

A cohomology theory for "odd polygon" relations -- algebraic imitations of Pachner moves in dimensions 3, 5, ... -- is constructed. Manifold invariants based on polygon relations and nontrivial polygon cocycles are proposed. Example calculation results are presented.

Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin

Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Baye

A. F. Krasnikov

In this paper we prove the theorem on freedom for free products with a single relation (analogous with the well-known result of Magnus) and a generalized Freiheitssatz for free products (analogous with the well-known result of Romanovski)

Qinying Liu, Zilei Wang, Ruoxi Chen, Zhilin Li

Weakly-supervised temporal action localization (WTAL) intends to detect action instances with only weak supervision, e.g., video-level labels. The current~\textit{de facto} pipeline locates action instances by thresholding and grouping continuous high-score regions on temporal class activation sequences. In this route, the capacity of the model to recognize

Yong Xie, Dakuo Wang, Pin-Yu Chen, Jinjun Xiong

More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability is underexplored. In this paper, we experim

Yasith Jayawardana, Vikas G. Ashok, Sampath Jayarathna

Reusable data/code and reproducible analyses are foundational to quality research. This aspect, however, is often overlooked when designing interactive stream analysis workflows for time-series data (e.g., eye-tracking data). A mechanism to transmit informative metadata alongside data may allow such workflows to intelligently consume data, propagate metadata

Shabnam Behzad, Keisuke Sakaguchi, Nathan Schneider, Amir Zeldes

We present ELQA, a corpus of questions and answers in and about the English language. Collected from two online forums, the >70k questions (from English learners and others) cover wide-ranging topics including grammar, meaning, fluency, and etymology. The answers include descriptions of general properties of English vocabulary and grammar as well as explanat

Tenavi Nakamura-Zimmerer, Qi Gong, Wei Kang

Recent research shows that supervised learning can be an effective tool for designing near-optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the behavior of neural network controllers is still not well understood. In particular, some neural networks with high test accuracy can fail to even locally stabilize the dynamic system.

Yaojian Chen, Yong Liu, Xinmin Shi, Jiawei Song

High-performance classical simulator for quantum circuits, in particular the tensor network contraction algorithm, has become an important tool for the validation of noisy quantum computing. In order to address the memory limitations, the slicing technique is used to reduce the tensor dimensions, but it could also lead to additional computation overhead that

Shokoufe Faraji, Audrey Trova

We generalise the relativistic accretion thick disc model to the background of a spinning charged accelerating black hole described by the C-metric to study the effects of this background on the disc model. We show the properties of this accretion disc model and its dependence on the initial parameters. This background can be distinguishable from the Kerr sp

Shokoufe Faraji

We consider a static and axially symmetric metric containing two quadrupole parameters. In the present contribution, we study the quadrupole moments constraints on the properties of the relativistic accretion disc models, also explore the relation of oscillatory frequencies of charged particles to the frequencies of the twin high-frequency quasi-periodic osc

Xueyao Liu, Songbai Chen, Jiliang Jing

The polarized images of a synchrotron emitting ring are studied in the regular Hayward and Bardeen black hole spacetimes. These regular black holes carry a magnetic field in terms of gravity coupled to nonlinear electrodynamics. Results show that the main features of the polarization images of the emitting rings are similar in these two regular black hole sp

Shen Zhang, Yibo Wang, Qingqi Zeng, Jianlei Shen

The linear positive magnetoresistance (LPMR) is a widely observed phenomenon in topological materials, which is promising for potential applications on topological spintronics. However, its mechanism remains ambiguous yet and the effect is thus uncontrollable. Here, we report a quantitative scaling model that correlates the LPMR with the Berry curvature, bas

Shokoufe Faraji

This is the summary of the parallel session entitled "Time and Philosophy in Physics", chaired by Shokoufe Faraji at the sixteenth Marcel Grossmann Meeting. This parallel session aimed to discuss open issues related to Time and fundamental laws from different perspectives in a complementary point of view.

Jonathan Weinberger

We give structural results about bifibrations of (internal) $(\infty,1)$-categories with internal sums. This includes a higher version of Moens' Theorem, characterizing cartesian bifibrations with extensive aka stable and disjoint internal sums over lex bases as Artin gluings of lex functors. We also treat a generalized version of Moens' Theorem due to Strei

Jichao Yin, Hu Wang, Shuhao Li, Daozhen Guo

Efficient topology optimization based on the adaptive auxiliary reduced model reanalysis (AARMR) is proposed to improve computational efficiency and scale. In this method, a projection auxiliary reduced model (PARM) is integrated into the combined approximation reduced model (CARM) to reduce the dimension of the model in different aspects. First, the CARM re

Zhe Fei, Weixuan Xia

Stochastic clocks represent a class of time change methods for incorporating trading activity into continuous-time financial models, with the ability to deal with typical asymmetrical and tail risks in financial returns. In this paper we propose a significant improvement of stochastic clocks for the same objective but without decreasing the number of trades

Jinsheng Wei, Wei Peng, Guanming Lu, Yante Li

Micro-expression recognition (MER) is valuable because micro-expressions (MEs) can reveal genuine emotions. Most works take image sequences as input and cannot effectively explore ME information because subtle ME-related motions are easily submerged in unrelated information. Instead, the facial landmark is a low-dimensional and compact modality, which achiev

Yulong Chen, Ming Zhong, Xuefeng Bai, Naihao Deng

We propose the shared task of cross-lingual conversation summarization, \emph{ConvSumX Challenge}, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation. This task can be particularly useful due to the emergence of online meetings and conferences. We construct a new benchmark, covering

Naifan Li, Fan Song, Ying Zhang, Pengpeng Liang

Detection of rare objects (e.g., traffic cones, traffic barrels and traffic warning triangles) is an important perception task to improve the safety of autonomous driving. Training of such models typically requires a large number of annotated data which is expensive and time consuming to obtain. To address the above problem, an emerging approach is to apply

Karolina Janicka, Aleksander L. Wysocki, Kyungwha Park

The great success of point defects and dopants in semiconductors for quantum information processing has invigorated a search for molecules with analogous properties. Flexibility and tunability of desired properties in a large chemical space have great advantages over solid-state systems. The properties analogous to point defects were demonstrated in Cr(IV)-b

Moses Charikar, Erik Waingarten

We study the effect of Johnson-Lindenstrauss transforms in various projective clustering problems, generalizing recent results which only applied to center-based clustering [MMR19]. We ask the general question: for a Euclidean optimization problem and an accuracy parameter $ε\in (0, 1)$, what is the smallest target dimension $t \in \mathbb{N}$ such that a Jo

Yong-Geun Oh

We develop the gluing theory of contact instantons in the context of open strings and in the context of closed strings \emph{with vanishing charge}, for example in the symplectization context. This is one of the key ingredients for the study of (virtually) smooth moduli space of (bordered) contact instantons needed for the construction of contact instanton F

Ichiro Maruta, Toshiharu Sugie

This paper provides a method for obtaining a continuous-time model of a target system in closed-loop from input-output data alone, in the case where no knowledge of the controllers nor excitation signals is available and I/O data may suffer from unknown offsets. The proposed method is based on a fixed-pole observer model, which is a reasonable continuous-tim

R. K. Michael Thambynayagam

A few basic, intuitive, properties of the Navier-Stokes system of equations for incompressible fluid flows are discussed in this paper. We present a rephrased interpretation of the Navier-Stokes equation in a space having an arbitrary number of dimensions. We then derive spatially periodic solutions for the velocity and pressure fields that span an unbounded

Xiyuan Chen, Xingyu Li, Yi Zhou, Tianming Yang

Despite their outstanding performance in a broad spectrum of real-world tasks, deep artificial neural networks are sensitive to input noises, particularly adversarial perturbations. On the contrary, human and animal brains are much less vulnerable. In contrast to the one-shot inference performed by most deep neural networks, the brain often solves decision-m

Hung Viet Chu

Albiac and Wojtaszczyk introduced property (A) to characterize $1$-greedy bases. Later, Dilworth et al. generalized the concept to $C$-property (A), where the case $C = 1$ gives property (A). They (among other results) characterized greedy bases by unconditionality and $C$-property (A). In this paper, we extend the definition of the so-called A-property cons

Carlos Zapata-Carratala, Xerxes D. Arsiwalla, Taliesin Beynon

In this paper we investigate a ternary generalization of associativity by defining a diagrammatic calculus of hypergraphs that extends the usual notions of tensor networks, categories and relational algebras. In doing so we rediscover the ternary structures known as heaps and are able to give a more comprehensive treatment of their mergence in the context of

Yifan Zhao, Yulong Wei, Weihua Yang

The small cycle decompositions of line graph ($λ$-fold line graph) of complete graphs and complete bipartite graphs are studied by many papers. In particular, Colby and Rodger obtained necessary and sufficient conditions for the existence of a $C_{4}$-decomposition of the $λ$-fold line graph of $K_{n}$, and Ganesamurthy and Paulraja completely determined the

Tianchu Lyu, Chen Liang, Jihong Liu, Berry Campbell

Objective: To develop a rule-based algorithm that detects temporal information of clinical events during pregnancy for women with COVID-19 by inferring gestational weeks and delivery dates from Electronic Health Records (EHR) from the National COVID Cohort Collaborate (N3C). Materials and Methods: The EHR are normalized by the Observational Medical Outcomes

Patrick T. Bowen, Michael Boyarsky, Laura M. Pulido-Mancera, David R. Smith

We present a design strategy for selecting the effective polarizability distribution for a metasurface aperture needed to form a desired radiation pattern. A metasurface aperture consists of an array of subwavelength metamaterial elements, each of which can be conceptualized as a radiating, polarizable dipole. An ideal polarizability distribution can be dete

Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori, Rajgopal Kannan

Machine learning algorithms have shown potential to improve prefetching performance by accurately predicting future memory accesses. Existing approaches are based on the modeling of text prediction, considering prefetching as a classification problem for sequence prediction. However, the vast and sparse memory address space leads to large vocabulary, which m

Ramir Ristić, Emil Babić

A recent paper by Juntao Huo et al [Chin. Phys. Lett. 39 (2022) 0464011] reported a correlation between the entropy of mixing (and the corresponding energy state) and the thermal stability and mechanical parameters, for three Zr-Ti-Cu-Ni-Be metallic glasses (MG) including a high-entropy one (HEMG). The authors dismissed a possible compositional contribution

Yongsheng Bai, Bing Zha, Halil Sezen, Alper Yilmaz

This paper presents a few comprehensive experimental studies for automated Structural Damage Detection (SDD) in extreme events using deep learning methods for processing 2D images. In the first study, a 152-layer Residual network (ResNet) is utilized to classify multiple classes in eight SDD tasks, which include identification of scene levels, damage levels,

Mark Gluzman

Novel advanced policy gradient (APG) algorithms, such as proximal policy optimization (PPO), trust region policy optimization, and their variations, have become the dominant reinforcement learning (RL) algorithms because of their ease of implementation and good practical performance. This dissertation is concerned with theoretical justification and practical

Ahmed El Ouadrhiri, Ahmed Abdelhadi

Differential privacy (DP) is considered a de-facto standard for protecting users' privacy in data analysis, machine, and deep learning. Existing DP-based privacy-preserving training approaches consist of adding noise to the clients' gradients before sharing them with the server. However, implementing DP on the gradient is not efficient as the privacy

Xinyi Hu, Jasper C. H. Lee, Jimmy H. M. Lee, Allen Z. Zhong

This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving. Given an optimization problem solvable by a recursive algorithm satisfying simple conditions, we show how a corresponding learning algorithm can be constructed directly and methodically from the r

Ryan J Richards, Austen M Groener

Deep learning has significantly advanced and accelerated de novo molecular generation. Generative networks, namely Variational Autoencoders (VAEs) can not only randomly generate new molecules, but also alter molecular structures to optimize specific chemical properties which are pivotal for drug-discovery. While VAEs have been proposed and researched in the

Pankaj, Bramha Dutta Pandey, P. Suresh Kumar, Umesh Kumar Sharma

The proposed model is a study of the nature of dark energy through non-extensive Tsallis entropy. The method is based on the Karolyhazy relation which is a combined idea from quantum physics and general relativity. Dark energy is the energy density of quantum fluctuations in space-time. This is the key idea behind proposing agegraphic dark energy (ADE) model

Mundher Mohammed Taresh, Ningbo Zhu, Talal Ahmed Ali Ali, Mohammed Alghaili

Computer-aided diagnosis tools have experienced rapid growth and development in recent years. Among all, deep learning is the most sophisticated and popular tool. In this paper, researchers propose a novel deep learning model and apply it to COVID-19 diagnosis. Our model uses the tool of fractional calculus, which has the potential to improve the performance

Kevin Zeng, Alec J. Linot, Michael D. Graham

Deep reinforcement learning (RL) is a data-driven method capable of discovering complex control strategies for high-dimensional systems, making it promising for flow control applications. In particular, the present work is motivated by the goal of reducing energy dissipation in turbulent flows, and the example considered is the spatiotemporally chaotic dynam

Xiaolong He, Jiun-Shyan Chen

Characterization and modeling of path-dependent behaviors of complex materials by phenomenological models remains challenging due to difficulties in formulating mathematical expressions and internal state variables (ISVs) governing path-dependent behaviors. Data-driven machine learning models, such as deep neural networks and recurrent neural networks (RNNs)

Yang Liu

In this article, we intend to find a specific model which can satisfy the further refining dS swampland conjecture and scalar weak gravity conjecture (SWGC) simultaneously, in particular, Higgs inflation model and its two extensions: Higgs-Dilaton model and Palatini Higgs inflation. We find that although Higgs inflation model and Higgs-Dilaton model could sa

Juan Pablo Aguilera, Martín Diéguez, David Fernández-Duque, Brett McLean

We investigate a non-classical version of linear temporal logic whose propositional fragment is Gödel--Dummett logic (which is well known both as a superintuitionistic logic and a t-norm fuzzy logic). We define the logic using two natural semantics, a real-valued semantics and a bi-relational semantics, and show that these indeed define one and the same logi