August 2022 arXiv papers — page 61

Sheheeda Manakkadu, Sourav Dutta

This paper describes a new approach for approximating the inverse kinematics of a manipulator using an Ant Colony Optimization (ACO) based RBFN (Radial Basis Function Network). In this paper, a training solution using the ACO and the LMS (Least Mean Square) algorithm is presented in a two-phase training procedure. To settle the problem that the cluster resul

Barak Babayov, Yoram Louzoun

The alignment of two similar graphs from different domains is a well-studied problem. In many practical usages, there is no reliable information or labels over the vertices or edges, leaving structural similarity as the only information available to match such a graph. In such cases, one often assumes a small amount of already aligned vertices -- called a se

Fiona Anting Tan, Xinyu Zuo, See-Kiong Ng

Current causal text mining datasets vary in objectives, data coverage, and annotation schemes. These inconsistent efforts prevent modeling capabilities and fair comparisons of model performance. Furthermore, few datasets include cause-effect span annotations, which are needed for end-to-end causal relation extraction. To address these issues, we propose UniC

Shun Maeta

We consider the broadest concept of the gradient Yamabe soliton, the conformal gradient soliton. In this paper, we elucidate the structure of complete gradient conformal solitons under some assumption, and provide some applications to gradient Yamabe solitons. These results enhance the understanding gained from previous research. Furthermore, we give an affi

Satyam Kumar, Vadlamani Ravi

Of late, in order to have better acceptability among various domain, researchers have argued that machine intelligence algorithms must be able to provide explanations that humans can understand causally. This aspect, also known as causability, achieves a specific level of human-level explainability. A specific class of algorithms known as counterfactuals may

Shiyong Zeng, Ping Zhu, Ruijie Zhou, Dominique Frank Escande

Recent MHD simulations find that the reversal of the local resistive interchange parameter $D_R$ from negative to positive due to impurity radiation cooling is able to trigger the resistive tearing mode growth in a tokamak above a threshold in impurity level. A layer of perturbed Pfirsch-Schl\"{u}ter current density and resistivity are also induced by the im

Hoa T. Vu

We revisit the MaxSAT problem in the data stream model. In this problem, the stream consists of $m$ clauses that are disjunctions of literals drawn from $n$ Boolean variables. The objective is to find an assignment to the variables that maximizes the number of satisfied clauses. Chou et al. (FOCS 2020) showed that $\Omega(\sqrt{n})$ space is necessary to yie

Pranav Nuti, Jan Vondrák

In this paper, we investigate two variants of the secretary problem. In these variants, we are presented with a sequence of numbers $X_i$ that come from distributions $\mathcal{D}_i$, and that arrive in either random or adversarial order. We do not know what the distributions are, but we have access to a single sample $Y_i$ from each distribution $\mathcal{D

Hui-Fang Xue, Jia-Shu Niu, Jian-Ning Fu

Stellar structure and evolution theory is one of the basis in modern astronomy. Stellar inner structures and their evolutionary states can be precisely tested by asteroseismology, since the inner information is brought to the stellar surface by the global oscillating waves and becomes observable. For stellar evolutionary speed (i.e. how long time scale does

Haruki Kono, Tatsuya Kubokawa

In the problem of selecting variables in a multivariate linear regression model, we derive new Bayesian information criteria based on a prior mixing a smooth distribution and a delta distribution. Each of them can be interpreted as a fusion of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Inheriting their asymptotic pro

Emanuel Sommer, Karoline Bax, Claudia Czado

Accurately estimating risk measures for financial portfolios is critical for both financial institutions and regulators. However, many existing models operate at the aggregate portfolio level and thus fail to capture the complex cross-dependencies between portfolio components. To address this, a new approach is presented that uses vine copulas in combination

Oleg Gutik, Olha Popadiuk

We study the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}}$, which is introduced in the paper [O. Gutik and M. Mykhalenych, \emph{On some generalization of the bicyclic monoid}, Visnyk Lviv. Univ. Ser. Mech.-Mat. \textbf{90} (2020), 5--19 (in Ukrainian)], in the case when the family $\mathscr{F}_n$ generated by the set $\{0,1,\ldots,n\}$. We show that the

Nico G. Leumer

In this manuscript, we introduce (symmetric) Tetranacci polynomials $\xi_j$ as a twofold generalization of ordinary Tetranacci numbers, by considering both non unity coefficients and generic initial values in their recursive definition. The issue of these polynomials arose in condensed matter physics and the diagonalization of symmetric Toeplitz matrices hav

Zikai Tang, Hanyuan Deng

A new geometric background of graph invariants was introduced by Gutman, of which the simplest is the second Sombor index $SO_2$, defined as $SO_2=SO_2(G)=\sum_{uv\in E}\frac{|d^2_G(u)-d^2_G(v)|}{d^2_G(u)+d^2_G(v)}$, where $G = (V, E)$ is a simple graph and $d_G(v)$ denotes the degree of $v$ in $G$. In this paper, the chemical applicability of the second Som

Zhenzhen Liu, Guochao Wei, Jun-Jun Xiao

In the presence of crystalline symmetries, second-order topological insulators can be featured by the polarization which is believed identical to the Wannier center. In this Letter, we show that second-order topological insulators are present in the full process of topological phase transition between a pair of degenerate photonic bands resulting from the no

Toshko Boev, Georgi Georgiev

A common approach is present concerning the problem of Dirichlet, both for bounded 3D domains and their (unbounded) complements, regarding the fractional (3D) Poisson equation.

Yeonhong Park, Sunhong Min, Jae W. Lee

Recently, Graph Neural Networks (GNNs) have been receiving a spotlight as a powerful tool that can effectively serve various inference tasks on graph structured data. As the size of real-world graphs continues to scale, the GNN training system faces a scalability challenge. Distributed training is a popular approach to address this challenge by scaling out C

Tailin Chen, Desen Zhou, Jian Wang, Shidong Wang

In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel classes, particularly for fine-grained actions. Existing meta-learning frameworks typically rely on the body-level representations in spatial dimension, which limits the generalisa

Jajati Keshari Sahoo, Prdeep Boggarapu, Ratikanta Behera, M. Zuhair Nashed

Mosic and Djordjevic introduced the notation of the gDMP inverse for Hilbert space operators in [J. Spectr. Theory, 8(2):555-573, 2018] by considering generalized Drazin inverse with the Moore-Penrose inverse. This paper introduces two new classes of inverses: GD1 (generalized Drazin and inner) inverse and 1GD (inner and generalized Drazin) inverse for Hilbe

Avik Das, Devanjali Nandi Das

Purpose: In the context of a COVID pandemic in 2020-21, this paper attempts to capture the interconnectedness and volatility transmission dynamics. The nature of change in volatility spillover effects and time-varying conditional correlation among the G7 countries and India is investigated. Methodology: To assess the volatility spillover effects, the bivaria

Ziqi Xu, Jixue Liu, Debo Cheng, Jiuyong Li

Much research has been devoted to the problem of learning fair representations; however, they do not explicitly the relationship between latent representations. In many real-world applications, there may be causal relationships between latent representations. Furthermore, most fair representation learning methods focus on group-level fairness and are based o

Yunxiang Xie, Ercai Chen, Kexiang Yang

In this paper, we study the Feldman-Katok metric in random dynamical systems and establish corresponding fiber topological entropy formula, Brin-Katok local entropy formula and fiber Katok entropy formula by replacing Bowen metric with Feldman-Katok metric. It turns out that the Feldman-Katok metric is also the weakest metric that makes the entropy formulae

Rong Qiang Wei

There have been a lot of methods aimed at studying the limiting free energy per particle (LFEPP) for 3-dimensional (3D) Ising model in absence of an external magnetic field. These methods are elegant, but most of them are complicated and often require specialized knowledge and special skills. Here we approximate the LFEPP for Ising model from its functional

Gung-Min Gie, Youngjoon Hong, Chang-Yeol Jung

We propose a new semi-analytic physics informed neural network (PINN) to solve singularly perturbed boundary value problems. The PINN is a scientific machine learning framework that offers a promising perspective for finding numerical solutions to partial differential equations. The PINNs have shown impressive performance in solving various differential equa

Wen O. Wang, Jixun K. Ding, Yoni Schattner, Edwin W. Huang

Many metallic quantum materials display anomalous transport phenomena that defy a Fermi liquid description. Here, we use numerical methods to calculate thermal and charge transport in the doped Hubbard model and observe a cross-over separating high- and low-temperature behaviors. Distinct from the behavior at high temperatures, the Lorenz number $L$ becomes

Pengcheng Zuo, Dong Ma, Yang Chen

A photonics-enabled wavelet-like transform system, characterized by multi-resolution time-frequency analysis, is proposed based on a typical stimulated Brillouin scattering (SBS) pump-probe setup using an optical nonlinear frequency-sweep signal. In the pump path, a continuous-wave optical signal is injected into an SBS medium to generate an SBS gain. In the

Gaurush Hiranandani

Given a learning problem with real-world tradeoffs, which cost function should the model be trained to optimize? This is the metric selection problem in machine learning. Despite its practical interest, there is limited formal guidance on how to select metrics for machine learning applications. This thesis outlines metric elicitation as a principled framewor

Pan Xie, Qipeng Zhang, Taiyi Peng, Hao Tang

The Sign Language Production (SLP) project aims to automatically translate spoken languages into sign sequences. Our approach focuses on the transformation of sign gloss sequences into their corresponding sign pose sequences (G2P). In this paper, we present a novel solution for this task by converting the continuous pose space generation problem into a discr

Shan Jin, Minghua Xu, Yiwei Cai

Side-channel attacks (SCAs), which infer secret information (for example secret keys) by exploiting information that leaks from the implementation (such as power consumption), have been shown to be a non-negligible threat to modern cryptographic implementations and devices in recent years. Hence, how to prevent side-channel attacks on cryptographic devices h

Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain

We consider the problem of OOD generalization, where the goal is to train a model that performs well on test distributions that are different from the training distribution. Deep learning models are known to be fragile to such shifts and can suffer large accuracy drops even for slightly different test distributions. We propose a new method - DAFT - based on

Stanley H. Chan

The one-bit quanta image sensor (QIS) is a photon-counting device that captures image intensities using binary bits. Assuming that the analog voltage generated at the floating diffusion of the photodiode follows a Poisson-Gaussian distribution, the sensor produces either a ``1'' if the voltage is above a certain threshold or ``0'' if it is below the threshol

Tuan Thanh Nguyen, Kui Cai, Han Mao Kiah, Kees A. Schouhamer Immink

In this work, we study two types of constraints on two-dimensional binary arrays. In particular, given $p,\epsilon>0$, we study (i) The $p$-bounded constraint: a binary vector of size $m$ is said to be $p$-bounded if its weight is at most $pm$, and (ii) The $\epsilon$-balanced constraint: a binary vector of size $m$ is said to be $\epsilon$-balanced if its w

Yun-Cheng Wang, Xiou Ge, Bin Wang, C. -C. Jay Kuo

Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs). Most prior KGC work focuses on learning embeddings for entities and relations through a simple scoring function. Yet, a higher-dimensional embedding space is usually required for a better reasoning capability, which leads to a larger model size

Shijia Zhao, Mingying Zhong

The spectrum structure of the linearized relativistic Boltzmann equation around a global Maxwellian is studied in this paper. Based on the spectrum analysis, we establish the optimal time-convergence rates of the global solution to the Cauchy problem for the relativistic Boltzmann equation.

Alvaro Zamora, Elliott Slaughter, Tom Abel

In this paper, we introduce our open source implementation of the Coupled Discrete Unified Gas Kinetic Scheme (CDUGKS) of https://journals.aps.org/pre/abstract/10.1103/PhysRevE.98.053310, a phase space scheme capable of handling a wide range of flow regimes. We demonstrate its performance on several problems including a number of well known test problems fro

Jonathan Boretsky, Christopher Eur, Lauren Williams

A flag positroid of ranks $\boldsymbol{r}:=(r_1<\dots <r_k)$ on $[n]$ is a flag matroid that can be realized by a real $r_k \times n$ matrix $A$ such that the $r_i \times r_i$ minors of $A$ involving rows $1,2,\dots,r_i$ are nonnegative for all $1\leq i \leq k$. In this paper we explore the polyhedral and tropical geometry of flag positroids, particularly wh

Zheqi Lv, Feng Wang, Shengyu Zhang, Kun Kuang

In an era of information explosion, recommendation systems play an important role in people's daily life by facilitating content exploration. It is known that user activeness, i.e., number of behaviors, tends to follow a long-tail distribution, where the majority of users are with low activeness. In practice, we observe that tail users suffer from significan

Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang

Generalized text representations are the foundation of many natural language understanding tasks. To fully utilize the different corpus, it is inevitable that models need to understand the relevance among them. However, many methods ignore the relevance and adopt a single-channel model (a coarse paradigm) directly for all tasks, which lacks enough rationalit

Song Wu, Kaichao You, Weihua He, Chen Yang

Video frame interpolation is a challenging task due to the ever-changing real-world scene. Previous methods often calculate the bi-directional optical flows and then predict the intermediate optical flows under the linear motion assumptions, leading to isotropic intermediate flow generation. Follow-up research obtained anisotropic adjustment through estimate

Guanzi Chen, Jiying Zhang, Xi Xiao, Yang Li

Recently, test time adaptation (TTA) has attracted increasing attention due to its power of handling the distribution shift issue in the real world. Unlike what has been developed for convolutional neural networks (CNNs) for image data, TTA is less explored for Graph Neural Networks (GNNs). There is still a lack of efficient algorithms tailored for graphs wi

Wilfrid Azariah, Fransiscus Asisi Bimo, Chih-Wei Lin, Ray-Guang Cheng

The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN deployment and operation. This paper provides a comprehensive survey of the open RAN development. We briefly summarized the RAN evolution history an

Sourav Chatterjee, Kaumudibikash Goswami, Rishab Chatterjee, Urbasi Sinha

Quantum key distribution (QKD) is a cryptographic protocol to enable two parties to share a secure key string, which can be used in one-time pad cryptosystem. There has been an ongoing surge of interest in implementing long-haul photonic-implementation of QKD protocols. However, the endeavour is challenging in many aspects. In particular, one of the major ch

Luciano Dyballa, Steven W. Zucker

Invoking the manifold assumption in machine learning requires knowledge of the manifold's geometry and dimension, and theory dictates how many samples are required. However, in applications data are limited, sampling may not be uniform, and manifold properties are unknown and (possibly) non-pure; this implies that neighborhoods must adapt to the local struct

Zhiwen Cao, Dongfang Liu, Qifan Wang, Yingjie Chen

Facial pose estimation refers to the task of predicting face orientation from a single RGB image. It is an important research topic with a wide range of applications in computer vision. Label distribution learning (LDL) based methods have been recently proposed for facial pose estimation, which achieve promising results. However, there are two major issues i

Yi-Ying Wang, Shao-Peng Tang, Zhi-Ping Jin, Yi-Zhong Fan

The multi-messenger data of neutron star merger events are promising for constraining the Hubble constant. So far, GW170817 is still the unique gravitational wave event with multi-wavelength electromagnetic counterparts. In particular, its radio and X-ray emission have been measured in the past $\sim 3-5$ years. In this work, we fit the long-lasting X-ray, o

M. Mohseni, I. Abdolhosseini Sarsari, S. Karbasizadeh, P. Udvarhelyi

Basic vacancy defects in twodimensional silicon carbide (2D-SiC) are examined by means of density functional theory calculations to explore their magneto-optical properties as well as their potential in quantum technologies. In particular, the characteristic hyperfine tensors and optical excited states of carbon-vacancy, silicon-vacancy, and carbon antisite-

Carles Tardío Pi, Jorge Castillo Medina, Pablo Padilla Longoria

Numerous biological systems exhibit transitions to synchronised oscillations via a population-density-dependant mechanism known as quorum sensing. Here we propose a model system, based on spatially distributed limit-cycle oscillators, that allows us to capture the dynamics of synchronization fronts by taking the continuum limit as the number of coupled oscil

Xin Tan, Longyin Zhang, Guodong Zhou

It is well known that translations generated by an excellent document-level neural machine translation (NMT) model are consistent and coherent. However, existing sentence-level evaluation metrics like BLEU can hardly reflect the model's performance at the document level. To tackle this issue, we propose a Discourse Cohesion Evaluation Method (DCoEM) in this

Guido Cavraro, Zhenyi Yuan, Manish K. Singh, Jorge Cortés

This paper considers the problem of voltage regulation in distribution networks. The primary motivation is to keep voltages within preassigned operating limits by commanding the reactive power output of distributed energy resources (DERs) deployed in the grid. We develop a framework for developing local Volt/Var control that comprises two main steps. In the

Shengcheng Yu, Chunrong Fang, Xin Li, Yuchen Ling

Software applications have been playing an increasingly important role in various aspects of society. In particular, mobile apps and web apps are the most prevalent among all applications and are widely used in various industries as well as in people's daily lives. To help ensure mobile and web app quality, many approaches have been introduced to improve app

Rui Yang, Yang Li

In response to the damage to electric power transmission systems caused by typhoon disasters in coastal areas, a planning-targeted resilience assessment framework that considers the impact of multiple factors is established to accurately find the weak links of the transmission system and improve the system resilience. Firstly, this paper constructs the atten

Jeff Greensite, Kazue Matsuyama

We point out that in superconductors there may exist localized quantum excitations of the electric and condensate fields surrounding a static charge, which cannot be interpreted as simply the ground state of the screened charge plus some number of massive photons. This is illustrated via a lattice Monte Carlo calculation of the energy spectrum of a pair of s

Zhu-yao Jin, Jia-shun Yan, Jun Jing

We propose a nuclear-spin-polarization protocol in a general evolution-and-measurement framework. The protocol works in a spin-star configuration, where the central spin is coupled to the surrounding bath (nuclear) spins by flip-flop interaction of equal strength and is subject to a sequence of projective measurements on its ground state. Then a nondetermini

Nguyen Tu Cuong, Nguyen Tuan Long, Hoang Le Truong

This paper shows that if $R$ is a homomorphic image of a Cohen-Macaulay local ring, then $R$-module $M$ is sequentially generalized Cohen-Macaulay if and only if the difference between Hilbert coefficients and arithmetic degrees for all distinguished parameter ideals of $M$ are bounded.

Yuxuan Han, Zhiyi Huang, Yang Wang, Rui Zhang

Given a spectrally sparse signal $\mathbf{y} = \sum_{i=1}^s x_i\mathbf{f}(\tau_i) \in \mathbb{C}^{2n+1}$ consisting of $s$ complex sinusoids, we consider the super-resolution problem, which is about estimating frequency components $\{\tau_i\}_{i=1}^s$ of $\mathbf y$. We consider the OMP-type algorithms for super-resolution, which is more efficient than other

Fu-An Chao, Tien-Hong Lo, Tzu-I Wu, Yao-Ting Sung

As an indispensable ingredient of computer-assisted pronunciation training (CAPT), automatic pronunciation assessment (APA) plays a pivotal role in aiding self-directed language learners by providing multi-aspect and timely feedback. However, there are at least two potential obstacles that might hinder its performance for practical use. On one hand, most of

Michael Hoff, Hoang Le Truong

We show that the affine cones over a general Fano-Mukai fourfold of genus $g=7$, $8$ and $9$ are flexible. Equivalently, there is an infinitely transitive action of the special automorphism group on such affine cones. In particular, any Mukai fourfold of genus $7,8$ and $9$ is $\Bbb A^2$-cylindrical.

Dinh Dũng

We investigate the approximation of weighted integrals over $\mathbb{R}^d$ for integrands from weighted Sobolev spaces of mixed smoothness. We prove upper and lower bounds of the convergence rate of optimal quadratures with respect to $n$ integration nodes for functions from these spaces. In the one-dimensional case $(d=1)$, we obtain the right convergence r

Lin Su, Xiang Yan, Xilei Zhao

Many cities around the world have introduced dockless micromobility services in recent years and witnessed their rapid growth. Shared dockless e-scooters have the potential to benefit neighborhoods that lack access to station-based bikeshare services, but they may also exacerbate the existing spatial disparities. While some studies have examined the equity o

Jared Markowitz, Ryan W. Gardner, Ashley Llorens, Raman Arora

Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-making, where gains and losses are valued differently and outlying outcomes are given increased consideration. It also fails to capitalize on opportunities to improve safety and/or pe

Joshua Erlich

We present a first analysis of a nonperturbative approach to quantum gravity based on a representation of quantum field theory in terms of stochastic processes. The stochastic description accommodates a physical Lorentz-invariant ultraviolet regulator that provides a novel description of physics at ultra-short distances. In a stochastic composite gravity mod

Daiki Hatanaka, Motoki Asano, Hajime Okamoto, Hiroshi Yamaguchi

Establishing a way to control magnetic dynamics and elementary excitations (magnons) is crucial to fundamental physics and the search for novel phenomena and functions in magnetic solid-state systems. Electromagnetic waves have been developed as means of driving and sensing in magnonic and spintronics devices used in magnetic spectroscopy, non-volatile memor

Unggi Lee, Yonghyun Park, Yujin Kim, Seongyune Choi

Knowledge tracing (KT) is a field of study that predicts the future performance of students based on prior performance datasets collected from educational applications such as intelligent tutoring systems, learning management systems, and online courses. Some previous studies on KT have concentrated only on the interpretability of the model, whereas others h

Nan Chen, Yinling Zhang

Discovering the underlying dynamics of complex systems from data is an important practical topic. Constrained optimization algorithms are widely utilized and lead to many successes. Yet, such purely data-driven methods may bring about incorrect physics in the presence of random noise and cannot easily handle the situation with incomplete data. In this paper,

Yu Song, Madhav V. Chitturi, David A. Noyce

This paper develops a test scenario specification procedure using crash sequence analysis and Bayesian network modeling. Intersection two-vehicle crash data was obtained from the 2016 to 2018 National Highway Traffic Safety Administration Crash Report Sampling System database. Vehicles involved in the crashes are specifically renumbered based on their initia

Mamadou Yauck

This paper deals with the estimation of exogeneous peer effects for partially observed networks under the new inferential paradigm of design identification, which characterizes the missing data challenge arising with sampled networks with the central idea that two full data versions which are topologically compatible with the observed data may give rise to t

Shang Liu, Wenjie Ji

We present a general approach to the bulk-boundary correspondence of noninvertible topological phases, including both topological and fracton orders. This is achieved by a novel bulk construction protocol where solvable $(d+1)$-dimensional bulk models with noninvertible topology are constructed from the so-called generalized Ising (GI) models in $d$ dimensio

Hong-Ao Yang, Hao-Yu Wei, Bing-Yang Cao

Topological semimetal states which are constrained by symmetries and give birth to innovative excitations are the frontiers of topological quantum matter. Nodal chains in which two nodal rings connect at one point were first discovered in non-symmorphic electronic systems and then generalized to symmorphic phononic systems. In this work, we identify a new cl

Yue You, Renkai Ma, Xinning Gui

This review reports the user experience of symptom checkers, aiming to characterize users studied in the existing literature, identify the aspects of user experience of symptom checkers that have been studied, and offer design suggestions. Our literature search resulted in 31 publications. We found that (1) most symptom checker users are relatively young; (2

Lele Cao, Sonja Horn, Vilhelm von Ehrenheim, Richard Anselmo Stahl

Investment professionals rely on extrapolating company revenue into the future (i.e. revenue forecast) to approximate the valuation of scaleups (private companies in a high-growth stage) and inform their investment decision. This task is manual and empirical, leaving the forecast quality heavily dependent on the investment professionals' experiences and insi

A. Gilad Kusne, Austin McDannald

Autonomous materials research systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. As these systems grow in number, capability, and complexity, a new challenge arises - how will they work together across large facilities? We explore one solution to this question - a multi-agent laboratory control frame-work. We d

Franklin Wang, Jian Ge, Kevin Willis

Although many near-Earth objects have been found by ground-based telescopes, some fast-moving ones, especially those near detection limits, have been missed by observatories. We developed a convolutional neural network for detecting faint fast-moving near-Earth objects. It was trained with artificial streaks generated from simulations and was able to find th

Ghodai Abdelrahman, Sherif Abdelfattah, Qing Wang, Yu Lin

Online education has gained an increasing importance over the last decade for providing affordable high-quality education to students worldwide. This has been further magnified during the global pandemic as more students switched to study online. The majority of online education tasks, e.g., course recommendation, exercise recommendation, or automated evalua

Giovanni Rosa, Simone Scalabrino, Rocco Oliveto

Background. Containerization technologies are widely adopted in the DevOps workflow. The most commonly used one is Docker, which requires developers to define a specification file (Dockerfile) to build the image used for creating containers. There are several best practice rules for writing Dockerfiles, but the developers do not always follow them. Violation

Alison B. Ma, Alexander Lerch

Labeling and maintaining a commercial sound effects library is a time-consuming task exacerbated by databases that continually grow in size and undergo taxonomy updates. Moreover, sound search and taxonomy creation are complicated by non-uniform metadata, an unrelenting problem even with the introduction of a new industry standard, the Universal Category Sys

Wolfgang Bangerth, Chris R. Johnson, Dennis K. Njeru, Bart van Bloemen Waanders

In inverse problems, one attempts to infer spatially variable functions from indirect measurements of a system. To practitioners of inverse problems, the concept of "information" is familiar when discussing key questions such as which parts of the function can be inferred accurately and which cannot. For example, it is generally understood that we can identi

David Kadish, Arezoo Sarkheyli-Hägele, Jose Font, Diederick C. Niehorster

Augmented reality (AR) games are a rich environment for researching and testing computational systems that provide subtle user guidance and training. In particular computer systems that aim to augment a user's situation awareness benefit from the range of sensors and computing power available in AR headsets. In this work-in-progress paper, we present a new e

Tsutomu Shimada

We shall study three subjects of the Jacobi-Perron Algorithm of dimension 2. First, we study the "ideal convergence". About the approximations (p_n/r_n, q_n/r_n) to (A, B) (where A and B are positive real numbers, r_n, p_n and q_n are natural numbers), Showing some inequalities and evaluations of |p_n-Ar_n| and |q_n-Br_n|, we shall prove some sufficient cond

Elena Braverman, Alexandra Rodkina

We explore stabilization for nonlinear systems of difference equations with modified Target-Oriented Control and a chosen equilibrium as a target, both in deterministic and stochastic settings. The influence of stochastic components in the control parameters is explored. The results are tested on the H\'{e}non and the Lozi maps.

Mumtaz Hussain, Bixuan Li, Nikita Shulga

We present a detailed Hausdorff dimension analysis of the set of real numbers where the product of consecutive partial quotients in their continued fraction expansion grow at a certain rate but the growth of the single partial quotient is at a different rate. We consider the set \begin{equation*} \FF(\Phi_1,\Phi_2) \defeq \EE(\Phi_1) \backslash \EE(\Phi_2)=\

E. Cruz-Albaro, A. Gutiérrez-Rodríguez, J. I. Aranda, F. Ramírez-Zavaleta

In this paper, using the Bestest Little Higgs Model (BLHM) we calculate at the one-loop level the contributions to the Anomalous Magnetic Dipole Moment (AMDM) and Anomalous Weak Magnetic Dipole Moment (AWMDM) of the tau-lepton. The implications from this model are study, emphasizing the contributions of the new physics induced by the new scalar and vector bo

Ariel Molinuevo, Federico Quallbrunn

We determine the structure of the singular locus of generic codimension-$q$ logarithmic foliations and its relation with the unfoldings of said foliations. In the case where the ambient variety is the projective space $\mathbb{P}^n$ we calculate the graded ideal defining the scheme of persistent singularities.

Cem Ates Musluoglu, Charles Hovine, Alexander Bertrand

This paper studies the convergence conditions and properties of the distributed adaptive signal fusion (DASF) algorithm, the framework itself having been introduced in a `Part I' companion paper. The DASF algorithm can be used to solve linear signal and feature fusion optimization problems in a distributed fashion, and is in particular well-suited for solvin

Jorge A. Garcia, Angelos Alamanos

Complex agricultural problems concern many countries, as the economic motives are increasingly higher, and at the same time the consequences from the irrational resources use and emissions are becoming more evident. In this work we study three of the most common agricultural problems and model them through optimization techniques, showing ways to assess conf

Ziqi Wang

A fundamental limitation of various Equivalent Linearization Methods (ELMs) in nonlinear random vibration analysis is that they are approximate by their nature. A quantity of interest estimated from an ELM has no guarantee to be the same as the solution of the original nonlinear system. In this study, we tackle this fundamental limitation. We sequentially ad

I. Dahiri, M. Baouahi, M. Ouali, B. Manaut

In both absence and presence of a circularly polarized monochromatic electromagnetic pulse, we have analyzed the electron-nucleon scattering process, where the nucleon is assumed to be spinless with a spherical shape. We have provided the theoretical calculation of the differential cross section (DCS) by using the Dirac-Volkov formalism. This research paper

Oleksii Ivanytskyi, David Blaschke, Tobias Fischer, Andreas Bauswein

Based on a recently developed relativistic density functional approach to color-superconducting quark matter and a novel quark-hadron transition construction which phenomenologically accounts for the effects of inhomogeneous pasta phases and quark-hadron continuity, we construct a class of hybrid equations of state applicable at the regimes typical for compa

Logan Bishop-Van Horn, Irene P. Zhang, Emily N. Waite, Ian Mondragon-Shem

In this work we study the effect of engineered disorder on the local magnetic response of proximity coupled superconducting island arrays by comparing scanning Superconducting Quantum Interference Device (SQUID) susceptibility measurements to a model in which we treat the system as a network of one-dimensional (1D) superconductor-normal metal-superconductor

Mu Cai, Yixuan Li

Modern deep generative models can assign high likelihood to inputs drawn from outside the training distribution, posing threats to models in open-world deployments. While much research attention has been placed on defining new test-time measures of OOD uncertainty, these methods do not fundamentally change how deep generative models are regularized and optim

M. Rasel Mahmud, Michael Stewart, Alberto Cordova, John Quarles

Virtual Reality (VR) users often encounter postural instability, i.e., balance issues, which can be a significant impediment to universal usability and accessibility, particularly for those with balance impairments. Prior research has validated imbalance issues, but little effort has been made to mitigate them. We recruited 39 participants (with balance impa

Jiawei Yan, Gianluca Geloni

X-ray beams carrying orbital angular momentum (OAM) are an emerging tool for probing matter. Optical elements, such as spiral phase plates and zone plates, have been widely used to generate OAM light. However, these optics are challenging to use at x-ray free-electron lasers (XFELs) due to the high impinging intensities. Here, we propose a self-seeded FEL me

Boris Rubin

We obtain sharp norm estimates for fractional integrals generated by Radon transforms of three types in the n-dimensional real Euclidean space. The method relies on recent interpolation results for analytic families of operators.

Rohan Tan Bhowmik

Wildfires are increasingly impacting the environment, human health and safety. Among the top 20 California wildfires, those in 2020-2021 burned more acres than the last century combined. California's 2018 wildfire season caused damages of $148.5 billion. Among millions of impacted people, those living with disabilities (around 15% of the world population) ar

Phil A. LeMaitre, Russell B. Thompson

A representation of polymer self-consistent field theory equivalent to quantum density functional theory is given in terms of non-orthogonal basis sets. Molecular integrals and self-consistent equations for spherically symmetric systems using Gaussian basis functions are given, and the binding energies and radial electron densities of neutral atoms hydrogen

Eiichi Bannai, Tsuyoshi Miezaki, Hiroyuki Nakasora

Let $C$ be a four-weight binary code, which has all one vector. Furthermore, we assume that $C$ supports $t$-designs for all weights obtained from the Assmus--Mattson theorem. We previously showed that $t\leq 5$. In the present paper, we show an analogue of this result in the cases of five and six-weight codes.

Sejoon Oh, Ankur Bhardwaj, Jongseok Han, Sungchul Kim

Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose ISCON, which implici

M. A. Amooie, H. Ghadimi, B. Hajshirmohammadi, M. M. Khonsari

The accumulated entropy is used to evaluate the initiation of crack in flat notched specimen made of stainless steel 304. Two notch radii have been studied to capture the thermography of crack initiation and measure accumulated heat dissipation. It has been observed that under pull-push loading with an R-ratio of -1, the accumulated entropy for crack initiat

Alessandra Tafuro, Bappaditya Debnath, Andrea M. Zanchettin, Amir Ghalamzan E

This paper presents a novel probabilistic approach to deep robot learning from demonstrations (LfD). Deep movement primitives (DMPs) are deterministic LfD model that maps visual information directly into a robot trajectory. This paper extends DMPs and presents a deep probabilistic model that maps the visual information into a distribution of effective robot

Laurentiu G. Maxim, Jose Israel Rodriguez, Botong Wang, Lei Wu

The linear optimization degree gives an algebraic measure of complexity of optimizing a linear objective function over an algebraic model. Geometrically, it can be interpreted as the degree of a projection map on the {affine} conormal variety. Fixing an affine variety, our first result shows that the geometry of {this} conormal variety, expressed in terms of

Partha S. Dey, Grigory Terlov

We derive a Gaussian Central Limit Theorem for the sample quantiles based on locally dependent random variables with explicit convergence rate. Our approach is based on converting the problem to a sum of indicator random variables, applying Stein's method for local dependence, and bounding the distance between two normal distributions. We also generalize thi