October 2020 arXiv papers — page 47

Baoli Yin, Yang Liu, Hong Li, Zhimin Zhang

This article devotes to developing robust but simple correction techniques and efficient algorithms for a class of second-order time stepping methods, namely the shifted fractional trapezoidal rule (SFTR), for subdiffusion problems to resolve the initial singularity and nonlocality. The stability analysis and sharp error estimates in terms of the smoothness

P. Woitke, O. Herbort, Ch. Helling, E. Stüeken

We propose a classification of exoplanet atmospheres based on their H, C, O, N element abundances below about 600 K. Chemical equilibrium models were run for all combinations of H, C, N, O abundances, and three types of solutions were found, which are robust against variations of temperature, pressure and nitrogen abundance. Type A atmospheres contain H2O, C

Owen Wyn Roberts, Daniel Verscharen, Yasuhito Narita, Rumi Nakamura

We investigate compressive turbulence at sub-ion scales with measurements from the Magnetospheric MultiScale Mission. The tetrahedral configuration and high time resolution density data obtained by calibrating spacecraft potential allow an investigation of the turbulent density fluctuations in the solar wind and their three-dimensional structure in the sub-i

Inigo Alonso, Luis Riazuelo, Luis Montesano, Ana C. Murillo

LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems during their decision making processes. Deep neural networks are achieving state-of-the-art results on large public benchmarks on this task. Unfortunately, finding models that generalize well or adapt to additional domains, where data

Huan Fu, Shunming Li, Rongfei Jia, Mingming Gong

Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. The common routine is to map 2D images and 3D shapes into an embedding space and define (or learn) a shape similarity measure. While metric learning with some adaptation techniques seems to be a natural solution to shape similarit

Tim Genewein, Tom McGrath, Grégoire Déletang, Vladimir Mikulik

Probability trees are one of the simplest models of causal generative processes. They possess clean semantics and -- unlike causal Bayesian networks -- they can represent context-specific causal dependencies, which are necessary for e.g. causal induction. Yet, they have received little attention from the AI and ML community. Here we present concrete algorith

Solenne Gaucher

We consider a situation where an agent has $T$ ressources to be allocated to a larger number $N$ of actions. Each action can be completed at most once and results in a stochastic reward with unknown mean. The goal of the agent is to maximize her cumulative reward. Non trivial strategies are possible when side information on the actions is available, for exam

Yanwu Gu, Rajesh Mishra, Berthold-Georg Englert, Hui Khoon Ng

Characterizing the noise in the set of gate operations that form the building blocks of a quantum computational device is a necessity for assessing the quality of the device. Here, we introduce randomized linear gate set tomography, an easy-to-implement gate set tomography procedure that combines the idea of state-preparation-and-measurement-error-free chara

Mehdi Benallegue, Jean-Paul Laumond, Alain Berthoz

During the steady gait, humans stabilize their head around the vertical orientation. While there are sensori-cognitive explanations for this phenomenon, its mechanical e fect on the body dynamics remains un-explored. In this study, we take profit from the similarities that human steady gait share with the locomotion of passive dynamics robots. We introduce a

Haidong Liu, Roberto Svaldi

We give a criterion for a nef divisor $D$ to be semiample on a Calabi--Yau threefold $X$ when $D^3=0=c_2(X)\cdot D$ and $c_3(X)\neq 0$. As a direct consequence, we show that on such a variety $X$, if $D$ is strictly nef and $\nu(D)\neq 1$, then $D$ is ample; we also show that if there exists a nef non-ample divisor $D$ with $D\not\equiv 0$, then $X$ contains

Nora Brambilla

Effective Quantum Field Theories and QCD Lattice methods have become more and more complementary and mutually supportive in the study of Hard Probes. I present some of the progress that this alliance already delivered and I discuss future opportunities.

Wen-Chin Huang, Yi-Chiao Wu, Tomoki Hayashi, Tomoki Toda

We present a novel approach to any-to-one (A2O) voice conversion (VC) in a sequence-to-sequence (seq2seq) framework. A2O VC aims to convert any speaker, including those unseen during training, to a fixed target speaker. We utilize vq-wav2vec (VQW2V), a discretized self-supervised speech representation that was learned from massive unlabeled data, which is as

Jingzhao Zhang, Aditya Menon, Andreas Veit, Srinadh Bhojanapalli

The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes access to an \emph{unlabelled} test sample. This sample may be used to estimate the test label distribution, and to then train a suitably re-weighted classifier. While approaches usi

Othmane Marfoq, Chuan Xu, Giovanni Neglia, Richard Vidal

Federated learning usually employs a client-server architecture where an orchestrator iteratively aggregates model updates from remote clients and pushes them back a refined model. This approach may be inefficient in cross-silo settings, as close-by data silos with high-speed access links may exchange information faster than with the orchestrator, and the or

Alexander Herega

The theme of the article is the application of the Poincare section method for visual classification of attractors in the four-dimensional phase space; the purpose of the study is to introduce consideration of three-dimensional Poincare sections, and develop an algorithm for their use for classification of four-dimensional flows by the type of attractors. Th

Aaron S. G. Robotham

In this work we compute a reasonably comprehensive set of tables for current and next generation survey facility filter conversions. Almost all useful transforms are included with the ProSpect software package described in Robotham et al (2020). Users are free to provide their own filters and compute their own transforms, where the included package examples

Abidha V P, Pradeesha Ashok

Given a bichromatic point set $P=\textbf{R} \cup \textbf{B}$ of red and blue points, a separator is an object of a certain type that separates $\textbf{R}$ and $\textbf{B}$. We study the geometric separability problem when the separator is a) rectangular annulus of fixed orientation b) rectangular annulus of arbitrary orientation c) square annulus of fixed o

Andrea Beck, Marius Kurz

This work presents a review of the current state of research in data-driven turbulence closure modeling. It offers a perspective on the challenges and open issues, but also on the advantages and promises of machine learning methods applied to parameter estimation, model identification, closure term reconstruction and beyond, mostly from the perspective of La

Hao Song, Zihan Zhang, Tian Cui2, Chris J. Pickard

Lanthanum hydrides, containing hydrogen framework structures under compression, display a superconducting state with a high observed critical temperature. However, this phenomenon has so far only been observed at very high pressures. Here, we computationally search for superconductors with very high critical temperatures, but at much lower pressures. We unco

Yongsun Lee, Jongshin Lee, Soo Min Oh, Deokjae Lee

Simplicial complex (SC) representation is an elegant mathematical framework for representing the effect of complexes or groups with higher-order interactions in a variety of complex systems ranging from brain networks to social relationships. Here, we explore the homological percolation transitions (HPTs) of growing SCs using empirical datasets and a model p

Richard Moot

We present a proof-theoretic analysis of the logic NL$\lambda$ (Barker \& Shan 2014, Barker 2019). We notably introduce a novel calculus of proof nets and prove it is sound and complete with respect to the sequent calculus for the logic. We study decidability and complexity of the logic using this new calculus, proving a new upper bound for complexity of the

A. G. Aleksejevs, S. G. Barkanova, Yu. M. Bystritskiy, V. A. Zykunov

The Standard Model radiative corrections to the Bhabha scattering process are considered within the one-loop approximation. Both virtual corrections and corrections for the real photon emission are taken into consideration. The calculation was performed at the energy assumed at the Belle II (Japan) facility.

Gabriel Frisch, Jean-Benoist Léger, Yves Grandvalet

Missing data can be informative. Ignoring this information can lead to misleading conclusions when the data model does not allow information to be extracted from the missing data. We propose a co-clustering model, based on the Latent Block Model, that aims to take advantage of this nonignorable nonresponses, also known as Missing Not At Random data (MNAR). A

Negar Heidari, Alexandros Iosifidis

Graph convolutional networks (GCNs) have been very successful in modeling non-Euclidean data structures, like sequences of body skeletons forming actions modeled as spatio-temporal graphs. Most GCN-based action recognition methods use deep feed-forward networks with high computational complexity to process all skeletons in an action. This leads to a high num

Chenni Xu, Itzhack Dana, Li-Gang Wang, Patrick Sebbah

Light propagation on a two-dimensional curved surface embedded in a three-dimensional space has attracted increasing attention as an analog model of four-dimensional curved spacetime in laboratory. Despite recent developments in modern cosmology on the dynamics and evolution of the universe, investigation of nonlinear dynamics of light in non-Euclidean geome

Liyan Sun, Jianxiong Wu, Xinghao Ding, Yue Huang

Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such learning framework is built on laborious manual annotation with restrict demands for expertise, leading to insufficient high-quality labels. To overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid

Liu Wang, Ruiqing Li, Jiaxin Zhu, Guangdong Bai

Ever since the beginning of the outbreak of the COVID-19 pandemic, researchers from interdisciplinary domains have worked together to fight against the crisis. The open source community, plays a vital role in coping with the pandemic which is inherently a collaborative process. Plenty of COVID-19 related datasets, tools, software, deep learning models, are c

Carlo Marcati, Joost A. A. Opschoor, Philipp C. Petersen, Christoph Schwab

We prove exponential expressivity with stable ReLU Neural Networks (ReLU NNs) in $H^1(\Omega)$ for weighted analytic function classes in certain polytopal domains $\Omega$, in space dimension $d=2,3$. Functions in these classes are locally analytic on open subdomains $D\subset \Omega$, but may exhibit isolated point singularities in the interior of $\Omega$

Hang Zhu, Zihao Wang

Feature matching is an important technique to identify a single object in different images. It helps machines to construct recognition of a specific object from multiple perspectives. For years, feature matching has been commonly used in various computer vision applications, like traffic surveillance, self-driving, and other systems. With the arise of Comput

Jonathan Homann, Wen-Chi Kuo, Bruce A. Watson

We extend the Koopman-von Neumann convergence condition on the Ces\`{a}ro mean to the context of a Dedekind complete Riesz space with weak order unit. As a consequence, a characterisation of conditional weak mixing is given in the Riesz space setting. The results are applied to convergence in $L^1$.

Nasrin Sultana, Jeffrey Chan, A. K. Qin, Tabinda Sarwar

The Travelling Salesman Problem (TSP) is a classical combinatorial optimisation problem. Deep learning has been successfully extended to meta-learning, where previous solving efforts assist in learning how to optimise future optimisation instances. In recent years, learning to optimise approaches have shown success in solving TSP problems. However, they focu

Aurelio Patelli

The Landau equation is a kinetic equation based on the weak coupling approximation of the interaction between the particles. In the framework of dry active matter this new kinetic equation relies on the weak coupling approximation of both the alignment strength and the magnitude of the angular noise, instead of the hypothesis of diluteness. Therefore, it is

Sara Fucini, Matteo Rinaldi, Sergio Scopetta

The measurement of nuclear generalized parton distributions (GPDs) in hard exclusive processes, such as deeply virtual Compton Scattering (DVCS), will be one of the main achievements of a new generation of experiments at high luminosity, such as those under way at the Jefferson Laboratory (JLab) with the 12 GeV electron beam and, above all, those planned at

Xiao Chai, Di Lao, Kazuya Fujimoto, Chandra Raman

Recent theoretical and experimental research has explored magnetic solitons in binary Bose-Einstein condensates (BECs). Here we demonstrate that such solitons are part of an SO(3) soliton family when embedded within a full three-component spin-1 manifold with spin-rotational symmetry. To showcase this, we have experimentally created a new type of domain wall

N. Guillaume, M. Azzaz, S. Blonkowski, E. Jalaguier

In this work, we propose a novel integration in order to significantly reduce the High Resistance State vari-ability and to improve thermal stability in Oxide-based Resistive Random Access Memory (OxRRAM) devices. A novel device featuring a metallic liner, acting as a parallel resistance, is presented. To assess the effect of this solution, we compare the re

Alois Würger

We theoretically study the thermoelectric response of ionic conductors to an applied temperaturegradient. As a main result we find that open and closed systems with respect to charge exchange,result in different expressions for the thermopower which may even take opposite signs. For theexperimentally most relevant zero-current steady state, we show that the

K. Sruthi, C. D. Ravikumar

We present correlations involving central intensity ratio (CIR) of 52 early type galaxies, including 24 ellipticals and 28 lenticulars, selected from low density environment in the nearby (< 30 Mpc) universe. CIR is found to be negatively and significantly correlated with the mass of the central super massive black hole, central velocity dispersion, absolute

Alois Würger

We show that the huge Seebeck coefficients observed recently for ionic conductors, arise from a ratchet effect where activated jumps between neighbor sites are rectified by a temperature gradient, thus driving mobile ions towards the cold. For complex systems with mobile molecules like water or polyethylene glycol, there is an even more efficient diffusiopho

Jean-Yves Welschinger

We recently defined a property of Morse shellability (and tileability) of finite simplicial complexes which extends the classical one and its relations with discrete Morse theory. We now prove that the product of two Morse tileable or shellable simplicial complexes carries Morse tileable or shellable triangulations under some tameness condition, and that any

Léo Girardin, Danielle Hilhorst

The paper is concerned with a singular limit for the bistable traveling wave problem in a very large class of two-species fully nonlinear parabolic systems with competitive reaction terms. Assuming existence of traveling waves and enough compactness, we derive and characterize the limiting problem. The assumptions and results are discussed in detail. The fre

Yang Ma, Jiasen Niu, Wenyu Xing, Yunyan Yao

Superconductivity has been one of the most fascinating quantum states of matter for over several decades. Among the superconducting materials, LaAlO3/SrTiO3 interface is of particularly interest since superconductivity exists between two insulating materials, which provides it with various unique applications compared with bulk superconductors and makes it a

Huanying Sun, Liwen Sang, Haihua Wu, Zilong Zhang

The ultra-wide bandgap of diamond distinguishes it from other semiconductors, in that all known defects have deep energy levels that are inactive at room temperature. Here, we present the effect of deep defects on the mechanical energy dissipation of single-crystal diamond experimentally and theoretically up to 973 K. Energy dissipation is found to increase

J. de Boer, C. Ginski, G. Chauvin, F. Menard

During the evolution of protoplanetary disks into planetary systems we expect to detect signatures that trace mechanisms such as planet-disk interaction. Protoplanetary disks display a large variety of structures in recently published high-spatial resolution images. However, the three-dimensional morphology of these disks is often difficult to infer from the

Ruchuan Ou, Michael Heinrich Baumann, Lars Grüne, Timm Faulwasser

This paper presents a simulation study on turnpike phenomena in stochastic optimal control problems. We employ the framework of Polynomial Chaos Expansions (PCE) to investigate the presence of turnpikes in stochastic LQ problems. Our findings indicate that turnpikes can be observed in the evolution of PCE coefficients as well as in the evolution of statistic

Alice Allen, Gábor Csányi, Geneviève Dusson, Christoph Ortner

We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent machine learned potentials. We bring these two key ideas together to bridge the gap between established empirical force fields that have a high

Dayang Nur Zulhijah Awang Jesemi, Hamimah Ujir, Irwandi Hipiny, Sarah Flora Samson Juan

Facial features deformed according to the intended facial expression. Specific facial features are associated with specific facial expression, i.e. happy means the deformation of mouth. This paper presents the study of facial feature deformation for each facial expression by using an optical flow algorithm and segmented into three different regions of intere

Abhinav Ramesh Kashyap, Devamanyu Hazarika, Min-Yen Kan, Roger Zimmermann

Domain divergence plays a significant role in estimating the performance of a model in new domains. While there is a significant literature on divergence measures, researchers find it hard to choose an appropriate divergence for a given NLP application. We address this shortcoming by both surveying the literature and through an empirical study. We develop a

Yinqian Sun, Yi Zeng, Tielin Zhang

Despite advances in artificial intelligence models, neural networks still cannot achieve human performance, partly due to differences in how information is encoded and processed compared to human brain. Information in an artificial neural network (ANN) is represented using a statistical method and processed as a fitting function, enabling handling of structu

Yin-Jyun Luo, Yuen-Jen Lin, Li Su

Singing voice correction (SVC) is an appealing application for amateur singers. Commercial products automate SVC by snapping pitch contours to equal-tempered scales, which could lead to deadpan modifications. Together with the neglect of rhythmic errors, extensive manual corrections are still necessary. In this paper, we present a streamlined system to autom

Nikunj Gupta, Rohit Ashiwal, Bine Brank, Sateesh K. Peddoju

The HPC community shows a keen interest in creating diversity in the CPU ecosystem. The advent of Arm-based processors provides an alternative to the existing HPC ecosystem, which is primarily dominated by x86 processors. In this paper, we port an Asynchronous Many-Task runtime system based on the ParalleX model, i.e., High Performance ParalleX (HPX), and ev

Ming Zhang, Jie Jiang

Due to the acceleration of the black hole, the circular orbits of the photons will deviate from the equatorial plane and the property of the black hole shadow will change. We find that the latitude of the circular orbit increases with the increasing acceleration and then show that the observer's inclination angles which make the shadow radius and the shadow

Kohei Soga

We introduce a class of controlled random walks on a grid in $\mathbb{T}^d$ and investigate global properties of action minimizing random walks for a certain action functional together with Hamilton-Jacobi equations on the grid. This yields an analogue of weak KAM theory, which recovers a part of original weak KAM theory through the hyperbolic scaling limit.

Kicheon Kang

We introduce the local field interaction approach to Dirac magnetic monopoles. Our analysis reveals two physically different types of a monopole. The first type is free of singularity, and the field angular momentum plays an essential role in the interaction. The second type is described as an endpoint of an invisible semi-infinite flux tube (a Dirac string)

Andi Han, Junbin Gao

In this paper, we propose a variant of Riemannian stochastic recursive gradient method that can achieve second-order convergence guarantee and escape saddle points using simple perturbation. The idea is to perturb the iterates when gradient is small and carry out stochastic recursive gradient updates over tangent space. This avoids the complication of exploi

Kun Fang, Qinghua Tao, Yingwen Wu, Tao Li

Deep Neural Networks (DNNs) are vulnerable to invisible perturbations on the images generated by adversarial attacks, which raises researches on the adversarial robustness of DNNs. A series of methods represented by the adversarial training and its variants have proven as one of the most effective techniques in enhancing the DNN robustness. Generally, advers

Feiguo Chen

With meshfree and fully Lagrangian features of particle methods, smoothed particle hydrodynamics (SPH) is suitable to achieve high-accurate simulations of multiphase flows with large interfacial deformations, discontinuities, and multi-physics. In this review, the basic concept of SPH is first briefly introduced. Then, various implementations of SPH in regar

Yunpeng Ren, Ziao Wang, Yiyuan Wang, Xiaofeng Zhang

Automatically generating financial report from a piece of news is quite a challenging task. Apparently, the difficulty of this task lies in the lack of sufficient background knowledge to effectively generate long financial report. To address this issue, this paper proposes the conditional variational autoencoders (CVAE) based approach which distills external

Gaosheng Zhu

In this paper, we give the definition of Maslov-type index of the discrete Hamiltonian system, and obtain the relation of Morse index and Maslov-type index of the discrete Hamiltonian system which is a generalization of case $\omega=1$ in \cite{RoS1}, \cite{RoS2} and \cite{Maz1} to case $\omega \in {\bf U}$ via direct method which is different from that of \

Akshaj Veldanda, Siddharth Garg

Deep neural networks (DNNs) demonstrate superior performance in various fields, including scrutiny and security. However, recent studies have shown that DNNs are vulnerable to backdoor attacks. Several defenses were proposed in the past to defend DNNs against such backdoor attacks. In this work, we conduct a critical analysis and identify common pitfalls in

Chul Moon, Xinlei Wang, Johan Lim

The area under a receiver operating characteristic curve (AUC) is a useful tool to assess the performance of continuous-scale diagnostic tests on binary classification. In this article, we propose an empirical likelihood (EL) method to construct confidence intervals for the AUC from data collected by ranked set sampling (RSS). The proposed EL-based method en

Taotao Jing, Bingrong Xu, Jingjing Li, Zhengming Ding

Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly focus on practical knowledge transfer through domain alignment. Unfortunately, they ignore the fairness issue when the auxiliary source is extremely imbalanced across different cat

Zhilin Wang, Weizhe Lin, Xiaodong Wu

While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness. We propose a new task to capture this richness based on an unlikely setting: movie characters. We sought to capture theme-level similarities between movie characters that were community-curated into 20,000 themes. By introducing a tw

Moritz Lichter, Pascal Schweitzer

In the quest for a logic capturing PTime the next natural classes of structures to consider are those with bounded color class size. We present a canonization procedure for graphs with dihedral color classes of bounded size in the logic of Choiceless Polynomial Time (CPT), which then captures PTime on this class of structures. This is the first result of thi

Qianqian Ma, Alex Olshevsky

We consider the problem of reconstructing a rank-one matrix from a revealed subset of its entries when some of the revealed entries are corrupted with perturbations that are unknown and can be arbitrarily large. It is not known which revealed entries are corrupted. We propose a new algorithm combining alternating minimization with extreme-value filtering and

Sanyuan Chen, Yu Wu, Zhuo Chen, Takuya Yoshioka

With its strong modeling capacity that comes from a multi-head and multi-layer structure, Transformer is a very powerful model for learning a sequential representation and has been successfully applied to speech separation recently. However, multi-channel speech separation sometimes does not necessarily need such a heavy structure for all time frames especia

Takuhiro Ogino, Ryui Kaneko, Satoshi Morita, Shunsuke Furukawa

We investigate a quantum phase transition between a Neel phase and a valence bond solid (VBS) phase, in each of which a different Z2 symmetry is broken, in a spin-1/2 two-leg XXZ ladder with a four-spin interaction. The model can be viewed as a one-dimensional variant of the celebrated J-Q model on a square lattice. By means of variational uniform matrix pro

Cheng Meng, Rui Xie, Abhyuday Mandal, Xinlian Zhang

We consider a measurement constrained supervised learning problem, that is, (1) full sample of the predictors are given; (2) the response observations are unavailable and expensive to measure. Thus, it is ideal to select a subsample of predictor observations, measure the corresponding responses, and then fit the supervised learning model on the subsample of

Kyriakos Papadopoulos, Nazli Kurt

We clarify and discuss a misunderstanding between uniform completeness and metric completeness, that has appeared in the literature in a study on the Alexandrov topology for a spacetime.

Kai Fukami, Takaaki Murata, Kai Zhang, Koji Fukagata

We perform a sparse identification of nonlinear dynamics (SINDy) for low-dimensionalized complex flow phenomena. We first apply the SINDy with two regression methods, the thresholded least square algorithm (TLSA) and the adaptive Lasso (Alasso) which show reasonable ability with a wide range of sparsity constant in our preliminary tests, to a two-dimensional

Yuxi Li, Ning Xu, Jinlong Peng, John See

In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process to produce more robust representations. By relying on the accurate reference mask in the starting frame, we show that the error propagation problem can be mitigated. Next, we introd

Yahya Sattar, Zubair Khalid

The depletion and variations of groundwater storage~(GWS) are of critical importance for sustainable groundwater management. In this work, we use Gravity Recovery and Climate Experiment (GRACE) to estimate variations in the terrestrial water storage~(TWS) and use it in conjunction with the Global Land Data Assimilation System~(GLDAS) data to extract GWS vari

Rubungo Andre Niyongabo, Hong Qu, Julia Kreutzer, Li Huang

Recent progress in text classification has been focused on high-resource languages such as English and Chinese. For low-resource languages, amongst them most African languages, the lack of well-annotated data and effective preprocessing, is hindering the progress and the transfer of successful methods. In this paper, we introduce two news datasets (KINNEWS a

Mengyi Shan, TJ Tsai

This paper tackles the problem of verifying the authenticity of speech recordings from world leaders. Whereas previous work on detecting deep fake or tampered audio focus on scrutinizing an audio recording in isolation, we instead reframe the problem and focus on cross-verifying a questionable recording against trusted references. We present a method for cro

Zihao Qi, Yongjun Xu, James J. Zhang, Xiangui Zhao

The paper concerns the Gelfand-Kirillov dimension and the generating series of nonsymmetric operads. An analogue of Bergman's gap theorem is proved, namely, no finitely generated locally finite nonsymmetric operad has Gelfand-Kirillov dimension strictly between $1$ and $2$. For every $r\in \{0\}\cup \{1\}\cup [2,\infty)$ or $r=\infty$, we construct a single-

Shiyi Yang, Peilun Wu, Hui Guo

Network intrusion detection (NID) is an essential defense strategy that is used to discover the trace of suspicious user behaviour in large-scale cyberspace, and machine learning (ML), due to its capability of automation and intelligence, has been gradually adopted as a mainstream hunting method in recent years. However, traditional ML based network intrusio

Yiwei Bao, Yang Chen, Siming Liu

DAMPE observation on the cosmic ray electron spectrum hints a narrow excess at $\sim$ 1.4 TeV. Although the excess can be ascribed to dark matter particles, pulsars and pulsar wind nebulae are believed to be a more natural astrophysical origin: electrons injected from nearby pulsars at their early ages can form a bump-like feature in the spectrum due to radi

Carlos Medel-Ramirez, Hilario Medel-Lopez

The importance of the working document is that it allows the analysis of the information and the status of cases associated with (SARS-CoV-2) COVID-19 as open data at the municipal, state and national level, with a daily record of patients, according to a age, sex, comorbidities, for the condition of (SARS-CoV-2) COVID-19 according to the following character

Digvijay Boob, Qi Deng, Guanghui Lan, Yilin Wang

Nonconvex sparse models have received significant attention in high-dimensional machine learning. In this paper, we study a new model consisting of a general convex or nonconvex objectives and a variety of continuous nonconvex sparsity-inducing constraints. For this constrained model, we propose a novel proximal point algorithm that solves a sequence of conv

Sabah Suhail, Rasheed Hussain, Raja Jurdak, Choong Seon Hong

Industrial processes rely on sensory data for critical decision-making processes. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the trustworthiness of data. To this end, we envision a blockchain-based framework for the Industrial Internet of Things (IIoT) to address the issues of data management and securi

Sho Takemori, Masahiro Sato

The RKHS bandit problem (also called kernelized multi-armed bandit problem) is an online optimization problem of non-linear functions with noisy feedback. Although the problem has been extensively studied, there are unsatisfactory results for some problems compared to the well-studied linear bandit case. Specifically, there is no general algorithm for the ad

Elyes Balti, Brian K. Johnson

In this work, we present a framework analysis of millimeter waves (mmWaves) vehicular communications systems. Communications between vehicles take place through a cooperative relay which acts as an intermediary base station (BS). The relay is equipped with multiple transmit and receive antennas and it employs decode-and-forward (DF) to process the signal. Al

Lili Ju, Xiao Li, Zhonghua Qiao, Jiang Yang

A large class of semilinear parabolic equations satisfy the maximum bound principle (MBP) in the sense that the time-dependent solution preserves for any time a uniform pointwise bound imposed by its initial and boundary conditions. Investigation on numerical schemes of these equations with preservation of the MBP has attracted increasingly attentions in rec

Noriaki Kitazawa

Future precision measurements of CMB polarizations can shed new light on the problem so called Hubble tension. The Hubble tension comes from the difference of the evolutions of the Hubble parameter which are determined with two different distance ladders. The standard distance ladder with the observation of Cepheid variables and type Ia supernovae gives larg

Priyank Agrawal, Jinglin Chen, Nan Jiang

This paper studies regret minimization with randomized value functions in reinforcement learning. In tabular finite-horizon Markov Decision Processes, we introduce a clipping variant of one classical Thompson Sampling (TS)-like algorithm, randomized least-squares value iteration (RLSVI). Our $\tilde{\mathrm{O}}(H^2S\sqrt{AT})$ high-probability worst-case reg

Fan Zhou

We say a subset $C$ of an abelian group $G$ \textit{arises as a minimal additive complement} if there is some other subset $W$ of $G$ such that $C+W=\{c+w:c\in C,\ w\in W\}=G$ and such that there is no proper subset $C'\supset C$ such that $C'+W=G$. In their recent paper, Burcroff and Luntzlara studied, among many other things, the conditions under which "ev

Pritam Chakraborty, Ashutosh Kumar Pandey, Sadhana Dash

The three-dimensional femtoscopic correlations of pions and kaons are presented for Pb$-$Pb collisions at \rootsNN = 5.02 TeV within the framework of (3+1)D viscous hydrodynamics combined with THERMINATOR 2 code for statistical hadronization. The femtoscopic radii for pions and kaons are obtained as a function of pair transverse momentum and centrality in al

M. M. Tsventoukh

A model has been proposed for estimation of plasma parameters of explosive electron emission pulses in vacuum arc discharge. It based on transition through the critical state during the explosion and allow to predict the cathode spot plasma parameters for various materials. The cathode flare plasma ions kinetic energy was estimated to be of about 100 times c

D. Hara, M. S. Bahramy, S. Murakami

In systems with time-reversal symmetry, the orbital magnetization is zero in equilibrium. Recently, it has been proposed that the orbital magnetization can be induced by an electric current in a helical crystal structure in the same manner as that in a classical solenoid. In this paper, we extend this theory and study the current-induced orbital magnetizatio

Xia Han, Zhibin Liang

In this paper, we study an optimal reinsurance-investment problem in a risk model with two dependent classes of insurance business, where the two claim number processes are correlated through a common shock component. We assume that the insurer can purchase per-loss reinsurance for each line of business and invest its surplus in a financial market consisting

Di Jin, Xiangchen Song, Zhizhi Yu, Ziyang Liu

Graph convolutional networks (GCNs), aiming to integrate high-order neighborhood information through stacked graph convolution layers, have demonstrated remarkable power in many network analysis tasks. However, topological limitations, including over-smoothing and local topology homophily, limit its capability to represent networks. Existing studies only per

Fei Zhao, Zhen Wu, Xinyu Dai

Aspect-level sentiment classification (ASC) aims to detect the sentiment polarity of a given opinion target in a sentence. In neural network-based methods for ASC, most works employ the attention mechanism to capture the corresponding sentiment words of the opinion target, then aggregate them as evidence to infer the sentiment of the target. However, aspect-

Menglong Xu, Shengqiang Li, Xiao-Lei Zhang

Recently, several studies reported that dot-product selfattention (SA) may not be indispensable to the state-of-theart Transformer models. Motivated by the fact that dense synthesizer attention (DSA), which dispenses with dot products and pairwise interactions, achieved competitive results in many language processing tasks, in this paper, we first propose a

Lewis A. Williamson, P. B. Blakie

Ferromagnetic spin-1 Bose-Einstein condensates in the broken-axisymmetric phase support polar-core spin vortices (PCVs), which are intimately linked to the nonequilibrium dynamics of the system. For a purely transversely magnetized system, the Turner point-vortex model predicts that PCVs behave like massive charged particles interacting via a two-dimensional

R. Abiya, G. Ambika

We study the undirected divisibility graph in which the vertex set is a finite subset of consecutive natural numbers up to N.We derive analytical expressions for measures of the graph like degree, clustering, geodesic distance and centrality in terms of the floor functions and the divisor functions. We discuss how these measures depend on the vertex labels a

Jindong Jiang, Sungjin Ahn

Reconciling symbolic and distributed representations is a crucial challenge that can potentially resolve the limitations of current deep learning. Remarkable advances in this direction have been achieved recently via generative object-centric representation models. While learning a recognition model that infers object-centric symbolic representations like bo

M. Porrati

This is a review of selected topics from recent work on symmetry charges in asymptotically flat spacetime done by the author in collaboration with U. Kol and R. Javadinezhad. First we reinterpret the reality constraint on the boundary graviton as the gauge fixing of a new local symmetry, called dual supertranslations. This symmetry extends the BMS group and

Tetsuya Ito

Birman-Menasco proved that there are finitely many knots having a given genus and braid index. We give a quantitative version of Birman-Menasco finiteness theorem, an estimate of the crossing number of knots in terms of genus and braid index. This has various applications of crossing numbers, such as, the crossing number of connected sum or satellites.

Suhua Tang, Petar Popovski, Chao Zhang, Sadao Obana

IoT systems typically involve separate data collection and processing, and the former faces the scalability issue when the number of nodes increases. For some tasks, only the result of data fusion is needed. Then, the whole process can be realized in an efficient way, integrating the data collection and fusion in one step by over-the-air computation (AirComp

Xiaogang Zhu, Shigang Liu, Xian Li, Sheng Wen

Fuzzing is one of the most effective technique to identify potential software vulnerabilities. Most of the fuzzers aim to improve the code coverage, and there is lack of directedness (e.g., fuzz the specified path in a software). In this paper, we proposed a deep learning (DL) guided directed fuzzing for software vulnerability detection, named DeFuzz. DeFuzz

Dongling Xiao, Yu-Kun Li, Han Zhang, Yu Sun

Coarse-grained linguistic information, such as named entities or phrases, facilitates adequately representation learning in pre-training. Previous works mainly focus on extending the objective of BERT's Masked Language Modeling (MLM) from masking individual tokens to contiguous sequences of n tokens. We argue that such contiguously masking method neglects to