October 2020 arXiv papers — page 38

Abouzeid M. Shalaby

In this work, we use a specific parameterization of the hypergeometric approximants ( the one by Mera et.al in Phys. Rev. Let. 115, 143001 (2015)) to approximate the seven-loop critical exponent $\nu$ for the $O(2)$-symmetric $\phi^4$ model. Our prediction gives the result $\nu=0.6711(7)$ which is compatible with the value $\nu=0.6709(1)$ from the famous exp

Yong Kiam Tan, André Platzer

Stability is required for real world controlled systems as it ensures that those systems can tolerate small, real world perturbations around their desired operating states. This paper shows how stability for continuous systems modeled by ordinary differential equations (ODEs) can be formally verified in differential dynamic logic (dL). The key insight is to

Bartłomiej Kiczek, Marek Rogatko, Karol I. Wysokiński

The anomalous spin Hall conductivity in the holographic model of Dirac semimetals with two Dirac nodes protected by the crystal symmetry has been elaborated. Such system besides the chiral anomaly possesses another anomaly which is related to the $\mathbb{Z}_2$ topological charge of the system. The holographic model of the system contains matter action with

Masahiro Kaneko, Danushka Bollegala

Prior work investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors one can increase the accuracy of a given set of pre-trained word embeddings. However, theoretically, this post-processing step is equivalent to applying a lin

Ayako Itaba, Izuru Mori

For a $3$-dimensional quantum polynomial algebra $A=\mathcal{A}(E,\sigma)$, Artin-Tate-Van den Bergh showed that $A$ is finite over its center if and only if $|\sigma|<\infty$. Moreover, Artin showed that if $A$ is finite over its center and $E\neq \mathbb{P}^2$, then $A$ has a fat point module, which plays an important role in noncommutative algebraic geome

Yin Cao, Turab Iqbal, Qiuqiang Kong, Fengyan An

Polyphonic sound event localization and detection (SELD), which jointly performs sound event detection (SED) and direction-of-arrival (DoA) estimation, detects the type and occurrence time of sound events as well as their corresponding DoA angles simultaneously. We study the SELD task from a multi-task learning perspective. Two open problems are addressed in

Mohamed Khedidji, Danila Amoroso, Hania Djani

Hyperferroelectrics are receiving a growing interest thanks to their unique property to retain a spontaneous polarization even in presence of a depolarizing field. Nevertheless, general microscopic mechanisms driving hyperferroelectricity, which is ascribed to the softening of a polar $LO$ mode, are still missing. Here, by means of phonons calculations and f

L. M. Volkova, D. V. Marinin

The structural-magnetic models of 25 AFM kagome cuprates similar to herbertsmithite (ZnCu3(OH)6Cl2) - a perspective spin liquid - have been calculated and analyzed. Main correlations between the structure and magnetic properties of these compounds were revealed. It has been demonstrated that, in all AFM kagome cuprates, including herbertsmithite, there exist

Alexei A. Mailybaev

We consider general infinite-dimensional dynamical systems with the Galilean and spatiotemporal scaling symmetry groups. Introducing the equivalence relation with respect to temporal scalings and Galilean transformations, we define a representative set containing a single element within each equivalence class. Temporal scalings and Galilean transformations d

Maria Grazia Concilio, Murari Soundararajan, Lucio Frydman, Ilya Kuprov

At the magnetic fields of common NMR instruments, electron Zeeman frequencies are too high for efficient electron-nuclear dipolar cross-relaxation to occur in solution. The rate of that process fades with the electron Zeeman frequency as omega^{-2} - in the absence of isotropic hyperfine couplings, liquid state dynamic nuclear polarisation (DNP) in high-fiel

Jan Haskovec

We present a direct proof of asymptotic consensus in the nonlinear Hegselmann-Krause model with transmission-type delay, where the communication weights depend on the particle distance in phase space. Our approach is based on an explicit estimate of the shrinkage of the group diameter on finite time intervals and avoids the usage of Lyapunov-type functionals

Pooja Sahlot, A. M. Awasthi

First- and second-harmonic dielectric susceptibilities are maidenly studied on Samarium Orthoferrite of mesoscopic/500 nm and nanoscopic/55 nm grainsizes. Magneto-electrically coupled to the antiferromagnetic and spin-reorientation transitions, fundamental and harmonic dielectricity consistently reflect the global/local polarization effects of crystallite-si

Shion Maeda, Nicolas Chauvet, Hayato Saigo, Hirokazu Hori

Collective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of applications such as resource management in social infrastru

Mingyang Qian, Yi Fu, Xiao Tan, Yingying Li

Video segmentation approaches are of great importance for numerous vision tasks especially in video manipulation for entertainment. Due to the challenges associated with acquiring high-quality per-frame segmentation annotations and large video datasets with different environments at scale, learning approaches shows overall higher accuracy on test dataset but

Pritam Das, Mrinal Kumar Das, Najimuddin Khan

We study a model of neutrino and dark matter within the framework of a minimal extended seesaw. This model is based on $A_4$ flavour symmetry along with the discrete $Z_3\times Z_4$ symmetry to stabilize the dark matter and construct desired mass matrices for neutrino mass. Five-zero textures are imposed in the final $4\times4$ active-sterile mass matrix, wh

Nirnai Rao, Elie Aljalbout, Axel Sauer, Sami Haddadin

In recent years, challenging control problems became solvable with deep reinforcement learning (RL). To be able to use RL for large-scale real-world applications, a certain degree of reliability in their performance is necessary. Reported results of state-of-the-art algorithms are often difficult to reproduce. One reason for this is that certain implementati

Parvez Ahmad, Saqib Qamar, Linlin Shen, Adnan Saeed

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-context

Chen Griner, Johannes Zerwas, Andreas Blenk, Manya Ghobadi

This paper presents a performance analysis of the design space of optical datacenter networks, including both demand-oblivious (static or dynamic) and demand-aware networks. We formally show that the number of specific optical switch types which should be used in an optimized datacenter network, depends on the traffic pattern, and in particular, the flow siz

Hanlu Wu, Tengfei Ma, Lingfei Wu, Shouling Ji

Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-experts, an aggregation model that learns the true label by incorporating the source credibility is required. In this paper, we propose a novel framework based on graph neural networ

Satoshi Kumabe

In this paper, we give a formula for the number of rational points on the Dwork hypersurfaces of degree six over finite fields by using Greene's finite-field hypergeometric function, which is a generalization of Goodson's formula for the Dwork hypersurfaces of degree four. Our formula is also a higher-dimensional and a finite field analogue of Matsumoto-Tera

Chao Sun, Guoqiang Hu

In this work, we study the distributed Nash equilibrium seeking problem for monotone generalized noncooperative games with set constraints and shared affine inequality constraints. A distributed regularized penalty method is proposed. The idea is to use a differentiable penalty function with a time-varying penalty parameter to deal with the inequality constr

Nigel G. Bean, Małgorzata M. O'Reilly, Zbigniew Palmowski

Stochastic fluid-fluid models (SFFMs) offer powerful modeling ability for a wide range of real-life systems of significance. The existing theoretical framework for this class of models is in terms of operator-analytic methods. For the first time, we establish matrix-analytic methods for the efficient analysis of SFFMs. We illustrate the theory with numerical

Ze Zhou

This paper studies circle patterns from the viewpoint of configurations. By using the topological degree theory, we extend the Koebe-Andreev-Thurston Theorem to include circle patterns with obtuse exterior intersection angles. As a consequence, we obtain a generalized Andreev's Theorem which allows obtuse dihedral angles.

Elina Shishkina

The paper contains the inversion formula for the weighted spherical mean. The interest to reconstruction a function by its integral by sphere grews tremendously in the last six decades, stimulated by the spectrum of new problems and methods of image reconstruction. We consider a generalization of the classical spherical mean and its inverse in the case when

Yujing Wang, Jun Ren, Weixuan Zhang, Lu He

The realization of robust strong coupling and entanglement between distant quantum emitters (QEs) is very important for scalable quantum information processes. However, it is hard to achieve it based on conventional systems. Here, we propose theoretically and demonstrate numerically a scheme to realize such strong coupling and entanglement. Our scheme is bas

Luminiţa Barbu, Gheorghe Moroşanu

In this paper we consider in a bounded domain $\Omega \subset \mathbb{R}^N$ with smooth boundary an eigenvalue problem for the negative $(p,q)$-Laplacian with a Steklov-like boundary condition, where $p,\, q\in (1,\infty)$, $p\neq q$, including the open case $p\in (1,\infty)$, $q\in (1, 2)$, $p\neq q$. A full description of the set of eigenvalues of this pro

Yujing Wang, Jun Ren, Weixuan Zhang, Lu He

The realization of robust coherent energy transfer with a long range from a donor to an acceptor has many important applications in the field of quantum optics. However, it is hard to be realized using conventional schemes. Here, we demonstrate theoretically that the robust energy transfer can be achieved using a photonic crystal platform, which includes the

Sahan Hemachandra, Ranga Rodrigo, Chamira Edussooriya

Light field saliency detection -- important due to utility in many vision tasks -- still lacks speed and can improve in accuracy. Due to the formulation of the saliency detection problem in light fields as a segmentation task or a memorizing task, existing approaches consume unnecessarily large amounts of computational resources for training, and have longer

Thien-Minh Nguyen, Muqing Cao, Shenghai Yuan, Yang Lyu

In recent years, thanks to the continuously reduced cost and weight of 3D Lidar, the applications of this type of sensor in robotics community have become increasingly popular. Despite many progresses, estimation drift and tracking loss are still prevalent concerns associated with these systems. However, in theory these issues can be resolved with the use of

Shaofan Liu, Liu Zhao

The definitions of gravitational work as well as work done by the total external force on a massive probe particle moving in generic spacetime backgrounds are proposed. These definitions are given in the form of scalar integrals and thus, are independent of coordinate choices. However, the dependence on the choice of observer field is essential and inevitabl

Shahar Hoory, Tzvika Shapira, Asaf Shabtai, Yuval Elovici

Recent research shows that neural networks models used for computer vision (e.g., YOLO and Fast R-CNN) are vulnerable to adversarial evasion attacks. Most of the existing real-world adversarial attacks against object detectors use an adversarial patch which is attached to the target object (e.g., a carefully crafted sticker placed on a stop sign). This metho

Gergő Nemes

We prove the enveloping property of the known divergent asymptotic expansions of the large real zeros of the cylinder and Airy functions, and thereby answering in the affirmative two conjectures posed by Elbert and Laforgia and by Fabijonas and Olver, respectively. The essence of the proof is the construction of analytic functions that return the zeros when

Minghua Chen, Fan Yu, Zhi Zhou

Based on the equivalence of A-stability and G-stability, the energy technique of the six-step BDF method for the heat equation has been discussed in [Akrivis, Chen, Yu, Zhou, Math. Comp., Revised]. Unfortunately, this theory is hard to extend the time-fractional PDEs. In this work, we consider three types of subdiffusion models, namely single-term, multi-ter

Melanie Bancilhon, Zhengliang Liu, Alvitta Ottley

Data visualizations are standard tools for assessing and communicating risks. However, it is not always clear which designs are optimal or how encoding choices might influence risk perception and decision-making. In this paper, we report the findings of a large-scale gambling game that immersed participants in an environment where their actions impacted thei

Antonio De Felice, Andreas Doll, François Larrouturou, Shinji Mukohyama

In the context of the recently proposed type-II minimally modified gravity theory, i.e. a metric theory of gravity with two local physical degrees of freedom that does not possess an Einstein frame, we study spherically symmetric vacuum solutions to explore the strong gravity regime. Despite the absence of extra degrees of freedom in the gravity sector, the

A. van Hameren, P. Kotko, K. Kutak, S. Sapeta

We discuss central-forward dijet production at LHC energies within the framework of high energy factorization. In our study, we profit from the recent progress on consistent merging of Sudakov resummation with small-$x$ effects, which allows us to compute two different gluon distributions which depend on longitudinal momentum, transverse momentum and the har

Chenmin Sun, Nikolay Tzvetkov

We continue our study of the cubic fractional NLS with very weak dispersion $\alpha>1$ and data distributed according to the Gibbs measure. We construct the natural strong solutions for $\alpha>\alpha_0=\frac{31-\sqrt{233}}{14}\approx 1.124$ which is strictly smaller than $\frac{8}{7}$, the threshold beyond which the first nontrivial Picard iteration has no

Ziyu Wang, Bin Dai, David Wipf, Jun Zhu

The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling. In this work, we present a possible explanation for this phenomenon, starting from the observation that a model's

Ross Cutler, Babak Naderi, Markus Loide, Sten Sootla

The quality of acoustic echo cancellers (AECs) in real-time communication systems is typically evaluated using objective metrics like ERLE and PESQ, and less commonly with lab-based subjective tests like ITU-T Rec. P.831. We will show that these objective measures are not well correlated to subjective measures. We then introduce an open-source crowdsourcing

Yuqiao Liu, Yudan Wang, Ying Zhao, Zhixiang Li

Transgender community is experiencing a huge disparity in mental health conditions compared with the general population. Interpreting the social medial data posted by transgender people may help us understand the sentiments of these sexual minority groups better and apply early interventions. In this study, we manually categorize 300 social media comments po

T. -N. Nguyen, M. -N. Tran, R. Kohn

We propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in-sample analysis and out-ofsample forecasting of the traditional conditional heteroskedastic models. In particular, we incorporate auxiliary deterministic processes, governed by recurrent neural networks, into the condition

Guoliang Cheng, Lele Liao, Hongsheng Chen, Jing Lu

The mismatch between the numerical and actual nonlinear models is a challenge to nonlinear acoustic echo cancellation (NAEC) when the nonlinear adaptive filter is utilized. To alleviate this problem, we combine a basis-generic expansion of the memoryless nonlinearity into semi-blind source separation (SBSS). By regarding all the basis functions of the far-en

Chao Liu, Heming Sun, Jiro Katto, Xiaoyang Zeng

Convolutional neural network (CNN)-based filters have achieved great success in video coding. However, in most previous works, individual models are needed for each quantization parameter (QP) band. This paper presents a generic method to help an arbitrary CNN-filter handle different quantization noise. We model the quantization noise problem and implement a

Wen Sun, Shiyu Lei, Lu Wang, Zhiqiang Liu

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial devices to achieve Industry 4.0 benefits. In this paper, we consider a new architecture of digital twin empowered Industrial IoT where digital twins capture the characteristics of industrial devices to assist federated learning. Notic

Sathvik Nair, Mahesh Srinivasan, Stephan Meylan

Understanding context-dependent variation in word meanings is a key aspect of human language comprehension supported by the lexicon. Lexicographic resources (e.g., WordNet) capture only some of this context-dependent variation; for example, they often do not encode how closely senses, or discretized word meanings, are related to one another. Our work investi

Yunlei Shi, Zhaopeng Chen, Hongxu Liu, Sebastian Riedel

Contact-rich manipulation tasks are commonly found in modern manufacturing settings. However, manually designing a robot controller is considered hard for traditional control methods as the controller requires an effective combination of modalities and vastly different characteristics. In this paper, we firstly consider incorporating operational space visual

Edo Cohen-Karlik, Avichai Ben David, Amir Globerson

In many machine learning problems the output should not depend on the order of the input. Such "permutation invariant" functions have been studied extensively recently. Here we argue that temporal architectures such as RNNs are highly relevant for such problems, despite the inherent dependence of RNNs on order. We show that RNNs can be regularized towards pe

Johan P. Boetker

Purpose: The aim of this work is to demonstrate that convolutional neural networks (CNN) can be applied to extremely sparse image libraries by subdivision of the original image datasets. Methods: Image datasets from a conventional digital camera was created and scanning electron microscopy (SEM) measurements were obtained from the literature. The image datas

Shulin He, Hao Li, Xueliang Zhang

This paper introduces an improved target speaker extractor, referred to as Speakerfilter-Pro, based on our previous Speakerfilter model. The Speakerfilter uses a bi-direction gated recurrent unit (BGRU) module to characterize the target speaker from anchor speech and use a convolutional recurrent network (CRN) module to separate the target speech from a nois

Chris S. Hanson, Thomas L. Duvall, Aaron C. Birch, Laurent Gizon

Context: Giant-cell convection is believed to be an important component of solar dynamics. For example, it is expected to play a crucial role in maintaining the Sun's differential rotation. Aims: We re-examine early reports of giant convective cells detected using correlation analysis of Dopplergrams. We extend this analysis using 19 years of space and groun

Yair Judkovsky, Aviv Ofir, Oded Aharonson

Non-Keplerian dynamics of planetary orbits manifest in the transit light-curve as variations of different types. In addition to Transit Timing Variations (TTV's), the shape of the transits contains additional information on variations in the geometry of the orbit. This study presents an analytic approach to light-curve fitting: dynamical variations in the or

Sujoy Datta, Debnarayan Jana

The journey of theoretical study on semiconductors is reviewed in a non-conventional way. We have started with the basic introduction of Hartree-Fock method and introduce the fundamentals of Density Functional Theory (DFT). From the oldest Local Density Approximations (LDA) to the most recent developments of semi-local corrections [Generalised Gradient Appro

Gongqi Lin, Yuan Miao, Xiaoyong Yang, Wenwu Ou

Commonsense knowledge is critical in human reading comprehension. While machine comprehension has made significant progress in recent years, the ability in handling commonsense knowledge remains limited. Synonyms are one of the most widely used commonsense knowledge. Constructing adversarial dataset is an important approach to find weak points of machine com

Edith Cohen, Ofir Geri, Tamas Sarlos, Uri Stemmer

Common datasets have the form of elements with keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of key and frequency pairs. A weighted sample of keys by (a function of) frequency is a highly versatile summary that provides a sparse set of representative keys and supports approximate evaluations of query stati

Hirofumi Inaguma, Yosuke Higuchi, Kevin Duh, Tatsuya Kawahara

Fast inference speed is an important goal towards real-world deployment of speech translation (ST) systems. End-to-end (E2E) models based on the encoder-decoder architecture are more suitable for this goal than traditional cascaded systems, but their effectiveness regarding decoding speed has not been explored so far. Inspired by recent progress in non-autor

Louise Gillian C. Bautista, Prospero C. Naval

Eye movements are intricate and dynamic biosignals that contain a wealth of cognitive information about the subject. However, these are ambiguous signals and therefore require meticulous feature engineering to be used by machine learning algorithms. We instead propose to learn feature vectors of eye movements in a self-supervised manner. We adopt a contrasti

Arnaud Münch, Emmanuel Trélat

The exact distributed controllability of the semilinear wave equation $y_{tt}-y_{xx} + g(y)=f \,1_{\omega}$, assuming that $g$ satisfies the growth condition $\vert g(s)\vert /(\vert s\vert \log^{2}(\vert s\vert))\rightarrow 0$ as $\vert s\vert \rightarrow \infty$ and that $g^\prime\in L^\infty_{loc}(\mathbb{R})$ has been obtained by Zuazua in the nineties.

Yu Song, Su-Huai Wei

The practical damage of silicon bipolar devices subjected to mixed ionization and displacement irradiations is usually evaluated by the sum of separated ionization and displacement damages. However, recent experiments show clear difference between the practical and summed damages, indicating significant irradiation synergistic effects (ISEs). Understanding t

Sourendu Gupta, Jajati K. Nayak, Sushant K. Singh

We examine the thermalization of an ensemble of the octet of pseudoscalar mesons, in the isospin symmetric limit, whose interactions are constrained through chiral symmetry, unitarity, and measurements. The reaction amplitudes generate all resonances up to masses of about 2 GeV, with twelve input parameters, namely f_pi, three masses, and eight low energy co

Yo Matsubara, Toshimichi Usuba

In this paper, we investigate properties of countable stationary towers. We derive the regularity properties of sets of reals in $L(\mathbf R)$ from some properties of countable stationary towers without explicit use of strong large cardinals such as Woodin cardinals. We also introduce the notion of semiprecipitousness and investigate its relation to precipi

Baisakh, Rakesh Mohanty

In this paper we propose a novel online deterministic list update algorithm known as Move-To-Front-or-Logarithmic Position (MFLP). Our proposed algorithm MFLP achieves a competitive ratio of 2 for larger list with respect to static optimum offline algorithm, whereas MFLP is not competitive for smaller list. We also show that MFLP is 2 competitive with respec

Dessislava H. Kochloukova

For a finitely generated group $G$ we calculate the Bieri-Neumann-Strebel-Renz invariant $\Sigma^1(\X(G))$ for the weak commutativity construction $\X(G)$. Identifying $S(\X(G))$ with $S(\X(G) / W(G))$ we show $\Sigma^2(\X(G),\Z) \subseteq \Sigma^2(\X(G)/ W(G),\Z)$ and $\Sigma^2(\X(G)) \subseteq $ $ \Sigma^2(\X(G)/ W(G))$ that are equalities when $W(G)$ is f

Zhaoning Li, Jiangtao Ren

Chest imaging reports describe the results of chest radiography procedures. Automatic extraction of abnormal imaging signs from chest imaging reports has a pivotal role in clinical research and a wide range of downstream medical tasks. However, there are few studies on information extraction from Chinese chest imaging reports. In this paper, we formulate che

Indrajit Ghosh, Anirban Bhattacharya, Debdeep Pati

A systematic approach to finding variational approximation in an otherwise intractable non-conjugate model is to exploit the general principle of convex duality by minorizing the marginal likelihood that renders the problem tractable. While such approaches are popular in the context of variational inference in non-conjugate Bayesian models, theoretical guara

Isao Yagi, Yuji Masuda, Takanobu Mizuta

Many empirical studies have discussed market liquidity, which is regarded as a measure of a booming financial market. Further, various indicators for objectively evaluating market liquidity have also been proposed and their merits have been discussed. In recent years, the impact of high-frequency traders (HFTs) on financial markets has been a focal concern,

XueGuang Zhang, Yanqiu Feng, Huan Chen, QiRong Yuan

Narrow emission-line galaxies can be distinguished in the well-known BPT diagrams through narrow emission line properties. However, there are no boundaries visible to the naked eye between type-2 AGN and HII galaxies in BPT diagrams, besides the extreme dividing lines expected by theoretical photoionization models. Here, based on powerful t-SNE technique app

Isao Yagi, Shunya Maruyama, Takanobu Mizuta

A leveraged ETF is a fund aimed at achieving a rate of return several times greater than that of the underlying asset such as Nikkei 225 futures. Recently, it has been suggested that rebalancing trades of a leveraged ETF may destabilize the financial markets. An empirical study using an agent-based simulation indicated that a rebalancing trade strategy could

Tomohiro Tokunaga, Michael J. Lyons

We are exploring the design and implementation of artificial expressions, kinetic audio-visual representations of real-time physiological data that reflect emotional and cognitive state. In this work, we demonstrate a prototype, the Enactive Mandala, which maps real-time EEG signals to modulate ambient music and animated visual music. Transparent real-time a

Dan D. Erdmann-Pham, Wonjun Son, Khanh Dao Duc, Yun S. Song

The totally asymmetric simple exclusion process (TASEP), which describes the stochastic dynamics of interacting particles on a lattice, has been actively studied over the past several decades and applied to model important biological transport processes. Here we present a software package, called EGGTART (Extensive GUI gives TASEP-realization in real time),

Tin Lai, Philippe Morere

We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation. This framework, the Markov Intent Process, features a collection of skills which are each specialised to perform a single task well. Skills are aware of their intended effects and are able to analyse planning goals to delegate planning to the best-suite

Jiani Li, Waseem Abbas, Xenofon Koutsoukos

Distributed multi-task learning provides significant advantages in multi-agent networks with heterogeneous data sources where agents aim to learn distinct but correlated models simultaneously.However, distributed algorithms for learning relatedness among tasks are not resilient in the presence of Byzantine agents. In this paper, we present an approach for By

Xiaoying Li, Suyuan Peng, Jian Du

In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept "knowledge unit" and "knowmetrics" for measuring knowledge. However, the definition of "computable knowledge object" remains controversial so far in different fields. For example, it is defined as 1) quantitative scientific concept in natural science and engineering, 2) knowledge po

Shuangyang Li, Weijie Yuan, Zhiqiang Wei, Jinhong Yuan

Orthogonal time frequency space (OTFS) modulation has attracted substantial attention recently due to its great potential of providing reliable communications in high-mobility scenarios. In this paper, we propose a novel hybrid signal detection algorithm for OTFS modulation. By characterizing the input-output relationship of OTFS modulation, we derive the ne

Gemeng Zhang, Aiying Zhang, Biao Cai, Zhuozhuo Tu

Functional connectivity (FC) has been widely used to study brain network interactions underlying the emerging cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation between brain regions. Although FC is proved to be a good starting point to understand the brain organization, it fails to tell the causal relati

Sayyed M. Zahiri, Ali Ahmadvand

In recent years, social media platforms have hosted an explosion of hate speech and objectionable content. The urgent need for effective automatic hate speech detection models have drawn remarkable investment from companies and researchers. Social media posts are generally short and their semantics could drastically be altered by even a single token. Thus, i

C. Adams, R. Alfaro, G. Ambrosi, M. Ambrosio

For the first time in the history of ground-based $\gamma$-ray astronomy, the on-axis performance of the dual mirror, aspheric, aplanatic Schwarzschild-Couder optical system has been demonstrated in a $9.7$-m aperture imaging atmospheric Cherenkov telescope. The novel design of the prototype Schwarzschild-Couder Telescope (pSCT) is motivated by the need of t

Andreea Sistrunk, Vanessa Cedeno, Subhodip Biswas

This paper studies the feasibility of synthetic data generation for mission-critical applications. The emphasis is on synthetic data generation for anomalous detection in complex social networks. In particular, the development of a heuristic generative model, capable of creating data for anomalous rare activities in complex social networks is sought. To this

Ning Zeng, Pengfei Han, Di Liu, Zhiqiang Liu

The world-wide lockdown in response to the COVID-19 pandemic in year 2020 led to economic slowdown and large reduction of fossil fuel CO2 emissions, but it is unclear how much it would reduce atmospheric CO2 concentration, and whether it can be observed. We estimated that a 7.9% reduction in emissions for 4 months would result in a 0.25 ppm decrease in the N

Jin Hong Kuan, Aadesh Salecha

To investigate the origin of cooperative behaviors, we developed an evolutionary model of sequential strategies and tested our model with computer simulations. The sequential strategies represented by stochastic machines were evaluated through games of Iterated Prisoner's Dilemma (IPD) with other agents in the population, allowing co-evolution to occur. We e

Chen Cui, Ning Yang, Philip S. Yu

Most existing random walk based network embedding methods often follow only one of two principles, homophily or structural equivalence. In real world networks, however, nodes exhibit a mixture of homophily and structural equivalence, which requires adaptive network embedding that can adaptively preserve both homophily and structural equivalence for different

Mingming Nie, Shu-Wei Huang

We theoretically study the nature of parametrically driven dissipative Kerr soliton (PD-DKS) in a doubly resonant degenerate micro-optical parametric oscillator (DR-D{\mu}OPO) with the cooperation of \c{hi}(2) and \c{hi}(3) nonlinearities. Lifting the assumption of close-to-zero group velocity mismatch (GVM) that requires extensive dispersion engineering, we

Michelle A. Lee, Brent Yi, Roberto Martín-Martín, Silvio Savarese

Leveraging multimodal information with recursive Bayesian filters improves performance and robustness of state estimation, as recursive filters can combine different modalities according to their uncertainties. Prior work has studied how to optimally fuse different sensor modalities with analytical state estimation algorithms. However, deriving the dynamics

Zhanibek Omarov, Selcuk Haciomeroglu

We present a non-destructive beam profile imaging concept that utilizes machine learning tools, namely genetic algorithm with a gradient descent-like minimization. Electromagnetic fields around a charged beam carry information about its transverse profile. The electrodes of a stripline-type beam position monitor (with eight probes in this study) can pick up

Lirong Xia, Weiqiang Zheng

The computational complexity of winner determination under common voting rules is a classical and fundamental topic in the field of computational social choice. Previous work has established the NP-hardness of winner determination under some commonly-studied voting rules, especially the Kemeny rule and the Slater rule. In a recent blue-sky paper, Baumeister,

S. S. Kubakaddi

We have carried out a theoretical investigation of hot electron power loss $P$, involving electron-acoustic phonon interaction, as a function of twist angle $\theta$, electron temperature $T_e$ and electron density $n_s$ in twisted bilayer graphene (tBLG). It is found that as $\theta$ decreases closer to magic angle $\theta_m$, $P$ enhances strongly and $\th

Takeyuki Sasai, Hironori Fujisawa

We consider robust low rank matrix estimation as a trace regression when outputs are contaminated by adversaries. The adversaries are allowed to add arbitrary values to arbitrary outputs. Such values can depend on any samples. We deal with matrix compressed sensing, including lasso as a partial problem, and matrix completion, and then we obtain sharp estimat

Jiangning Zhang, Xianfang Zeng, Chao Xu, Jun Chen

Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio. However, current methods can only reenact a special person once the model is trained or need extra operations such as 3D rendering and image post-fusion on the premise of generating vivid faces. To solve the above challenge, we propos

Yongfa Chen

We get optimal lower bounds for the eigenvalues of the submanifold Dirac operator on locally reducible Riemannian manifolds in terms of intrinsic and extrinsic expressions. The limiting-cases are also studied. As a corollary, one gets several known results in this direction.

Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting Hsiang Wang

Detecting statistical interactions between input features is a crucial and challenging task. Recent advances demonstrate that it is possible to extract learned interactions from trained neural networks. It has also been observed that, in neural networks, any interacting features must follow a strongly weighted connection to common hidden units. Motivated by

Qiang Zeng, Jiangwei Shang, H. Chau Nguyen, Xiangdong Zhang

Quantum steering is a recently-defined form of quantum correlation which lies at the heart of quantum mechanics. In difference from other types of quantum correlations, quantum steering is inherently asymmetric, which implies that it could manifest in one direction but not in the opposite direction. This rather peculiar phenomenon, known as one-way steering,

Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey

Tractable contextual bandit algorithms often rely on the realizability assumption - i.e., that the true expected reward model belongs to a known class, such as linear functions. In this work, we present a tractable bandit algorithm that is not sensitive to the realizability assumption and computationally reduces to solving a constrained regression problem in

Z. S. Lim, A. Ariando

Several challenges in designing an operational Skyrmion racetrack memory are well-known. Among those challenges, a few contradictions can be identified if researchers were to rely only on metallic materials. Hence, expanding the exploration on Skyrmion Physics into oxide materials is essential to bridge the contradicting gap. In this topical review, we first

Gavin Lee, Qing Zhang, Jane W. Liang, Theodore Huang

Identifying individuals who are at high risk of cancer due to inherited germline mutations is critical for effective implementation of personalized prevention strategies. Most existing models to identify these individuals focus on specific syndromes by including family and personal history for a small number of cancers. Recent evidence from multi-gene panel

Kasyap Chakravadhanula

Among the most impactful diabetic complications are diabetic retinopathy, the leading cause of blindness among working class adults, and cardiovascular disease, the leading cause of death worldwide. This study describes the development of improved machine learning based screening of these conditions. First, a random forest model was developed by retrospectiv

Kyungyong Lee, Li Li, Michelle Rabideau, Ralf Schiffler

The Markov numbers are the positive integers that appear in the solutions of the equation $x^2+y^2+z^2=3xyz$. These numbers are a classical subject in number theory and have important ramifications in hyperbolic geometry, algebraic geometry and combinatorics. It is known that the Markov numbers can be labeled by the lattice points $(q,p)$ in the first quadra

Jian-Guo Zhang, Kazuma Hashimoto, Wenhao Liu, Chien-Sheng Wu

Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nea

Shuangyang Li, Jinhong Yuan, Weijie Yuan, Zhiqiang Wei

Orthogonal time frequency space (OTFS) modulation is a recently developed multi-carrier multi-slot transmission scheme for wireless communications in high-mobility environments. In this paper, the error performance of coded OTFS modulation over high-mobility channels is investigated. We start from the study of conditional pairwise-error probability (PEP) of

Danni Ma, Neville Ryant, Mark Liberman

Pre-trained acoustic representations such as wav2vec and DeCoAR have attained impressive word error rates (WER) for speech recognition benchmarks, particularly when labeled data is limited. But little is known about what phonetic properties these various representations acquire, and how well they encode transferable features of speech. We compare features fr

Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan

COVID-19 pandemic has an unprecedented impact all over the world since early 2020. During this public health crisis, reliable forecasting of the disease becomes critical for resource allocation and administrative planning. The results from compartmental models such as SIR and SEIR are popularly referred by CDC and news media. With more and more COVID-19 data

Zhiqiang Wei, Weijie Yuan, Shuangyang Li, Jinhong Yuan

In this paper, we investigate the impacts of transmitter and receiver windows on the performance of orthogonal time-frequency space (OTFS) modulation and propose window designs to improve the OTFS channel estimation and data detection performance. In particular, assuming ideal pulse shaping filters at the transceiver, we derive the impacts of windowing on th