December 2020 arXiv papers — page 126

Wouter Groeneveld, Joost Vennekens, Kris Aerts

Peer assessment is a popular technique for a more fine-grained evaluation of individual students in group projects. Its effect on the evaluation is well studied. However, its effects on the learning abilities of students are often overlooked. In this paper, we explore self-evaluation, motivation, and study time of students in relation to peer assessment, as

Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li

The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate the scale variance in object representation by performing instance-level assignments. Nevertheless, this strategy ignores the distinct characteristics of different sub-regions in an instance. To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level

Dmitry Chirkov, Alex Giacomini, Sergey A. Pavluchenko, Alexey Toporensky

In this paper we perform systematic investigation of all possible solutions with static compact extra dimensions and expanding three-dimensional subspace (``our Universe''). Unlike previous papers, we consider extra-dimensional subspace to be constant-curvature manifold with both signs of spatial curvature. We provide a scheme how to build solutions

Malhar Jere, Maghav Kumar, Farinaz Koushanfar

Convolutional Neural Networks (CNNs) have made significant progress on several computer vision benchmarks, but are fraught with numerous non-human biases such as vulnerability to adversarial samples. Their lack of explainability makes identification and rectification of these biases difficult, and understanding their generalization behavior remains an open p

Rong Zhang, Qiaolin He

In this article, we develop a least--squares/fictitious domain method for direct simulation of fluid particle motion with Navier slip boundary condition at the fluid--particle interface. Let $Ω$ and $B$ be two bounded domains of $\mathbb{R}^{d}$ such that $\overline{B} \subset Ω$. The motion of solid particle $B$ is governed by Newton's equations. Our go

Gi-Hwan Shin, Young-Seok Kweon, Minji Lee

Memory is an essential element in people's daily life based on experience. So far, many studies have analyzed electroencephalogram (EEG) signals at encoding to predict later remembered items, but few studies have predicted long-term memory only with EEG signals of successful short-term memory. Therefore, we aim to predict long-term memory using deep neur

Wang Pan, Qipu Deng, Jiadong Li, Zhi Wang

With the outbreak of COVID-19, how to mitigate and suppress its spread is a big issue to the government. Department of public health need powerful models to model and predict the trend and scale of such pandemic. And models that could evaluate the effect of the public policy are also essential to the fight with the COVID-19. A main limitation of existing mod

Takehito Suzuki

Flows within porous media play important roles in many scientific and industrial systems. However, the case wherein such flows become turbulent has not been completely understood, particularly, from a mathematical viewpoint. In this study, the $k-\varepsilon$ model (the variable $k$ denotes the turbulent kinetic energy per unit mass and $\varepsilon$ the dis

Marco C Lam, Robert J Smith, Josh Veitch-Michaelis, Iain A Steele

We are reporting the updates in version 0.2.0 of the Automated SpectroPhotometric REDuction (ASPIRED) pipeline, designed for common use on different instruments. The default settings support many typical long-slit spectrometer configurations, whilst it also offers a flexible set of functions for users to refine and tailor-make their automated pipelines to an

Taehyeon Kim, Jaeyeon Ahn, Nakyil Kim, Seyoung Yun

In the machine learning algorithms, the choice of the hyperparameter is often an art more than a science, requiring labor-intensive search with expert experience. Therefore, automation on hyperparameter optimization to exclude human intervention is a great appeal, especially for the black-box functions. Recently, there have been increasing demands of solving

Chenfeng Miao, Shuang Liang, Zhencheng Liu, Minchuan Chen

In this work, we address the Text-to-Speech (TTS) task by proposing a non-autoregressive architecture called EfficientTTS. Unlike the dominant non-autoregressive TTS models, which are trained with the need of external aligners, EfficientTTS optimizes all its parameters with a stable, end-to-end training procedure, while allowing for synthesizing high quality

Q. B. Chen, S. Frauendorf

We discuss in depth the application of the classical concepts for interpreting the quantal results from the triaxial rotor core without and with odd-particle. The corresponding limitations caused by the discreteness and finiteness of the angular momentum Hilbert space and the extraction of the relevant features from the complex wave function and distribution

Peng Zou, Huaisong Zhao, Lianyi He, Xia-Ji Liu

We theoretically investigate dynamic structure factors of a strongly interacting Fermi superfluid near an orbital Feshbach resonance with random phase approximation, and find their dynamical characters during the phase transition between a balanced conventional Bardeen-Cooper-Schrieffer superfluid and a polarized Sarma superfluid by continuously varying the

Takuya Hirose, Nobuhito Maru

We further study the cancellation of the one-loop corrections to the scalar mass in a six dimensional SU(2) gauge theory with higher dimensional operators, which is compactified on a torus with magnetic flux. Higher dimensional operators also contribute to the corrections to the scalar mass nontrivially. We explicitly show by the diagrammatic calculations th

Yeonjeong Koo, Yongchul Kim, Soo Ho Choi, Hyeongwoo Lee

The tunability of the bandgap, absorption and emission energies, photoluminescence (PL) quantum yield, exciton transport, and energy transfer in transition metal dichalcogenide (TMD) monolayers provides a new class of functions for a wide range of ultrathin photonic devices. Recent strain-engineering approaches have enabled us to tune some of these propertie

Anusha Lalitha, Anatoly Khina, Tara Javidi

We consider the problem of communication over the binary symmetric channel with feedback, where the information sequence is made available in a causal, possibly random, fashion. We develop a real-time variant of the renowned Horstein scheme and provide analytical guarantees for its error-probability exponential decay rate. We further use the scheme to stabil

Tianyi Zhang, Jiankun Wang, Max Q. -H. Meng

Sampling-based path planning is a popular methodology for robot path planning. With a uniform sampling strategy to explore the state space, a feasible path can be found without the complex geometric modeling of the configuration space. However, the quality of initial solution is not guaranteed and the convergence speed to the optimal solution is slow. In thi

Weikui Ye, Wei Luo, Zhaoyang Yin

In this paper, we mainly investigate the Cauchy problem of the non-resistive MHD equation. We first establish the local existence in the homogeneous Besov space $\dot{B}^{\frac{d}{p}-1}_{p,1}\times \dot{B}^{\frac{d}{p}}_{p,1}$ with $p<\infty$, and give a lifespan $T$ of the solution which depends on the norm of the Littlewood-Paley decomposition of the initi

Bonaventure F. P. Dossou, Alena Iureva, Sayali R. Rajhans, Vamsi S. Pidikiti

Each year, over 2.5 million people, most of them in developed countries, die from pneumonia [1]. Since many studies have proved pneumonia is successfully treatable when timely and correctly diagnosed, many of diagnosis aids have been developed, with AI-based methods achieving high accuracies [2]. However, currently, the usage of AI in pneumonia detection is

Pratap Roy, K. Banerjee, A. K. Saha, C. Bhattacharya

Detailed characterization of the newly available plastic scintillator (EJ-299-33A) having the pulse shape discrimination (PSD) property has been carried out in case of a large-sized detector (5 in.$\times$5 in.). The pulse height response of the scintillator for nearly mono-energetic neutrons has been reported in case of neutron energies E$_n$ =3, 6 and 9 Me

Byunggill Joe, Jihun Hamm, Sung Ju Hwang, Sooel Son

Although deep neural networks have shown promising performances on various tasks, they are susceptible to incorrect predictions induced by imperceptibly small perturbations in inputs. A large number of previous works proposed to detect adversarial attacks. Yet, most of them cannot effectively detect them against adaptive whitebox attacks where an adversary h

Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li

The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. Nevertheless, the intrinsic geometric constraint forces it to focus on the regions with close spatial distance, hindering the effective long-range interactions. To relax the geometric constraint, we give the analysis by reformulating it

Mario Fischer, Juergen Wassner

Deep Convolutional Neural Networks (CNNs) have become state-of-the art for computer vision and other signal processing tasks due to their superior accuracy. In recent years, large efforts have been made to reduce the computational costs of CNNs in order to achieve real-time operation on low-power embedded devices. Towards this goal we present BinArray, a cus

Yiming Lei, Haiping Zhu, Junping Zhang, Hongming Shan

Deep learning-based methods have achieved promising performance in early detection and classification of lung nodules, most of which discard unsure nodules and simply deal with a binary classification -- malignant vs benign. Recently, an unsure data model (UDM) was proposed to incorporate those unsure nodules by formulating this problem as an ordinal regress

Achilleas Mavrakis, Konstantinos Kritos

The $N$-body problem is of historical significance because it was the first implementation of the Newtonian dynamical laws for the description of our Solar System. Motivated by this, the project&#39;s goal is to revisit this problem for small $N$ and find a solution for the trajectories of specific two-body and three-body configurations as well as the planet

Kun Su, Xiulong Liu, Eli Shlizerman

We propose a novel system that takes as an input body movements of a musician playing a musical instrument and generates music in an unsupervised setting. Learning to generate multi-instrumental music from videos without labeling the instruments is a challenging problem. To achieve the transformation, we built a pipeline named &#39;Multi-instrumentalistNet&#

Kengo Nagashima, Yasunori Sato, Chikuma Hamada

In pharmacogenomic studies, biomedical researchers commonly analyze the association between genotype and biological response by using the Kruskal--Wallis test or one-way analysis of variance (ANOVA) after logarithmic transformation of the obtained data. However, because these methods detect unexpected biological response patterns, the power for detecting the

Paul-Aymeric McRae, Michael Hilke

Quantum Machine Learning (QML) algorithms to solve classifications problems have been made available thanks to recent advancements in quantum computation. While the number of qubits are still relatively small, they have been used for &#34;quantum enhancement&#34; of machine learning. An important question is related to the efficacy of such protocols. We eval

Jessica Mink, Warren R. Brown, Igor V. Chilingarian, Daniel Fabricant

We announce the public release of 141,531 moderate-dispersion optical spectra of 72,247 objects acquired over the past 25 years with the FAST Spectrograph on the Fred L. Whipple Observatory 1.5-meter Tillinghast telescope. We describe the data acquisition and processing so that scientists can understand the spectra. We highlight some of the largest FAST surv

Yu-Chia Lin, Yuji Matsumoto, Pin-Gao Gu

Recent observations have revealed the existence of multiple-planet systems composed of Earth-mass planets around late M dwarfs. Most of their orbits are close to commensurabilities, which suggests that planets were commonly trapped in resonant chains in their formation around low-mass stars. We investigate the formation of multiple-planet systems in resonant

Lili Wang, Chongyang Gao, Jason Wei, Weicheng Ma

The field of NLP has seen unprecedented achievements in recent years. Most notably, with the advent of large-scale pre-trained Transformer-based language models, such as BERT, there has been a noticeable improvement in text representation. It is, however, unclear whether these improvements translate to noisy user-generated text, such as tweets. In this paper

Jiansheng Fang, Yanwu Xu, Xiaoqing Zhang, Yan Hu

Deep hashing methods have been proved to be effective for the large-scale medical image search assisting reference-based diagnosis for clinicians. However, when the salient region plays a maximal discriminative role in ophthalmic image, existing deep hashing methods do not fully exploit the learning ability of the deep network to capture the features of sali

Shama, Goutam Sheet, Yogesh Singh

We report the growth and magneto-transport studies of Pd$_{3}$Bi$_{2}$S$_{2}$ (PBS) thin films synthesized by pulsed laser deposition (PLD) technique. The magneto-transport study on pristine and post annealed films show the presence of more than one type of charge carrier with a carrier concentration in the range $0.6$ - $2.26~\times$ 10$^{21}$ cm$^{-3}$ and

Pratap Roy, S. Mukhopadhyay, Mamta Aggarwal, Deepak Pandit

Neutron kinetic energy spectra in coincidence with low-energy $γ$-ray multiplicities have been measured around $A\approx $ 110 in the $^{16}$O, $^{20}$Ne + $^{93}$Nb reactions in a compound nuclear excitation energy range of $\approx $ 90 - 140 MeV. The excitation energy (temperature) and angular momentum (spin) dependence of the inverse level density parame

Zhizhong Huang, Shouzhen Chen, Junping Zhang, Hongming Shan

Face aging is to render a given face to predict its future appearance, which plays an important role in the information forensics and security field as the appearance of the face typically varies with age. Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically use a

Tsuguo Mogami

Is multiplication really necessary for deep neural networks? Here we propose just adding two IEEE754 floating-point numbers with an integer-add instruction in place of a floating-point multiplication instruction. We show that ResNet can be trained using this operation with competitive classification accuracy. Our proposal did not require any methods to solve

Sangdoo Yun, Seong Joon Oh, Byeongho Heo, Dongyoon Han

State-of-the-art video action classifiers often suffer from overfitting. They tend to be biased towards specific objects and scene cues, rather than the foreground action content, leading to sub-optimal generalization performances. Recent data augmentation strategies have been reported to address the overfitting problems in static image classifiers. Despite

Basir Ahamed Khan, Supriya Chatterjee, Golam Ali Sekh, Benoy Talukdar

This \textquoteleft research-survey&#39; is meant for beginners in the studies of integrable systems. Here we outline some analytical methods for dealing with a class of nonlinear partial differential equations. We pay special attention to \textquoteleft inverse spectral transform&#39;, \textquoteleft Lax pair representation&#39;, and \textquoteleft zero-cur

Shibo Zhao, Peng Wang, Hengrui Zhang, Zheng Fang

To achieve robust motion estimation in visually degraded environments, thermal odometry has been an attraction in the robotics community. However, most thermal odometry methods are purely based on classical feature extractors, which is difficult to establish robust correspondences in successive frames due to sudden photometric changes and large thermal noise

Shreyansh Surana, Smit Detroja, Saurabh Tiwari

GitHub repositories consist of various detailed information about the project contributors, the number of commits and its contributors, releases, pull requests, programming languages, and issues. However, no systematic dataset of open source projects exists which features detailed information about the repositories on GitHub for knowledge acquisition and min

Yuanqiang Zhou, Dewei Li, Yugeng Xi

This paper investigates the data-driven predictive control problems for a class of continuous-time industrial processes with completely unknown dynamics. The proposed approach employs the data-driven technique to get the system matrices online, using input-output measurements. Then, a model-free predictive control approach is designed to implement the recedi

Zhiqiang Tan

For discrete-time survival data, conditional likelihood inference in Cox&#39;s hazard odds model is theoretically desirable but exact calculation is numerical intractable with a moderate to large number of tied events. Unconditional maximum likelihood estimation over both regression coefficients and baseline hazard probabilities can be problematic with a lar

Glen Frost

In this work we study the problem of writing a Hermitian polynomial as a Hermitian sum of squares modulo a Hermitian ideal. We investigate a novel idea of Putinar-Scheiderer to obtain necessary matrix positivity conditions for Hermitian polynomials to be Hermitian sums of squares modulo Hermitian ideals. We show that the conditions are sufficient for a class

Zhaoting Li, Jiankun Wang, Max Q. -H. Meng

Robot path planning is difficult to solve due to the contradiction between optimality of results and complexity of algorithms, even in 2D environments. To find an optimal path, the algorithm needs to search all the state space, which costs a lot of computation resource. To address this issue, we present a novel recurrent generative model (RGM) which generate

Han Gao, Zhengshuo Li

Optimally operating an integrated electricity-gas system (IEGS) is significant for the energy sector. However, the IEGS operation model&#39;s nonconvexity makes it challenging to solve the optimal dispatch problem in the IEGS. This letter proposes an improved Benders decomposition (IBD) algorithm catering to a commonly used steady-state dispatch model of the

Elías Castellanos, Guillermo Chacón-Acosta, Jorge Mastache

In the present work, we analyze the structural configuration of a collection of generic non-relativistic bosons forming a gravitational bound Bose-Einstein condensate that we interpreted as a non-relativistic boson star. We prove that the system&#39;s behavior can be obtained by analyzing its fundamental constituent&#39;s properties, i.e., the single particl

Junjie Wu, Wei Chen

High energy efficiency and low latency have always been the significant goals pursued by the designer of wireless networks. One efficient way to achieve these goals is cross-layer scheduling based on the system states in different layers, such as queuing state and channel state. However, most existing works in cross-layer design focus on the scheduling based

Haokui Zhang, Ying Li, Yenan Jiang, Peng Wang

Recently, hyperspectral image (HSI) classification approaches based on deep learning (DL) models have been proposed and shown promising performance. However, because of very limited available training samples and massive model parameters, DL methods may suffer from overfitting. In this paper, we propose an end-to-end 3-D lightweight convolutional neural netw

Bingyu Liu, Yuhong Guo, Jieping Ye, Weihong Deng

Recent domain adaptation methods have demonstrated impressive improvement on unsupervised domain adaptation problems. However, in the semi-supervised domain adaptation (SSDA) setting where the target domain has a few labeled instances available, these methods can fail to improve performance. Inspired by the effectiveness of pseudo-labels in domain adaptation

Matthew Francis-Landau

This paper introduces mFST, a new Python library for working with Finite-State Machines based on OpenFST. mFST is a thin wrapper for OpenFST and exposes all of OpenFST&#39;s methods for manipulating FSTs. Additionally, mFST is the only Python wrapper for OpenFST that exposes OpenFST&#39;s ability to define a custom semirings. This makes mFST ideal for develo

Jiansheng Fang, Xiaoqing Zhang, Yan Hu, Yanwu Xu

Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix factorization applied usually in pattern recognition, LFM models user-item interactions as inner products of factor vectors of user and item

Maozhu Dai, Hal S. Stern

Many studies include a goal of determining whether there is treatment effect heterogeneity across different subpopulations. In this paper, we propose a U-statistic-based non-parametric test of the null hypothesis that the treatment effects are identical in different subgroups. The proposed test provides more power than the standard parametric test when the u

Haosen Shang, Duo Pan, Xiaogang Zhang, Xiaobo Xue

A thermal atomic ensemble-based laser source with superior frequency stability is proposed that relies on the accumulated contributions from an abundance of nonzero-transverse-velocity atomic ensembles. Compared with the traditional case in which only atoms with near-zero transverse velocities are utilized, the amplitude of the optical Ramsey fringes for a t

Qunwei Li, Shaofeng Zou, Wenliang Zhong

The first provably efficient algorithm for learning graph neural networks (GNNs) with one hidden layer for node information convolution is provided in this paper. Two types of GNNs are investigated, depending on whether labels are attached to nodes or graphs. A comprehensive framework for designing and analyzing convergence of GNN training algorithms is deve

Zhou Lu

We prove a Johns theorem for simplices in $R^d$ with positive dilation factor $d+2$, which improves the previously known $d^2$ upper bound. This bound is tight in view of the $d$ lower bound. Furthermore, we give an example that $d$ isn&#39;t the optimal lower bound when $d=2$. Our results answered both questions regarding Johns theorem for simplices with po

Stephen E. Moore

We present the analysis of interior penalty discontinuous Galerkin Isogeometric Analysis (dGIGA) for the biharmonic problem on orientable surfaces $Ω\subset \mathbb{R}^3.$ Here, we consider a surface consisting of several non-overlapping patches as typical in multipatch dGIGA. Due to the non-overlapping nature of the patches, we construct NURBS approximation

Kashumi Madampe, Rashina Hoda, John Grundy

Agile processes are now widely practiced by software engineering (SE) teams, and the agile manifesto claims that agile methods support responding to changes well. However, no study appears to have researched whether this is accurate in reality. Requirements changes (RCs) are inevitable in any software development environment, and we wanted to acquire a holis

Rahil N. Valani, Anja C. Slim, Tapio P. Simula

Vertically vibrating a liquid bath at two frequencies, $f$ and $f/2$, having a relative phase difference $Δϕ_0$ can give rise to self-propelled superwalking droplets on the liquid surface. We have numerically investigated such superwalking droplets with the two driving frequencies slightly detuned, resulting in the phase difference $Δϕ(t)$ varying linearly w

Yixin Tan, Xiaomeng Wang, Tao Jia

The hyponym-hypernym relation is an essential element in the semantic network. Identifying the hypernym from a definition is an important task in natural language processing and semantic analysis. While a public dictionary such as WordNet works for common words, its application in domain-specific scenarios is limited. Existing tools for hypernym extraction e

Fan Wang, Jiangxin Yang, Yanlong Cao, Yanpeng Cao

Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution optical sensors, facilitating better-performing target detection and autonomous navigation in a wide range of robotics applications. It is noted that the state-of-the-art SR methods are typically trained and tested using single-channel inputs, neglecting

Anurag Anshu, Shalev Ben-David, Srijita Kundu

We investigate query-to-communication lifting theorems for models related to the quantum adversary bounds. Our results are as follows: 1. We show that the classical adversary bound lifts to a lower bound on randomized communication complexity with a constant-sized gadget. We also show that the classical adversary bound is a strictly stronger lower bound tech

Zijie Lin, Ercai Chen

In this paper, we consider which kind of invariant measure on hereditary subshifts is not Gibbs measure. For the hereditary closure of a subshift $(X,S)$, we prove that in some situation, the invariant measure $ν*B_{p,1-p}$ can not be a Gibbs measure where $ν$ is an invariant measure on $(X,S)$. As an application, we show that for some $\B$-free subshifts, t

A. Vazquez-Castro, D. Rusca, H. Zbinden

We study information theoretical security for space links between a satellite and a ground-station. Quantum key distribution (QKD) is a well established method for information theoretical secure communication, giving the eavesdropper unlimited access to the channel and technological resources only limited by the laws of quantum physics. But QKD for space lin

Zhaokuan Lu, Eric D. Dupuis, Viral K. Patel, Ayyoub M. Momen

Experimental and numerical investigations are performed to provide an assessment of the transport behavior of an ultrasonic oscillatory two-phase flow in a microchannel. The work is inspired by the flow observed in an innovative ultrasonic fabric drying device using a piezoelectric bimorph transducer with microchannels, where a water-air two-phase flow is tr

Shagufta Mehnaz, Ninghui Li, Elisa Bertino

Increasing use of ML technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to better understand if these ML technologies are introducing leakages of sensitive and proprietary training data. In this paper, we focus on one kind of model inversion attacks, where the adversary knows

Ochoa Pablo, Virginia N. Vera de Serio

In this paper we analyze the optimal value function $v$ associated to a general parametric optimization problems via the theory of viscosity solutions. The novelty is that we obtain regularity properties of $v$ by showing that it is a viscosity solution to a set of first-order equations. As a consequence, in Banach spaces, we provide sufficient conditions fo

Yang Fu, Linjie Yang, Ding Liu, Thomas S. Huang

Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features for the detection, segmentation, and tracking of objects and they suffer in the video scenario due to several distinct challenges such as motion blur and drastic appearance change

Sayaka Kamei, Sébastien Tixeuil

We consider the problem of constructing a maximum independent set with mobile myopic luminous robots on a grid network whose size is finite but unknown to the robots. In this setting, the robots enter the grid network one-by-one from a corner of the grid, and they eventually have to be disseminated on the grid nodes so that the occupied positions form a maxi

Lingling Tan, Dingguo Wang, Tiwei Zhao

In this paper, we study ideal approximation theory associated to almost $n$-exact structures in extension closed subcategories of $n$-angulated categories. For $n=3$, an $n$-angulated category is nothing but a classical triangulated category. Moreover, since every exact category can be embedded as an extension closed subcategory of a triangulated category, t

Yasith Jayawardana, Alexander C. Nwala, Gavindya Jayawardena, Jian Wu

The vastness of the web imposes a prohibitive cost on building large-scale search engines with limited resources. Crawl frontiers thus need to be optimized to improve the coverage and freshness of crawled content. In this paper, we propose an approach for modeling the dynamics of change in the web using archived copies of webpages. To evaluate its utility, w

Jeremy Blackstone, Wei Hu, Alric Althoff, Armaiti Ardeshiricham

Classic hardware verification techniques (e.g., X-propagation and fault-propagation) and more recent hardware security verification techniques based on information flow tracking (IFT) aim to understand how information passes, affects, and otherwise modifies a circuit. These techniques all have separate usage scenarios, but when dissected into their core func

Nguyen Duy Cuong, Alexander Y. Kruger

We propose a unifying general framework of quantitative primal and dual sufficient and necessary error bound conditions covering linear and nonlinear, local and global settings. The function is not assumed to possess any particular structure apart from the standard assumptions of lower semicontinuity in the case of sufficient conditions and (in some cases) c

Matthew Buican, Linfeng Li, Rajath Radhakrishnan

Long ago, Arad and Herzog (AH) conjectured that, in finite simple groups, the product of two conjugacy classes of length greater than one is never a single conjugacy class. We discuss implications of this conjecture for non-abelian anyons in 2+1-dimensional discrete gauge theories. Thinking in this way also suggests closely related statements about finite si

Tanapon Lilasathapornkit, David Rey, Wei Liu, Meead Saberi

The estimation of pedestrian traffic in urban areas is often performed with computationally intensive microscopic models that usually suffer from scalability issues in large-scale footpath networks. In this study, we present a new macroscopic user equilibrium traffic assignment problem (UE-pTAP) framework for pedestrian networks while taking into account fun

Lushan Song, Guopeng Lin, Jiaxuan Wang, Haoqi Wu

Nowadays, gathering high-quality training data from multiple data sources with privacy preservation is a crucial challenge to training high-performance machine learning models. The potential solutions could break the barriers among isolated data corpus, and consequently enlarge the range of data available for processing. To this end, both academic researcher

Daniel Seita, Pete Florence, Jonathan Tompson, Erwin Coumans

Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional configuration spaces of deformables, compared to rigid objects, make manipulation difficult not only for multi-step planning, but even for goal specification. Goals cannot be as easi

Joseph Lorenzetti, Andrew McClellan, Charbel Farhat, Marco Pavone

Model predictive controllers use dynamics models to solve constrained optimal control problems. However, computational requirements for real-time control have limited their use to systems with low-dimensional models. Nevertheless, high-dimensional models arise in many settings, for example discretization methods for generating finite-dimensional approximatio

Filip Cornell

Choosing a suitable filtering function for the Mapper algorithm can be difficult due to its arbitrariness and domain-specific requirements. Finding a general filtering function that can be applied across domains is therefore of interest, since it would improve the representation of manifolds in higher dimensions. In this extended abstract, we propose that to

Qiang Luo, Jize Zhao, Xiaoqun Wang, Hae-Young Kee

The key to unraveling intriguing phenomena observed in various Kitaev materials lies in understanding the interplay of Kitaev ($K$) interaction and a symmetric off-diagonal $Γ$ interaction. To provide insight into the challenging problems, we study the quantum phase diagram of a bond-alternating spin-$1/2$ $g_x$-$g_y$ $K$-$Γ$ chain by density-matrix renormal

Giovanni Pistone

This is a revised version of a tutorial lecture that I presented at the Ècole de Physique des Houches on July 26-31 2020. Topics include Non-parametric Information Geometry, the Statistical bundle, exponential Orlicz spaces, and Gaussian Orlicz-Sobolev spaces.

Iryna Banakh, Taras Banakh, Serhii Bardyla

A subset $A$ of a semigroup $S$ is called a $chain$ ($antichain$) if $xy\in\{x,y\}$ ($xy\notin\{x,y\}$) for any (distinct) elements $x,y\in S$. A semigroup $S$ is called ($anti$)$chain$-$finite$ if $S$ contains no infinite (anti)chains. We prove that each antichain-finite semigroup $S$ is periodic and for every idempotent $e$ of $S$ the set $\sqrt[\infty]{e}

Aida Nematzadeh, Zahra Shekarchi, Thomas L. Griffiths, Suzanne Stevenson

Children learn word meanings by tapping into the commonalities across different situations in which words are used and overcome the high level of uncertainty involved in early word learning experiences. We propose a modeling framework to investigate the role of mutual exclusivity bias - asserting one-to-one mappings between words and their meanings - in redu

Dong Wang, Yuewei Yang, Chenyang Tao, Zhe Gan

Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts. However, ad-hoc visual explanations of model decisions often reveal an alarming level of reliance on exploiting non-causal visual cues that strongly correlate with the target label in training data. As such, deep neural

Pouya Rezazadeh Kalehbasti, Hayato Ushijima-Mwesigwa, Avradip Mandal, Indradeep Ghosh

Recent advances in specialized hardware for solving optimization problems such quantum computers, quantum annealers, and CMOS annealers give rise to new ways for solving real-word complex problems. However, given current and near-term hardware limitations, the number of variables required to express a large real-world problem easily exceeds the hardware capa

Saul Ancari, Xu Cheng

In this paper, we mainly study immersed self-expander hypersurfaces in Euclidean space whose mean curvatures have some linear growth controls. We discuss the volume growths and the finiteness of the weighted volumes. We prove some theorems that characterize the hyperplanes through the origin as self-expanders. We estimate upper bound of the bottom of the spe

Chao Pan, Siheng Chen, Antonio Ortega

Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. Furthermore, spatio-temporal graph neural networks lack theoretical interpretation. To address these issues, we put forth

Lu Yu, Xialei Liu, Joost van de Weijer

In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used fo

Aadarsh Sahoo, Rameswar Panda, Rogerio Feris, Kate Saenko

Partial domain adaptation which assumes that the unknown target label space is a subset of the source label space has attracted much attention in computer vision. Despite recent progress, existing methods often suffer from three key problems: negative transfer, lack of discriminability, and domain invariance in the latent space. To alleviate the above issues

Alexander Fedotov

For the almost Mathieu operator with a small coupling constant, for a series of spectral gaps, we describe the asymptotic locations of the gaps and get lower bounds for their lengths. The results are obtained by analysing a monodromy matrix.

Kengo Nagashima, Hisashi Noma, Yasunori Sato, Masahiko Gosho

In single-arm clinical trials with survival outcomes, the Kaplan-Meier estimator and its confidence interval are widely used to assess survival probability and median survival time. Since the asymptotic normality of the Kaplan-Meier estimator is a common result, the sample size calculation methods have not been studied in depth. An existing sample size calcu

Wenlei Zhang, Ravi K. Saripalli, Jacob M. Leamer, Ryan T. Glasser

In contrast to Bell's inequalities which test the correlations between multiple spatially separated systems, the Leggett-Garg inequalities test the temporal correlations between measurements of a single system. We experimentally demonstrate the violation of the Leggett-Garg inequality in a classical optical system using only the polarization degree-of-freedo

Felix Voigtlaender

We generalize the classical universal approximation theorem for neural networks to the case of complex-valued neural networks. Precisely, we consider feedforward networks with a complex activation function $\sigma : \mathbb{C} \to \mathbb{C}$ in which each neuron performs the operation $\mathbb{C}^N \to \mathbb{C}, z \mapsto \sigma(b + w^T z)$ with weights $

Herbert Edelsbrunner, Anton Nikitenko

The approximation of a circle with the edges of a fine square grid distorts the perimeter by a factor about $\tfrac{4}{\pi}$. We prove that this factor is the same on average (in the ergodic sense) for approximations of any rectifiable curve by the edges of any non-exotic Delaunay mosaic (known as Voronoi path), and extend the results to all dimensions, gene

Athanasios G. Tzikas

We examine the thermodynamic behavior of a static neutral regular (non-singular) black hole enclosed in a finite isothermal cavity. The cavity enclosure helps us investigate black hole systems in a canonical or a grand canonical ensemble. Here we demonstrate the derivation of the reduced action for the general metric of a regular black hole in a cavity by co

Tudor Giurgica-Tiron, Iordanis Kerenidis, Farrokh Labib, Anupam Prakash

We design and analyze two new low depth algorithms for amplitude estimation (AE) achieving an optimal tradeoff between the quantum speedup and circuit depth. For $\beta \in (0,1]$, our algorithms require $N= \tilde{O}( \frac{1}{ \epsilon^{1+\beta}})$ oracle calls and require the oracle to be called sequentially $D= O( \frac{1}{ \epsilon^{1-\beta}})$ times to

Ioannis Chiotellis, Daniel Cremers

Can we learn how to explore unknown spaces efficiently? To answer this question, we study the problem of Online Graph Exploration, the online version of the Traveling Salesperson Problem. We reformulate graph exploration as a reinforcement learning problem and apply Direct Future Prediction (Dosovitskiy and Koltun, 2017) to solve it. As the graph is discover

Botond Feczkó, Dániel Divényi, Ádám Sleisz, Dávid Csercsik

In this work we propose a heuristic clearing method of day-ahead electricity markets. In the first part of the process, a computationally less demanding problem is solved using an approximation of the cumulative demand and supply curves, which are derived via the aggregation of simple bids. Based on the outcome of this problem, estimated ranges for the clear

Juan M. Z. Pretel, Sergio E. Jorás, Ribamar R. R. Reis, José D. V. Arbañil

We examine the static structure configurations and radial stability of compact stars within the context of $f(R, T)$ gravity, with $R$ and $T$ standing for the Ricci scalar and trace of the energy-momentum tensor, respectively. Considering the $f(R, T)=R+2βT$ functional form, with $β$ being a constant, we derive the corresponding hydrostatic equilibrium equa

C. Paz-Soldan

A database of DIII-D plasmas without ELMs compares the operating space and plasma performance of stationary no-ELM regimes: RMP-ELM suppression, QH-mode, I-mode, EDA H-mode, L-mode, and negative triangularity L-mode (Neg-D). Operational space is documented in terms of engineering and physics parameters, revealing divergent constraints. Some operational space

Svetlana Roudenko, Zhongming Wang, Kai Yang

We consider the generalized Benjamin-Ono (gBO) equation on the real line, $ u_t + \partial_x (-\mathcal H u_{x} + \tfrac1{m} u^m) = 0, x \in \mathbb R, m = 2,3,4,5$, and perform numerical study of its solutions. We first compute the ground state solution to $-Q -\mathcal H Q^\prime +\frac1{m} Q^m = 0$ via Petviashvili's iteration method. We then investigate