March 2020 arXiv papers — page 7

Yoshihiko Suyama

The aim of this paper is to verify that the study of generic conformally flat hypersurfaces in 4-dimensional space forms is reduced to a surface theory in the standard 3-sphere. The conformal structure of generic conformally flat (local-)hypersurfaces is characterized as conformally flat (local-)3-metrics with the Guichard condition. Then, there is a certain

Zhentao Tang, Yuanheng Zhu, Dongbin Zhao, Simon M. Lucas

The Fighting Game AI Competition (FTGAIC) provides a challenging benchmark for 2-player video game AI. The challenge arises from the large action space, diverse styles of characters and abilities, and the real-time nature of the game. In this paper, we propose a novel algorithm that combines Rolling Horizon Evolution Algorithm (RHEA) with opponent model lear

Enze Xie, Wenjia Wang, Wenhai Wang, Mingyu Ding

Transparent objects such as windows and bottles made by glass widely exist in the real world. Segmenting transparent objects is challenging because these objects have diverse appearance inherited from the image background, making them had similar appearance with their surroundings. Besides the technical difficulty of this task, only a few previous datasets w

Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang

We consider class incremental learning (CIL) problem, in which a learning agent continuously learns new classes from incrementally arriving training data batches and aims to predict well on all the classes learned so far. The main challenge of the problem is the catastrophic forgetting, and for the exemplar-memory based CIL methods, it is generally known tha

Lingrui Ge, Jiangong You

The arithmetic version of Anderson localization (AL), i.e., AL with explicit arithmetic description on both the localization frequency and the localization phase, was first given by Jitomirskaya \cite{J} for the almost Mathieu operators (AMO). Later, the result was generalized by Bourgain and Jitomirskaya \cite{bj02} to a class of {\it one dimensional} quasi

Hau-Tieng Wu

Motivated by analyzing complicated time series, nonlinear-type time-frequency analysis became an active research topic in the past decades. Those developed tools have been applied to various problems. In this article, we review those developed tools and summarize their applications to high-frequency biomedical signals.

Kentaro Nishimura, Naoki Yamamoto

We study the ground states of low-density hadronic matter and high-density color-flavor locked color superconducting phase in three-flavor QCD at finite baryon chemical potential under rotation. We find that, in both cases under sufficiently fast rotation, the combination of the rotation-induced topological term for the eta' meson and the QCD anomaly leads t

Katsunori Iwasaki, Yuta Takada

A hypergeometric group is a matrix group modeled on the monodromy group of a generalized hypergeometric differential equation. This article presents a fruitful interaction between the theory of hypergeometric groups and dynamics on K3 surfaces by showing that a certain class of hypergeometric groups and related lattices lead to a lot of K3 surface automorphi

Boxiao Pan, Haoye Cai, De-An Huang, Kuan-Hui Lee

Video captioning is a challenging task that requires a deep understanding of visual scenes. State-of-the-art methods generate captions using either scene-level or object-level information but without explicitly modeling object interactions. Thus, they often fail to make visually grounded predictions, and are sensitive to spurious correlations. In this paper,

Sheng-Quan Wang, Rui-Qing Meng, Xing-Gang Wu, Long Chen

The bottom quark forward-backward asymmetry $A_{\rm{FB}}$ is a key observable in electron-positron collisions at the $Z^{0}$ peak. In this paper, we employ the Principle of Maximum Conformality (PMC) to fix the $\alpha_s$-running behavior of the next-to-next-to-leading order QCD corrections to $A_{\rm{FB}}$. The resulting PMC scale for this $A_{\rm{FB}}$ is

Qiang Zhang, Xuezhi Zhao

Indices of fixed point classes play a central role in Nielsen fixed point theory. Jiang-Wang-Zhang proved that for selfmaps of graphs and surfaces, the index of any fixed point class has an upper bound called its characteristic. In this paper, we study the difference between the index and the characteristic for selfmaps of graphs. First, for free groups, we

Sandip Mondal1, Tathagata Paul, Arindam Ghosh, V. Venkataraman

Gate controllable electronic trap detection method has been demonstrated by regulating the gate potential of MIS devices. This method is based on shift of capacitance voltage (CV) curve as well as flatband voltage (VFB) measure in less than 10 micro-seconds due to injection or ejection of electrons through the metal gate. Using this method, an electronic tra

Mouloud Goubi

Our aim in this work is to give explicit formula of the linear processes solution of autoregressive time series AR(2) with hint of generating functions theory by using the Horadam numbers and polynomials.

Randell Heyman

Let $N$ be a positive number. We give an asymptotic formula for the sum of $\tau(\gcd(a,b))$ for all $a$ and $b$ with $ab \le N$.

Hanyu Song, Yingjian Wang, David B. Dunson

In many modern applications, there is interest in analyzing enormous data sets that cannot be easily moved across computers or loaded into memory on a single computer. In such settings, it is very common to be interested in clustering. Existing distributed clustering algorithms are mostly distance or density based without a likelihood specification, precludi

Valerio Faraoni, Andrea Giusti, Jeremy Côté

Following an existing procedure in general relativity, the turnaround radius of a spherical structure is studied in scalar-tensor gravity using a new prescription for the analog of the Hawking-Hayward quasilocal mass in this class of theories.

Lancelot Dupont, Christophe Jouffrais, Simon T. Perrault

Visually impaired people encounter many challenges in their everyday life, especially when it comes to navigating and representing space. The issue of shopping is addressed mostly on the level of navigation and product detection, but conveying clues about the object position to the user is rarely implemented. This work presents a prototype of vibrotactile wr

Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson

We consider inference (filtering) problems over probabilistic graphical models with aggregate data generated by a large population of individuals. We propose a new efficient belief propagation type algorithm over tree-structured graphs with polynomial computational complexity as well as a global convergence guarantee. This is in contrast to previous methods

Rodrigo A. Schulz, Carlos H. Coimbra-Araújo, Samuel W. S. Costiche

In the present article we introduce an epidemiological model for the investigation of the spread of epidemics caused by viruses. The model is applied specifically to COVID-19, the disease caused by the SARS-Cov-2 virus (aka "novel coronavirus"). The SIR (Susceptible - Infectious - Recovered) model is used as a basis for studying the evolution of the epidemic

Takehiro Yonehara

We examine the effect of structural disorder and dynamical lattice fluctuation on charge migration dynamics starting from a birth of local exciton in a quantum network of molecular aggregates by using model Hamiltonians having complicate interactions. Here all monomers are supposed to be the same for simplicity. A natural use of inherent sparsity of Hamilton

Shaochi Hu, Donghao Xu, Huijing Zhao

This work addresses on the following problem: given a set of unsynchronized history observations of two scenes that are correlative on their dynamic changes, the purpose is to learn a cross-scene predictor, so that with the observation of one scene, a robot can onlinely predict the dynamic state of another. A method is proposed to solve the problem via model

Koichi Funakubo, Eibun Senaha

We elucidate a magnetic mass effect on a sphaleron energy that is crucial for baryon number preservation needed for successful electroweak baryogenesis. It is found that the sphaleron energy increases in response to the magnetic mass. As an application, we study the sphaleron energy and electroweak phase transition with the magnetic mass in a two-Higgs-doubl

MengNi Chen, Yuanhong Zhong, Yi Feng, Di Li

Studies have shown that the use of pulsar timing arrays (PTAs) is among the approaches with the highest potential to detect very low-frequency gravitational waves in the near future. Although the capture of gravitational waves (GWs) by PTAs has not been reported yet, many related theoretical studies and some meaningful detection limits have been reported. In

Jianming Deng, Xingan Jiang, Yanyu Liu, Wei Zhao

The conductive domain wall (CDW) is extensively investigated in ferroelectrics, which can be considered as a quasi-two-dimensional reconfigurable conducting channel embedded into an insulating material. Therefore, it is highly important for the application of ferroelectric nanoelectronics. Hitherto, most CDW investigations are restricted in oxides, and limit

Jilin Mei, Huijing Zhao

We propose a graph neural network(GNN) based method to incorporate scene context for the semantic segmentation of 3D LiDAR data. The problem is defined as building a graph to represent the topology of a center segment with its neighborhoods, then inferring the segment label. The node of graph is generated from the segment on range image, which is suitable fo

Shinji Fujita, Hidetoshi Sano, Rei Enokiya, Katsuhiro Hayashi

Herein, we present results from observations of the 12CO (J=1-0), 13CO (J=1-0), and 12CO (J=2-1) emission lines toward the Carina nebula complex (CNC) obtained with the Mopra and NANTEN2 telescopes. We focused on massive-star-forming regions associated with the CNC including the three star clusters Tr14, Tr15, and Tr16, and the isolated WR-star HD92740. We f

Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi

Multi-agent interacting systems are prevalent in the world, from pure physical systems to complicated social dynamic systems. In many applications, effective understanding of the situation and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision making and planning. In this paper, we propose a gen

Libo Feng, Pinghui Zhuang, Fawang Liu, Ian Turner

In this paper, we propose high-order numerical methods for the Riesz space fractional advection-dispersion equations (RSFADE) on a {f}inite domain. The RSFADE is obtained from the standard advection-dispersion equation by replacing the first-order and second-order space derivative with the Riesz fractional derivatives of order $\alpha\in(0,1)$ and $\beta\in(

Aiping Xiong, Tianhao Wang, Ninghui Li, Somesh Jha

Differential privacy protects an individual's privacy by perturbing data on an aggregated level (DP) or individual level (LDP). We report four online human-subject experiments investigating the effects of using different approaches to communicate differential privacy techniques to laypersons in a health app data collection setting. Experiments 1 and 2 invest

Ruofan Chen

We study Landau-Zener transitions in a fermionic dissipative environment where a two-level (up and down states) system is coupled to two metallic leads kept with different chemical potentials at zero temperature. The dynamics of the system is simulated by an iterative numerically exact influence functional path integral method. In the pure Landau-Zener probl

J. P. Ma, G. P. Zhang

The first moment the chirality-odd twist-3 parton distribution $e(x)$ is related to the pion-nucleon $\sigma$-term which is important for phenomenology. However, the possible existence of a singular contribution proportional to $\delta(x)$ in the distribution prevents from the determination of the $\sigma$-term with $e(x)$ from experiment. There are two appr

Yikan Liu

This article is concerned with the derivation of numerical reconstruction schemes for the inverse moving source problem on determining source profiles in (time-fractional) evolution equations. As a continuation of the theoretical result on the uniqueness, we adopt a minimization procedure with regularization to construct iterative thresholding schemes for th

Alexander Madurowicz

A mission to the focus of the solar gravitational lens could produce images with unprecedented angular resolution and sensitivity. In the context of trying to resolve the time variable thermal signature of continents on other Earth-like exoplanets, we develop an approach to improve the image reconstruction performance by using azimuthal variations in the Ein

Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma

Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based attention model, U-Net$_{At}$, to enhance adversarial speech signals. Specifically, we evaluate the model performance by interpretable speech recognition metrics and discuss the m

Oishee Banerjee

In \cite{Vakil13} Vakil and Wood made several conjectures on the topology of symmetric powers of geometrically irreducible varieties based on their computations on motivic zeta functions. Two of those conjectures are about subspaces of $\Sym^n(\P^1)$. In this note, we disprove one of them thereby obtaining a counterexample to the principle of Occcam's razor

Eunchong Kim, Yujin Yang, Ann Zabludoff, Paul Smith

"Ly$\alpha$ nebulae" are giant ($\sim$100 kpc), glowing gas clouds in the distant universe. The origin of their extended Ly$\alpha$ emission remains a mystery. Some models posit that Ly$\alpha$ emission is produced when the cloud is photoionized by UV emission from embedded or nearby sources, while others suggest that the Ly$\alpha$ photons originate from an

Kazuo Hosomichi

We briefly review some of the important developments in the last decade in the theory of multiple M2-branes and AdS4/CFT3 correspondence. Taking the examples of the superconformal index, free energy on S^3 and entropy of charged black holes, we illustrate how the large N limit was studied and the correspondence was checked.

Johann Brehmer, Kyle Cranmer

We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold. Combining aspects of normalizing flows, GANs, autoencoders, and energy-based models, they have the potential to represent datasets with a manifold structure more faithfully a

Hao-Hsiang Yang, Chao-Han Huck Yang, Yi-Chang James Tsai

Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many computer vision problems. In this paper, we propose a Y-net that is named for its structure. This network reconstructs clear images by aggregating multi-scale features maps. Additionally,

Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak

Existing metric learning losses can be categorized into two classes: pair-based and proxy-based losses. The former class can leverage fine-grained semantic relations between data points, but slows convergence in general due to its high training complexity. In contrast, the latter class enables fast and reliable convergence, but cannot consider the rich data-

Siqi Li, Changqing Zou, Yipeng Li, Xibin Zhao

This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images. Compared with previous methods which use only the semantic features extracted from RGB-D images

Meng Hua, Qingqing Wu, Derrick Wing Kwan Ng, Jun Zhao

This paper investigates intelligent reflecting surface (IRS)-aided multicell wireless networks, where an IRS is deployed to assist the joint processing coordinated multipoint (JP-CoMP) transmission from multiple base stations (BSs) to multiple cell-edge users. By taking into account the fairness among cell-edge users, we aim at maximizing the minimum achieva

Matt Olfat, Stephen Sloan, Pedro Hespanhol, Matt Porter

Attack detection and mitigation strategies for cyberphysical systems (CPS) are an active area of research, and researchers have developed a variety of attack-detection tools such as dynamic watermarking. However, such methods often make assumptions that are difficult to guarantee, such as exact knowledge of the distribution of measurement noise. Here, we dev

Lisa Singh, Shweta Bansal, Leticia Bode, Ceren Budak

Since December 2019, COVID-19 has been spreading rapidly across the world. Not surprisingly, conversation about COVID-19 is also increasing. This article is a first look at the amount of conversation taking place on social media, specifically Twitter, with respect to COVID-19, the themes of discussion, where the discussion is emerging from, myths shared abou

Yuta Michimura, Kentaro Komori

Probing the boundary between classical and quantum mechanics has been one of the central themes in modern physics. Recently, experiments to precisely measure the force acting on milligram scale oscillators with optical cavities are attracting interest as promising tools to test quantum mechanics, decoherence mechanisms, and gravitational physics. In this pap

Volodymyr Vovchenko, Oleh Savchuk, Roman V. Poberezhnyuk, Mark I. Gorenstein

We derive the relation between cumulants of a conserved charge measured in a subvolume of a thermal system and the corresponding grand-canonical susceptibilities, taking into account exact global conservation of that charge. The derivation is presented for an arbitrary equation of state, with the assumption that the subvolume is sufficiently large to be clos

Simson L. Garfinkel, J. Spencer Love

A file system standard for use with write-once media such as digital compact disks is proposed. The file system is designed to work with any operating system and a variety of physical media. Although the implementation is simple, it provides a a full-featured and high-performance alternative to conventional file systems on traditional, multiple-write media s

Rabin Yu Acharya, Sreeja Chowdhury, Fatemeh Ganji, Domenic Forte

Hardware intellectual property (IP) theft is a major issue in today's globalized supply chain. To address it, numerous logic locking and obfuscation techniques have been proposed. While locking initially focused on digital integrated circuits (ICs), there have been recent attempts to extend it to analog ICs, which are easier to reverse engineer and to copy t

Tong Zhou, Changxing Ding, Shaowen Lin, Xinchao Wang

High-fidelity face completion is a challenging task due to the rich and subtle facial textures involved. What makes it more complicated is the correlations between different facial components, for example, the symmetry in texture and structure between both eyes. While recent works adopted the attention mechanism to learn the contextual relations among elemen

Xuan Lin

Accurately predicting drug-target binding affinity (DTA) in silico is a key task in drug discovery. Most of the conventional DTA prediction methods are simulation-based, which rely heavily on domain knowledge or the assumption of having the 3D structure of the targets, which are often difficult to obtain. Meanwhile, traditional machine learning-based methods

Yong Zhang, Xiangnan Yu, HongGuang Sun, Geoffrey R. Tick

The coronavirus disease 2019 (COVID-19) pandemic radically impacts our lives, while the transmission/infection and recovery dynamics of COVID-19 remain obscure. A time-dependent Susceptible, Exposed, Infectious, and Recovered (SEIR) model was proposed and applied to fit and then predict the time series of COVID-19 evolution observed in the last three months

Jonghyeon Min

The financial market trend forecasting method is emerging as a hot topic in financial markets today. Many challenges still currently remain, and various researches related thereto have been actively conducted. Especially, recent research of neural network-based financial market trend prediction has attracted much attention. However, previous researches do no

Ramya Tekumalla, Juan M. Banda

With the increase in popularity of deep learning models for natural language processing (NLP) tasks, in the field of Pharmacovigilance, more specifically for the identification of Adverse Drug Reactions (ADRs), there is an inherent need for large-scale social-media datasets aimed at such tasks. With most researchers allocating large amounts of time to crawl

Anirudh Vemula, Wen Sun, J. Andrew Bagnell

Parameter space exploration methods with black-box optimization have recently been shown to outperform state-of-the-art approaches in continuous control reinforcement learning domains. In this paper, we examine reasons why these methods work better and the situations in which they are worse than traditional action space exploration methods. Through a simple

Beibei Hu, Ling Zhang, Qinghong Li, Ning Zhang

In this paper, we utilize Fokas method to investigate the initial-boundary value problems (IBVPs) of the fourth-order dispersive nonlinear Schr\"{o}dinger (FODNLS) equation on the half-line, which can simulate the nonlinear transmission and interaction of ultrashort pulses in the high-speed optical fiber transmission system, and describe the nonlinear spin e

Hao Tang, Xiaojuan Qi, Guolei Sun, Dan Xu

We propose a novel ECGAN for the challenging semantic image synthesis task. Although considerable improvement has been achieved, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic labels do not provide detailed structural information, making it difficult to synthesize local details and struc

Yang Zhang, Wen-qi Wang, Yu-bo Ma, Jun Wang

In this paper, the effect of the space-time dimension on effective thermodynamic quantities in (n+2)-dimensional Reissoner-Nordstrom-de Sitter space has been stud ied. Based on derived effective thermodynamic quantities, conditions for the phase transition are obtained. The result shows that the accelerating cosmic expansion can be attained by the entropy fo

Shengling Wang, Lina Shi, Junshan Zhang, Xiuzhen Cheng

The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data

Guangyao Shi, Lifeng Zhou, Pratap Tokekar

The multiple-path orienteering problem asks for paths for a team of robots that maximize the total reward collected while satisfying budget constraints on the path length. This problem models many multi-robot routing tasks such as exploring unknown environments and information gathering for environmental monitoring. In this paper, we focus on how to make the

Kenneth F. Caluya, Abhishek Halder

How to steer a given joint state probability density function to another over finite horizon subject to a controlled stochastic dynamics with hard state (sample path) constraints? In applications, state constraints may encode safety requirements such as obstacle avoidance. In this paper, we perform the feedback synthesis for minimum control effort density st

Ramya Tekumalla, Juan M. Banda

There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from Twitter and Reddit. However, the vast majority of those works do not share their code or data fo

Yang Chen, Kaiming Zhao, Yueqiang Zhao

In this paper, we prove that every invertible $2$-local or local automorphism of a simple generalized Witt algebra over any field of characteristic $0$ is an automorphism. In particular, every $2$-local or local automorphism of Witt algebras $W_n$ is an automorphism for all $n\in \mathbb{N}$. But some simple generalized Witt algebras indeed have $2$-local (a

Jiewei Ding, Guoxing Xia

In this paper, the feasibility of the electron-positron collider based on plasma accelerator is proved by simulation experiments. Starting from the 17.612cm proton beam provided by SPS (super proton synchrotron), the simulation shows that the long proton beam can form several high quality proton bunches through seeded self-modulation in the first half of the

Guang-Wei Li

Rotation and binary interaction play important roles in understanding the nature of massive stars (>8 Msun). Some interesting transients, such as the long-duration gamma-ray bursts, are thought to be originated from fast-rotating massive stars. Because the strong stellar wind can effectively spin down a metal-rich massive star with fast rotation, it is very

Efren M. Benavides

This article presents a new model to predict the evolution of infective diseases under uncertainty or low-quality information, just as it has happened in the initial scenario during the CoVid-19 spread in China and Europe. The model has been used to predict the death rate in Spain but can be used to predict the demand of ICUs or mechanical ventilators under

Benjamin D. Oppenheimer, Akos Bogdan, Robert A. Crain, John A. ZuHone

We simulate stacked observations of nearby hot X-ray coronae associated with galaxies in the EAGLE and Illustris-TNG hydrodynamic simulations. A forward modeling pipeline is developed to predict 4-year eROSITA observations and stacked image analysis, including the effects of instrumental and astrophysical backgrounds. We propose an experiment to stack z~0.01

Benjamin Avanzi, Greg Taylor, Bernard Wong, Alan Xian

The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we extend the Markov-modulated Poisson process framework through the introduction of a flexible frequency perturbation measure. This contribution enables known information of observed

Vadlamani Ravi, Vadlamani Madhav

It is well-known that disciplines such as mechanical engineering, electrical engineering, civil engineering, aerospace engineering, chemical engineering and software engineering witnessed successful applications of reliability engineering concepts. However, the concept of reliability in its strict sense is missing in financial services. Therefore, in order t

Simran K, Sriram S, Vinayakumar R, Soman KP

In recent years, the amount of Cyber Security data generated in the form of unstructured texts, for example, social media resources, blogs, articles, and so on has exceptionally increased. Named Entity Recognition (NER) is an initial step towards converting this unstructured data into structured data which can be used by a lot of applications. The existing m

Wolfgang Wieland

Jackiw-Teitelboim (JT) gravity is a 1+1-dimensional toy model for quantum gravity in four spacetime dimensions. In the absence of matter, JT gravity is a topological field theory and there are no local observables. The introduction of a boundary changes the situation. What was a un-physical gauge direction before turns into a physical boundary degree of free

Srikanth Malla, Behzad Dariush, Chiho Choi

We consider the problem of predicting the future trajectory of scene agents from egocentric views obtained from a moving platform. This problem is important in a variety of domains, particularly for autonomous systems making reactive or strategic decisions in navigation. In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Ta

Bogdan Ungureanu, Mehul P. Makwana, Richard V. Craster, Sebastien Guenneau

We combine two different fields, topological physics and graded metamaterials to design a topological metasurface to control and redirect elastic waves. We strategically design a two-dimensional crystalline perforated elastic plate, using a square lattice, %consisting of bore holes, that hosts symmetry-induced topological edge states. By concurrently allowin

Simran K, Prathiksha Balakrishna, Vinayakumar R, Soman KP

In recent days, the amount of Cyber Security text data shared via social media resources mainly Twitter has increased. An accurate analysis of this data can help to develop cyber threat situational awareness framework for a cyber threat. This work proposes a deep learning based approach for tweet data analysis. To convert the tweets into numerical representa

Graham M. Harper, Curtis N. DeWitt, Matthew J. Richter, Edward F. Guinan

In 2019 October Betelgeuse began a decline in V-band brightness that went beyond the minimum expected from its quasi-periodic ~420 day cycle, becoming the faintest in recorded photometric history. Observations obtained in 2019 December with VLT/SPHERE (Montarges 2020) have shown that the southern half of the star has become markedly fainter than in 2019 Janu

Simran K, Prathiksha Balakrishna, Vinayakumar Ravi, Soman KP

Deep learning is a state of the art method for a lot of applications. The main issue is that most of the real-time data is highly imbalanced in nature. In order to avoid bias in training, cost-sensitive approach can be used. In this paper, we propose cost-sensitive deep learning based frameworks and the performance of the frameworks is evaluated on three dif

Wennie Tabib, Kshitij Goel, John Yao, Curtis Boirum

This paper presents a method for cave surveying in total darkness using an autonomous aerial vehicle equipped with a depth camera for mapping, downward-facing camera for state estimation, and forward and downward lights. Traditional methods of cave surveying are labor-intensive and dangerous due to the risk of hypothermia when collecting data over extended p

Samuel Harford, Fazle Karim, Houshang Darabi

Classification models for the multivariate time series have gained significant importance in the research community, but not much research has been done on generating adversarial samples for these models. Such samples of adversaries could become a security concern. In this paper, we propose transforming the existing adversarial transformation network (ATN) o

G. M. Peccini, L. S. Moriggi, M. V. T. Machado

We study the diffractive jet production in electron-ion collisions in the kinematical region where the mass $M_X$ of the diffractive final state is larger than $Q^2$. Based on parton saturation framework predictions are done for the kinematics of future or possible $eA$ machines as the EIC, LHeC, HE-LHeC and FCC-eA. We analyze the differential cross section

Abdulrahman Alabdulkareem, Jean Honorio

In this paper we analyze the necessary number of samples to estimate the gradient of any multidimensional smooth (possibly non-convex) function in a zero-order stochastic oracle model. In this model, an estimator has access to noisy values of the function, in order to produce the estimate of the gradient. We also provide an analysis on the sufficient number

Zhichao Lu, Kalyanmoy Deb, Vishnu Naresh Boddeti

Convolutional neural networks have witnessed remarkable improvements in computational efficiency in recent years. A key driving force has been the idea of trading-off model expressivity and efficiency through a combination of $1\times 1$ and depth-wise separable convolutions in lieu of a standard convolutional layer. The price of the efficiency, however, is

Rabiah A. Rahmat, Melissa A. Humphries, Jeremy J. Austin, Adrian M. T. Linacre

Heat alters colour and crystallinity of teeth by destruction of the organic content and inducing hydroxyapatite crystal growth. The colour and crystallite changes can be quantified using spectrophotometric and x-ray diffraction analyses, however these analyses are not commonly used in combination to evaluate burned dental remains. In this study, thirty-nine

Michael B. Lund

In this paper we assess the correlation between recent observing runs (2018 and 2019) and inclement weather, and demonstrate that these observing runs have seen much more rainfall than would otherwise be expected, an increase of over 200%. We further look at a number of observatory sites in areas that are facing or will face drought, and suggest that a stron

Aida Amini, Antoine Bosselut, Bhavana Dalvi Mishra, Yejin Choi

Procedural texts often describe processes (e.g., photosynthesis and cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural reading comprehension by translating the text into a general formalism that represents processes as a sequence of transitions over entity attributes (e.g., location, temperature).

Tian Yao, Benjian Lv, Kaishun Wang

In this paper, by shifting technique we study $t$-intersecting families for direct products where the ground set is divided into several parts. Assuming the size of each part is sufficiently large, we determine all extremal $t$-intersecting families for direct products. We also prove that every largest $t$-intersecting subfamily of a more general family intr

Jamileh Beik Mohammadi, Andrew D. Kent

Understanding the magnetization dynamics induced by spin transfer torques in perpendicularly magnetized magnetic tunnel junction nanopillars and its dependence on material parameters is critical to optimizing device performance. Here we present a micromagnetic study of spin-torque switching in a disk-shaped element as a function of the free layer's exchange

Thomas Huettemann

The "fundamental theorem" for algebraic $K$-theory expresses the $K$-groups of a Laurent polynomial ring $L[t,t^{-1}]$ as a direct sum of two copies of the $K$-groups of $L$ (with a degree shift in one copy), and certain "nil" groups of $L$. It is shown here that a modified version of this result generalises to strongly $\mathbb{Z}$-graded ri

Thomas Huettemann, Tasha Montgomery

A Laurent polynomial ring $A[t,1/t]$ with coefficients in a unital ring $A$ determines a category of quasi-coherent sheaves on the projective line over $A$; its $K$-theory is known to split into a direct sum of two copies of the $K$-theory of $A$. In this paper, the result is generalised to the case of an arbitrary strongly $\mathbb{Z}$-graded ring $R$ in pl

Zitao Chen, Guanpeng Li, Karthik Pattabiraman

The adoption of deep neural networks (DNNs) in safety-critical domains has engendered serious reliability concerns. A prominent example is hardware transient faults that are growing in frequency due to the progressive technology scaling, and can lead to failures in DNNs. This work proposes Ranger, a low-cost fault corrector, which directly rectifies the faul

Koun Shirai

Many properties of solids such as the glass state are commonly treated as nonequilibrium phenomena, which involve many conceptual difficulties. However, few studies have addressed the problem of understanding equilibrium itself. Equilibrium is commonly assessed based on the assumption that its thermodynamic state should be determined solely by temperature an

Esther Banaian, Elizabeth Kelley

We give an explicit combinatorial formula for the Laurent expansion of any arc or closed curve on an unpunctured triangulated orbifold. We do this by extending the snake graph construction of Musiker, Schiffler, and Williams to unpunctured orbifolds. In the case of an ordinary arc, this gives a combinatorial proof of positivity to the generalized cluster alg

Diego Lorenzo-Oliveira, Jorge Meléndez, Geisa Ponte, Jhon Yana Galarza

We present a detailed analysis of the possible future Sun's rotational evolution scenario based on the 8 Gyr-old solar twin HIP 102152. Using HARPS high-cadence observations (and TESS light curves), we analyzed the modulation of a variety of activity proxies (Ca II, HI Balmer, and Na I lines), finding a strong rotational signal of 35.7 $\pm$ 1.4 days ($\log

Zhichao Lu, Vivek Rathod, Ronny Votel, Jonathan Huang

Traditionally multi-object tracking and object detection are performed using separate systems with most prior works focusing exclusively on one of these aspects over the other. Tracking systems clearly benefit from having access to accurate detections, however and there is ample evidence in literature that detectors can benefit from tracking which, for examp

Rudrasis Chakraborty

Many measurements in computer vision and machine learning manifest as non-Euclidean data samples. Several researchers recently extended a number of deep neural network architectures for manifold valued data samples. Researchers have proposed models for manifold valued spatial data which are common in medical image processing including processing of diffusion

Manohar Karki, Junghwan Cho, Seokhwan Ko

Data augmentation can effectively resolve a scarcity of images when training machine-learning algorithms. It can make them more robust to unseen images. We present a lesion conditional Generative Adversarial Network LcGAN to generate synthetic Computed Tomography (CT) images for data augmentation. A lesion conditional image (segmented mask) is an input to bo

Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe

We present 3D-MPA, a method for instance segmentation on 3D point clouds. Given an input point cloud, we propose an object-centric approach where each point votes for its object center. We sample object proposals from the predicted object centers. Then, we learn proposal features from grouped point features that voted for the same object center. A graph conv

Calvin Murdock, Simon Lucey

Choosing a deep neural network architecture is a fundamental problem in applications that require balancing performance and parameter efficiency. Standard approaches rely on ad-hoc engineering or computationally expensive validation on a specific dataset. We instead attempt to quantify networks by their intrinsic capacity for unique and robust representation

Xingyi Yang, Xuehai He, Jinyu Zhao, Yichen Zhang

During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-powered diagnosis methods of COVID-19 based on CTs. To address this issue, we build an open-sourced

Steven M. Kreyche, Jason W. Barnes, Billy L. Quarles, Jack J. Lissauer

Previous studies have shown that planets that rotate retrograde (backwards with respect to their orbital motion) generally experience less severe obliquity variations than those that rotate prograde (the same direction as their orbital motion). Here we examine retrograde-rotating planets on eccentric orbits and find a previously unknown secular spin-orbit re

Ali Amini, Adele Khavari, Clayton Molter, Allen J. Ehrlicher

Glasses have numerous applications due to their exceptional transparency, however, poor fracture and impact resistance limit their applications as an engineering material. One relatively recent approach to improve the mechanical properties of materials is through bio-inspiration. Structural biological composites such as nacre, the protective inner layer of m

Anh Tran, Jing Sun, Dehao Liu, Tim Wildey

Integrated Computational Materials Engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinder