November 2019 arXiv papers — page 66

Hua Chen, Patrick M. Wensing, Wei Zhang

This paper considers the optimal control problem of an extended spring-loaded inverted pendulum (SLIP) model with two additional actuators for active leg length and hip torque modulation. These additional features arise naturally in practice, allowing for consideration of swing leg kinematics during flight and active control over stance dynamics. On the othe

Satoshi Tsutsui, Yanwei Fu, David Crandall

One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes. To alleviate this problem, an off-the-shelf image generator can be applied to synthesize additional training images, but these synthesized images are often not helpful for actually improving the accuracy of one-shot fine-grained rec

Juan Polo, Piero Naldesi, Anna Minguzzi, Luigi Amico

We study the ground state of two interacting bosonic particles confined in a ring-shaped lattice potential and subjected to a synthetic magnetic flux. The system is described by the Bose-Hubbard model and solved exactly through a plane-wave Ansatz of the wave function. We obtain energies and correlation functions of the system both for repulsive and attracti

R. K. Pandey, A. K. Verma

In this note we establish existence of solutions of singular boundary value problem $-(p(x)y^{\prime }(x))^{\prime}=q(x)f(x,y,py&#39;)$ for $0< x\leq b$ and $y&#39;(0)=0$, $α_{1}y(b)+β_{1}p(b)y^{\prime}(b)=γ_{1}$ with $p(0)=0$ and $q(x)$ is integrable. Regions of multiple solutions have also been determined.

Haozhe Wu, Zhiyuan Hu, Jia Jia, Yaohua Bu

Online Social Networks (OSNs) evolve through two pervasive behaviors: follow and unfollow, which respectively signify relationship creation and relationship dissolution. Researches on social network evolution mainly focus on the follow behavior, while the unfollow behavior has largely been ignored. Mining unfollow behavior is challenging because user&#39;s d

Andrew Weinfeld

We create several families of bases for the symmetric polynomials. From these bases we prove that certain Schur symmetric polynomials form a basis for quotients of symmetric polynomials that generalize the cohomology and the quantum cohomology of the Grassmannian. Our work also provides an alternative proof of a result due to Grinberg.

Siddharth Dawar, Vikram Goyal, Debajyoti Bera

High-utility itemset mining finds itemsets from a transaction database with utility no less than a fixed user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its item. Several algorithms were proposed to mine high-utility itemsets. However, no state-of-the-art algorithm performs consistently good across dense and sparse

Xilong Liang, Jingkun Zhao, Yuqin Chen, Wenbo Zuo

Since Gaia DR2 was released, many velocity structures in the disk have been revealed such as large scale ridge-like patterns in the phase space. Both kinematic information and stellar elemental abundances are needed to reveal their evolution history. We have used labels from the APOGEE survey to predict elemental abundances for a huge amount of low resolutio

Yuan Zhou, Fuhui Zhou, Yongpeng Wu, Rose Qingyang Hu

Subcarrier assignment is of crucial importance in wideband cognitive radio (CR) networks. In order to tackle the challenge that the traditional optimization-based methods are inappropriate in the dynamic spectrum access environment, an independent Q-learning-based scheme is proposed for the case that the secondary users (SUs) cannot exchange information whil

William Y. C. Chen, Julia Q. D. Du, Jack C. D. Zhao

This paper is concerned with a class of partition functions $a(n)$ introduced by Radu and defined in terms of eta-quotients. By utilizing the transformation laws of Newman, Schoeneberg and Robins, and Radu&#39;s algorithms, we present an algorithm to find Ramanujan-type identities for $a(mn+t)$. While this algorithm is not guaranteed to succeed, it applies t

Kui Yu, Xianjie Guo, Lin Liu, Jiuyong Li

Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships betwe

Farzam Nosrati, Ali Mortezapour, Rosario Lo Franco

Experimental validation and control of quantum traits for an open quantum system are important for any quantum information purpose. We consider a traveling atom qubit as a quantum memory with adjustable velocity inside a leaky cavity, adopting a quantum witness as a figure of merit for quantumness assessment. We show that this model constitutes an inherent p

Qingchao Zhang, Yunmei Chen

Variational method and deep learning method are two mainstream powerful approaches to solve inverse problems in computer vision. To take advantages of advanced optimization algorithms and powerful representation ability of deep neural networks, we propose a novel deep network for image reconstruction. The architecture of this network is inspired by our propo

N. Teshima

The DeeMe experiment is planned to search for muon-to-electron conversion at J-PARC MLF. Our goal is to measure the process with a single event sensitivity of $1 \times 10^{-13}$ or $2 \times 10^{-14}$ for a graphite or silicon carbide target. That is one or two orders of magnitude better than the current upper limits, $7 \times 10^{-13}$ for a gold target b

Biqiong Yu, Guichuan Yu, Jeff Walter, Vipul Chaturvedi

Electrolyte-based transistors utilizing ionic liquids/gels have been highly successful in the study of charge-density-controlled phenomena, particularly in oxides. Experimental probes beyond transport have played a significant role, despite challenges to their application in electric double-layer transistors. Here, we demonstrate application of synchrotron s

Robert M. Oliveira, Rodrigo C. de Lamare

In this paper, we present the concept of non-uniform channel polarization and a scheme for rate-compatible polar code construction for any codeword length using additive white Gaussian noise (AWGN) channels and the successive cancellation (SC) decoder. A Non-Uniform Polarization technique based on the Gaussian Approximation (NUPGA) is developed and an effici

Khushboo Suman, Yogesh M. Joshi

The evolution of viscoelastic properties near the sol-gel transition is studied by performing oscillatory rheological measurements on two different types of systems: a colloidal dispersion and a thermo-responsive polymer solution under isothermal and non-isothermal conditions. While undergoing sol-gel transition, both the systems pass through a critical poin

Sudheer K. Mishra, A. K. Srivastava

We analyze the eruption of a coronal plasma channel (CPC) and an overlying flux rope using \textit{Atmospheric Imaging Assembly/Solar Dynamic Observatory} (AIA/SDO) and \textit{Solar TErrestrial RElations Observatory} (STEREO)-A spacecraft data. The CPC erupted first with its low and very faint coronal signature. Later, above the CPC, a diffuse and thin flux

Mario DeFranco

We evaluate the multiple zeta values $ζ(\{2\}^k)$ by proving a certain factorization property. The proof uses a combinatorial bijection and elementary telescoping series. We show how the infinite product for the sine function in fact implies its power series and other trigonometric properties. We define two constants, which we call pi-frequency and pi-amplit

Prashant S. Emani, Jonathan Warrell, Alan Anticevic, Stefan Bekiranov

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challe

Fabian V. Filipp

Machine learning (ML), deep learning (DL), and artificial intelligence (AI) are of increasing importance in biomedicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. High-throughput technologies are delivering growing vol

Randy S. Conklin

Gravitational wave echoes may imply physics beyond general relativity; however, several approaches to searching for echoes require fitting the data to toy templates which are not physically motivated. Here, I create a procedure which outputs a more realistic form for echoes, starting from initial conditions, by calculating the modulated transfer function.

R. J. Kinnear, R. R. Mazumdar

We study Granger causality in the context of wide-sense stationary time series, where our focus is on the topological aspects of the underlying causality graph. We establish sufficient conditions (in particular, we develop the notion of a &#34;strongly causal&#34; graph topology) under which the true causality graph can be recovered via pairwise causality te

J. A. Sende, T. Löhne

Resolved images suggest that asymmetric structures are a common feature of cold debris disks. While planets close to these disks are rarely detected, their hidden presence and gravitational perturbations provide plausible explanations for some of these features. To put constraints on the properties of yet undetected planetary companions, we aim to predict wh

S. Yanai, G. A. Steele

We study the coherence of flux-tunable Josephson junction resonators made with two different fabrication processes. In the first process, devices are made using a single step of evaporation in which the resonator and the junctions of the SQUID are made at the same time. In the second process, devices are made with an identical geometry, but in which the reso

Ghada El-Hadad, Doaa Shawky, Ashraf Badawi

This poster presents the conceptual framework of the Adaptive Learning Guidance System ALGS. The system aims to propose a model for adaptive learning environments where two major concerns arising from past studies are being addressed; the marginal role of the teacher, and the need for a big data approach. Most past studies marginalized the teacher role in ad

Hyunji Jane Bae, Adrian Lozano-Duran, Beverley J. McKeon

The nonlinear mechanism in the self-sustaining process (SSP) of wall-bounded turbulence is investigated. Resolvent analysis is used to identify the principal forcing mode which produces the maximum amplification of the velocities in numerical simulations of the minimal channel for the buffer layer and a modified logarithmic (log) layer. The wavenumbers targe

Yue Wang, Joseph Ibrahim, Hongtu Zhu

Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJM) framework, proposed in Li and Luo (2017), investigates the association between repeated clinical measures and survival data, while adjusting for both high-dimensional images and

Xingyi Liu, Keshab K. Parhi

This paper describes a novel approach to synthesize molecular reactions to train a perceptron, i.e., a single-layered neural network, with sigmoidal activation function. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations

Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun

In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations. Most of existing RLfD methods require demonstrations to be perfect and sufficient, which yet is unrealistic to meet in practice. To work on imperfect demonstrations, we first def

He Wang, Feixiang He, Zhexi Peng, Yongliang Yang

Adversarial attack has inspired great interest in computer vision, by showing that classification-based solutions are prone to imperceptible attack in many tasks. In this paper, we propose a method, SMART, to attack action recognizers which rely on 3D skeletal motions. Our method involves an innovative perceptual loss which ensures the imperceptibility of th

Martin Larocca, Esteban A. Calzetta, Diego A. Wisniacki

The successful application of Quantum Optimal Control (QOC) over the past decades unlocked the possibility of directing the dynamics of quantum systems. Nevertheless, solutions obtained from QOC algorithms are usually highly irregular, making them unsuitable for direct experimental implementation. In this paper, we propose a method to reshape those unattract

Ethan Che

We study a seller who sells a single good to multiple bidders with uncertainty over the joint distribution of bidders' valuations, as well as bidders' higher-order beliefs about their opponents. The seller only knows the (possibly asymmetric) means of the marginal distributions of each bidder's valuation and the range. An adversarial nature chooses the worst

Qian Lou, Bo Feng, Geoffrey C. Fox, Lei Jiang

Big data is one of the cornerstones to enabling and training deep neural networks (DNNs). Because of the lack of expertise, to gain benefits from their data, average users have to rely on and upload their private data to big data companies they may not trust. Due to the compliance, legal, or privacy constraints, most users are willing to contribute only thei

Shayan Gharib, Konstantinos Drossos, Eemi Fagerlund, Tuomas Virtanen

The performance of sound event detection methods can significantly degrade when they are used in unseen conditions (e.g. recording devices, ambient noise). Domain adaptation is a promising way to tackle this problem. In this paper, we present VOICe, the first dataset for the development and evaluation of domain adaptation methods for sound event detection. V

He Zhang, Fanli Shan, Hong Fang, Xing Zhang

An accurate algorithm is proposed to improve the prediction of a particle in collision with a moving wall within the direct simulation Monte Carlo (DSMC) framework for the simulation of unsteady rarefied flows. This algorithm is able to predict the particle-wall collision in a coupled manner by removing the assumption employed by the approximate algorithm, i

Alexander I. Bobenko, Tim Hoffmann, Thilo Rörig

We introduce orthogonal ring patterns consisting of pairs of concentric circles generalizing circle patterns. We show that orthogonal ring patterns are governed by the same equation as circle patterns. For every ring pattern there exists a one parameter family of patterns that interpolates between a circle pattern and its dual. We construct ring pattern anal

Avner P. Cohen, Guy Malamud, Shay I. Heizler

This article studies the propagation of supersonic radiative Marshak waves. These waves are radiation dominated, and play an important role in inertial confinement fusion and in astrophysical and laboratory systems. For that reason, this phenomenon has attracted considerable experimental attention in recent decades in several different facilities. The presen

Katerina Chatziioannou, Sophia Han

The composition of neutron stars at the extreme densities reached in their cores is currently unknown. Besides nuclear matter of normal neutrons and protons, the cores of neutron stars might harbor exotic matter such as deconfined quarks. In this paper we study strong hadron-quark phase transitions in the context of gravitational wave observations of inspira

Prashant Saxena, Basant Lal Sharma

We derive the equations of nonlinear electroelastostatics using three different variational formulations involving the deformation function and an independent field variable representing the electric character - considering either one of the electric field $\mathbb{E}$, electric displacement $\mathbb{D}$, or electric polarization $\mathbb{P}$. The first vari

Tristan Bice

We take the abstract basis approach to classical domain theory and extend it to quantitative domains. In doing so, we provide dual characterisations of distance domains (some new even in the classical case) as well as unifying and extending previous formal ball dualities, namely the Kostanek-Waszkiewicz and Romaguero-Valero theorems. In passing, we also char

Saeid Asgari Taghanaki, Kumar Abhishek, Ghassan Hamarneh

Over-parameterized deep models usually over-fit to a given training distribution, which makes them sensitive to small changes and out-of-distribution samples at inference time, leading to low generalization performance. To this end, several model-based and randomized data-dependent regularization methods are applied, such as data augmentation, which prevents

Michael P. Leung

This paper studies causal inference in randomized experiments under network interference. Commonly used models of interference posit that treatments assigned to alters beyond a certain network distance from the ego have no effect on the ego's response. However, this assumption is violated in common models of social interactions. We propose a substantially we

Jelena Grbić, Abigail Linton

As part of various obstruction theories, non-trivial Massey products have been studied in symplectic and complex geometry, commutative algebra and topology for a long time. We introduce a general approach to constructing non-trivial Massey products in the cohomology of moment-angle complexes, using homotopy theoretical and combinatorial methods. Our approach

Vincent Guigues, Alexander Shapiro, Yi Cheng

We investigate the dual of a Multistage Stochastic Linear Program (MSLP) to study two questions for this class of problems. The first of these questions is the study of the optimal value of the problem as a function of the involved parameters. For this sensitivity analysis problem, we provide formulas for the derivatives of the value function with respect to

Jennifer A. Loe, Ronald B. Morgan

We present a polynomial preconditioner for solving large systems of linear equations. The polynomial is derived from the minimum residual polynomial (the GMRES polynomial) and is more straightforward to compute and implement than many previous polynomial preconditioners. Our current implementation of this polynomial using its roots is naturally more stable t

Mateusz Borkowski

Optical Feshbach resonances (OFRs) allow one to control cold atomic scattering, produce ultracold molecules and study atomic interactions via photoassociation spectroscopy. In the limit of ultracold s-wave collisions the strength of an optical Feshbach resonance can be expressed via an energy-independent parameter called the optical length. Here we give full

Shuo Liu, Gil Keren, Björn Schuller

N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression. The functionalities are realised based on two neural network models sharing the same architecture, but trained separately. The models are comprised of stacks of residual blocks, each condit

Tiancheng Xu, Boyuan Tian, Yuhao Zhu

Machine perception applications are increasingly moving toward manipulating and processing 3D point cloud. This paper focuses on point cloud registration, a key primitive of 3D data processing widely used in high-level tasks such as odometry, simultaneous localization and mapping, and 3D reconstruction. As these applications are routinely deployed in energy-

Per Bäck

In this note, we introduce a notion of multi-parameter formal deformations of ternary hom-Nambu-Lie algebras. Within this framework, we construct formal deformations of the three-dimensional Jacobian determinant and of the cross-product in four-dimensional Euclidean space. We also conclude that the previously defined ternary q-Virasoro-Witt algebra is a form

Satish Ramakrishna

The act of measurement on a quantum state is supposed to "collapse" the state into one of several eigenstates of the operator corresponding to the observable being measured. This measurement process is sometimes described as outside standard quantum-mechanical evolution and not calculable from Schr\"odinger's equation. There are two general approaches to the

Xiaohui Bei, Zihao Li, Jinyan Liu, Shengxin Liu

We study the problem of fair division when the resources contain both divisible and indivisible goods. Classic fairness notions such as envy-freeness (EF) and envy-freeness up to one good (EF1) cannot be directly applied to the mixed goods setting. In this work, we propose a new fairness notion envy-freeness for mixed goods (EFM), which is a direct generaliz

Qitong Wang, Yi Zheng, Margrit Betke

Understanding the meaning of text in images of natural scenes like highway signs or store front emblems is particularly challenging if the text is foreshortened in the image or the letters are artistically distorted. We introduce a pipeline-based text spotting framework that can both detect and recognize text in various fonts, shapes, and orientations in nat

Miroslav Georgiev

Quantizing the transfer of energy and momentum between interacting particles, we obtain a quantum impulse equation and relations that the corresponding mechanical power, force and torque satisfy. In addition to the energy-frequency and momentum-wavelength relations, we introduce the power-frequency and force-wavelength analogs, respectively. Further, we obta

Robert Grupp, Mathias Unberath, Cong Gao, Rachel Hegeman

Fluoroscopy is the standard imaging modality used to guide hip surgery and is therefore a natural sensor for computer-assisted navigation. In order to efficiently solve the complex registration problems presented during navigation, human-assisted annotations of the intraoperative image are typically required. This manual initialization interferes with the su

Tomohiko Konno

We verified that the deep learning method named reading periodic table introduced by ref. Deep Learning Model for Finding New Superconductors, which utilizes deep learning to read the periodic table and the laws of the elements, is applicable not only for superconductors, for which the method was originally applied but also for other problems of materials by

Eric Amar

Let $S$ be a sequence of points in ${\mathbb{D}}^{n}.$ Suppose that $S$ is $H^{p}$ interpolating. Then we prove that the sequence $S$ is Carleson, provided that $p>2.$ We also give a sufficient condition, in terms of dual boundedness and Carleson measure, for $S$ to be an $H^{p}$ interpolating sequence.

M. Sánchez-Barquilla, R. E. F. Silva, J. Feist

Strong coupling of quantum emitters with confined electromagnetic modes of nanophotonic structures may be used to change optical, chemical and transport properties of materials, with significant theoretical effort invested towards a better understanding of this phenomenon. However, a full theoretical description of both matter and light is an extremely chall

Shishun Tian, Lu Zhang, Wenbin Zou, Xia Li

The Depth-Image-Based-Rendering (DIBR) is one of the main fundamental technique to generate new views in 3D video applications, such as Multi-View Videos (MVV), Free-Viewpoint Videos (FVV) and Virtual Reality (VR). However, the quality assessment of DIBR-synthesized views is quite different from the traditional 2D images/videos. In recent years, several effo

Tianyu Wang, Xiaowei Hu, Qiong Wang, Pheng-Ann Heng

Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, we design LISA, named after Light-guided

Zhihang Yuan, Bingzhe Wu, Zheng Liang, Shiwan Zhao

Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN). In contrast to static methods (e.g. weight pruning), dynamic inference adaptively adjusts the inference process according to each input sample, which can considerably reduce the computational cost on &#34;easy&#34; samples w

Mohammad Moltafet, Markus Leinonen, Marian Codreanu

Freshness of status update packets is essential for enabling services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of single-server multi-source queueing models under a first-come first-served (FCFS) serving policy. In the considered model, each source independently generates sta

Francesco Caravelli

In the present Letter we discuss the origin of the vertex population inversion observed experimentally in the mediated Artificial Square Ice. An interaction modifier is a disc-shaped magnetic nanoisland (a dot) which is placed at the center of a vertex to mediate the interaction between the nearby islands. We show that the inversion is of entropic origin, an

Jinmingwu Jiang, Kaigui Wu, Haiyang Liu, Ren Zhang

Cooperative path-finding in multi-agent systems demands scalable solutions to navigate agents from their origins to destinations without conflict. Despite the breadth of research, scalability remains hampered by increased computational demands in complex environments. This study introduces the multi-agent RRT* potential field (MA-RRT*PF), an innovative algor

Pietro Caputo, Matteo Quattropani

We study convergence to stationarity for random walks on dynamic random digraphs with given degree sequences. The digraphs undergo full regeneration at independent geometrically distributed random time intervals with parameter $α$. Relaxation to stationarity is the result of a competition between regeneration and mixing on the static digraph. When the number

Min Jin Chong, David Forsyth

This paper shows that two commonly used evaluation metrics for generative models, the Fréchet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected value of the score computed for a finite sample set is not the true value of the score. Worse, the paper shows that the bias term depends on the particular model being evaluated, so mo

Luca Fabbri

In a series of recent papers, we have introduced an object that was constructed on the connection but which was proven to be a tensor: this object, thus called tensorial connection, has been defined and some of its properties have been given. In the present paper, we intend to present all the results found so far, complementing them with some new ones, in a

Andreas Galanis, Leslie Ann Goldberg, Heng Guo, Kuan Yang

We give the first efficient algorithm to approximately count the number of solutions in the random $k$-SAT model when the density of the formula scales exponentially with $k$. The best previous counting algorithm for the permissive version of the model was due to Montanari and Shah and was based on the correlation decay method, which works up to densities $(

Chris Vavaliaris, Miguel Beneitez, Dan S. Henningson

Subcritical transition to turbulence in spatially developing boundary layer flows can be triggered efficiently by finite amplitude perturbations. In this work, we employ adjoint-based optimization to identify optimal initial perturbations in the Blasius boundary layer, culminating in the computation of the subcritical transition critical energy threshold and

Wenjun Yu, Xiangliang Kong, Yuanxiao Xi, Xiande Zhang

Let $\{(A_i,B_i)\}_{i=1}^{m}$ be a collection of pairs of sets with $|A_i|=a$ and $|B_i|=b$ for $1\leq i\leq m$. Suppose that $A_i\cap B_j=\emptyset$ if and only if $i=j$, then by the famous Bollob\'{a}s theorem, we have the size of this collection $m\leq {a+b\choose a}$. In this paper, we consider a variant of this problem by setting $\{A_i\}_{i=1}^{m}$ to

Thibaut Lemoine

We compute the large N limit of the partition function of the Euclidean Yang--Mills measure with structure group SU(N) or U(N) on all closed compact surfaces, orientable or not, excepted for the sphere and the projective plane. This limit is finite as opposed to the case of the sphere and presumably the projective plane. We expect that the results we present

Doried Ghader

Twistronics is currently one of the most active research fields in condensed matter physics, following the discovery of correlated insulating and superconducting phases in twisted bilayer graphene (tBLG). Here, we present a magnonic analogue of tBLG. We study magnons in twisted ferromagnetic bilayers (tFBL), including exchange and Dzyaloshinskii-Moriya inter

Maria Choufany, Davide Martinetti, Rachid Senoussi, Cindy E. Morris

The movement of atmospheric air masses can be seen as a continuous and complex flow of particles hovering over our planet. It can however be locally simplified by considering three-dimensional trajectories of air masses connecting distant areas of the globe during a given period of time. In this paper, we present a mathematical framework to construct spatial

Takashi Furuya

We study the direct and inverse scattering problem for the semilinear Schr\"{o}dinger equation $\Delta u+a(x,u)+k^2u=0$ in $\mathbb{R}^d$. We show well-posedness in the direct problem for small solutions based on the Banach fixed point theorem, and the solution has the certain asymptotic behavior at infinity. We also show the inverse problem that the semilin

Mohit Lal Bera, Maciej Lewenstein, Manabendra Nath Bera

A heat engine operating in the one-shot finite-size regime, where systems composed of a small number of quantum particles interact with hot and cold baths and are restricted to one-shot measurements, delivers fluctuating work. Further, engines with lesser fluctuation produce a lesser amount of deterministic work. Hence, the heat-to-work conversion efficiency

Richard Varro

We are interested in the evolution operators defined on commutative and nonassociative algebras when the scalar field is of characteristic 2. We distinguish four types: nilpotent, quasi-constant, ultimately periodic and plenary train operators. They are studied and classified for non baric and for baric algebras.

Anupama Unnikrishnan, Damian Markham

Authenticated teleportation aims to certify the transmission of a quantum state through teleportation, even in the presence of an adversary. This scenario can be pictured in terms of an untrusted source distributing a Bell state between two parties who wish to verify it using some simple tests. We propose a protocol that achieves this goal in a practical way

Morteza Raeisi, Florent Bonneu, Edith Gabriel

Because most natural phenomena exhibit dependence at multiple scales like locations of earthquakes or forest fire occurrences, spatio-temporal single-scale point process models are unrealistic in many applications. This motivates us to construct generalizations of classical Gibbs models. In this paper, we extend the Geyer saturation point process model to th

Lucas Böttcher

We study graphs that are formed by independently-positioned needles (i.e., line segments) in the unit square. To mathematically characterize the graph structure, we derive the probability that two line segments intersect and determine related quantities such as the distribution of intersections, given a certain number of line segments $N$. We interpret inter

Xiaowei Hu, Tianyu Wang, Chi-Wing Fu, Yitong Jiang

Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on various benchmark data, their performance is still limited for general real-world situations. In this work, we collected shadow images for multiple scenarios and compiled a new dat

Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Linxiao Yang

Self-supervised (SS) learning is a powerful approach for representation learning using unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) training. Specifically, SS tasks were proposed to address the catastrophic forgetting issue in the GAN discriminator. In this work, we perform an in-depth analysis to understand how SS t

Sneha Kamath, Jithin Ravi, Bikash Kumar Dey

The distributed coded caching problem has been studied extensively in the recent past. While the known coded caching schemes achieve an improved transmission rate, they violate the privacy of the users since in these schemes the demand of one user is revealed to others in the delivery phase. In this paper, we consider the coded caching problem under the cons

Geesara Prathap, Roman Fedorenko, Alexandr Klimchik

We propose a novel filter for segmenting the regions of interest from LiDAR 3D point cloud for multirotor aerial vehicles. It is specially targeted for real-time applications and works on sparse LiDAR point clouds without preliminary mapping. We use this filter as a crucial component of fast obstacle avoidance system for agriculture drone operating at low al

Hongwei Dong, Siyu Zhang, Bin Zou, Lamei Zhang

Convolutional neural networks (CNNs) have shown good performance in polarimetric synthetic aperture radar (PolSAR) image classification due to the automation of feature engineering. Excellent hand-crafted architectures of CNNs incorporated the wisdom of human experts, which is an important reason for CNN&#39;s success. However, the design of the architecture

Rundong Wang, Xu He, Runsheng Yu, Wei Qiu

We consider the problem of the limited-bandwidth communication for multi-agent reinforcement learning, where agents cooperate with the assistance of a communication protocol and a scheduler. The protocol and scheduler jointly determine which agent is communicating what message and to whom. Under the limited bandwidth constraint, a communication protocol is r

Zahra Haghani

In this paper, we will consider cosmological implications of the Maxwell theory coupled to a non-local $U(1)$-symmetric term. It is well-known that the theory in flat space time, reduces to the Proca theory. However, it will be shown that in curved space time the resulting theory will differ from the coupled Einstein-Proca system. The cosmological perturbati

Zi-Yu Tang, Bin Wang, Eleftherios Papantonopoulos

We consider Maxwell-f(R) gravity and obtain an exact charged black hole solution with dynamic curvature in D-dimensions. Considering a spherically symmetric metric ansatz and without specifying the form of f(R) we fnd a general black hole solution in D-dimensions. This general black hole solution can be reduced to the Reissner-Nordström (RN) black hole in D-

Syed Sabyel Haider, Mujeeb ur Rehman, Thabet Abdeljawad

In this article, a new definition of fractional Hilfer difference operator is introduced. Definition based properties are developed and utilized to construct fixed point operator for fractional order Hilfer difference equations with initial condition. We acquire some conditions for existence, uniqueness, Ulam-Hyers and Ulam-Hyers-Rassias stability. Modified

Hideo Bannai, Juha Kärkkäinen, Dominik Köppl, Marcin Picatkowski

The Burrows-Wheeler transform (BWT) is a permutation whose applications are prevalent in data compression and text indexing. The bijective BWT (BBWT) is a bijective variant of it. Although it is known that the BWT can be constructed in linear time for integer alphabets by using a linear time suffix array construction algorithm, it was up to now only conjectu

Hilmi E. Egilmez, Oguzhan Teke, Amir Said, Vadim Seregin

In many video coding systems, separable transforms (such as two-dimensional DCT-2) have been used to code block residual signals obtained after prediction. This paper proposes a parametric approach to build graph-based separable transforms (GBSTs) for video coding. Specifically, a GBST is derived from a pair of line graphs, whose weights are determined based

Andrey Filippov, Oleg Dzhimiev

Long Wave Infrared (LWIR) cameras provide images regardles of the ambient illumination, they tolerate fog and are not blinded by the incoming car headlights. These features make LWIR cameras attractive for autonomous navigation, security and military applications. Thermal images can be used similarly to the visible range ones, including 3D scene reconstructi

Vladimir N. Potapov

We obtain the following results. For any prime $q$ the minimal Hamming distance between distinct regular $q$-ary bent functions of $2n$ variables is equal to $q^n$. The number of $q$-ary regular bent functions at the distance $q^n$ from the quadratic bent function $Q_n=x_1x_2+\dots+x_{2n-1}x_{2n}$ is equal to $q^n(q^{n-1}+1)\cdots(q+1)(q-1)$ for $q>2$. The H

Ponugoti Nikhila, Sabari Nathan, Elmer Jeto Gomes Ataide, Alfredo Illanes

Ultrasound is a useful technique for diagnosing thyroid nodules. Benign and malignant nodules that automatically discriminate in the ultrasound pictures can provide diagnostic recommendations or, improve diagnostic accuracy in the absence of specialists. The main issue here is how to collect suitable features for this particular task. We suggest here a techn

Zhiqin Chen, Andrea Tagliasacchi, Hao Zhang

Polygonal meshes are ubiquitous in the digital 3D domain, yet they have only played a minor role in the deep learning revolution. Leading methods for learning generative models of shapes rely on implicit functions, and generate meshes only after expensive iso-surfacing routines. To overcome these challenges, we are inspired by a classical spatial data struct

Riashat Islam, Komal K. Teru, Deepak Sharma, Joelle Pineau

Off-policy deep reinforcement learning (RL) algorithms are incapable of learning solely from batch offline data without online interactions with the environment, due to the phenomenon known as \textit{extrapolation error}. This is often due to past data available in the replay buffer that may be quite different from the data distribution under the current po

Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali

There is growing interest in graph pattern mining (GPM) problems such as motif counting. GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. However, existing systems may take hours to mine even simple patterns in moderate-sized graphs, which significantly limit

Wenbin Li, Lei Wang, Xingxing Zhang, Lei Qi

This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem. This is because the commonly assumed sample-level distribution consistency between the

Komal K. Teru, Etienne Denis, William L. Hamilton

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations. However, these embedding-based methods do not explicitly capture the compositional logical rules underlying the knowledge graph, and they are limited to the transductive setting, where the f

Nazgol Tavabi, Homa Hosseinmardi, Jennifer L. Villatte, Andrés Abeliuk

Continuous collection of physiological data from wearable sensors enables temporal characterization of individual behaviors. Understanding the relation between an individual&#39;s behavioral patterns and psychological states can help identify strategies to improve quality of life. One challenge in analyzing physiological data is extracting the underlying beh

Frank Ban, David Woodruff, Qiuyi Zhang

The classical low rank approximation problem is to find a rank $k$ matrix $UV$ (where $U$ has $k$ columns and $V$ has $k$ rows) that minimizes the Frobenius norm of $A - UV$. Although this problem can be solved efficiently, we study an NP-hard variant of this problem that involves weights and regularization. A previous paper of [Razenshteyn et al. &#39;16] d