November 2019 arXiv papers — page 63

Sang Won Bae

In this paper, we study the problem of computing a minimum-width double-strip or parallelogram annulus that encloses a given set of $n$ points in the plane. A double-strip is a closed region in the plane whose boundary consists of four parallel lines and a parallelogram annulus is a closed region between two edge-parallel parallelograms. We present several f

Rajshekhar Vishweshwar Bhat, Rahul Vaze, Mehul Motani

In the emerging fifth generation (5G) technology, communication nodes are expected to support two crucial classes of information traffic, namely, the enhanced mobile broadband (eMBB) traffic with high data rate requirements, and ultra-reliable low-latency communications (URLLC) traffic with strict requirements on latency and reliability. The URLLC traffic, w

Yifan Zhang, Peilin Zhao, Shuaicheng Niu, Qingyao Wu

Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced. In practice, it is

Arman Arian, Ozgur Yilmaz

Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representation. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalized as multiplying the signal with a "measurement matrix". Most of matrices used in CS are random matrice

Z. Mahhouti, H. El Moussaoui, T. Mahfoud, M. Hamedoun

In this work, a successful synthesis of magnetic cobalt ferrite (CoFe2O4) nanoparticles is presented. The synthesized CoFe2O4 nanoparticles have a spherical shape and highly monodisperse in the selected solvent. The effect of different reaction conditions such as temperature, reaction time and varying capping agents on the phase and morphology is studied. Sc

Magnus Aa. Gjennestad, Mathias Winkler, Alex Hansen

We perform more than 6000 steady-state simulations with a dynamic pore network model, corresponding to a large span in viscosity ratios and capillary numbers. From these simulations, dimensionless quantities such as relative permeabilities, residual saturations, mobility ratios and fractional flows are computed. Relative permeabilities and residual saturatio

Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu

Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for regularization. While deep transfer learning can usually boost the performance with better accuracy and faster convergence, transferring weights from inappropriate networks hurts t

Nikolay G. Moshchevitin, Ilya D. Shkredov

We prove that for any prime $p$ there is a divisible by $p$ number $q = O(p^{30})$ such that for a certain positive integer $a$ coprime with $q$ the ratio $a/q$ has bounded partial quotients. In the other direction we show that there is an absolute constant $C>0$ such that for any prime $p$ exist divisible by $p$ number $q = O(p^{C})$ and a number $a$, $a$ c

Mikhail Fomichev, Max Maass, Matthias Hollick

Reproducibility and realistic datasets are crucial for advancing research. Unfortunately, they are often neglected as valid scientific contributions in many young disciplines, with computer science being no exception. In this article, we show the challenges encountered when reproducing the work of others, collecting realistic data in the wild, and ensuring t

Nello Blaser, Morten Brun

The alpha complex efficiently computes persistent homology of a point cloud $X$ in Euclidean space when the dimension $d$ is low. Given a subset $A$ of $X$, relative persistent homology can be computed as the persistent homology of the relative Čech complex. But this is not computationally feasible for larger point clouds. The aim of this note is to present

Fengzhuo Zhang, Kai Gu, Yuan Shen

Accurate location information is indispensable for the emerging applications of \ac{iov}, such as automatic driving and formation control. In the real scenario, vision-based localization has demonstrated superior performance to other localization methods for its stability and flexibility. In this paper, a scheme of cooperative vision-based localization with

Heewon Kim, Seokil Hong, Bohyung Han, Heesoo Myeong

We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even generate compositional feature maps using several different base operations. FGNAS runs efficiently in spite of significantly large search space compared to other methods because it tr

Kazunori Kodaka

Let $A$ and $B$ be $σ$-unital $C^*$-algebras and $X$ and $Y$ an $A-A$-equivalence bimodule and a $B-B$-equivalence bimodule, respectively. Also, let $A\rtimes_X \mathbb{Z}$ and $B\rtimes_Y \mathbb{Z}$ be the crossed products of $A$ and $B$ by $X$ and $Y$, respectively. Furthermore, let $A\subset A\rtimes_X \mathbb{Z}$ and $B\subset B\rtimes_Y \mathbb{Z}$ be

Shulin Qin, Gengsheng Wang, Huaiqiang Yu

In this paper, we present some properties of time optimal controls for linear ODEs with the ball-type control constraint. More precisely, for an optimal control, we build up an upper bound for the number of its switching points; show that it jumps from one direction to the reverse direction at each switching point; give its dynamic behaviour between two cons

Mohamed Vall

This paper deals with exact formulas for the resolvent kernels and exact solutions of time dependent Schrödinger equations under a uniform magnetic field on the hyperbolic half plane $\H$, and under a diatomic molecular Morse potential on the real line $\R$.

Wu Shi, Tak-Wai Hui, Ziwei Liu, Dahua Lin

Existing unconditional generative models mainly focus on modeling general objects, such as faces and indoor scenes. Fashion textures, another important type of visual elements around us, have not been extensively studied. In this work, we propose an effective generative model for fashion textures and also comprehensively investigate the key components involv

Hua Lan, Jirong Ma, Zengfu Wang, Quan Pan

This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP)

Yu Nakahata, Jun Kawahara, Takashi Horiyama, Shin-ichi Minato

Given graphs $G$ and $H$, we propose a method to implicitly enumerate topological-minor-embeddings of $H$ in $G$ using decision diagrams. We show a useful application of our method to enumerating subgraphs characterized by forbidden topological minors, that is, planar, outerplanar, series-parallel, and cactus subgraphs. Computational experiments show that ou

Zhibin Gao, Mengyang Li, Jian-Sheng Wang

We report a previously unknown monolayer borophene allotrope and we call it super-B with a flat structure based on the ab initio calculations. It has good thermal, dynamical, and mechanical stability compared with many other typical borophenes. We find that super-B has a fascinating chemical bond environment consisting of standard sp, sp2 hybridizations, and

Jun Guo, Philipp Walk, Hamid Jafarkhani

To provide a reliable wireless uplink for users in a given ground area, one can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another application, one can use UAVs to collect data from sensors on the ground. For a power-efficient and scalable deployment of such flying BSs, directional antennas can be utilized to efficiently cover arbitrar

Tatsuya Yanagisawa, Hiroyuki Hidaka, Hiroshi Amitsuka, Sergei Zherlitsyn

Ultrasonic investigations of the single-site quadrupolar Kondo effect in diluted Pr system Y$_{0.966}$Pr$_{0.034}$Ir$_2$Zn$_{20}$ are reported. The elastic constant $(C_{11}-C_{12})/2$ is measured down to ~40 mK using ultrasound for the dilute system Y$_{0.966}$Pr$_{0.034}$Ir$_2$Zn$_{20}$ and the pure compound YIr$_2$Zn$_{20}$. We found that the elastic cons

Aleksandar Haber

We use Machine Learning (ML) and system identification validation approaches to estimate neural network models of large-scale Deformable Mirrors (DMs) used in Adaptive Optics (AO) systems. To obtain the training, validation, and test data sets, we simulate a realistic large-scale Finite Element (FE) model of a faceplate DM. The estimated models reproduce the

S. K. Padhi, M. Ghanashyam Krishna

In this review, room temperature (RT) precipitation of the nanocrystalline-ceria (nc-ceria) re-dispersed and subsequently size-reduced by 20 kHz probe sonication in 25 % ethylene glycol/ 75 % DI-water mixed media is investigated. The sonication result in three nanostructured products: (1) water-soluble supernatant nc-ceria (Ce_Sl@RT), (2) settled gelatinous

Jingshi Cui, Haoxiang Wang, Chenye Wu, Yang Yu

Data analytics and machine learning techniques are being rapidly adopted into the power system, including power system control as well as electricity market design. In this paper, from an adversarial machine learning point of view, we examine the vulnerability of data-driven electricity market design. More precisely, we follow the idea that consumer's lo

Chih-Wei Huang, Po-Chen Chen

With the explosive growth in demand for mobile traffic, one of the promising solutions is to offload cellular traffic to small base stations for better system efficiency. Due to increasing system complexity, network operators are facing severe challenges and looking for machine learning-based solutions. In this work, we propose an energy-aware mobile traffic

Suzuna Shibasaki, Masaki Inoue, Mitsuru Arahata, Vijay Gupta

This paper is devoted to a consumer-preferred community-level energy management system (CEMS), in which a system manager allows consumers their selfish decisions of power-saving while regulating the overall demand-supply imbalance. The key structure of the system is to weakly control consumers: the controller sends the allowable range of the power-saving amo

Bing Wang, Klaus Mueller

We test the hypothesis whether transforming a data matrix into a 3D shaded surface or even a volumetric display can be more appealing to humans than a scatterplot since it makes direct use of the innate 3D scene understanding capabilities of the human visual system. We also test whether 3D shaded displays can add a significant amount of information to the vi

Cong Hao, Yao Chen, Xinheng Liu, Atif Sarwari

The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-efficiency implementations. In this position paper, we argue that a simultaneous DNN/implementation co-design methodology, named Neural Architecture and Implementation Search (NAIS

Parthapratim Biswas, Durga Paudel, Raymond Atta-Fynn, Stephen R. Elliott

The paper presents an $ab$ $initio$ study of temperature-induced nanostructural evolution of hydrogen-rich voids in amorphous silicon. By using large $a$-Si models, obtained from classical molecular-dynamics simulations, with a realistic void-volume density of 0.2%, the dynamics of Si and H atoms on the surface of the nanometer-size cavities were studied and

Sergey V. Bogovalov, Dmitry Khangulyan, Alexander V. Koldoba, Galina V. Ustyugova

We present a numerical study of the properties of the flow produced by the collision of a magnetized anisotropic pulsar wind with the circumbinary environment. We focus on studying the impact of the high wind magnetization on the geometrical structure of the shocked flow. This work is an extension of our earlier studies that focused on a purely hydrodynamic

Chong Sun, Ushnish Ray, Zhi-Hao Cui, Miles Stoudenmire

We describe a formulation of the density matrix embedding theory at finite temperature. We present a generalization of the ground-state bath orbital construction that embeds a mean-field finite-temperature density matrix up to a given order in the Hamiltonian, or the Hamiltonian up to a given order in the density matrix. We assess the performance of the fini

Dmitry Khangulyan, Masanori Arakawa, Felix Aharonian

The Crab Nebula is an extreme particle accelerator boosting the energy of electrons up to a few PeV ($10^{15} \ \rm eV$), close to the maximum energy allowed by theory. The physical conditions in the acceleration site and the nature of the acceleration process itself remain highly uncertain. The key information about the highest energy accelerated particles

Hengyue Li, Junya Otsuki, Makoto Naka, Sumio Ishihara

Optical responses in an excitonic insulating (EI) system with strong electron correlation are studied. We adopt the two-orbital Hubbard model with a finite energy difference between the two orbitals where the spin state degree of freedom exists. This model is analyzed by the variational cluster approach. In order to include the local electron correlation eff

Jiawei Hou, Haofei Kuang, Sören Schwertfeger

We present a novel area matching algorithm for merging two different 2D grid maps. There are many approaches to address this problem, nevertheless, most previous work is built on some assumptions, such as rigid transformation, or similar scale and modalities of two maps. In this work we propose a 2D map matching algorithm based on area segmentation. We trans

Hongliang Lu, Xingxing Yu, Xiaofan Yuan

In this paper, we study degree conditions for the existence of large matchings in uniform hypergraphs. We prove that for integers $k,l,n$ with $k\ge 3$, $k/2<l<k$, and $n$ large, if $H$ is a $k$-uniform hypergraph on $n$ vertices and $δ_{l}(H)>{n-l\choose k-l}-{(n-l)-(\lceil n/k \rceil-2)\choose 2}$, then $H$ has a matching covering all but a constant number

Neil D. Barrie, Shao-Feng Ge, Tsutomu T. Yanagida

The democratic mass matrix is an intriguing possibility for explaining the observed fermion mixings due to its inherent hierarchical mass eigenvalues and large mixing angles. Nevertheless, two of the three mass eigenvalues are zero if the flavor democracy is exact, in obvious contradiction with the experimental observations. One possibility is breaking the f

Yahui Liu, Furao Shen, Jian Zhao

Context-aware recommender systems (CARS), which consider rich side information to improve recommendation performance, have caught more and more attention in both academia and industry. How to predict user preferences from diverse contextual features is the core of CARS. Several recent models pay attention to user behaviors and use specifically designed struc

Arman Arian, Ozgur Yilmaz

Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalized as multiplying the signal with a &#34;measurement matrix&#34;. If the measurement satisfies the so-called re

Kai Hu, Barnabas Poczos

In this paper, we propose a novel regularization method, RotationOut, for neural networks. Different from Dropout that handles each neuron/channel independently, RotationOut regards its input layer as an entire vector and introduces regularization by randomly rotating the vector. RotationOut can also be used in convolutional layers and recurrent layers with

Boris Pittel

Consider a cyclically ordered collection of $r$ equinumerous agent sets with strict preferences of every agent over the agents from the next agent set. A weakly stable cyclic matching is a partition of the set of agents into disjoint union of $r$-long cycles, one agent from each set per cycle, such that there are no destabilizing $r$-long cycles, i.e. cycles

Paul Dupuis, Yixiang Mao

This paper develops a new divergence that generalizes relative entropy and can be used to compare probability measures without a requirement of absolute continuity. We establish properties of the divergence, and in particular derive and exploit a representation as an infimum convolution of optimal transport cost and relative entropy. Also included are exampl

Ignavier Ng, Shengyu Zhu, Zhitang Chen, Zhuangyan Fang

Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees. Recently, a new approach has transformed the combinatorial structure learning problem into a continuous one and then solved it using gradient-based optimization

Dian Ang Yap, Nicholas Roberts, Vinay Uday Prabhu

Kernel sparsity (&#34;dying ReLUs&#34;) and lack of diversity are commonly observed in CNN kernels, which decreases model capacity. Drawing inspiration from information theory and wireless communications, we demonstrate the intersection of coding theory and deep learning through the Grassmannian subspace packing problem in CNNs. We propose Grassmannian packi

Michael Whitmeyer, Jonathan Liu

This report will be a literature review on a result in algorithmic discrepancy theory. We will begin by providing a quick overview on discrepancy theory and some major results in the field, and then focus on an important result by Shachar Lovett and Raghu Meka. We restate the main algorithm and ideas of the paper, and rewrite proofs for some of the major res

Jonathan Reiner, Abhay Kumar Nayak, Amit Tulchinsky, Aviram Steinbok

Following significant progress in the visualization and characterization of Majorana end modes in hybrid systems of semiconducting nanowires and superconducting islands, much attention is devoted to the investigation of the electronic structure at the buried interface between the semiconductor and the superconductor. The properties of that interface and the

Dongwon Park, Dong Un Kang, Jisoo Kim, Se Young Chun

Multi-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales. MS approaches have been effective especially for severe blurs induced by large motions in high spatial scale since thos

Andrea Boskovic, Qinyi Chen, Dominik Kufel, Zijie Zhou

In order for an e-commerce platform to maximize its revenue, it must recommend customers items they are most likely to purchase. However, the company often has business constraints on these items, such as the number of each item in stock. In this work, our goal is to recommend items to users as they arrive on a webpage sequentially, in an online manner, in o

Zhijie Dong, Haitao Ma

We study the convolution algebra $H_{*}(Z^θ_{W})$ of homology on diagram automorphism fixed point quiver variety and prove that there exists an algebra homomorphism from the universal enveloping algebra of the diagram automorphism fixed algebra of the split quiver to $H_{*}(Z^θ_{W})$.

Qiang Huang, Jianhui Bu, Weijian Xie, Shengwen Yang

Question Answering (QA) systems are used to provide proper responses to users&#39; questions automatically. Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem. Given a question, the aim of the task is to find the most similar question from a QA knowledge base. In this paper, we pro

Zhipeng Gao, Yuhao Wang, Xiaodong Liu, Fuhui Zhou

Channel estimation is of crucial importance in massive multiple-input multiple-output (m-MIMO) visible light communication (VLC) systems. In order to tackle this problem, a fast and flexible denoising convolutional neural network (FFDNet)-based channel estimation scheme for m-MIMO VLC systems was proposed. The channel matrix of the m-MIMO VLC channel is iden

Jiangchao Shi, Xiaobo Yang, Jin Hu

Based on transformation optics, a strategy is proposed to expose the inner one-dimensional space of a wave field inside a beam volume to the surface of the propagation medium and extend the space from one-dimensional to two-dimensional, allowing the corresponding field distribution to be detected directly and more subtly, which is important in optical signal

Xijie Huang, Moustafa Alzantot, Mani Srivastava

Deep neural networks have achieved state-of-the-art performance on various tasks. However, lack of interpretability and transparency makes it easier for malicious attackers to inject trojan backdoor into the neural networks, which will make the model behave abnormally when a backdoor sample with a specific trigger is input. In this paper, we propose NeuronIn

Tatsuhiro Misumi, Toshiaki Fujimori, Etsuko Itou, Muneto Nitta

We report the results of the lattice simulation of the ${\mathbb C} P^{N-1}$ sigma model on $S_{s}^{1}$(large) $\times$ $S_τ^{1}$(small). We take a sufficiently large ratio of the circumferences to approximate the model on ${\mathbb R} \times S^1$. For periodic boundary condition imposed in the $S_τ^{1}$ direction, we show that the expectation value of the P

Jun-Feng Hou, Zhi Xu, Shu Yuan, Yu-Chao Chen

This paper is devoted to the primary spectro-polarimetric observation performed at the New Vacuum Solar Telescope of China since 2017, and our aim is to precisely evaluate the real polarimetric accuracy and sensitivity of this polarimetry by using full Stokes spectro-polarimetric observations of the photospheric line Fe I 532.4 nm. In the work, we briefly de

Guosheng Jiang, Zhehui Wang, Jintian Zhu

For $n\geq2,$ we obtain Liouville type theorems for minimal surface equations in half space $\mathbf R^n_+$ with affine Dirichlet boundary value or constant Neumann boundary value.

Hao Chen, Hesham A Rakha

This paper develops an automatic freeway bottleneck identification and visualization algorithm using a combination of image processing techniques and traffic flow theory. Unlike previous studies that are based solely on loop detector data, the proposed method can use traffic measurements from various sensing technologies. Four steps are included in the propo

Suda Bharadwaj, Louis Ly, Bo Wu, Richard Tsai

This paper studies a two-player game with a quantitative surveillance requirement on an adversarial target moving in a discrete state space and a secondary objective to maximize short-term visibility of the environment. We impose the surveillance requirement as a temporal logic constraint.We then use a greedy approach to determine vantage points that optimiz

Baishakhi Ray, Prem Devanbu, Vladimir Filkov

Berger et al., published in TOPLAS 2019, is a critique of our 2014 FSE conference abstract and its archival version, the 2017 CACM paper: A Large-Scale Study of Programming Languages and Code Quality in Github. In their paper Berger et al. make academic claims about the veracity of our work. Here, we respond to their technical and scientific critiques aimed

Jiu-Gang Dong, Seung-Yeal Ha, Jinwook Jung, Doheon Kim

We present an emergent stochastic flocking dynamics of the Cucker-Smale (CS) ensemble under randomly switching topologies. The evolution of the CS ensemble with randomly switching topologies involves two random components (switching times and choices of network topologies at switching instant). First, we allow switching times for the network topology to be r

Armin Najarpour Foroushani, Sujaya Neupane, Pablo De Heredia Pastor, Christopher C. Pack

A main challenge for the development of cortical visual prostheses is to spatially localize individual spots of light, called phosphenes, by assigning appropriate stimulating parameters to implanted electrodes. Imitating the natural responses to phosphene-like stimuli at different positions can help in designing a systematic procedure to determine these para

Ren Li, Changjiang Cai, Georgios Georgakis, Srikrishna Karanam

We consider the problem of human pose estimation. While much recent work has focused on the RGB domain, these techniques are inherently under-constrained since there can be many 3D configurations that explain the same 2D projection. To this end, we propose a new method that uses RGB-D data to estimate a parametric human mesh model. Our key innovations includ

Elif Surer, Mustafa Erkayaoğlu, Zeynep Nur Öztürk, Furkan Yücel

Emergency training and planning provide structured curricula, rule-based action items, and interdisciplinary collaborative entities to imitate and teach real-life tasks. This rule-based structure enables the curricula to be transferred into other systematic learning platforms such as serious games ---games that have additional purposes rather than only enter

Yayun Fu, Wenjun Cai, Yushun Wang

The main objective of this paper is to present an efficient structure-preserving scheme, which is based on the idea of the scalar auxiliary variable approach, for solving the space fractional nonlinear Schrödinger equation. First, we reformulate the equation as a Hamiltonian system, and obtain a new equivalent system via introducing a scalar variable. Then,

Guy Blanc, Jane Lange, Li-Yang Tan

Consider the following heuristic for building a decision tree for a function $f : \{0,1\}^n \to \{\pm 1\}$. Place the most influential variable $x_i$ of $f$ at the root, and recurse on the subfunctions $f_{x_i=0}$ and $f_{x_i=1}$ on the left and right subtrees respectively; terminate once the tree is an $\varepsilon$-approximation of $f$. We analyze the qual

Jingze Li, Xiantao Li

We present a class of exponential integrators to compute solutions of the stochastic Schrödinger equation arising from the modeling of open quantum systems. In order to be able to implement the methods within the same framework as the deterministic counterpart, we express the solution using the Kunita&#39;s representation. With appropriate truncations, the s

James East

We give a thorough structural analysis of the principal one-sided ideals of arbitrary semigroups, and then apply this to full transformation semigroups and symmetric inverse monoids. One-sided ideals of these semigroups naturally occur as semigroups of transformations with restricted range or kernel.

Bora Uyumazturk, Amirhossein Kiani, Pranav Rajpurkar, Alex Wang

While artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, the question of how best to incorporate these algorithms into clinical workflows remains relatively unexplored. We investigated how AI can affect pathologist performance on the task of differentiating between two subtypes of primary liver cancer,

Elisa G. M. Ferreira

In this talk I present a new model of a unified dark sector, where late-time cosmic acceleration emerges from the dark matter (DM) superfluid framework. We will start by reviewing the dark matter superfluid model and show how it describes the dynamics of DM in large and small scales. Then we will show that if the superfluid consists of a mixture of two disti

Lia X. Harrington, Jason W. Wei, Arief A. Suriawinata, Todd A. Mackenzie

Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence. We used natural language processing to extract polyp morphological characteristics from 953 polyp-presenting patients&#39; electronic medical records. We used subsequent colonoscopy r

Jianyu Wang, Anit Kumar Sahu, Zhouyi Yang, Gauri Joshi

This paper studies the problem of error-runtime trade-off, typically encountered in decentralized training based on stochastic gradient descent (SGD) using a given network. While a denser (sparser) network topology results in faster (slower) error convergence in terms of iterations, it incurs more (less) communication time/delay per iteration. In this paper,

Rui Yan, Zongying Shi, Yisheng Zhong

This paper considers a subspace guarding game in high-dimensional space which consists of a play subspace and a target subspace. Two faster defenders cooperate to protect the target subspace by capturing an attacker which strives to enter the target subspace from the play subspace without being captured. A closed-form solution is provided from the perspectiv

Qian Wang, Khalid N. Ismail, Toby P. Breckon

The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towa

José Guillermo Méndez-Bermúdez, Iván Guillén-Escamilla, Juan Carlos Mixteco-Sánchez, Gloria Arlette Méndez-Maldonado

We performed extensive molecular dynamics simulations to obtain pressure-density phase diagram, orientational order parameter, pair correlation functions and translational diffusion coefficients of Gay-Berne fluids. Different sets of parameters were employed for the Gay-Berne potential, in particular we studied the cases $κ&#39;=5, 10, 15, 20$ and $κ=3$, $μ=

Ioann Vasilyev

In this article we prove a multidimensional version of the Nazarov lemma. The proof is based on an appropriate generalisation of the regularised system of intervals introduced by Havin, Nazarov and Mashreghi to several dimensions.

Eduard Gorbunov, Darina Dvinskikh, Alexander Gasnikov

We consider stochastic convex optimization problems with affine constraints and develop several methods using either primal or dual approach to solve it. In the primal case, we use a special penalization technique to make the initial problem more convenient for using optimization methods. We propose algorithms to solve it based on Similar Triangles Method wi

Fernando Fantino, Juan Hidalgo, Adriana Mejia Castano, Carla Morschbacher

We calculate all irreducible representations over a subfamily of pointed Hopf algebras with group-likes the dihedral group analyzing the possible decompositions of the restriction to the dihedral group and calculating the Jacobson radical of the Hopf algebra

Joseph Lorenzetti, Marco Pavone

The control of constrained systems using model predictive control (MPC) becomes more challenging when full state information is not available and when the nominal system model and measurements are corrupted by noise. Since these conditions are often seen in practical scenarios, techniques such as robust output feedback MPC have been developed to address them

Clément L. Canonne, Xi Chen, Gautam Kamath, Amit Levi

We give a nearly-optimal algorithm for testing uniformity of distributions supported on $\{-1,1\}^n$, which makes $\tilde O (\sqrt{n}/\varepsilon^2)$ queries to a subcube conditional sampling oracle (Bhattacharyya and Chakraborty (2018)). The key technical component is a natural notion of random restriction for distributions on $\{-1,1\}^n$, and a quantitati

Anastasiya Ivanova, Fedor Stonyakin, Dmitry Pasechnyuk, Evgeniya Vorontsova

Network utility maximization is the most important problem in network traffic management. Given the growth of modern communication networks, we consider the utility maximization problem in a network with a large number of connections (links) that are used by a huge number of users. To solve this problem an adaptive mirror descent algorithm for many constrain

Alex Kokot, Charles Johnson

We consider path-connected sets of matrices and the induced paths between eigenvalues. We discuss the equivalence relation generated by these paths, and how it relates to the presence of higher multiplicity eigenvalues realized by the set. Particular interest is applied to the convex hull of matrices, where additional characterizations are provided of this p

Domagoj Bradac, Anupam Gupta, Sahil Singla, Goran Zuzic

In classical secretary problems, a sequence of $n$ elements arrive in a uniformly random order, and we want to choose a single item, or a set of size $K$. The random order model allows us to escape from the strong lower bounds for the adversarial order setting, and excellent algorithms are known in this setting. However, one worrying aspect of these results

Mengmi Zhang, Claire Tseng, Gabriel Kreiman

Context plays an important role in visual recognition. Recent studies have shown that visual recognition networks can be fooled by placing objects in inconsistent contexts (e.g., a cow in the ocean). To model the role of contextual information in visual recognition, we systematically investigated ten critical properties of where, when, and how context modula

Tyler Ard, Robert Austin Dollar, Ardalan Vahidi, Yaozhong Zhang

This paper studies the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the anticipative mode, more immediately beneficial when automated vehicle fleet penetration is low, and 2. the connected mode, beneficial in coordinated car-following scenarios

Haichao Yu, Haoxiang Li, Honghui Shi, Thomas S. Huang

We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-widths, by truncating the least significant bits, to support dynamic speed and accuracy trade-off. When all l

Malik Matwi

The canonical formulation of general relativity is based on decomposition space--time manifold $M$ into $ R\times \Sigma$, this decomposition has to preserve the invariance of general relativity, invariance under general coordinates, and local Lorentz transformations. These symmetries associate with conserved currents that are coupled to gravity. In this pap

Ivan Kostov

We construct an effective Quantum Field Theory for the wrapping effects in 1+1 dimensional models of factorised scattering. The recently developed graph-theoretical approach to TBA gives the perturbative desctiption of this QFT. For the sake of simplicity we limit ourselves to scattering matrices for a single neutral particle and no bound state poles, such a

Gregory W. Horndeski

In this paper I shall show how notions of Finsler geometry can be used to construct a new type of geometry using a scalar field, f, on the cotangent bundle of a differentiable manifold, M. This new geometry will be called Lorentzian Cofinsler geometry. This geometry will enable me to use the second vertical derivatives of f, along with the differential of th

Sina Masnadi, Joseph J. LaViola, Xiaofan Zhu, Karthik Desingh

In this paper, we present an easy to use sketch-based interface to extract geometries and generate affordance files from 3D point clouds for robot-object interaction tasks. Using our system, even novice users can perform robot task planning by employing such sketch tools. Our focus in this paper is employing human-in-the-loop approach to assist in the genera

Zhou Fang, Arjan van der Schaft, Chuanhou Gao

In this paper, we provide a graphic formulation of non-isothermal reaction systems and show that a non-isothermal detailed balanced network system converges (locally) asymptotically to the unique equilibrium within the invariant manifold determined by the initial condition. To model thermal effects, the proposed modeling approach extends the classical chemic

Cole Hugelmeyer

We prove that for every smooth Jordan curve $\gamma$, if $X$ is the set of all $r \in [0,1]$ so that there is an inscribed rectangle in $\gamma$ of aspect ratio $\tan(r\cdot \pi/4)$, then the Lebesgue measure of $X$ is at least $1/3$. To do this, we study sets of disjoint homologically nontrivial projective planes smoothly embedded in $\mathbb{R}\times \math

Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan, Rheeya Uppaal

Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks. However, they exhibit several weaknesses in practice, including (a) inability to use uncertainty sampling with black-box models, (b) lack of robustness to labeling noise, and (c) lack of transparency. In response, we propose a transparent bat

Nishanth Sasankan, Atul Kedia, Motohiko Kusakabe, Grant J Mathews

Big-bang nucleosynthesis (BBN) is valuable as a means to constrain the physics of the early universe and it is the only probe of the radiation-dominated epoch. A fundamental assumption in BBN is that the nuclear velocity distributions obey Maxwell-Boltzmann (MB) statistics as they do in stars. Specifically, the BBN epoch is characterized by a dilute baryon p

Florentin Smarandache

In this paper we prove that Neutrosophic Set (NS) is an extension of Intuitionistic Fuzzy Set (IFS) no matter if the sum of single-valued neutrosophic components is < 1, or > 1, or = 1. For the case when the sum of components is 1 (as in IFS), after applying the neutrosophic aggregation operators one gets a different result from that of applying the intuitio

Shriya Soma, Debades Bandyopadhyay

We investigate gross properties of binary components and remnant in GW170817 using equations of state within the finite temperature field theoretical models. We also adopt finite temperature equations of state in the density dependent hadron field theory for this study. Properties of binary components are studied using zero temperature equations of state. Pa

Domenico Giuliano, Luca Lepori, Andrea Nava

We propose that a pertinently engineered double superconducting island connected to two spinless one-dimensional conducting leads can work as a tunable (iso)spin Kondo and charge Kondo system, with the lead index regarded as an effective isospin degree of freedom. We evidence how, by tuning a single gate voltage applied to the island, it is possible to make

A. Skopenkov

A 2018 paper by A. Levine and T. Lidman outlines a proof of the following interesting result in topology of manifolds: there is a compact smooth 4-manifold $W$ with boundary such that $W$ is homotopy equivalent to $S^2$ but there does not exist an embedding $S^2\to W$ which is a homotopy equivalence and is simplicial for some triangulations of $W$ and of $S^

Giuseppe C. Calafiore, Giulia Fracastoro

The nearest-centroid classifier is a simple linear-time classifier based on computing the centroids of the data classes in the training phase, and then assigning a new datum to the class corresponding to its nearest centroid. Thanks to its very low computational cost, the nearest-centroid classifier is still widely used in machine learning, despite the devel

Özlem Tugfe Demir, Emil Björnson

This paper considers the joint impact of non-linear hardware impairments at the base station (BS) and user equipments (UEs) on the uplink performance of single-cell massive MIMO (multiple-input multiple-output) in practical Rician fading environments. First, Bussgang decomposition-based effective channels and distortion characteristics are analytically deriv

Mark Gross, Valentino Tosatti, Yuguang Zhang

We prove that the twisted Kahler-Einstein metrics that arise on the base of certain holomorphic fiber space with Calabi-Yau fibers have conical-type singularities along the discriminant locus. These fiber spaces arise naturally when studying the collapsing of Ricci-flat Kahler metrics on Calabi-Yau manifolds, and of the Kahler-Ricci flow on compact Kahler ma

Haotian Gu, Xin Guo, Xiaoli Wei, Renyuan Xu

Dynamic programming principle (DPP) is fundamental for control and optimization, including Markov decision problems (MDPs), reinforcement learning (RL), and more recently mean-field controls (MFCs). However, in the learning framework of MFCs, DPP has not been rigorously established, despite its critical importance for algorithm designs. In this paper, we fir