November 2019 arXiv papers — page 64

José Nuno S. Gomes, Carlos Trallero-Giner, Nuno M. R. Peres, Mikhail I. Vasilevskiy

We describe exciton-polariton modes formed by the interaction between excitons in a 2D layer of a transition metal dichalcogenide embedded in a cylindrical microcavity and the microcavity photons. For this, an expression for the excitonic susceptibility of a semiconductor disk placed in the symmetry plane perpendicular to the axis of the microcavity is deriv

Tsu-Jui Fu, Xin Eric Wang, Matthew Peterson, Scott Grafton

Vision-and-Language Navigation (VLN) is a task where agents must decide how to move through a 3D environment to reach a goal by grounding natural language instructions to the visual surroundings. One of the problems of the VLN task is data scarcity since it is difficult to collect enough navigation paths with human-annotated instructions for interactive envi

Simon Apers, Ronald de Wolf

Graph sparsification underlies a large number of algorithms, ranging from approximation algorithms for cut problems to solvers for linear systems in the graph Laplacian. In its strongest form, "spectral sparsification" reduces the number of edges to near-linear in the number of nodes, while approximately preserving the cut and spectral structure of the graph

Dirk Erhard, Tertuliano Franco, Guilherme Reis

We define here a \textit{directed edge reinforced random walk} on a connected locally finite graph. As the name suggests, this walk keeps track of its past, and gives a bias towards directed edges previously crossed proportional to the exponential of the number of crossings. The model is inspired by the so called \textit{Ant Mill phenomenon}, in which a grou

Maya Bechler-Speicher

A family of closed simple (i.e., Jordan) curves is $m$-intersecting if any pair of its curves have at most $m$ points of common intersection. We say that a pair of such curves touch if they intersect at a single point of common tangency. In this work we show that any $m$-intersecting family of $n$ Jordan curves in general position in the plane contains $O\le

Zhehui Chen, Simon Mak, C. F. Jeff Wu

The Expected Improvement (EI) method, proposed by Jones et al. (1998), is a widely-used Bayesian optimization method, which makes use of a fitted Gaussian process model for efficient black-box optimization. However, one key drawback of EI is that it is overly greedy in exploiting the fitted Gaussian process model for optimization, which results in suboptimal

Santiago Capriotti, Jordi Gaset, Narciso Román-Roy, Leandro Salomone

This work is mainly devoted to constructing a multisymplectic description of Lovelock's gravity, which is an extension of General Relativity. We establish a Griffiths variational problem for the Lovelock Lagrangian, obtaining the geometric form of the corresponding field equations. We give the unified Lagrangian--Hamiltonian formulation of this model and

Nail Akar, Ozancan Dogan, Eray Unsal Atay

Bufferless and single-buffer queueing systems have recently been shown to be effective in coping with escalated Age of Information (AoI) figures arising in single-source status update systems with large buffers and FCFS scheduling. In this paper, for the single-source scenario, we propose a numerical algorithm for obtaining the exact distributions of both th

Zhibo Wang, Shen Yan, Xiaoyu Zhang, Niels Lobo

(Very early draft)Traditional supervised learning keeps pushing convolution neural network(CNN) achieving state-of-art performance. However, lack of large-scale annotation data is always a big problem due to the high cost of it, even ImageNet dataset is over-fitted by complex models now. The success of unsupervised learning method represented by the Bert mod

Leonard Hardiman

Let $\mathcal{C}$ be a spherical fusion category. The goal of this article is to present the tube category of $\mathcal{C}$, denoted $\mathcal{TC}$, as giving an alternative graphical perspective on the Drinfeld centre of $\mathcal{C}$, denoted $Z(\mathcal{C})$. We then exploit this perspective to obtain an alternative proof of the equivalence between $Z(\ma

Mohammed Brahimi, Yvain Quéau, Bjoern Haefner, Daniel Cremers

Uncalibrated photometric stereo aims at estimating the 3D-shape of a surface, given a set of images captured from the same viewing angle, but under unknown, varying illumination. While the theoretical foundations of this inverse problem under directional lighting are well-established, there is a lack of mathematical evidence for the uniqueness of a solution

Farhad Mehdifar, Charalampos P. Bechlioulis, Farzad Hashemzadeh, Mahdi Baradarannia

This paper presents a novel control protocol for robust distance-based formation control with prescribed performance in which agents are subjected to unknown external disturbances. Connectivity maintenance and collision avoidance among neighboring agents are also handled by the appropriate design of certain performance bounds that constrain the inter-agent d

D. Gallacher, M. Boulay

Future large liquid argon direct dark matter detectors can benefit greatly from an efficient surface background rejection technique. To aid the development of these large scale detectors a test stand, Argon-1, has been constructed at Carleton University, Ottawa, Canada, in the noble liquid detector development lab. It aims to test a novel surface background

Ernesto Frodden, Diego Hidalgo

These notes provide a detailed catalog of surface charge formulas for different classes of gravity theories. The present catalog reviews and extends the existing literature on the topic. Part of the focus is on reviewing the method to compute quasi-local surface charges for gauge theories in order to clarify conceptual issues and their range of applicability

Anne Boutet de Monvel, Iryna Karpenko, Dmitry Shepelsky

The paper aims at developing the Riemann-Hilbert problem approach to the modified Camassa-Holm (mCH) equation in the case when the solution is assumed to approach a non-zero constant at the both infinities of the space variable. In this case, the spectral problem for the associated Lax pair equation has a continuous spectrum, which allows formulating the inv

Soham Chandra, Muktish Acharyya

We have studied a spin-1/2, ABA, Ising trilayer system with two different types of interactions, in-plane ferromagnetic and out-of-plane anti-ferromagnetic, among the lattice sites, on different layers. In the pure case, devoid of any impurity, we employed Monte-Carlo method with single spin-flip Metropolis algorithm to find out the anti-ferromagnetic critic

Yunming Zhang, Ajay Brahmakshatriya, Xinyi Chen, Laxman Dhulipala

Many graph problems can be solved using ordered parallel graph algorithms that achieve significant speedup over their unordered counterparts by reducing redundant work. This paper introduces a new priority-based extension to GraphIt, a domain-specific language for writing graph applications, to simplify writing high-performance parallel ordered graph algorit

Bérenger Bramas, Muhammad Hassan, Benjamin Stamm

This article is the second in a series of two papers concerning the mathematical study of a boundary integral equation of the second kind that describes the interaction of $N$ dielectric spherical particles undergoing mutual polarisation. The first article presented the numerical analysis of the Galerkin method used to solve this boundary integral equation a

Amit Boyarski, Sanketh Vedula, Alex Bronstein

Deep Matrix Factorization (DMF) is an emerging approach to the problem of matrix completion. Recent works have established that gradient descent applied to a DMF model induces an implicit regularization on the rank of the recovered matrix. In this work we interpret the DMF model through the lens of spectral geometry. This allows us to incorporate explicit re

Tom Carroll, Clifford Gilmore

Bounded weighted composition operators, as well as compact weighted composition operators, on Fock spaces have been characterised. This characterisation is refined to the extent that the question of whether weighted composition operators on the Fock space can be supercyclic is answered in the negative.

Jungkai Chen, Meng Chen, Chen Jiang

In this short note, we give a refinement of our previous work (arXiv:1803.05553) stating that for a projective $3$-fold $X$ of general type with either $p_g(X)\leq 4$ or $p_g(X)\geq 11$, $$\text{vol}(X)\geq \frac{4}{3}p_g(X)-{\frac{10}{3}}.$$

Hyeonbae Kang, Xiaofei Li, Shigeru Sakaguchi

The polarization tensor is a geometric quantity associated with a domain. It is a signature of the small inclusion's existence inside a domain and used in the small volume expansion method to reconstruct small inclusions by boundary measurements. In this paper, we consider the question of the polarization tensor vanishing structure of general shape. The

Martin Gauch, Juliane Mai, Jimmy Lin

Accurate streamflow prediction largely relies on historical meteorological records and streamflow measurements. For many regions, however, such data are only scarcely available. Facing this problem, many studies simply trained their machine learning models on the region's available data, leaving possible repercussions of this strategy unclear. In this st

Pritam Das, Mrinal Kumar Das, Najimuddin Khan

We study a model of neutrino and dark matter within the framework of a minimal extended seesaw. This framework is based on $A_4$ flavor symmetry along with the discrete $Z_4$ symmetry to stabilize the dark matter and construct desired mass matrices for neutrino mass. We use a non-trivial Dirac mass matrix with broken $μ- τ$ symmetry to generate the leptonic

Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang

By decomposing the visual tracking task into two subproblems as classification for pixel category and regression for object bounding box at this pixel, we propose a novel fully convolutional Siamese network to solve visual tracking end-to-end in a per-pixel manner. The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for

Simone Raponi, Roberto Di Pietro

Single-factor password-based authentication is generally the norm to access on-line Web-sites. While single-factor authentication is well known to be a weak form of authentication, a further concern arises when considering the possibility for an attacker to recover the user passwords by leveraging the loopholes in the password recovery mechanisms. Indeed, th

Zhan-Dong Mei

This paper is concerned with exponential stability of a class of infinite dimensional coupled systems. It is proved that under some admissibility conditions, the considered infinite dimensional coupled system is governed by a $C_0$-semigroup. Furthermore, if both the free subsystems are governed by exponentially stable $C_0$-semigroups, then so is the couple

Xiang Fu, Lawrence Reeves, Linxiao Xu

In this paper we study affine reflection subgroups in arbitrary infinite Coxeter groups of finite rank. In particular, we study the distribution of roots of Coxeter groups in the root subsystems associated with affine reflection subgroups. We give a characterization of limit roots arising from affine reflection subgroups. We also give a characterization of w

Dan Jonsson

This article includes a survey of the historical development and theoretical structure of the pre-modern theory of magnitudes and numbers. In Part 1, work, insights and controversies related to quantity calculus from Euler onward are reviewed. In Parts 2 and 3, we define scalable monoids and, as a special case, quantity spaces; both can be regarded as univer

Or Isaacs, Oran Shayer, Michael Lindenbaum

Current successful approaches for generic (non-semantic) segmentation rely mostly on edge detection and have leveraged the strengths of deep learning mainly by improving the edge detection stage in the algorithmic pipeline. This is in contrast to semantic and instance segmentation, where DNNs are applied directly to generate pixel-wise segment representation

Nico Schlömer

This article revisits Kobayasi's and Yosiki's algorithm for conversion of OSA-UCS into XYZ cooordinates. It corrects some mistakes on the involved functions and initial guesses and shows that that hundreds of thousands of coordinates can be converted in less than a second with full accuracy.

Francesco Ortelli, Sara van de Geer

We study the theoretical properties of image denoising via total variation penalized least-squares. We define the total vatiation in terms of the two-dimensional total discrete derivative of the image and show that it gives rise to denoised images that are piecewise constant on rectangular sets. We prove that, if the true image is piecewise constant on just

Wen-Hui Zhu, Jian-Guo Liu

Under investigation is a generalized (3 + 1)-dimensional B-type Kadomtsev-Petviashvili equation with variable coefficients in fluid dynamics. Based on the Hirota's bilinear form and the positive quadratic function, abundant lump solutions are obtained.The interaction solutions between lump solutions and other solitons are also presented. Their dynamical beha

Ana Ozaki, Cosimo Persia, Andrea Mazzullo

We investigate the complexity of learning query inseparable ELH ontologies in a variant of Angluin's exact learning model. Given a fixed data instance A* and a query language Q, we are interested in computing an ontology H that entails the same queries as a target ontology T on A*, that is, H and T are inseparable w.r.t. A* and Q. The learner is allowed

Binh An Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

In recent years, Vietnamese Named Entity Recognition (NER) systems have had a great breakthrough when using Deep Neural Network methods. This paper describes the primary errors of the state-of-the-art NER systems on Vietnamese language. After conducting experiments on BLSTM-CNN-CRF and BLSTM-CRF models with different word embeddings on the Vietnamese NER dat

Ali Maalaoui

Given a three dimensional pseudo-Einstein CR manifold $(M,T^{1,0}M,θ)$, we study the existence of a contact structure conformal to $θ$ for which the logarithmic Hardy-Littlewood-Sobolev (LHLS) inequality holds. Our approach closely follows \cite{Ok1} in the Riemannian setting. For this purpose, we introduce the notion of Robin mass as the constant term appea

Alexander Pfeiffer, Robert M. Reeve, Kelvin Elphick, Atsufumi Hirohata

Spin transport phenomena underpin an extensive range of spintronic effects. In particular spin transport across interfaces occurs in most device concepts, but is so far poorly understood. As interface properties strongly impact spin transport, one needs to characterize and correlate them to the fabrication method. Here we investigate pure spin current transp

Zakhar Kabluchko, Christoph Thaele

The typical cell of a Voronoi tessellation generated by $n+1$ uniformly distributed random points on the $d$-dimensional unit sphere $\mathbb S^d$ is studied. Its $f$-vector is identified in distribution with the $f$-vector of a beta' polytope generated by $n$ random points in $\mathbb R^d$. Explicit formulae for the expected $f$-vector are provided for any

Marco Cantarini, Alessandro Gambini, Alessandro Zaccagnini

We consider weighted averages of the number of representations of an even integer as a sum of two prime numbers, where each summand lies in a given arithmetic progression modulo a common integer $q$. Our result is uniform in a suitable range for $q$.

Timothy J Burchell, Thomas J Bridges

This paper studies the linear stability problem for solitary wave solutions of Hamiltonian PDEs. The linear stability problem is formulated in terms of the Evans function, a complex analytic function denoted by $D(\lambda)$, where $\lambda$ is the spectral parameter. The main result is the introduction of a new factor, denoted $\Pi$, in the Pego & Weinstein

Christian Táfula

Let $\chi_D$ be the Dirichlet character associated to $\mathbb{Q}(\sqrt{D})$ where $D < 0$ is a fundamental discriminant. Improving Granville-Stark [DOI:10.1007/s002229900036], we show that \[ \frac{L'}{L}(1,\chi_D) = \frac{1}{6}\, \mathrm{height}(j(\tau_D)) - \frac{1}{2}\log|D| + C + o_{D\to -\infty}(1), \] where $\tau_D = \frac 12(-\delta+\sqrt{D})$ for $D

Yotam Gafni, Ron Lavi, Moshe Tennenholtz

VCG is a classical combinatorial auction that maximizes social welfare. However, while the standard single-item Vickrey auction is false-name-proof, a major failure of multi-item VCG is its vulnerability to false-name attacks. This occurs already in the natural bare minimum model in which there are two identical items and bidders are single-minded. Previous

Ron Levie, Çağkan Yapar, Gitta Kutyniok, Giuseppe Caire

In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point $x$ (transmitter location) to any point $y$ on a planar domain. For applications such as user-cell site association and device-to-device link scheduling, an accurate knowledge of the pathloss function for all pairs of transm

Xinyun Chen, Wenxiao Wang, Chris Bender, Yiming Ding

Training deep neural networks from scratch could be computationally expensive and requires a lot of training data. Recent work has explored different watermarking techniques to protect the pre-trained deep neural networks from potential copyright infringements. However, these techniques could be vulnerable to watermark removal attacks. In this work, we propo

Yongbin Ruan, Yingchun Zhang, Jie Zhou

We first develop theories of differential rings of quasi-Siegel modular and quasi-Siegel Jacobi forms for genus two. Then we apply them to the Eynard-Orantin topological recursion of certain local Calabi-Yau threefolds equipped with branes, whose mirror curves are genus two hyperelliptic curves. By the proof of the Remodeling Conjecture, we prove that the co

Zhuo Yang, Yufei Han, Guoxian Yu, Qiang Yang

We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each

Peilan Wang, Jun Fang, Huiping Duan, Hongbin Li

In this paper, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to

Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel

Deep neural networks are known to be vulnerable to adversarial attacks. Current methods of defense from such attacks are based on either implicit or explicit regularization, e.g., adversarial training. Randomized smoothing, the averaging of the classifier outputs over a random distribution centered in the sample, has been shown to guarantee the performance o

Hyejeong Choi, Johan Lim, Minjung Kwak, Seongoh Park

We construct a procedure to test the stochastic order of two samples of interval-valued data. We propose a test statistic which belongs to U-statistic and derive its asymptotic distribution under the null hypothesis. We compare the performance of the newly proposed method with the existing one-sided bivariate Kolmogorov-Smirnov test using real data and simul

Ting Gong, Zhonghua Ji, Jiaqi Du, Yanting Zhao

We have demonstrated microwave-assisted coherent control of ultracold $^{85}$Rb$^{133}$Cs molecules with a ladder-type configuration of rotational states. A probe microwave (MW) field is used to couple a lower state $X^1\Sigma^+(v=0, J=1)$ and a middle state $X^1\Sigma^+(v=0, J=2)$, while a control MW field couples the middle state and a upper state $X^1\Sig

Changjian Fu, Yasuaki Gyoda

We introduce a new function on the set of pairs of cluster variables via $f$-vectors, which we call it the compatibility degree (of cluster complexes). The compatibility degree is a natural generalization of the classical compatibility degree introduced by Fomin and Zelevinsky. In particular, we prove that the compatibility degree has the duality property, t

Qin Zou, Zheng Zhang, Ling Cao, Long Chen

Hash coding has been widely used in approximate nearest neighbor search for large-scale image retrieval. Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes. While some newly introduced images may contain undefined semantic labels, which we c

Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii

Neural network quantization enables the deployment of large models on resource-constrained devices. Current post-training quantization methods fall short in terms of accuracy for INT4 (or lower) but provide reasonable accuracy for INT8 (or above). In this work, we study the effect of quantization on the structure of the loss landscape. Additionally, we show

Yanxun Chang, Simone Costa, Tao Feng, Xiaomiao Wang

This paper introduces almost partitionable sets to generalize the known concept of partitionable sets. These notions provide a unified frame to construct $\mathbb{Z}$-cyclic patterned starter whist tournaments and cyclic balanced sampling plans excluding contiguous units. The existences of partitionable sets and almost partitionable sets are investigated. As

Alexander Zapryagaev

Presburger Arithmetic $\mathop{\mathbf{PrA}}\nolimits$ is the true theory of natural numbers with addition. We consider linear orderings interpretable in Presburger Arithmetic and establish various necessary and sufficient conditions for interpretability depending on dimension $n$ of interpretation. We note this problem is relevant to the interpretations of

Shibo Zhang, Yuqi Zhao, Dzung Tri Nguyen, Runsheng Xu

We present the design, implementation, and evaluation of a multi-sensor low-power necklace &#39;NeckSense&#39; for automatically and unobtrusively capturing fine-grained information about an individual&#39;s eating activity and eating episodes, across an entire waking-day in a naturalistic setting. The NeckSense fuses and classifies the proximity of the neck

Vikas Yadav, Steven Bethard, Mihai Surdeanu

We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence selection method can be coupled with any

B. S. Ham

The Copenhagen interpretation has been long-lasted, whose core concepts are in the Heisenberg&#39;s uncertainty principle and nonlocal correlation of EPR. The second-order anticorrelation on a beam splitter represents these phenomena where it cannot be achieved classically. Here, the anticorrelation of nonclassicality on a beam splitter is interpreted in a p

Tao Li, Ke Wang, Chunqiang Xu, Qiang Hou

Very recently, NiTe2 has been reported to be a type II Dirac semimetal with Dirac nodes near the Fermi surface. Furthermore, it is unveiled that NiTe2 presents the Hall Effect, which is ascribed to orbital magnetoresistance. The physical properties behavior of NiTe2 under high pressure attracts us. In this paper, we investigate the electrical properties of p

Feng Sun, Ting Luo, Lin Li, Aijun Hong

We use the modified Becke-Johnson exchange potential (mBJ) with the spin-orbit coupling effect (SOC) to study effects of biaxial strain and local constant potential on electronic structure of monolayer SnSe. Our results show the fundamental band gap size can be tuned via biaxial strain. Compressive strain (tensile strain) can narrow (enlarge) band gap, and c

Yue-Hui Lu, Bao-Zong Wang, Xiong-Jun Liu

There is an immense effort in search for various types of Weyl semimetals, of which the most fundamental phase consists of the minimal number of i.e. two Weyl points, but is hard to engineer in solids. Here we demonstrate how such fundamental Weyl semimetal can be realized in a maneuverable optical Raman lattice, with which the three-dimensional (3D) spin-or

Gregory Vaksman, Michael Elad, Peyman Milanfar

Image denoising is a well studied problem with an extensive activity that has spread over several decades. Despite the many available denoising algorithms, the quest for simple, powerful and fast denoisers is still an active and vibrant topic of research. Leading classical denoising methods are typically designed to exploit the inner structure in images by m

Atsufumi Honda, Kosuke Naokawa, Kentaro Saji, Masaaki Umehara

Consider an oriented curve $Γ$ in a domain $D$ in the plane $\boldsymbol R^2$. Thinking of $D$ as a piece of paper, one can make a curved folding in the Euclidean space $\boldsymbol R^3$. This can be expressed as the image of an &#34;origami map&#34; $Φ:D\to \boldsymbol R^3$ such that $Γ$ is the singular set of $Φ$, the word &#34;origami&#34; coming from the

Jing-Rebecca Li, Try Nguyen Tran, Van-Dang Nguyen

The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium such as brain tissue can be modeled by the Bloch-Torrey partial differential equation. The spatial integral of the solution of this equation in realistic geometry provides a gold-standard reference model for the diffusion M

Yilang Zhang, Neal N. Xiong, Zheng Wei, Xin Yuan

As a novel method eliminating chromatic aberration on objects, computational color constancy has becoming a fundamental prerequisite for many computer vision applications. Among algorithms performing this task, the learning-based ones have achieved great success in recent years. However, they fail to fully consider the spatial information of images, leaving

David Kempe

Distortion-based analysis has established itself as a fruitful framework for comparing voting mechanisms. m voters and n candidates are jointly embedded in an (unknown) metric space, and the voters submit rankings of candidates by non-decreasing distance from themselves. Based on the submitted rankings, the social choice rule chooses a winning candidate; the

Daniel Ketover, Yevgeny Liokumovich, Antoine Song

Let $H$ be a strongly irreducible Heegaard surface in a closed oriented Riemannian $3$-manifold. We prove that $H$ is either isotopic to a minimal surface of index at most one or isotopic to the boundary of a tubular neighborhood about a non-orientable minimal surface with a vertical handle attached. This confirms a long-standing conjecture of J. Pitts and J

Ziyi Kou, Guofeng Cui, Shaojie Wang, Wentian Zhao

Weakly Supervised Object Localization (WSOL) methods generate both classification and localization results by learning from only image category labels. Previous methods usually utilize class activation map (CAM) to obtain target object regions. However, most of them only focus on improving foreground object parts in CAM, but ignore the important effect of it

Fuxun Yu, Di Wang, Yinpeng Chen, Nikolaos Karianakis

Current state-of-the-art object detectors can have significant performance drop when deployed in the wild due to domain gaps with training data. Unsupervised Domain Adaptation (UDA) is a promising approach to adapt models for new domains/environments without any expensive label cost. However, without ground truth labels, most prior works on UDA for object de

Vyjayanthi Chari, Justin Davis, Ryan Moruzzi

The goal of this paper is to understand the graded limit of a family of irreducible prime representations of the quantum affine algebra associated to a simply-laced simple Lie algebra $\mathfrak{g}$. This family was introduced by David Hernandez and Bernard Leclerc in the context of monoidal categorification of cluster algebras. The graded limit of a member

Yasamin Nazari

We give the first Congested Clique algorithm that computes a sparse hopset with polylogarithmic hopbound in polylogarithmic time. Given a graph $G=(V,E)$, a $(β,ε)$-hopset $H$ with &#34;hopbound&#34; $β$, is a set of edges added to $G$ such that for any pair of nodes $u$ and $v$ in $G$ there is a path with at most $β$ hops in $G \cup H$ with length within $(

Kezhou Yang, Abhronil Sengupta

Emulating various facets of computing principles of the brain can potentially lead to the development of neuro-computers that are able to exhibit brain-like cognitive capabilities. In this letter, we propose a magnetoelectronic neuron that utilizes noise as a computing resource and is able to encode information over time through the independent control of ex

Joseph C. Schindler

This article provides a pedagogically oriented introduction to geometric (Clifford) calculus on pseudo-Riemannian manifolds. Unlike usual approaches to the topic, which rely on embedding the geometric algebra either within a tensor algebra or within a vector manifold framework, here we define geometric calculus directly, by elementary methods. In particular

Jaewoo Park, Ick Hoon Jin, Michael Schweinberger

Doubly-intractable posterior distributions arise in many applications of statistics concerned with discrete and dependent data, including physics, spatial statistics, machine learning, the social sciences, and other fields. A specific example is psychometrics, which has adapted high-dimensional Ising models from machine learning, with a view to studying the

Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan

Transformers have increasingly outperformed gated RNNs in obtaining new state-of-the-art results on supervised tasks involving text sequences. Inspired by this trend, we study the question of how Transformer-based models can improve the performance of sequential decision-making agents. We present the Working Memory Graph (WMG), an agent that employs multi-he

Zhichao Huang, Tong Zhang

We present a new method for black-box adversarial attack. Unlike previous methods that combined transfer-based and scored-based methods by using the gradient or initialization of a surrogate white-box model, this new method tries to learn a low-dimensional embedding using a pretrained model, and then performs efficient search within the embedding space to at

Wonjune Choi, Ki Hoon Lee, Yong Baek Kim

Unambiguous identification of fractionalized excitations in quantum spin liquids has been a long-standing issue in correlated topological phases. Conventional spectroscopic probes, such as the dynamical spin structure factor, can only detect composites of fractionalized excitations, leading to a broad continuum in energy. Lacking a clear signature in convent

Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang

This paper studies model-inversion attacks, in which the access to a model is abused to infer information about the training data. Since its first introduction, such attacks have raised serious concerns given that training data usually contain privacy-sensitive information. Thus far, successful model-inversion attacks have only been demonstrated on simple mo

David Harel, Assaf Marron, Joseph Sifakis

The potential benefits of autonomous systems have been driving intensive development of such systems, and of supporting tools and methodologies. However, there are still major issues to be dealt with before such development becomes commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly availabl

Yongqi Zhang, Quanming Yao, Lei Chen

Knowledge graph (KG) embedding is well-known in learning representations of KGs. Many models have been proposed to learn the interactions between entities and relations of the triplets. However, long-term information among multiple triplets is also important to KG. In this work, based on the relational paths, which are composed of a sequence of triplets, we

Mahmudul Islam, Md Shajedul Hoque Thakur, Satyajit Mojumder, Abdullah Al Amin

Functionally graded material (FGM) is a class of advanced materials, consisting of two (or more) different constituents, that possesses a continuously varying composition profile. With the advancement of nanotechnology, applications of FGMs have shifted from their conventional usage towards sophisticated micro and nanoscale electronics and energy conversion

Yifan Ren, Tongsuo Wu

In this note, we use the concept of a polynomial ring to give an elementary proof to Cayley-Hamilton Theorem. We also give an elementary proof to Birkhoff theorem on Bi-stochastic matrices.

Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu

Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption. One simple idea is to use the leave-one-out error of each training point to indicate its importance. Recent

Carlos Gershenson

Self-organization offers a promising approach for designing adaptive systems. Given the inherent complexity of most cyber-physical systems, adaptivity is desired, as predictability is limited. Here I summarize different concepts and approaches that can facilitate self-organization in cyber-physical systems, and thus be exploited for design. Then I mention re

Hongqian Qin

Deep learning has achieved impressive prediction performance in the field of sequence learning recently. Dissolved oxygen prediction, as a kind of time-series forecasting, is suitable for this technique. Although many researchers have developed hybrid models or variant models based on deep learning techniques, there is no comprehensive and sound comparison a

Rafael Díaz Hernández Rojas, Giorgio Parisi, Federico Ricci-Tersenghi

Jamming is a phenomenon shared by a wide variety of systems, such as granular materials, foams, and glasses in their high density regime. This has motivated the development of a theoretical framework capable of explaining many of their static critical properties with a unified approach. However the dynamics occurring in the vicinity of the jamming point has

Matt Groff

A New Number Theoretic Transform(NTT), which is a form of FFT, is introduced, that is faster than FFTs. Also, a multiplication algorithm is introduced that uses this to perform integer multiplication faster than O(n log n). It uses preprocessing to achieve an upper bounds of (n log n/(log log n/ log log log n). Also, we explore the possibility of O(n) time m

Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu

Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications. As real-world graphs are often incomplete and noisy, treating them as ground-truth information, which is a common practice in mos

Tamara Dimitrova, Kristijan Petrovski, Ljupco Kocarev

We develop graphlet analysis for multiplex networks and discuss how this analysis can be extended to multilayer and multilevel networks as well as to graphs with node and/or link categorical attributes. The analysis has been adapted for two typical examples of multiplexes: economic trade data represented as a 957-plex network and 75 social networks each repr

Jianxiong Li, Uwe Thumm

To solve the time-dependent Schrödinger equation in spatially inhomogeneous pulses of electromagnetic radiation, we propose an iterative semi-classical complex trajectory approach. In numerical applications, we validate this method against ab initio numerical solutions by scrutinizing (a) electronic states in combined Coulomb and spatially homogeneous laser

Christine Bauer

Socio-demographic user profiles are currently regarded as the most convenient base for successful personalized advertising. However, signs point to the dormant power of context recognition. While technologies that can sense the environment are increasingly advanced, questions such as what to sense and how to adapt to a consumer&#39;s context are largely unan

Hanqing Huang, Kezia Irene, Nahyun Ryu

Although the voice search system is getting more and more developed, some people still have difficulties when searching for information with the voice search system. This paper is a pilot study to compare the search performance of people using voice search and typed search using Baidu search system. We surveyed and interviewed 40 Chinese students who have be

Guangxiang Zhao, Xu Sun, Jingjing Xu, Zhiyuan Zhang

In sequence to sequence learning, the self-attention mechanism proves to be highly effective, and achieves significant improvements in many tasks. However, the self-attention mechanism is not without its own flaws. Although self-attention can model extremely long dependencies, the attention in deep layers tends to overconcentrate on a single token, leading t

Xiaomei Chen

Denton, Parke, Tao and Zhang gave a new method which determines eigenvectors from eigenvalues for Hermitian matrices with distinct eigenvalues. In this short note, we extend the above result to general Hermitian matrices.

Vikas Ramachandra

In this paper, we predict severity of extreme weather events (tropical storms, hurricanes, etc.) using buoy data time series variables such as wind speed and air temperature. The prediction/forecasting method is based on various forecasting and machine learning models. The following steps are used. Data sources for the buoys and weather events are identified

Venkat R. Dasari, Mee Seong Im, Lubjana Beshaj

Classical optimization algorithms in machine learning often take a long time to compute when applied to a multi-dimensional problem and require a huge amount of CPU and GPU resource. Quantum parallelism has a potential to speed up machine learning algorithms. We describe a generic mathematical model to leverage quantum parallelism to speed-up machine learnin

Yiheng Han, Wang Zhao, Jia Pan, Zipeng Ye

Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish collision free subspace from in-collision su

Chenlu Xiang, Shunqing Zhang, Shugong Xu, Xiaojing Chen

Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Th

B. Muraleetharan, K. Thirulogasanthar

For a bounded right linear operators $A$, in a right quaternionic Hilbert space $V_{\mathbb{H}}^{R}$, following the complex formalism, we study the Berberian extension $A^\circ$, which is an extension of $A$ in a right quaternionic Hilbert space obtained from $V_{\mathbb{H}}^{R}$. In the complex setting, the important feature of the Berberian extension is th

Olga Soloveva, Pierre Moreau, Elena Bratkovskaya

We calculate transport coefficients of the quark-gluon plasma (QGP) within the dynamical quasiparticle model (DQPM) by explicitly computing the parton interaction rates as a function of temperature $T$ and baryon chemical potential $μ_B$ on the basis of the DQPM couplings and partonic propagators. The latter are extracted from lattice QCD by matching the equ