October 2020 arXiv papers — page 80

Martin Holeček

The automation of document processing is gaining recent attention due to the great potential to reduce manual work through improved methods and hardware. Neural networks have been successfully applied before - even though they have been trained only on relatively small datasets with hundreds of documents so far. To successfully explore deep learning techniqu

Michael Fishman, Nishanth Kumar, Cameron Allen, Natasha Danas

A general-purpose planning agent requires an open-scope world model: one rich enough to tackle any of the wide range of tasks it may be asked to solve over its operational lifetime. This stands in contrast with typical planning approaches, where the scope of a model is limited to a specific family of tasks that share significant structure. Unfortunately, pla

Nathan Canen, Kyungchul Song

Decomposition methods are often used for producing counterfactual predictions in non-strategic settings. When the outcome of interest arises from a game-theoretic setting where agents are better off by deviating from their strategies after a new policy, such predictions, despite their practical simplicity, are hard to justify. We present conditions in Bayesi

Piotr Pokora, Tomasz Szemberg, Justyna Szpond

Felix Klein in course of his study of the regular icosahedron and its symmetries encountered a highly symmetric configuration of $60$ points in ${\mathbb P}^3$. This configuration has appeared in various guises, perhaps post notably as the configuration of points dual to the $60$ reflection planes in the group $G_{31}$ in the Shephard-Todd list. In the prese

Thibaud Lemanissier, Jérôme Poineau

This text contributes to the foundations of the theory of global Berkovich spaces, that is to say Berkovich spaces over Banach rings with nice properties such as $\mathbf{Z}$, rings of integers of number fields, discrete valuation rings, hybrid fields, etc. We focus on three main themes that had not been investigated so far: category, topology and cohomology

James S. Milne

We prove, following Deligne and André, that the Hodge classes on abelian varieties of CM-type can be expressed in terms of divisor classes and split Weil classes, and we describe some consequences. In particular, we show that Grothendieck's standard conjecture of Lefschetz type implies the Hodge conjecture for abelian varieties (Abdulali, André, ...).

Swapnil Yadav, Kazi Alam, K. A. Muttalib, Dong Wang

We consider several limiting cases of the joint probability distribution for a random matrix ensemble with an additional interaction term controlled by an exponent $γ$ (called the $γ$-ensembles). The effective potential, which is essentially the single-particle confining potential for an equivalent ensemble with $γ=1$ (called the Muttalib-Borodin ensemble),

Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik

Graph neural networks (GNNs) can process graphs of different sizes, but their ability to generalize across sizes, specifically from small to large graphs, is still not well understood. In this paper, we identify an important type of data where generalization from small to large graphs is challenging: graph distributions for which the local structure depends

Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales

Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks. However, existing SNNs are usually heuristically motivated, and often rely on adversarial training, which is computationally costly. We propose a new SNN that achieves state-of-the-art performance wit

Alex Hanna, Tina M. Park

At the heart of what drives the bulk of innovation and activity in Silicon Valley and elsewhere is scalability. This unwavering commitment to scalability -- to identify strategies for efficient growth -- is at the heart of what we refer to as "scale thinking." Whether people are aware of it or not, scale thinking is all-encompassing. It is not just a

Nikolai S. Mosyagin, Alexander V. Oleynichenko, Andrei Zaitsevskii, Artur V. Kudrin

The intercombination $a^3\Pi - X^1\Sigma^+$ Cameron system of carbon monoxide has been computationally studied in the framework of multi-reference Fock space coupled cluster method with the use of generalized relativistic pseudopotential model for the effective introducing the relativity in all-electron correlation treatment. The extremely weak $a^3\Pi_{\Ome

Samuel Pfrommer, Fernando Gama, Alejandro Ribeiro

Network data can be conveniently modeled as a graph signal, where data values are assigned to the nodes of a graph describing the underlying network topology. Successful learning from network data requires methods that effectively exploit this graph structure. Graph neural networks (GNNs) provide one such method and have exhibited promising performance on a

Jinghan Yang, Adith Boloor, Ayan Chakrabarti, Xuan Zhang

There is considerable evidence that deep neural networks are vulnerable to adversarial perturbations applied directly to their digital inputs. However, it remains an open question whether this translates to vulnerabilities in real systems. For example, an attack on self-driving cars would in practice entail modifying the driving environment, which then impac

Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan

We propose a theoretical framework for approximate planning and learning in partially observed systems. Our framework is based on the fundamental notion of information state. We provide two equivalent definitions of information state -- i) a function of history which is sufficient to compute the expected reward and predict its next value; ii) equivalently, a

Alan Haynes, Juan J. Ramirez

Recently, the first author together with Jens Marklof studied generalizations of the classical three distance theorem to higher dimensional toral rotations, giving upper bounds in all dimensions for the corresponding numbers of distances with respect to any flat Riemannian metric. In dimension two they proved a five distance theorem, which is best possible.

Soufiane Lamghari, Guillaume-Alexandre Bilodeau, Nicolas Saunier

Human action recognition (HAR) in videos is a fundamental research topic in computer vision. It consists mainly in understanding actions performed by humans based on a sequence of visual observations. In recent years, HAR have witnessed significant progress, especially with the emergence of deep learning models. However, most of existing approaches for actio

Harold D. Chiang, Bing Yang Tan

This paper studies the asymptotic properties of and alternative inference methods for kernel density estimation (KDE) for dyadic data. We first establish uniform convergence rates for dyadic KDE. Secondly, we propose a modified jackknife empirical likelihood procedure for inference. The proposed test statistic is asymptotically pivotal regardless of presence

Rosie Hayward, Fabio Biancalana

We study the evolution equations for gravitational waves, which are derived using the full metric to raise and lower indices. This method ensures full consistency between the Ricci tensor and all gauge restrictions and requirements, and allows a meaningful expansion of all tensors up to second order, avoiding several inconsistencies and contradictions observ

Makoto Muto, Tamotsu Onozaki, Yoshitaka Saiki

Two decades of studies have found significant regional differences in the timing of transitions in national business cycles and their durations. Earlier studies partly detect regional synchronization during business cycle expansions and contractions in Europe, the United States, and Japan. We examine this possibility applying a sophisticated method for ident

Junsoo Park, Yi Xia, Vidvuds Ozoliņš, Anubhav Jain

Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified bands and/or scattering models. In this study, we apply more rigorous scattering treatments to more realistic model band structures - upward-parabolic bands that inflect to an inverte

J. S. Fabila-Carrasco, Fernando Lledó, Olaf Post

In this article, we relate the spectrum of the discrete magnetic Laplacian (DML) on a finite simple graph with two structural properties of the graph: the existence of a perfect matching and the existence of a Hamiltonian cycle of the underlying graph. In particular, we give a family of spectral obstructions parametrised by the magnetic potential for the gra

Hartmut Wachter

We consider time-dependent Schrödinger equations for a free nonrelativistic particle on the three-dimensional $q$-deformed Euclidean space. We determine plane wave solutions to these Schrödinger equations and show that they form a complete orthonormal system. We derive $q$-deformed expressions for propagators of a nonrelativistic particle. Considerations abo

Makoto Muto, Yoshitaka Saiki

Synchronization is a phenomenon in which a pair of fluctuations adjust their rhythms when interacting with each other. We measure the degree of synchronization between the U.S. dollar (USD) and euro exchange rates and between the USD and Japanese yen exchange rates on the basis of purchasing power parity (PPP) over time. We employ a method of synchronization

Zvika Brakerski, Noah Stephens-Davidowitz, Vinod Vaikuntanathan

In this work, we show the first worst-case to average-case reduction for the classical $k$-SUM problem. A $k$-SUM instance is a collection of $m$ integers, and the goal of the $k$-SUM problem is to find a subset of $k$ elements that sums to $0$. In the average-case version, the $m$ elements are chosen uniformly at random from some interval $[-u,u]$. We consi

Barkın Tuncer, Emre Özkan

In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects' extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. Th

Andrew Hanlon, Jeff Hicks

We study homological mirror symmetry for toric varieties, exploring the relationship between various Fukaya-Seidel categories which have been employed for constructing the mirror to a toric variety. In particular, we realize tropical Lagrangian sections as objects of a partially wrapped category and construct a Lagrangian correspondence mirror to the inclusi

Kisetsu Tsuge, Hidetoshi Sano, Kengo Tachihara, Kenji Bekki

The galactic tidal interaction is a possible mechanism to trigger the active star formation in galaxies. Recent analyses using the Hi data in the Large Magellanic Cloud (LMC) proposed that the tidally driven colliding HI flows, induced by the galactic interaction with the Small Magellanic Cloud (SMC), triggered high-mass star formation in the southeastern HI

Ricardo Campos, Pedro Tamaroff

The classical Hochschild--Kostant--Rosenberg (HKR) theorem computes the Hochschild homology and cohomology of smooth commutative algebras. In this paper, we generalise this result to other kinds of algebraic structures. Our main insight is that producing HKR isomorphisms for other types of algebras is directly related to computing quasi-free resolutions in t

Roman Pol, Piotr Zakrzewski

We study a strengthening of the notion of a perfectly meager set. We say that that a subset $A$ of a perfect Polish space $X$ is countably perfectly meager in $X$, if for every sequence of perfect subsets $\{P_n: n \in {\mathbb N}\}$ of $X$, there exists an $F_σ$-set $F$ in $X$ such that $A \subseteq F$ and $F\cap P_n$ is meager in $P_n$ for each $n$. We giv

Ryuichiro Kitano, Norikazu Yamada, Masahito Yamazaki

We study $θ$ dependence of the vacuum energy for the 4d SU(2) pure Yang-Mills theory by lattice numerical simulations. The response of topological excitations to the smearing procedure is investigated in detail, in order to extract topological information from smeared gauge configurations. We determine the first two coefficients in the $θ$ expansion of the v

Josué Corujo

The purpose of this paper is to provide a complete description of the eigenvalues of the generator of a neutral multi-type Moran model, and the applications to the study of the speed of convergence to stationarity. The Moran model we consider is a non-reversible in general, continuous-time Markov chain with an unknown stationary distribution. Specifically, w

Devraj Ranjan, Sankar Narayanan, Kai Kadau, Anirban Patra

A Crystal Plasticity Finite Element (CPFE) framework is proposed for modeling the non-Schmid yield behavior of L12 type Ni3Al crystals and Ni-based superalloys. This framework relies on the estimation of the non-Schmid model parameters directly from the orientation- and temperature-dependent experimental yield stress data. The inelastic deformation model for

G. Bonfanti, L. Diago-Cisneros

In this report we study the quantum transport of charge carriers for low dimensional systems with spin-orbit coupling by means of Heisenberg's inequalities. To develop our analysis, an accurate \emph{gendanken} experiment was carefully put together, mainly based on the spin-field effect transistor phenomenology and taking into account several wide accept

Naoki Kubota

We consider the simple random walk in i.i.d. nonnegative potentials on the $d$-dimensional cubic lattice $\mathbb{Z}^d$ ($d \geq 1$). In this model, the so-called Lyapunov exponent describes the cost of traveling for the simple random walk in the potential. The Lyapunov exponent depends on the distribution function of the potential, and the aim of this artic

Jonas Kornprobst, Thomas F. Eibert

A combined source integral equation (CSIE) is constructed on the basis of the electric field integral equation (EFIE) to solve electromagnetic radiation and scattering problems containing perfect electrically conducting bodies. It is discretized with Rao-Wilton-Glisson basis functions only, for both electric and magnetic surface current densities. The combin

Ariana Torres-Knoop, Verena Schamboeck, Nitish Govindarajan, Pieter D. Iedema

Thermo-mechanical properties of polymer networks depend on functionality of the monomer precursors -- an association that is frequently exploited in materials science. We use molecular simulations to generate spatial networks from chemically different monomers with identical functionality and show that such networks have several universal graph-theoretical p

James Aaronson, David Ellis, Imre Leader

We show that the Union-Closed Conjecture holds for the union-closed family generated by the cyclic translates of any fixed set.

Michelangelo Bin, Daniele Astolfi, Lorenzo Marconi

Robustness is a basic property of any control system. In the context of linear output regulation, it was proved that embedding an internal model of the exogenous signals is necessary and sufficient to achieve tracking of the desired reference signals in spite of external disturbances and parametric uncertainties. This result is commonly known as "interna

Sita Rani, Aman Kataria, Vishal Sharma, Smarajit Ghosh

Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly to numerous application areas. The stormy expansion of smart devices and their credence for data transfer using wireless mechanics boosts their susceptibility to cyber-attacks. Consequently, the rate of cybercrime is

Philippe Robert, Gaetan Vignoud

Mathematical models of biological neural networks are associated to a rich and complex class of stochastic processes. In this paper, we consider a simple {\em plastic} neural network whose {\em connectivity/synaptic strength} $(W(t))$ depends on a set of activity-dependent processes to model {\em synaptic plasticity}, a well-studied mechanism from neuroscien

Zhaojing Luo, Sai Ho Yeung, Meihui Zhang, Kaiping Zheng

With the ever-increasing adoption of machine learning for data analytics, maintaining a machine learning pipeline is becoming more complex as both the datasets and trained models evolve with time. In a collaborative environment, the changes and updates due to pipeline evolution often cause cumbersome coordination and maintenance work, raising the costs and m

Guanghui Zhu, Zhuoer Xu, Xu Guo, Chunfeng Yuan

Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to Automated Feature Engineering (AutoFE) to replace expensive human labor. However, existing methods are computationally demanding due to treating AutoFE as a coarse-grained black-bo

Xingyao Chen, Eduard P. Kontar, Nicolina Chrysaphi, Natasha L. S. Jeffrey

Recent developments in astronomical radio telescopes opened new opportunities in imaging and spectroscopy of solar radio bursts at sub-second timescales. Imaging in narrow frequency bands has revealed temporal variations in the positions and source sizes that do not fit into the standard picture of type III solar radio bursts, and require a better understand

Sungu Kim, Makrand A. Khanwale, Robbyn K. Anand, Baskar Ganapathysubramanian

Finite element modeling of charged species transport has enabled analysis, design, and optimization of a diverse array of electrochemical and electrokinetic devices. These systems are represented by the Poisson-Nernst-Planck equations coupled with the Navier-Stokes equation, with a key quantity of interest being the current at the system boundaries. Accurate

Hanzi Huang, Yetian Huang, Yu He, Haoshuo Chen

We experimentally demonstrate a record net capacity per wavelength of 1.23~Tb/s over a single silicon-on-insulator (SOI) multimode waveguide for optical interconnects employing on-chip mode-division multiplexing and 11$\times$11 multiple-in-multiple-out (MIMO) digital signal processing.

Ling Liang, Defeng Sun, Kim-Chuan Toh

In this paper, we adopt the augmented Lagrangian method (ALM) to solve convex quadratic second-order cone programming problems (SOCPs). Fruitful results on the efficiency of the ALM have been established in the literature. Recently, it has been shown in [Cui, Sun, and Toh, {\em Math. Program.}, 178 (2019), pp. 381--415] that if the quadratic growth condition

Mario Vazquez Corte

I formulate and characterize the following two-stage choice behavior. The decision maker is endowed with two preferences. She shortlists all maximal alternatives according to the first preference. If the first preference is decisive, in the sense that it shortlists a unique alternative, then that alternative is the choice. If multiple alternatives are shortl

Fatima Haouari, Maram Hasanain, Reem Suwaileh, Tamer Elsayed

In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020. We collected 138 verified claims, mostly from popular fact-checking websites, and identified 9.4K relevant tweets to those claims. Tweets were manually-annotated by verac

Ofer Busani, Timo Seppäläinen

We derive a lower bound for the probability that a random walk with i.i.d.\ increments and small negative drift $μ$ exceeds the value $x>0$ by time $N$. When the moment generating functions are bounded in an interval around the origin, this probability can be bounded below by $1-O(x|μ| \log N)$. The approach is elementary and does not use strong approximatio

Min Zhang, Yao Shu, Kun He

Finite-sum optimization plays an important role in the area of machine learning, and hence has triggered a surge of interest in recent years. To address this optimization problem, various randomized incremental gradient methods have been proposed with guaranteed upper and lower complexity bounds for their convergence. Nonetheless, these lower bounds rely on

Ariane Nunes-Alves, Daria B. Kokh, Rebecca C. Wade

The protein-ligand residence time, tau, influences molecular function in biological networks and has been recognized as an important determinant of drug efficacy. To predict tau, computational methods must overcome the problem that tau often exceeds the timescales accessible to conventional molecular dynamics (MD) simulation. Here, we apply the tau-Random Ac

Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Hamed Haddadi

Training deep neural networks via federated learning allows clients to share, instead of the original data, only the model trained on their data. Prior work has demonstrated that in practice a client's private information, unrelated to the main learning task, can be discovered from the model's gradients, which compromises the promised privacy protect

Pedro Aniceto, Gabriel Lopes Cardoso, Suresh Nampuri

We approach the problem of constructing an explicit holographic dictionary for the AdS$_2$/CFT$_1$ correspondence in the context of higher derivative gravitational actions in AdS$_2$ space-times. These actions are obtained by an $S^2$ reduction of four-dimensional ${\cal N}=2$ Wilsonian effective actions with Weyl squared interactions restricted to constant

F. J. Vaquero-Caballero, D. Charlton, M. E. Mousa-Pasandi, D. J. Ives

Perturbed spectra are modelled to estimate OSNR for a single channel amplified link. Perturbation-dependent nonlinear noise is separated from constant ASE noise using a set of propagated perturbed spectra. A least mean square fitting is used to estimate OSNR with standard deviation of 0.16 dB.

Ángel L. Corps, Rafael A. Molina, Armando Relaño

Disordered interacting spin chains that undergo a many-body localization transition are characterized by two limiting behaviors where the dynamics are chaotic and integrable. However, the transition region between them is not fully understood yet. We propose here a possible finite-size precursor of a critical point that shows a typical finite-size scaling an

Jonas Kornprobst, Thomas F. Eibert

The combined source integral equation (CSIE) for the electric field on the surface of a perfect electrically conducting scatterer can be discretized very accurately with lowest-order Rao-Wilton-Glisson basis and testing functions if the combined-source (CS) condition is enforced in weak form. We introduce a technique to accelerate the iterative solution for

Chenjia Bai, Peng Liu, Kaiyu Liu, Lingxiao Wang

Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or even totally disregarded. Significant advances based on intrinsic motivation show promising results in simple environments but often get stuck in environments with multimodal and stochastic dynamics. In t

Kush Kinra, Manil T. Mohan

In this work, we consider the two and three-dimensional stochastic convective Brinkman-Forchheimer (2D and 3D SCBF) equations driven by irregular additive white noise $$\mathrm{d}\boldsymbol{u}-[\mu \Delta\boldsymbol{u}-(\boldsymbol{u}\cdot\nabla)\boldsymbol{u}-\alpha\boldsymbol{u}-\beta|\boldsymbol{u}|^{r-1}\boldsymbol{u}-\nabla p]\mathrm{d} t=\boldsymbol{f

Boyuan Ma, Xiang Yin, Di Wu, Xiaojuan Ban

The general aim of multi-focus image fusion is to gather focused regions of different images to generate a unique all-in-focus fused image. Deep learning based methods become the mainstream of image fusion by virtue of its powerful feature representation ability. However, most of the existing deep learning structures failed to balance fusion quality and end-

Barbara Franci, Sergio Grammatico

Generative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: a generator and a discriminator. These two neural networks compete against each other through an adversarial process that can be modeled as a stochastic Nash equilibrium problem. Since the associated training process is challenging, it is fundamenta

Matteo Bonato, Isabella Prandoni, Gianfranco De Zotti, Marisa Brienza

We present a study of the 1173 sources brighter than $S_{1.4\,\rm GHz}= 120\,μ$Jy detected over an area of $\simeq 1.4\,\hbox{deg}^{2}$ in the Lockman Hole field. Exploiting the multi-band information available in this field for $\sim$79% of the sample, sources have been classified into radio loud (RL) active galactic nuclei (AGNs), star forming galaxies (SF

Mouna Abdelli, Carlos Castro

We propose a numerical method to approximate the exact averaged boundary control of a family of wave equations depending on an unknown parameter sigma. More precisely the control, independent of sigma, that drives an initial data to a family of final states at time t = T, whose average in sigma is given. The idea is to project the control problem in the fini

Xingguang Zhong, Yuwei Wu, Dong Wang, Qianhao Wang

In this paper, we present a method to efficiently generate large, free, and guaranteed convex space among arbitrarily cluttered obstacles. Our method operates directly on point clouds, avoids expensive calculations, and processes thousands of points within a few milliseconds, which extremely suits embedded platforms. The base stone of our method is sphere fl

Arkin Dharawat, Ismini Lourentzou, Alex Morales, ChengXiang Zhai

Given the widespread dissemination of inaccurate medical advice related to the 2019 coronavirus pandemic (COVID-19), such as fake remedies, treatments and prevention suggestions, misinformation detection has emerged as an open problem of high importance and interest for the research community. Several works study health misinformation detection, yet little a

William D. Piñeros, Tsvi Tlusty

The design of small scale non-equilibrium steady states (NESS) is a challenging, open ended question. While similar equilibrium problems are tractable using standard thermodynamics, a generalized description for non-equilibrium systems is lacking, making the design problem particularly difficult. Here we show we can exploit the large deviation behavior of a

Yu-Chuan Cheng, Ting-Heng Hsieh, Jih-Chiang Tsai, Tzay-Ming Hong

All children enjoy inflating balloons and twisting them into different shapes and animals. Snapping the balloon into two separate compartments is a necessary step that bears resemblance to the pinch-off phenomenon for water droplet detached from the faucet. In addition to testing whether balloons exhibit the properties of self-similarity and memory effect th

Pavlos Avgoustinakis, Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Andreas L. Symeonidis

In this work, we address the problem of audio-based near-duplicate video retrieval. We propose the Audio Similarity Learning (AuSiL) approach that effectively captures temporal patterns of audio similarity between video pairs. For the robust similarity calculation between two videos, we first extract representative audio-based video descriptors by leveraging

Hiroshi Ishimoto

We prove Ibukiyama's conjectures on Siegel modular forms of half-integral weight and of degree 2 by using Arthur's multiplicity formula on the split odd special orthogonal group $\SO_5$ and Gan-Ichino's multiplicity formula on the metaplectic group $\Mp_4$. In the proof, the representation theory of the Jacobi groups also plays an important role.

Bing-Jyun Tsao, Roland Haas, Antonios Tsokaros

The initial condition problem for a binary neutron star system requires a Poisson equation solver for the velocity potential with a Neumann-like boundary condition on the surface of the star. Difficulties that arise in this boundary value problem are: a) the boundary is not known a-priori, but constitutes part of the solution of the problem; b) various terms

Weicheng Zang, Yossi Shvartzvald, Andrzej Udalski, Jennifer C. Yee

We report the discovery and analysis of a planet in the microlensing event OGLE-2018-BLG-0799. The planetary signal was observed by several ground-based telescopes, and the planet-host mass ratio is $q = (2.65 \pm 0.16) \times 10^{-3}$. The ground-based observations yield a constraint on the angular Einstein radius $\theta_{\rm E}$, and the microlensing para

Jiale Guo, Ziyao Liu, Kwok-Yan Lam, Jun Zhao

The pervasive adoption of Internet-connected digital services has led to a growing concern in the personal data privacy of their customers. On the other hand, machine learning (ML) techniques have been widely adopted by digital service providers to improve operational productivity and customer satisfaction. ML inevitably accesses and processes users' per

Tsuyoshi Ishizone, Tomoyuki Higuchi, Kazuyuki Nakamura

Variational inference (VI) combined with Bayesian nonlinear filtering produces state-of-the-art results for latent time-series modeling. A body of recent work has focused on sequential Monte Carlo (SMC) and its variants, e.g., forward filtering backward simulation (FFBSi). Although these studies have succeeded, serious problems remain in particle degeneracy

Jin Xu, Yinuo Guo, Junfeng Hu

Copying mechanism has been commonly used in neural paraphrasing networks and other text generation tasks, in which some important words in the input sequence are preserved in the output sequence. Similarly, in machine translation, we notice that there are certain words or phrases appearing in all good translations of one source text, and these words tend to

Parisa Ghane, Gahangir Hossain

Technology advancements made it easy to measure non-invasive and high-quality electroencephalograph (EEG) signals from human's brain. Hence, development of robust and high-performance AI algorithms becomes crucial to properly process the EEG signals and recognize the patterns, which lead to an appropriate control signal. Despite the advancements in proce

Luigi De Masi

We establish a partial rectifiability result for the free boundary of a $k$-varifold $V$. Namely, we first refine a theorem of Grüter and Jost by showing that the first variation of a general varifold with free boundary is a Radon measure. Next we show that if the mean curvature $H$ of $V$ is in $L^p$ for some $p \in [1,k]$, then the set of points where the

N. Dimakis, Genly Leon, Andronikos Paliathanasis

We present exact solutions in Einstein-aether theory in a static spherically symmetric background space with a spacelike aether field, as a difference with the usual selection of timelike aether field. We assume a coupling between the scalar field and the aether field introduced in the aether coefficients. The exact spacetimes describe hairy black hole solut

Xiaogang Wang, Marcelo H Ang, Gim Hee Lee

Point clouds are often sparse and incomplete, which imposes difficulties for real-world applications. Existing shape completion methods tend to generate rough shapes without fine-grained details. Considering this, we introduce a two-branch network for shape completion. The first branch is a cascaded shape completion sub-network to synthesize complete objects

Yuan Cai

This paper concerns the time growth of the highest-order energy of the systems of incompressible isotropic elastodynamics in two space dimensions. The global well-posedness of smooth solutions near equilibrium was first obtained by Lei [31] where the highest-order generalized energy may have certain growth in time. We improve above result and show that the h

Qiaoyuan Cheng, Engui Fan

We study the long-time asymptotic behavior of the focusing Fokas-Lenells (FL) equation $$ u_{xt}+αβ^2u-2iαβu_x-αu_{xx}-iαβ^2|u|^2u_x=0 \label{cs} $$ with generic initial data in a Sobolev space which supports bright soliton solutions. The FL equation is an integrable generalization of the well-known Schrodinger equation, and also linked to the derivative Sch

Linchen Qian, Jiasong Chen, Timur Urakov, Weiyong Gu

Ambiguity is inevitable in medical images, which often results in different image interpretations (e.g. object boundaries or segmentation maps) from different human experts. Thus, a model that learns the ambiguity and outputs a probability distribution of the target, would be valuable for medical applications to assess the uncertainty of diagnosis. In this p

Meng Cao, Yue Dong, Jiapeng Wu, Jackie Chi Kit Cheung

Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre-trained with self-supervised methods. However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge. We propose a post-editing corrector module to address this

Letian Chen, Rohan Paleja, Matthew Gombolay

Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration. However, modern LfD techniques, e.g. inverse reinforcement learning (IRL), assume users provide at least stochastically optimal demonstrations. This assumption fails to hold in most real-wo

Jasel Berra-Montiel, Alberto Molgado, Eduardo Torres-Cordero

Guided by recent developments towards the implementation of the deformation quantization program within the Loop Quantum Cosmology (LQC) formalism, in this paper we address the introduction of both the integral and differential representation of the star product for LQC. To this end, we consider the Weyl quantization map for cylindrical functions defined on

Shuxi Zeng, Murat Ali Bayir, Joesph J. Pfeiffer, Denis Charles

It is often critical for prediction models to be robust to distributional shifts between training and testing data. From a causal perspective, the challenge is to distinguish the stable causal relationships from the unstable spurious correlations across shifts. We describe a causal transfer random forest (CTRF) that combines existing training data with a sma

Wen-Hao Zhang, Xiao Liu, Peng Yin, Xing-Xiang Peng

Quantum state verification provides an efficient approach to characterize the reliability of quantum devices for generating certain target states. The figure of merit of a specific strategy is the estimated infidelity $ε$ of the tested state to the target state, given a certain number of performed measurements n. Entangled measurements constitute the globall

Ahmed H. Qureshi, Jiangeng Dong, Asfiya Baig, Michael C. Yip

Constrained motion planning is a challenging field of research, aiming for computationally efficient methods that can find a collision-free path on the constraint manifolds between a given start and goal configuration. These planning problems come up surprisingly frequently, such as in robot manipulation for performing daily life assistive tasks. However, fe

Aaron Hudson, Ali Shojaie

Differences between biological networks corresponding to disease conditions can help delineate the underlying disease mechanisms. Existing methods for differential network analysis do not account for dependence of networks on covariates. As a result, these approaches may detect spurious differential connections induced by the effect of the covariates on both

Ziqi Ma, Pranav Gokhale, Tian-Xing Zheng, Sisi Zhou

Quantum sensing is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use optimization techniques to search for encoder and decoder circuits that scalably improve sensitivity under given application and noise charac

Wenjuan Li, Huiju Wang, Dunyan Yan

We consider pointwise convergence of Schrödinger means $e^{it_{n}Δ}f(x)$ for $f \in H^{s}(\mathbb{R}^{2})$ and decreasing sequences $\{t_{n}\}_{n=1}^{\infty}$ converging to zero. The main theorem improves the previous results of [Sjölin, JFAA, 2018] and [Sjölin-Strömberg, JMAA, 2020] in $\mathbb{R}^{2}$. This study is based on investigating properties of Sch

Xiaoyang Ma, Jun Zhou, Tong Yang, Dechun Li

Phase stability and properties of two-dimensional phosphorus carbide, P$_x$C$_{1-x}$, are investigated using the first-principles method in combination with cluster expansion and Monte Carlo simulation. Monolayer P$_x$C$_{1-x}$ is found to be a phase separating system which indicates difficulty in fabricating monolayer P$_x$C$_{1-x}$ or crystalline P$_x$C$_{

Kunal Sawarkar, Meenkakshi Kodati

ML Data Curation process typically consist of heterogeneous & federated source systems with varied schema structures; requiring curation process to standardize metadata from different schemas to an inter-operable schema. This manual process of Metadata Harmonization & cataloging slows efficiency of ML-Ops lifecycle. We demonstrate automation of this step wit

Osman Asif Malik, Hayato Ushijima-Mwesigwa, Arnab Roy, Avradip Mandal

Many fundamental problems in data mining can be reduced to one or more NP-hard combinatorial optimization problems. Recent advances in novel technologies such as quantum and quantum-inspired hardware promise a substantial speedup for solving these problems compared to when using general purpose computers but often require the problem to be modeled in a speci

Mykola Matviichuk, Brent Pym, Travis Schedler

We establish a local model for the moduli space of holomorphic symplectic structures with logarithmic poles, near the locus of structures whose polar divisor is normal crossings. In contrast to the case without poles, the moduli space is singular: when the cohomology class of a symplectic structure satisfies certain linear equations with integer coefficients

Cole Hawkins, Xing Liu, Zheng Zhang

While post-training model compression can greatly reduce the inference cost of a deep neural network, uncompressed training still consumes a huge amount of hardware resources, run-time and energy. It is highly desirable to directly train a compact neural network from scratch with low memory and low computational cost. Low-rank tensor decomposition is one of

Jacob Imola, Takao Murakami, Kamalika Chaudhuri

Differentially private analysis of graphs is widely used for releasing statistics from sensitive graphs while still preserving user privacy. Most existing algorithms however are in a centralized privacy model, where a trusted data curator holds the entire graph. As this model raises a number of privacy and security issues -- such as, the trustworthiness of t

Matthew Ragoza, Tomohide Masuda, David Ryan Koes

Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models. Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent space. These methods have been increasingly successful at generating two dimensional molecules as SMILES strings

Jesse Haviland, Peter Corke

We present NEO, a fast and purely reactive motion controller for manipulators which can avoid static and dynamic obstacles while moving to the desired end-effector pose. Additionally, our controller maximises the manipulability of the robot during the trajectory, while avoiding joint position and velocity limits. NEO is wrapped into a strictly convex quadrat

Junyu He, Xin Ye, Junyu Lin, Mingyang Guo

We report the investigation on dipolar collisions in rotational state mixtures of ultracold bosonic $^{23}$Na$^{87}$Rb molecules. The large resonant dipole-dipole interaction between molecules in rotational states of opposite parities brings about significant modifications to their collisions, even when an electric field is not present. In this work, this ef

Shikib Mehri, Mihail Eric

A key challenge of dialog systems research is to effectively and efficiently adapt to new domains. A scalable paradigm for adaptation necessitates the development of generalizable models that perform well in few-shot settings. In this paper, we focus on the intent classification problem which aims to identify user intents given utterances addressed to the di

J. Albert, D. Budker, K. Chance, I. E. Gordon

This article is the second in a pair of articles on the topic of the generation of a two-color artificial star (which we term a "laser photometric ratio star," or LPRS) of de-excitation light from neutral sodium atoms in the mesosphere, for use in precision telescopic measurements in astronomy and atmospheric physics, and more specifically for the ca