arXiv papers from June 2020

Saeed Rashidi, Matthew Denton, Srinivas Sridharan, Sudarshan Srinivasan

Deep Learning (DL) training platforms are built by interconnecting multiple DL accelerators (e.g., GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandwidth. However, as we identify in this work, driving this bandwidth is quite challenging. This is because there is a pernicious balance between using the accelerator's compute and m

Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood

We introduce a novel objective for training deep generative time-series models with discrete latent variables for which supervision is only sparsely available. This instance of semi-supervised learning is challenging for existing methods, because the exponential number of possible discrete latent configurations results in high variance gradient estimators. W

Juyang Weng

Universal Turing Machines [29, 10, 18] are well known in computer science but they are about manual programming for general purposes. Although human children perform conscious learning (i.e., learning while being conscious) from infancy [24, 23, 14, 4], it is unknown that Universal Turing Machiness can facilitate not only our understanding of Autonomous Prog

Robert Konik, Márton Lájer, Giuseppe Mussardo

One of the most striking but mysterious properties of the sinh-Gordon model (ShG) is the $b \rightarrow 1/b$ self-duality of its $S$-matrix, of which there is no trace in its Lagrangian formulation. Here $b$ is the coupling appearing in the model's eponymous hyperbolic cosine present in its Lagrangian, $\cosh(b\phi)$. In this paper we develop truncated spect

Guanghui Lan, Edwin Romeijn, Zhiqiang Zhou

Conditional gradient methods have attracted much attention in both machine learning and optimization communities recently. These simple methods can guarantee the generation of sparse solutions. In addition, without the computation of full gradients, they can handle huge-scale problems sometimes even with an exponentially increasing number of decision variabl

Guantao Chen, Xiaowei Yu, Yi Zhao

For a given graph $H$, the Ramsey number $r(H)$ is the minimum $N$ such that any 2-edge-coloring of the complete graph $K_N$ yields a monochromatic copy of $H$. Given a positive integer $n$, a \emph{fan }$F_n$ is a graph formed by $n$ triangles that share one common vertex. We show that ${9n}/{2}-5\le r(F_n)\le {11n}/{2} + 6$ for any $n$. This improves previ

Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda

We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed to first fit the training data with clean labels during an "early learning" phase, before eventually memorizing the examples with false labels. We prove that early learning and me

Ana M. Bianco, Graciela Boente, Wenceslao Gonzalez-Manteiga

The Receiver Operating Characteristic (ROC) curve is a useful tool that measures the discriminating power of a continuous variable or the accuracy of a pharmaceutical or medical test to distinguish between two conditions or classes. In certain situations, the practitioner may be able to measure some covariates related to the diagnostic variable which can inc

Nicholas Choma, Daniel Murnane, Xiangyang Ju, Paolo Calafiura

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segment

Yingjie Fei, Zhuoran Yang, Zhaoran Wang, Qiaomin Xie

We consider reinforcement learning (RL) in episodic MDPs with adversarial full-information reward feedback and unknown fixed transition kernels. We propose two model-free policy optimization algorithms, POWER and POWER++, and establish guarantees for their dynamic regret. Compared with the classical notion of static regret, dynamic regret is a stronger notio

Eric Wong, Tim Schneider, Joerg Schmitt, Frank R. Schmidt

Recent work has shown that it is possible to learn neural networks with provable guarantees on the output of the model when subject to input perturbations, however these works have focused primarily on defending against adversarial examples for image classifiers. In this paper, we study how these provable guarantees can be naturally applied to other real wor

Aminollah Khormali, DaeHun Nyang, David Mohaisen

Deep learning models are widely used in a range of application areas, such as computer vision, computer security, etc. However, deep learning models are vulnerable to Adversarial Examples (AEs),carefully crafted samples to deceive those models. Recent studies have introduced new adversarial attack methods, but, to the best of our knowledge, none provided gua

Eric Dodds, Jack Culpepper, Simao Herdade, Yang Zhang

Image retrieval with natural language feedback offers the promise of catalog search based on fine-grained visual features that go beyond objects and binary attributes, facilitating real-world applications such as e-commerce. Our Modality-Agnostic Attention Fusion (MAAF) model combines image and text features and outperforms existing approaches on two visual

Anurag Kumar, Vamsi Krishna Ithapu

An important problem in machine auditory perception is to recognize and detect sound events. In this paper, we propose a sequential self-teaching approach to learning sounds. Our main proposition is that it is harder to learn sounds in adverse situations such as from weakly labeled and/or noisy labeled data, and in these situations a single stage of learning

José Lipovetzky, Mariano Garcia-Inza, Macarena Rodríguez Cañete, Gabriel Redin

We present the results after 2.5 years in or-bit of Total Ionizing Dose (TID) measurements done using Metal Oxide Semiconductor (MOS) dosimeters on the MeMOSat board. The MeMOSat board was launched on July 19th 2014 at the BugSat-1 "Tita" microsatellite developed by Satellogic to stay at LEO. We used as dosimeters p-channel Commercial Off The Shelf (COTS) MO

Antonino Marciano, Deen Chen, Filippo Fabrocini*, Chris Fields

Our work intends to show that: (1) Quantum Neural Networks (QNN) can be mapped onto spinnetworks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theories (TQFT); (2) Deep Neural Networks (DNN) are a subcase of QNN, in the sense that they emerge as the semiclassical limit of QNN;

G. A. Sanca, M. Barella, F. Gomez Marlasca, G. Rodríguez

In this work the first results obtained by LabOSat-01 platform are presented. This platform was designed for testing custom devices on board of small satellites. Two LabOSat-01 type boards were launched and placed into Low Earth Orbit (LEO) on May 30, 2016. We present here an analysis of data collected by one of these boards during the first days of mission.

Kumar Pratik, Bhaskar D. Rao, Max Welling

In this paper, we present a novel neural network for MIMO symbol detection. It is motivated by several important considerations in wireless communication systems; permutation equivariance and a variable number of users. The neural detector learns an iterative decoding algorithm that is implemented as a stack of iterative units. Each iterative unit is a neura

Changchang Xi

We establish relations between representation dimensions of two algebras connected by a Frobenius bimodule or extension. Consequently, upper bounds and equality formulas for representation dimensions of group algebras, symmetric separably equivalent algebras and crossed products are obtained. Particularly, for any subgroup H of a finite group G, if [G:H] is

Olga K. Sil'chenko, Alexei Yu. Kniazev, Ekaterina M. Chudakova

We have obtained imaging data in two photometric bands, g and r, for a sample of 42 isolated lenticular galaxies with the Las Cumbres Observatory one-meter telescope network. We have analyzed the structure of their large-scale stellar disks. The parameters of surface brightness distributions have been determined including the radial profile shapes and disk t

Enrico Speranza, Nora Weickgenannt

The relativistic treatment of spin is a fundamental subject which has an old history. In various physical contexts it is necessary to separate the relativistic total angular momentum into an orbital and spin contribution. However, such decomposition is affected by ambiguities since one can always redefine the orbital and spin part through the so-called pseud

Justin A. Kasin, Ioannis Papastathopoulos

Modelling wildfire occurrences is important for disaster management including prevention, detection and suppression of large catastrophic events. We present a spatial Poisson hurdle model for exploring geographical variation of monthly counts of wildfire occurrences and apply it to Indonesia and Australia. The model includes two a priori independent spatiall

Patrick Dondl, Matteo Novaga, Stephan Wojtowytsch, Steve Wolff-Vorbeck

We consider a version of Gamow's liquid drop model with a short range attractive perimeter-penalizing potential and a long-range Coulomb interaction of a uniformly charged mass in $\R^3$. Here we constrain ourselves to minimizing among the class of shapes that are columnar, i.e., constant in one spatial direction. Using the standard perimeter in the energy w

Yassine El Ouahidi, Matis Feller, Matthieu Talagas, Bastien Pasdeloup

In this paper, we introduce a novel and interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies. Contrary to existing approaches, we propose here to capture complex patterns in the repartitions of their cells using histograms, and compare subjects on the basis of these repartitions. We describe her

Kasra Hejazi, Xiao Chen, Leon Balents

We propose a method for studying the strong interaction regimes in twisted bilayer graphene using hybrid Wannier functions, that are Wannier-like in one direction and Bloch-like in the other. We focus on the active bands as given by the continuum model proposed by Bistritzer and MacDonald, and discuss the properties of corresponding hybrid Wannier functions.

Sapan Karki, Brett Altschul

Lorentz- and CPT-violating models of electrodynamics with Chern-Simons terms are typically plagued by various sorts of instabilities. However, when the Chern-Simons term arises from a slow time variation in a pseudoscalar field with an axion-like electromagnetic coupling, the total energy of the theory is bounded below. We examine the behavior of such a theo

Georgios Kotsalis, Guanghui Lan, Arkadi Nemirovski

This work addresses the finite-horizon robust covariance control problem for discrete-time, partially observable, linear system affected by random zero mean noise and deterministic but unknown disturbances restricted to lie in what is called ellitopic uncertainty set (e.g., finite intersection of centered at the origin ellipsoids/elliptic cylinders). Perform

Maarten Van Segbroeck, Harish Mallidih, Brian King, I-Fan Chen

Acoustic models in real-time speech recognition systems typically stack multiple unidirectional LSTM layers to process the acoustic frames over time. Performance improvements over vanilla LSTM architectures have been reported by prepending a stack of frequency-LSTM (FLSTM) layers to the time LSTM. These FLSTM layers can learn a more robust input feature to t

Dayu Zhu, Zhaocheng Liu, Lakshmi Raju, Andrew S. Kim

Flat optics foresees a new era of ultra-compact optical devices, where metasurfaces serve as the foundation. Conventional designs of metasurfaces start with a certain structure as the prototype, followed by an extensive parametric sweep to accommodate the requirements of phase and amplitude of the emerging light. Regardless of how computation-consuming the p

Ruben Bueno, Naomichi Hatano

First we report that the adjacency matrices of real-world complex networks systematically have null eigenspaces with much higher dimensions than that of random networks. These null eigenvalues are caused by duplication mechanisms leading to structures with local symmetries which should be more present in complex organizations. The associated eigenvectors of

Mazen Ali, Anthony Nouy

We study the approximation by tensor networks (TNs) of functions from classical smoothness classes. The considered approximation tool combines a tensorization of functions in $L^p([0,1))$, which allows to identify a univariate function with a multivariate function (or tensor), and the use of tree tensor networks (the tensor train format) for exploiting low-r

André Reggio, Robin Delabays, Philippe Jacquod

Inspired by the Deffuant and Hegselmann-Krause models of opinion dynamics, we extend the Kuramoto model to account for confidence bounds, i.e., vanishing interactions between pairs of oscillators when their phases differ by more than a certain value. We focus on Kuramoto oscillators with peaked, bimodal distribution of natural frequencies. We show that, in t

Heather Z. Brooks, Unchitta Kanjanasaratool, Yacoub H. Kureh, Mason A. Porter

The COVID-19 pandemic has led to significant changes in how people are currently living their lives. To determine how to best reduce the effects of the pandemic and start reopening societies, governments have drawn insights from mathematical models of the spread of infectious diseases. In this article, we give an introduction to a family of mathematical mode

Fábio Machado Gil, Nuno M. Garcia, Bárbara Matos, Nuno Pombo

The User Datagram Protocol (UDP) and other similar protocols send the application data from the source machine to the destination machine inside segments, without foreseeing nor allowing for any type of control on the transmission or success metrics. These protocols are very convenient for e.g. real time data transmission. But when the reliability of the tra

Lucas Miranda, Riya Paul, Benno Pütz, Bertram Müller-Myhsok

Psychiatric disorders have historically been classified using symptom information alone. With the advent of new technologies that allowed researchers to investigate brain mechanisms more directly, interest in the mechanistic rationale behind defined pathologies and aetiology redefinition has greatly increased. This is particularly appealing for the field of

David A. Plaisted

A version of the situation calculus in which situations are represented as first-order terms is presented. Fluents can be computed from the term structure, and actions on the situations correspond to rewrite rules on the terms. Actions that only depend on or influence a subset of the fluents can be described as rewrite rules that operate on subterms of the t

Ivan Voitalov, Pim van der Hoorn, Maksim Kitsak, Fragkiskos Papadopoulos

Maximum entropy null models of networks come in different flavors that depend on the type of constraints under which entropy is maximized. If the constraints are on degree sequences or distributions, we are dealing with configuration models. If the degree sequence is constrained exactly, the corresponding microcanonical ensemble of random graphs with a given

Juliane Capaverde, Ariane M. Masuda, Virgínia M. Rodrigues

Let $\mathbb{F}_q$ be a finite field of odd characteristic. We study R\'edei functions that induce permutations over $\mathbb{P}^1(\mathbb{F}_q)$ whose cycle decomposition contains only cycles of length $1$ and $j$, for an integer $j\geq 2$. When $j$ is $4$ or a prime number, we give necessary and sufficient conditions for a R\'edei permutation of this type

Filomena Feo, Juan Luis Vázquez, Bruno Volzone

We prove the existence of self-similar fundamental solutions (SSF) of the anisotropic porous medium equation in the suitable fast diffusion range. Each of such SSF solutions is uniquely determined by its mass. We also obtain the asymptotic behaviour of all finite-mass solutions in terms of the family of self-similar fundamental solutions. Time decay rates ar

Elena A. Kaye, Emily A. Aherne, Cihan Duzgol, Ida Häggström

Purpose: To investigate feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN). Materials and Methods: Raw data from the prostate DWI scans were retrospectively gathered (between July 2018 and Jul

Cheng Shu

We compute the character table of $\text{GL}_n(\mathbb{F}_q)\rtimes\!<\!\sigma\!>\!$, $\sigma$ being an order 2 exterior automorphism.

Chris Lin, Gerald J. Sun, Krishna C. Bulusu, Jonathan R. Dry

Graph Neural Networks (GNNs) are versatile, powerful machine learning methods that enable graph structure and feature representation learning, and have applications across many domains. For applications critically requiring interpretation, attention-based GNNs have been leveraged. However, these approaches either rely on specific model architectures or lack

Mazen Ali, Anthony Nouy

We study the approximation of functions by tensor networks (TNs). We show that Lebesgue $L^p$-spaces in one dimension can be identified with tensor product spaces of arbitrary order through tensorization. We use this tensor product structure to define subsets of $L^p$ of rank-structured functions of finite representation complexity. These subsets are then us

Seong Soon Jo, Akshay Singh, Liqiu Yang, Subodh C. Tiwari

A thorough understanding of native oxides is essential for designing semiconductor devices. Here we report a study of the rate and mechanisms of spontaneous oxidation of bulk single crystals of ZrS$_x$Se$_{2-x}$ alloys and MoS$_2$. ZrS$_x$Se$_{2-x}$ alloys oxidize rapidly, and the oxidation rate increases with Se content. Oxidation of basal surfaces is initi

Yacine Aoun

For a family of random-cluster models with cluster weights $q\geq 1$, we prove that the probability that $0$ is connected to $x$ is asymptotically equal to $\tfrac{1}{q}\chi(\beta)^{2}\beta J_{0,x}$. The method developed in this article can be applied to any spin model for which there exists a random-cluster representation which is one-monotonic.

Luis Enrique Adame, Luis Manuel Rivera, Ana Laura Trujillo-Negrete

Let $G$ be a simple graph of order $n$. The double vertex graph $F_2(G)$ of $G$ is the graph whose vertices are the $2$-subsets of $V(G)$, where two vertices are adjacent in $F_2(G)$ if their symmetric difference is a pair of adjacent vertices in $G$. A generalization of this graph is the complete double vertex graph $M_2(G)$ of $G$, defined as the graph who

Océane Boulais, Ben Woodward, Brian Schlining, Lonny Lundsten

Thousands of hours of marine video data are collected annually from remotely operated vehicles (ROVs) and other underwater assets. However, current manual methods of analysis impede the full utilization of collected data for real time algorithms for ROV and large biodiversity analyses. FathomNet is a novel baseline image training set, optimized to accelerate

Zhe Huang, Aamir Hasan, Kazuki Shin, Ruohua Li

Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians. Critical insights from human intention and behavioral patterns need to be integrated to effectively forecast long-term pedestrian behavior. Thus, we propose a framework incorporating a Mutable Intention Filter and a Warp LSTM (MIF-WLSTM) to simul

Hojin Jang, Syed Suleman Abbas Zaidi, Xavier Boix, Neeraj Prasad

Deep Convolutional Neural Networks (DCNNs) have demonstrated impressive robustness to recognize objects under transformations (eg. blur or noise) when these transformations are included in the training set. A hypothesis to explain such robustness is that DCNNs develop invariant neural representations that remain unaltered when the image is transformed. Howev

Wojciech Czerwiński, Georg Zetzsche

We study the problem of regular separability of languages of vector addition systems with states (VASS). It asks whether for two given VASS languages K and L, there exists a regular language R that includes K and is disjoint from L. While decidability of the problem in full generality remains an open question, there are several subclasses for which decidabil

Igor Protasov, Ksenia Protasova

Given a discrete group $G$, we identify the Stone-$\check C$ech compactification $\beta G$ with the set of all ultrafilters on $G$ and put $G^\ast =\beta G\setminus G$. The action $G$ on $G$ by the conjugations $(g,x)\mapsto g^{-1}xg$ induces the action of $G$ on $G^\ast$ by $(g, p)\mapsto p^g $, $p^g = \{ g^{-1} Pg: P\in p\}$. We study interplays between th

Brianna Lacy, Adam Burrows

JWST will provide moderate resolution transit spectra with continuous wavelength coverage from the optical to the mid-infrared for the first time. In this paper, we illustrate how different aerosol species, size-distributions, and spatial distributions encode information in JWST transit spectra. We use the transit spectral modeling code METIS, along with Mie

Imene Trigui, Sofiene Affes, Marco Di Renzo, Dushantha Nalin K. Jayakody

In this paper, we develop an innovative approach to quantitatively characterize the performance of ultra-dense wireless networks in a plethora of propagation environments. The proposed framework has the potential of significantly simplifying the cumbersome procedure of analyzing the coverage probability and allowing the remarkable unification of single- and

Paulo Cesar Ventura, Yamir Moreno, Francisco A. Rodrigues

Diseases and other contagion phenomena in nature and society can interact asymmetrically, such that one can benefit from the other, which in turn impairs the first, in analogy with predator-prey systems. Here, we consider two models for interacting disease-like dynamics with asymmetric interactions and different associated time scales. Using rate equations f

Linchuan Wei, Andres Gomez, Simge Kucukyavuz

Motivated by modern regression applications, in this paper, we study the convexification of a class of convex optimization problems with indicator variables and combinatorial constraints on the indicators. Unlike most of the previous work on convexification of sparse regression problems, we simultaneously consider the nonlinear non-separable objective, indic

Andrew Giffin, Brian Reich, Shu Yang, Ana Rappold

Many spatial phenomena exhibit treatment interference where treatments at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal inference do not apply. We propose a new causal framework to recover direct and spill-over effects in the presence of spatial i

Noam R. Izenberg, Diana M. Gentry, David J. Smith, Martha S. Gilmore

Ancient Venus and Earth may have been similar in crucial ways for the development of life, such as liquid water oceans, land-ocean interfaces, favorable chemical ingredients and energy pathways. If life ever developed on, or was transported to, early Venus from elsewhere, it might have thrived, expanded and then survived the changes that have led to an inhos

Laila Abouzaid, Essaid Sabir, Halima Elbiaze, Ahmed Errami

Nowadays, Unmanned Aerial Vehicles (UAVs) are being used in several novel applications, especially in the telecommunication domain. However, ensuring UAV communication and networking for the purpose of a specific application is still challenging. Indeed, due to the mobility of a UAV in a vast area, permanent connectivity over the back-haul is very sporadic a

Ahmed J. Zerouali

Following Boalch-Yamakawa and Meinrenken, we consider a certain class of moduli spaces on bordered surfaces from a quasi-Hamiltonian perspective. For a given Lie group $G$, these character varieties parametrize flat $G$-connections on "twisted" local systems, in the sense that the transition functions take values in $G\rtimes\mathrm{Aut}(G)$. After reviewing

Alexander Bork, Sebastian Junges, Joost-Pieter Katoen, Tim Quatmann

The verification problem in MDPs asks whether, for any policy resolving the nondeterminism, the probability that something bad happens is bounded by some given threshold. This verification problem is often overly pessimistic, as the policies it considers may depend on the complete system state. This paper considers the verification problem for partially obse

Goodarz Mehr, Azim Eskandarian

This paper presents an on-board advance warning system for vehicles based on a probabilistic prediction model that advises them on when to change lanes to reach a highway diverge on time. The system is based on a model that estimates the probability of reaching a goal state on the road using one or multiple lane changes. This estimate is based on several tra

Russell Schwartz, Pratap Tokekar

The problem of assigning agents to tasks is a central computational challenge in many multi-agent autonomous systems. However, in the real world, agents are not always perfect and may fail due to a number of reasons. A motivating application is where the agents are robots that operate in the physical world and are susceptible to failures. This paper studies

Siting Liu, Levon Nurbekyan

We extend the methods from Nurbekyan, Saude "Fourier approximation methods for first-order nonlocal mean-field games" [Port. Math. 75 (2018), no. 3-4] and Liu, Jacobs, Li, Nurbekyan, Osher "Computational methods for nonlocal mean field games with applications" [arXiv:2004.12210] to a class of non-potential mean-field game (MFG) systems with mixed couplings.

N. G. Kruzhilin, S. Yu. Orevkov

Boileau and Rudolph called a link $L$ in the $3$-sphere a $\bf C$-boundary if it can be realized as the intersection of an algebraic curve $A$ in $\bf C^2$ with the boundary of a smooth embedded $4$-ball $B$. They showed that some links are not $\bf C$-boundaries. We say that $L$ is a strong $\bf C$-boundary if $A\setminus B$ is connected. In particular, all

Joseph Y. Cheng, Hanlin Goh, Kaan Dogrusoz, Oncel Tuzel

Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100). To handle these challenges, we propose a self-supervised approach based on contrastive learning to model biosignals with a reduced reliance on labeled data and with fewer subjects. In this regime of limi

Santanu Pakhira, N. S. Sangeetha, V. Smetana, A. -V. Mudring

The ternary-arsenide compound BaCo2As2 was previously proposed to be in proximity to a quantum-critical point where long-range ferromagnetic (FM) order is suppressed by quantum fluctuations. Here we report the effect of Ir substitution for Co on the magnetic and thermal properties of Ba[Co(1-x)Ir(x)]2As2 (0 <= x <= 0.25) single crystals. These compositions a

Suzie Brown, Paul A. Jenkins, Adam M. Johansen, Jere Koskela

We present simple conditions under which the limiting genealogical process associated with a class of interacting particle systems with non-neutral selection mechanisms, as the number of particles grows, is a time-rescaled Kingman coalescent. Sequential Monte Carlo algorithms are popular methods for approximating integrals in problems such as non-linear filt

Amir Rasouli

Vision-based prediction algorithms have a wide range of applications including autonomous driving, surveillance, human-robot interaction, weather prediction. The objective of this paper is to provide an overview of the field in the past five years with a particular focus on deep learning approaches. For this purpose, we categorize these algorithms into video

Matthias Maier, Martin Kronbichler

We discuss the efficient implementation of a high-performance second-order collocation-type finite-element scheme for solving the compressible Euler equations of gas dynamics on unstructured meshes. The solver is based on the convex limiting technique introduced by Guermond et al. (SIAM J. Sci. Comput. 40, A3211-A3239, 2018). As such it is invariant-domain p

Abhinav Sagar, RajKumar Soundrapandiyan

In this paper, we present a novel neural network using multi scale feature fusion at various scales for accurate and efficient semantic image segmentation. We used ResNet based feature extractor, dilated convolutional layers in downsampling part, atrous convolutional layers in the upsampling part and used concat operation to merge them. A new attention modul

Stepan Orevkov

We prove that if a quasipositive link can be represented by an alternating diagram satisfying the condition that no pair of Seifert circles is connected by a single crossing, then the diagram is positive and the link is strongly quasipositive.

Tajdar Mufti

Studying extent of dynamical generation of mass in a quantum field theory, which may have non-perturbative attributes, is an essential step towards complete understanding of the theory. It has historic relevance to interactions including fermions. However, as search of further fundamental scalars continues, inquiring into the possibility of dynamical generat

Zahed Allahyari, Artem R. Oganov

Organizing a chemical space so that elements with similar properties would take neighboring places in a sequence can help to predict new materials. In this paper, we propose a universal method of generating such a one-dimensional sequence of elements, i.e. at arbitrary pressure, which could be used to create a well-structured chemical space of materials and

Szabolcs Ivan

A linear ordering is called context-free if it is the lexicographic ordering of some context-free language and is called scattered if it has no dense subordering. Each scattered ordering has an associated ordinal, called its rank. It is known that scattered context-free (regular, resp.) orderings have rank less than $\omega^\omega$ ($\omega$, resp). In this

Biao Ma

In this paper, we study the curve shortening flow (CSF) on Riemann surfaces. We generalize Huisken's comparison function to Riemann surfaces and surfaces with conic singularities. We reprove the Gage-Hamilton-Grayson theorem on surfaces. We also prove that for embedded simple closed curves, CSF can not touch conic singularities with cone angles $\leq \pi$.

Hansol Lee, René F. Kizilcec

Predictive models for identifying at-risk students early can help teaching staff direct resources to better support them, but there is a growing concern about the fairness of algorithmic systems in education. Predictive models may inadvertently introduce bias in who receives support and thereby exacerbate existing inequities. We examine this issue by buildin

Daxi Zhang, Amir Behbahanian, Nicholas A. Roberts

In this article, we are proposing a thorough analysis of the cross, and the in-plane thermal conductivity of thin-film materials based on the 3$\omega$ method. The analysis accommodates a 2D mathematical heat transfer model of a semi-infinite body and the details of the sample preparation followed by the measurement process. The presented mathematical model

Mariya Romanova, Vojtěch Vlček

We present two new developments for computing excited state energies within the $GW$ approximation. First, calculations of the Green's function and the screened Coulomb interaction are decomposed into two parts: one is deterministic while the other relies on stochastic sampling. Second, this separation allows constructing a subspace self-energy, which contai

Sebastian Junges, Nils Jansen, Sanjit A. Seshia

Partially-Observable Markov Decision Processes (POMDPs) are a well-known stochastic model for sequential decision making under limited information. We consider the EXPTIME-hard problem of synthesising policies that almost-surely reach some goal state without ever visiting a bad state. In particular, we are interested in computing the winning region, that is,

Jiaqi Jiang, Mingkun Chen, Jonathan A. Fan

The data sciences revolution is poised to transform the way photonic systems are simulated and designed. Photonics are in many ways an ideal substrate for machine learning: the objective of much of computational electromagnetics is the capture of non-linear relationships in high dimensional spaces, which is the core strength of neural networks. Additionally,

Sahar Siddiqui, Elena Sizikova, Gemma Roig, Najib J. Majaj

Scene text recognition models have advanced greatly in recent years. Inspired by human reading we characterize two important scene text recognition models by measuring their domains i.e. the range of stimulus images that they can read. The domain specifies the ability of readers to generalize to different word lengths, fonts, and amounts of occlusion. These

Constantinos Skordis, Tom Zlosnik

We propose a relativistic gravitational theory leading to modified Newtonian dynamics, a paradigm that explains the observed universal galactic acceleration scale and related phenomenology. We discuss phenomenological requirements leading to its construction and demonstrate its agreement with the observed cosmic microwave background and matter power spectra

Semih Cayci, Atilla Eryilmaz, R. Srikant

Time-constrained decision processes have been ubiquitous in many fundamental applications in physics, biology and computer science. Recently, restart strategies have gained significant attention for boosting the efficiency of time-constrained processes by expediting the completion times. In this work, we investigate the bandit problem with controlled restart

Xiao-Yue Gong, David Simchi-Levi

Motivated by the episodic version of the classical inventory control problem, we propose a new Q-learning-based algorithm, Elimination-Based Half-Q-Learning (HQL), that enjoys improved efficiency over existing algorithms for a wide variety of problems in the one-sided-feedback setting. We also provide a simpler variant of the algorithm, Full-Q-Learning (FQL)

Wilten Nicola, Sue Ann Campbell

The synchronization of different brain regions is widely observed under both normal and pathological conditions such as epilepsy. However, the relationship between the dynamics of these brain regions, the connectivity between them, and the ability to synchronize remains an open question. We investigated the problem of inter-region synchronization in networks

Hans Christianson, Dylan Muckerman

The purpose of this paper is to study the effect of conformal perturbations on the local smoothing effect for the Schr\"odinger equation on surfaces of revolution. The paper \cite{ChWu-lsm} studied the Schr\"odinger equation on surfaces of revolution with one trapped orbit. The dynamics near this trapping were unstable, but degenerately so. Beginning from th

Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Culatana

Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples. Existing approaches search globally for the optimal examples to label, scaling linearly or even quadratically with the unlabeled data. In this paper, we improve the computational efficiency of active learning and search methods

Dinuka Sahabandu, Shana Moothedath, Joey Allen, Linda Bushnell

Advanced Persistent Threats (APTs) are stealthy attacks that threaten the security and privacy of sensitive information. Interactions of APTs with victim system introduce information flows that are recorded in the system logs. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism for detecting APTs. DIFT taints information flows origina

Michael Gfrerer

A Trace-Finite-Cell-Method for the numerical analysis of thin shells is presented combining concepts of the TraceFEM and the Finite-Cell-Method. As an underlying shell model we use the Koiter model, which we re-derive in strong form based on first principles of continuum mechanics by recasting well-known relations formulated in local coordinates to a formula

Amir Hertz, Rana Hanocka, Raja Giryes, Daniel Cohen-Or

Recently, deep generative adversarial networks for image generation have advanced rapidly; yet, only a small amount of research has focused on generative models for irregular structures, particularly meshes. Nonetheless, mesh generation and synthesis remains a fundamental topic in computer graphics. In this work, we propose a novel framework for synthesizing

Rasim Volga Ovali, Ramazan Sahin, Alpan Bek, Mehmet Emre Tasgin

A recent study shows that: when a long lifetime particle is positioned near a plasmonic metal nanoparticle, lifetime of plasmon oscillations extends, but, "only" near that long-life particle [PRB 101, 035416 (2020)]. Here, we show that this phenomenon can be utilized for ultrahigh (single-molecule) resolution ultrafast apertureless (scattering) SNOM applicat

Peter C. Sercel, Zeev Valy Vardeny, Alexander L. Efros

We demonstrate theoretically that non-chiral perovskite layers can exhibit circular dichroism (CD) in the absence of a magnetic field and without chiral activation by chiral molecules. The effect is shown to be due to splitting of helical excitonic states which can form in structures of orthorhombic or lower symmetry that exhibit Rashba spin effects. The sel

T. Byvank, D. A. Endrizzi, C. B. Forest, S. J. Langendorf

We present experimental data providing evidence for the formation of transient ($\sim 20~\mu$s) plasmas that are simultaneously weakly magnetized (i.e., Hall magnetization parameter $\omega \tau > 1$) and dominated by thermal pressure (i.e., ratio of thermal-to-magnetic pressure $\beta > 1$). Particle collisional mean free paths are an appreciable fraction o

Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li

Transformers are one of the most important machine learning workloads today. Training one is a very compute-intensive task, often taking days or weeks, and significant attention has been given to optimizing transformers. Despite this, existing implementations do not efficiently utilize GPUs. We find that data movement is the key bottleneck when training. Due

Amir Babak Aazami

On a smooth $n$-manifold $M$ with $n \geq 3$, we study pairs $(g,T)$ consisting of a Riemannian metric $g$ and a unit length closed vector field $T$. Motivated by how Ricci solitons generalize Einstein metrics via a distinguished vector field, we propose to generalize space forms by considering those pairs $(g,T)$ whose corresponding Lorentzian metric $g_{\s

Christopher D. C. Hawthorne

The problem of characterizing which automatic sets of integers are stable is here solved. Given a positive integer $d$ and a subset $A\subseteq \mathbb{Z}$ whose set of representations base $d$ is recognized by a finite automaton, a necessary condition is found for $x+y\in A$ to be a stable formula in $\operatorname{Th}(\mathbb{Z},+,A)$. Combined with a theo

Mostafa Fazly, Wen Yang

We develop a monotonicity formula for solutions of the fractional Toda system $$ (-\Delta)^s f_\alpha = e^{-(f_{\alpha+1}-f_\alpha)} - e^{-(f_\alpha-f_{\alpha-1})} \quad \text{in} \ \ \mathbb R^n,$$ when $0<s<1$, $\alpha=1,\cdots,Q$, $f_0=-\infty$, $f_{Q+1}=\infty$ and $Q \ge2$ is the number of equations in this system. We then apply this formula, technical

Thomas Stuttard, Mikkel Jensen

Neutrinos undergoing stochastic perturbations as they propagate experience decoherence, damping neutrino oscillations over distance. Such perturbations may result from fluctuations in space-time itself if gravity is a quantum force, including interactions between neutrinos and virtual black holes. In this work we model the influence of heuristic neutrino-vir

Boyuan Pan, Yazheng Yang, Kaizhao Liang, Bhavya Kailkhura

Recent advances in maximizing mutual information (MI) between the source and target have demonstrated its effectiveness in text generation. However, previous works paid little attention to modeling the backward network of MI (i.e., dependency from the target to the source), which is crucial to the tightness of the variational information maximization lower b

Maria Vasilyeva, Aleksei Tyrylgin, Donald L. Brown, Anirban Mondal

In this paper, we consider the numerical solution of the poroelasticity problem with stochastic properties. We present a Two-stage Markov Chain Monte Carlo method for geomechanical subsidence. In this work, we study two techniques of preconditioning: (MS) multiscale method for model order reduction and (ML) machine learning technique. The purpose of precondi