October 2020 arXiv papers — page 53

Rose Wagstaffe

We prove that the 2-category of skeletally small abelian categories with exact monoidal structures is anti-equivalent to the 2-category of fp-hom-closed definable additive categories satisfying an exactness criterion. For a fixed finitely accessible category $\mathcal{C}$ with products and a monoidal structure satisfying the appropriate assumptions, we provi

Zeyi Huang, Yang Zou, Vijayakumar Bhagavatula, Dong Huang

Weakly Supervised Object Detection (WSOD) has emerged as an effective tool to train object detectors using only the image-level category labels. However, without object-level labels, WSOD detectors are prone to detect bounding boxes on salient objects, clustered objects and discriminative object parts. Moreover, the image-level category labels do not enforce

Marcel Celaya, Georg Loho, Chi Ho Yuen

In a previous work, we gave a construction of (not necessarily realizable) oriented matroids from a triangulation of a product of two simplices. In this follow-up paper, we use a variant of Viro's patchworking to derive a topological representation of the oriented matroid directly from the polyhedral structure of the triangulation, hence finding a combin

Matthew L. Leavitt, Ari Morcos

Methods for understanding the decisions of and mechanisms underlying deep neural networks (DNNs) typically rely on building intuition by emphasizing sensory or semantic features of individual examples. For instance, methods aim to visualize the components of an input which are "important" to a network's decision, or to measure the semantic proper

Ruilin Xu, Rundi Wu, Yuko Ishiwaka, Carl Vondrick

We introduce a deep learning model for speech denoising, a long-standing challenge in audio analysis arising in numerous applications. Our approach is based on a key observation about human speech: there is often a short pause between each sentence or word. In a recorded speech signal, those pauses introduce a series of time periods during which only noise i

G. Dufour, S. B. Charnley

The origin of bistable solutions in the kinetic equations describing the chemistry of dense interstellar clouds is explained as being due to the autocatalysis and feedback of oxygen nuclei from the oxygen dimer (O2). We identify four autocatalytic processes that can operate in dense molecular clouds, driven respectively by reactions of H+, He+, C+, and S+ wi

Metod Jazbec, Barna Pásztor, Felix Faltings, Nino Antulov-Fantulin

We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a nonprofit organization that crawls a large part of the web. We develop a processing pipeline to identify news articles associated

Arthur Delarue, Ross Anderson, Christian Tjandraatmadja

Value-function-based methods have long played an important role in reinforcement learning. However, finding the best next action given a value function of arbitrary complexity is nontrivial when the action space is too large for enumeration. We develop a framework for value-function-based deep reinforcement learning with a combinatorial action space, in whic

Constantinos Daskalakis, Themis Gouleakis, Christos Tzamos, Manolis Zampetakis

We provide a computationally and statistically efficient estimator for the classical problem of truncated linear regression, where the dependent variable $y = w^T x + ε$ and its corresponding vector of covariates $x \in R^k$ are only revealed if the dependent variable falls in some subset $S \subseteq R$; otherwise the existence of the pair $(x, y)$ is hidde

Martin Kong

Most quantum compiler transformations and qubit allocation techniques to date are either peep-hole focused or rely on sliding windows that depend on a number of external parameters. Thus, global optimization criteria are still lacking. In this paper we explore the synergies and impact of affine loop transformations in the context of qubit allocation and mapp

Florian Frick, Michael Harrison

Given a space X we study the topology of the space of embeddings of X into $\mathbb{R}^d$ through the combinatorics of triangulations of X. We give a simple combinatorial formula for upper bounds for the largest dimension of a sphere that antipodally maps into the space of embeddings. This result summarizes and extends results about the nonembeddability of c

Baladitya Yellapragada, Sascha Hornhauer, Kiersten Snyder, Stella Yu

Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-intensive annotations and are restricted to the specific trained task. Here, we employed an unsupervised network with Non-Parametric Instance Discrimination (NPID) to grade age-rel

Murphy Yuezhen Niu, Andrew M. Dai, Li Li, Augustus Odena

Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers. A similar exponential separation has yet to be established in generative models through quantum sample learning: given samples from an n-qubit computation, can we learn the underlying quantum distribution using m

Avia Efrat, Omer Levy

Supervised machine learning provides the learner with a set of input-output examples of the target task. Humans, however, can also learn to perform new tasks from instructions in natural language. Can machines learn to understand instructions as well? We present the Turking Test, which examines a model's ability to follow natural language instructions of

Luis Miralles-Pechuán, Fernando Jiménez, José Manuel García

Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. This system works like a global auction where advertisers bid to display their impressions in the publishers' ad slots. The most popular system to select which advertiser wins each auction is the Generalized second-price auction in which the advertiser th

Tuan Manh Lai, Trung Bui, Doo Soon Kim, Quan Hung Tran

Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a w

Anamaria Crisan, Shannah Fisher, Jennifer L. Gardy, Tamara Munzner

Genomic Epidemiology (genEpi) is a branch of public health that uses many different data types including tabular, network, genomic, and geographic, to identify and contain outbreaks of deadly diseases. Due to the volume and variety of data, it is challenging for genEpi domain experts to conduct data reconnaissance; that is, have an overview of the data they

Badr M. Abdullah, Jacek Kudera, Tania Avgustinova, Bernd Möbius

Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification. In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective

Yaochen Xie, Zhengyang Wang, Shuiwang Ji

Self-supervised frameworks that learn denoising models with merely individual noisy images have shown strong capability and promising performance in various image denoising tasks. Existing self-supervised denoising frameworks are mostly built upon the same theoretical foundation, where the denoising models are required to be J-invariant. However, our analyse

Paul La Plante, Peter K. G. Williams, Joshua S. Dillon

The Hydrogen Epoch of Reionization Array (HERA) is a radio telescope in the Karoo desert of South Africa endeavoring to observe Cosmic Dawn and the Epoch of Reionization. When fully constructed, it will consist of 350 antennas and generate over 60 terabytes (TB) of data each night. In order to keep pace with the relatively large rate of data, we have develop

Chenguang Wang, Xiao Liu, Dawn Song

This paper shows how to construct knowledge graphs (KGs) from pre-trained language models (e.g., BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are built in either a supervised or semi-supervised manner, requiring humans to create knowledge. Recent deep language models automatically acquire knowledge from large-scale corpora via

David Lowell, Brian E. Howard, Zachary C. Lipton, Byron C. Wallace

Unsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation. While UDA has gained popularity for text classification, open questions linger over

Joseph M. Renes, Renato Renner

A central claim in quantum cryptography is that secrecy can be proved rigorously, based on the assumption that the relevant information-processing systems obey the laws of quantum physics. This claim has recently been challenged by Bernstein (arXiv:1803.04520). He argues that the laws of physics may also entail an unavoidable leakage of any classical informa

Christopher Plumberg

Understanding the origins of collective, fluid-like behavior in ultrarelativistic nuclear collisions constitutes one of the biggest open challenges in the field. In this Letter, it is argued that certain features in the multiplicity dependence of the source sizes extracted using pion interferometry may be understood quite naturally if small systems evolve hy

Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan, Nathanael Teissier

Balancing the privacy-utility tradeoff is a crucial requirement of many practical machine learning systems that deal with sensitive customer data. A popular approach for privacy-preserving text analysis is noise injection, in which text data is first mapped into a continuous embedding space, perturbed by sampling a spherical noise from an appropriate distrib

Karl Pertsch, Youngwoon Lee, Joseph J. Lim

Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for leveraging prior knowledge is to transfer skills learned on prior tasks to the new task. However, as the amount of prior experience increases, the number of transferable skills gr

Esther Robb, Wen-Sheng Chu, Abhishek Kumar, Jia-Bin Huang

Generative Adversarial Networks (GANs) have shown remarkable performance in image synthesis tasks, but typically require a large number of training samples to achieve high-quality synthesis. This paper proposes a simple and effective method, Few-Shot GAN (FSGAN), for adapting GANs in few-shot settings (less than 100 images). FSGAN repurposes component analys

Michael Block, Mustapha Laatiaoui, Sebastian Raeder

The interest to perform laser spectroscopy in the heaviest elements arises from the strong impact of relativistic effects, electron correlations and quantum electrodynamics on their atomic structure. Once this atomic structure is well understood, laser spectroscopy also provides access to nuclear properties such as spins, mean square charge radii and electro

Jun Yamada, Youngwoon Lee, Gautam Salhotra, Karl Pertsch

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goal

Rafal Powalski, Tomasz Stanislawek

In this paper, we introduce a new approach to dealing with the problem of case-sensitiveness in Language Modelling (LM). We propose simple architecture modification to the RoBERTa language model, accompanied by a new tokenization strategy, which we named Unified Case LM (UniCase). We tested our solution on the GLUE benchmark, which led to increased performan

Anita Liebenau, Letícia Mattos, Walner Mendonça, Jozef Skokan

We say that $G \to (F,H)$ if, in every edge colouring $c: E(G) \to \{1,2\}$, we can find either a $1$-coloured copy of $F$ or a $2$-coloured copy of $H$. The well-known Kohayakawa--Kreuter conjecture states that the threshold for the property $G(n,p) \to (F,H)$ is equal to $n^{-1/m_{2}(F,H)}$, where $m_{2}(F,H)$ is given by \[ m_{2}(F,H):= \max \left\{\dfrac

Douglas G. Macharet, Armando Alves Neto, Daigo Shishika

Different applications, such as environmental monitoring and military operations, demand the observation of predefined target locations, and an autonomous mobile robot can assist in these tasks. In this context, the Orienteering Problem (OP) is a well-known routing problem, in which the goal is to maximize the objective function by visiting the most rewardin

Friedemann Zenke, Emre O. Neftci

Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the promise for scalable, low-power information processing on temporal data streams. Yet, to solve real-world problems, these networks need to be trained. However, training on neuromorphic substrates creates significant challenges due to the offline character and the required

Ronak Pradeep, Xueguang Ma, Rodrigo Nogueira, Jimmy Lin

This work describes the adaptation of a pretrained sequence-to-sequence model to the task of scientific claim verification in the biomedical domain. We propose VERT5ERINI that exploits T5 for abstract retrieval, sentence selection and label prediction, which are three critical sub-tasks of claim verification. We evaluate our pipeline on SCIFACT, a newly cura

Efthymia Tsamoura, Loizos Michael

Despite significant progress in the development of neural-symbolic frameworks, the question of how to integrate a neural and a symbolic system in a \emph{compositional} manner remains open. Our work seeks to fill this gap by treating these two systems as black boxes to be integrated as modules into a single architecture, without making assumptions on their i

Arghya Chattopadhyay, Suvankar Dutta, Debangshu Mukherjee, Neetu

We establish a connection between time evolution of free Fermi droplets and partition function of \emph{generalised} \emph{q}-deformed Yang-Mills theories on Riemann surfaces. Classical phases of $(0+1)$ dimensional unitary matrix models can be characterised by free Fermi droplets in two dimensions. We quantise these droplets and find that the modes satisfy

Gabor Lugosi, Shahar Mendelson

We consider the problem of estimating the mean of a random vector based on $N$ independent, identically distributed observations. We prove the existence of an estimator that has a near-optimal error in all directions in which the variance of the one dimensional marginal of the random vector is not too small: with probability $1-δ$, the procedure returns $\wh

R. Inanc Baykur, Noriyuki Hamada

We develop techniques to construct explicit symplectic Lefschetz fibrations over the 2-sphere with any prescribed signature and any spin type when the signature is divisible by 16. This solves a long-standing conjecture on the existence of such fibrations with positive signature. As applications, we produce symplectic 4-manifolds that are homeomorphic but no

Adithya Krishna, André van Schaik, Chetan Singh Thakur

Particle filtering is a recursive Bayesian estimation technique that has gained popularity recently for tracking and localization applications. It uses Monte Carlo simulation and has proven to be a very reliable technique to model non-Gaussian and non-linear elements of physical systems. Particle filters outperform various other traditional filters like Kalm

Semin Yoo

Nondegenerate quadratic forms over $p$-adic fields are classified by their dimension, discriminant, and Hasse invariant. This paper uses these three invariants, elementary facts about $p$-adic fields and the theory of quadratic forms to determine which types of quadratic spaces -- including degenerate cases -- can be embedded in the Euclidean $p$-adic space

Yun-Ning Hung, Gordon Wichern, Jonathan Le Roux

Most music source separation systems require large collections of isolated sources for training, which can be difficult to obtain. In this work, we use musical scores, which are comparatively easy to obtain, as a weak label for training a source separation system. In contrast with previous score-informed separation approaches, our system does not require iso

Yuki Nagai, Akinori Tanaka, Akio Tomiya

In this paper, we develop the self-learning Monte-Carlo (SLMC) algorithm for non-abelian gauge theory with dynamical fermions in four dimensions to resolve the autocorrelation problem in lattice QCD. We perform simulations with the dynamical staggered fermions and plaquette gauge action by both in HMC and SLMC for zero and finite temperature to examine the v

Shehzad Afzal, Sohaib Ghani, Hank C. Jenkins-Smith, David S. Ebert

Public health officials dealing with pandemics like COVID-19 have to evaluate and prepare response plans. This planning phase requires not only looking into the spatiotemporal dynamics and impact of the pandemic using simulation models, but they also need to plan and ensure the availability of resources under different spread scenarios. To this end, we have

Ruosong Wang, Dean P. Foster, Sham M. Kakade

Offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinforcement learning coupled with function approximation methods (to deal with the curse of dimensionality) can provide a means to help alleviate the excessive sample complexity burde

Rohit Agrawal, Kapil Ahuja, Dhaarna Maheshwari, Akash Kumar

Routing of the nets in Field Programmable Gate Array (FPGA) design flow is one of the most time consuming steps. Although Versatile Place and Route (VPR), which is a commonly used algorithm for this purpose, routes effectively, it is slow in execution. One way to accelerate this design flow is to use parallelization. Since VPR is intrinsically sequential, a

Khalil Ayadi, Awatef Azaza, Salah Beldi

While Roth's theorem states that the irrationality measure of all the irrational algebraic numbers is 2, and the same holds true over function fields in characteristic zero, some counter-examples were found over function fields in positive characteristic. This was put forward first by Mahler in 1949, in his fundamental paper on Diophantine approximation

Tor Nordam, Jørgen Skancke, Rodrigo Duran, Chris Barker

The main focus of marine oil spill modelling is often on where the oil will end up, i.e., on the horizontal transport. However, due to current shear, wind drag, and the different physical, chemical and biological processes that affect oil differently on the surface and in the water column, modelling the vertical distribution of the oil is essential for model

K L Bhanu Moorthy, Moneish Kumar, Ramanathan Subramaniam, Vineet Gandhi

We present GAZED- eye GAZe-guided EDiting for videos captured by a solitary, static, wide-angle and high-resolution camera. Eye-gaze has been effectively employed in computational applications as a cue to capture interesting scene content; we employ gaze as a proxy to select shots for inclusion in the edited video. Given the original video, scene content and

James Ren Hou Lee, Alexander Wong

The ability to interpret social cues comes naturally for most people, but for those living with Autism Spectrum Disorder (ASD), some experience a deficiency in this area. This paper presents the development of a multimodal augmented reality (AR) system which combines the use of computer vision and deep convolutional neural networks (CNN) in order to assist i

Ben S. Southworth, Wayne Mitchell, Andreas Hessenthaler, Federico Danieli

Two of the most popular parallel-in-time methods are Parareal and multigrid-reduction-in-time (MGRIT). Recently, a general convergence theory was developed in Southworth (2019) for linear two-level MGRIT/Parareal that provides necessary and sufficient conditions for convergence, with tight bounds on worst-case convergence factors. This paper starts by provid

Manuel Müller, Rukan Nasri, Lars Tiemann, Irene Fernandez-Cuesta

We present here a novel resist formulation with active thiol groups at the surface. The material is UV curable, and can be patterned at the micro- and nanoscale by UV nanoimprint lithography. The resist formulation development, its processing, patterning and surface characterization are presented here. In addition, a possible application, including its use t

Tian Xu, Ziniu Li, Yang Yu

Imitation learning trains a policy by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understanding needs further studies. In this paper, we firstly analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning and generative

Michael Sjoholmsierchio, Britta Hale, Daniel Lukaszewski, Geoffrey G. Xie

Software-defined networking (SDN) has become a fundamental technology for data centers and 5G networks. In an SDN network, routing and traffic management decisions are made by a centralized controller and communicated to switches via a control channel. Transport Layer Security (TLS) has been proposed as its single security layer; however, use of TLS is optio

Raphael L. V. Fortulan, Luís F. C. Alberto

In this paper, we discuss a method to find the most influential power system parameters to the probabilistic transient stability assessment problem---finding the probability distribution of the critical clearing time. We perform the parameter selection by employing a sensitivity analysis combined with a principal component analysis. First, we determine the s

Erica J. Gonzales, Justin R. Crepp, Eric B. Bechter, Charlotte M. Wood

The physical properties of faint stellar and substellar objects often rely on indirect, model-dependent estimates. For example, the masses of brown dwarfs are usually inferred using evolutionary models, which are age dependent and have yet to be properly calibrated. With the goal of identifying new benchmark objects to test low-mass stellar and substellar mo

J. A. Secrest, J. M. Conroy, H. G. Miller

The Maximum Entropy (MaxEnt) technique is applied to the derivation of the Gaussian Dispersion Plume Model as well as to more complex transport phenomena such as the one-dimensional advection equation, the one-dimensional diffusion equation, the one dimensional advection-diffusion equation, and finally to the multi-dimensional advection-diffusion equation. F

Ruosong Wang, Hanrui Zhang, Devendra Singh Chaplot, Denis Garagić

We study planning with submodular objective functions, where instead of maximizing the cumulative reward, the goal is to maximize the objective value induced by a submodular function. Our framework subsumes standard planning and submodular maximization with cardinality constraints as special cases, and thus many practical applications can be naturally formul

Saurabh Kataria, Jesús Villalba, Najim Dehak

Deep learning based speech denoising still suffers from the challenge of improving perceptual quality of enhanced signals. We introduce a generalized framework called Perceptual Ensemble Regularization Loss (PERL) built on the idea of perceptual losses. Perceptual loss discourages distortion to certain speech properties and we analyze it using six large-scal

Andrea Agazzi, Jianfeng Lu

We study the problem of policy optimization for infinite-horizon discounted Markov Decision Processes with softmax policy and nonlinear function approximation trained with policy gradient algorithms. We concentrate on the training dynamics in the mean-field regime, modeling e.g., the behavior of wide single hidden layer neural networks, when exploration is e

Michael L. Wick, Kate Silverstein, Jean-Baptiste Tristan, Adam Pocock

It's been said that "Language Models are Unsupervised Multitask Learners." Indeed, self-supervised language models trained on "positive" examples of English text generalize in desirable ways to many natural language tasks. But if such models can stray so far from an initial self-supervision objective, a wayward model might generalize in u

Mark Allen, Dennis Kriventsov, Henrik Shahgholian

We prove a boundary Harnack principle in Lipschitz domains with small constant for fully nonlinear and $p$-Laplace type equations with a right hand side, as well as for the Laplace equation on nontangentially accessible domains under extra conditions. The approach is completely new and gives a systematic approach for proving similar results for a variety of

Johannes E. M. Mosig, Shikib Mehri, Thomas Kober

We present STAR, a schema-guided task-oriented dialog dataset consisting of 127,833 utterances and knowledge base queries across 5,820 task-oriented dialogs in 13 domains that is especially designed to facilitate task and domain transfer learning in task-oriented dialog. Furthermore, we propose a scalable crowd-sourcing paradigm to collect arbitrarily large

Rohan Tondulkar, Manisha Dubey, P. K. Srijith, Michal Lukasik

Social media has provided a platform for users to gather and share information and stay updated with the news. Such networks also provide a platform to users where they can engage in conversations. However, such micro-blogging platforms like Twitter restricts the length of text. Due to paucity of sufficient word occurrences in such posts, classification of t

Ye Ren

A Lie algebra is said to be generalised reductive if it is a direct sum of a semisimple Lie algebra and a commutative radical. In this paper we extend the BGG category $\mathcal{O}$ over complex semisimple Lie algebras to the category $\mathcal {O'}$ over complex generalised reductive Lie algebras. Then we make a preliminary research on the highest weigh

Cristina Feier, Carsten Lutz, Frank Wolter

We consider ontology-mediated queries (OMQs) based on expressive description logics of the ALC family and (unions) of conjunctive queries, studying the rewritability into OMQs based on instance queries (IQs). Our results include exact characterizations of when such a rewriting is possible and tight complexity bounds for deciding rewritability. We also give a

Chirag Modi, Francois Lanusse, Uros Seljak

We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations. We implement and validate the accuracy of a novel multi-grid scheme based on multiresolution pyramids to compute large scale forces efficiently on distributed platforms. We explore the scaling of th

J. T. Francis, M. S. Tame

The addition of a photon into the same mode as a coherent state produces a nonclassical state that has interesting features, including quadrature squeezing and a sub-Poissonian photon-number distribution. The squeezed nature of photon-added coherent (PAC) states potentially offers an advantage in quantum sensing applications. Previous theoretical works have

Ipek Ganiyusufoglu, L. Minh Ngô, Nedko Savov, Sezer Karaoglu

For deepfake detection, video-level detectors have not been explored as extensively as image-level detectors, which do not exploit temporal data. In this paper, we empirically show that existing approaches on image and sequence classifiers generalize poorly to new manipulation techniques. To this end, we propose spatio-temporal features, modeled by 3D CNNs,

Pierre Bourhis, Carsten Lutz

We study query containment in three closely related formalisms: monadic disjunctive Datalog (MDDLog), MMSNP (a logical generalization of constraint satisfaction problems), and ontology-mediated queries (OMQs) based on expressive description logics and unions of conjunctive queries. Containment in MMSNP was known to be decidable due to a result by Feder and V

Kai Li, Ying Li, Lei Cheng, Qingjiang Shi

There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems. In particular, two types of codebooks (namely Type I and Type II codebooks) are introduced

Chutong Gao, Weihao Wang, Leyuan Shi

The parallel machine scheduling problem has been a popular topic for many years due to its theoretical and practical importance. This paper addresses the robust makespan optimization problem on unrelated parallel machine scheduling with sequence-dependent setup times, where the processing times are uncertain, and the only knowledge is the intervals they take

Chenyang Lei, Yazhou Xing, Qifeng Chen

Applying image processing algorithms independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. Unlike most previou

Ahsan Z. Khan, Gregory W. Moore

We describe how categorical BPS data including chain complexes of solitons, CPT pairings, and interior amplitudes jump across a wall of marginal stability in two-dimensional $\mathcal{N}=(2,2)$ models. We show that our jump formulas hold if and only if the $A_{\infty}$-categories of half-BPS branes constructed on either side of the wall are homotopy equivale

Mikko Lauri, Frans A. Oliehoek

Multi-agent active perception is a task where a team of agents cooperatively gathers observations to compute a joint estimate of a hidden variable. The task is decentralized and the joint estimate can only be computed after the task ends by fusing observations of all agents. The objective is to maximize the accuracy of the estimate. The accuracy is quantifie

Ilani Axelrod-Freed

Valid hook configurations are combinatorial objects used to understand West's stack sorting map as well as cumulants in noncommutative probability theory. We show a bijection between reduced valid hook configurations on 312-avoiding permutations with the maximal allowed number of points and 3D-Dyck words, proving a conjecture of Sankar's. We extend t

Waad Almasri, Dimitri Bettebghor, Fakhreddine Ababsa, Florence Danglade

Mechanical product engineering often must comply with manufacturing or geometric constraints related to the shaping process. Mechanical design hence should rely on robust and fast tools to explore complex shapes, typically for design for additive manufacturing (DfAM). Topology optimization is such a powerful tool, yet integrating geometric constraints (shape

Jasper P. Huijing, Richard P. Dwight, Martin Schmelzer

In this short note we apply the recently proposed data-driven RANS closure modelling framework of Schmelzer et al. (2020) to fully three-dimensional, high Reynolds number flows: namely wall-mounted cubes and cuboids at Re=40,000, and a cylinder at Re=140,000. For each flow, a new RANS closure is generated using sparse symbolic regression based on LES or DES

Sophie Mathieu, Rainer von Sachs, Véronique Delouille, Laure Lefèvre

In many applications, a control procedure is required to detect potential deviations in a panel of serially correlated processes. It is common that the processes are corrupted by noise and that no prior information about the in-control data are available for that purpose. This paper suggests a general nonparametric monitoring scheme for supervising such a pa

Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon

For composite nonsmooth optimization problems, Forward-Backward algorithm achieves model identification (e.g. support identification for the Lasso) after a finite number of iterations, provided the objective function is regular enough. Results concerning coordinate descent are scarcer and model identification has only been shown for specific estimators, the

Junjiao Tian, Yen-Cheng Liu, Nathan Glaser, Yen-Chang Hsu

Neural Networks can perform poorly when the training label distribution is heavily imbalanced, as well as when the testing data differs from the training distribution. In order to deal with shift in the testing label distribution, which imbalance causes, we motivate the problem from the perspective of an optimal Bayes classifier and derive a post-training pr

Melissa C. Brindise, Brett A. Meyers, Shelby Kutty, Pavlos P. Vlachos

We present a method for unsupervised segmentation of echocardiograms (echo). The method uses an iterative Dijkstra's algorithm, a strategic node selection, and a novel cost matrix formulation based on intensity peak prominence and is thus termed the "Prominence Iterative Dijkstra's" algorithm, or ProID. Although the current analysis focuses o

Mario Krnic, Nicusor Minculete

Based on a suitable improvement of a triangle inequality, we derive new mutual bounds for $p$-angular distance $α_p[x,y]=\big\Vert \Vert x\Vert^{p-1}x- \Vert y\Vert^{p-1}y\big\Vert$, in a normed linear space $X$. We show that our estimates are more accurate than the previously known upper bounds established by Dragomir, Hile and Maligranda. Next, we give sev

Mateus Schmidt

This is an extensive survey of the techniques used to formulate generalizations of the Mittag-Leffler Theorem from complex analysis. With the techniques of the theory of differential forms, sheaves and cohomology, we are able to define the notion of a Mittag-Leffler Problem on a Riemann surface as a problem of passage of data from local to global, and discus

R. James Cotton, Fabian H. Sinz, Andreas S. Tolias

In recent years, artificial neural networks have achieved state-of-the-art performance for predicting the responses of neurons in the visual cortex to natural stimuli. However, they require a time consuming parameter optimization process for accurately modeling the tuning function of newly observed neurons, which prohibits many applications including real-ti

Chia-Hao Liu, Christopher J. Wright, Ran Gu, Sasaank Bandi

We validate the use of matrix factorization for the automatic identification of relevant components from atomic pair distribution function (PDF) data. We also present a newly developed software infrastructure for analyzing the PDF data arriving in streaming manner. We then apply two matrix factorization techniques, Principal Component Analysis (PCA) and Non-

Augustin Arnault, Baptiste Hanssens, Nicolas Riche

Urban sound classification has been achieving remarkable progress and is still an active research area in audio pattern recognition. In particular, it allows to monitor the noise pollution, which becomes a growing concern for large cities. The contribution of this paper is two-fold. First, we present our DCASE 2020 task 5 winning solution which aims at helpi

Zhengxia Zou

This paper proposes a vision-based method for video sky replacement and harmonization, which can automatically generate realistic and dramatic sky backgrounds in videos with controllable styles. Different from previous sky editing methods that either focus on static photos or require inertial measurement units integrated in smartphones on shooting videos, ou

Robert James Scales, David Edward John Armstrong, Angus J Wilkinson, Bo-Shiuan Li

The fracture properties of as-cast TiVNbTa, a refractory high-entropy alloy (RHEA), were investigated using four-point bending tests from -139C to 20C under a strain-rate of 10-3 s-1. From those tests and fractography, the conditional fracture toughness values and the brittle-to-ductile transition temperature were obtained. The brittle-to-ductile transition

Masahiro Sato, Hiroaki Ishizuka

Photovoltaic effect, e.g., solar cells, converts light into DC electric current. This phenomenon takes place in various setups such as in noncentrosymmetric crystals and semiconductor pn junctions. Recently, we proposed a theory for producing DC spin current in magnets using electromagnetic waves, i.e., the spin-current counterpart of the solar cells. Our ca

Andrew Garmon, Vinay Ramakrishnaiah, Danny Perez

The constant increase in parallelism available on large-scale distributed computers poses major scalability challenges to many scientific applications. A common strategy to improve scalability is to express the algorithm in terms of independent tasks that can be executed concurrently on a runtime system. In this manuscript, we consider a generalization of th

Willem M. Schouten-Straatman, Hermen Jan Hupkes

We investigate the impact of spatial-temporal discretisation schemes on the dynamics of a class of reaction-diffusion equations that includes the FitzHugh-Nagumo system. For the temporal discretisation we consider the family of six backward differential formula (BDF) methods, which includes the well-known backward-Euler scheme. The spatial discretisations ca

Paweł Idziak, Piotr Kawałek, Jacek Krzaczkowski, Armin Weiß

The study of the complexity of the equation satisfiability problem in finite groups had been initiated by Goldmann and Russell (2002) where they showed that this problem is in polynomial time for nilpotent groups while it is NP-complete for non-solvable groups. Since then, several results have appeared showing that the problem can be solved in polynomial tim

Vikas Bajpai, Anukriti Bansal, Kshitiz Verma, Sanjay Agarwal

Rainfall is a natural process which is of utmost importance in various areas including water cycle, ground water recharging, disaster management and economic cycle. Accurate prediction of rainfall intensity is a challenging task and its exact prediction helps in every aspect. In this paper, we propose a deep and wide rainfall prediction model (DWRPM) and eva

Benjamin Ricaud, Nicolas Aspert, Volodymyr Miz

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatically sample user-defined relevant parts of a network. Starting from a few selected places in the network

Eric Balkanski, Harrison Chase, Kojin Oshiba, Alexander Rilee

We initiate the study of adversarial attacks on models for binary (i.e. black and white) image classification. Although there has been a great deal of work on attacking models for colored and grayscale images, little is known about attacks on models for binary images. Models trained to classify binary images are used in text recognition applications such as

Hascoet Tristan, Yihao Zhang, Persch Andreas, Ryoichi Takashima

Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure maintenance is to continuously monitor (i.e., quantify the level of safety and reliability) the state of very large structures. Meanwhile, computer vision has made impressive strides

Wonjun Choi, Munkyu Kang, Jin Hee Hong, Ori Katz

Ultrathin lensless fibre endoscopes offer minimally invasive investigation, but they mostly operate as a rigid type due to the need for prior calibration of a fibre probe. Furthermore, most implementations work in fluorescence mode rather than label-free imaging mode, making them unsuitable for medicine and industry. Herein, we report a fully flexible ultrat

Shashank Mishra, Soumyadipta Maiti, Beena Rai

Refractory high entropy alloys (R-HEAs) are having properties and uses as high strength and high hardness materials for ambient and high temperature, aerospace and nuclear radiation tolerance applications, orthopedic applications etc. The mechanical properties like yield strength and ductility of TaNbHfZr R-HEA depend on the local nanostructure and chemical

Augustin Chevallier, Paul Fearnhead, Matthew Sutton

A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-reversible, can mix better than standard MCMC algorithms, and can use subsampling ideas to speed up computation in big data scenarios. However, current PDMP samplers can only sample f

Aman Madaan, Dheeraj Rajagopal, Yiming Yang, Abhilasha Ravichander

Reasoning about events and tracking their influences is fundamental to understanding processes. In this paper, we present EIGEN - a method to leverage pre-trained language models to generate event influences conditioned on a context, nature of their influence, and the distance in a reasoning chain. We also derive a new dataset for research and evaluation of