April 2020 arXiv papers — page 7

Weirong Huang, Zhou Xie, Wenjie Zhong, Ying Mei

The primary task of the 1.26-m telescope jointly operated by the National Astronomical Observatory and Guangzhou University is photometric observations of the g, r, and i bands. A data processing pipeline system was set up with mature software packages, such as IRAF, SExtractor, and SCAMP, to process approximately 5 GB of observational data automatically eve

Yong Kiam Tan, André Platzer

This article presents an axiomatic approach for deductive verification of existence and liveness for ordinary differential equations (ODEs) with differential dynamic logic (dL). The approach yields proofs that the solution of a given ODE exists long enough to reach a given target region without leaving a given evolution domain. Numerous subtleties complicate

Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan

Reading long documents to answer open-domain questions remains challenging in natural language understanding. In this paper, we introduce a new model, called RikiNet, which reads Wikipedia pages for natural question answering. RikiNet contains a dynamic paragraph dual-attention reader and a multi-level cascaded answer predictor. The reader dynamically repres

Nisha Godani, Gauranga C. Samanta

Morris and Thorne \cite{morris1} proposed traversable wormholes, hypothetical connecting tools, using the concept of Einstein's general theory of relativity. In this paper, the modification of general relativity (in particular $f(R,T)$ theory of gravity defined by Harko et al. \cite{harko}) is considered, to study the traversable wormhole solutions. The func

Saravanan Ramanathan, Nitin Shivaraman, Seima Suryasekaran, Arvind Easwaran

Artificial Intelligence (AI) and Internet of Things (IoT) applications are rapidly growing in today's world where they are continuously connected to the internet and process, store and exchange information among the devices and the environment. The cloud and edge platform is very crucial to these applications due to their inherent compute-intensive and resou

David Buttsworth, Timothy Buttsworth

Surface heat transfer in convective and radiative environments is sometimes measured by recording the surface temperature history in a transient experiment and interpreting this surface temperature with the aid of a suitable model for transient conduction within the substrate. The semi-infinite one-dimensional model is often adopted, and several well-develop

Risheng Liu, Zi Li, Xin Fan, Chenying Zhao

Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contrast, recent deep learning based approaches can provide fast deformation estimation. These heuristic network architectures are fully data-driven and thus lack explicit geometric const

Ya-Peng Zhao

We present a non-extensive version of the Polyakov-Nambu-Jona-Lasinio model which is based on the non-extentive statistical mechanics. This new statistics is characterized by a dimensionless non-extensivity parameter $q$ that accounts for all possible effects violating the assumptions of the Boltzmann-Gibbs statistics (when $q\rightarrow1$, it returns to the

Peiran Li, Fang Guo, Jingbo Shang

Aspect classification, identifying aspects of text segments, facilitates numerous applications, such as sentiment analysis and review summarization. To alleviate the human effort on annotating massive texts, in this paper, we study the problem of classifying aspects based on only a few user-provided seed words for pre-defined aspects. The major challenge lie

Yifan Zhang, Shuaicheng Niu, Zhen Qiu, Ying Wei

The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer from lung infection, so one important diagnostic method is to screen chest radiography images, e.g., X-Ray or CT images. However, such examinations are time-consuming and labor-inte

Kristen C. Allen, Alex Davis, Tamar Krishnamurti

During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to pr

Bingxin Zhu, Haijun Wu

This paper addresses several aspects of the linear Hybridizable Discontinuous Galerkin Method (HDG) for the Helmholtz equation with impedance boundary condition at high frequency. First, error estimates with explicit dependence on the wave number $k$ for the HDG approximations to the exact solution $u$ and its negative gradient $\mathbf{q}=-\nabla u$ are der

Tengteng Zhang, Yiqin Yu, Jing Mei, Zefang Tang

The PICO framework (Population, Intervention, Comparison, and Outcome) is usually used to formulate evidence in the medical domain. The major task of PICO extraction is to extract sentences from medical literature and classify them into each class. However, in most circumstances, there will be more than one evidences in an extracted sentence even it has been

Guangyu Ren, Tianhong Dai, Panagiotis Barmpoutis, Tania Stathaki

Salient object detection has achieved great improvement by using the Fully Convolution Network (FCN). However, the FCN-based U-shape architecture may cause the dilution problem in the high-level semantic information during the up-sample operations in the top-down pathway. Thus, it can weaken the ability of salient object localization and produce degraded bou

Takeru Matsuda, Tasuku Soma

Matrix scaling is a classical problem with a wide range of applications. It is known that the Sinkhorn algorithm for matrix scaling is interpreted as alternating e-projections from the viewpoint of classical information geometry. Recently, a generalization of matrix scaling to completely positive maps called operator scaling has been found to appear in vario

Pratyush Sarkar, Dale Winter

The aim of this paper is to establish exponential mixing of frame flows for convex cocompact hyperbolic manifolds of arbitrary dimension with respect to the Bowen-Margulis-Sullivan measure. Some immediate applications include an asymptotic formula for matrix coefficients with an exponential error term as well as the exponential equidistribution of holonomy o

Jia-Chen Gu, Zhen-Hua Ling, Quan Liu, Zhigang Chen

The challenges of building knowledge-grounded retrieval-based chatbots lie in how to ground a conversation on its background knowledge and how to match response candidates with both context and knowledge simultaneously. This paper proposes a method named Filtering before Iteratively REferring (FIRE) for this task. In this method, a context filter and a knowl

Fabricio Perez, Miguel Angel Moreles, Hector Morales

This work is concerned with the estimation of radial velocities of sea surface elevations. The data, is a noisy along-track interferometric synthetic aperture radar (AT-INSAR) image. We assume the Velocity Bunching Model. This model relates the complex AT-INSAR image at a point in the image plane, with the radial velocity of a scatterer point in the sea surf

André C. R. Martins

There are several opinion dynamics models where extremism is defined as part of their characteristics. However, the way extremism is implemented in each model does not correspond to equivalent definitions. While some models focus on one aspect of the problem, others focus on different characteristics. This paper shows how each model only captures part of the

Xiaoteng Ma, Junyao Chen, Li Xia, Jun Yang

We present Distributional Soft Actor-Critic (DSAC), a distributional reinforcement learning (RL) algorithm that combines the strengths of distributional information of accumulated rewards and entropy-driven exploration from Soft Actor-Critic (SAC) algorithm. DSAC models the randomness in both action and rewards, surpassing baseline performances on various co

Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts

Neural networks have recently achieved human-level performance on various challenging natural language processing (NLP) tasks, but it is notoriously difficult to understand why a neural network produced a particular prediction. In this paper, we leverage the text-to-text framework proposed by Raffel et al.(2019) to train language models to output a natural t

Metin Ozturk, Attai Ibrahim Abubakar, Rao Naveed Bin Rais, Mona Jaber

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by j

Gabrielle Ras, Ning Xie, Marcel van Gerven, Derek Doran

Deep neural networks (DNNs) have become a proven and indispensable machine learning tool. As a black-box model, it remains difficult to diagnose what aspects of the model's input drive the decisions of a DNN. In countless real-world domains, from legislation and law enforcement to healthcare, such diagnosis is essential to ensure that DNN decisions are drive

Yen-Chen Pan

Ejecta velocity of type Ia supernovae (SNe Ia) is one powerful tool to differentiate between progenitor scenarios and explosion mechanisms. Here we revisit the relation between photospheric Si II 6355 velocities (Vsi) and host-galaxy properties with ~280 SNe Ia. A more stringent criterion on the phase of SN spectra is adopted to classify SNe Ia in terms of t

Linyang Li, Xipeng Qiu

Gradient-based adversarial training is widely used in improving the robustness of neural networks, while it cannot be easily adapted to natural language processing tasks since the embedding space is discrete. In natural language processing fields, virtual adversarial training is introduced since texts are discrete and cannot be perturbed by gradients directl

X. Y. Han, Adrian S. Lewis

Optimal matrices for problems involving the matrix numerical radius often have fields of values that are disks, a phenomenon associated with partial smoothness. Such matrices are highly structured: we experiment in particular with the proximal mapping for the radius, which often maps n-by-n random matrix inputs into a particular manifold of disk matrices tha

Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian

Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data. We introduce Ra

Huaixin Cao, Yuxing Du, Zhihua Guo, Kanyuan Han

Since masking of quantum information was introduced by Modi et al. in [PRL 120, 230501 (2018)], many discussions on this topic have been published. In this paper, we consider relationship between quantum multipartite maskers (QMMs) and quantum error-correcting codes (QECCs). We say that a subset $Q$ of pure states of a system $K$ can be masked by an operator

Zihang Meng, Sathya N. Ravi, Vikas Singh

Consider a learning algorithm, which involves an internal call to an optimization routine such as a generalized eigenvalue problem, a cone programming problem or even sorting. Integrating such a method as a layer(s) within a trainable deep neural network (DNN) in an efficient and numerically stable way is not straightforward -- for instance, only recently, s

Zinah A. Khalid, Raheam A. Al-Saphory

The purpose of this paper is to explore the concept of the regional boundary asymptotic gradient full order observer (RBAGFO-observer) in connection with the characterizations of sensors structures. Then, we present various results related to different types of measurements, domains and boundary conditions for distributed parameter systems (DPSS) in paraboli

Weimin Ye, Yang Gao, Jianlong Liu

Bound states in the continuum (BICs), circularly polarized states (C points) and degenerate states are all of three types of singular points of polarization in the momentum space. For photonic crystal slabs (PhCSs) with linearly polarized far fields, BICs were found to be the centers of polarization vortices and attracted more attention in the previous studi

Weiqiang Xie, Lin Chang, Haowen Shu, Justin C. Norman

Aluminum gallium arsenide (AlGaAs) and related III-V semiconductors have excellent optoelectronic properties. They also possess strong material nonlinearity as well as high refractive indices. In view of these properties, AlGaAs is a promising candidate for integrated photonics, including both linear and nonlinear devices, passive and active devices, and ass

Srijan Bharati Das, Tuneer Chakraborty, Shravan M. Hanasoge, Jeroen Tromp

Departures from standard spherically symmetric solar models, in the form of perturbations such as global and local-scale flows and structural asphericities, result in the splitting of eigenfrequencies in the observed spectrum of solar oscillations. Drawing from prevalent ideas in normal-mode coupling theory in geophysical literature, we devise a procedure th

Michal Lukasik, Boris Dadachev, Gonçalo Simões, Kishore Papineni

Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as information retrieval or text summarization. In this work, we propose three transformer-based architectures and provide comprehensive comparisons with previously proposed approaches on th

Bo Jin, Sean Symon, Simon J. Illingworth

Energy transfer mechanisms for vortex shedding behind a 2D cylinder at a Reynolds number of Re=100 are investigated. We first characterize the energy balances achieved by the true cylinder flow -- both for the flow as a whole and for each of its most energetic harmonic frequencies. It is found that viscous dissipation balances production when each is conside

Krishna Dasaratha

Motivated by epidemics such as COVID-19, we study the spread of a contagious disease when behavior responds to the disease's prevalence. We extend the SIR epidemiological model to include endogenous meeting rates. Individuals benefit from economic activity, but activity involves interactions with potentially infected individuals. The main focus is a theoreti

Gayatri Bhat, Avneesh Saluja, Melody Dye, Jan Florjanczyk

While natural language understanding of long-form documents is still an open challenge, such documents often contain structural information that can inform the design of models for encoding them. Movie scripts are an example of such richly structured text - scripts are segmented into scenes, which are further decomposed into dialogue and descriptive componen

Lihua Lei, Xiaodong Li, Xingmei Lou

We study the hierarchy of communities in real-world networks under a generic stochastic block model, in which the connection probabilities are structured in a binary tree. Under such model, a standard recursive bi-partitioning algorithm is dividing the network into two communities based on the Fiedler vector of the unnormalized graph Laplacian and repeating

Peng Qi, Yuhao Zhang, Christopher D. Manning

We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how t

Teng Fei, Duong H. Phong, Sebastien Picard, Xiangwen Zhang

It is shown that bounds of all orders of derivative would follow from uniform bounds for the metric and the torsion 1-form, for a flow in non-K\"ahler geometry which can be interpreted as either a flow for the Type IIB string or the Anomaly flow with source term and zero slope parameter. A key ingredient in the proof is a formulation of this flow unifying it

Jugurta Montalvão, Jânio Canuto, Luiz Miranda

Intrinsic dimension and differential entropy estimators are studied in this paper, including their systematic bias. A pragmatic approach for joint estimation and bias correction of these two fundamental measures is proposed. Shared steps on both estimators are highlighted, along with their useful consequences to data analysis. It is shown that both estimator

Alysson Bessani, Eduardo Alchieri, João Sousa, André Oliveira

The popularization of blockchains leads to a resurgence of interest in Byzantine Fault-Tolerant (BFT) state machine replication protocols. However, much of the work on this topic focuses on the underlying consensus protocols, with emphasis on their lack of scalability, leaving other subtle limitations unaddressed. These limitations are related to the effects

Ruy Fabila-Monroy, Jesús Leaños, Ana Laura Trujillo-Negrete

Let $k$ and $n$ be integers such that $1\leq k \leq n-1$, and let $G$ be a simple graph of order $n$. The $k$-token graph $F_k(G)$ of $G$ is the graph whose vertices are the $k$-subsets of $V(G)$, where two vertices are adjacent in $F_k(G)$ whenever their symmetric difference is an edge of $G$. In this paper we show that if $G$ is a tree, then the connectivi

Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin

Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we investigate the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of regular convolutions. We discover that re

Huda Khayrallah, Brian Thompson, Matt Post, Philipp Koehn

Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the re

Brian Thompson, Philipp Koehn

We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring. Our method results in 61% relative reduction in error compared to the best previously published result on the WMT16 document alignment shared task. Our method improves downstream MT performance on web-scraped Sinha

Nikolai Leonenko, Ravindi Nanayakkara, Andriy Olenko

The R\'enyi function plays an important role in the analysis of multifractal random fields. For random fields on the sphere, there are three models in the literature where the R\'enyi function is known explicitly. The theoretical part of the article presents multifractal random fields on the sphere and develops specific models where the R\'enyi function can

Robert Bassett, Julio Deride

We study statistical estimators computed using iterative optimization methods that are not run until completion. Classical results on maximum likelihood estimators (MLEs) assert that a one-step estimator (OSE), in which a single Newton-Raphson iteration is performed from a starting point with certain properties, is asymptotically equivalent to the MLE. We fu

Ayaka Shimizu

The warping degree of an oriented knot diagram is the minimal number of crossing changes which are required to obtain a monotone knot diagram from the diagram. The minimal warping degree of a knot is the minimal value of the warping degree for all oriented minimal diagrams of the knot. In this paper, all prime alternating knots with minimal warping degree tw

Wuwei Lan, Yang Chen, Wei Xu, Alan Ritter

Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual zero-shot transfer. However, their performance on Arabic information extraction (IE) tasks is not very well studied. In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT,

Mina Aganagic

We derive two geometric approaches to categorification of quantum invariants of links associated to an arbitrary compact simple Lie group $^L{G}$. In part I, we describe the first approach, based on an equivariant derived category of coherent sheaves on ${\cal X}$, the moduli space of singular $G$-monopoles, where $G$ is related to $^LG$ by Langlands duality

Katsuki Chousa, Masaaki Nagata, Masaaki Nishino

In this paper, we propose a method to extract bilingual texts automatically from noisy parallel corpora by framing the problem as a token-level span prediction, such as SQuAD-style Reading Comprehension. To extract a span of the target document that is a translation of a given source sentence (span), we use either QANet or multilingual BERT. QANet can be tra

Masaaki Nagata, Chousa Katsuki, Masaaki Nishino

We present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. As this is equivalent to a SQuAD v2.0 style question answering task, we then solve this problem by using multi

Saeid Foroughi-Abari, Adam Ritz

High-luminosity fixed target experiments provide impressive sensitivity to new light weakly coupled degrees of freedom. We revisit the minimal case of a scalar singlet $S$ coupled to the Standard Model through the Higgs portal, that decays visibly to leptons for scalar masses below the di-pion threshold. The dataset from the LSND experiment is found to impos

Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi

Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, each span is assigned a class label based

Julian Michael, Jan A. Botha, Ian Tenney

The success of pretrained contextual encoders, such as ELMo and BERT, has brought a great deal of interest in what these models learn: do they, without explicit supervision, learn to encode meaningful notions of linguistic structure? If so, how is this structure encoded? To investigate this, we introduce latent subclass learning (LSL): a modification to exis

Sergey Khrapak, Lenaic Couedel

In this paper we compare different theoretical approaches to describe the dispersion of collective modes in Yukawa fluids when the inter-particle coupling is relatively weak, so that kinetic and potential contributions to the dispersion relation compete. Thorough comparison with the results from molecular dymamics simulation allows us to conclude that in the

Thomas Vojta, Samuel Halladay, Sarah Skinner, Skirmantas Janušonis

Fractional Brownian motion (FBM), a non-Markovian self-similar Gaussian stochastic process with long-ranged correlations, represents a widely applied, paradigmatic mathematical model of anomalous diffusion. We report the results of large-scale computer simulations of FBM in one, two, and three dimensions in the presence of reflecting boundaries that confine

Gang Cao

Novel materials, which often exhibit surprising or even revolutionary physical properties, are necessary for critical advances in technologies. Simultaneous control of structural and physical properties via a small electrical current is of great significance both fundamentally and technologically. Recent studies demonstrate that a combination of strong spin-

Gábor Czédli

For an integer $n\geq 5$, H. Strietz (1975) and L. Z\'adori (1986) proved that the lattice Part$(n)$ of all partitions of $\{1,2,\dots,n\}$ is four-generated. Developing L. Z\'adori's particularly elegant construction further, we prove that even the $k$-th direct power Part$(n)^k$ of Part$(n)$ is four-generated for many but only finitely many exponents $k$.

E. S. Morchenko

In connection with a published critique, the author justifies the use of a motionless homogeneous plane layer of pure hydrogen plasma that is near local thermodynamic equilibrium (LTE) for analyzing the characteristics of the radiation from a chromospheric condensation of thickness $\Delta{}z_m=10\text{ km}$ in a gas dynamic model of stellar flares. It is sh

Qingfu Zhu, Weinan Zhang, Ting Liu, William Yang Wang

Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses. In this paper, we propose to explore potential responses by counterfactual reasoning. Given an observed response, the counterfactual reasoning model automatically infers the outcome of an alternative policy that could have been taken. The

Elham Kalantari, Sergey Loyka, Halim Yanikomeroglu, Abbas Yongacoglu

An optimal base station (BS) location depends on the traffic (user) distribution, propagation pathloss and many system parameters, which renders its analytical study difficult so that numerical algorithms are widely used instead. In this paper, the problem is studied analytically. First, it is formulated as a convex optimization problem to minimize the total

I-Shing Hu

We are going to show that on bounded Lipschitz domain $D$: both $C_{c}^{\infty}(D)$, the set of smooth functions on $D$ with compact support, and $C_{0}^{\infty}(D)$, the set of smooth functions on $D$ with (extension) zero boundary, are dense in $W^{1,p}\left(D\right)$, $p\in[1,\infty)$. A proof can be found in Ne\v{c}as's monograph \cite{key-2}, Theorem 4.

Ayshwarya Saktheeswaran, Arjun Srinivasan, John Stasko

Interaction plays a vital role during visual network exploration as users need to engage with both elements in the view (e.g., nodes, links) and interface controls (e.g., sliders, dropdown menus). Particularly as the size and complexity of a network grow, interactive displays supporting multimodal input (e.g., touch, speech, pen, gaze) exhibit the potential

Alonso Botero, Matthias Christandl, Péter Vrana

We consider a family of positive operator valued measures associated with representations of compact connected Lie groups. For many independent copies of a single state and a tensor power representation we show that the observed probability distributions converge to the value of the moment map. For invertible states we prove that the measures satisfy the lar

Ji Ma, Ivan Korotkov, Yinfei Yang, Keith Hall

A major obstacle to the wide-spread adoption of neural retrieval models is that they require large supervised training sets to surpass traditional term-based techniques, which are constructed from raw corpora. In this paper, we propose an approach to zero-shot learning for passage retrieval that uses synthetic question generation to close this gap. The quest

Emmanuel de Dieu Nkou, Guy Martial Nkiet

In this paper, we introduce a wavelet-based method for estimating the EDR space in Li's semiparametric regression model for achieving dimension reduction. This method is obtained by using linear wavelet estimators of the density and regression functions that are involved in the covariance matrix of conditional expectation whose spectral analysis gives the ED

Sayak Mukherjee, He Bai, Aranya Chakrabortty

We present a set of model-free, reduced-dimensional reinforcement learning (RL) based optimal control designs for linear time-invariant singularly perturbed (SP) systems. We first present a state-feedback and output-feedback based RL control design for a generic SP system with unknown state and input matrices. We take advantage of the underlying time-scale s

Taehee Jung, Dongyeop Kang, Hua Cheng, Lucas Mentch

Most classification models work by first predicting a posterior probability distribution over all classes and then selecting that class with the largest estimated probability. In many settings however, the quality of posterior probability itself (e.g., 65% chance having diabetes), gives more reliable information than the final predicted class alone. When the

Ehsan Kourkchi, R. Brent Tully, Gagandeep S. Anand, Helene M. Courtois

This study is a part of the Cosmicflows-4 project with the aim of measuring the distances of more than ~10,000 spiral galaxies in the local universe up to ~15,000 km/s. New HI linewidth information has come primarily from the Arecibo Legacy Fast ALFA Survey. Photometry of our sample galaxies has been carried out in optical (SDSS u, g, r, i, z) and infrared (

E. E. Boos, V. E. Bunichev, L. V. Dudko, M. A. Perfilov

The Effective Field Theory (EFT) approach is widely used in the search for possible deviations from the predictions of the Standard Model. Such an approximation of possible BSM physics is valid up to a certain levels of energy scale and accuracy. In this article, we investigate potential limitation of the EFT approach related to unitarity to describe possibl

Edward H. Kennedy

Heterogeneous effect estimation plays a crucial role in causal inference, with applications across medicine and social science. Many methods for estimating conditional average treatment effects (CATEs) have been proposed in recent years, but there are important theoretical gaps in understanding if and when such methods are optimal. This is especially true wh

Max Gronke, Pierre Ocvirk, Charlotte Mason, Jorryt Matthee

The decline in abundance of Lyman-$\alpha$ (Ly$\alpha$) emitting galaxies at $z \gtrsim 6$ is a powerful and commonly used probe to constrain the progress of cosmic reionization. We use the CoDaII simulation, which is a radiation hydrodynamic simulation featuring a box of $\sim 94$ comoving Mpc side length, to compute the Ly$\alpha$ transmission properties o

Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler

Since its introduction in 2018, EPIC-KITCHENS has attracted attention as the largest egocentric video benchmark, offering a unique viewpoint on people's interaction with objects, their attention, and even intention. In this paper, we detail how this large-scale dataset was captured by 32 participants in their native kitchen environments, and densely annotate

Na Young Ahn, Jun Eun Park, Dong Hoon Lee, Paul C. Hong

There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we

Husheng Li

In the control of many autonomous subsystems, such as autonomous vehicles or UAV networks, a centralized control may be hindered by the prohibitive complexity, limited communication bandwidth, or private information of subsystems. Therefore, it is desirable for the control center to coordinate the controls of subsystems by designing mechanisms such as pricin

A. Chiotellis, P. Boumis, Z. T. Spetsieri

One of the key methods for determining the unknown nature of Type Ia supernovae (SNe Ia) is the search for traces of interaction between the SN ejecta and the circumstellar structures at the resulting supernova remnants (SNRs Ia). So far, the observables that we receive from well-studied SNRs Ia cannot be explained self-consistently by any model presented in

Yuchen Liu, David Wentzlaff, S. Y. Kung

Channel pruning has received ever-increasing focus on network compression. In particular, class-discrimination based channel pruning has made major headway, as it fits seamlessly with the classification objective of CNNs and provides good explainability. Prior works singly propose and evaluate their discriminant functions, while further study on the effectiv

Shruti Agarwal, Tarek El-Gaaly, Hany Farid, Ser-Nam Lim

Synthetically-generated audios and videos -- so-called deep fakes -- continue to capture the imagination of the computer-graphics and computer-vision communities. At the same time, the democratization of access to technology that can create sophisticated manipulated video of anybody saying anything continues to be of concern because of its power to disrupt d

Alex Alochukwu, Peter Dankelmann

Let $G$ be a finite, connected graph. The eccentricity of a vertex $v$ of $G$ is the distance from $v$ to a vertex farthest from $v$. The average eccentricity of $G$ is the arithmetic mean of the eccentricities of the vertices of $G$. We show that the average eccentricity of a connected graph $G$ of girth at least six is at most $\frac{9}{2} \lceil \frac{n}{

John X. Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby

While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training

Ondřej Texler, David Futschik, Michal Kučera, Ondřej Jamriška

In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of the sequence. Its key advantage is that the resulting stylization is semantically meaningful, i.e., specific parts of moving objects are stylized according to the artist's intentio

Juan A. Paredes, Dennis S. Bernstein

This paper presents a system identification technique for systems whose output is asymptotically periodic under constant inputs. The model used for system identification is a discrete-time Lur'e model consisting of asymptotically stable linear dynamics, a time delay, a washout filter, and a static nonlinear feedback mapping. For all sufficiently large scalin

Gustavo Lopez, Omar Bravo

The quantization of the forced harmonic oscillator is studied with the quantum variable ($x,\hat v$), with the commutation relation $[x,\hat v]=i\hbar/m$, and using a Shr\"odinger's like equation on these variable, and associating a linear operator to a constant of motion $K(x,v,t)$ of the classical system, The comparison with the quantization in the space (

Matthew Purri, Kristin Dana

The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical prop

J. Kalinowski, W. Kotlarski, P. Sopicki, A. F. Zarnecki

One of the important goals of the proposed future $e^+e^-$ collider experiments is the search for dark matter particles using different experimental approaches. The most general search approach is based on the mono-photon signature, which is expected when production of the invisible final state is accompanied by a hard photon from initial state radiation. An

Ashish Kumar, Anindya S. Chakrabarti, Anirban Chakraborti, Tushar Nandi

Distress propagation occurs in connected networks, its rate and extent being dependent on network topology. To study this, we choose economic production networks as a paradigm. An economic network can be examined at many levels: linkages among individual agents (microscopic), among firms/sectors (mesoscopic) or among countries (macroscopic). New emergent dyn

V. Mossa, K. Stöckel, F. Cavanna, F. Ferraro

Among the reactions involved in the production and destruction of deuterium during Big Bang Nucleosynthesis, the deuterium-burning D(p,gamma)3He reaction has the largest uncertainty and limits the precision of theoretical estimates of primordial deuterium abundance. Here we report the results of a careful commissioning of the experimental setup used to measu

Dongyoon Shin, Hyeji Kim, Jihyuk Gong, Uijeong Jeong

This paper uses Coanda Effect to reduce motors, the source of noise, and finds low noise materials with sufficient lift force so that it can achieve acoustical stealth UAVs.According to NASA research [1], the noise of UAVs is better heard to people. But there must be some moments when we need to operate the drones quietly, so how can we reduce the noise? In

Kai-Cheng Yang, Christopher Torres-Lugo, Filippo Menczer

As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it are also growing. Here we estimate the prevalence of links to low-credibility information on Twitter during the outbreak, and the role of bots in spreading these links. We find that the combined volume of tweets linking to low-credibility informatio

Y. Wunderlich, P. Kroenert, F. Afzal, A. Thiel

We revisit Moravcsik's theorem on the unique extraction of amplitudes from polarization observables, which has been originally published in 1985. The proof is (re-) written in a more formal and detailed way and the theorem is corrected for the special case of an odd number of amplitudes (this case was treated incorrectly in the original publication). Moravcs

Eric L. Goodman, Chase Zimmerman, Corey Hudson

One of deep learning's attractive benefits is the ability to automatically extract relevant features for a target problem from largely raw data, instead of utilizing human engineered and error prone handcrafted features. While deep learning has shown success in fields such as image classification and natural language processing, its application for feature e

Thibaut Vidal, Rafael Martinelli, Tuan Anh Pham, Minh Hoàng Hà

Vehicle routing algorithms usually reformulate the road network into a complete graph in which each arc represents the shortest path between two locations. Studies on time-dependent routing followed this model and therefore defined the speed functions on the complete graph. We argue that this model is often inadequate, in particular for arc routing problems

Imants Platais, Massimo Robberto, Andrea Bellini, Vera Kozhurina-Platais

We present a catalog of high-precision proper motions in the Orion Nebula Cluster (ONC), based on Treasury Program observations with the Hubble Space Telescope's (HST) ACS/WFC camera. Our catalog contains 2,454 objects in the magnitude range of $14.2<m_{\rm F775W}<24.7$, thus probing the stellar masses of the ONC from $\sim$0.4 $M_\odot$ down to $\sim$0.02 $

Tianyu Li, Matthew Butrovich, Amadou Ngom, Wan Shen Lim

The proliferation of modern data processing tools has given rise to open-source columnar data formats. The advantage of these formats is that they help organizations avoid repeatedly converting data to a new format for each application. These formats, however, are read-only, and organizations must use a heavy-weight transformation process to load data from o

Rebecca L. Jayne, Kailash C. Misra

For $n \geq 2$ consider the affine Lie algebra $\widehat{s\ell}(n)$ with simple roots $\{\alpha_i \mid 0 \leq i \leq n-1\}$. Let $V(k\Lambda_0), \, k \in \mathbb{Z}_{\geq 1}$ denote the integrable highest weight $\widehat{s\ell}(n)$-module with highest weight $k\Lambda_0$. It is known that there are finitely many maximal dominant weights of $V(k\Lambda_0)$.

Nikolay Shcherbina

We give a geometric condition on a compact subset of a complex manifold which is necessary and sufficient for the existence of a smooth strictly plurisubharmonic function defined in a neighbourhood of this set.

David Yevick

This paper examines several applications of principal component analysis (PCA) to physical systems. The first of these demonstrates that the principal components in a basis of appropriate system variables can be employed to identify physically conserved quantities. That is, if the general form of a physical symmetry law is known, the PCA can identify an alge

Ke Yang, Bao-Min Gu, Shao-Wen Wei, Yu-Xiao Liu

A novel four-dimensional Einstein-Gauss-Bonnet gravity was formulated by D. Glavan and C. Lin [Phys. Rev. Lett. 124, 081301 (2020)], which is intended to bypass the Lovelock's theorem and to yield a non-trivial contribution to the four-dimensional gravitational dynamics. However, the validity and consistency of this theory has been called into question recen