October 2020 arXiv papers — page 66

S. Minhal Gardezi, Harris Pirie, William Dorrell, Jennifer E. Hoffman

Twisted van der Waals (vdW) heterostructures have recently emerged as an attractive platform to study tunable correlated electron systems. However, the quantum mechanical nature of vdW heterostructures makes their theoretical and experimental exploration laborious and expensive. Here we present a simple platform to mimic the behavior of twisted vdW heterostr

A. Kargaran, G. R. Jafari

Structural balance in social complex networks has been modeled with two types of triplet interactions. First, the interaction that only considers dynamic role for links or relationships (Heider balance), and second, the interaction that considers both individual opinions (nodes) and relationships in network dynamics (coevolutionary balance). The question is,

Neha Srikanth, Junyi Jessy Li

Much of modern-day text simplification research focuses on sentence-level simplification, transforming original, more complex sentences into simplified versions. However, adding content can often be useful when difficult concepts and reasoning need to be explained. In this work, we present the first data-driven study of content addition in text simplificatio

S. B. Prasad, B. C. Mulkerin, A. M. Martin

We have employed the theory of harmonically trapped dipolar Bose-Einstein condensates to examine the influence of a uniform magnetic field that rotates at an arbitrary angle to its own orientation. This is achieved by semi-analytically solving the dipolar superfluid hydrodynamics of this system within the Thomas-Fermi approximation and by allowing the body f

Aaron Naber

In this short note we review some known results on the structure and regularity of spaces with lower Ricci curvature bounds. We present some known and new open questions about next steps.

Yi Tang, Yuanman Li, Wenbin Zou

Since the wide employment of deep learning frameworks in video salient object detection, the accuracy of the recent approaches has made stunning progress. These approaches mainly adopt the sequential modules, based on optical flow or recurrent neural network (RNN), to learn robust spatiotemporal features. These modules are effective but significantly increas

Yi-Hao Chen, Pavel Sidorenko, Frank Wise

We start an environmentally-stable linear Mamyshev oscillator with electronically-controlled modulated pump and a moving filter. It delivers a 21-nJ pulse that becomes 65 fs in duration after a compressor. Reliable starting into stable mode-locking is found achievable with a modulated mode-locked state observed only when the modulation frequency is larger th

F. P. Hartwig, L. Wang, G. Davey Smith, N. M. Davies

Background: Instrumental variables (IVs) can be used to provide evidence as to whether a treatment X has a causal effect on an outcome Y. Even if the instrument Z satisfies the three core IV assumptions of relevance, independence and the exclusion restriction, further assumptions are required to identify the average causal effect (ACE) of X on Y. Sufficient

Tong Zhao, Bo Ni, Wenhao Yu, Zhichun Guo

The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behaviors. However, most of them detect the anomalies once the users have performed many such behaviors. Their performance is substantially hindered when the users' observed data is lim

Chaitanya Manapragada, Heitor M Gomes, Mahsa Salehi, Albert Bifet

Decision tree ensembles are widely used in practice. In this work, we study in ensemble settings the effectiveness of replacing the split strategy for the state-of-the-art online tree learner, Hoeffding Tree, with a rigorous but more eager splitting strategy that we had previously published as Hoeffding AnyTime Tree. Hoeffding AnyTime Tree (HATT), uses the H

Jun Gu, Tzonelih Hwang

Recently, Li et al. (Int J Theor Phys: DOI: 10.1007/s10773-020-04588-w, 2020) proposed a multiparty quantum key agreement protocol via non-maximally entangled cluster states. They claimed that the proposed protocol can help all the involved participants have equal influence on the final shared key. However, this study points out a loophole that makes Li et a

Dongkeun Lee, Wonmin Son

For the identification of non-trivial quantum phase, we exploit a Bell-type correlation that is applied to the one-dimensional spin-1 XXZ chain. It is found that our generalization of bipartite Bell correlation can take a decomposed form of transverse spin correlation together with high-order terms. The formulation of density-matrix renormalisation group is

Manami Roy, Ralf Schmidt, Shaoyun Yi

We find the number $s_k(p,\Omega)$ of cuspidal automorphic representations of $\mathrm{GSp}(4,\mathbb{A}_{\mathbb{Q}})$ with trivial central character such that the archimedean component is a holomorphic discrete series representation of weight $k\ge 3$, and the non-archimedean component at $p$ is an Iwahori-spherical representation of type $\Omega$ and unra

Xin Liu, Bin Li, Pengyi Shi, Lei Ying

This paper considers constrained online dispatching with unknown arrival, reward and constraint distributions. We propose a novel online dispatching algorithm, named POND, standing for Pessimistic-Optimistic oNline Dispatching, which achieves $O(\sqrt{T})$ regret and $O(1)$ constraint violation. Both bounds are sharp. Our experiments on synthetic and real da

Xinhui Wu, Jesse Chan

We present a high-order entropy stable discontinuous Galerkin (ESDG) method for nonlinear conservation laws on both multi-dimensional domains and on networks constructed from one-dimensional domains. These methods utilize treatments of multi-dimensional interfaces and network junctions which retain entropy stability when coupling together entropy stable disc

Leszek Szczecinski

In this work we develop a new algorithm for rating of teams (or players) in one-on-one games by exploiting the observed difference of the game-points (such as goals), also known as a margin of victory (MOV). Our objective is to obtain the Elo-style algorithm whose operation is simple to implement and to understand intuitively. This is done in three steps: fi

Jesús Arturo Jiménez González

Two integral quadratic unit forms are called strongly Gram congruent if their upper triangular Gram matrices are Z-congruent. The paper gives a combinatorial strong Gram invariant for those unit forms that are non-negative of Dynkin type A, within the framework introduced in [Fundamenta Informaticae 184(1):49-82, 2021], and uses it to determine all correspon

Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril

Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production. Despite considerable effort by the catalysis community to apply machine learning models to the computational catalyst discovery process, it remains an open challenge to build

Rohan Rao, Amit Moscovich, Amit Singer

Motivated by the 2D class averaging problem in single-particle cryo-electron microscopy (cryo-EM), we present a k-means algorithm based on a rotationally-invariant Wasserstein metric for images. Unlike existing methods that are based on Euclidean ($L_2$) distances, we prove that the Wasserstein metric better accommodates for the out-of-plane angular differen

Yuan Gao, Tiejun Li, Xiaoguang Li, Jian-Guo Liu

We present a data-driven point of view for rare events, which represent conformational transitions in biochemical reactions modeled by over-damped Langevin dynamics on manifolds in high dimensions. We first reinterpret the transition state theory and the transition path theory from the optimal control viewpoint. Given point clouds sampled from a reaction dyn

Zhen Xiang, David J. Miller, George Kesidis

Backdoor attacks (BAs) are an emerging form of adversarial attack typically against deep neural network image classifiers. The attacker aims to have the classifier learn to classify to a target class when test images from one or more source classes contain a backdoor pattern, while maintaining high accuracy on all clean test images. Reverse-Engineering-based

Wenjin Zhao, Elliott Runburg, Zaiyao Fei, Joshua Mutch

Evidence for the quantum spin Hall (QSH) effect has been reported in several experimental systems in the form of approximately quantized edge conductance. However, the most fundamental feature of the QSH effect, spin-momentum locking in the edge channels, has never been demonstrated experimentally. Here, we report clear evidence for spin-momentum locking in

Yuzhuo Liu, Hangting Chen, YunWang, Pengyuan Zhang

Access to large corpora with strongly labelled sound events is expensive and difficult in engineering applications. Much research turns to address the problem of how to detect both the types and the timestamps of sound events with weak labels that only specify the types. This task can be treated as a multiple instance learning (MIL) problem, and the key to i

Liang Liu, Ya-Feng Liu

A great amount of endeavour has recently been devoted to the joint device activity detection and channel estimation problem in massive machine-type communications. This paper targets at two practical issues along this line that have not been addressed before: asynchronous transmission from uncoordinated users and efficient algorithms for real-time implementa

Vijayaraghavan Murali, Lee Gross, Rebecca Qian, Satish Chandra

We explore the application of Information Retrieval (IR) based bug localization methods at a large industrial setting, Facebook. Facebook's code base evolves rapidly, with thousands of code changes being committed to a monolithic repository every day. When a bug is detected, it is often time-sensitive and imperative to identify the commit causing the bug in

Vijayaraghavan Murali, Edward Yao, Umang Mathur, Satish Chandra

Despite engineering workflows that aim to prevent buggy code from being deployed, bugs still make their way into the Facebook app. When symptoms of these bugs, such as user submitted reports and automatically captured crashes, are reported, finding their root causes is an important step in resolving them. However, at Facebook's scale of billions of users, a

Jingru Yan, Xingzhi Zhan

Given a graph $H$ and a positive integer $n,$ the Tur\'{a}n number of $H$ for the order $n,$ denoted ${\rm ex}(n,H),$ is the maximum size of a simple graph of order $n$ not containing $H$ as a subgraph. The book with $p$ pages, denoted $B_p$, is the graph that consists of $p$ triangles sharing a common edge. Bollob\'{a}s and Erd\H{o}s initiated the research

Diego Alonso-Orán, Christian Rohde, Hao Tang

In this paper, we establish a local theory, i.e., existence, uniqueness and blow-up criterion, for a general family of singular SDEs in some Hilbert space. The key requirement is an approximation property that allows us to embed the singular drift and diffusion mappings into a hierarchy of regular mappings that are invariant with respect to the Hilbert space

Tian Gu, Jeremy M. G. Taylor, Bhramar Mukherjee

It is becoming increasingly common for researchers to consider incorporating external information from large studies to improve the accuracy of statistical inference instead of relying on a modestly sized dataset collected internally. With some new predictors only available internally, we aim to build improved regression models based on individual-level data

Stefano Liberati, Giovanni Tricella, Andrea Trombettoni

We study the back-reaction associated with Hawking evaporation of an acoustic canonical analogue black hole in a Bose-Einstein condensate. We show that the emission of Hawking radiation induces a local back-reaction on the condensate, perturbing it in the near-horizon region, and a global back-reaction in the density distribution of the atoms. We discuss how

Erika Ordog

The canonical sylvan resolution is a resolution of an arbitrary monomial ideal over a polynomial ring that is minimal and has an explicit combinatorial formula for the differential. The differential is a weighted sum over lattice paths of weights of chain-link fences, which are sequences of faces that are linked to each other via higher-dimensional analogues

Daniel Bruder, Xun Fu, Ram Vasudevan

Nonlinear dynamical systems can be made easier to control by lifting them into the space of observable functions, where their evolution is described by the linear Koopman operator. This paper describes how the Koopman operator can be used to generate approximate linear, bilinear, and nonlinear model realizations from data, and argues in favor of bilinear rea

Yufeng Ye, Kaidong Peng, Mahdi Naghiloo, Gregory Cunningham

Strong nonlinear coupling of superconducting qubits and/or photons is a critical building block for quantum information processing. Due to the perturbative nature of the Josephson nonlinearity, linear coupling is often used in the dispersive regime to approximate nonlinear coupling. However, this dispersive coupling is weak and the underlying linear coupling

Dingdong Yi, Shaoyang Ning, Chia-Jung Chang, S. C. Kou

Big data generated from the Internet offer great potential for predictive analysis. Here we focus on using online users' Internet search data to forecast unemployment initial claims weeks into the future, which provides timely insights into the direction of the economy. To this end, we present a novel method PRISM (Penalized Regression with Inferred Seasonal

Ashok K. Singal

We study in detail the electromagnetic fields and the Poynting flux in the case of a uniformly accelerated charge, in order to examine whether such a charge does `emit' radiation, especially in view of the widely accepted fact that there is no radiation reaction on the charge. Our concern, in particular, is with the Poynting flow computed at large distances

Wen-Ya Wu, Chun-Zheng Wang, Qi-Ye Shou, Yu-Gang Ma

The chiral magnetic wave (CMW) is sought using the charge asymmetry ($A_{\rm ch}$) dependence of anisotropic flow in heavy-ion collisions. The charge dependent transverse momentum ($p_{\rm T}$), however, could play a role as a background. With the string fragmentation models, including PYTHIA, we demonstrate the origin of the $A_{\rm ch}-p_{\rm T}$ correlati

Runzhe Yang, Jingxiao Chen, Karthik Narasimhan

In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent's person

Feng Ji, Hui Feng, Hang Sheng, Wee Peng Tay

A continuous-time graph signal can be viewed as a time series of graph signals. It generalizes both the classical continuous-time signal and ordinary graph signal. Therefore, such a signal can be considered as a function on two domains: the graph domain and the time domain. In this paper, we consider the sampling theory of bandlimited continuous-time graph s

Michael H. Stanley, Debra F. Laefer

This paper introduces five new density and accuracy metrics for aerial point clouds that address the complexity and objectives of modern, dense laser scans of urban scenes. The five metrics describe (1) vertical surface density (points per area on vertical surfaces); (2) vertical density as a function of horizontal density; (3) vertical surface accuracy; and

Fanjun Bu, Chien-Ming Huang

As robots perform manipulation tasks and interact with objects, it is probable that they accidentally drop objects (e.g., due to an inadequate grasp of an unfamiliar object) that subsequently bounce out of their visual fields. To enable robots to recover from such errors, we draw upon the concept of object permanence-objects remain in existence even when the

Janni Yuval, Paul A. O'Gorman, Chris N. Hill

A promising approach to improve climate-model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data-driven. However, neural networks (NNs) often lead to instabilities and climate drift when coupled to an atmospheric model. Here we learn an NN parameterization from a hi

E. P. Mattos, A. Vidiella-Barranco

We present an alternative method for describing the evolution of a mode of the quantized electromagnetic field in contact with a finite temperature thermal bath. We use the expansion of the field density operator in terms of coherent states and the related Glauber-Sudarshan $P$-function in phase space. The method allows us to obtain analytical expressions of

Tomoki Tokuda, Okito Yamashita, Junichiro Yoshimoto

In neuroscience, the functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. Using fMRI, the functional connectivity (FC) between brain regions can be inferred, which has contributed to a number of findings of the fundamental properties of the brain. As an important clinical application of FC, clustering of subje

Henric Krawczynski

We present a generally applicable formalism for modeling the emission, absorption, reflection, and reprocessing of radiation by moving plasma streams close to a Kerr Black hole. The formalism can be used to investigate the observational signatures of a wide range of phenomena, including: (i) the reflection of coronal X-ray radiation off plasma plunging from

Marcin Pitera, Thorsten Schmidt

While the {estimation} of risk is an important question in the daily business of banking and insurance, many existing plug-in estimation procedures suffer from an unnecessary bias. This often leads to the underestimation of risk and negatively impacts backtesting results, especially in small sample cases. In this article we show that the link between estimat

Yingyi Zhang, Chengzhi Zhang

The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading f

Amanda K. Robinson, Tijl Grootswagers, Sophia M. Shatek, Jack Gerboni

Humans can covertly track the position of an object, even if the object is temporarily occluded. What are the neural mechanisms underlying our capacity to track moving objects when there is no physical stimulus for the brain to track? One possibility is that the brain 'fills-in' information about imagined objects using internally generated representa

Gil I. Shamir, Dong Lin, Lorenzo Coviello

Deep networks are gradually penetrating almost every domain in our lives due to their amazing success. However, with substantive performance accuracy improvements comes the price of \emph{irreproducibility}. Two identical models, trained on the exact same training dataset may exhibit large differences in predictions on individual examples even when average a

Santu Sardar, Amit K. Mishra, Mohammed Z. A. Khan

The period after the COVID-19 wave is called the Echo-period. Estimation of crowd size in an outdoor environment is essential in the Echo-period. Making a simple and flexible working system for the same is the need of the hour. This article proposes and evaluates a non-intrusive, passive, and costeffective solution for crowd size estimation in an outdoor env

Sandeep Ummethala, Juned N. Kemal, Ahmed S. Alam, Matthias Lauermann

Electro-optic (EO) modulators rely on interaction of optical and electrical signals with second-order nonlinear media. For the optical signal, this interaction can be strongly enhanced by using dielectric slot-waveguide structures that exploit a field discontinuity at the interface between a high-index waveguide core and the low-index EO cladding. In contras

William Hui

Recently, the use of pre-trained model to build neural network based on transfer learning methodology is increasingly popular. These pre-trained models present the benefit of using less computing resources to train model with smaller amount of training data. The rise of state-of-the-art models such as BERT, XLNet and GPT boost accuracy and benefit as a base

Daniel Spokoyny, Taylor Berg-Kirkpatrick

We conduct a large scale empirical investigation of contextualized number prediction in running text. Specifically, we consider two tasks: (1)masked number prediction-predicting a missing numerical value within a sentence, and (2)numerical anomaly detection-detecting an errorful numeric value within a sentence. We experiment with novel combinations of contex

Feras A. Batarseh, Munisamy Gopinath

The global food supply chain (starting at farms and ending with consumers) has been seriously disrupted by many outlier events such as trade wars, the China demand shock, natural disasters, and pandemics. Outlier events create uncertainty along the entire supply chain in addition to intervening policy responses to mitigate their adverse effects. Artificial I

Oktay K. Pashaev, Merve Ozvatan

By using definition of Golden derivative, corresponding Golden exponential function and Fibonomial coefficients, we introduce generating functions for Bernoulli-Fibonacci polynomials and related numbers. Properties of these polynomials and numbers are studied in parallel with usual Bernoulli counterparts. Fibonacci numbers and Golden ratio are intrinsically

Xiao-Jun Yang

In this paper, the scaling-law vector calculus, which is related to the connection between the vector calculus and the scaling law in fractal geometry, is addressed based on the Leibniz derivative and Stieltjes integral for the first time. The Gauss-Ostrogradsky-like theorem, Stokes-like theorem, Green-like theorem, and Green-like identities are considered i

Victor J. W. Guo

Let $E_n$ be the $n$-th Euler number and $(a)_n=a(a+1)\cdots (a+n-1)$ the rising factorial. Let $p>3$ be a prime. In 2012, Sun proved the that $$ \sum^{(p-1)/2}_{k=0}(-1)^k(4k+1)\frac{(\frac{1}{2})_k^3}{k!^3} \equiv p(-1)^{(p-1)/2}+p^3E_{p-3} \pmod{p^4}, $$ which is a refinement of a famous supercongruence of Van Hamme. In 2016, Chen, Xie, and He established

Yawen Zhang

We proposed a purely 3-D geometrical solution to Mathematics Magazine Problem 2065. Let $\mathcal{Q}$ be a cube centered at the origin of $\mathbb{R}^3$. Choose a unit vector $(a,b,c)$ uniformly at random on the surface of the unit sphere $a^2+b^2+c^2=1$, and let $Π$ be the plane $ax+by+cz=0$ through the origin and normal to $(a,b,c)$. What is the probabilit

Sachithra Lokuge, Darshana Sedera, Vanessa Cooper, Frada Burstein

The advent of digital technologies such as social media, mobile, analytics, cloud computing and internet-of-things has provided unique opportunities for organizations to engage in innovations that are affordable, easy-to-use, easy-to-learn and easy-to-implement. Transformations through such technologies often have positive impacts on business processes, prod

Darshana Sedera, Sachithra Lokuge

Today, technologies, devices and systems play a major role in our lives. Anecdotal commentary suggests that such technologies and our interactions with them create a false sense of perfectionism about life, events and its outcomes. While it is admirable to strive for better outcomes, constant and sometimes unrealistic expectations create a psychological cond

Sachithra Lokuge

Even though rural entrepreneurship and innovation has been studied for decades, the advent of social media, mobile, analytics, cloud computing and internet of things - also referred as digital technologies - (Nambisan 2013, Yoo et al. 2012) has provided new opportunities and challenges for this vast discipline. As a result, we see new business models, new pr

Niklas Kühl, Dominik Martin, Clemens Wolff, Melanie Volkamer

The obligation to wear masks in times of pandemics reduces the risk of spreading viruses. In case of the COVID-19 pandemic in 2020, many governments recommended or even obligated their citizens to wear masks as an effective countermeasure. In order to continuously monitor the compliance of this policy measure in public spaces like restaurants or tram station

Simon Kasif

Technological advances of virtually every kind pose risks to society including fairness and bias. We review a long-standing wisdom that a widespread practical deployment of any technology may produce adverse side effects misusing the knowhow. This includes AI but AI systems are not solely responsible for societal risks. We describe some of the common and AI

Yunlu Wang, Cheng Yang, Menghan Hu, Jian Zhang

This paper presents an unobtrusive solution that can automatically identify deep breath when a person is walking past the global depth camera. Existing non-contact breath assessments achieve satisfactory results under restricted conditions when human body stays relatively still. When someone moves forward, the breath signals detected by depth camera are hidd

Nouredine Nour, Reda Belhaj-Soullami, Cédric Buron, Alain Peres

Smart Grids of collaborative netted radars accelerate kill chains through more efficient cross-cueing over centralized command and control. In this paper, we propose two novel reward-based learning approaches to decentralized netted radar coordination based on black-box optimization and Reinforcement Learning (RL). To make the RL approach tractable, we use a

Paulo R C Mendes, Antonio J G Busson, Sérgio Colcher, Daniel Schwabe

Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluste

Fadi Boutros, Naser Damer, Kiran Raja, Raghavendra Ramachandra

Augmented and virtual reality is being deployed in different fields of applications. Such applications might involve accessing or processing critical and sensitive information, which requires strict and continuous access control. Given that Head-Mounted Displays (HMD) developed for such applications commonly contains internal cameras for gaze tracking purpos

Luze Xu, Marcia Fampa, Jon Lee

Generalized inverses are important in statistics and other areas of applied matrix algebra. A \emph{generalized inverse} of a real matrix $A$ is a matrix $H$ that satisfies the Moore-Penrose (M-P) property $AHA=A$. If $H$ also satisfies the M-P property $HAH=H$, then it is called \emph{reflexive}. Reflexivity of a generalized inverse is equivalent to minimum

Ta-Hsin Li, Huijing Jiang, Kevin Tran, Gigi Yuen-Reed

There is strong interest among healthcare payers to identify emerging healthcare cost drivers to support early intervention. However, many challenges arise in analyzing large, high dimensional, and noisy healthcare data. In this paper, we propose a systematic approach that utilizes hierarchical search strategies and enhanced statistical process control (SPC)

Saeed Karimi-Bidhendi, Jun Guo, Hamid Jafarkhani

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as an optimization problem with the total power consumption of the network as its

Kiran Bisht, Arun Kumar

Fuzzy time series forecasting methods are very popular among researchers for predicting future values as they are not based on the strict assumptions of traditional time series forecasting methods. Non-stochastic methods of fuzzy time series forecasting are preferred by the researchers as they provide more significant forecasting results. There are generally

Mohammed K. A. Kaabar, Francisco Martínez, José Francisco Gómez-Aguilar, Behzad Ghanbari

In this paper, a modified nonlinear Schrödinger equation with spatio-temporal dispersion is formulated in the senses of Caputo fractional derivative and conformable derivative. A new generalized double Laplace transform coupled with Adomian decomposition method has been defined and applied to solve the newly formulated nonlinear Schrödinger equation with spa

Santiago R. C. Fernandez, Marcos R. R. Gesualdi

In this work, we present the computational simulations of holographic metasurfaces to generation of the optical non-diffracting beams. The metasurfaces are designed by the holographic technique and the computer-generated holograms (CGHs) of optical non-diffracting beams are generated computationally. These holographic metasurfaces (HMS) are obtained by model

Cristina Rottondi, Francesco Malandrino, Andrea Bianco, Carla Fabiana Chiasserini

Single-task UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication support, and other specific tasks. When the geographical area of operation of single-task missions is common, e.g., in post-disaster recovery scenarios, it is more efficient to have multiple tasks carried out as part of a single UAV mission. In these sc

Chi-Liang Liu, Tsung-Yuan Hsu, Yung-Sung Chuang, Hung-yi Lee

Recently, multilingual BERT works remarkably well on cross-lingual transfer tasks, superior to static non-contextualized word embeddings. In this work, we provide an in-depth experimental study to supplement the existing literature of cross-lingual ability. We compare the cross-lingual ability of non-contextualized and contextualized representation model wit

A. Simonič, T. Trudgian, C. L. Turnage-Butterbaugh

We make explicit an argument of Heath-Brown concerning large and small gaps between nontrivial zeroes of the Riemann zeta-function, $ζ(s)$. In particular, we provide the first unconditional results on gaps (large and small) which hold for a positive proportion of zeroes. To do this we prove explicit bounds on the second and fourth power moments of $S(t+h)-S(

Young-Suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy

Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR. At the same time, self-learning techniques have helped push the performance boundaries of other natural language processing applications, s

Byron Chin, Allan Sly

This paper is motivated by the reconstruction problem on the sparse stochastic block model. The paper "Belief Propagation, robust reconstruction and optimal recovery of block models" by Mossel, Neeman, and Sly provided and proved a reconstruction algorithm that recovers an optimal fraction of the communities in the 2 community case. The main step in

Petr Horava, Christopher J. Mogni

We extend our study of the large-$N$ expansion of general non-equilibrium many-body systems with matrix degrees of freedom $M$, and its dual description as a sum over surface topologies in a dual string theory, to the Keldysh-rotated version of the Schwinger-Keldysh formalism. The Keldysh rotation trades the original fields $M_\pm$ -- defined as the values o

Ramon Fernandez Astudillo, Miguel Ballesteros, Tahira Naseem, Austin Blodgett

Modeling the parser state is key to good performance in transition-based parsing. Recurrent Neural Networks considerably improved the performance of transition-based systems by modelling the global state, e.g. stack-LSTM parsers, or local state modeling of contextualized features, e.g. Bi-LSTM parsers. Given the success of Transformer architectures in recent

Nyasha Mushambi, Bruce A. Watson, Bertin Zinsou

In a Dedekind complete Riesz space, $E$, we show that if $(P_n)$ is a sequence of band projections in $E$ then $$\limsup\limits_{n\to \infty} P_n - \liminf\limits_{n\to \infty} P_n = \limsup\limits_{n\to \infty} P_n(I-P_{n+1}).$$ This identity is used to obtain conditional extensions in a Dedekind complete Riesz spaces with weak order unit and conditional ex

Phillip Nguyen, Alex Silence, David Darais, Joseph P. Near

Differential privacy offers a formal privacy guarantee for individuals, but many deployments of differentially private systems require a trusted third party (the data curator). We propose DuetSGX, a system that uses secure hardware (Intel's SGX) to eliminate the need for a trusted data curator. Data owners submit encrypted data that can be decrypted only

Rajeev Yasarla, Jeya Maria Jose Valanarasu, Vishal M. Patel

Removal of rain streaks from a single image is an extremely challenging problem since the rainy images often contain rain streaks of different size, shape, direction and density. Most recent methods for deraining use a deep network following a generic "encoder-decoder" architecture which captures low-level features across the initial layers and high-

Enrique T. R. Pinto, Leonardo S. Resende

The LMS algorithm is one of the most widely used techniques in adaptive filtering. Accurate modeling of the algorithm in various circumstances is paramount to achieving an efficient adaptive Wiener filter design process. In the recent decades, concerns have been raised on studying improper signals and providing an accurate model of the LMS algorithm for both

Siddarth Srinivasan, Sandesh Adhikary, Jacob Miller, Guillaume Rabusseau

Modeling joint probability distributions over sequences has been studied from many perspectives. The physics community developed matrix product states, a tensor-train decomposition for probabilistic modeling, motivated by the need to tractably model many-body systems. But similar models have also been studied in the stochastic processes and weighted automata

Wei-Fan Chen, Khalid Al-Khatib, Henning Wachsmuth, Benno Stein

Media organizations bear great reponsibility because of their considerable influence on shaping beliefs and positions of our society. Any form of media can contain overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such form of imbalanced news coverage can be exposed

Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su

An acknowledged weakness of neural networks is their vulnerability to adversarial perturbations to the inputs. To improve the robustness of these models, one of the most popular defense mechanisms is to alternatively maximize the loss over the constrained perturbations (or called adversaries) on the inputs using projected gradient ascent and minimize over we

Wei-Fan Chen, Khalid Al-Khatib, Benno Stein, Henning Wachsmuth

Media plays an important role in shaping public opinion. Biased media can influence people in undesirable directions and hence should be unmasked as such. We observe that featurebased and neural text classification approaches which rely only on the distribution of low-level lexical information fail to detect media bias. This weakness becomes most noticeable

Elman Mansimov, Mitchell Stern, Mia Chen, Orhan Firat

In this paper, we offer a preliminary investigation into the task of in-image machine translation: transforming an image containing text in one language into an image containing the same text in another language. We propose an end-to-end neural model for this task inspired by recent approaches to neural machine translation, and demonstrate promising initial

Zheng Ma, Salvatore Torquato

In their seminal paper on scattering by an inhomogeneous solid, Debye and coworkers proposed a simple exponentially decaying function for the two-point correlation function of an idealized class of two-phase random media. Such {\it Debye random media}, which have been shown to be realizable, are singularly distinct from all other models of two-phase media in

Tiago Mota, Mohan Sridharan

A robot's ability to provide descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing such transparency is particularly challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning algorithms. Towards addressing this challenge, our architecture couples the complem

Emet Hirsch

The study of the combinatorial game Nim and its variants is rich and varied, but little is known of the game Nim with a Pass. It is Nim, but once per game a player is permitted to skip their turn but this can only be done if a nonempty pile remains. In this paper we define a new binary operation on games which we use to prove novel properties of Nim with a P

Scott Osborne, Garth Warner

The purpose of this note is to announce the results of our investigation into the role played by the continuous spectrum in the development of the Selberg trace formula vis-à-vis a pair $(G,Γ)$. For the sake of simplicity, we shall restrict ourselves to a "rank-2" situation, a case in point being when $G = \textbf{SL}(3,\mathbb{R}), \ Γ= \textbf{SL}(

Luke Finnerty, Kirsten Larson, B. T. Soifer, Lee Armus

We present rest-frame optical spectroscopic observations of 24 Hot Dust-Obscured Galaxies (Hot DOGs) at redshifts 1.7-4.6 with KECK/NIRES. Our targets are selected based on their extreme red colors to be the highest luminosity sources from the WISE infrared survey. In 20 sources with well-detected emission we fit the key [O III], H$β$, H$α$, [N II], and [S I

Andreea B. Alexandru, George J. Pappas

As large amounts of data are circulated both from users to a cloud server and between users, there is a critical need for privately aggregating the shared data. This paper considers the problem of private weighted sum aggregation with secret weights, where an aggregator wants to compute the weighted sum of the local data of some agents. Depending on the priv

Marco Alecci, Riccardo Cestaro, Mauro Conti, Ketan Kanishka

Android virtualization enables an app to create a virtual environment, in which other apps can run. Originally designed to overcome the limitations of mobile apps dimensions, malicious developers soon started exploiting this technique to design novel attacks. As a consequence, researchers proposed new defence mechanisms that enable apps to detect whether the

Weiyun Jiang, Kaiqi Zhang, Colin Yu Lin, Feng Xing

Recommendation systems, social network analysis, medical imaging, and data mining often involve processing sparse high-dimensional data. Such high-dimensional data are naturally represented as tensors, and they cannot be efficiently processed by conventional matrix or vector computations. Sparse Tucker decomposition is an important algorithm for compressing

Maria Emilia Descotte

In the sixties, Grothendieck developed the theory of pro-objects over a category. The fundamental property of the category $Pro(C)$ is that there is an embedding $C \stackrel{c}{\rightarrow} Pro(C)$, $Pro(C)$ is closed under small cofiltered limits, and these are free in the sense that for any category $E$ closed under small cofiltered limits, pre-compositio

Edwin Chau, Jamie Haddock

Matrix factorization techniques compute low-rank product approximations of high dimensional data matrices and as a result, are often employed in recommender systems and collaborative filtering applications. However, many algorithms for this task utilize an exact least-squares solver whose computation is time consuming and memory-expensive. In this paper we d

Alexander V. Bezrukov

In this paper it is demonstrated that the application of principal components analysis for regional cluster modelling and analysis is essential in the situations where there is significant multicollinearity among several parameters, especially when the dimensionality of regional data is measured in tens. The proposed principal components model allows for sam

Yanzhao Wu, Ling Liu, Zhongwei Xie, Juhyun Bae

Ensemble learning is gaining renewed interests in recent years. This paper presents EnsembleBench, a holistic framework for evaluating and recommending high diversity and high accuracy ensembles. The design of EnsembleBench offers three novel features: (1) EnsembleBench introduces a set of quantitative metrics for assessing the quality of ensembles and for c