April 2020 arXiv papers — page 30

Uriel Feige, Vadim Grinberg

In the well known planted clique problem, a clique (or alternatively, an independent set) of size $k$ is planted at random in an Erdos-Renyi random $G(n, p)$ graph, and the goal is to design an algorithm that finds the maximum clique (or independent set) in the resulting graph. We introduce a variation on this problem, where instead of planting the clique at

Min Ru, Paul Vojta

In this paper, we introduce the notion of an Evertse-Ferretti Nevanlinna constant and compare it with the birational Nevanlinna constant introduced by the authors in a recent joint paper. We then use it to recover several previously known results. This includes a 1999 example of Faltings from his Baker's Garden article.

Darshit Mehta, Kenji Aono, Shantanu Chakrabartty

Continuous, battery-free operation of sensor nodes requires ultra-low-power sensing and data-logging techniques. Here we report that by directly coupling a sensor/transducer signal into globally asymptotically stable monotonic dynamical systems based on Fowler-Nordheim quantum tunneling, one can achieve self-powered sensing at an energy budget that is curren

Liangji Fang, Qinhong Jiang, Jianping Shi, Bolei Zhou

Making accurate motion prediction of the surrounding traffic agents such as pedestrians, vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction methods have attempted to learn to directly regress the exact future position or its distribution from massive amount of trajectory data. However, it remains difficult for thes

Seong-Ho Kwon, Hyo-Sung Ahn

This paper discusses generalized weak rigidity theory, and aims to apply the theory to formation control problems with a gradient flow law. The generalized weak rigidity theory is utilized in order that desired formations are characterized by a general set of pure inter-agent distances and angles. As the first result of its applications, the paper provides a

Zhao Jianglin

The aim of this paper is to reemphasize the money theory of exchange which is centered on the function of exchange medium of money, and make a contribution towards linearization of the quantity equation of exchange. A dynamical quantity equation is presented and an important balanced path of economic evolution is derived. To understand the business cycle we

Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh

The ever-growing advances of deep learning in many areas including vision, recommendation systems, natural language processing, etc., have led to the adoption of Deep Neural Networks (DNNs) in production systems. The availability of large datasets and high computational power are the main contributors to these advances. The datasets are usually crowdsourced

Alejandro Alés, Fernando Lanzini

The elastic properties of $Cu-Al-Mn$ alloys with compositions along the $Cu_3Al \to Cu_2AlMn$ line and $bcc$-based structures, are studied by means of first-principles calculations. From the calculated elastic constants, the Zener's anisotropy, sound velocities and Debye temperature are determined. The theoretical results compare well with the available expe

Yashar Naeimi, Farzan Kooben, Mohamad Hanif Moallem

An important parameter that affects PV panel performance of a solar power system is the incident solar radiation with the panel or panel's area of exposure to the sun. The direction and tilt angle of a PV panel are two important factors in PV system design. This paper itself presents the calculation of the optimum installation angles for the seasonal adjusti

James F. Mather, Radostin D. Simitev

Convection and magnetic field generation in the Earth and planetary interiors are driven by both thermal and compositional gradients. In this work numerical simulations of finite-amplitude double-diffusive convection and dynamo action in rapidly rotating spherical shells full of incompressible two-component electrically-conducting fluid are reported. Four di

Anuj Srivastava

This paper develops an agent-level simulation model, termed ALPS, for simulating the spread of an infectious disease in a confined community. The mechanism of transmission is agent-to-agent contact, using parameters reported for Corona COVID-19 pandemic. The main goal of the ALPS simulation is analyze effects of preventive measures -- imposition and lifting

Aksel Kobiałka, Andrzej Ptok

Theoretical research suggests a emergence of the Majorana bound states at the ends of the nanowires. Experimental verifications of said concept has already been executed, e.g., in superconductor/semiconductor nanowire devices where interplay between superconducting gap, spin-orbit coupling and external magnetic field allows for creation of zero-energy bound

Tao Yuan, Hangxin Liu, Lifeng Fan, Zilong Zheng

Aiming to understand how human (false-)belief--a core socio-cognitive ability--would affect human interactions with robots, this paper proposes to adopt a graphical model to unify the representation of object states, robot knowledge, and human (false-)beliefs. Specifically, a parse graph (pg) is learned from a single-view spatiotemporal parsing by aggregatin

Jun Zhang, Yao-Kun Lei, Zhen Zhang, Junhan Chang

Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are comple

Arda Akdemir, Tetsuo Shibuya, Tunga Güngör

Morphological information is important for many sequence labeling tasks in Natural Language Processing (NLP). Yet, existing approaches rely heavily on manual annotations or external software to capture this information. In this study, we propose using subword contextual embeddings to capture the morphological information for languages with rich morphology. I

Zeyu Zhang, Hangxin Liu, Ziyuan Jiao, Yixin Zhu

We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations while keeping tracks of all evacuees' locations. A population density map, obtained on-the-fly by aggregating locations of evacuees from user-end Augmented Reality (AR) devices, is used to model th

Chao Zheng, Kun Yang, Xin Wan

We investigate the longitudinal conductance of a disordered three-dimensional (3D) quantum Hall system within a tight-binding lattice model using numerical Thouless conductance calculations. For the bulk, we confirm that the mobility edges are independent of the propagating directions in this anisotropic system. As disorder increases, the conductance peak of

Thierry Alboussiere, Kamel Drif, Franck Plunian

With materials of anisotropic electrical conductivity, it is possible to generate a dynamo with a simple velocity field, of the type precluded by Cowling's theorems with isotropic materials. Following a previous study by Ruderman and Ruzmaikin [1] who considered the dynamo effect induced by a uniform shear flow, we determine the conditions for the dynamo thr

Furkan Şahbaz, Mehmet C. Onbaşlı

Detailed understanding of spin dynamics in magnetic nanomaterials is necessary for developing ultrafast, low-energy and high-density spintronic logic and memory. Here, we develop micromagnetic models and analytical solutions to elucidate the effect of increasing damping and uniaxial anisotropy on magnetic field pulse-assisted switching time, energy and field

Tyler Meadows, Gail S. K. Wolkowicz

We introduce a model of the growth of a single microorganism in a self-cycling fermentor in which an arbitrary number of resources are limiting, and impulses are triggered when the concentration of one specific substrate reaches a predetermined level. The model is in the form of a system of impulsive differential equations. We consider the operation of the r

Giada Cianfarani Carnevale, Corrado Lattanzio

The aim of this paper is to investigate the singular relaxation limits for the Euler-Korteweg and the Navier-Stokes-Korteweg system in the high friction regime. We shall prove that the viscosity term is present only in higher orders in the proposed scaling and therefore it does not affect the limiting dynamics, and the two models share the same equilibrium e

Halgurd S. Maghdid, Kayhan Zrar Ghafoor

The emergence of novel COVID-19 causing an overload in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to ensure extreme social distancing of entire population and hence slowing down the epidemic spread. Further, authorities u

Jiaao Chen, Zichao Yang, Diyi Yang

This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. Moreover, we leverage recent advances in data augmentation to guess low-entropy labels for unlabeled data,

Abhishek Kumar, Trisha Mittal, Dinesh Manocha

We present MCQA, a learning-based algorithm for multimodal question answering. MCQA explicitly fuses and aligns the multimodal input (i.e. text, audio, and video), which forms the context for the query (question and answer). Our approach fuses and aligns the question and the answer within this context. Moreover, we use the notion of co-attention to perform c

Kaijen Cheng, Hongming You, Ting-Hui Yang

In this work, we revisit the classical Holling type II three species food chain model with a different viewpoint. Two critical parameters $\lambda_1$ and $\lambda_2$ dependent on other six parameters are defined. We show that local stabilities and existence of all equilibria can be reformulated by $\lambda_1$ and $\lambda_2$, and the complete classifications

Yurii Kolomoitsev, Elijah Liflyand

In this paper an asymptotic formula is given for the Lebesgue constants generated by the anisotropically dilated $d$-dimensional simplex. Contrary to many preceding results established only in dimension two, the obtained ones are proved in any dimension. Also, the "rational" and "irrational" parts are both united and separated in one formula.

Martin Magill, Andrew M. Nagel, Hendrick W. de Haan

The neural network method of solving differential equations is used to approximate the electric potential and corresponding electric field in the slit-well microfluidic device. The device's geometry is non-convex, making this a challenging problem to solve using the neural network method. To validate the method, the neural network solutions are compared to a

Leticia Decker, Daniel Leite, Luca Giommi, Daniele Bonacorsi

Detection of anomalous behaviors in data centers is crucial to predictive maintenance and data safety. With data centers, we mean any computer network that allows users to transmit and exchange data and information. In particular, we focus on the Tier-1 data center of the Italian Institute for Nuclear Physics (INFN), which supports the high-energy physics ex

Yifei Sun, Charles E. McCulloch, Kieren A. Marr, Chiung-Yu Huang

Although increasingly used as a data resource for assembling cohorts, electronic health records (EHRs) pose many analytic challenges. In particular, a patient's health status influences when and what data are recorded, generating sampling bias in the collected data. In this paper, we consider recurrent event analysis using EHR data. Conventional regression m

Puskar Bhattarai, Kamal Wagle, Chandra Shahi, Yoh Yamamoto

The Perdew-Zunger(PZ) self-interaction correction (SIC) was designed to correct the one-electron limit of any approximate density functional for the exchange-correlation (xc) energy, while yielding no correction to the exact functional. Unfortunately, it spoils the slowly-varying-in-space limits of the uncorrected approximate functionals, where those functio

Aniket Pokale, Aditya Aggarwal, K. Madhava Krishna

This paper presents a new system to obtain dense object reconstructions along with 6-DoF poses from a single image. Geared towards high fidelity reconstruction, several recent approaches leverage implicit surface representations and deep neural networks to estimate a 3D mesh of an object, given a single image. However, all such approaches recover only the sh

Huayu Li, Xiwen Chen, Haiyu Wu, Zaoyi Chi

Digital in-line holography is commonly used to reconstruct 3D images from 2D holograms for microscopic objects. One of the technical challenges that arise in the signal processing stage is removing the twin image that is caused by the phase-conjugate wavefront from the recorded holograms. Twin image removal is typically formulated as a non-linear inverse pro

Samuele Giraudo

Pairs of graded graphs, together with the Fomin property of graded graph duality, are rich combinatorial structures providing among other a framework for enumeration. The prototypical example is the one of the Young graded graph of integer partitions, allowing us to connect number of standard Young tableaux and numbers of permutations. Here, we use operads,

Donald V. Reames

We revisit a multi-spacecraft study of the element abundances of solar energetic particles (SEPs) in the 23 January 2012 event, where the power-law pattern of enhancements versus the mass-to-charge ratio A/Q for the elements C through Fe was partly disrupted by a break near Mg, which turned out to be an unfortunate distraction. In the current article we find

Quanmin Guo

For an ideal gas consisting N molecules within a volume V, the volume accessible to each molecule at an instantaneous time is V/N. The rest of the volume, (N-1)(V/N), is occupied by other (N-1) molecules. The textbook assumption that a molecule can access any location inside the volume V at one instantaneous in time is wrong leading to the Gibbs paradox. By

Yuanhao Xiong, Cho-Jui Hsieh

Adversarial attack has recently become a tremendous threat to deep learning models. To improve the robustness of machine learning models, adversarial training, formulated as a minimax optimization problem, has been recognized as one of the most effective defense mechanisms. However, the non-convex and non-concave property poses a great challenge to the minim

Koosha Zarei, Reza Farahbakhsh, Noel Crespi, Gareth Tyson

The novel coronavirus (COVID-19) pandemic outbreak is drastically shaping and reshaping many aspects of our life, with a huge impact on our social life. In this era of lockdown policies in most of the major cities around the world, we see a huge increase in people and professional engagement in social media. Social media is playing an important role in news

Vladimir Djordjić, Milana Pavić-Čolić, Nikola Spasojević

In this paper, we consider the kinetic model of continuous type describing a polyatomic gas in two different settings corresponding to a different choice of the functional space used to define macroscopic quantities. Such a model introduces a single continuous variable supposed to capture all the phenomena related to the more complex structure of a molecule

Polyxeni Spilioti

In this paper, we study the twisted Ruelle zeta function associated with the geodesic flow of a compact, hyperbolic, odd-dimensional manifold $X$. The twisted Ruelle zeta function is associated with an acyclic representation $\chi\colon \pi_{1}(X) \rightarrow \GL_{n}(\C)$, which is close enough to an acyclic, unitary representation. In this case, the twisted

Yaron Fairstein, Ariel Kulik, Joseph, Naor

We study the problem of maximizing a monotone submodular function subject to a Multiple Knapsack constraint. The input is a set $I$ of items, each has a non-negative weight, and a set of bins of arbitrary capacities. Also, we are given a submodular, monotone and non-negative function $f$ over subsets of the items. The objective is to find a packing of a subs

Avah Banerjee, Guoli Ding

In this paper we study the mincut problem in the online setting. We consider two distinct models: A) competitive analysis and B) regret analysis. In the competitive setting we consider the vertex arrival model; whenever a new vertex arrives it's neighborhood with respect to the set of known vertices is revealed. An online algorithm must make an irrevocable d

Eduard Eiben, Robert Ganian, Thekla Hamm, Fabian Klute

Algorithmic extension problems of partial graph representations such as planar graph drawings or geometric intersection representations are of growing interest in topological graph theory and graph drawing. In such an extension problem, we are given a tuple $(G,H,\mathcal{H})$ consisting of a graph $G$, a connected subgraph $H$ of $G$ and a drawing $\mathcal

Alev Kelleci, Luiz C. B. da Silva

We consider the extrinsic geometry of surfaces in simply isotropic space, a three-dimensional space equipped with a rank 2 metric of index zero. Since the metric is degenerate, a surface normal cannot be unequivocally defined based on metric properties only. To understand the contrast between distinct choices of an isotropic Gauss map, here we study surfaces

Ilyas Noor Bhatti, Imtiaz Noor Bhatti, Rabindra Nath Mahato, M. A. H. Ahsan

Structural, magnetic, and dielectric properties of Sm$_2$CoMnO$_6$ have been studied. X-ray diffraction and Rietveld analysis show that the sample crystallizes in the monoclinic structure with \textit{P2$_1$/n} space group. Magnetic study shows that the sample undergoes a paramagnetic to ferromagnetic phase transition around $T_c$ $\sim$148 K and a low-tempe

Shailendra Rajput, Asher Yahalom, Hong Qin

In a previous paper we have shown that Newton's third law cannot strictly hold in a distributed system of which the different parts are at a finite distance from each other. This is due to the finite speed of signal propagation which cannot exceed the speed of light at vacuum, which in turn means that when summing the total force in the system the force does

Jason Khadka, Jean-Daniel Julien, Karen Alim

Plant organ outgrowth superficially appears like the continuous mechanical deformation of a sheet of cells. Yet, how precisely cells as individual mechanical entities can act to morph a tissue reliably and efficiently into three dimensions during outgrowth is still puzzling especially when cells are tightly connected as in plant tissue. In plants, the mechan

A. Tiribocchi, A. Montessori, M. Lauricella, F. Bonaccorso

Understanding the fluid-structure interaction is crucial for an optimal design and manufacturing of soft mesoscale materials. Multi-core emulsions are a class of soft fluids assembled from cluster configurations of deformable oil-water double droplets (cores), often employed as building-blocks for the realisation of devices of interest in bio-technology, suc

C. Inserra, M. Sullivan, C. R. Angus, E. Macaulay

We present the first Hubble diagram of superluminous supernovae (SLSNe) out to a redshift of two, together with constraints on the matter density, $\Omega_{\rm M}$, and the dark energy equation-of-state parameter, $w(\equiv p/\rho)$. We build a sample of 20 cosmologically useful SLSNe~I based on light curve and spectroscopy quality cuts. We confirm the robus

Shubhankar Mohan, Aditi Chaudhary, Prachie Gupta, Ritu Tiwari

This paper proposes an idea of building an interface to merge the existing technologies like Image processing, Internet of Things, Sixth sense, etc. at one place to reduce the hardware restrictions imposed on a user and improve the responsiveness of the system. The wearable device comprises of a camera, a projector, and its own gesture-controlled environment

Shangyu Xie, Han Wang, Yuan Hong, My Thai

The smart grid incentivizes distributed agents with local generation (e.g., smart homes, and microgrids) to establish multi-agent systems for enhanced reliability and energy consumption efficiency. Distributed energy trading has emerged as one of the most important multi-agent systems on the power grid by enabling agents to sell their excessive local energy

Cristóbal A. Navarro, Felipe A. Quezada, Nancy Hitschfeld, Raimundo Vega

This work proposes a new approach for mapping GPU threads onto a family of discrete embedded 2D fractals. A block-space map $\lambda: \mathbb{Z}_{\mathbb{E}}^{2} \mapsto \mathbb{Z}_{\mathbb{F}}^{2}$ is proposed, from Euclidean parallel space $\mathbb{E}$ to embedded fractal space $\mathbb{F}$, that maps in $\mathcal{O}(\log_2 \log_2(n))$ time and uses no mor

Dawei Chen, Jiangtao Wang, Wenjie Ruan, Qiang Ni

Because of its important role in health policy-shaping, population health monitoring (PHM) is considered a fundamental block for public health services. However, traditional public health data collection approaches, such as clinic-visit-based data integration or health surveys, could be very costly and time-consuming. To address this challenge, this paper pr

Stefano Ascenzi, Gor Oganesyan, Om S. Salafia, Marica Branchesi

The X-ray emission of gamma-ray bursts (GRBs) is often characterized by an initial steep decay, followed by a nearly constant emission phase (so called "plateau") which can extend up to thousands of seconds. While the steep decay is usually interpreted as the tail of the prompt gamma-ray flash, the long-lasting plateau is commonly associated to the emission

Ozan Sener, Vladlen Koltun

We are interested in derivative-free optimization of high-dimensional functions. The sample complexity of existing methods is high and depends on problem dimensionality, unlike the dimensionality-independent rates of first-order methods. The recent success of deep learning suggests that many datasets lie on low-dimensional manifolds that can be represented b

Yitong Li, Wenying Ji, Simaan M. AbouRizk

Abstraction of operation processes is a fundamental step for simulation modeling. To reliably abstract an operation process, modelers rely on text information to study and understand details of operations. Aiming at reducing modelers' interpretation load and ensuring the reliability of the abstracted information, this research proposes a systematic methodolo

Lior Sidi, Hadar Klein

E-Learning systems (ELS) and Intelligent Tutoring Systems (ITS) play a significant part in today's education programs. Sequencing questions is the art of generating a personalized quiz for a target learner. A personalized test will enrich the learner's experience and will contribute to a more effective and efficient learning process. In this paper, we used t

Kamran Javid, Will Handley, Mike Hobson, Anthony Lasenby

We conduct a thorough analysis of the relationship between the out-of-sample performance and the Bayesian evidence (marginal likelihood) of Bayesian neural networks (BNNs), as well as looking at the performance of ensembles of BNNs, both using the Boston housing dataset. Using the state-of-the-art in nested sampling, we numerically sample the full (non-Gauss

Siting Liu, Matthew Jacobs, Wuchen Li, Levon Nurbekyan

We introduce a novel framework to model and solve mean-field game systems with nonlocal interactions. Our approach relies on kernel-based representations of mean-field interactions and feature-space expansions in the spirit of kernel methods in machine learning. We demonstrate the flexibility of our approach by modeling various interaction scenarios between

Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang

Invariance (defined in a general sense) has been one of the most effective priors for representation learning. Direct factorization of parametric models is feasible only for a small range of invariances, while regularization approaches, despite improved generality, lead to nonconvex optimization. In this work, we develop a convex representation learning algo

A. Javili, S. Firooz, A. T. McBride, P. Steinmann

Peridynamics (PD) is a non-local continuum formulation. The original version of PD was restricted to bond-based interactions. Bond-based PD is geometrically exact and its kinematics are similar to classical continuum mechanics (CCM). However, it cannot capture the Poisson effect correctly. This shortcoming was addressed via state-based PD, but the kinematics

Claus Michael Ringel

Let A be a finite-dimensional algebra. If A is self-injective, then all modules are reflexive. Marczinzik recently has asked whether A has to be self-injective in case all the simple modules are reflexive. Here, we exhibit an 8-dimensional algebra which is not self-injective, but such that all simple modules are reflexive (actually, for this example, the sim

Abigail Z. Jacobs, Michaelanne Dye

We propose a mixed-methods approach to understanding the human infrastructure underlying StreetNet (SNET), a distributed, community-run intranet that serves as the primary 'Internet' in Havana, Cuba. We bridge ethnographic studies and the study of social networks and organizations to understand the way that power is embedded in the structure of Havana's SNET

John R. Edison, Rebecca L. Siegelman, Zdenek Preisler, Joyjit Kundu

Diamine-appended metal{organic frameworks (MOFs) of the form Mg2(dobpdc)(diamine)2 adsorb CO2 in a cooperative fashion, exhibiting an abrupt change in CO2 occupancy with pressure or temperature. This change is accompanied by hysteresis. While hysteresis is suggestive of a firstorder phase transition, we show that hysteretic temperature-occupancy curves assoc

Aniket Patra, Birgit Hillebrecht, Anne E. B. Nielsen

Trial states describing anyonic quasiholes in the Laughlin state were found early on, and it is therefore natural to expect that one should also be able to create anyonic quasielectrons. Nevertheless, the existing trial wavefunctions for quasielectrons show behaviors that are not compatible with the expected topological properties or their construction invol

Eduardo Nigri, Nivio Ziviani, Fabio Cappabianco, Augusto Antunes

Deep Convolutional Neural Networks (CNNs) are becoming prominent models for semi-automated diagnosis of Alzheimer's Disease (AD) using brain Magnetic Resonance Imaging (MRI). Although being highly accurate, deep CNN models lack transparency and interpretability, precluding adequate clinical reasoning and not complying with most current regulatory demands. On

Seddigheh Tizchang, Seyed Mohsen Etesami

In this paper, we explore the potential of the LHC to measure the rate of $\mathrm{p}\mathrm{p}\rightarrow \mathrm{p}~ WW\gamma~\mathrm{p}$ process, also to probe the new effective couplings contributing to the $WW\gamma$ and $WW\gamma\gamma$ vertices. The analysis is performed at the $\sqrt{s}=13$ TeV, in the di-leptonic decay channel, and assuming 300 $fb^

Marichelo Garcia-Venegas, Diego A. Mercado-Ravell, Carlos A. Carballo-Monsivais

In this work, orientation detection using Deep Learning is acknowledged for a particularly vulnerable class of road users,the cyclists. Knowing the cyclists' orientation is of great relevance since it provides a good notion about their future trajectory, which is crucial to avoid accidents in the context of intelligent transportation systems. Using Transfer

Rustem Khasanov, Ritu Gupta, Debarchan Das, Andreas Leithe-Jasper

The puzzling situation where some thermodynamic quantities require a single-gap description, while others need a more complex gap scenario, is discussed. Our approach reveals that in some cases, the conclusions based on measurements of only one thermodynamic quantity may lead to conflicting results. As an example, temperature evolutions of the electronic spe

Zahra Barani, Fariborz Kargar, Amirmahdi Mohammadzadeh, Sahar Naghibi

We describe a method for scalable synthesis of epoxy composites with graphene and few-layer graphene fillers, and report on the electromagnetic interference (EMI) shielding and thermal properties of such composites at elevated temperatures. The tested materials reveal excellent total EMI shielding of ~65 dB (~105 dB) at a thickness of 1 mm(~2 mm) in the X-ba

Menglong Xu, Xiao-Lei Zhang

One difficult problem of keyword spotting is how to miniaturize its memory footprint while maintain a high precision. Although convolutional neural networks have shown to be effective to the small-footprint keyword spotting problem, they still need hundreds of thousands of parameters to achieve good performance. In this paper, we propose an efficient model b

Alexander Jung, Yasmin SarcheshmehPour

We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundary and seed nodes. While spectral clustering methods are guided by a minimization of the graph Laplacian quadratic form, nLasso minimizes the total

Mengjie Zhao, Philipp Dufter, Yadollah Yaghoobzadeh, Hinrich Schütze

Pretrained language models have achieved a new state of the art on many NLP tasks, but there are still many open questions about how and why they work so well. We investigate the contextualization of words in BERT. We quantify the amount of contextualization, i.e., how well words are interpreted in context, by studying the extent to which semantic classes of

Gengchen Mai, Krzysztof Janowicz, Ling Cai, Rui Zhu

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. For those models that consider spac

Amine Kechaou, Manuel Martinez, Monica Haurilet, Rainer Stiefelhagen

In this work, we present Detective - an attentive object detector that identifies objects in images in a sequential manner. Our network is based on an encoder-decoder architecture, where the encoder is a convolutional neural network, and the decoder is a convolutional recurrent neural network coupled with an attention mechanism. At each iteration, our decode

Félix del Teso, David Gómez-Castro, Juan Luis Vázquez

In this paper we study how the (normalised) Gagliardo semi-norms $[u]_{W^{s,p} (\mathbb{R}^n)}$ control translations. In particular, we prove that $\| u(\cdot + y) - u \|_{L^p (\mathbb{R}^n)} \le C [ u ] _{W^{s,p} (\mathbb{R}^n)} |y|^s$ for $n\geq1$, $s \in [0,1]$ and $p \in [1,+\infty]$, where $C$ depends only on $n$. We then obtain a corresponding higher-o

Monjurul Meem, Sourangsu Banerji, Apratim Majumder, Christian Pies

It is generally thought that correcting chromatic aberrations in imaging requires multiple surfaces. Here, we show that by allowing the phase in the image plane of a flat lens to be a free parameter, it is possible to correct chromatic aberrations over a large continuous bandwidth with a single diffractive surface. We experimentally demonstrate imaging using

Georg Rehm, Peter Bourgonje, Stefanie Hegele, Florian Kintzel

In all domains and sectors, the demand for intelligent systems to support the processing and generation of digital content is rapidly increasing. The availability of vast amounts of content and the pressure to publish new content quickly and in rapid succession requires faster, more efficient and smarter processing and generation methods. With a consortium o

Arunava Mandal, Riddhi Shah

We study properties and the structure of Cartan subgroups in a connected Lie group. We obtain a characterisation of Cartan subgroups which generalises W\"ustner's structure theorem for the same. We show that Cartan subgroups are same as those of the centralizers of maximal compact subgroups of the radical. Moreover, we describe a recipe for constructing Cart

Wenhe Zhang, Chi Zhang, Yixin Zhu, Song-Chun Zhu

As a comprehensive indicator of mathematical thinking and intelligence, the number sense (Dehaene 2011) bridges the induction of symbolic concepts and the competence of problem-solving. To endow such a crucial cognitive ability to machine intelligence, we propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically ge

Tran Huu Phat, Toan T. Nguyen

Exploring the significant impacts of topological charge on the holographic phase transitions and conductivity we start from an Einstein - Maxwell system coupled with a charged scalar field in Anti - de Sitter spacetime. In our set up, the corresponding black hole (BH) is chosen to be the topological AdS one where the pressure is identified with the cosmologi

Xian-Hui Ge, Sang-Jin Sin

We study charged black hole solutions in 4-dimensional (4D) Einstein-Gauss-Bonnet-Maxwell theory to the linearized perturbation level. We first compute the shear viscosity to entropy density ratio. We then demonstrate how bulk causal structure analysis imposes a upper bound on the Gauss-Bonnet coupling constant in the AdS space. Causality constrains the valu

Georg Rehm, Karolina Zaczynska, Julián Moreno-Schneider, Malte Ostendorff

Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop AI-based technologies that are verified b

Paraskevi C. Divari

In this work, the supernova neutrino(SN) charged-current interactions with Gd odd isotopes (A=155 and 157) are studied. We use measured spectra and the quasiparticle-phonon model (MQPM) to calculate the charged current response of odd Gd isotopes to supernova neutrinos. Flux-averaged cross-sections are obtained considering quasi-thermal neutrino spectra.

Hristu Culetu

A static, spherically symmetric spacetime with negative pressures is conjectured inside a star. The gravitational field is repulsive and so a central singularity is avoided. The positive energy density and the pressures of the imperfect fluid are finite everywhere. The Tolman-Komar energy of the space is negative, as for a de Sitter geometry. From the Darmoi

Amiram Moshaiov, Michael Zadok

Evolving Counter-Propagation Neuro-Controllers (CPNCs), rather than the traditional Feed-Forward Neuro-Controllers (FFNCs), has recently been suggested and tested using simulated robot navigation. It has been demon-strated that both convergence rate and final performance obtained by evolving CPNCs are superior to those obtained by evolving FFNCs. In this pap

David Barozzini, Lorenzo Clemente, Thomas Colcombet, Paweł Parys

In this work we prove decidability of the model-checking problem for safe recursion schemes against properties defined by alternating B-automata. We then exploit this result to show how to compute downward closures of languages of finite trees recognized by safe recursion schemes. Higher-order recursion schemes are an expressive formalism used to define lang

Daniel Groos, Heri Ramampiaro, Espen A. F. Ihlen

Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimation generally do not meet the requirements of real-life applications. The proliferation of deep learning techniques has resulted in the development of many advanced approaches. Howev

Chong Oh Lee, Jin Young Kim, Mu-In Park

We study gravitational perturbations of electrically charged black holes in (3+1)-dimensional Einstein-Born-Infeld gravity with a positive cosmological constant. For the axial perturbations, we obtain a set of decoupled Schrodinger-type equations, whose formal expressions, in terms of metric functions, are the same as those without cosmological constant, cor

Nirav Diwan, Devansh Batra, Ganesh Bagler

Traditional cooking recipes follow a structure which can be modelled very well if the rules and semantics of the different sections of the recipe text are analyzed and represented accurately. We propose a structure that can accurately represent the recipe as well as a pipeline to infer the best representation of the recipe in this uniform structure. The Ingr

Tim Witschel, Christian Wressnegger

Current schemes to detect cheating in online games often build on the assumption that the applied cheat takes actions that are drastically different from normal behavior. For instance, an Aimbot for a first-person shooter is used by an amateur player to increase his/her capabilities many times over. Attempts to evade detection would require to reduce the int

Sebastian Engelke, Jevgenijs Ivanovs

Extreme value statistics provides accurate estimates for the small occurrence probabilities of rare events. While theory and statistical tools for univariate extremes are well-developed, methods for high-dimensional and complex data sets are still scarce. Appropriate notions of sparsity and connections to other fields such as machine learning, graphical mode

CMS Collaboration

The first direct, model-independent measurement is presented of the modulus of the Cabibbo-Kobayashi-Maskawa (CKM) matrix elements $|V_\mathrm{tb}|$, $|V_\mathrm{td}|$, and $|V_\mathrm{ts}|$, in final states enriched in single top quark $t$-channel events. The analysis uses proton-proton collision data from the LHC, collected during 2016 by the CMS experimen

Francisco Rubilar, Leonardo Schultz

Let $\mathrm{SL}(n,\mathbb{R})$ be the special linear group and $\mathfrak{sl}(n,\mathbb{R})$ its Lie algebra. We study geometric properties associated to the adjoint orbits in the simplest non-trivial case, namely, those of $\mathfrak{sl}(2,\mathbb{R})$. In particular, we show that just three possibilities arise: either the adjoint orbit is a one-sheeted hy

Christopher Bellman, Paul C. van Oorschot

Best practices for Internet of Things (IoT) security have recently attracted considerable attention worldwide from industry and governments, while academic research has highlighted the failure of many IoT product manufacturers to follow accepted practices. We explore not the failure to follow best practices, but rather a surprising lack of understanding, and

Dongzhan Zhou, Xinchi Zhou, Hongwen Zhang, Shuai Yi

In this paper, we propose a general and efficient pre-training paradigm, Montage pre-training, for object detection. Montage pre-training needs only the target detection dataset while taking only 1/4 computational resources compared to the widely adopted ImageNet pre-training.To build such an efficient paradigm, we reduce the potential redundancy by carefull

Taylor Brysiewicz

We develop a collection of numerical algorithms which connect ideas from polyhedral geometry and algebraic geometry. The first algorithm we develop functions as a numerical oracle for the Newton polytope of a hypersurface and is based on ideas of Hauenstein and Sottile. Additionally, we construct a numerical tropical membership algorithm which uses the forme

Kevin Flaherty, A. Meredith Hughes, Jacob B. Simon, Chunhua Qi

Turbulence is a crucial factor in many models of planet formation, but it has only been directly constrained among a small number of planet forming disks. Building on the upper limits on turbulence placed in disks around HD 163296 and TW Hya, we present ALMA CO J=2-1 line observations at $\sim0.3"$ (20-50 au) resolution and 80 ms$^{-1}$ channel spacing of th

Daniel Dobin, Alexander Dobin

We investigated the dependence of Covid-19 attack rates on demographic and socioeconomic factors for the communities in Suffolk County (Long Island, New York State), presently the 5th most-affected county in the United States. Confirming the previous observations that minorities are disproportionately impacted by the Covid-19 disease, we found that the attac

M. S. Churilova

The regularization proposed in [D.~Glavan and C.~Lin, Phys.\ Rev.\ Lett.\ {\bf 124}, 081301 (2020)] led to the black hole solutions which turned out to be the solutions of the consistent well-defined $4$-dimensional Einstein-Gauss-Bonnet theory of gravity suggested in [K.~Aoki, M.~Gorji and S.~Mukohyama, arXiv:2005.03859]. Recently the quasinormal modes of b

Ziyue Wang, Xingyu Guo, Shuzhe Shi, Pengfei Zhuang

We study fermion mass correction to chiral kinetic equations in electromagnetic fields. Different from the chiral limit where fermion number density is the only independent distribution, the number and spin densities are coupled to each other for massive fermion systems. To the first order in $\hbar$, we derived the quantum correction to the classical on-she