October 2020 arXiv papers — page 81

Rakesh Shrestha, Zhiwen Fan, Qingkun Su, Zuozhuo Dai

Deep learning based 3D shape generation methods generally utilize latent features extracted from color images to encode the semantics of objects and guide the shape generation process. These color image semantics only implicitly encode 3D information, potentially limiting the accuracy of the generated shapes. In this paper we propose a multi-view mesh genera

I. Assani, R. S. Hallyburton, S. McMahon, S. Schmidt

We study the coefficients of the Taylor series expansion of powers of the function $ψ(x)=\frac{1-\sqrt{1-x}}{x}$, where the Brunel operator $A\equiv A(T)$ is defined as $ψ(T)$ for any mean-bounded $T$. We prove several new precise estimates regarding the Taylor coefficients of $ψ^n$ for $n\in\mathbb{N}$. We apply these estimates to give an elementary proof t

Felix Arjun Kuhn, Claudio Bruderer, Simon Birrer, Adam Amara

We present a combined cosmic shear analysis of the modeling of line-of-sight distortions on strongly lensed extended arcs and galaxy shape measurements in the COSMOS field. We develop a framework to predict the covariance of strong lensing and galaxy shape measurements of cosmic shear on the basis of the small scale matter power-spectrum. The weak lensing me

Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere

Checkpoints play an important role in training long running machine learning (ML) models. Checkpoints take a snapshot of an ML model and store it in a non-volatile memory so that they can be used to recover from failures to ensure rapid training progress. In addition, they are used for online training to improve inference prediction accuracy with continuous

Robert David, Jared Duke, Advait Jain, Vijay Janapa Reddi

Deep learning inference on embedded devices is a burgeoning field with myriad applications because tiny embedded devices are omnipresent. But we must overcome major challenges before we can benefit from this opportunity. Embedded processors are severely resource constrained. Their nearest mobile counterparts exhibit at least a 100 -- 1,000x difference in com

Ting-Wei Pan, Ji-Feng Yang

The closed-form Brükner $G$ matrix for nuclear matter is computed in the $^1S_0$ channel of EFT($\not\!\!π$) and renormalized in nonperturbative context. The nuclear medium environment yields additional constraints that are consistent with off-shell $T$ matrix renormalization, keeping the power counting intact and simplifying the running behaviors of the EFT

Çağla Aksoy, Alper Ahmetoğlu, Tunga Güngör

Recent works show that learning contextualized embeddings for words is beneficial for downstream tasks. BERT is one successful example of this approach. It learns embeddings by solving two tasks, which are masked language model (masked LM) and the next sentence prediction (NSP). The pre-training of BERT can also be framed as a multitask learning problem. In

Xilin Liu, Hongjie Zhu, Tian Qiu, Srihari Y. Sritharan

Sensory feedback is critical to the performance of neural prostheses that restore movement control after neurological injury. Recent advances in direct neural control of paralyzed arms present new requirements for miniaturized, low-power sensor systems. To address this challenge, we developed a fully-integrated wireless sensor-brain-machine interface (SBMI)

Zhonggan Huang

This paper derives an explicit formula for the effective diffusion tensor by using the solutions to some effective cell problems after homogenizing Road effective boundary conditions (EBCs). The concept of Road EBCs was proposed recently by H. Li and X. Wang, and in this paper, we extend the effective conditions on closed curves to those on patterns, especia

Mohamed Maher, Perseverance Munga Ngoy, Aleksandrs Rebriks, Cagri Ozcinar

Boosting sales of e-commerce services is guaranteed once users find more matching items to their interests in a short time. Consequently, recommendation systems have become a crucial part of any successful e-commerce services. Although various recommendation techniques could be used in e-commerce, a considerable amount of attention has been drawn to session-

Yuto Omae, Jun Toyotani, Kazuyuki Hara, Yasuhiro Gon

As of 2020, COVID-19 is spreading in the world. In Japan, the Ministry of Health, Labor and Welfare developed COVID-19 Contact-Confirming Application (COCOA). The researches to examine the effect of COCOA are still not sufficient. We develop a mathematical model to examine the effect of COCOA and show examined result.

Nabit Bajwa, Kedar Bajwa, Atif Rana, M. Faique Shakeel

We investigate the generalizability of deep convolutional neural network (CNN) on the task of disease classification from chest x-rays collected over multiple sites. We systematically train the model using datasets from three independent sites with different patient populations: National Institute of Health (NIH), Stanford University Medical Centre (CheXpert

Yunchao Wei, Shuai Zheng, Ming-Ming Cheng, Hang Zhao

Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotated datasets. The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect

Jiantao Wu, Lin Wang

Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning. This improves interpretability, enables the performance of down-stream tasks, and enables controllable generative models. However, this domain is challenged by the abstract notion and incomplete theories to support unsupervised

Mohiuddin Md Abdul Qudar, Vijay Mago

Twitter is a well-known microblogging social site where users express their views and opinions in real-time. As a result, tweets tend to contain valuable information. With the advancements of deep learning in the domain of natural language processing, extracting meaningful information from tweets has become a growing interest among natural language researche

Ryszard Frankiewicz, Joanna Jureczko

In this note we give equivalence of the existence of K-partitions with the existence of the precipitous ideal which is essentially topological. This way we strengthen the main result of Frankiewczi and Kunen (1987).

M. I. Ayzatsky

A new approach to the description of inhomogeneous disk-loaded waveguides (chains of coupled resonators) is proposed. New matrix difference equations based on the technique of coupled integral equations and the decomposition method are obtained. Various approximate approaches have been developed, including the WKB approximation.

Jiaying Cai

Carathéodory's well-known conjecture states that every sufficiently smooth, closed convex surface in three dimensional Euclidean space admits at least two umbilic points. It has been established that the conjecture is true for all rotationally symmetric surfaces; in this paper, we investigate the umbilic points of two families of surfaces without rotatio

Rongxing Xu, Xuding Zhu

A graph $G$ is called $3$-choice critical if $G$ is not $2$-choosable but any proper subgraph is $2$-choosable. A graph $G$ is strongly fractional $r$-choosable if $G$ is $(a,b)$-choosable for all positive integers $a,b$ for which $a/b \ge r$. The strong fractional choice number of $G$ is $ch_f^s(G) = \inf \{r: G $ is strongly fractional $r$-choosable$\}$. T

David Masser

Alan Baker, Fields Medallist, died on 4th February 2018 in Cambridge England after a severe stroke a few days earlier. In 1970 he was awarded the Fields Medal at the International Congress in Nice on the basis of his outstanding work on linear forms in logarithms and its consequences. Since then he received many honours including the prestigious Adams Prize

Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin Zhu

Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest to lower teaching complexity, several teaching models and complexity measures have been proposed for both the batch settings (e.g., worst-case, recursive, preference-based, and non-

Jonathan T. Barron

We present a generalization of Schlick's bias and gain functions -- simple parametric curve-shaped functions for inputs in [0, 1]. Our single function includes both bias and gain as special cases, and is able to describe other smooth and monotonic curves with variable degrees of asymmetry.

Tiancheng Sun, Zexiang Xu, Xiuming Zhang, Sean Fanello

The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subject under different light sources, one obtains the light transport matrix of that subject, which enables image-based relighting in novel environments. However, due to the finite nu

Sourabh Kulkarni, Kinjal Divesh Shah, Nimar Arora, Xiaoyan Wang

We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and evaluation code for a number of models as well as implementations in some common PPLs. All of the benchmark code and PPL implementations are available on Github. We welcome contributi

Ana Salta, Rui Prada, Francisco S. Melo

Game AI competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tools to facilitate the research on their core topics and provide means for comparing and sharing results. A compe

Sai Sharath Japa, Rekabdar Banafsheh

Knowledge Base, represents facts about the world, often in some form of subsumption ontology, rather than implicitly, embedded in procedural code, the way a conventional computer program does. While there is a rapid growth in knowledge bases, it poses a challenge of retrieving information from them. Knowledge Base Question Answering is one of the promising a

J. M. Sullivan, S. Nance, J. Craig Wheeler

We previously proposed that Betelgeuse might have been spun up by accreting a companion of about 1 solar mass. Here we explore in more detail the possible systematics of such a merger and a larger range of accreted masses. We use the stellar evolutionary code MESA to add angular momentum to a primary star in core helium burning, core carbon burning, or shell

Joshua Alexander Osborne, Fatemeh Bagheri, Amir Shahmoradi

The potential existence of two separate classes of Long-duration Gamma-Ray Bursts (LGRBs) with and without radio afterglow emission, corresponding to radio-bright/loud and radio-dark/quiet populations, has been recently argued and favored in the GRB literature. The radio-quiet LGRBs have been found to have, on average, lower total isotropic gamma-ray emissio

Tracy Qi Dong, Jon Wakefield

Measles is a significant source of global disease burden and child mortality. Measles vaccination through routine immunization (RI) programs in high-burden settings remains a challenge due to poor health care infrastructure and access. Supplementary immunization activities (SIA) in the form of vaccination campaigns are therefore implemented to prevent measle

Andreas Schreiber

We want to analyze visually, to what extend team members and external developers contribute to open-source projects. This gives a high-level impression about collaboration in that projects. We achieve this by recording provenance of the development process and use graph drawing on the resulting provenance graph. Our graph drawings show, which developers are

Hamed Jalali, Gjergji Kasneci

Distributed Gaussian processes (DGPs) are prominent local approximation methods to scale Gaussian processes (GPs) to large datasets. Instead of a global estimation, they train local experts by dividing the training set into subsets, thus reducing the time complexity. This strategy is based on the conditional independence assumption, which basically means tha

Manu Goyal, Judith Austin-Strohbehn, Sean J. Sun, Karen Rodriguez

Background: Deep learning has great potential to assist with detecting and triaging critical findings such as pneumoperitoneum on medical images. To be clinically useful, the performance of this technology still needs to be validated for generalizability across different types of imaging systems. Materials and Methods: This retrospective study included 1,287

Lucas Esclapez, Félix Collin-Bastiani, Eleonore Riber, Bénédicte Cuenot

Ignition capability is a critical design constraint for aeronautical gas turbines. However the current trend toward overall lean burn is detrimental to the engine ignition and relight and the ignition system must be adapted to ensure a fast and reliable light-round in all circumstances. As ignition is a stochastic phenomenon, the optimization of an ignition

Xiaotian Xie, Dimitrios Katselis, Carolyn L. Beck, R. Srikant

We consider the problem of identifying parameters of a particular class of Markov chains, called Bernoulli Autoregressive (BAR) processes. The structure of any BAR model is encoded by a directed graph. Incoming edges to a node in the graph indicate that the state of the node at a particular time instant is influenced by the states of the corresponding parent

Houda Hani, Moez Khenissi

This paper is concerned with approximation of blow-up phenomena in nonlinear parabolic problems. We consider the equation u_t = u_xx +|u|^p -b(x)|u_x|^q in a bounded domain, we study the behavior of the semidiscrete problem. Under some assumptions we show existence and unicity of the semidiscrete solution, we show that it blows up in a finite time and we pro

Sibi C. Sethuraman, Pranav Kompally, Saraju P. Mohanty, Uma Choppali

Smart healthcare which is built as healthcare Cyber-Physical System (H-CPS) from Internet-of-Medical-Things (IoMT) is becoming more important than before. Medical devices and their connectivity through Internet with alongwith the electronics health record (EHR) and AI analytics making H-CPS possible. IoMT-end devices like wearables and implantables are key f

Thanh Tran, Yifan Hu, Changwei Hu, Kevin Yen

We present our HABERTOR model for detecting hatespeech in large scale user-generated content. Inspired by the recent success of the BERT model, we propose several modifications to BERT to enhance the performance on the downstream hatespeech classification task. HABERTOR inherits BERT's architecture, but is different in four aspects: (i) it generates its

Faming Liang, Jingnan Xue, Bochao Jia

This paper proposes an innovative method for constructing confidence intervals and assessing p-values in statistical inference for high-dimensional linear models. The proposed method has successfully broken the high-dimensional inference problem into a series of low-dimensional inference problems: For each regression coefficient $β_i$, the confidence interva

Michael Hoffmann, Jayson Lynch, Andrew Winslow

We consider the computational complexity of winning this turn (mate-in-1 or "finding lethal") in Hearthstone as well as several other single turn puzzle types introduced in the Boomsday Lab expansion. We consider three natural generalizations of Hearthstone (in which hand size, board size, and deck size scale) and prove the various puzzle types in ea

Mi Zhang, Faen Zhang, Nicholas D. Lane, Yuanchao Shu

The era of edge computing has arrived. Although the Internet is the backbone of edge computing, its true value lies at the intersection of gathering data from sensors and extracting meaningful information from the sensor data. We envision that in the near future, majority of edge devices will be equipped with machine intelligence powered by deep learning. Ho

Dmitry Bankov, Evgeny Khorov, Andrey Lyakhov

LoRaWAN is a popular low power wide area network technology widely used in many scenarios, such as environmental monitoring and smart cities. Different applications demand various quality of service (QoS), and their service within a single network requires special solutions for QoS provision. We consider the problem of QoS provision in heterogeneous LoRaWAN

Bridger Herman, Francesca Samsel, Annie Bares, Seth Johnson

We, as a society, need artists to help us interpret and explain science, but what does an artist's studio look like when today's science is built upon the language of large, increasingly complex data? This paper presents a data visualization design interface that lifts the barriers for artists to engage with actively studied, 3D multivariate datasets

Aref Asvadishirehjini, Murat Kantarcioglu, Bradley Malin

Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a wide range of applications ranging from self-driving cars to COVID-19 treatment discovery. To support the computational power necessary to learn a DNN, cloud environments with dedicated hardware support have emerged as critical infrastructure. However, there are many

Víctor Hernández-Santamaría, Kévin Le Balc'h, Liliana Peralta

In this paper, we prove the small-time global null-controllability of forward (resp. backward) semilinear stochastic parabolic equations with globally Lipschitz nonlinearities in the drift and diffusion terms (resp. in the drift term). In particular, we solve the open question posed by S. Tang and X. Zhang, in 2009. We propose a new twist on a classical stra

S. Aghababaei, H. Moradpour, G. Rezaei, S. Khorshidian

The effect of Generalized Uncertainty Principle (GUP) on Berry phase is studied using the perturbation approach and up to the first order of approximation. Thereinafter, the obtained results are extended to a quantum ring in which two types of spin-orbit interactions, including Rashba and Dresselhaus interactions, can be felt by electrons. Comparing the fina

Razyeh Behbahani, Martin L. Plumer, Ivan Saika-Voivod

We extend a renormalization group-based course-graining method for micromagnetic simulations to include properly scaled magnetostatic interactions. We apply the method in simulations of dynamic hysteresis loops at clinically relevant sweep rates and at 310 K of iron oxide nanoparticles (NPs) of the kind that have been used in preclinical studies of magnetic

Manjunath Narayana, Andreas Kolling, Lucio Nardelli, Phil Fong

A robot understands its world through the raw information it senses from its surroundings. This raw information is not suitable as a shared representation between the robot and its user. A semantic map, containing high-level information that both the robot and user understand, is better suited to be a shared representation. We use the semantic map as the use

Emily Rudman

The Hochschild homology of the ring $k[x_1,x_2,\ldots,x_d]/(x_1,x_2,\ldots,x_d)^2$ has been known and calculated several ways. This paper uses those calculations to calculate cyclic, negative cyclic, and periodic cyclic homology of $k[x_1,x_2,\ldots,x_d]/(x_1,x_2,\ldots,x_d)^2$ over $k$.

Bryce Sandlund, Sebastian Wild

We introduce the lazy search tree data structure. The lazy search tree is a comparison-based data structure on the pointer machine that supports order-based operations such as rank, select, membership, predecessor, successor, minimum, and maximum while providing dynamic operations insert, delete, change-key, split, and merge. We analyze the performance of ou

Gustav Uhre Jakobsen

The quantum field theoretic description of general relativity is a modern approach to gravity where gravitational force is carried by spin-2 gravitons. In the classical limit of this theory, general relativity as described by the Einstein field equations is obtained. This limit, where classical general relativity is derived from quantum field theory is the t

Sandhya Jagannathan, Ramkishor Sharma, T. R. Seshadri

Astrophysical magnetic fields decay primarily via two processes namely, ambipolar diffusion and turbulence. Constraints on the strength and the spectral index of non-helical magnetic fields have been derived earlier in the literature through the effect of the above mentioned processes on the Cosmic Microwave Background (CMB) radiation. A helical component of

Veronica Dexheimer, Jorge Noronha, Jacquelyn Noronha-Hostler, Claudia Ratti

With the computational power and algorithmic improvements available today, the ongoing STAR/RHIC and HADES/GSI experiments, the future FAIR and NICA facilities becoming operational, and the new precise measurements from NICER and LIGO/VIRGO, the high-energy nuclear physics and astrophysics communities are in the unique position to set very stringent constrai

William Thomson, Neelanjan Bhowmik, Toby P. Breckon

Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat). In this work, we investigate different Convolutional Neural Network (CNN) architectures and their variants for the non-temporal real-time bounds detection of fire pixel regions in video (or still) imagery. Two reduced complexity compact CNN architect

Alessandro Coretti, Lamberto Rondoni, Sara Bonella

It has recently been pointed out that Hamiltonian particle systems in constant magnetic fields satisfy generalized time-reversal symmetries that enable to prove useful statistical relationships based on equilibrium phase-space probability distributions without the need to invert, as commonly considered necessary, the magnetic field. Among these relations, th

Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao

Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed based on some heuristic assumptions. They often suffer from unstable performance, poor applicability, and high computational cost in complex tasks where their assumptions do not h

Mohammad Mozaffari, Y. -P. Eric Wang, Kittipong Kittichokechai

The 5th generation (5G) new radio (NR) is designed to support a wide range of use cases, requirements, and services from enhanced mobile broadband (eMBB) to ultra-reliable low-latency communications (URLLC). NR signals are designed to meet stringent requirements, and in particular the physical downlink control channel (PDCCH) which requires careful considera

Neji Mensi, Danda B. Rawat, Elyes Balti

The concept of intelligent transportation systems (ITS) is considered to be a highly promising area of research due to its diversity of unique features. It is based mainly on the wireless vehicular network (WVN), where vehicles can perform sophisticated services such as sharing real-time safety information. To ensure high-quality service, WVN needs to solve

Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao

We study knowledge-grounded dialogue generation with pre-trained language models. To leverage the redundant external knowledge under capacity constraint, we propose equipping response generation defined by a pre-trained language model with a knowledge selection module, and an unsupervised approach to jointly optimizing knowledge selection and response genera

Nicola Quercioli

Group equivariant operators are playing a more and more relevant role in machine learning and topological data analysis. In this paper we present some new results concerning the construction of $G$-equivariant non-expansive operators (GENEOs) from a space $\varPhi$ of real-valued bounded continuous functions on a topological space $X$ to $\varPhi$ itself. Th

Wei Wang, Piji Li, Hai-Tao Zheng

Story generation is a challenging task, which demands to maintain consistency of the plots and characters throughout the story. Previous works have shown that GPT2, a large-scale language model, has achieved good performance on story generation. However, we observe that several serious issues still exist in the stories generated by GPT2 which can be categori

Alvaro Cabrejas-Egea, Colm Connaughton

Reinforcement Learning is proving a successful tool that can manage urban intersections with a fraction of the effort required to curate traditional traffic controllers. However, literature on the introduction and control of pedestrians to such intersections is scarce. Furthermore, it is unclear what traffic state variables should be used as reward to obtain

Seong-Mi Seo

We consider a two-dimensional Coulomb gas confined to a disk when the external potential is radially symmetric. In the presence of a hard-wall constraint effective to change the equilibrium, the density of the equilibrium measure acquires a singular component at the hard wall. In the determinantal case, we study the local statistics of Coulomb particles at t

Luca Pappalardo, Leo Ferres, Manuel Sacasa, Ciro Cattuto

Home detection, assigning a phone device to its home antenna, is a ubiquitous part of most studies in the literature on mobile phone data. Despite its widespread use, home detection relies on a few assumptions that are difficult to check without ground truth, i.e., where the individual that owns the device resides. In this paper, we provide an unprecedented

Bommareddy Poojitha, B. H. Reddy, Aprajita Joshi, Ankit Kumar

Here we have investigated the role of electron-phonon coupling on the Raman spectrum of narrow bandgap semiconductors APd3O4 (A = Ca, Sr) and hole-doped system Sr0.85Li0.15Pd3O4. Four Raman active phonons are observed at room temperature for all three compounds as predicted by factor group analysis. The lowest energy phonon (~190/202 cm-1) associated with Pd

Anas M. Al-Oraiqat, Alexander Y. Ivanov, Yuriy A. Ivanov

The problem of optimization of the rolling dynamics model is considered. That providing safe movement at high frequency when interacting with the railway. Moreover, allowing to evaluate the dynamic parameters when designing new and modernizing existing locomotives. The object of this research is a rail transport dynamic system model. The article's purpos

Yoko Nagafuchi, Yin Lin, Kaushal Mamgain, Abolfazl Asudeh

Given a data set, misleading conclusions can be drawn from it by cherry-picking selected samples. One important class of conclusions is a trend derived from a data set of values over time. Our goal is to evaluate whether the 'trends' described by the extracted samples are representative of the true situation represented in the data. We demonstrate Mi

Gene Patrick S. Rible, Nicolette Ann A. Arriola, Manuel C. Ramos

To properly simulate and implement a quadcopter flight control for intended load and flight conditions, the quadcopter model must have parameters on various relationships including propeller thrust-torque, thrust-PWM, and thrust--angular speed to a certain level of accuracy. Thrust-torque modeling requires an expensive reaction torque measurement sensor. In

Hongjie Xu, Yuran Yu, Junhua Hu, Zhuo Wang

The argyrodite sulfides are getting more and more attractive as highly promising solid-state electrolytes (SSEs) for high-performance all-solid-state batteries (ASSBs), owing to their high ionic conductivity, adequate plasticity, and decent mechanical strength. However, their poor incompatibility with Li metal anode and high voltage cathodes and as well as s

Lucas Barberis

Using geometrical arguments it is shown that Cancer Stem Cells (CSC) must be confined inside solid tumors under natural situations. Aided by an agent-based model and percolation theory, the probability for a CSC to be at the border of a colony is estimated. This probability is estimated as a function of the CSCs self-renewal probability $p_s$, i.e. the chanc

José Miguel Pérez-Álvarez, Adrian Mos

In this paper we present the modeling support infrastructure for domain-specific application definition. This consists of a set of meta-models and the associated generators to allow the definition of reusable and domain-specific behavior blocks, which can later be used to compose complex behaviors. In addition we also present the related visual languages tha

L. Diago-Cisneros, Eduardo Serna, I. Rodríguez Vargas, R. Pérez-Álvarez

We foretell a new pseudospin-dependent phenomenon in mono-layer graphene (MLG), which is numerically simulated \emph{via} an innovator nano-spintronic device. We proposed a novel theoretical procedure for describing the dynamics of Dirac fermions, departing from classic theoretical modelling. More importantly, we have found appealing evidences of wiggling an

Sayan Banerjee, S Divakar Bhat, Subhasis Chaudhuri, Rajbabu Velmurugan

In this paper, we propose a novel framework for multi-image co-segmentation using class agnostic meta-learning strategy by generalizing to new classes given only a small number of training samples for each new class. We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous emb

Lukasz Kaczmarczyk, Hoang Nguyen, Zahur Ullah, Mebratu Wakeni

The formulation of a new prism finite element is presented for the nonlinear analysis of solid shells subject to large strains and large displacements. The element is based on hierarchical, heterogeneous, and anisotropic shape functions. As with other solid shell formulations, only displacement degrees of freedom are required to describe the shell kinematics

Hanbo Yu, Junfeng Wang, Jonathan C. Tan

We present a high spatial resolution Chandra X-ray study of two Infrared Dark Clouds (IRDCs), G034.43+00.24 and G035.39-00.33, which are expected to be in the early phases of star cluster formation. We detect 112 and 209 valid X-ray point sources towards G034.43+00.24 and G035.39-00.33, respectively. We cross-match the X-ray point sources with 2MASS, GLIMPSE

Misha Gavrilovich

We give category-theoretic reformulations of stability, NIP, NTP, and non-dividing by observing that their characterisations in terms of indiscernible sequences are naturally expressed as Quillen lifting properties %(negation) of certain morphisms associated with linear orders, in a certain category extending the categories of topological spaces and of simpl

Buyang Li, Jiang Yang, Zhi Zhou

A new class of high-order maximum principle preserving numerical methods is proposed for solving parabolic equations, with application to the semilinear Allen--Cahn equation. The proposed method consists of a $k$th-order multistep exponential integrator in time, and a lumped mass finite element method in space with piecewise $r$th-order polynomials and Gauss

Pradyumna Reddy, Paul Guerrero, Matt Fisher, Wilmot Li

Patterns, which are collections of elements arranged in regular or near-regular arrangements, are an important graphic art form and widely used due to their elegant simplicity and aesthetic appeal. When a pattern is encoded as a flat image without the underlying structure, manually editing the pattern is tedious and challenging as one has to both preserve th

Paolo Piovano, Igor Velčić

The continuum model related to the Winterbottom problem, i.e., the problem of determining the equilibrium shape of crystalline drops resting on a substrate, is derived in dimension two by means of a rigorous discrete-to-continuum passage by $Γ$-convergence of atomistic models taking into consideration the atomic interactions of the drop particles both among

Rhys J. J. Poulton, Chris Power, Aaron S. G. Robotham, Pascal J. Elahi

Predicting the merger timescale ($τ_{\rm merge}$) of merging dark matter halos, based on their orbital parameters and the structural properties of their hosts, is a fundamental problem in gravitational dynamics that has important consequences for our understanding of cosmological structure formation and galaxy formation. Previous models predicting $τ_{\rm me

Huanhao Li, Chi Man Woo, Tianting Zhong, Zhipeng Yu

Optical focusing through/inside scattering media, like multimode fiber and biological tissues, has significant impact in biomedicine yet considered challenging due to strong scattering nature of light. Previously, promising progress has been made, benefiting from the iterative optical wavefront shaping, with which deep-tissue high-resolution optical focusing

Yiqi Hu, Yihong Qi, Yu You, Shicheng Zhang

Magnetic-free optical isolators are critical components for the realization of integrated optical systems. The underlying physics of passive nonlinear optical isolators is not solely about breaking the Lorentz reciprocity without requiring any external bias. Indeed, one major obstacle to the operation of Kerr-type nonlinear optical isolators was found to be

Ludovic A. R. Capelli, Zhenjiang Hu, Timothy A. K. Zakian, Nick Brown

The vertex-centric programming model, designed to improve the programmability in graph processing application writing, has attracted great attention over the years. However, shared memory frameworks that implement the vertex-centric interface all expose a common tradeoff: programmability against memory efficiency and performance. Our approach, iPregel, prese

Shamim Sk, Sudhir K. Pandey

First-principles phonon calculations along with density functional theory (DFT) play an important role to study the dynamical and thermal properties of materials. Here, we investigate the effect of exchange correlation (XC) functionals on the vibrational and thermodynamic properties of CoSi. The role of 3s(2p) orbitals of Co(Si) as core/valence states on the

Sarah Jenkins, Roy W. Chantrell, Richard F. L. Evans

Antiferromagnetic spintronic devices have the potential to outperform conventional ferromagnetic devices due to their ultrafast dynamics and high data density. A challenge in designing these devices is the control and detection of the orientation of the anti-ferromagnet. One of the most promising ways to achieve this is through the exchange bias effect. This

Pengshuai Li, Xinsong Zhang, Weijia Jia, Wei Zhao

Distant supervision has been a widely used method for neural relation extraction for its convenience of automatically labeling datasets. However, existing works on distantly supervised relation extraction suffer from the low quality of test set, which leads to considerable biased performance evaluation. These biases not only result in unfair evaluations but

Mariusz Bojarski, Chenyi Chen, Joyjit Daw, Alperen Değirmenci

Four years ago, an experimental system known as PilotNet became the first NVIDIA system to steer an autonomous car along a roadway. This system represents a departure from the classical approach for self-driving in which the process is manually decomposed into a series of modules, each performing a different task. In PilotNet, on the other hand, a single dee

Anna Roubícková, Lucy MacGregor, Nick Brown, Oliver Thomson Brown

Exploration using borehole drilling is a key activity in determining the most appropriate locations for the petroleum industry to develop oil fields. However, estimating the amount of Oil In Place (OIP) relies on computing with a very significant number of geological models, which, due to the ever increasing capability to capture and refine data, is becoming

Nick Brown, Rupert Nash, Gordon Gibb, Bianca Prodan

Technological advances are creating exciting new opportunities that have the potential to move HPC well beyond traditional computational workloads. In this paper we focus on the potential for HPC to be instrumental in responding to disasters such as wildfires, hurricanes, extreme flooding, earthquakes, tsunamis, winter weather conditions, and accidents. Driv

Paul Grandgeorge, Changyeob Baek, Harmeet Singh, Paul Johanns

Networks of flexible filaments often involve regions of tight contact. Predictively understanding the equilibrium configurations of these systems is challenging due to intricate couplings between topology, geometry, large nonlinear deformations, and friction. Here, we perform an in-depth study of a simple yet canonical problem that captures the essence of co

Mohamed Bader, Ismail Shahin, Abdelfatah Hassan

Recently there has been a formidable work which has been put up from the people who are working in the frontlines such as hospitals, clinics, and labs alongside researchers and scientists who are also putting tremendous efforts in the fight against COVID-19 pandemic. Due to the preposterous spread of the virus, the integration of the artificial intelligence

Behrooz Khadem, Amin Masoumi, M. S. Farash

Wireless Body Sensor Network (WBSN) is a developing technology with constraints in energy consumption, coverage radius, communication reliability. Also, communications between nodes contain very sensitive personal information in which sometimes due to the presence of hostile environments, there are a wide range of security risks. As such, designing authentic

Prashansa Agrawal, Parwat Singh Anjana, Sathya Peri

The surge in the spread of misleading information, lies, propaganda, and false facts, frequently known as fake news, raised questions concerning social media's influence in today's fast-moving democratic society. The widespread and rapid dissemination of fake news cost us in many ways. For example, individual or societal costs by hampering elections

Mohamed Ali Souibgui, Yousri Kessentini

Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images.

Daniel Zeltner, Benedikt Schmid, Gabor Csiszar, Orsolya Csiszar

Over the past few years, deep neural networks have shown excellent results in multiple tasks, however, there is still an increasing need to address the problem of interpretability to improve model transparency, performance, and safety. Achieving eXplainable Artificial Intelligence (XAI) by combining neural networks with continuous logic and multi-criteria de

Bingrong Yu, Shun Zhou

In this talk, we present a recent investigation of the sufficient and necessary conditions for CP conservation in the leptonic sector with massive Majorana neutrinos in terms of CP-odd weak-basis invariants. The number of weak-basis invariants to guarantee CP conservation in the leptonic sector is clarified and a new set of invariants are advocated for the d

Sara Renjit, Sumam Mary Idicula

With the popularity of social media, communications through blogs, Facebook, Twitter, and other plat-forms have increased. Initially, English was the only medium of communication. Fortunately, now we can communicate in any language. It has led to people using English and their own native or mother tongue language in a mixed form. Sometimes, comments in other

Dariusz Graczyk, Grzegorz Pietrzynski, Ian B. Thompson, Wolfgang Gieren

We present a new study of late-type eclipsing binary stars in the Small Magellanic Cloud (SMC) undertaken with the aim of improving the distance determination to this important galaxy. A sample of 10 new detached, double-lined eclipsing binaries indentified from the OGLE variable star catalogues and consisting of F- and G-type giant components has been analy

Evgeny Yu. Panov

Under a precise genuine nonlinearity assumption we establish the decay of entropy solutions of a multidimensional scalar conservation law with merely continuous flux and with initial data being a sum of periodic function and a function vanishing at infinity (in the sense of measure).

Mert Kilickaya, Noureldien Hussein, Efstratios Gavves, Arnold Smeulders

Human-object interaction recognition aims for identifying the relationship between a human subject and an object. Researchers incorporate global scene context into the early layers of deep Convolutional Neural Networks as a solution. They report a significant increase in the performance since generally interactions are correlated with the scene (\ie riding b

P. Belli, R. Bernabei, V. B. Brudanin, F. Cappella

Studies on double beta decay processes in $^{106}$Cd were performed by using a cadmium tungstate scintillator enriched in $^{106}$Cd at 66% ($^{106}$CdWO$_4$) with two CdWO$_4$ scintillation counters (with natural Cd composition). No effect was observed in the data accumulated over 26033 h. New improved half-life limits were set on the different channels and