May 2020 arXiv papers — page 7

Abdelaziz Lakhfif

Advances in NLP, knowledge representation and computer graphic technologies can provide us insights into the development of educational tool for Deaf people. Actual education materials and tools for deaf pupils present several problems, since textbooks are designed to support normal students in the classroom and most of them are not suitable for people with

Abdelaziz Lakhfif, Mohamed Tayeb Laskri

in this paper, we present a corpus linguistics based approach applied to analyzing digitized classical multilingual novels and narrative texts, from a semantic point of view. Digitized novels such as "the hobbit (Tolkien J. R. R., 1937)" and "the hound of the Baskervilles (Doyle A. C. 1901-1902)", which were widely translated to dozens of languages, provide

Weimin Zhou, Hua Li, Mark A. Anastasio

Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems because the IO sets an upper performance limit among all observers. When joint signal detection and localization

Thiago de Paula Oliveira, Rafael de Andrade Moral

The continuously growing number of COVID-19 cases pressures healthcare services worldwide. Accurate short-term forecasting is thus vital to support country-level policy making. The strategies adopted by countries to combat the pandemic vary, generating different uncertainty levels about the actual number of cases. Accounting for the hierarchical structure of

Rocío Joo, Simona Picardi, Matthew E. Boone, Thomas A. Clay

Movement is fundamental to life, shaping population dynamics, biodiversity patterns, and ecosystem structure. Recent advances in tracking technology have enabled fundamental questions about movement to be tackled, leading to the development of the movement ecology framework (MEF), considered a milestone in the field [1]. The MEF introduced an integrative the

Luis A. Martinez, Yaniv J. Rosen, Jonathan L. DuBois

We demonstrate the application of pattern recognition algorithms via hidden Markov models (HMM) for qubit readout. This scheme provides a state-path trajectory approach capable of detecting qubit state transitions and makes for a robust classification scheme with higher starting state assignment fidelity than when compared to a multivariate Gaussian (MVG) or

P. Abratenko, M. Alrashed, R. An, J. Anthony

We report on the first measurement of flux-integrated single differential cross sections for charged-current (CC) muon neutrino ($\nu_\mu$) scattering on argon with a muon and a proton in the final state, $^{40}$Ar($\nu_\mu$,$\mu$p)X. The measurement was carried out using the Booster Neutrino Beam at Fermi National Accelerator Laboratory and the MicroBooNE l

Pranav Hosangadi, Ashok Gopalarathnam

A low-order method is presented for aerodynamic prediction of wings operating at near-stall and post-stall flight conditions. The method is intended for use in design, modeling, and simulation. In this method, the flow separation due to stall is modeled in a vortex-lattice framework as an effective reduction in the camber, or "decambering." For each section

K. Ammari, S. El Alaoui, M. Ouzahra

We consider linear control systems of the form $\dot{y}(t)=Ay(t)-\mu B C y(t)$ where $\mu$ is a positive real parameter, $A$ is the state operator and generates a linear $C_0-$semigroup of contractions $S(t) $ on a Banach space $X$, $B$ and $C$ are respectively the operators of control and observability, which are defined in appropriate spaces in which they

Guanyu Hu, Zhihua Ma, Insu Paek

Item response theory (IRT) is a popular modeling paradigm for measuring subject latent traits and item properties according to discrete responses in tests or questionnaires. There are very limited discussions on heterogeneity pattern detection for both items and individuals. In this paper, we introduce a nonparametric Bayesian approach for clustering items a

Derek Onken, Samy Wu Fung, Xingjian Li, Lars Ruthotto

A normalizing flow is an invertible mapping between an arbitrary probability distribution and a standard normal distribution; it can be used for density estimation and statistical inference. Computing the flow follows the change of variables formula and thus requires invertibility of the mapping and an efficient way to compute the determinant of its Jacobian

Vinícius Magno de Oliveira Coelho, Gustavo Barbosa Libotte, Francisco Duarte Moura Neto, Gustavo Mendes Platt

Mathematical formulations of real world optimization studies frequently present characteristics such as non-linearity, discontinuity and high complexity. This class of problems may also exhibit a high number of global minimum/maximum points, especially for optimization problems arising from nonlinear algebraic systems (where null minima correspond to the sol

Patrick Del Vecchio, Mario Lodari, Amir Sammak, Giordano Scappucci

A clear signature of Zeeman split states crossing is observed in Landau fan diagram of strained germanium two-dimensional hole gas. The underlying mechanisms are discussed based on a perturbative model yielding a closed formula for the critical magnetic fields. These fields depend strongly on the energy difference between the top-most and the neighboring val

Gustavo Barbosa Libotte, Fran Sérgio Lobato, Francisco Duarte Moura Neto, Gustavo Mendes Platt

Mathematical models simulate various events under different conditions, enabling an early overview of the system to be implemented in practice, reducing the waste of resources and in less time. In project optimization, these models play a fundamental role, allowing to obtain parameters and attributes capable of enhancing product performance, reducing costs a

Sharmishtaa Seshamani, Leila Elabbady, Casey Schneider-Mizell, Gayathri Mahalingam

Morphology based analysis of cell types has been an area of great interest to the neuroscience community for several decades. Recently, high resolution electron microscopy (EM) datasets of the mouse brain have opened up opportunities for data analysis at a level of detail that was previously impossible. These datasets are very large in nature and thus, manua

Nina Pardal

A graph is circle if there is a family of chords in a circle such that two vertices are adjacent if the corresponding chords cross each other. There are diverse characterizations of circle graphs, many of them using the notions of local complementation or split decomposition. However, there are no known structural characterization by minimal forbidden induce

Jerome Bolte, Edouard Pauwels, Rodolfo Rios-Zertuche

We consider the long-term dynamics of the vanishing stepsize subgradient method in the case when the objective function is neither smooth nor convex. We assume that this function is locally Lipschitz and path differentiable, i.e., admits a chain rule. Our study departs from other works in the sense that we focus on the behavoir of the oscillations, and to do

Liang Wang, Hyojoon Kim, Prateek Mittal, Jennifer Rexford

Recent advances in programmable switch hardware offer a fresh opportunity to protect user privacy. This paper presents PINOT, a lightweight in-network anonymity solution that runs at line rate within the memory and processing constraints of hardware switches. PINOT encrypts a client's IPv4 address with an efficient encryption scheme to hide the address from

Luigi Barletti, Giovanni Frosali, Elisa Giovannini

Starting from the detailed description of the single-collision decoherence mechanism proposed by Adami, Hauray and Negulescu, we derive a Wigner equation endowed with a decoherence term of a fairly general form. This equation is shown to contain well known decoherence models, such as the Wigner-Fokker-Planck equation, as particular cases. The effect of the d

Youngjun Lee, Dongwook Lee

Discrete updates of numerical partial differential equations (PDEs) rely on two branches of temporal integration. The first branch is the widely-adopted, traditionally popular approach of the method-of-lines (MOL) formulation, in which multi-stage Runge-Kutta (RK) methods have shown great success in solving ordinary differential equations (ODEs) at high-orde

Juan S. Molano, Carlos A. Arango

A mathematical phase-space representation of the 1-dimensional Schr\"odinger equation is employed to obtain bound and resonance states of the rotationally excited H$_2$ molecule. The structure of the phase-space tangent field is analyzed and related to the behavior of the wave function in classically allowed and forbidden regions. In this phase-space represe

Hyeokhyen Kwon, Catherine Tong, Harish Haresamudram, Yan Gao

The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR). Labeled data in human activity recognition is scarce and hard to come by, as sensor data collection is expensive, and the annotation is time-consuming and error-prone. To address this pr

Sudhanshu Chanpuriya, Cameron Musco

The skip-gram model for learning word embeddings (Mikolov et al. 2013) has been widely popular, and DeepWalk (Perozzi et al. 2014), among other methods, has extended the model to learning node representations from networks. Recent work of Qiu et al. (2018) provides a closed-form expression for the DeepWalk objective, obviating the need for sampling for small

Giulia Vilone, Luca Longo

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability. A plethora of methods to tackle this problem have been propos

Simon B. Jäger, Luca Dell'Anna, Giovanna Morigi

We analyze the properties of the edge states of the one-dimensional Kitaev model with long-range anisotropic pairing and tunneling. Tunneling and pairing are assumed to decay algebraically with exponents $\alpha$ and $\beta$, respectively, and $\alpha,\beta>1$. We determine analytically the decay of the edges modes. We show that the decay is exponential for

K. C. Hammond, C. Zhu, T. Brown, K. Corrigan

The development of stellarators that use permanent magnet arrays to shape their confining magnetic fields has been a topic of recent interest, but the requirements for how such magnets must be shaped, manufactured, and assembled remain to be determined. To address these open questions, we have performed a study of geometric concepts for magnet arrays with th

Hung Viet Chu

Zeckendorf proved that every positive integer has a unique representation as a sum of non-consecutive Fibonacci numbers. A natural generalization of this theorem is to look at the sequence defined as follows: for $n\ge 2$, let $F_{n,1} = F_{n,2} = \cdots = F_{n,n} = 1$ and $F_{n, m+1} = F_{n, m} + F_{n, m+1-n}$ for all $m\ge n$. It is known that every positi

Marianne Rakic, John Guttag, Adrian V. Dalca

Changes over time in brain anatomy can provide important insight for treatment design or scientific analyses. We present a method that predicts how a brain MRI for an individual will change over time. We model changes using a diffeomorphic deformation field that we predict using function using convolutional neural networks. Given a predicted deformation fiel

Ramin Nikzad-Langerodi, Florian Sobieczky

The problem of transferring calibrations from a primary to a secondary instrument, i.e. calibration transfer (CT), has been a matter of considerable research in chemometrics over the past decades. Current state-of-the-art (SoA) methods like (piecewise) direct standardization perform well when suitable transfer standards are available. However, stable calibra

Filip Ilievski, Daniel Garijo, Hans Chalupsky, Naren Teja Divvala

Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications. While KGs have become a mainstream technology, the RDF/SPARQL-centric toolset for operating with them at scale is heterogeneous, difficult to integrate and only covers a subset of the operations that are commonly needed in dat

Grigory Knyazev, Daria Ignatyeva, Ivan Sopko, Vladimir Belotelov

In this work we have performed an analysis of electrostriction mechanism of optical to acoustical energy conversion on the interface of two materials with low optical absorption. We compared this method of conversion with widely used thermal conversion based on thin metal film. It was shown, that the contribution of electrostriction mechanism is significantl

Eric Wu, Kevin Wu, William Lotter

Data scarcity and class imbalance are two fundamental challenges in many machine learning applications to healthcare. Breast cancer classification in mammography exemplifies these challenges, with a malignancy rate of around 0.5% in a screening population, which is compounded by the relatively small size of lesions (~1% of the image) in malignant cases. Simu

Andrea Aspri, Elena Beretta, Alberto Gandolfi, Etienne Wasmer

We adapt a SEIRD differential model with asymptomatic population and Covid deaths, which we call SEAIRD, to simulate the evolution of COVID-19, and add a control function affecting both the diffusion of the virus and GDP, featuring all direct and indirect containment policies; to model feasibility, the control is assumed to be a piece-wise linear function sa

Patrick D. Aleo, Simon J. Lock, Donna J. Cox, Stuart A. Levy

Scientific visualization tools are currently not optimized to create cinematic, production-quality representations of numerical data for the purpose of science communication. In our pipeline \texttt{Estra}, we outline a step-by-step process from a raw simulation into a finished render as a way to teach non-experts in the field of visualization how to achieve

Bianca Lassen-Schmidt, Alessa Hering, Stefan Krass, Hans Meine

Fully-automatic lung lobe segmentation is challenging due to anatomical variations, pathologies, and incomplete fissures. We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss function to emphasize the lobar boundaries. To validate the performance of the proposed method we compare

Chenglong Ye, Reza Ghanadan, Jie Ding

In collaborative learning, learners coordinate to enhance each of their learning performances. From the perspective of any learner, a critical challenge is to filter out unqualified collaborators. We propose a framework named meta clustering to address the challenge. Unlike the classical problem of clustering data points, meta clustering categorizes learners

Phuong X. Nguyen, Wang-Kong Tse

A circularly polarized a.c. pump field illuminated near resonance on two-dimensional transition metal dichalcogenides (TMDs) produces an anomalous Hall effect in response to a d.c. bias field. In this work, we develop a theory for this photo-induced anomalous Hall effect in undoped TMDs irradiated by a strong coherent laser field. The strong field renormaliz

Qi Chang, Hui Qu, Yikai Zhang, Mert Sabuncu

In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN). Our proposed framework aims to train a central generator learns from distributed discriminator, and use the generated synthetic image solely to train the segmentation model.We validat

Lu Zhang, Siyang Wang, N. Anders Petersson

We develop a fourth order accurate finite difference method for the three dimensional elastic wave equation in isotropic media with the piecewise smooth material property. In our model, the material property can be discontinuous at curved interfaces. The governing equations are discretized in second order form on curvilinear meshes by using a fourth order fi

So Hirata

It is shown analytically and numerically that the finite-temperature many-body perturbation theory in the grand canonical ensemble has zero radius of convergence at zero temperature when the energy ordering or degree of degeneracy for the ground state changes with the perturbation strength. When the degeneracy of the reference state is partially or fully lif

Daniel J. Lawson, Vinesh Solanki, Igor Yanovich, Johannes Dellert

Integrating datasets from different disciplines is hard because the data are often qualitatively different in meaning, scale, and reliability. When two datasets describe the same entities, many scientific questions can be phrased around whether the (dis)similarities between entities are conserved across such different data. Our method, CLARITY, quantifies co

Zhuojia Shen, Komail Dharsee, John Criswell

Remote code disclosure attacks threaten embedded systems as they allow attackers to steal intellectual property or to find reusable code for use in control-flow hijacking attacks. Execute-only memory (XOM) prevents remote code disclosures, but existing XOM solutions either require a memory management unit that is not available on ARM embedded systems or incu

Luyao Shi, Deepta Rajan, Shafiq Abedin, Manikanta Srikar Yellapragada

Pulmonary Embolism (PE) is a life-threatening disorder associated with high mortality and morbidity. Prompt diagnosis and immediate initiation of therapeutic action is important. We explored a deep learning model to detect PE on volumetric contrast-enhanced chest CT scans using a 2-stage training strategy. First, a residual convolutional neural network (ResN

Stephen A Lauer, Alexandria C Brown, Nicholas G Reich

Forecasting transmission of infectious diseases, especially for vector-borne diseases, poses unique challenges for researchers. Behaviors of and interactions between viruses, vectors, hosts, and the environment each play a part in determining the transmission of a disease. Public health surveillance systems and other sources provide valuable data that can be

Martin Markl, Christopher L. Rogers

We characterize $A_\infty$-structures that are transfers over a chain homotopy equivalence or a quasi-isomorphism, answering a question posed by D. Sullivan. Along the way, we present an obstruction theory for weak $A_\infty$-morphisms over an arbitrary commutative ring. We then generalize our results to ${\mathcal P}_\infty$-structures over a field of chara

Brendan Pass

We show that the variance of a probability measure $\mu$ on a compact subset $X$ of a complete metric space $M$ is bounded by the square of the circumradius $R$ of the canonical embedding of $X$ into the space $P(M)$ of probability measures on $M$, equipped with the Wasserstein metric. When barycenters of measures on $X$ are unique (such as on CAT($0$) space

Liang Chen, Kostyantyn Sirenko, Ping Li, Hakan Bagci

Incorporation of plasmonic nanostructures in the design of photoconductive devices (PCDs) has significantly improved their optical-to-terahertz conversion efficiency. However, this improvement comes at the cost of increased complexity for the design and simulation of these devices. Indeed, accurate and efficient modeling of multiphysics processes and intrica

Steven Lantz, Kevin McDermott, Michael Reid, Daniel Riley

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimati

Alireza Sheikh, Alexandre Graell i Amat, Gianluigi Liva, Alex Alvarado

We propose a novel soft-aided iterative decoding algorithm for product codes (PCs). The proposed algorithm, named iterative bounded distance decoding with combined reliability (iBDD-CR), enhances the conventional iterative bounded distance decoding (iBDD) of PCs by exploiting some level of soft information. In particular, iBDD-CR can be seen as a modificatio

Edward T. Samulski, Alexandros G. Vanakaras, Demetri J. Photinos

We review the physics underlying Meyer's conjecture of how macroscopic-scale twist and bend conspire within the Frank-Oseen elasticity theory of nematics to create a heliconical arrangement of the uniaxial, apolar nematic director, the so-called "twist bend nematic" $\rm N_{TB}$. We show that since 2011 a second, lower-temperature nematic phase observed in o

Meghdad Saeedian, Maxi San Miguel, Raul Toral

We consider a general model in which there is a coupled dynamics of node states and links states in a network. This coupled dynamics coevolves with dynamical changes of the topology of the network caused by a link rewiring mechanism. Such coevolution model features the interaction of the local dynamics of node and link states with the nonlocal dynamics of li

Brian D. Leahy, Won-Dong Jang, Helen Y. Yang, Robbert Struyven

A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy. Time-lapse microscopy provides clinicians with a wealth of information for selecting embryos. However, the resulting movies of embryos are currently analyzed manually, which is time consuming and

Alicja Wierzcholska, Stefan Wagner

The X-ray spectrum of extreme HBL type blazars is located in the synchrotron branch of the broadband spectral energy distribution (SED), at energies below the peak. A joint fit of the extrapolated X-ray spectra together with a host galaxy template allows characterizing the synchrotron branch in the SED. The X-ray spectrum is usually characterized either with

Rachel E. Lim, Darren C. Pagan, Donald E. Boyce, Joel V. Bernier

Hexagonal metals have anisotropic coefficients of thermal expansion causing grain-level internal stresses during heating. High energy x-ray diffraction microscopy, a non-destructive, in situ, micromechanical and microstructural characterization technique, has been used to determine the anisotropic coefficients of thermal expansion (CTEs) for Ti-7Al. Two samp

Scott Schneider, Xavier Guerin, Shaohan Hu, Kun-Lung Wu

We present the architecture of a cloud native version of IBM Streams, with Kubernetes as our target platform. Streams is a general purpose streaming system with its own platform for managing applications and the compute clusters that execute those applications. Cloud native Streams replaces that platform with Kubernetes. By using Kubernetes as its platform,

Nishanth Thumbavanam Arun, Nathan Gaw, Praveer Singh, Ken Chang

Saliency maps have become a widely used method to assess which areas of the input image are most pertinent to the prediction of a trained neural network. However, in the context of medical imaging, there is no study to our knowledge that has examined the efficacy of these techniques and quantified them using overlap with ground truth bounding boxes. In this

Sarod Yatawatta

Mitigation of radio frequency interference (RFI) is essential to deliver science-ready radio interferometric data to astronomers. In this paper, using dual polarized radio interferometers, we propose to use the polarization information of post-correlation interference signals to detect and mitigate them. We use the directional statistics of the polarized sig

Ranendu Adhikary, Kaustav Bose, Satwik Mukherjee, Bodhayan Roy

We resolve the longstanding open problem concerning the computational complexity of Max Cut on interval graphs by showing that it is NP-complete.

Mickael Tardy, Diana Mateus

We work on the breast imaging malignancy segmentation task while focusing on the training process instead of network complexity. We designed a training process based on a modified U-Net, increasing the overall segmentation performances by using both, benign and malignant data for training. Our approach makes use of only a small amount of annotated data and r

Vasiliki Kondyli, Mehul Bhatt, Jakob Suchan

We develop a human-centred, cognitive model of visuospatial complexity in everyday, naturalistic driving conditions. With a focus on visual perception, the model incorporates quantitative, structural, and dynamic attributes identifiable in the chosen context; the human-centred basis of the model lies in its behavioural evaluation with human subjects with res

Papia Chowdhury

The recent appearance of COVID-19 virus has created a global crisis due to unavailability of any vaccine or drug that can effectively and deterministically work against it. Naturally, different possibilities (including herbal medicines having known therapeutic significance) have been explored by the scientists. The systematic scientific study (beginning with

Christopher A. George, Eduardo A. Barrera, Kenric P. Nelson

We review three recently-proposed classifier quality metrics and consider their suitability for large-scale classification challenges such as applying convolutional neural networks to the 1000-class ImageNet dataset. These metrics, referred to as the "geometric accuracy," "decisiveness," and "robustness," are based on the generalized mean ($\rho$ equals 0, 1

Shan Li, Baoxu Shi, Jaewon Yang, Ji Yan

As the world's largest professional network, LinkedIn wants to create economic opportunity for everyone in the global workforce. One of its most critical missions is matching jobs with processionals. Improving job targeting accuracy and hire efficiency align with LinkedIn's Member First Motto. To achieve those goals, we need to understand unstructured job po

Riccardo Giubilato, Andrea Masili, Sebastiano Chiodini, Marco Pertile

In this paper we present a simulation framework for the evaluation of the navigation and localization metrological performances of a robotic platform. The simulator, based on ROS (Robot Operating System) Gazebo, is targeted to a planetary-like research vehicle which allows to test various perception and navigation approaches for specific environment conditio

Daniel de las Heras, Matthias Schmidt

We present a fundamental classification of forces relevant in nonequilibrium structure formation under collective flow in Brownian many-body systems. The internal one-body force field is systematically split into contributions relevant for the spatial structure and for the coupled motion. We demonstrate that both contributions can be obtained straightforward

Lin Bai, Yecheng Lyu, Xinming Huang

In this paper, a scalable neural network hardware architecture for image segmentation is proposed. By sharing the same computing resources, both convolution and deconvolution operations are handled by the same process element array. In addition, access to on-chip and off-chip memories is optimized to alleviate the burden introduced by partial sum. As an exam

Marjan Hosseinia, Eduard Dragut, Arjun Mukherjee

We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format.

Serge Cantat, Junyi Xie

We proved that, in characteristic 0, if two dominant endomorphisms of the projective plane of degree at least 2 are conjugate by some birational transformation, then they are conjugate by an automorphism. We also gave counterexamples in positive characteristic.

Piotr Sworowski, Waldemar Sieg

We characterise the class of uniform limits of functions from Pawlak's class $\mathcal B_1^{**}$. The resulting class $u\mathscr S_1$, which contains functions with the oscillation rank one, is discussed in connection with its linear span. We apply a general, topological space, setting in our discussion.

Lin Bai, Yecheng Lyu, Xin Xu, Xinming Huang

LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization. This paper presents an FPGA-based deep learning platform for real-time point cloud processing targeted on autonomous vehicles. The software driver for the Velodyne LiDAR sensor is modified and moved into the on-chip processor system, whil

Ivan Kovynyov, Axel Buerck, Ralf Mikut

This study uses 125 responses from companies of all sizes headquartered in Germany, Switzerland, France and UK to reveal perceptions of the drivers of organisational agility. It further investigates current understanding of managing principles of multiple organisational dimensions such as culture, values, leadership, organisational structure, processes and o

Ciro S. Campolina

Models on logarithmic lattices have recently been proposed as an alternative approach to the study of multi-scale nonlinear physics. Here, we introduce LogLatt, an efficient MATLAB library for the calculus between functions on multi-dimensional logarithmic lattices. The applicabilities include common differential operators, norms, and convolutions, and opera

Khuong An Nguyen, Zhiyuan Luo, Chris Watkins

Contact tracing is widely considered as an effective procedure in the fight against epidemic diseases. However, one of the challenges for technology based contact tracing is the high number of false positives, questioning its trust-worthiness and efficiency amongst the wider population for mass adoption. To this end, this paper proposes a novel, yet practica

Shaurya Kaushal, Abhineet Singh Rajput, Soumyadeep Bhattacharya, M. Vidyasagar

A quantitative COVID-19 model that incorporates hidden asymptomatic patients is developed, and an analytic solution in parametric form is given. The model incorporates the impact of lockdown and resulting spatial migration of population due to announcement of lockdown. A method is presented for estimating the model parameters from real-world data. It is show

Vuk Lesi, Zivana Jakovljevic, Miroslav Pajic

With ever-expanding computation and communication capabilities of modern embedded platforms, Internet of Things (IoT) technologies enable development of Reconfigurable Manufacturing Systems---a new generation of highly modularized industrial equipment suitable for highly-customized manufacturing. Sequential control in these systems is largely based on discre

David J. Smith, Alessandro Prete, Angela E. Taylor, Niki Karavitaki

A simplified mathematical model of oral hydrocortisone delivery in adrenal insufficiency is described; the model is based on three components (gastric hydrocortisone, free serum cortisol and bound serum cortisol) and is formulated in terms of linear kinetics, taking into account the dynamics of glucocorticoid-protein binding. Motivated by the need to optimis

A. D. O. Tisbury, D. J. Needham, A. Tzella

In Part II of this series of papers, we consider an initial-boundary value problem for the Kolmogorov--Petrovskii--Piscounov (KPP) type equation with a discontinuous cut-off in the reaction function at concentration $u=u_c$. For fixed cut-off value $u_c \in (0,1)$, we apply the method of matched asymptotic coordinate expansions to obtain the complete large-t

David L. Duncan, Wesley J. Engelbrecht

By working with coefficients in $\mathbb{Z}$ or $\mathbb{R}$, one can define two different notions of stability for a sandpile on a graph. We call a sandpile immutable when these notions agree. Our main results give linear-algebraic characterizations for large classes of immutable sandpiles.

Margarita Kostré, Christof Schütte, Frank Noé, Mauricio J. del Razo

Open biochemical systems of interacting molecules are ubiquitous in life-related processes. However, established computational methodologies, like molecular dynamics, are still mostly constrained to closed systems and timescales too small to be relevant for life processes. Alternatively, particle-based reaction-diffusion models are currently the most accurat

Marina Groshaus, André Luiz Pires Guedes

A biclique of a graph is a maximal complete bipartite subgraph. The biclique graph of a graph $G$, $KB(G)$, defined as the intersection graph of the bicliques of $G$, was introduced and characterized in 2010. However, this characterization does not lead to polynomial time recognition algorithms. The time complexity of its recognition problem remains open. Th

Lorena Aguirre Salazar, Stan Alama, Lia Bronsard

We consider two nonlocal variational models arising in physical contexts. The first is the Thomas-Fermi-Dirac-von Weiz\"{a}cker (TFDW) model, introduced in the study of ionization of atoms and molecules, and the second is the liquid drop model with external potential, proposed by Gamow in the context of nuclear structure. It has been observed that the two mo

Haw-minn Lu

Most machine learning models, especially artificial neural networks, require numerical, not categorical data. We briefly describe the advantages and disadvantages of common encoding schemes. For example, one-hot encoding is commonly used for attributes with a few unrelated categories and word embeddings for attributes with many related categories (e.g., word

Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa, Mikel Artetxe

We propose a modular architecture of language-specific encoder-decoders that constitutes a multilingual machine translation system that can be incrementally extended to new languages without the need for retraining the existing system when adding new languages. Differently from previous works, we simultaneously train $N$ languages in all translation directio

David Kent, Sonia Chernova

As robot autonomy improves, robots are increasingly being considered in the role of autonomous observation systems -- free-flying cameras capable of actively tracking human activity within some predefined area of interest. In this work, we formulate the autonomous observation problem through multi-objective optimization, presenting a novel Semi-MDP formulati

Igor P. Ivanov, Francisco Vazão

We discuss a rather common but often unnoticed pitfall which arises when deriving the bounded-from-below (BFB) conditions in multi-Higgs models with softly broken global symmetries. Namely, necessary and sufficient BFB conditions derived for the case with an exact symmetry can be ruined by introducing soft symmetry breaking terms. Using $S_4$ and $A_4$-symme

Timothy Y. Chow, Jennifer Paulhus

Suppose that $\chi_\lambda$ and $\chi_\mu$ are distinct irreducible characters of the symmetric group $S_n$. We give an algorithm that, in time polynomial in $n$, constructs $\pi\in S_n$ such that $\chi_\lambda(\pi)$ is provably different from $\chi_\mu(\pi)$. In fact, we show a little more. Suppose $f=\chi_\lambda$ for some irreducible character $\chi_\lamb

David Pesquera, Ekaterina Khestanova, Massimo Ghidini, Sen Zhang

The strain dependent functional properties of epitaxial transition metal oxide films can be significantly modified via substrate selection. However, large lattice mismatches preclude dislocation-free epitaxial growth on ferroelectric substrates, whose strain states are modified by applied electric fields. Here we overcome this mismatch problem by depositing

Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li

It has been advocated that medical imaging systems and reconstruction algorithms should be assessed and optimized by use of objective measures of image quality that quantify the performance of an observer at specific diagnostic tasks. One important source of variability that can significantly limit observer performance is variation in the objects to-be-image

Andrew D. Davis, Youssef Marzouk, Aaron Smith, Natesh Pillai

Many Bayesian inference problems involve target distributions whose density functions are computationally expensive to evaluate. Replacing the target density with a local approximation based on a small number of carefully chosen density evaluations can significantly reduce the computational expense of Markov chain Monte Carlo (MCMC) sampling. Moreover, conti

Nikolay Arefyev, Boris Sheludko, Alexander Podolskiy, Alexander Panchenko

Lexical substitution in context is an extremely powerful technology that can be used as a backbone of various NLP applications, such as word sense induction, lexical relation extraction, data augmentation, etc. In this paper, we present a large-scale comparative study of popular neural language and masked language models (LMs and MLMs), such as context2vec,

Onel L. A. López, Nurul Huda Mahmood, Hirley Alves, Matti Latva-aho

Wireless Energy Transfer (WET) is a promising solution for powering massive Internet of Things deployments. An important question is whether the costly Channel State Information (CSI) acquisition procedure is necessary for optimum performance. In this paper, we shed some light into this matter by evaluating CSI-based and CSI-free multi-antenna WET schemes in

Newton Solorzano, Benedito Leandro

In this paper we study spherically symmetric metrics on a symmetric space in $\mathbb{R}^n$ with scalar and constant flag curvature and we also obtain families of this type of metrics. Many explicit examples are provided for Douglas metrics with scalar and constant flag curvature. Furthermore, new examples of projectively flat Finsler metrics are given. We a

Thomas Weng, Amith Pallankize, Yimin Tang, Oliver Kroemer

State-of-the-art object grasping methods rely on depth sensing to plan robust grasps, but commercially available depth sensors fail to detect transparent and specular objects. To improve grasping performance on such objects, we introduce a method for learning a multi-modal perception model by bootstrapping from an existing uni-modal model. This transfer lear

Gabriel García, Rocío del Amor, Adrián Colomer, Valery Naranjo

Nowadays, glaucoma is the leading cause of blindness worldwide. We propose in this paper two different deep-learning-based approaches to address glaucoma detection just from raw circumpapillary OCT images. The first one is based on the development of convolutional neural networks (CNNs) trained from scratch. The second one lies in fine-tuning some of the mos

William R. Rowe, Dmitry V. Skryabin, Andrey V. Gorbach

We analyse Raman-induced self-frequency shift in two-component solitons supported by both quadratic and cubic nonlinearities. Treating Raman terms as a perturbation, we derive expressions for soliton velocity and frequency shifts of the fundamental frequency and second harmonic soliton components. We find these predictions compare well with simulations of so

Wiebke Toussaint, Aaron Yi Ding

Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision intelligent services. This survey moves beyond existing ML algorithms and cloud-driven design to investigate the less-explored systems, scaling and socio-technical aspects for consolidating ML and IoT. It covers the latest developments (up to 2020) on scaling and dis

Karine Miras, Eliseo Ferrante, A. E. Eiben

Evolutionary robot systems are usually affected by the properties of the environment indirectly through selection. In this paper, we present and investigate a system where the environment also has a direct effect: through regulation. We propose a novel robot encoding method where a genotype encodes multiple possible phenotypes, and the incarnation of a robot

C. D. Pruitt, R. J. Charity, L. G. Sobotka, J. M. Elson

The neutron total cross sections $\sigma_{tot}$ of $^{16,18}$O, $^{58,64}$Ni, $^{103}$Rh, and $^{112,124}$Sn have been measured at the Los Alamos Neutron Science Center (LANSCE) from low to intermediate energies (3 $\leq E_{lab} \leq$ 450 MeV) by leveraging waveform-digitizer technology. The $\sigma_{tot}$ relative differences between isotopes are presented,

Alberto Sánchez

We study thermodynamics and geometrothermodynamics of a particular black hole configuration with a nonlinear source. We use the mass as fundamental equation, from which it follows that the curvature radius must be considered as a thermodynamic variable, leading to an extended equilibrium space. Using the formalism of geometrothermodynamics, we show that the

Nicole M. Lloyd-Ronning, Jarrett L. Johnson, Aycin Aykutalp

Gamma-ray burst (GRB) data suggest that the jets from GRBs in the high redshift universe are more narrowly collimated than those at lower redshifts. This implies that we detect relatively fewer long GRB progenitor systems (i.e. massive stars) at high redshifts, because a greater fraction of GRBs have their jets pointed away from us. As a result, estimates of