March 2020 arXiv papers — page 51

Qian Qin, James P. Hobert

Over the last three decades, there has been a considerable effort within the applied probability community to develop techniques for bounding the convergence rates of general state space Markov chains. Most of these results assume the existence of drift and minorization (d\&m) conditions. It has often been observed that convergence rate bounds based on singl

Evangelia Gergatsouli, Brendan Lucier, Christos Tzamos

In many practical applications, heuristic or approximation algorithms are used to efficiently solve the task at hand. However their solutions frequently do not satisfy natural monotonicity properties of optimal solutions. In this work we develop algorithms that are able to restore monotonicity in the parameters of interest. Specifically, given oracle access

Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell

Continual learning aims to learn new tasks without forgetting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are significantly less prone to forgetting and propose a novel hybrid continual learning framew

F. Kopp, M. B. Gay Ducati, M. V. T. Machado

The central exclusive production of $\chi_{c}$ and $\chi_{b}$ at the LHC, RHIC and Tevatron energies is analyzed, using the recent unintegrated parton distribution (UGDs) functions available in the package TMDlib. Comparison with data is performed, which tests the underlying assumptions basing the theoretical approach and it can constrain the unintegrated gl

Qing Luo, Zedi Cheng, Junkai Fan, Lijuan Tan

We experimentally demonstrate a real-time quantum random number generator by using a room-temperature single-photon emitter from the defect in a commercial gallium nitride wafer. Thanks to the brightness of our single photon emitter, the raw bit generation rate is ~1.8 MHz, and the unbiased bit generation rate is ~420 kHz after von Neumann's randomness extra

Dor Gabay

A density-dependent conformal killing vector (CKV) field is attained from a conformally transformed action composed of a unique constraint and a Klein-Gordon field. The CKV is re-expressed into an information identity and studied in its integro-differential form for both null and time-like geodesics. It is conjectured that the identity corresponds to a gener

Ru-Yu Lai, Gunther Uhlmann, Yang Yang

We consider an inverse problem for the Boltzmann equation with nonlinear collision operator in dimensions $n\geq 2$. We show that the kinetic collision kernel can be uniquely determined from the incoming-to-outgoing mappings on the boundary of the domain provided that the kernel satisfies a monotonicity condition. Furthermore, a reconstruction formula is als

P. T. Chruściel, Sk Jahanur Hoque, Tomasz Smołka

We derive a formula for the total canonical energy, and its flux, of weak gravitational waves on a de Sitter background.

Douglas D. Novaes, Gabriel A. R. Vielma

Understanding how tangential singularities evolve under smoothing processes was one of the first problem concerning regularization of Filippov systems. In this paper, we are interested in $C^n$-regularizations of Filippov systems around visible regular-tangential singularities of even multiplicity. More specifically, using Fenichel Theory and Blow-up Methods

Pablo Bueno, Horacio Casini

Exploiting the split property of quantum field theories (QFTs), a notion of von Neumann entropy associated to pairs of spatial subregions has been recently proposed both in the holographic context -- where it has been argued to be related to the entanglement wedge cross section -- and for general QFTs. We argue that the definition of this "reflected entropy"

Francesco Pittaluga, Zaid Tasneem, Justin Folden, Brevin Tilmon

We present a proof-of-concept LIDAR design that allows adaptive real-time measurements according to dynamically specified measurement patterns. We describe our optical setup and calibration, which enables fast sparse depth measurements using a scanning MEMS (micro-electro-mechanical) mirror. We validate the efficacy of our prototype LIDAR design by testing o

Ghafour Ahani, Di Yuan

The notion of age of information (AoI) has become an important performance metric in network and control systems. Information freshness, represented by AoI, naturally arises in the context of caching. We address optimal scheduling of cache updates for a time-slotted system where the contents vary in size. There is limited capacity for the cache and for makin

Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson

In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific quest

Seyed Mohssen Ghafari

Trust can be defined as a measure to determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust predictio

Bhusan Chettri, Tomi Kinnunen, Emmanouil Benetos

Automatic speaker verification (ASV) systems are highly vulnerable to presentation attacks, also called spoofing attacks. Replay is among the simplest attacks to mount - yet difficult to detect reliably. The generalization failure of spoofing countermeasures (CMs) has driven the community to study various alternative deep learning CMs. The majority of them a

Antonis Kontaxakis, Nikos Giatrakos, Antonios Deligiannakis

In this work, we detail the design and structure of a Synopses Data Engine (SDE) which combines the virtues of parallel processing and stream summarization towards delivering interactive analytics at extreme scale. Our SDE is built on top of Apache Flink and implements a synopsis-as-a-service paradigm. In that it achieves (a) concurrently maintaining thousan

Guohui Ding, Joewie J. Koh, Kelly Merckaert, Bram Vanderborght

We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equili

Anna Fariha, Suman Nath, Alexandra Meliou

Runtime nondeterminism is a fact of life in modern database applications. Previous research has shown that nondeterminism can cause applications to intermittently crash, become unresponsive, or experience data corruption. We propose Adaptive Interventional Debugging (AID) for debugging such intermittent failures. AID combines existing statistical debugging,

Tao Xu, Jingtong Zhang, Yuquan Zhu, Jie Wang

Polar metals characterized by the simultaneous coexistence of ferroelectric distortions and metallicity have attracted tremendous attention. Developing such materials at low dimensions remains challenging since both conducting electrons and reduced dimensions are supposed to quench ferroelectricity. Here, based on first-principles calculations, we report the

Shi Li, Sai Vikneshwar Mani Jayaraman, Atri Rudra

In this paper, we initiate a theoretical study of what we call the join covering problem. We are given a natural join query instance $Q$ on $n$ attributes and $m$ relations $(R_i)_{i \in [m]}$. Let $J_{Q} = \ \Join_{i=1}^m R_i$ denote the join output of $Q$. In addition to $Q$, we are given a parameter $\Delta: 1\le \Delta\le n$ and our goal is to compute th

Daniel A. Lemus, James Charles, Tillmann Kubis

The nonequilibrium Green's function (NEGF) method is often used to predict transport in atomistically resolved nanodevices and yields an immense numerical load when inelastic scattering on phonons is included. To ease this load, this work extends the atomistic mode space approach of Ref. [1] to include inelastic scattering on optical and acoustic phonons in

Du Su, Ali Yekkehkhany, Yi Lu, Wenmiao Lu

We propose a new application of embedding techniques for problem retrieval in adaptive tutoring. The objective is to retrieve problems whose mathematical concepts are similar. There are two challenges: First, like sentences, problems helpful to tutoring are never exactly the same in terms of the underlying concepts. Instead, good problems mix concepts in inn

R. Leplaideur, F. Watbled

We extend results on quadratic pressure and convergence of Gibbs mesures from previous joined work of the authors to the Curie-Weiss-Potts model. We define the notion of equilibrium state for the quadratic pressure and show that under some conditions on the maxima for some auxiliary function, the Gibbs measure converges to a convex combination of eigen-measu

Naoki Fujita, Akihiro Higashitani

A Newton-Okounkov body is a convex body constructed from a projective variety with a globally generated line bundle and with a higher rank valuation on the function field, which gives a systematic method of constructing toric degenerations of projective varieties. Its combinatorial properties heavily depend on the choice of a valuation, and it is a fundament

Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang

Deep reinforcement learning (RL) has achieved great empirical successes in various domains. However, the large search space of neural networks requires a large amount of data, which makes the current RL algorithms not sample efficient. Motivated by the fact that many environments with continuous state space have smooth transitions, we propose to learn a smoo

Boris Aronov, Esther Ezra, Micha Sharir

We present subquadratic algorithms, in the algebraic decision-tree model of computation, for detecting whether there exists a triple of points, belonging to three respective sets $A$, $B$, and $C$ of points in the plane, that satisfy a certain polynomial equation or two equations. The best known instance of such a problem is testing for the existence of a co

Patrick Steyrleithner, Gerhard Hensler, Alessandro Boselli

Ram-pressure stripping (RPS) is a well observed phenomenon of massive spiral galaxies passing through the hot intra-cluster medium (ICM) of galaxy clusters. For dwarf galaxies (DGs) within a cluster, the transformation from gaseous to gas-poor systems by RPS is not easily observed and must happen in the outskirts of clusters. In a few objects in close by gal

Laurent Lessard, Peter Seiler

Iterative first-order methods such as gradient descent and its variants are widely used for solving optimization and machine learning problems. There has been recent interest in analytic or numerically efficient methods for computing worst-case performance bounds for such algorithms, for example over the class of strongly convex loss functions. A popular app

Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis

Performance of adaptive control policies is assessed through the regret with respect to the optimal regulator, which reflects the increase in the operating cost due to uncertainty about the dynamics parameters. However, available results in the literature do not provide a quantitative characterization of the effect of the unknown parameters on the regret. Fu

Haishan Ye, Luo Luo, Zhihua Zhang

Many machine learning models involve solving optimization problems. Thus, it is important to deal with a large-scale optimization problem in big data applications. Recently, subsampled Newton methods have emerged to attract much attention due to their efficiency at each iteration, rectified a weakness in the ordinary Newton method of suffering a high cost in

Talley Amir, James Aspnes, David Doty, Mahsa Eftekhari

The standard population protocol model assumes that when two agents interact, each observes the entire state of the other agent. We initiate the study of $\textit{message complexity}$ for population protocols, where the state of an agent is divided into an externally-visible $\textit{message}$ and an internal component, where only the message can be observed

Andreas Brendel, Walter Kellermann

Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-varying acoustic scenarios. In this paper, we derive fast converging update rules from a negentropy perspective, which are based on the Majorize-Minimize (MM) principle and eigenva

Jonathan J. Harris, Ching-Hua Chen, Mohammed J. Zaki

Whereas it has become easier for individuals to track their personal health data (e.g., heart rate, step count, food log), there is still a wide chasm between the collection of data and the generation of meaningful explanations to help users better understand what their data means to them. With an increased comprehension of their data, users will be able to

Thibault Duhamel, Mariane Maynard, Froduald Kabanza

Being able to infer the goal of people we observe, interact with, or read stories about is one of the hallmarks of human intelligence. A prominent idea in current goal-recognition research is to infer the likelihood of an agent's goal from the estimations of the costs of plans to the different goals the agent might have. Different approaches implement this i

Orgest Zaka

This paper introduces a description of Endomorphisms of the translation group in an affine plane, will define the addition and composition of the set of endomorphisms and specify the neutral elements associated with these two actions and present the Endomorphism algebra thereof will distinguish the Trace-preserving endomorphism algebra in affine plane, and p

Zhongxia Zhang, Meng Wu

In this paper, we propose an unsupervised data-driven approach to predict real-time locational marginal prices (RTLMPs). The proposed approach is built upon a general data structure for organizing system-wide heterogeneous market data streams into the format of market data images and videos. Leveraging this general data structure, the system-wide RTLMP predi

Sumit K. Mandal, Ganapati Bhat, Janardhan Rao Doppa, Partha Pratim Pande

Mobile platforms must satisfy the contradictory requirements of fast response time and minimum energy consumption as a function of dynamically changing applications. To address this need, system-on-chips (SoC) that are at the heart of these devices provide a variety of control knobs, such as the number of active cores and their voltage/frequency levels. Cont

Rafik Zitouni, Hacene Bouaroua, Benaoumeur Senouci

Software Defined Radio (SDR) platforms are useful tools to design new wireless technologies or to improve specifications of existing ones. The IEEE 802.11p is the de-facto standard for Wireless Vehicular Ad-hoc NETworks (VANETs). It has been implemented on GNU Radio SDR [1], which experiences frames decoding/encoding latency. In this paper an FPGA based SDR

D. Bazeia, M. A. Marques, R. Menezes

We deal with planar vortex structures in Maxwell-Higgs models in the presence of a generalized magnetic permeability. The model under investigation engenders a real parameter that controls the behavior of the tail of the solutions and of the quantities associated to them. As the parameter gets larger, the solutions attain their boundary values faster, unveil

Benjamin H Savitzky, Lauren A Hughes, Steven E Zeltmann, Hamish G Brown

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full 2D image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-S

Walter Carnielli, Marcelo E. Coniglio, David Fuenmayor

It is customary to expect from a logical system that it can be algebraizable, in the sense that an algebraic companion of the deductive machinery can always be found. Since the inception of da Costa's paraconsistent calculi $C_n$, algebraic equivalents for such systems have been sought. It is known, however, that these systems are not self-extensional (i.e.,

Kristian Snyder, Brennan Thomas, Ming-Lun Lu, Rashmi Jha

Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for

Elisa Gugliotta, Marco Dinarelli

This article describes the constitution process of the first morpho-syntactically annotated Tunisian Arabish Corpus (TArC). Arabish, also known as Arabizi, is a spontaneous coding of Arabic dialects in Latin characters and arithmographs (numbers used as letters). This code-system was developed by Arabic-speaking users of social media in order to facilitate t

Anton Kapustin, Lev Spodyneiko

We define and study analogs of the Thouless charge pump for many-body gapped systems in dimension $D$. We show how to attach a topological invariant to a $D$-dimensional family of such systems, provided all of them have an on-site $U(1)$ symmetry. For a large class of families we argue that this topological invariant is an integer. In the case of gapped syst

Maxim Naumov, John Kim, Dheevatsa Mudigere, Srinivas Sridharan

Large-scale training is important to ensure high performance and accuracy of machine-learning models. At Facebook we use many different models, including computer vision, video and language models. However, in this paper we focus on the deep learning recommendation models (DLRMs), which are responsible for more than 50% of the training demand in our data cen

Yuichiro Nakai, David Shih, Scott Thomas

Tagging jets of strongly interacting particles initiated by energetic strange quarks is one of the few largely unexplored Standard Model object classification problems remaining in high energy collider physics. In this paper we investigate the purest version of this classification problem in the form of distinguishing strange-quark jets from down-quark jets.

Karen Bodie, Maximilian Brunner, Michael Pantic, Stefan Walser

This paper presents and validates active interaction force control and planning for fully actuated and omnidirectional aerial manipulation platforms, with the goal of aerial contact inspection in unstructured environments. We present a variable axis-selective impedance control which integrates direct force control for intentional interaction, using feedback

Alessandro Carbotti, Giovanni E. Comi

Taking inspiration from a recent paper by Bergounioux, Leaci, Nardi and Tomarelli we study the Riemann-Liouville fractional Sobolev space $W^{s, p}_{RL, a+}(I)$, for $I = (a, b)$ for some $a, b \in \mathbb{R}, a < b$, $s \in (0, 1)$ and $p \in [1, \infty]$; that is, the space of functions $u \in L^{p}(I)$ such that the left Riemann-Liouville $(1 - s)$-fracti

Timothy Kieras, Muhammad Junaid Farooq, Quanyan Zhu

Supply chain security threats pose new challenges to security risk modeling techniques for complex ICT systems such as the IoT. With established techniques drawn from attack trees and reliability analysis providing needed points of reference, graph-based analysis can provide a framework for considering the role of suppliers in such systems. We present such a

Tony C. W. Mok, Albert C. S. Chung

Diffeomorphic deformable image registration is crucial in many medical image studies, as it offers unique, special properties including topology preservation and invertibility of the transformation. Recent deep learning-based deformable image registration methods achieve fast image registration by leveraging a convolutional neural network (CNN) to learn the

Chen Lu, Yongyou Zhang, Yu Zhang, Ming Zhang

Starting from a realistic extended Hubbard model for a $p_{x,y}$-orbital tight-binding model on the Honeycomb lattice, we perform a thorough investigation on the possible electron instabilities in the MA-TBG near the van Hove (VH) dopings. Here we focus on the interplay between the approximate SU(2)$\times$SU(2) symmetry and the $D_3$ symmetry, which leads t

Mike Allenspach, Karen Bodie, Maximilian Brunner, Luca Rinsoz

Omnidirectional micro aerial vehicles are a growing field of research, with demonstrated advantages for aerial interaction and uninhibited observation. While systems with complete pose omnidirectionality and high hover efficiency have been developed independently, a robust system that combines the two has not been demonstrated to date. This paper presents th

R. M. G. de Albuquerque, J. F. Gameiro, S. H. P. Alencar, J. J. G. Lima

Although the Orion Nebula Cluster is one of the most studied clusters in the solar neighborhood, the evolution of the very low-mass members ($M_* < 0.25 \, M_\odot$) has not been fully addressed due to their faintness. Our goal is to verify if some young and very low-mass objects in the Orion Nebula Cluster show evidence of ongoing accretion using broadband

Alessandro Bazzi, Alberto Zanella, Ioannis Sarris, Vincent Martinez

In the last few years, two technologies have been developed to enable direct exchange of information between vehicles. These technologies, currently seen as alternatives, are ITS-G5, as commonly referred in Europe, and sidelink LTE-vehicle-to-everything (LTE-V2X) (one of the solutions of the so-called cellular-V2X, C-V2X). For this reason, the attention has

Luis Vasquez

In this study equilibrium isotope effects (EIEs) on evaporation to several organic solvents (bromobenzene, dibromomethane, ethanol, methanol, and trichloromethane) in the pure phase are estimated employing Kohn-Sham Density functional theory (KS-DFT) along with full Hessian vibrational analysis (FHVA) and partial Hessian vibrational analysis (PHVA). Both FHV

Stefan Borgwardt, Veronika Thost

Ontology-based query answering (OBQA) augments classical query answering in databases by domain knowledge encoded in an ontology. Systems for OBQA use the ontological knowledge to infer new information that is not explicitly given in the data. Moreover, they usually employ the open-world assumption, which means that knowledge that is not stated explicitly in

Thomas Muehlenstaedt, Maria Lanzerath

In this article, an adaption of an algorithm for the creation of experimental designs by Lekivetz and Jones (2015) is suggested, dealing with constraints around randomization. Split-plot design of experiments is used, when the levels of some factors cannot be modified as easily as others. While most split-plot designs deal in the context of I-optimal or D-op

Haroldo C. D. Lima Junior, Luís C. B. Crispino, Atsushi Higuchi

Tidal forces are an important feature of General Relativity, which are related to the curvature tensor. We analyze the tidal tensor in Kerr spacetime, with emphasis on the case along the symmetry axis of the Kerr black hole, noting that tidal forces may vanish at a certain point, unlike in the Schwarzschild spacetime, using Boyer-Lindquist coordinates. We st

Yingying Li, Qinran Hu, Na Li

One challenge in the optimization and control of societal systems is to handle the unknown and uncertain user behavior. This paper focuses on residential demand response (DR) and proposes a closed-loop learning scheme to address these issues. In particular, we consider DR programs where an aggregator calls upon residential users to change their demand so tha

Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj

In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data into a new subspace optimized for ellipsoidal encapsulation of target class data. We provide both linear and non-linear formulations for the proposed method. The method takes into

Zilong Zhao, Sophie Cerf, Bogdan Robu, Nicolas Marchand

Convolutional Neural Network (CNN) has become the most used method for image classification tasks. During its training the learning rate and the gradient are two key factors to tune for influencing the convergence speed of the model. Usual learning rate strategies are time-based i.e. monotonous decay over time. Recent state-of-the-art techniques focus on ada

Hazel Browne

We present several results regarding the connectivity of McKay quivers of finite-dimensional complex representations of finite groups, with no restriction on the faithfulness or self-duality of the representations. We give examples of McKay quivers, as well as quivers that cannot arise as McKay quivers, and discuss a necessary and sufficient condition for tw

Omar Reyad

The novel Coronavirus (COVID-19) is an infectious disease caused by a new virus called COVID-19 or 2019-nCoV that first identified in Wuhan, China. The disease causes respiratory illness (such as the flu) with other symptoms such as a cough, fever, and in more severe cases, difficulty breathing. This new Coronavirus seems to be very infectious and has spread

Takahiko Mizuno, Eiji Hase, Takeo Minamikawa, Yu Tokizane

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool for quantitative fluorescence imaging because fluorescence lifetime is independent of concentration of fluorescent molecules or excitation/detection efficiency and is robust to photobleaching. However, since FLIM is based on point-to-point measurements, mechanical scanning of a focal spot is

Stephen E. Nagler, D. Alan Tennant

The early 1990s saw the first useful application of pulsed neutron spectroscopy to the study of excitations in low dimensional magnetic systems, with Roger Cowley as a key participant in important early experiments. Since that time the technique has blossomed as a powerful tool utilizing vastly improved neutron instrumentation coupled with more powerful puls

Seydou-Samba Diop, Lars Fritz, Matthias Vojta, Stephan Rachel

Band structures of topological insulators are characterized by non-local topological invariants. Consequently, proposals for the experimental detection using local probes are rare. A recent paper [Slager et al., Phys. Rev. B 92, 085126 (2015)] has argued, based on theoretical results for a particular class of models, that insulators with topologically trivia

Mariangela Settimo

The DAMIC (Dark Matter in CCDs) experiment searches for the interactions of dark matter particles with the nuclei and the electrons in the silicon bulk of thick fully depleted charge-coupled devices (CCDs). Because of the low noise and low dark current, DAMIC CCDs are sensitive to the ionization signals expected from low-mass dark matter particles ($< 10$ Ge

Dingkang Wang, Lucas Magee, Bing-Xing Huo, Samik Banerjee

Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory. However, the tree-like shapes of neurons cannot be described easily as points in a vector space (the subtraction of two neuronal shapes is not a meaningful operation), and methods from computational topology are better suited to their analysis. Here we int

Giuseppe de Vito, Pietro Ricci, Lapo Turrini, Vladislav Gavryusev

Light-sheet microscopy (LSM) is a powerful imaging technique that uses a planar illumination oriented orthogonally to the detection axis. Two-photon (2P) LSM is a variant of LSM that exploits the 2P absorption effect for sample excitation. The light polarization state plays a significant, and often overlooked, role in 2P absorption processes. The scope of th

Helen Naumann, Thorsten Theobald

The $\mathcal{S}$-cone provides a common framework for cones of polynomials or exponential sums which establish non-negativity upon the arithmetic-geometric inequality, in particular for sums of non-negative circuit polynomials (SONC) or sums of arithmetic-geometric exponentials (SAGE). In this paper, we study the $\mathcal{S}$-cone and its dual from the vie

Holger Dette, Xin Liu, Rong-Xian Yue

The determination of an optimal design for a given regression problem is an intricate optimization problem, especially for models with multivariate predictors. Design admissibility and invariance are main tools to reduce the complexity of the optimization problem and have been successfully applied for models with univariate predictors. In particular several

Junais, S. Boissier, B. Epinat, P. Amram

Malin 1 is the largest known low surface brightness (LSB) galaxy, the archetype of so-called giant LSBs. The structure and the origin of such galaxies are still poorly understood, especially due to the lack of high-resolution kinematics and spectroscopic data. We use emission lines from spectroscopic observations of Malin 1 aiming to bring new constraints on

Zeliang Liu, Haoyan Wei, Tianyu Huang, C. T. Wu

In the paper, we present an integrated data-driven modeling framework based on process modeling, material homogenization, mechanistic machine learning, and concurrent multiscale simulation. We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics

Klaudiusz Czudek, Tomasz Szarek

The central limit theorem for Markov chains generated by iterated function systems consisting of orientation preserving homeomorphisms of the interval is proved. We study also ergodicity of such systems.

Yu Tao, John A. Schneeloch, Adam A. Aczel, Despina Louca

Elastic neutron scattering on a single crystal and powder X-ray diffraction measurements were carried out to investigate how the crystal structure evolves as a function of temperature in the Weyl semimetal WTe$_{2}$. A sharp transition from the low-temperature orthorhombic phase (T$_{d}$) to the high-temperature monoclinic phase (1T$^{\prime}$) was observed

Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff, Baosen Zhang

This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints. Despite its success in many domains, reinforcement learning is challenging to apply to problems with hard constraints, especially if both the state variables and actions are constrained. Previous works seeking to ensure constraint satisfa

Ophir Gozes, Hayit Greenspan

In this paper, we present a deep learning-based image processing technique for extraction of bone structures in chest radiographs using a U-Net FCNN. The U-Net was trained to accomplish the task in a fully supervised setting. To create the training image pairs, we employed simulated X-Ray or Digitally Reconstructed Radiographs (DRR), derived from 664 CT scan

Zhaoshuo Li, Amirreza Shaban, Jean-Gabriel Simard, Dinesh Rabindran

Purpose: We describe a 3D multi-view perception system for the da Vinci surgical system to enable Operating room (OR) scene understanding and context awareness. Methods: Our proposed system is comprised of four Time-of-Flight (ToF) cameras rigidly attached to strategic locations on the daVinci Xi patient side cart (PSC). The cameras are registered to the rob

Roberto Percacci

I review here some motivations to consider a theory of gravity based on independent metric and connection, and its status as a quantum theory.

Kamil Skarzynski, Marcin Stepniak, Waldemar Bartyna, Stanislaw Ambroszkiewicz

Humans are considered as integral components of Human-Robot Collaboration (HRC) systems, not only as object (e.g. in health care), but also as operators and service providers in manufacturing. Sophisticated and complex tasks are to be collaboratively executed by devices (robots) and humans. We introduce a generic ontology for HRC systems. Description of huma

Francis Adjei, Mieczyslaw Dabkowski, Samreen Khan, Viswanath Ramakrishna

Algorithmic methods for the explicit inversion of the indefinite double covering maps are proposed. These are based on either the Givens decomposition or the polar decomposition of the given matrix in the proper, indefinite orthogonal group with signature (p,q). As a by-product we establish that the preimage in the covering group, of a positive matrix in the

Jie Luo, Guangshen Ma, Sarah Frisken, Parikshit Juvekar

With the increasing availability of new image registration approaches, an unbiased evaluation is becoming more needed so that clinicians can choose the most suitable approaches for their applications. Current evaluations typically use landmarks in manually annotated datasets. As a result, the quality of annotations is crucial for unbiased comparisons. Even t

Zhiming Zhang, Rachel Myers, Kenji Watanabe, Takashi Taniguchi

Using scanning probe microscopy and spectroscopy, we explore the spatial symmetry of the electronic wavefunctions of twisted bilayer graphene at the "magic angle" of 1.1 degrees. This small twist angle leads to a long wavelength moir\'e unit cell on the order of 13 nm and the appearance of two flat bands. As the twist angle is decreased, correlation effects

Simeon Krastnikov, Florian Kerschbaum, Douglas Stebila

A major algorithmic challenge in designing applications intended for secure remote execution is ensuring that they are oblivious to their inputs, in the sense that their memory access patterns do not leak sensitive information to the server. This problem is particularly relevant to cloud databases that wish to allow queries over the client's encrypted data.

Thomas E. Gilsdorf, Mohammad Khavanin

We extend existence and uniqueness results of [4] for nonlinear integro-differential equations of Volterra type between real locally complete vector spaces

N. V. Maslova, D. O. Revin

A subgroup $H$ of a group $G$ is said to be {\it pronormal} in $G$ if $H$ and $H^g$ are conjugate in $\langle H, H^g \rangle$ for each $g \in G$. Some problems in Finite Group Theory, Combinatorics, and Permutation Group Theory were solved in terms of pronormality, therefore, the question of pronormality of a given subgroup in a given group is of interest. S

Roland Herzog

The preconditioned iterative solution of large-scale saddle-point systems is of great importance in numerous application areas, many of them involving partial differential equations. Robustness with respect to certain problem parameters is often a concern, and it can be addressed by identifying proper scalings of preconditioner building blocks. In this paper

Stefan Tappe

The goal of this note is to present a simple mathematical model with two parameters for the number of deaths due to the corona (COVID-19) virus. The model only requires basic knowledge in differential calculus, and can also be understood by pupils attending secondary school. The model can easily be implemented on a computer, and we will illustrate it on the

Andreas Höring, Jie Liu, Feng Shao

Let $X$ be a Fano manifold. While the properties of the anticanonical divisor $-K_X$ and its multiples have been studied by many authors, the positivity of the tangent bundle $T_X$ is much more elusive. We give a complete characterisation of the pseudoeffectivity of $T_X$ for del Pezzo surfaces, hypersurfaces in the projective space and del Pezzo threefolds.

R. Jacobs, C. E. Larson

Let PR$[n]$ be the graph whose vertices are $2,3,\ldots,n$ with vertex $v$ adjacent to vertex $w$ if and only if $\gcd(v,w)>1$. It is shown that $\pi(n)$, the the number of primes no more than $n$, equals the Lov\'{a}sz number of this graph. This result suggests new avenues for graph-theoretic investigations of number-theoretic problems.

Josua Kottke, Daniel Utt, Mathilde Laurent-Brocq, Adnan Fareed

Tracer diffusion of all constituting elements is studied at various temperatures in a series of (CoCrFeMn)$_{100-x}$Ni$_x$ alloys with compositions ranging from pure Ni to the equiatomic CoCrFeMnNi high-entropy alloy. At a given homologous temperature, the measured tracer diffusion coefficients change non-monotonically along the transition from pure Ni to th

Christina K. Gilligan, Brian Chaboyer, Jeffrey D. Cummings, Dougal Mackey

We examine four ancient Large Magellanic Cloud (LMC) globular clusters (GCs) for evidence of multiple stellar populations using the Advanced Camera for Surveys and Wide Field Camera 3 on the Hubble Space Telescope Programme GO-14164. NGC 1466, NGC 1841, and NGC 2257 all show evidence for a redder, secondary population along the main-sequence. Reticulum does

Trygve K. Løken, Jean Rabault, Erin E. Thomas, Malte Müller

Increased economic activity and research interest in the Arctic raise the need for better wave forecasts in the marginal ice zone (MIZ). Mathematical and numerical models of wave propagation in sea ice would benefit from more in situ data for validation. This study presents shipborne wave measurements from the MIZ where altimeter readings are corrected for s

Neha Gupta, Arun Kumar, Nikolai Leonenko

In this article, we introduce Skellam process of order k and its running average. We also discuss the time-changed Skellam process of order k. In particular we discuss space-fractional Skellam process and tempered space-fractional Skellam process via time changes in Poisson process by independent stable subordinator and tempered stable subordinator, respecti

Kerry Ojakian, Riste Skrekovski, Aleksandra Tepeh

Recently the notion of $k$-rainbow total domination was introduced for a graph $G$, motivated by a desire to reduce the problem of computing the total domination number of the generalized prism $G \Box K_k$ to an integer labeling problem on $G$. In this paper we further demonstrate usefulness of the labeling approach, presenting bounds on the rainbow total d

Valeria G. Ramírez-Preciado, Alexandre Román-Lópes, Carlos G. Román-Zuñiga, Jesús Hernandez

We present a semi-empirical spectral classification scheme for normal B-type stars using near-infrared spectra (1.5-1.7 $\mu$m) from the SDSS APOGEE2-N DR14 database. The main motivation for working with B-type stars is their importance in the evolution of young stellar clusters, however we also take advantage of having a numerous sample (316 stars) of B-typ

Fionn D. Malone, Shuai Zhang, Miguel A. Morales

We outline how auxiliary-field quantum Monte Carlo (AFQMC) can leverage graphical processing units (GPUs) to accelerate the simulation of solid state sytems. By exploiting conservation of crystal momentum in the one- and two-electron integrals we show how to efficiently formulate the algorithm to best utilize current GPU architectures. We provide a detailed

Melike Oguz-Alper, Li-Chun Zhang

We develop unbiased strategies to probabilistic T-wave snowball sampling from graphs, where the interest of estimation may concern finite-order subgraphs such as triangles, cycles or stars. Our approaches encompass also the finite-population sampling strategies to multiplicity sampling and adaptive cluster sampling, both of which can be recast as snowball sa

Qiaomei Li, Rachel Cummings, Yonatan Mintz

A key challenge for decision makers when incorporating black box machine learned models into practice is being able to understand the predictions provided by these models. One proposed set of methods is training surrogate explainer models which approximate the more complex model. Explainer methods are generally classified as either local or global, depending

Diana Cai, Rishit Sheth, Lester Mackey, Nicolo Fusi

Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples. However, many popular meta-learning algorithms, such as model-agnostic meta-learning (MAML), only assume access to the target samples for fine-tuning. In this work, we provide a general framework for meta-lear