March 2020 arXiv papers — page 40

Arka Sadhu, Kan Chen, Ram Nevatia

We explore the task of Video Object Grounding (VOG), which grounds objects in videos referred to in natural language descriptions. Previous methods apply image grounding based algorithms to address VOG, fail to explore the object relation information and suffer from limited generalization. Here, we investigate the role of object relations in VOG and propose

Akshay Jain, Elena Lopez-Aguilera, Ilker Demirkol

In this paper, we provide a novel application aware user association and resource allocation framework, i.e., AURA-5G, which utilizes a joint optimization strategy to accomplish the same. Concretely, our methodology considers all the real network constraints that will be prevalent in the 5G networks as well as practical deployment scenarios. Furthermore, AUR

Laurent Hébert-Dufresne, Dina Mistry, Benjamin M. Althouse

There is a rich history of models for the interaction of a biological contagion like influenza with the spread of related information such as an influenza vaccination campaign. Recent work on the spread of interacting contagions on networks has highlighted that these interacting contagions can have counter-intuitive interplay with network structure. Here we

Dimitris G. Giovanis, Michael D. Shields

This paper introduces a surrogate modeling scheme based on Grassmannian manifold learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The method exploits subspace-structured features of each solution by projecting it onto a Grassmann manifold. The method utilizes a solution clustering approach in order to identify region

P. L. Krapivsky, S. Redner

We investigate parking in a one-dimensional lot, where cars enter at a rate $\lambda$ and each attempts to park close to a target at the origin. Parked cars also depart at rate 1. An entering driver cannot see beyond the parked cars for more desirable open spots. We analyze a class of strategies in which a driver ignores open spots beyond $\tau L$, where $\t

Christopher M. Bender, Yang Li, Yifeng Shi, Michael K. Reiter

In this work we develop a novel Bayesian neural network methodology to achieve strong adversarial robustness without the need for online adversarial training. Unlike previous efforts in this direction, we do not rely solely on the stochasticity of network weights by minimizing the divergence between the learned parameter distribution and a prior. Instead, we

Moses Fayngold

The lately developed part of Quantum Bayesianism named QBism has been proclaimed by its authors a powerful interpretation of Quantum Physics. This article presents analysis of some aspects of QBism. The considered examples show inconsistencies in some basic statements of the discussed interpretation. In particular, the main quantum mechanical conundrum of me

Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi

This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain. It specifically investigates the use of Bilinear Convolutional Neural Network (B-CNN) to extract facial features during different levels of postoperative pain followed by modeling the temporal pattern using Recurrent

Amir Hossein Kargaran, Amir Neshastegaran, Iman Izadi, Ehsan Yazdian

An industrial process includes many devices, variables, and sub-processes that are physically or electronically interconnected. These interconnections imply some level of correlation between different process variables. Since most of the alarms in a process plant are defined on process variables, alarms are also correlated. However, this can be a nuisance to

Oscar Bettermann, Nelson Darkwah Oppong, Giulio Pasqualetti, Luis Riegger

We report on the direct optical production and spectroscopy of $^1\mathrm{S}_0\mbox{-}^3\mathrm{P}_0$ molecules with large binding energy using the clock transition of $^{171}\mathrm{Yb}$, and on the observation of the associated orbital Feshbach resonance near $1300\,\mathrm{G}$. We measure the magnetic field dependence of the closed-channel dimer and of th

Hassam Ullah Sheikh, Ladislau Bölöni

Many cooperative multi-agent problems require agents to learn individual tasks while contributing to the collective success of the group. This is a challenging task for current state-of-the-art multi-agent reinforcement algorithms that are designed to either maximize the global reward of the team or the individual local rewards. The problem is exacerbated wh

Jeremy Booher, José Felipe Voloch

In this paper, we prove that a smooth hyperbolic projective curve over a finite field can be recovered from L-functions associated to the Hilbert class field of the curve and its constant field extensions. As a consequence, we give a new proof of a result of Mochizuki and Tamagawa that two such curves with isomorphic fundamental groups are themselves isomorp

Apurva Gandhi, Shomik Jain

This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors. We created adversarial perturbations using the Fast Gradient Sign Method and the Carlini and Wagner L2 norm attack in both blackbox and whitebox settings. Detectors achieved over 95% accuracy on unperturbed deepfakes, but less than 27% accuracy on perturbe

Liwei Song, Prateek Mittal

Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the model. In this paper, we show that prior work on membership inference attacks may severely underestimate the privacy risks by relying solely on training custom neur

Ítalo M. de Lima, Emanuel V. de Souza, Francisco D. V. de Araújo

In view of the difficulties encountered in science teaching, specifically in the teaching of physics, studies aim to develop methodologies that help science teaching. In this context, the present work proposes the use of the Arduino platform as a methodological tool to improve the teaching of physics in high school. For this purpose, the objective of the wor

Alina Arseniev-Koehler, Jacob G. Foster

As we navigate our cultural environment, we learn cultural biases, like those around gender, social class, health, and body weight. It is unclear, however, exactly how public culture becomes private culture. In this paper, we provide a theoretical account of such cultural learning. We propose that neural word embeddings provide a parsimonious and cognitively

Martin Mayr, Martin Stumpf, Anguelos Nicolaou, Mathias Seuret

Most people think that their handwriting is unique and cannot be imitated by machines, especially not using completely new content. Current cursive handwriting synthesis is visually limited or needs user interaction. We show that subdividing the process into smaller subtasks makes it possible to imitate someone's handwriting with a high chance to be visually

Yuan Tian, Brian J. Reich

The max-stable process is an asymptotically justified model for spatial extremes. In particular, we focus on the hierarchical extreme-value process (HEVP), which is a particular max-stable process that is conducive to Bayesian computing. The HEVP and all max-stable process models are parametric and impose strong assumptions including that all marginal distri

William W. Symes

An extremely simple single-trace transmission example shows how an extended source formulation of full waveform inversion can produce an optimization problem without spurious local minima ("cycle skipping"). The data consist of a single trace recorded at a given distance from a point source. The velocity or slowness is presumed homogeneous, and the target so

Timothy Hosgood

In the previous part of this diptych, we defined the notion of an admissible simplicial connection, as well as explaining how H.I. Green constructed a resolution of coherent analytic sheaves by locally free sheaves on the \v{C}ech nerve. This paper seeks to apply these abstract formalisms, by showing that Green's barycentric simplicial connection is indeed a

Andrey Sarantsev

Convergence rate to the stationary distribution for continuous-time Markov processes can be studied using Lyapunov functions. Recent work by the author provided explicit rates of convergence in special case of a reflected jump-diffusion on a half-line. These results are proved for total variation distance and its generalizations: measure distances defined by

Liliang Ren, Zhuonan Hao

Convolutional Neural Networks (CNN) has been widely applied in the realm of computer vision. However, given the fact that CNN models are translation invariant, they are not aware of the coordinate information of each pixel. Thus the generalization ability of CNN will be limited since the coordinate information is crucial for a model to learn affine transform

Mahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley, Kirk Pruhs

We consider the problem of evaluating certain types of functional aggregation queries on relational data subject to additive inequalities. Such aggregation queries, with a smallish number of additive inequalities, arise naturally/commonly in many applications, particularly in learning applications. We give a relatively complete categorization of the computat

Sota Yamamoto, Reina Kaji, Hirotaka Sasakura, Satoru Adachi

Semiconductor quantum dots provide a spin-coupled system of an electron and nuclei via enhanced hyperfine interaction. We showed that the nuclear spin polarization in single quantum dots can have three stable branches under a longitudinal magnetic field. The states were accompanied by hysteresis loops around the boundaries of each branch with a change in the

Timo Schumann, Luca Galletti, Hanbyeol Jeong, Kaveh Ahadi

Superconductors that possess both broken spatial inversion symmetry and spin-orbit interactions exhibit a mix of spin singlet and triplet pairing. Here, we report on measurements of the superconducting properties of electron-doped, strained SrTiO3 films. These films have an enhanced superconducting transition temperature and were previously shown to undergo

Pietro Verzelli, Cesare Alippi, Lorenzo Livi, Peter Tino

Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is not trained (e.g., via gradient descent), making them appealing for analytical studies by a large community of researchers with backgrounds spanning from dynamical systems to neur

César Contreras, Tomoki Ohsawa

We extend the method of controlled Lagrangians with kinetic shaping to those mechanical systems on semidirect product Lie groups with broken symmetry, more specifically to the Euler--Poincar\'e equations with advected parameters. We find a matching condition for the controlled Lagrangian for such systems whose configuration manifold is a general semidirect p

H. Hotta, H. Iijima

We investigate the rising flux tube and the formation of sunspots in an unprecedentedly deep computational domain that covers the whole convection zone with a radiative magnetohydrodynamics simulation. Previous calculations had shallow computational boxes (< 30 Mm) and convection zones at a depth of 200 Mm. By using our new numerical code R2D2, we succeed in

Dongjin Lee, Rong Pan

Predictive Maintenance (PdM) can only be implemented when the online knowledge of system condition is available, and this has become available with deployment of on-equipment sensors. To date, most studies on predicting the remaining useful lifetime of a system have been focusing on either single-component systems or systems with deterministic reliability st

Lin Lin, Ersin Gogus, Oliver J. Roberts, Chryssa Kouveliotou

We present timing and time-integrated spectral analysis of 127 bursts from SGR J1935+2154. These bursts were observed with the Gamma-ray Burst Monitor on the Fermi Gamma-ray Space Telescope and the Burst Alert Telescope on the Neil Gehrels Swift Observatory during the source's four active episodes from 2014 to 2016. This activation frequency makes SGR J1935+

Bruno Barazani, Guillaume Dion, Jean-François Morissette, Louis Beaudoin

This study presents the design, fabrication, and test of a micro accelerometer with intrinsic processing capabilities, that integrates the functions of sensing and computing in the same MEMS. The device consists of an inertial mass electrostatically coupled to an oscillating beam through a gap of 8 {\mu}m. The motion of the inertial mass modulates an AC elec

Kunwoo Park, Taegyun Kim, Seunghyun Yoon, Meeyoung Cha

In digital environments where substantial amounts of information are shared online, news headlines play essential roles in the selection and diffusion of news articles. Some news articles attract audience attention by showing exaggerated or misleading headlines. This study addresses the \textit{headline incongruity} problem, in which a news headline makes cl

Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong

We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the existing state-of-the-art. Like Pseudo Labels, Meta Pseudo Labels has a teacher network to generate pseudo labels on unlabeled data to teach a student network. However, unlike Pseudo Labels w

Sanghamitra Dutta, Jianyu Wang, Gauri Joshi

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in runtime as it waits for the slowest workers (stragglers). Asynchronous methods can alleviate stragglers, but cause gradient staleness that can adversely affect the convergence error. In this work, we present a novel theoretical characterization of the speed

Ning-An Lai, Nico Michele Schiavone, Hiroyuki Takamura

In this paper we study several semilinear damped wave equations with "subcritical" nonlinearities, focusing on demonstrating lifespan estimates for energy solutions. Our main concern is on equations with scale-invariant damping and mass. Under different assumptions imposed on the initial data, lifespan estimates from above are clearly showed. The key fact is

Alireza Nooraiepour, Sina Rezaei Aghdam

Finite-length codes are learned for the Gaussian wiretap channel in an end-to-end manner assuming that the communication parties are equipped with deep neural networks (DNNs), and communicate through binary phase-shift keying (BPSK) modulation scheme. The goal is to find codes via DNNs which allow a pair of transmitter and receiver to communicate reliably an

Shaojun Guo, Xinghao Qiao

Modelling a large collection of functional time series arises in a broad spectral of real applications. Under such a scenario, not only the number of functional variables can be diverging with, or even larger than the number of temporally dependent functional observations, but each function itself is an infinite-dimensional object, posing a challenging task.

Yossi Arjevani, Michael Field

We consider the optimization problem associated with fitting two-layer ReLU networks with $k$ hidden neurons, where labels are assumed to be generated by a (teacher) neural network. We leverage the rich symmetry exhibited by such models to identify various families of critical points and express them as power series in $k^{-\frac{1}{2}}$. These expressions a

Elizabeth Melton

The identification and characterization of stellar members within a star-forming region are critical to many aspects of star formation, including formalization of the initial mass function, circumstellar disk evolution and star-formation history. Previous surveys of the Lupus star-forming region have identified members through infrared excess and accretion s

Danielle Cox, Todd Mullen, Richard Nowakowski

Originally proposed by Duffy et al., Diffusion is a variant of chip-firing in which chips from flow from places of high concentration to places of low concentration. In the variant, Perturbation Diffusion, the first step involves a "perturbation" in which some number of vertices send chips to each of their respective neighbours even though the rules of Diffu

Joon Hyeop Lee, Mina Pak, Hye-Ran Lee

The discovery of the coherence between galaxy rotation and neighbor motion in 1-Mpc scales has been reported recently. Following up the discovery, we investigate whether the neighbors in such dynamical coherence also present galaxy conformity, using the Calar Alto Legacy Integral Field Area Survey (CALIFA) data and the NASA-Sloan Atlas (NSA) catalog. We meas

Reed Milewicz, Gustavo Pinto, Paige Rodeghero

The development of scientific software is, more than ever, critical to the practice of science, and this is accompanied by a trend towards more open and collaborative efforts. Unfortunately, there has been little investigation into who is driving the evolution of such scientific software or how the collaboration happens. In this paper, we address this proble

Aleksandar Stanoev, Adnan Aijaz, Anthony Portelli, Michael Baddeley

Achieving closed-loop control over wireless is crucial in realizing the vision of Industry 4.0 and beyond. This demonstration shows the viability of closed-loop control over wireless through a high-performance wireless solution. The closed-loop control problem involves remote balancing of a two-wheeled robot that represents an inverted pendulum on wheels.

Gian Maria Campedelli, Alberto Aziani, Serena Favarin

This work investigates whether and how COVID-19 containment policies had an immediate impact on crime trends in Los Angeles. The analysis is conducted using Bayesian structural time-series and focuses on nine crime categories and on the overall crime count, daily monitored from January 1st 2017 to March 28th 2020. We concentrate on two post-intervention time

Florent Foucaud, Benjamin Gras, Anthony Perez, Florian Sikora

We study the complexity of the two dual covering and packing distance-based problems Broadcast Domination and Multipacking in digraphs. A dominating broadcast of a digraph $D$ is a function $f:V(D)\to\mathbb{N}$ such that for each vertex $v$ of $D$, there exists a vertex $t$ with $f(t)>0$ having a directed path to $v$ of length at most $f(t)$. The cost of $f

Danijel~Grgičin, Damir~Vurnek

In order to understand how live matter functions one needs to understand the interaction between polyelectrolytes. We discover a general dependence of polyelectrolyte conductivity valid in at least nine decades of polyelectrolyte concentration spanning dilute and semidilute pure water solutions. Furthermore, we showed that current state of the art theories c

Igor Dolinka

Building on the previous extensive study of Yang, Gould and the present author, we provide a more precise insight into the group-theoretical ramifications of the word problem for free idempotent generated semigroups over finite biordered sets. We prove that such word problems are in fact equivalent to the problem of computing intersections of cosets of certa

Jiani Li, Xenofon Koutsoukos

Distributed diffusion is a powerful algorithm for multi-task state estimation which enables networked agents to interact with neighbors to process input data and diffuse information across the network. Compared to a centralized approach, diffusion offers multiple advantages that include robustness to node and link failures. In this paper, we consider distrib

Adel Khalfallah, Fathi Haggui, Mohamed Mhamdi

In this paper, we establish some Schwarz type lemmas for mappings $\Phi$ satisfying the inhomogeneous biharmonic Dirichlet problem $ \Delta (\Delta(\Phi)) = g$ in $\mathbb{D}$, $\Phi=f$ on $\mathbb{T}$ and $\partial_n \Phi=h$ on $\mathbb{T}$, where $g$ is a continuous function on $\overline{\mathbb{D}}$, $f,h$ are continuous functions on $\mathbb{T}$, where

Qiufen Ni, Jianxiong Guo, Chuanhe Huang, Weili Wu

Community partition is an important problem in many areas such as biology network, social network. The objective of this problem is to analyse the relationships among data via the network topology. In this paper, we consider the community partition problem under IC model in social networks. We formulate the problem as a combinatorial optimization problem whi

E. Andriolo, N. Lambert, C. Papageorgakis

We explore various geometrical aspects of an action for six-dimensional chiral 2-forms based on the formalism of 1903.12196. We elucidate the coupling to general backgrounds and construct the full supersymmetric completion to an abelian (2,0) superconformal lagrangian including matter. We investigate the non-standard diffeomorphism properties of the fields a

Alan B. Cannaday, Curt H. Davis, Grant J. Scott, Blake Ruprecht

Here we demonstrate how Deep Neural Network (DNN) detections of multiple constitutive or component objects that are part of a larger, more complex, and encompassing feature can be spatially fused to improve the search, detection, and retrieval (ranking) of the larger complex feature. First, scores computed from a spatial clustering algorithm are normalized t

Emma S. Johnson, Shabbir Ahmed, Santanu S. Dey, Jean-Paul Watson

While transmission switching is known to reduce power generation costs, the difficulty of solving even DC optimal transmission switching (DCOTS) has prevented optimal transmission switching from becoming commonplace in real-time power systems operation. In this paper, we present a k-nearest neighbors (KNN) heuristic for DCOTS which relies on the insight that

Iroro Orife, David I. Adelani, Timi Fasubaa, Victor Williamson

Yor\`ub\'a is a widely spoken West African language with a writing system rich in orthographic and tonal diacritics. They provide morphological information, are crucial for lexical disambiguation, pronunciation and are vital for any computational Speech or Natural Language Processing tasks. However diacritic marks are commonly excluded from electronic texts

Jiani Li, Waseem Abbas, Xenofon Koutsoukos

In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusion-based information

T. G. Khunjua, K. G. Klimenko, R. N. Zhokhov

In this paper the phase structure of dense baryon matter composed of $u$ and $d$ quarks with two colors has been investigated in the presence of baryon $\mu_B$, isospin $\mu_I$ and chiral isospin $\mu_{I5}$ chemical potentials in the framework of Nambu--Jona-Lasinio model. In the chiral limit, it has been shown that the duality between phases with spontaneou

Enrique Gaztanaga

A Universe with finite age also has a finite causal scale. Larger scales can not affect our local measurements or modeling, but far away locations could have different cosmological parameters. The size of our causal Universe depends on the details of inflation and is usually assumed to be larger than our observable Universe today. To account for causality, w

Komal Kumari, Diego A. Donzis

Direct numerical simulations (DNS) are an indispensable tool for understanding the fundamental physics of turbulent flows. Because of their steep increase in computational cost with Reynolds number ($R_{\lambda}$), well-resolved DNS are realizable only on massively parallel supercomputers, even at moderate $R_{\lambda}$. However, at extreme scales, the commu

Xingjian Ding, Jianxiong Guo, Deying Li, Weili Wu

The world-changing blockchain technique provides a novel method to establish a secure, trusted and decentralized system for solving the security and personal privacy problems in Industrial Internet of Things (IIoT) applications. The mining process in blockchain requires miners to solve a proof-of-work puzzle, which requires high computational power. However,

Sisi Zhou, Liang Jiang

The quantum Fisher information (QFI), as a function of quantum states, measures the amount of information that a quantum state carries about an unknown parameter. The (entanglement-assisted) QFI of a quantum channel is defined to be the maximum QFI of the output state assuming an entangled input state over a single probe and an ancilla. In quantum metrology,

Jakub Maksymilian Fober

In this paper alternative method for real-time 3D model rasterization is given. Surfaces are drawn in perspective-map space which acts as a virtual camera lens. It can render single-pass 360{\deg} angle of view (AOV) image of unlimited shape, view-directions count and unrestrained projection geometry (e.g. direct lens distortion, projection mapping, curvilin

Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen, Shai Mazor

Optical character recognition (OCR) systems performance have improved significantly in the deep learning era. This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design. That said, deep learning based HTR is limited, as in every other task, by the number

F. L. Dubeibe, Euaggelos E. Zotos, Wei Chen

This paper deals with the derivation and analysis of a seventh-order generalization of the H\'enon-Heiles potential. The new potential has axial and reflection symmetries, and finite escape energy with three channels of escape. Based on SALI indicator and exits basins, the dynamic behavior of the seventh-order system is investigated qualitatively in cases of

Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning

Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sampl

Kyle Kingsbury, Peter Alvaro

Users who care about their data store it in databases, which (at least in principle) guarantee some form of transactional isolation. However, experience shows [Kleppmann 2019, Kingsbury and Patella 2019a] that many databases do not provide the isolation guarantees they claim. With the recent proliferation of new distributed databases, demand has grown for ch

Ifrah Idrees, Steven P. Reiss, Stefanie Tellex

Robots have the potential to improve health monitoring outcomes for the elderly by providing doctors, and caregivers with information about the person's behavior, health activities and their surrounding environment. Over the years, less work has been done to enable robots to preserve information for longer periods of time, on the order of months and years of

Vincent Desjacques, Evgeni Grishin, Yonadav Barry Ginat

Scalar, tensor waves induce oscillatory perturbations in Keplerian systems that can be probed with measurements of pulsar timing residuals. In this paper, we consider the imprint of coherent oscillations produced by ultralight axion dark matter on the Roemer time delay. We use the angle-action formalism to calculate the time evolution of the observed signal

Rui Li, Zach Shahn, Jun Li, Mingyu Lu

Counterfactual prediction is a fundamental task in decision-making. G-computation is a method for estimating expected counterfactual outcomes under dynamic time-varying treatment strategies. Existing G-computation implementations have mostly employed classical regression models with limited capacity to capture complex temporal and nonlinear dependence struct

Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Brian M. Sadler

Bayesian optimization is a framework for global search via maximum a posteriori updates rather than simulated annealing, and has gained prominence for decision-making under uncertainty. In this work, we cast Bayesian optimization as a multi-armed bandit problem, where the payoff function is sampled from a Gaussian process (GP). Further, we focus on action se

Renato Valladares Panaro

Software development innovations and advances in computing have enabled more complex and less costly computations in medical research (survival analysis), engineering studies (reliability analysis), and social sciences event analysis (historical analysis). As a result, many semi-parametric modeling efforts emerged when it comes to time-to-event data analysis

Hannah Bloom, Katharina Lodders, Heng Chen, Chen Zhao

Solar system materials are variably depleted in moderately volatile elements (MVEs) relative to the proto-solar composition. To address the origin of this MVE depletion, we conducted a systematic study of high-precision K isotopic composition on 16 carbonaceous chondrites (CCs) of types CM1-2, CO3, CV3, CR2, CK4-5 and CH3 and 28 ordinary chondrites (OCs) cov

Christopher Essex, Shantanu Basu, Janett Prehl, Karl Heinz Hoffmann

We introduce a new multi-power-law distribution for the Initial Mass Function (IMF) to explore its potential properties. It follows on prior work that introduced mechanisms accounting for mass accretion in star formation, developed within the framework of general evolution equations for the mass distribution of accreting and non-accreting (proto)stars. This

Pierre Ambrosini, Eva Hollemans, Charlotte F. Kweldam, Geert J. L. H. van Leenders

Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning taking into account other tumor growth patterns duri

Kyuhyoun Cho, Jongchul Chae

We infer the depth of the internal sources giving rise to three-minute umbral oscillations. Recent observations of ripple-like velocity patterns of umbral oscillations supported the notion that there exist internal sources exciting the umbral oscillations. We adopt the hypothesis that the fast magnetohydrodynamic (MHD) waves generated at a source below the p

Justin Q. Anderson, P. A. Praveen Janantha, Diego A. Alcala, Mingzhong Wu

We report the clean experimental realization of cubic-quintic complex Ginzburg-Landau physics in a single driven, damped system. Four numerically predicted categories of complex dynamical behavior and pattern formation are identified for bright and dark solitary waves propagating around an active magnetic thin film-based feedback ring: (1) periodic breathing

Han Gao, Jian-Xun Wang

Mathematical modeling and simulation of complex physical systems based on partial differential equations (PDEs) have been widely used in engineering and industrial applications. To enable reliable predictions, it is crucial yet challenging to calibrate the model by inferring unknown parameters/fields (e.g., boundary conditions, mechanical properties, and ope

Ekaterina Artemova, Amir Bakarov, Aleksey Artemov, Evgeny Burnaev

In this paper, our focus is the connection and influence of language technologies on the research in neurolinguistics. We present a review of brain imaging-based neurolinguistic studies with a focus on the natural language representations, such as word embeddings and pre-trained language models. Mutual enrichment of neurolinguistics and language technologies

Benjamin Antieau, Ben Williams

We prove the topological analogue of the period-index conjecture in each dimension away from a small set of primes.

Rong Du, Sindri Magnússon, Carlo Fischione

An important task in the Internet of Things (IoT) is field monitoring, where multiple IoT nodes take measurements and communicate them to the base station or the cloud for processing, inference, and analysis. This communication becomes costly when the measurements are high-dimensional (e.g., videos or time-series data). The IoT networks with limited bandwidt

P. S. Choong, H. Zainuddin, K. T. Chan, Sh. K. Said Husain

In this paper, we demonstrate that higher order singular value decomposition (HOSVD) can be used to identify special states in three qubits by local unitary (LU) operations. Since the matrix unfoldings of three qubits are related to their reduced density matrices, HOSVD simultaneously diagonalizes the one-body reduced density matrices of three qubits. From t

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler

The increasing complexity of computing systems places a tremendous burden on optimizing compilers, requiring ever more accurate and aggressive optimizations. Machine learning offers significant benefits for constructing optimization heuristics but there remains a gap between what state-of-the-art methods achieve and the performance of an optimal heuristic. C

Rita Fioresi, Fabio Gavarini

We investigate the notion of real form of complex Lie superalgebras and supergroups, both in the standard and graded version. Our functorial approach allows most naturally to go from the superalgebra to the supergroup and retrieve the real forms as fixed points, as in the ordinary setting. We also introduce a more general notion of compact real form for Lie

Danijel Grgičin

The electromagnetic potential is the only force relevant to understand polyelectrolytes and it should enables us to reveal all polyelectrolytes properties. We argue that dielectric spectroscopy probes the average dipole moment of the pure water polyelectrolyte probing this way the polyion - counterion potential. From this polyion potential can be evaluated i

André E. Botha, Wynand Dednam

We develop a simple 3-dimensional iterative map model to forecast the global spread of the coronavirus disease. Our model contains at most two fitting parameters, which we determine from the data supplied by the world health organisation for the total number of cases and new cases each day. We find that our model provides a surprisingly good fit to the curre

Daniel Irving Bernstein

We characterize the combinatorial types of symmetric frameworks in the plane that are minimally generically symmetry-forced infinitesimally rigid when the symmetry group consists of rotations and translations. Along the way, we use tropical geometry to show how a construction of Edmonds that associates a matroid to a submodular function can be used to give a

Yue Wei, Jason C. Hsu, Wei Chen, Emily Y. Chew

With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized clinical trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye di

Sichun Sun, Yun-Long Zhang

The axion objects such as axion mini-clusters and axion clouds around spinning black holes induce parametric resonances of electromagnetic waves through the axion-photon interaction. In particular, it has been known that the resonances from the axion with the mass around $10^{-6}$eV may explain the observed fast radio bursts (FRBs). Here we argue that simila

Wuchen Li

We propose to study the Hessian metric of a functional on the space of probability measures endowed with the Wasserstein $2$-metric. We name it transport Hessian metric, which contains and extends the classical Wasserstein-$2$ metric. We formulate several dynamical systems associated with transport Hessian metrics. Several connections between transport Hessi

Eric T. Moore, Johanna L. Turk, William P. Ford, Nathan J. Hoteling

The models and weights of prior trained Convolutional Neural Networks (CNN) created to perform automated isotopic classification of time-sequenced gamma-ray spectra, were utilized to provide source domain knowledge as training on new domains of potential interest. The previous results were achieved solely using modeled spectral data. In this work we attempt

Matt Emschwiller, David Gamarnik, Eren C. Kızıldağ, Ilias Zadik

In the context of neural network models, overparametrization refers to the phenomena whereby these models appear to generalize well on the unseen data, even though the number of parameters significantly exceeds the sample sizes, and the model perfectly fits the in-training data. A conventional explanation of this phenomena is based on self-regularization pro

Hamed Aslani, Davod Khojasteh Salkuyeh, Fatemeh Panjeh Ali Beik

We establish a new iterative method for solving a class of large and sparse linear systems of equations with three-by-three block coefficient matrices having saddle point structure. Convergence properties of the proposed method are studied in details and its induced preconditioner is examined for accelerating the convergence speed of generalized minimal resi

Sam Kaufman-Martin, Nicholas Naclerio, Pedro May, Paolo Luzzatto-Fegiz

Several novel wind energy systems produce wakes with annular cross-sections, which are qualitatively different from the wakes with circular cross-sections commonly generated by conventional horizontal-axis wind turbines and by compact obstacles. Since wind farms use arrays of hundreds of turbines, good analytical wake models are essential for efficient wind

Chris Bamford, Simon Lucas

Access to a fast and easily copied forward model of a game is essential for model-based reinforcement learning and for algorithms such as Monte Carlo tree search, and is also beneficial as a source of unlimited experience data for model-free algorithms. Learning forward models is an interesting and important challenge in order to address problems where a mod

N. Ahmadiniaz, T. E. Cowan, R. Sauerbrey, U. Schramm

Quantum electrodynamics predicts the vacuum to behave as a non-linear medium, including effects such as birefringence. However, for experimentally available field strengths, this vacuum polarizability is extremely small and thus very hard to measure. In analogy to the Heisenberg limit in quantum metrology, we study the minimum requirements for such a detecti

Marco Picariello, Paola Aliani

The data analysis on deaths in the Lombardy Region and of both the provinces of Bergamo and Brescia shows a twofold aspect on the trend of the epidemic: - all the data show a bias linked to the event of March 10th (day for which the Lombardy region data is partial) and the subsequent change in the way in which positive cases and deaths are calculated; - foll

Hansjoerg Albrecher, Martin Bladt, Mogens Bladt

We extend the construction principle of multivariate phase-type distributions to establish an analytically tractable class of heavy-tailed multivariate random variables whose marginal distributions are of Mittag-Leffler type with arbitrary index of regular variation. The construction can essentially be seen as allowing a scalar parameter to become matrix-val

Israel Quiros, Ricardo García-Salcedo, Tame Gonzalez, Jorge Luis Morales Martínez

In this paper we investigate the cosmological dynamics of an up to cubic curvature correction to General Relativity (GR) known as Cosmological Einsteinian Cubic Gravity (CECG), whose vacuum spectrum consists of the graviton exclusively and its cosmology is well-posed as an initial value problem. We are able to uncover the global asymptotic structure of the p

Rajasekaran M, K. Ganapathy Ayappa

Hydration or interfacial water present in biomolecules and inorganic solids have been shown to exhibit a dynamical transition upon supercooling. However, an understanding of the extent of the underlying surface hydrophilicity as well as the local distribution of hydrophilic/hydrophobic patches on the dynamical transition is unexplored. Here, we use molecular

Yujiang Wang, Tobias Ludwig, Bethany Little, Joe H Necus

Quantification of brain morphology has become an important cornerstone in understanding brain structure. Measures of cortical morphology such as thickness and surface area are frequently used to compare groups of subjects or characterise longitudinal changes. However, such measures are often treated as independent from each other. A recently described scalin

Bilgiday Yuce, Patrick Schaumont, Marc Witteman

Embedded software is developed under the assumption that hardware execution is always correct. Fault attacks break and exploit that assumption. Through the careful introduction of targeted faults, an adversary modifies the control-flow or data-flow integrity of software. The modified program execution is then analyzed and used as a source of information leak