November 2019 arXiv papers — page 18

Boris Sturman

The bulk photovoltaic effect (BPVE) -- generation of electric currents by light in noncentrosymmetric materials in the absence of electric fields and gradients -- has been intensively investigated in the end of the last century. The outcomes including all main aspects of this phenomenon were summarized in review and books. A new upsurge of interest to the BP

Nanying Yang, Danila O. Revin, Evgeny P. Vdovin

In the paper we prove (modulo the classification of finite simple groups) an analogue of the famous Baer-Suzuki theorem for the $\pi$-radical of a finite group, where $\pi$ is a set of primes

T. S. Jayram, Vincent Marois, Tomasz Kornuta, Vincent Albouy

Transfer learning has become the de facto standard in computer vision and natural language processing, especially where labeled data is scarce. Accuracy can be significantly improved by using pre-trained models and subsequent fine-tuning. In visual reasoning tasks, such as image question answering, transfer learning is more complex. In addition to transferri

Monowar Hasan, Sibin Mohan, Rodolfo Pellizzoni, Rakesh B. Bobba

We propose a design-time framework (named HYDRA-C) for integrating security tasks into partitioned real-time systems (RTS) running on multicore platforms. Our goal is to opportunistically execute security monitoring mechanisms in a 'continuous' manner -- i.e., as often as possible, across cores, to ensure that security tasks run with as few interruptions as

Chenghao Guo, Zhiyi Huang, Zhihao Gavin Tang, Xinzhi Zhang

This paper explores a theory of generalization for learning problems on product distributions, complementing the existing learning theories in the sense that it does not rely on any complexity measures of the hypothesis classes. The main contributions are two general sample complexity bounds: (1) $\tilde{O} \big( \frac{nk}{\epsilon^2} \big)$ samples are suff

Yi-Chen Chen, Zhaojun Yang, Ching-Feng Yeh, Mahaveer Jain

As one of the major sources in speech variability, accents have posed a grand challenge to the robustness of speech recognition systems. In this paper, our goal is to build a unified end-to-end speech recognition system that generalizes well across accents. For this purpose, we propose a novel pre-training framework AIPNet based on generative adversarial net

Lake Bu, Mihailo Isakov, Michel A. Kinsy

In Internet of Things (IoT) systems with security demands, there is often a need to distribute sensitive information (such as encryption keys, digital signatures, or login credentials, etc.) among the devices, so that it can be retrieved for confidential purposes at a later moment. However, this information cannot be entrusted to any one device, since the fa

Katsuki Chousa, Katsuhito Sudoh, Satoshi Nakamura

Simultaneous machine translation is a variant of machine translation that starts the translation process before the end of an input. This task faces a trade-off between translation accuracy and latency. We have to determine when we start the translation for observed inputs so far, to achieve good practical performance. In this work, we propose a neural machi

Mihailo Isakov, Vijay Gadepally, Karen M. Gettings, Michel A. Kinsy

Deep Neural Network (DNN) workloads are quickly moving from datacenters onto edge devices, for latency, privacy, or energy reasons. While datacenter networks can be protected using conventional cybersecurity measures, edge neural networks bring a host of new security challenges. Unlike classic IoT applications, edge neural networks are typically very compute

Sannidhan MS, Sunil Kumar Aithal, Abhir Bhandary

Computer Vision is considered to be one of the most important areas in research and has focused on developing many applications that has proved to be useful for both research and societal benefits. Today we have been witnessing many of the road mishaps happening just because of the lack of concentration while driving.As a part of avoiding this kind of disast

Xuhui Zhou, Yue Zhang, Leyang Cui, Dandan Huang

Contextualized representations trained over large raw text data have given remarkable improvements for NLP tasks including question answering and reading comprehension. There have been works showing that syntactic, semantic and word sense knowledge are contained in such representations, which explains why they benefit such tasks. However, relatively little w

Hao Liao, Qi-xin Liu, Ze-cheng Huang, Chi Ho Yeung

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users' rating and preferences

Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu

Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mit

Rong-Jun Qin, Jing-Cheng Pang, Yang Yu

Fictitious play with reinforcement learning is a general and effective framework for zero-sum games. However, using the current deep neural network models, the implementation of fictitious play faces crucial challenges. Neural network model training employs gradient descent approaches to update all connection weights, and thus is easy to forget the old oppon

Sandeep Nallan Chakravarthula, Md Nasir, Shao-Yen Tseng, Haoqi Li

Suicide is a major societal challenge globally, with a wide range of risk factors, from individual health, psychological and behavioral elements to socio-economic aspects. Military personnel, in particular, are at especially high risk. Crisis resources, while helpful, are often constrained by access to clinical visits or therapist availability, especially wh

Filipi Nascimento Silva, Aditya Tandon, Diego Raphael Amancio, Alessandro Flammini

The citations process for scientific papers has been studied extensively. But while the citations accrued by authors are the sum of the citations of their papers, translating the dynamics of citation accumulation from the paper to the author level is not trivial. Here we conduct a systematic study of the evolution of author citations, and in particular their

Jonathan Kress, Konrad Schöbel, Andreas Vollmer

Second-order superintegrable systems in dimensions two and three are essentially classified. With increasing dimension, however, the non-linear partial differential equations employed in current methods become unmanageable. Here we propose a new, algebraic-geometric approach to the classification problem - based on a proof that the classification space for i

Heng Yang, Luca Carlone

We study the problem of 3D shape reconstruction from 2D landmarks extracted in a single image. We adopt the 3D deformable shape model and formulate the reconstruction as a joint optimization of the camera pose and the linear shape parameters. Our first contribution is to apply Lasserre's hierarchy of convex Sums-of-Squares (SOS) relaxations to solve the shap

Jose Javier Otero Perez, Richard Sandberg

This article shows the importance of flow compressibility on the heat transfer in confined impinging jets, and how it is driven by both the Mach number and the wall heat-flux. Hence, we present a collection of cases at several Mach numbers with different heat-flux values applied at the impingement wall. The wall temperature scales linearly with the imposed h

Anton Alyakin, Yichen Qin, Carey E. Priebe

A t-test is considered a standard procedure for inference on population means and is widely used in scientific discovery. However, as a special case of a likelihood-ratio test, t-test often shows drastic performance degradation due to the deviations from its hard-to-verify distributional assumptions. Alternatively, in this article, we propose a new two-sampl

Tyler S. Smith, Fangfei Ming, Daniel González Trabada, César González

Two-dimensional melting is one of the most fascinating and poorly understood phase transitions in nature. Theoretical investigations often point to a two-step melting scenario involving unbinding of topological defects at two distinct temperatures. Here we report on a novel melting transition of a charge-ordered K-Sn alloy monolayer on a silicon substrate. M

Limeng Cui

With the prevalence of misinformation online, researchers have focused on developing various machine learning algorithms to detect fake news. However, users' perception of machine learning outcomes and related behaviors have been widely ignored. Hence, this paper proposed to bridge this gap by studying how to pass the detection results of machine learning to

Wenjie Li, Yao Li

The aim of this paper is to investigate various information-theoretic measures, including entropy, mutual information, and some systematic measures that based on mutual information, for a class of structured spiking neuronal network. In order to analyze and compute these information-theoretic measures for large networks, we coarse-grained the data by ignorin

Nserdin A. Ragab, Zhi-Lei She, Gang Chen

Production of light (anti-)nuclei and (anti-)hypertriton within midrapidity ($|y|<0.5$) and $p_T<3.0$ GeV/c in $pp$ interactions at $\sqrt{s}$ = 0.90, 2.76 and 7 TeV is investigated by the dynamically constrained phase space coalescence model (DCPC), combined with {\footnotesize{PACIAE}} model. The ALICE data for yields, ratios, as well as transverse momentu

Cheuk-Yin Wong

Among the states of $^{12}$C, there is an important subset of $K$=0 and $K$=$I$ planar intrinsic states in which the intrinsic motion of the nucleons are confined in the planar region defined by the three-alpha cluster or by their generated toroid. The intrinsic nuclear densities of these states are toroidal in nature. We study these $^{12}$C toroidal states

Jiaye Wu, Boris A. Malomed, H. Y. Fu, Qian Li

Dynamics of femtosecond pulses with the telecom carrier wavelength is investigated numerically in a subwavelength layer of an indium tin oxide (ITO) epsilon-near-zero (ENZ) material with high dispersion and high nonlinearity. Due to the subwavelength thickness of the ITO ENZ material, and the fact that the pulse's propagation time is shorter than its tempora

Francis Baek, Somin Park, Hyoungkwan Kim

Deep learning-based construction-site image analysis has recently made great progress with regard to accuracy and speed, but it requires a large amount of data. Acquiring sufficient amount of labeled construction-image data is a prerequisite for deep learning-based construction-image recognition and requires considerable time and effort. In this paper, we pr

Sachini Jayasekara, Aaron Harwood, Shanika Karunasekera

State-of-the-art distributed stream processing systems such as Apache Flink and Storm have recently included checkpointing to provide fault-tolerance for stateful applications. This is a necessary eventuality as these systems head into the Exascale regime, and is evidently more efficient than replication as state size grows. However current systems use a nom

Tatsuya Yanagisawa, Hiroyuki Hidaka, Hiroshi Amitsuka, Shintaro Nakamura

In this study, ultrasonic measurements were performed on a single crystal of cubic PrNi$_2$Cd$_{20}$, down to a temperature of 0.02 K, to investigate the crystalline electric field ground state and search for possible phase transitions at low temperatures. The elastic constant $(C_{11}-C_{12})/2$, which is related to the $\Gamma_3$-symmetry quadrupolar respo

David J. Schunter,, Regina K. Czech, Douglas P. Holmes

Confined thin structures are ubiquitous in nature. Spatial and length constraints have led to a number of novel packing strategies at both the micro-scale, as when DNA packages inside a capsid, and the macro-scale, seen in plant root development and the arrangement of the human intestinal tract. By varying the arc length of an elastic loop injected into an a

P. Mohan, T. An, J. Yang

The fast-rising blue optical transient AT2018cow indicated unusual early phase characteristics unlike relatively better studied explosive transients. Its afterglow may be produced by either a relativistically beamed (jetted) or intrinsically luminous (non-jetted) ejecta and carries observational signatures of the progenitor and environment. High resolution m

Thor Tepper-García, Joss Bland-Hawthorn, Di Li

We revisit the orbital history of the Triangulum galaxy (M33) around the Andromeda galaxy (M31) in view of the recent Gaia Data Release 2 proper motion measurements for both Local Group galaxies. Earlier studies consider highly idealised dynamical friction, but neglect the effects of dynamical mass loss. We show the latter process to be important using mutua

Wonhee Jeong, Hoseung Jang, Unjong Yu

We propose a method to make a highly clustered complex network within the configuration model. Using this method, we generated highly clustered random regular networks and analyzed the properties of them. We show that highly clustered random regular networks with appropriate parameters satisfy all the conditions of the small-world network: connectedness, hig

Alexander V. Proskurin, Anatoly M. Sagalakov

In the article the authors present a numerical method for modelling a laminar-turbulent transition in magnetohydrodynamic flows. The equations in the small magnetic Reynolds numbers approach is considered. Speed, pressure and electrical potential are decomposed to the sum of the state values and the finite amplitude perturbations. A solver based on the Necta

Gan Sun, Yang Cong, Qianqian Wang, Jun Li

In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we aim to explore the problem of spectral

Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo

Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from

Max Carlson, Robert M. Kirby, Hari Sundar

The study of fractional order differential operators is receiving renewed attention in many scientific fields. In order to accommodate researchers doing work in these areas, there is a need for highly scalable numerical methods for solving partial differential equations that involve fractional order operators on complex geometries. These operators have desir

H. Ruan, R. C. de Lamare

In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the s

Edward Goldstein

Background: Information on influenza-associated mortality in Russia is limited. Methods: Using previously developed methodology (Goldstein et al., Epidemiology 2012), we regressed the monthly rates of mortality for respiratory causes, as well as circulatory causes linearly against the monthly proxies for the incidence of influenza A/H3N2, A/H1N1 and B (obtai

SuperCDMS Collaboration, T. Aralis, T. Aramaki, I. J. Arnquist

We present an analysis of electron recoils in cryogenic germanium detectors operated during the SuperCDMS Soudan experiment. The data are used to set new constraints on the axioelectric coupling of axion-like particles and the kinetic mixing parameter of dark photons, assuming the respective species constitutes all of the galactic dark matter. This study cov

Akio Kawauchi

Every smooth homotopy 4-sphere is diffeomorphic to the 4-sphere.

Subhayan Mukherjee, Giuseppe Valenzise, Irene Cheng

Image Quality Assessment algorithms predict a quality score for a pristine or distorted input image, such that it correlates with human opinion. Traditional methods required a non-distorted "reference" version of the input image to compare with, in order to predict this score. However, recent "No-reference" methods circumvent this requirement by modelling th

Tommaso Ghigna, Tomotake Matsumura, Masashi Hazumi, Samantha Lynn Stever

LiteBIRD is a proposed JAXA satellite mission to measure the CMB B-mode polarization with unprecedented sensitivity ($\sigma_r\sim 0.001$). To achieve this goal, $4676$ state-of-the-art TES bolometers will observe the whole sky for 3 years from L2. These detectors, as well as the SQUID readout, are extremely susceptible to EMI and other instrumental disturba

Joseph Gatto, Ravi Lanka, Yumi Iwashita, Adrian Stoica

Have you ever wondered how your feature space is impacting the prediction of a specific sample in your dataset? In this paper, we introduce Single Sample Feature Importance (SSFI), which is an interpretable feature importance algorithm that allows for the identification of the most important features that contribute to the prediction of a single sample. When

Samuel Nkrumah

Predicting the three-dimensional (3D) functional structures of proteins remains an important computational milestone in molecular biology to be achieved. This feat is hinged on a clear understanding of the mechanism which proteins use to fold into their native structures. Since Levinthal's paradox, there has been a lot of progress in understanding this mecha

Yang Li, Guodong Long, Tao Shen, Tianyi Zhou

Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to denoise from wrongly labeled data, which however could be incompetent when there is only one sentence in a bag. In this paper, we propose a bran

Xiancong Lu, David Sénéchal

We investigate the proximity effect in a heterostructure of the topological insulator (TI) \BiSe\ deposited on the HTSC material BSCCO. The latter is described by the one-band Hubbard model and is treated with cluster dynamical mean field theory (CDMFT), the TI layers being included via the CDMFT self-consistency loop. The penetration of superconductivity in

Hao Tang, Hong Liu, Dan Xu, Philip H. S. Torr

State-of-the-art methods in image-to-image translation are capable of learning a mapping from a source domain to a target domain with unpaired image data. Though the existing methods have achieved promising results, they still produce visual artifacts, being able to translate low-level information but not high-level semantics of input images. One possible re

Andrey Melnikov

Backus (1962) developed his technique for homogenization of a layered structure solely within the context of linear elastic theory. In this paper we propose an extended use of Backus average for finitely deformed materials of a layered structure. We attempt to use two different approaches to account for large deformations. The first approach utilizes the con

Vahid Sazgari, Gerard Sullivan, Ismet I. Kaya

We present magneto-transport study in an InAs/GaSb double quantum well structure in the weak localization regime. As the charge carriers are depleted using a top gate electrode, we observe a crossover from weak anti-localization (WAL) to weak localization (WL), when the inelastic phase breaking time decreases below spin-orbit characteristic time as a result

Emery D. Berger, Petr Maj, Olga Vitek, Jan Vitek

Ray, Devanbu and Filkov issued a rebuttal of our TOPLAS paper "On the Impact of Programming Languages on Code Quality: A Reproduction Study". Our paper reproduced "A Large-Scale Study of Programming Languages and Code Quality in GitHub", which appeared at FSE 2014 and was subsequently republished as a CACM research highlight in 2017. This article is a rebutt

Pradyumna Chari, Chinmay Talegaonkar, Yunhao Ba, Achuta Kadambi

In this paper, we teach a machine to discover the laws of physics from video streams. We assume no prior knowledge of physics, beyond a temporal stream of bounding boxes. The problem is very difficult because a machine must learn not only a governing equation (e.g. projectile motion) but also the existence of governing parameters (e.g. velocities). We evalua

E. Bratkovskaya, J. Aichelin, A. Le Fevre, V. Kireyeu

We present the novel microscopic n-body dynamical transport approach PHQMD(Parton-Hadron-Quantum-Molecular-Dynamics) for the description of particle production and cluster formation in heavy-ion reactions at relativistic energies. The PHQMD extends the established PHSD (Parton-Hadron-String-Dynamics) transport approach by replacing the mean field by density

Ali Hyder, Yannick Sire

This paper is devoted to the construction of weak solutions to the singular constant $Q$-curvature problem. We build on several tools developed in the last years. This is the first construction of singular metrics on closed manifolds of sufficiently large dimension with constant (positive) $Q$-curvature.

Soosan Beheshti, Edward Nidoy, Faizan Rahman

Determining the correct number of clusters (CNC) is an important task in data clustering and has a critical effect on finalizing the partitioning results. K-means is one of the popular methods of clustering that requires CNC. Validity index methods use an additional optimization procedure to estimate the CNC for K-means. We propose an alternative validity in

Fateme Esmailie, Mathieu Francoeur, Tim Ameel

Magnetic cochlear implant surgery requires removal of a magnet via a heating process after implant insertion, which may cause thermal trauma within the ear. Intra-cochlear heat transfer analysis is required to ensure that the magnet removal phase is thermally safe. The objective of this work is to determine the safe range of input power density to detach the

Hugh Chen, Scott Lundberg, Su-In Lee

In healthcare, making the best possible predictions with complex models (e.g., neural networks, ensembles/stacks of different models) can impact patient welfare. In order to make these complex models explainable, we present DeepSHAP for mixed model types, a framework for layer wise propagation of Shapley values that builds upon DeepLIFT (an existing approach

Sara L. Ellison, Mallory D. Thorp, Lihwai Lin, Hsi-An Pan

Using a sample of 11,478 spaxels in 34 galaxies with molecular gas, star formation and stellar maps taken from the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we investigate the parameters that correlate with variations in star formation rates on kpc scales. We use a combination of correlation statistics and an artificial neural network to qu

Taushif Ahmed, Pulak Banerjee, Amlan Chakraborty, Prasanna K. Dhani

We present the first calculations of two-point two-loop form factors (FFs) with a two identical operators insertion in maximally supersymmetric Yang-Mills theory. In this article, we consider the supersymmetry protected half-BPS primary and unprotected Konishi operators. Unlike the FFs of a single operator insertion of the half-BPS primary, the FFs involving

Chia-Feng Chang, Yanou Cui

A zero initial velocity of the axion field is assumed in the conventional misalignment mechanism. We propose an alternative scenario where the initial velocity is nonzero, which may arise from an explicit breaking of the PQ symmetry in the early Universe. We demonstrate that, depending on the specifics about the initial velocity and the time order of the PQ

Sumit Vashishtha, Siddhartha Verma

A catastrophic bifurcation in non-linear dynamical systems, called crisis, often leads to their convergence to an undesirable non-chaotic state after some initial chaotic transients. Preventing such behavior has proved to be quite challenging. We demonstrate that deep Reinforcement Learning (RL) is able to restore chaos in a transiently-chaotic regime of the

Shu-Hao Yeh, Dezhen Song

Robust estimation of camera motion under the presence of outlier noise is a fundamental problem in robotics and computer vision. Despite existing efforts that focus on detecting motion and scene degeneracies, the best existing approach that builds on Random Consensus Sampling (RANSAC) still has non-negligible failure rate. Since a single failure can lead to

Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi

In this paper, the efficient hinging hyperplanes (EHH) neural network is proposed based on the model of hinging hyperplanes (HH). The EHH neural network is a distributed representation, the training of which involves solving several convex optimization problems and is fast. It is proved that for every EHH neural network, there is an equivalent adaptive hingi

Roel Dobbe, Werner van Westering, Stephan Liu, Daniel Arnold

Implementing state estimation in low and medium voltage power distribution is still challenging given the scale of many networks and the reliance of traditional methods on a large number of measurements. This paper proposes a method to improve voltage predictions in real-time by leveraging a limited set of real-time measurements. The method relies on Bayesia

Annelies De Meulenaere, Sonja Hohloch

We construct a 1-parameter family $F_t=(J, H_t)_{0 \leq t \leq 1}$ of integrable systems on a compact $4$-dimensional symplectic manifold $(M, \omega)$ that changes smoothly from a toric system $F_0$ with eight elliptic-elliptic singular points via toric type systems to a semitoric system $F_t$ for $ t^- < t < t^+$. These semitoric systems $F_t$ have precise

Daniel Rubin, Alex Townsend, Heather Wilber

By closely following a construction by Ganelius, we construct Faber rational functions that allow us to derive tight and explicit bounds on Zolotarev numbers. We use our results to bound the singular values of matrices, including complex-valued Cauchy matrices and Vandermonde matrices with nodes inside the unit disk. We construct Faber rational functions usi

Chao Tang, Yifei Fan, Anthony Yezzi

The safety and robustness of learning-based decision-making systems are under threats from adversarial examples, as imperceptible perturbations can mislead neural networks to completely different outputs. In this paper, we present an adaptive view of the issue via evaluating various test-time smoothing defense against white-box untargeted adversarial example

Yinheng Li, Junhao Wang, Yijie Cao

Portfolio management is the art and science in fiance that concerns continuous reallocation of funds and assets across financial instruments to meet the desired returns to risk profile. Deep reinforcement learning (RL) has gained increasing interest in portfolio management, where RL agents are trained base on financial data to optimize the asset reallocation

Beñat Mencia Uranga, Austen Lamacraft

We introduce Schr\"odingeRNN, a quantum inspired generative model for raw audio. Audio data is wave-like and is sampled from a continuous signal. Although generative modelling of raw audio has made great strides lately, relational inductive biases relevant to these two characteristics are mostly absent from models explored to date. Quantum Mechanics is a nat

L. M. Arutyunyan

We denote as an integral Remez inequality an inequality of the form $$ \|f\|_{L^{1}(\mu)} \le C(\Omega,\mu(A), X) \|f\|_{L^{1}(\mu_{A})}, $$ where $\mu_A$ is the normalised restriction of a measure $\mu$ to a set $A$. Let $\mu$ be the uniform distribution over a convex body A and $f$ be a polynomial of degree $d$. One can choose $C$ independent of the dimens

Anshul Choudhary, John F. Lindner, Elliott G. Holliday, Scott T. Miller

Conventional artificial neural networks are powerful tools in science and industry, but they can fail when applied to nonlinear systems where order and chaos coexist. We use neural networks that incorporate the structures and symmetries of Hamiltonian dynamics to predict phase space trajectories even as nonlinear systems transition from order to chaos. We de

Sushma Kurapati, Jayaram N. Chengalur, Peter Kamphuis, Simon Pustilnik

We construct mass models of eight gas rich dwarf galaxies that lie in the Lynx-Cancer void. From NFW fits to the dark matter halo profile, we find that the concentration parameters of halos of void dwarf galaxies are similar to those of dwarf galaxies in normal density regions. We also measure the slope of the central dark matter density profiles, obtained b

Sema Candemir, Richard D. White, Mutlu Demirer, Vikash Gupta

We propose a fully automated algorithm based on a deep learning framework enabling screening of a coronary computed tomography angiography (CCTA) examination for confident detection of the presence or absence of coronary artery atherosclerosis. The system starts with extracting the coronary arteries and their branches from CCTA datasets and representing them

Raul Castro Fernandez, Nan Tang, Mourad Ouzzani, Michael Stonebraker

Many data problems are solved when the right view of a combination of datasets is identified. Finding such a view is challenging because of the many tables spread across many databases, data lakes, and cloud storage in modern organizations. Finding relevant tables, and identifying how to combine them is a difficult and time-consuming process that hampers use

E. Epelbaum, H. Krebs, P. Reinert

We review a new generation of nuclear forces derived in chiral effective field theory using the recently proposed semilocal regularization method. We outline the conceptual foundations of nuclear chiral effective field theory, discuss all steps needed to compute nuclear observables starting from the effective chiral Lagrangian and consider selected applicati

Alexander Roitershtein, Reza Rastegar, Robert S. Chapkin, Ivan Ivanov

We study a generalized discrete-time multi-type Wright-Fisher population process. The mean-field dynamics of the stochastic process is induced by a general replicator difference equation. We prove several results regarding the asymptotic behavior of the model, focusing on the impact of the mean-field dynamics on it. One of the results is a limit theorem that

Srijani Mallik, Purbasha Sharangi, Biswajit Sahoo, Stefan Mattauch

The interface between organic semiconductor [OSC]/ferromagnetic [FM] material can exhibit ferromagnetism due to their orbital hybridization. Charge/spin transfer may occur from FM to OSC layer leading to the formation of `spinterface' i.e. the interface exhibiting a finite magnetic moment. In this work, the magnetic properties of Co/C$_{60}$ bilayer thin fil

Ahmed Hosny, Michael Schwier, Christoph Berger, Evin P Örnek

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others. Additionally, the availability of open-source computational frameworks has lowered the barriers to implementing state-of-the-art methods across multiple domains. Albeit lea

Manu Goyal, Thomas Knackstedt, Shaofeng Yan, Saeed Hassanpour

Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist c

S. Nagorny, C. Rusconi, S. Sorbino, J. W. Beeman

The growing interest in clarifying the controversial situation in the Dark Matter sector has driven the experimental efforts towards new ways to investigate the long-standing DAMA/LIBRA result. Among them, low-temperature calorimeters based on Na-containing scintillating crystals offer the possibility to clarify the nature of the measured signal via particle

Assia Abdellaoui, Farid Benamira

Li\'enard-type nonlinear one-dimensional oscillator is quantized using van Roos symmetric ordering recipe for the kinetic-like part of the new derived Hamiltonian. The corresponding Schr\"odinger equation is exactly solved in momuntum space via the approach of supersymmetric quantum mechanics (SUSYQM). The bound-states energy spectra and corresponding wave f

Yu Wang, Siddhartha Nalluri, Miroslav Pajic

There is a growing interest on formal methods-based robotic planning for temporal logic objectives. In this work, we extend the scope of existing synthesis methods to hyper-temporal logics. We are motivated by the fact that important planning objectives, such as optimality, robustness, and privacy, (maybe implicitly) involve the interrelation between multipl

Li Li

We introduce the fractional magnetic operator involving a magnetic potential and an electric potential. We formulate an inverse problem for the fractional magnetic operator. We determine the electric potential from the exterior partial measurements of the associated Dirichlet-to-Neumann map by using Runge approximation property.

Greg Bodwin, Santosh Vempala

We prove algorithmic weak and \Szemeredi{} regularity lemmas for several classes of sparse graphs in the literature, for which only weak regularity lemmas were previously known. These include core-dense graphs, low threshold rank graphs, and (a version of) $L^p$ upper regular graphs. More precisely, we define \emph{cut pseudorandom graphs}, we prove our regu

Nick Bultinck

In this work we consider general fermion systems in two spatial dimensions, both with and without charge conservation symmetry, which realize a nontrivial fermionic topological order with only Abelian anyons. We address the question of precisely how these quantum phases differ from their bosonic counterparts, both in terms of their edge physics and in the wa

Ben Green

We show that rounding to a delta-net in SO(3) is not close to a group operation, thus confirming a conjecture of Gowers and Long.

C. L. Baldwin, B. Swingle

We show that any SYK-like model with finite-body interactions among \textit{local} degrees of freedom, e.g., bosons or spins, has a fundamental difference from the standard fermionic model: the former fails to be described by an annealed free energy at low temperature. In this respect, such models more closely resemble spin glasses. We demonstrate this by tw

Paromita Dubey, Hans-Georg Müller

We propose a method to infer the presence and location of change-points in the distribution of a sequence of independent data taking values in a general metric space, where change-points are viewed as locations at which the distribution of the data sequence changes abruptly in terms of either its Fr\'echet mean or Fr\'echet variance or both. The proposed met

Limeng Cui, Siddharth Biswal, Lucas M. Glass, Greg Lever

Rare diseases affect hundreds of millions of people worldwide but are hard to detect since they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and are massively underdiagnosed. How do we reliably detect rare diseases with such low prevalence rates? How to further leverage patients with possibly uncertain diagnosis to improve

Flor Aguilar, Gabriela Araujo-Pardo, Natalia García-Colín

Let $G$ be a cubic graph and $\Pi$ be a polyhedral embedding of this graph. The extended graph, $G^{e},$ of $\Pi$ is the graph whose set of vertices is $V(G^{e})=V(G)$ and whose set of edges $E(G^{e})$ is equal to $E(G) \cup \mathcal{S}$, where $\mathcal{S}$ is constructed as follows: given two vertices $t_0$ and $t_3$ in $V(G^{e})$ we say $[t_0 t_3] \in \ma

Maíra Dutra

The out-of-equilibrium production of dark matter (DM) from standard model (SM) species in the early universe (freeze-in mechanism) is expected in many scenarios in which very heavy beyond the SM fields act as mediators. In this conference, I have talked about the freeze-in of scalar, fermionic and vector DM though the exchange of moduli fields \cite{chowdhur

Hildeberto Jardon-Kojakhmetov, Christian Kuehn

Canard cycles are periodic orbits that appear as special solutions of fast-slow systems (or singularly perturbed Ordinary Differential Equations). It is well known that canard cycles are difficult to detect, hard to reproduce numerically, and that they are sensible to exponentially small changes in parameters. In this paper we combine techniques from geometr

Kinwah Wu, Kaye Jiale Li, Ellis R. Owen, Li Ji

Large-scale outflows from starburst galaxies are multi-phase, multi-component fluids. Charge-exchange lines which originate from the interfacing surface between the neutral and ionised components are a useful diagnostic of the cold dense structures in the galactic outflow. From the charge-exchange lines observed in the nearby starburst galaxy M82, we conduct

David J. Schunter,, Matthew Boucher, Douglas P. Holmes

Frustration arises for a broad class of physical systems where confinement (geometric) or the presence of a perturbation (kinematic) prevents equilibration to a minimum energy state. By varying the diameter ratio and packing fraction in granular arrays surrounding a slowly elongating elastica, we characterize the resulting elastogranular interactions taking

Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal

Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics. However, this problem is also #P-complete, which leaves little hope for finding an exact solution that can be computed efficiently. While the problem admits a fully polynomial randomized approximation sche

Erfan Ebrahim Esfahani, Alireza Hosseini

Inspired by the first-order method of Malitsky and Pock, we propose a new variational framework for compressed MR image reconstruction which introduces the application of a rotation-invariant discretization of total variation functional into MR imaging while exploiting BM3D frame as a sparsifying transform. In the first step, we provide theoretical and numer

Jun Hou Fung

We provide computational tools to calculate the strict units of commutative ring spectra. We describe the Goerss-Hopkins-Miller spectral sequence for computing strict units of $E_\infty$-$H\mathbb{F}_p$-algebras, and use it to compute the strict units of polynomial and truncated polynomial rings, whose Postnikov towers we also analyze. We then sketch the cal

James A. Klimchuk

For some forms of steady heating, coronal loops are in a state of thermal nonequilibrium and evolve in a manner that includes accelerated cooling, often resulting in the formation of a cold condensation. This is frequently confused with thermal instability, but the two are in fact fundamentally different. We explain the distinction and discuss situations whe

Hermano Velten, Syrios Gomes

Recently Risaliti \& Lusso [Nature Astron. 3 (2019) 3 272] reported new measurements of the expansion rate of the Universe by constructing the Hubble diagram of 1598 quasars in the redshift range $0.5<z<5.5$. It is claimed a $4\sigma$ tension with the standard concordance $\Lambda$CDM concerning both the fractionary matter density $\Omega_{m0}$ and the dark

Hassene Aissi, S. Thomas McCormick, Maurice Queyranne

The parametric global minimum cut problem concerns a graph $G = (V,E)$ where the cost of each edge is an affine function of a parameter $\mu \in \mathbb{R}^d$ for some fixed dimension $d$. We consider the problems of finding the next breakpoint in a given direction, and finding a parameter value with maximum minimum cut value. We develop strongly polynomial