April 2019 arXiv papers — page 27

Runyu Zhang, Chaoshu Yang

Existing path lookup routines in file systems need to construct an auxiliary index in memory or traverse the dentries of the directory file sequentially, which brings either heavy writes or large timing cost. This paper designs a novel path lookup mechanism, Content-Indexed Browsing (CIB), for file systems on persistent memory, in which the structure of dire

Eloi Zablocki, Patrick Bordes, Benjamin Piwowarski, Laure Soulier

Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging auxiliary knowledge, such as semantic representations. A limitation of previous approaches is that only intrinsic properties of objects, e.g. their visual appearance, are taken into account while their context, e.g. the surrounding objects in the image, is ignored. Following the int

Raúl González-Jiménez, Alexis Nikolakopoulos, Natalie Jachowicz, José Manuel Udías

We study the impact of the description of the knockout nucleon wave function on electron- and neutrino-induced quasielastic and single-pion production cross sections. We work in a fully relativistic and quantum mechanical framework, where the relativistic mean-field model is used to describe the target nucleus. The focus is on Pauli blocking and the distorti

Vladimir Petrov Kostov

A hyperbolic polynomial (HP) is a real univariate polynomial with all roots real. By Descartes' rule of signs a HP with all coefficients nonvanishing has exactly $c$ positive and exactly $p$ negative roots counted with multiplicity, where $c$ and $p$ are the numbers of sign changes and sign preservations in the sequence of its coefficients. For $c=1$ and $2$

Juan Yao, Yadong Wu, Jahyun Koo, Binghai Yan

In large-scale computation of physics problems, one often encounters the problem of determining a multi-dimensional function, which can be time-consuming when computing each point in this multi-dimensional space is already time-demanding. In the work, we propose that the active learning algorithm can speed up such calculations. The basic idea is to fit a mul

José A. Carrasco, Giuseppe Marmo, Piergiulio Tempesta

We present a mathematical construction of new quantum information measures that generalize the notion of logarithmic negativity. Our approach is based on formal group theory. We shall prove that this family of generalized negativity functions, due their algebraic properties, is suitable for studying entanglement in many-body systems. Under mild hypotheses, t

Eli Aljadeff, Darrell Haile, Yakov Karasik

Let $k$ be a field containing an algebraically closed field of characteristic zero. If $G$ is a finite group and $D$ is a division algebra over $k$, finite dimensional over its center, we can associate to a faithful $G$-grading on $D$ a normal abelian subgroup $H$, a positive integer $d$ and an element of $Hom(M(H), k^\times)^G$, where $M(H)$ is the Schur mu

Ya-Dong Wu, Barry C. Sanders

We aim to devise feasible, efficient verification schemes for bosonic channels. To this end, we construct an average-fidelity witness that yields a tight lower bound for average fidelity plus a general framework for verifying optimal quantum channels. For both multi-mode unitary Gaussian channels and single-mode amplification channels, we present experimenta

Y. Matsubara

We study the moduli space of logarithmic connections of rank $2$ on $\mathbb{P}^1 \setminus \{ t_1, \dots, t_5 \}$ with fixed spectral data. The aim of this paper is to compute the cohomology of such space, and this computation will be used to extend the results of Geometric Langlands Correspondence due to D. Arinkin to the case where this type of connection

Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari

Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approach for assessing the generalizability of a model, but unfortunately, LOO does not scale well to large datasets. We propose a combination of using approximate inference techniques an

Konstantinos Drossos, Paul Magron, Tuomas Virtanen

A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the acoustic scene classification (ASC) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel

Jacques Darné

We consider the group of pure welded braids (also known as loop braids) up to (link-)homotopy. The pure welded braid group classically identifies, via the Artin action, with the group of basis-conjugating automorphisms of the free group, also known as the McCool group P $\Sigma$ n. It has been shown recently that its quotient by the homotopy relation identif

Kiwamu Yoshii, Kojiro Otoguro, Ayane Pygoscelis Sato, Yutaka Sumino

The injection of a fluid into another fluid causes a spatiotemporal pattern along the injection front. Viscous fingering is a well-known example when the replaced material is a viscous fluid. Notably, most fluids are, in reality, viscoelastic, i.e., they behave as an elastic solid over short timescales. For this reason, it is important to study the situation

Soumendra Nath Panja, Pascal Manuel, Sunil Nair

We report temperature dependent magnetization and neutron diffraction measurements on the corundum related magnetoelectric antiferromagnet Mn$_{4}$Ta$_{2}$O$_{9}$. Measurements performed on a single crystalline specimen reveal that the magnetization is anisotropic, and a weak ferromagnetic component emerges well within the antiferromagnetically ordered state

Monique Dauge, Michael A. Hall, San Vu Ngoc

This article introduces the notion of good labellings for asymptotic lattices in order to study joint spectra of quantum integrable systems from the point of view of inverse spectral theory. As an application, we consider a new spectral quantity for a quantum integrable system, the quantum rotation number. In the case of two degrees of freedom, we obtain a c

Ryuichi Sakamoto

The cut polytope of a graph is an important object in several fields, such as functional analysis, combinatorial optimization, and probability. For example, Sturmfels and Sullivant showed that the toric ideals of cut polytopes are useful in algebraic statistics. In the theory of lattice polytopes, the $h^{*}$-polynomial is one of the most important invariant

Hsu-kuang Chiu, Ehsan Adeli, Juan Carlos Niebles

Predicting the future is an important aspect for decision-making in robotics or autonomous driving systems, which heavily rely upon visual scene understanding. While prior work attempts to predict future video pixels, anticipate activities or forecast future scene semantic segments from segmentation of the preceding frames, methods that predict future semant

Benjamin K. Smeltzer, Eirik Æsøy, Anna Ådnøy, Simen Å. Ellingsen

A new inversion method for determining near-surface shear currents from a measured wave spectrum is introduced. The method is straightforward to implement and starts from the existing state-of-the-art technique of assigning effective depths to measured wavenumber-dependent Doppler shift velocities. A polynomial fit is performed, with the coefficients scaled

Manuela Bastidas, Carina Bringedal, Sorin Pop, Florin Radu

We propose an efficient numerical strategy for simulating fluid flow through porous media with highly oscillatory characteristics. Specifically, we consider non-linear diffusion models. This scheme is based on the classical homogenization theory and uses a locally mass-conservative formulation. In addition, we discuss some properties of the standard non-line

Conrad J. Burden, Albert C. Soewongsono

We consider the problem of estimating the elapsed time since the most recent common ancestor of a finite random sample drawn from a population which has evolved through a Bienayme-Galton-Watson branching process. More specifically, we are interested in the diffusion limit appropriate to a supercritical process in the near-critical limit evolving over a large

Zhen Tian, Wei Liu, Bo Yang, Xue-Dong Fu

The spatial-dependent propagation model has been successfully used to explain diverse observational phenomena, including the spectral hardening of cosmic-ray nuclei above $200$ GV, the large-scale dipole anisotropy and the diffusive gamma distribution. In this work, we further apply the spatial-dependent propagation model to both electrons and positrons. To

Cyril Letrouit

We address the problem of catching all speed $1$ geodesics of a Riemannian manifold with a moving ball: given a compact Riemannian manifold $(M,g)$ and small parameters $\varepsilon>0$ and $v>0$, is it possible to find $T>0$ and an absolutely continuous map $x:[0,T]\rightarrow M, t\mapsto x(t)$ satisfying $\|\dot{x}\|_{\infty}\leq v$ and such that any geodes

Nayana Gautam, Mrinal Kumar Das

We study the possibility of simultaneously addressing neutrino phenomenology and the dark matter in the framework of inverse seesaw. The model is the extension of the standard model by the addition of two right handed neutrinos and three sterile fermions which leads to a light sterile state with the mass in the keV range along with three light active neutrin

Zeren Simon Wang, Kechen Wang

Future lepton colliders such as the CEPC and FCC-ee would run as high-luminosity $Z-$boson factories, which offer a unique opportunity to study long-lived particles which couple to Z-bosons. In order to exemplify this particular advantage, in this work we consider one benchmark physics scenario where the long-lived lightest neutralinos pair $(\tilde{\chi}_1^

Chiara Amorino, Arnaud Gloter

The problem of integrated volatility estimation for the solution X of a stochastic differential equation with L{\'e}vy-type jumps is considered under discrete high-frequency observations in both short and long time horizon. We provide an asymptotic expansion for the integrated volatility that gives us, in detail, the contribution deriving from the jump part.

Matthieu Barreau, Frédéric Gouaisbaut, Alexandre Seuret

This paper deals with the exponential stability of a drilling pipe controlled by a PI controller. The model used leads to a coupled ODE / PDE and is consequently of infinite dimension. Using recent advances in time-delay systems, we derive a new Lyapunov functional based on an state extension made up of projections of the Riemann coordinates. Two cases will

Chong-Xing Yue, Ming-Ze Liu, Yu-Chen Guo

We explore the possibility of searching for axionlike particle (ALP) by $ep$ collisions via the subprocess $e^{-}γ\rightarrow e^{-}a \rightarrow e^{-}γγ$. Sensitivities to the effective ALP-photon coupling $g_{aγγ}$ for its mass in the range of 10 GeV $< M_{a}< 3$ TeV are obtained for the LHeC and its high-energy upgrade, FCC-eh. Comparing to existing bounds

Raul Fuentes-Azcatl, Hector Dominguez

Most of the existing classical CO$_2$ models fail to reproduce some or many experimental properties such as surface tension, vapor pressure, density and dielectric constant at difference thermodynamic conditions. Therfore, it is proposed a new computational model to capture better structural, dynamical and thermodynamic properties for CO$_2$ . By scaling the

Xuan Zhu, Yue Cheng, Jinye Peng, Rongzhi Wang

Generative adversarial network (GAN) for image super-resolution (SR) has attracted enormous interests in recent years. However, the GAN-based SR methods only use image discriminator to distinguish SR images and high-resolution (HR) images. Image discriminator fails to discriminate images accurately since image features cannot be fully expressed. In this pape

Xu Zhu

In an episodic Markov Decision Process (MDP) problem, an online algorithm chooses from a set of actions in a sequence of $H$ trials, where $H$ is the episode length, in order to maximize the total payoff of the chosen actions. Q-learning, as the most popular model-free reinforcement learning (RL) algorithm, directly parameterizes and updates value functions

Kavita Yadav, Mohit K. Sharma, Sanjay Singh, K. Mukherjee

We present a detailed study of structural, magnetic and thermodynamic properties of a series of Heusler alloys Fe2-xMnxCrAl (x=0, 0.25, 0.5, 0.75 and 1). Structural investigation of this series is carried out using high resolution synchrotron X-ray diffraction. Results suggest that with increasing Mn concentration, the L21 structure of Fe2CrAl is destabilize

A. D. Ioffe

The key element of the approach to the theory of necessary conditions in optimal control discussed in the paper is reduction of the original constrained problem to unconstrained minimization with subsequent application of a suitable mechanism of local analysis to characterize minima of (necessarily nonsmooth) functionals that appear after reduction. Using un

Jozef Genzor, Andrej Gendiar, Tomotoshi Nishino

Phase transition of the classical Ising model on the Sierpi\'{n}ski carpet, which has the fractal dimension $\log_3^{~} 8 \approx 1.8927$, is studied by an adapted variant of the higher-order tensor renormalization group method. The second-order phase transition is observed at the critical temperature $T_{\rm c}^{~} \approx 1.478$. Position dependence of loc

Tao Li

This work showcases a new approach for causal discovery by leveraging user experiments and recent advances in photo-realistic image editing, demonstrating a potential of identifying causal factors and understanding complex systems counterfactually. We introduce the beauty learning problem as an example, which has been discussed metaphysically for centuries a

Nicolas Crampé, Luc Vinet, Alexei Zhedanov

We introduce Heun algebras of Lie type. They are obtained from bispectral pairs associated to simple or solvable Lie algebras of dimension three or four. For $\mathfrak{su}(2)$, this leads to the Heun-Krawtchouk algebra. The corresponding Heun-Krawtchouk operator is identified as the Hamiltonian of the quantum analogue of the Zhukovski-Voltera gyrostat. For

Daniel J. Rankin, Lydéric Bocquet, David M. Huang

Transport of liquid mixtures through porous membranes is central to processes such as desalination, chemical separations and energy harvesting, with ultrathin membranes made from novel 2D nanomaterials showing exceptional promise. Here we derive, for the first time, general equations for the solution and solute fluxes through a circular pore in an ultrathin

Yonghao He, Dezhong Xu, Lifang Wu, Meng Jian

Face detection, as a fundamental technology for various applications, is always deployed on edge devices which have limited memory storage and low computing power. This paper introduces a Light and Fast Face Detector (LFFD) for edge devices. The proposed method is anchor-free and belongs to the one-stage category. Specifically, we rethink the importance of r

Nikolaj Tatti

We consider the problem of defining the significance of an itemset. We say that the itemset is significant if we are surprised by its frequency when compared to the frequencies of its sub-itemsets. In other words, we estimate the frequency of the itemset from the frequencies of its sub-itemsets and compute the deviation between the real value and the estimat

Nimit S. Sohoni, Christopher R. Aberger, Megan Leszczynski, Jian Zhang

Memory is increasingly often the bottleneck when training neural network models. Despite this, techniques to lower the overall memory requirements of training have been less widely studied compared to the extensive literature on reducing the memory requirements of inference. In this paper we study a fundamental question: How much memory is actually needed to

C. A. Bertulani

I present a brief summary of the first three decades of studies of pygmy resonances in nuclei and their relation to the symmetry energy of nuclear matter. I discuss the first experiments and theories dedicated to study the electromagnetic response in halo nuclei and how a low energy peak was initially identified as a candidate for the pygmy resonance. This i

Hao Sun, Xianxu Zeng, Tao Xu, Gang Peng

Uterine cancer, also known as endometrial cancer, can seriously affect the female reproductive organs, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. However, due to the limited capability of modeling the complicated relationships between histopathological images and their interpretations, these computer-aided di

Mohammad Bahrami, Narges Chinichian, Ali Hosseiny, Gholamreza Jafari

General Motors or a local business, which one is better to be stimulated in post-crisis recessions, where government stimulation is meant to overcome recessions? Due to the budget constraints, it is quite relevant to ask how one can increase the chance of economic recovery. One of the key elements to answer this question is to understand metastable features

Nguyen Thanh Phuc, Akihito Ishizaki

The precise determination of the excitation energies in condensed-phase molecular systems is important for understanding system-environment interactions as well as for the prerequisite input data of theoretical models used to study the dynamics of the system. The excitation energies are usually determined by fitting of the measured optical spectra that conta

T. Niwase, M. Wada, P. Schury, H. Haba

We have developed a novel detector, referred to as an "$\alpha$-ToF detector", for correlated measurements of atomic masses and decay properties of low-yield, short-lived radioactive isotopes using a multi-reflection time-of-flight mass spectrograph. By correlating measured time-of-flight signals with decay events, it will be possible to suppress background

Rohit Voleti, Stephanie Woolridge, Julie M. Liss, Melissa Milanovic

Several studies have shown that speech and language features, automatically extracted from clinical interviews or spontaneous discourse, have diagnostic value for mental disorders such as schizophrenia and bipolar disorder. They typically make use of a large feature set to train a classifier for distinguishing between two groups of interest, i.e. a clinical

Gushu Li, Yufei Ding, Yuan Xie

To bridge the gap between limited hardware access and the huge demand for experiments for Noisy Intermediate-Scale Quantum (NISQ) computing system study, a simulator which can capture the modeling of both the quantum processor and its classical control system to realize early-stage evaluation and design space exploration, is naturally invoked but still missi

Hongzhu Li, Weiqiang Wang

The connectionist temporal classification (CTC) enables end-to-end sequence learning by maximizing the probability of correctly recognizing sequences during training. The outputs of a CTC-trained model tend to form a series of spikes separated by strongly predicted blanks, know as the spiky problem. To figure out the reason for it, we reinterpret the CTC tra

Laura Kreidberg, Rodrigo Luger, Megan Bedell

Observations of the Kepler-1625 system with the Kepler and Hubble Space Telescopes have suggested the presence of a candidate exomoon, Kepler-1625b I, a Neptune-radius satellite orbiting a long-period Jovian planet. Here we present a new analysis of the Hubble observations, using an independent data reduction pipeline. We find that the transit light curve is

Chol-Hui Yun, Mi-Kyong Ri

We estimate the bounds of box-counting dimension of hidden variable fractal interpolation functions (HVFIFs) and hidden variable bivariate fractal interpolation functions (HVBFIFs) with four function contractivity factors and present analytic properties of HVFIFs which are constructed to ensure more flexibility and diversity in modeling natural phenomena. Fi

Cole Comfort

The real stabilizer fragment of quantum mechanics was shown to have a complete axiomatization in terms of the angle-free fragment of the ZX-calculus. This fragment of the ZX-calculus---although abstractly elegant---is stated in terms of identities, such as spider fusion which generally do not have interpretations as circuit transformations. We complete the c

Wei-Na Li, Zhengyun Zhang, Jianshe Ma, Xiaohao Wang

We propose a three-dimensional nonlinear diffusion method to implement the similar autofocusing function of multiple micro-objects and simultaneously remove the defocused images, which can distinguish the locations of certain sized scattering particles that are overlapping along z-axis. It is applied to all of the reconstruction slices that are generated fro

Wen Huang, Zeng Lian, Xiao Ma, Leiye Xu

In this article, we consider the weighted ergodic optimization problem Axiom A attractors of a $C^2$ flow on a compact smooth manifold. The main result obtained in this paper is that for a generic observable from function space $\mc C^{0,\a}$ ($\a\in(0,1]$) or $\mc C^1$ the minimizing measure is unique and is supported on a periodic orbit.

Simon J. Illingworth

The linear amplification mechanisms leading to streamwise-constant large-scale structures in laminar and turbulent channel flows are considered. A key feature of the analysis is that the Orr--Sommerfeld and Squire operators are each considered separately. Physically this corresponds to considering two separate processes: (i) the response of wall-normal veloc

Sylvie Corteel, Jang Soo Kim

Recently the authors introduced lecture hall tableaux in their study of multivariate little $q$-Jacobi polynomials. In this paper, we enumerate bounded lecture hall tableaux. We show that their enumeration is closely related to standard and semistandard Young tableaux. We also show that the number of bounded lecture hall tableaux is the coefficient of the Sc

Ting-Ting Jiang, Jiang-Jing Wang, Lu Lu, Chuan-Sheng Ma

Fast and reversible phase transitions in chalcogenide phase-change materials (PCMs), in particular, Ge-Sb-Te compounds, are not only of fundamental interests, but also make PCMs based random access memory (PRAM) a leading candidate for non-volatile memory and neuromorphic computing devices. To RESET the memory cell, crystalline Ge-Sb-Te has to undergo phase

Jeremy Martin, Douglas Alpuche, Kristina Bodeman, Lamont Brown

We investigate Apple&#39;s Bluetooth Low Energy (BLE) Continuity protocol, designed to support interoperability and communication between iOS and macOS devices, and show that the price for this seamless experience is leakage of identifying information and behavioral data to passive adversaries. First, we reverse engineer numerous Continuity protocol message

Le Duc Anh, Takashi Yamashita, Hiroki Yamasaki, Daisei Araki

A major challenge in spin-based electronics is reducing power consumption for magnetization switching of ferromagnets, which is being implemented by injecting a large spin-polarized current. The alternative approach is to control the magnetic anisotropy (MA) of the ferromagnet by an electric field. However, the voltage-induced MA is too weak to deterministic

F. F. Karpeshin

The concept of collinear spontaneous true ternary fission of 252Cf is subject to critical analysis. The conclusion is that the collinear flight of the fragments turns out to be a natural and most probable mode. The collinearity arises in the model on the prescission stage as a result of the account of the principles of the collective Bohr&#39;s model. It is

Tran Phuong Thao, Yukiko Sawaya, Hoang-Quoc Nguyen-Son, Akira Yamada

Homograph attack is a way that attackers deceive victims about which website domain name they are communicating with by exploiting the fact that many characters look alike. The attack becomes serious and is raising broad attention when recently many brand domains have been attacked such as Apple Inc., Adobe Inc., Lloyds Bank, etc. We first design a survey of

Songyan Hou, Xingli Wang, Landobasa Y. M. Tobing, Zhenwei Xie

Strong interactions between surface plasmons in ultra-compact nanocavities and excitons in two dimensional materials have attracted wide interests for its prospective realization of polariton devices at room temperature. Here, we propose a continuous transition from weak coupling to strong coupling between excitons in MoS2 monolayer and highly localized plas

Aurel Bulgac

In cold atoms and in the crust of neutron stars the pairing gap can reach values comparable with the Fermi energy. While in nuclei the neutron gap is smaller, it is still of the order of a few percent of the Fermi energy. The pairing mechanism in these systems is due to short range attractive interactions between fermions and the size of the Cooper pair is e

Sultan Albarakati, Cheng Tan, Zhong-Jia Chen, James G. Partridge

Van der Waals (vdW) ferromagnetic materials are rapidly establishing themselves as effective building blocks for next generation spintronic devices. When layered with non-magnetic vdW materials, such as graphene and/or topological insulators, vdW heterostructures can be assembled (with no requirement for lattice matching) to provide otherwise unattainable de

Martin Formanek, Andrew Steinmetz, Johann Rafelski

We study a covariant classical model of neutral point particles with magnetic moment interacting with external electromagnetic fields. Classical dynamical equations which reproduce a correct behavior in the non-relativistic limit are introduced. We also discuss the non-uniqueness of the covariant torque equation. The focus of this work is on Dirac neutrino b

Thomas Uriot

In this paper, we propose an extension to an existing algorithm (instance-MIR) which tackles the multiple instance regression (MIR) problem, also known as distribution regression. The MIR setting arises when the data is a collection of bags, where each bag consists of several instances which correspond to the same and unique real-valued label. The goal of a

J. A. Melendez, R. J. Furnstahl, D. R. Phillips, M. T. Pratola

Effective field theories (EFTs) organize the description of complex systems into an infinite sequence of decreasing importance. Predictions are made with a finite number of terms, which induces a truncation error that is often left unquantified. We formalize the notion of EFT convergence and propose a Bayesian truncation error model for predictions that are

Caterina Negulescu, Kushan Wijesundara

During the past earthquakes, different low ductile failure modes are observed in the gravity design structures and thus, the most of existing damage indices may fail to assess the damage of gravity design structures accurately in referring to the two main performance levels: immediate occupancy and ultimate limit state. Therefore, this study investigates the

Imen Gnaba, Peng Wang, Xavier Legrand, Damien Soulat

The weak properties in the thickness of the laminates in terms of rigidity, fatigue and impact resistance involve delamination sensitivity for thick structures. This issue has led to the development of three-dimensional (3D) preforms with reinforcement through-the-thickness, making it possible to combine good mechanical performance in-plane and out-of-plane.

Nicholas Waytowich, Sean L. Barton, Vernon Lawhern, Ethan Stump

While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of {\em reward sparsity}. This is especially true for tasks such as training an agent to play StarCraft II, a real-time strate

Maroua Nouiri, Damien Trentesaux, Abdelghani Bekrar

Industry 4.0 is concerned with sustainable development constraints. In this context, we propose a multi-agent architecture, named EasySched, aiming at elaborating predictive and reactive scheduling as the result of a coordination between systems producing goods and systems producing renewable energy. The validation of this architecture is original, and was c

Simon M. Lucas, Vanessa Volz

This paper provides a detailed investigation of using the Kullback-Leibler (KL) Divergence as a way to compare and analyse game-levels, and hence to use the measure as the objective function of an evolutionary algorithm to evolve new levels. We describe the benefits of its asymmetry for level analysis and demonstrate how (not surprisingly) the quality of the

Juan Leni, John Levine, John Quigley

Modeling strategic conflict from a game theoretical perspective involves dealing with epistemic uncertainty. Payoff uncertainty models are typically restricted to simple probability models due to computational restrictions. Recent breakthroughs Artificial Intelligence (AI) research applied to Poker have resulted in novel approximation approaches such as coun

Harris Partaourides, Kostantinos Papadamou, Nicolas Kourtellis, Ilias Leontiadis

Modern deep learning approaches have achieved groundbreaking performance in modeling and classifying sequential data. Specifically, attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recognition in dyadic conversations. In contr

Ranjan Satapathy, Aalind Singh, Erik Cambria

With the current upsurge in the usage of social media platforms, the trend of using short text (microtext) in place of standard words has seen a significant rise. The usage of microtext poses a considerable performance issue in concept-level sentiment analysis, since models are trained on standard words. This paper discusses the impact of coupling sub-symbol

Jonathan F. Schonfeld

I argue that the marquis characteristics of the quantum-mechanical double-slit experiment (point detection, random distribution, Born rule) can be explained using Schroedinger&#39;s equation alone, if one takes into account that, for any atom in a detector, there is a small but nonzero gap between its excitation energy and the excitation energies of all othe

Bharath Ramesh, Andres Ussa, Luca Della Vedova, Hong Yang

We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high temporal resolution (order of microseconds), low power consumption (few hundred mW) and wide dynamic range (120 dB) as attractive properties. Howe

Zhen Lin, Mohammad Alshboul, Yan Solihin, Huiyang Zhou

Given its high integration density, high speed, byte addressability, and low standby power, non-volatile or persistent memory is expected to supplement/replace DRAM as main memory. Through persistency programming models (which define durability ordering of stores) and durable transaction constructs, the programmer can provide recoverable data structure (RDS)

Yujia Bao, Zhengyi Deng, Yan Wang, Heeyoon Kim

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germ

Mi-Kyong Ri, Chol-Hui Yun

In this paper, we analyze the smoothness and stability of hidden variable recurrent fractal interpolation functions (HVRFIF) with function contractivity factors introduced in Ref. 1. The HVRFIF is a hidden variable fractal interpolation function (HVFIF) constructed by recurrent iterated function system (RIFS) with function contractivity factors. An attractor

Manie Tadayon, Greg Pottie

Contributions: Prior studies on education have mostly followed the model of the cross sectional study, namely, examining the pretest and the posttest scores. This paper shows that students&#39; knowledge throughout the intervention can be estimated by time series analysis using a hidden Markov model. Background: Analyzing time series and the interaction betw

Peter B. Shalen

Let ${\mathfrak M}$ be a closed, orientable, hyperbolic 3-orbifold such that $π_1({\mathfrak M})$ contains no hyperbolic triangle group. We show that strict upper bounds of 0.07625, 0.1525 and 0.22875 for ${\rm vol}\ {\mathfrak M}$ imply respective upper bounds of 23, 43 and 79 for $\dim H_1({\mathfrak M};{\mathbb F}_2 )$. Stronger results hold if we assume

Thanh-Toan Do, Khoa Le, Tuan Hoang, Huu Le

Representing images by compact hash codes is an attractive approach for large-scale content-based image retrieval. In most state-of-the-art hashing-based image retrieval systems, for each image, local descriptors are first aggregated as a global representation vector. This global vector is then subjected to a hashing function to generate a binary hash code.

David Garofalo

Observations of radio loud active galaxies in the XMM-Newton archive by Mehdipour & Costantini show a strong anti-correlation between the column density of the ionized wind and the radio loudness parameter, providing evidence that jets may thrive in thin disks. This is in contrast with decades of analytic and numerical work suggesting jet formation is contin

Matthias Kahl, Thomas Kriechbaumer, Daniel Jorde, Anwar Ul Haq

Non-intrusive load monitoring (NILM) is a modern and still expanding technique, helping to understand fundamental energy consumption patterns and appliance characteristics. Appliance event detection is an elementary step in the NILM pipeline. Unfortunately, several types of appliances (e.g., switching mode power supply (SMPS) or multi-state) are known to cha

Yuhu Guo, Han Xiao, Yidong Chen, Xiaodong Shi

Event-based camera is a bio-inspired vision sensor that records intensity changes (called event) asynchronously in each pixel. As an instance of event-based camera, Dynamic and Active-pixel Vision Sensor (DAVIS) combines a standard camera and an event-based camera. However, traditional models could not deal with the event stream asynchronously. To analyze th

Umesh Kumar Verma, Sudhanshu Shekhar Chaurasia, Sudeshna Sinha

Many biological and chemical systems exhibit collective behavior in response to the change in their population density. These elements or cells communicate with each other via dynamical agents or signaling molecules. In this work, we explore the dynamics of nonlinear oscillators, specifically Stuart-Landau oscillators and Rayleigh oscillators, interacting gl

Hajar Doubabi, Issam Salhi, Mohammed Chennani, Najib Essounbouli

Appropriate control contributes essentially in the design of efficient DC-DC converters. With this intention, a study deals with the synthesis of a controller for DC-DC Three-level Boost converter (TLBC), has been addressed. The studied TLBC, known as nonlinear system, has been locally modeled using transfer function models. For instance, PI controllers were

Paola Favati, Grazia Lotti, Ornella Menchi, Francesco Romani

Spectral clustering is a powerful method for finding structure in a dataset through the eigenvectors of a similarity matrix. It often outperforms traditional clustering algorithms such as $k$-means when the structure of the individual clusters is highly non-convex. Its accuracy depends on how the similarity between pairs of data points is defined. Two import

Wojciech Górny

It is shown that solutions to the anisotropic least gradient problem for boundary data $f \in L^p(\partialΩ)$ lie in $L^{\frac{Np}{N-1}}(Ω)$; the exponent is shown to be optimal. Moreover, the solutions are shown to be locally bounded with explicit bounds on the rate of blow-up of the solution near the boundary in two settings: in the anisotropic case on the

Taylor J. Smith, Kai Salomaa

A two-dimensional finite automaton has a read-only input head that moves in four directions on a finite array of cells labelled by symbols of the input alphabet. A three-way two-dimensional automaton is prohibited from making upward moves, while a two-way two-dimensional automaton can only move downward and rightward. We show that the language emptiness prob

Ibrahim H. Khawaji, Alyssa N. Brigeman, Osama O. Awadelkarim, Akhlesh Lakhtakia

Degradation of metal-insulator-semiconductor (MIS) capacitors of gold/Parylene-C/Pentacene under constant voltage stress (CVS) was investigated to explore the electrical stability and reliability of Parylene C as a gate dielectric in flexible electronics. A stress voltage of fixed magnitude as high as 20 V, both negative and positive in polarity, was applied

Minsu Cho, Chinmay Hegde

We propose a new algorithm for hyperparameter selection in machine learning algorithms. The algorithm is a novel modification of Harmonica, a spectral hyperparameter selection approach using sparse recovery methods. In particular, we show that a special encoding of hyperparameter space enables a natural group-sparse recovery formulation, which when coupled w

Mariem Ben Fadhel, Kofi Nyarko

Anomaly detection is the process of finding data points that deviate from a baseline. In a real-life setting, anomalies are usually unknown or extremely rare. Moreover, the detection must be accomplished in a timely manner or the risk of corrupting the system might grow exponentially. In this work, we propose a two level framework for detecting anomalies in

A. J. Rogers, C. J. Farrugia, R. B. Torbert

We present a numerical algorithm aimed at identifying ion diffusion regions (IDRs) in the geomagnetic tail, and test its applicability. We use 5 criteria applied in three stages. (i) Correlated reversals (within 90 s) of Vx and Bz (at least 2 nT about zero; GSM coordinates); (ii) Detection of Hall electric and magnetic field signatures; and (iii) strong (>10

Thomas M. Loughin, Yan Zhang

Most methods for identifying location effects in unreplicated fractional factorial designs assume homoscedasticity of the response values. However, dispersion effects in the underlying process may create heteroscedasticity in the response values. This heteroscedasticity may go undetected when identification of location effects is pursued. Indeed, methods for

Yen-Chi Chen, Mauricio Sadinle

Pattern-mixture models provide a transparent approach for handling missing data, where the full-data distribution is factorized in a way that explicitly shows the parts that can be estimated from observed data alone, and the parts that require identifying restrictions. We introduce a nonparametric estimator of the full-data distribution based on the pattern-

Victor Araujo, Rodolfo Migon Favaretto, Paulo Knob, Soraia Raupp Musse

This work aims to evaluate people&#39;s perception regarding geometric features, personalities and emotions characteristics in virtual humans. For this, we use as a basis, a dataset containing the tracking files of pedestrians captured from spontaneous videos and visualized them as identical virtual humans. The goal is to focus on their behavior and not bein

Xinlei Pan, Weiyao Wang, Xiaoshuai Zhang, Bo Li

Machine learning has been widely applied to various applications, some of which involve training with privacy-sensitive data. A modest number of data breaches have been studied, including credit card information in natural language data and identities from face dataset. However, most of these studies focus on supervised learning models. As deep reinforcement

Frédéric Joucken, Eberth A. Quezada-López, Jose Avila, Chaoyu Chen

Hexagonal boron nitride (hBN) is the supporting substrate of choice for two-dimensional material devices because it is atomically flat and chemically inert. However, due to the small size of mechanically exfoliated hBN flakes, electronic structure studies of 2D materials supported by hBN using angle-resolved photoemission spectroscopy (ARPES) are challenging

Cacey Stevens Bester, Noah Cox, Hu Zheng, Robert P. Behringer

When a solid projectile impacts a granular target, it experiences a drag force and abruptly comes to rest as its momentum transfers to the grains. An empirical drag force law successfully describes the force experienced by the projectile, and the corresponding grain-scale mechanisms have been deciphered for normal impacts. However, there is little work explo

Aitor Arronte-Alvarez, Francisco Gomez-Martin

In this paper we present an attentional neural network for folk song classification. We introduce the concept of musical motif embedding, and show how using melodic local context we are able to model monophonic folk song motifs using the skipgram version of the word2vec algorithm. We use the motif embeddings to represent folk songs from Germany, China, and S