September 2019 arXiv papers — page 43

Sherzod R. Otajonov, Eduard N. Tsoy, Fatkhulla Kh. Abdullaev

The dynamics of quantum droplets in 1D is analyzed on the basis of the variational approach (VA). It is shown that the VA based on the super-Gaussian function gives a good approximation of stationary states. The period of small oscillations of the perturbed droplet is obtained. It is found numerically that oscillations are almost undamped for many periods. B

Hayoung Chung, Oded Amir, H. Alicia Kim

At elevated temperature environments, elastic structures experience a change of the stress-free state of the body that can strongly influence the optimal topology of the structure. This work presents level-set based topology optimization of structures undergoing large deformations due to thermal and mechanical loads. The nonlinear analysis model is construct

Takafumi Nishino, Thomas D. Dunstan

This paper presents a theory based on the law of momentum conservation to define and help analyse the problem of large wind farm aerodynamics. The theory splits the problem into two sub-problems; namely an 'external' (or farm-scale) problem, which is a time-dependent problem considering large-scale motions of the atmospheric boundary layer (ABL) to assess th

Kenju Otsuka

Synchronization of chaos among a single pair of transverse modes featuring intermode dynamical non-independence, resulting from cross-saturation of modal population inversions and coherent modal field coupling, is explored in a thin-slice solid-state laser with coupled orthogonally polarized transverse modes operating in quasi-locked states. The chaotic sign

Lin Chang, Weiqiang Xie, Haowen Shu, Qifan Yang

Recent advances in nonlinear optics have revolutionized the area of integrated photonics, providing on-chip solutions to a wide range of new applications. Currently, the state of the art integrated nonlinear photonic devices are mainly based on dielectric material platforms, such as Si3N4 and SiO2. While semiconductor materials hold much higher nonlinear coe

Fearghal Kearney, Han Lin Shang, Lisa Sheenan

Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant models can uncover predictable patterns in

Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan

Two-stage deep object detectors generate a set of regions-of-interest (RoI) in the first stage, then, in the second stage, identify objects among the proposed RoIs that sufficiently overlap with a ground truth (GT) box. The second stage is known to suffer from a bias towards RoIs that have low intersection-over-union (IoU) with the associated GT boxes. To ad

Garima Punetha, H. C. Chandola

The mechanism of color confinement has been studied in the framework of SU (3) color gauge theory in terms of abelian fields and monopoles extracted by adopting magnetic symmetry. The existence of the mechanism of color confinement corresponds to the dual Meissner effect caused by monopoles. The two length scales, i.e. the penetration depth and coherence len

Dennis Gaitsgory

We study factorization algebras on configuration spaces of points on the curved, colored by elements of the root lattice. We show that the factorization algebra attached to Lusztig's quantum group can be obtained as a direct image of a twisted Whittaker sheaf on the Zastava space.

Deepank Verma, Arnab Jana

The satellite imagery classification task is fundamental to spatial knowledge discovery. Several image classification methods are used to create standardized Land use and Land cover (LULC) maps, which facilitate research on spatial and ecological processes and human activities. Local Climate Zones (LCZ) classification maps are an example of standardized maps

Qiaoqiao Ding, Gaoyu Chen, Xiaoqun Zhang, Qiu Huang

Low dose X-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops image reconstruction methods with deep learning (DL) regularization for LDCT. Our methods are based on unrolling of proximal forward-backward splitting (PFBS) framework with data-driven image regularization via deep neural networks. In contrast with PFBS-IR th

Vladyslav Oles, Nathan Lemons, Alexander Panchenko

Gromov-Hausdorff distances measure shape difference between the objects representable as compact metric spaces, e.g. point clouds, manifolds, or graphs. Computing any Gromov-Hausdorff distance is equivalent to solving an NP-Hard optimization problem, deeming the notion impractical for applications. In this paper we propose polynomial algorithm for estimating

Abhinav Aggarwal, Shivam Kakkar, Pawan Kumar

A parallel and nested version of a frequency filtering preconditioner is proposed for linear systems corresponding to diffusion equation on a structured grid. The proposed preconditioner is found to be robust with respect to jumps in the diffusion coefficients. The storage requirement for the preconditioner is O(N),where N is number of rows of matrix, hence,

Ataru Tanikawa, Ken'ichi Nomoto, Naohito Nakasato, Keiichi Maeda

We have studied double-detonation explosions in double-degenerate (DD) systems with different companion white dwarfs (WD) for modeling type Ia supernovae (SNe Ia) by means of high-resolution smoothed particle hydrodynamics (SPH) simulations. We have found that only the primary WDs explode in some of the DD systems, while the explosions of the primary WDs ind

Chen Jia, Hong Qian, Michael Q. Zhang

Stochastic oscillations in individual cells are usually characterized by a non-monotonic power spectrum with an oscillatory autocorrelation function. Here we develop an analytical approach of stochastic oscillations in a minimal hybrid model of stochastic gene expression including promoter state switching, protein synthesis and degradation, as well as a gene

Zehui Yao, Boyan Zhang, Zhiyong Wang, Wanli Ouyang

Generative adversarial networks (GANs) have demonstrated great success in generating various visual content. However, images generated by existing GANs are often of attributes (e.g., smiling expression) learned from one image domain. As a result, generating images of multiple attributes requires many real samples possessing multiple attributes which are very

Yuhang Liu, Luming Wang, Xiang Li, Yang Wang

Providing architectural support is crucial for newly arising applications to achieve high performance and high system efficiency. Currently there is a trend in designing accelerators for special applications, while arguably a debate is sparked whether we should customize architecture for each application. In this study, we introduce what we refer to as Gene-

Yuqiang Li, Qiang Yao

The deviation principles of record numbers in random walk models have not been completely investigated, especially for the non-nearest neighbor cases. In this paper, we derive the asymptotic probabilities of large and moderate deviations for the number of "weak records"(or "ladder points") in two kinds of one-dimensional non-nearest neighbor random walks. Th

Michio Seto

Indefinite Schwarz-Pick inequalities for holomorphic self-maps of the bidisk are given as application of the spectral theory on Hilbert modules.

Rahul Roy, Hideki Tanemura

Ben-Ari and Schinazi (2016) introduced a stochastic model to study `virus-like evolving population with high mutation rate'. This model is a birth and death model with an individual at birth being either a mutant with a random fitness parameter in $[0,1]$ or having one of the existing fitness parameters with uniform probability; whereas a death event removes

Ameya Vaidya, Feng Mai, Yue Ning

With the recent rise of toxicity in online conversations on social media platforms, using modern machine learning algorithms for toxic comment detection has become a central focus of many online applications. Researchers and companies have developed a variety of models to identify toxicity in online conversations, reviews, or comments with mixed successes. H

Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han

Many attempts have been done to extend the great success of convolutional neural networks (CNNs) achieved on high-end GPU servers to portable devices such as smart phones. Providing compression and acceleration service of deep learning models on the cloud is therefore of significance and is attractive for end users. However, existing network compression and

Sameer Kumar, Victor Bitorff, Dehao Chen, Chiachen Chou

The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0.6 training benchmark demonstrates the scalability of a suite of industry relevant ML models. MLPerf defines a suite of models, datasets and rules to follow when benchmarking to ensure results are comparable across hardware, frameworks and companies. Using this suite of models, we disc

Xiao-Chuan Xu

For the direct problem, we give the asymptotic distribution of the (real and non-real) transmission eigenvalues for the Schrodinger operator on the half line. For the inverse problem, we prove that the potential can be uniquely determined by all transmission eigenvalues, the parameter in the boundary condition and some non-zero value related to the potential

Kookjin Lee, Kevin Carlberg

This work proposes an approach for latent-dynamics learning that exactly enforces physical conservation laws. The method comprises two steps. First, the method computes a low-dimensional embedding of the high-dimensional dynamical-system state using deep convolutional autoencoders. This defines a low-dimensional nonlinear manifold on which the state is subse

L. N. Granda, G. D. Rojas

The modified gravity is considered in the framework of the holographic dark energy. An analysis of the autonomous system, the critical points and their stability is presented. Unlike the dark energy models based on $f(R)$, it is found that working in the holographic frame enriches the possibility of accelerated and matter type points for different cosmologic

Haoshan Zhu, Chao Lian, Bryan M. Wong, Jory A. Yarmoff

The neutralization probability of low energy Na$^{+}$ ions scattered from In- and As-rich InAs(001) surfaces is measured by time-of-flight spectroscopy. It is found that the neutralization probability for projectiles scattered from As sites is larger than from In sites for both types of surfaces. A modification of the resonant charge transfer model is propos

Jürgen Lisenfeld, Alexander Bilmes, Anthony Megrant, Rami Barends

Superconducting integrated circuits have demonstrated a tremendous potential to realize integrated quantum computing processors. However, the downside of the solid-state approach is that superconducting qubits suffer strongly from energy dissipation and environmental fluctuations caused by atomic-scale defects in device materials. Further progress towards up

Aya E. Higuchi, Yoko Oya, Satoshi Yamamoto

We have detected the submillimeter-wave fine-structure transition (3P1-3P0) of 13C, [13C I], in the gaseous debris disk of 49 Ceti with the Atacama Large Millimeter/submillimeter Array (ALMA). Recently, the [C I] 3P1-3P0 emission has been spatially resolved in this source with ALMA. In this dataset, the F=3/2-1/2 hyperfine component of [13C I], which is blue

Ernest K. Ryu, Bang Cong Vu

We consider the monotone inclusion problem with a sum of 3 operators, in which 2 are monotone and 1 is monotone-Lipschitz. The classical Douglas--Rachford and Forward-backward-forward methods respectively solve the monotone inclusion problem with a sum of 2 monotone operators and a sum of 1 monotone and 1 monotone-Lipschitz operators. We first present a meth

D. F. Jimenez, L. N. Granda, E. Elizalde

A scalar-tensor model with Gauss-Bonnet and non-minimal kinetic couplings is considered, in which ghost modes are eliminated via a Lagrange multiplier constraint. A reconstruction procedure is deviced for the scalar potential and Lagrange multiplier, valid for any given cosmological scenario. In particular, inflationary and dark energy cosmologies of differe

Hanamichi Kawamura

In this paper, we introduce the hypermultiple gamma functions of BM-type and prove the asymptotic expansion of these functions.

Vladislav Levshinskii, Maxim Polyakov, Alexander Losev, Alexander Khoperskov

The method of microwave radiometry is one of the areas of medical diagnosis of breast cancer. It is based on analysis of the spatial distribution of internal and surface tissue temperatures, which are measured in the microwave (RTM) and infrared (IR) ranges. Complex mathematical and computer models describing complex physical and biological processes within

Viktor Zenkov, Jason Laska

Criminals use malware to disrupt cyber-systems. The number of these malware-vulnerable systems is increasing quickly as common systems, such as vehicles, routers, and lightbulbs, become increasingly interconnected cyber-systems. To address the scale of this problem, analysts divide malware into classes and develop, for each class, a specialized defense. In t

Anis Koubaa, Abdulrahman Aldawood, Bassel Saeed, Abdullatif Hadid

Smart agriculture is an evolving trend in agriculture industry, where sensors are embedded into plants to collect vital data and help in decision making to ensure higher quality of crops and prevent pests, disease, and other possible threats. In Saudi Arabia, growing palms is the most important agricultural activity, and there is an increasing need to levera

Vivekraj V. K., Debashis Sen, Balasubramanian Raman

Video skimming, also known as dynamic video summarization, generates a temporally abridged version of a given video. Skimming can be achieved by identifying significant components either in uni-modal or multi-modal features extracted from the video. Being dynamic in nature, video skimming, through temporal connectivity, allows better understanding of the vid

Thomas Andersen

The recent experimental proposals by Bose et al. and Marletto et al. (BMV) outline a way to test for the quantum nature of gravity by measuring gravitationally induced differential phase accumulation over the superposed paths of two 10^-14kg masses. These authors outline the expected outcome of these experiments for semi-classical, quantum gravity and collap

Pedro Cisneros-Velarde, Noah E. Friedkin, Anton V. Proskurnikov, Francesco Bullo

Structural balance is a classic property of signed graphs satisfying Heider's seminal axioms. Mathematical sociologists have studied balance theory since its inception in the 1040s. Recent research has focused on the development of dynamic models explaining the emergence of structural balance. In this paper, we introduce a novel class of parsimonious dyn

Jincheng Shi, Jianhua Zeng

We analyze the existence and stability of two kinds of self-trapped spatially localized gap modes, gap solitons and truncated nonlinear Bloch waves, in one-and two-dimensional optical or matter-wave media with self-focusing nonlinearity, supported by a combination of linear and nonlinear periodic lattice potentials. The former is found to be stable once plac

Liangwei Zeng, Jianhua Zeng

Competing nonlinearities, such as the cubic (Kerr) and quintic nonlinear terms whose strengths are of opposite signs (the coefficients in front of the nonlinearities), exist in various physical media (in particular, in optical and matter-wave media). A benign competition between self-focusing cubic and self-defocusing quintic nonlinear nonlinearities (known

Ronghua Xu, Gowri Sankar Ramachandran, Yu Chen, Bhaskar Krishnamachari

To promote the benefits of the Internet of Things (IoT) in smart communities and smart cities, a real-time data marketplace middleware platform, called the Intelligent IoT Integrator (I3), has been recently proposed. While facilitating the easy exchanges of real-time IoT data streams between device owners and third-party applications through the marketplace,

Amir Karami

Large textual corpora are often represented by the document-term frequency matrix whose elements are the frequency of terms; however, this matrix has two problems: sparsity and high dimensionality. Four dimension reduction strategies are used to address these problems. Of the four strategies, unsupervised feature transformation (UFT) is a popular and efficie

Mikhail N. Sergeenko

Mesons containing light and heavy quarks are studied. Interaction of quarks is described by the funnel-type potential with the distant dependent strong coupling, $α_§(r)$. Free particle hypothesis for the bound state is developed: quark and antiquark move as free particles in of the bound system. Relativistic two-body wave equation with position dependent pa

Érika S. Rosas-Quezada, Gabriela Ramírez-de-la-Rosa, Esaú Villatoro-Tello

Engaged costumers are a very import part of current social media marketing. Public figures and brands have to be very careful about what to post online. That is why the need for accurate strategies for anticipating the impact of a post written for an online audience is critical to any public brand. Therefore, in this paper, we propose a method to predict the

Kasper Green Larsen, Michael Mitzenmacher, Charalampos E. Tsourakakis

We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover $n$ discrete variables $g_i \in \{0, \ldots, k-1\}$ (up to some global offset) given noisy observations of a set of their pairwise differences $\{(g_i - g_j) \bmod k\}$; specifically, with probability $\frac{1}{k}+δ$ for some $δ>

Abhishek Santra, Kanthi Sannappa Komar, Sanjukta Bhowmick, Sharma Chakravarthy

Datasets of real-world applications are characterized by entities of different types, which are defined by multiple features and connected via varied types of relationships. A critical challenge for these datasets is developing models and computations to support flexible analysis, i.e., the ability to compute varied types of analysis objectives in an efficie

Guy D. Rosin, Kira Radinsky

Though languages can evolve slowly, they can also react strongly to dramatic world events. By studying the connection between words and events, it is possible to identify which events change our vocabulary and in what way. In this work, we tackle the task of creating timelines - records of historical "turning points", represented by either words or e

Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H. Ballard

Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment. Human knowledge of how to solve these tasks can be incorporated using imitation learning, where the agent learns to imitate human demonstrated decisions. However, human guidance is not limited to the demonstrations. Other types of guidance could be

Hadi Ahmadi, Derek Small

Attribute-based access control (ABAC) promises a powerful way of formalizing access policies in support of a wide range of access management scenarios. Efficient implementation of ABAC in its general form is still a challenge, especially when addressing the complexity of privacy regulations and access management required to support the explosive growth of so

Jeancarlo Campos Leão, Alberto H. F. Laender, Pedro O. S. Vaz de Melo

Community detection is key to understand the structure of complex networks. However, the lack of appropriate evaluation strategies for this specific task may produce biased and incorrect results that might invalidate further analyses or applications based on such networks. In this context, the main contribution of this paper is an approach that supports a ro

Andrzej Bożek

We introduce a concept of a Goldbach factorization graph (GFG) $F_n$, which can be constructed for each even integer $n$ greater than 2. We prove that, if $n$ does not satisfy the binary Goldbach conjecture (BGC), then $F_n$ contains a special source strongly connected component (exceptional autonomous component, EAC). We analyse existence and properties of

Nelda Jaque, Bernardo San Martín

In arXiv:1801.01238 a variation of Bowen's topological entropy that can be applied to the study of discontinuous semiflows on compact metric spaces was introduced. The main novetly is the use of certain family of pseudosemimetrics associated to the semiflow, in a such a way that in the continuous case they coincide with the classical Bowen's entropie

Francesco Fanelli

In the present paper we study the incompressible and fast rotation limit for the barotropic Navier-Stokes equations with Coriolis force, in the case when the Mach number $\rm Ma$ is large with respect to the Rossby number $\rm Ro$: namely, we focus on the regime ${\rm Ro}\ll{\rm Ma}$. For this, we follow a recent approach by Danchin and Mucha in \cite{D-M} a

Boitumelo Ruf, Thomas Pollok, Martin Weinmann

Online augmentation of an oblique aerial image sequence with structural information is an essential aspect in the process of 3D scene interpretation and analysis. One key aspect in this is the efficient dense image matching and depth estimation. Here, the Semi-Global Matching (SGM) approach has proven to be one of the most widely used algorithms for efficien

Dana Cerna, Laura Rebollo-Neira

The purpose of sparse modelling of ECG signals is to represent an ECG record, given by sample points, as a linear combination of as few elementary components as possible. This can be achieved by creating a redundant set, called a dictionary, from where the elementary components are selected. The success in sparsely representing an ECG record depends on the n

Ichiro Oda

It is shown that in the quadratic gravity based on Weyl's conformal geometry, the Planck mass scale can be generated from quantum effects of the gravitational field and the Weyl gauge field via the Coleman-Weinberg mechanism where a local scale symmetry, that is, conformal symmetry, is broken. At the same time, the Weyl gauge field acquires mass less tha

Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli

Deep neural network controllers for autonomous driving have recently benefited from significant performance improvements, and have begun deployment in the real world. Prior to their widespread adoption, safety guarantees are needed on the controller behaviour that properly take account of the uncertainty within the model as well as sensor noise. Bayesian neu

Joseph E. Lawrence, Theo Fletcher, Lachlan P. Lindoy, David E. Manolopoulos

We present a simple interpolation formula for the rate of an electron transfer reaction as a function of the electronic coupling strength. The formula only requires the calculation of Fermi Golden Rule and Born-Oppenheimer rates and so can be combined with any methods that are able to calculate these rates. We first demonstrate the accuracy of the formula by

Jonathan Conejeros, Fabio Armando Tal

This paper studies homeomorphisms of the closed annulus that are isotopic to the identity from the viewpoint of rotation theory, using a newly developed forcing theory for surface homeomorphisms. Our first result is a solution to the so called strong form of Boyland's Conjecture on the closed annulus: Assume $f$ is a homeomorphism of $\overline{\mathbb{A

Ethan Fahnestock, Siddharth Patki, Thomas M. Howard

Language is an effective medium for bi-directional communication in human-robot teams. To infer the meaning of many instructions, robots need to construct a model of their surroundings that describe the spatial, semantic, and metric properties of objects from observations and prior information about the environment. Recent algorithms condition the expression

Yifeng Shi, Junier Oliva, Marc Niethammer

Modern methods for learning over graph input data have shown the fruitfulness of accounting for relationships among elements in a collection. However, most methods that learn over set input data use only rudimentary approaches to exploit intra-collection relationships. In this work we introduce Deep Message Passing on Sets (DMPS), a novel method that incorpo

Collin Cunningham

In this paper, we describe a method designed to add precision to radiation simulations in the adaptive mesh refinement cosmological hydrodynamics code ENZO. We build upon the geometric correction factor described in \textit{ENZO+MORAY: radiation hydrodynamics adaptive mesh refinement simulations with adaptive ray tracing} (Wise and Abel 2011) which accounts

Ratan Kumar Bera, Devshree Mandal, Amita Das, Sudip Sengupta

In this paper, wakefields driven by a relativistic electron beam in a cold homogeneous plasma is studied using 2-D fluid simulation techniques. It has been shown that in the limit when the transverse size of a rigid beam is greater than the longitudinal extension, the wake wave acquires purely an electrostatic form and the simulation results show a good agre

Andrea L. Gallo, Linda. V. Saal

In this work, we consider a family of Gelfand pairs $(K \ltimes N, N)$ (in short $(K,N)$) where $N$ is a two step nilpotent Lie group, and $K$ is the group of orthogonal automorphisms of $N$. This family has a nice analytic property: almost all these 2-step nilpotent Lie group have square integrable representations. In this cases, following Moore-Wolf's

Jan Goedgebeur, Davide Mattiolo, Giuseppe Mazzuoccolo

It is well-known that the circular flow number of a bridgeless cubic graph can be computed in terms of certain partitions of its vertex-set with prescribed properties. In the present paper, we first study some of these properties that turn out to be useful in order to design a more efficient algorithm for the computation of the circular flow number of a brid

Mai M. A. Ahmed, Wael Z. Tawfik, M. A. K. Elfayoumi, M. Abdel-Hafiez

The modification and tailoring the characteristics of nanostructured materials are of great interest due to controllable and unusual inherent properties in such materials. A simple spray pyrolysis technique is used to prepare pure and La-doped ZnO films. The influence of La concentration (0, 0.33, 0.45, 0.66, 0.92 and 1.04 at. %) on the structural, optical,

Hossein Rastgoftar, Ella Atkins

This paper applies the principles of continuum mechanics to safely and resiliently coordinate a multi-agent team. A hybrid automation with two operation modes, Homogeneous Deformation Mode (HDM) and Containment Exclusion Mode (CEM), are developed to robustly manage group coordination in the presence of unpredicted agent failures. HDM becomes active when all

Perry Sakkaris, Ryan Sudhakaran

The circuit model of quantum computation is reformulated as a multilayer network theory [3] called a Quantum Multiverse Network (QuMvN). The QuMvN formulation allows us to interpret the quantum wave function as a combination of ergodic Markov Chains where each Markov Chain occupies a different layer in the QuMvN structure. Layers of a QuMvN are separable com

Sauptik Dhar, Vladimir Cherkassky

This paper extends the idea of Universum learning [1, 2] to single-class learning problems. We propose Single Class Universum-SVM setting that incorporates a priori knowledge (in the form of additional data samples) into the single class estimation problem. These additional data samples or Universum belong to the same application domain as (positive) data sa

Junmo Sung, Brian L. Evans

In millimeter wave (mmWave) communication systems, beamforming with large antenna arrays is critical to overcome high path losses. Separating all-digital beamforming into analog and digital stages can provide the large reduction in power consumption and small loss in spectral efficiency needed for practical implementations. Developing algorithms with this fa

Louis-Pierre Arguin, Jack Hanson

We identify simple mechanisms that prevents the onset of disorder chaos for the Ising spin glass model on $\mathbb Z^d$. This was first shown by Chatterjee in the case of Gaussian couplings. We present three proofs of the theorem for general couplings with continuous distribution based on the presence in the coupling realization of stabilizing features of po

Thomas Boquet, Laure Delisle, Denis Kochetkov, Nathan Schucher

Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance. We build on these efforts and take them further. We offer a principled experimental design methodology, based on linear mixed models, to study and separate the effects of multiple fa

Junmo Sung, Brian L. Evans

Hybrid beamforming (HB) architectures are attractive for wireless communication systems with large antenna arrays because the analog beamforming stage can significantly reduce the number of RF transceivers and hence power consumption. In HB systems, channel estimation (CE) becomes challenging due to indirect access by the baseband processing to the communica

Alena Sorokina, Aidana Karipbayeva, Zhenisbek Assylbekov

We perform an empirical evaluation of several methods of low-rank approximation in the problem of obtaining PMI-based word embeddings. All word vectors were trained on parts of a large corpus extracted from English Wikipedia (enwik9) which was divided into two equal-sized datasets, from which PMI matrices were obtained. A repeated measures design was used in

Saket S. Chaturvedi, Kajol Gupta, Vaishali Ninawe, Prakash S. Prasad

Diabetic Retinopathy is a critical health problem influences 100 million individuals worldwide, and these figures are expected to rise, particularly in Asia. Diabetic Retinopathy is a chronic eye disease which can lead to irreversible vision loss. Considering the visual complexity of retinal images, the early-stage diagnosis of Diabetic Retinopathy can be ch

Arit Kumar Bishwas, Ashish Mani, Vasile Palade

In this paper, we have proposed a deep quantum SVM formulation, and further demonstrated a quantum-clustering framework based on the quantum deep SVM formulation, deep convolutional neural networks, and quantum K-Means clustering. We have investigated the run time computational complexity of the proposed quantum deep clustering framework and compared with th

Hyodong Lee, James J. DiCarlo

Deep convolutional neural networks are biologically driven models that resemble the hierarchical structure of primate visual cortex and are the current best predictors of the neural responses measured along the ventral stream. However, the networks lack topographic properties that are present in the visual cortex, such as orientation maps in primary visual c

Alem Fitwi, Yu Chen, Sencun Zhu

Witnessing the increasingly pervasive deployment of security video surveillance systems(VSS), more and more individuals have become concerned with the issues of privacy violations. While the majority of the public have a favorable view of surveillance in terms of crime deterrence, individuals do not accept the invasive monitoring of their private life. To da

Payam Delgosha, Venkat Anantharam

Graphical data is comprised of a graph with marks on its edges and vertices. The mark indicates the value of some attribute associated to the respective edge or vertex. Examples of such data arise in social networks, molecular and systems biology, and web graphs, as well as in several other application areas. Our goal is to design schemes that can efficientl

Paul Bruillard, Julia Yael Plavnik, Eric C. Rowell, Qing Zhang

We develop categorical and number theoretical tools for the classification of super-modular categories. We apply these tools to obtain a partial classification of super-modular categories of rank $8$. In particular we find three distinct families of prime categories in rank $8$ in contrast to the lower rank cases for which there is only one such family.

C. Barsanti, A. Bonelli, M. Bruno, V. Ivantoc

An analysis of a group of seven variables stars, classed by ASAS-SN as uncertain RRab, is performed comparing their position in a H-R diagram with respect to a sample of variables of the same type built from public astronomical databases.

Iliya D. Iliev, Chengzhi Li, Jiang Yu

We study arbitrary cubic perturbations of the symmetric 8-loop Hamiltonian, which are linear with respect to the small parameter. It is shown that when the first 4 coefficients in the expansion of the displacement functions corresponding to both period annuli inside the loop vanish, the system becomes integrable with the following three strata in the center

Kaixu Huang, Fanman Meng, Hongliang Li, Shuai Chen

Class activation map (CAM) highlights regions of classes based on classification network, which is widely used in weakly supervised tasks. However, it faces the problem that the class activation regions are usually small and local. Although several efforts paid to the second step (the CAM generation step) have partially enhanced the generation, we believe su

Fathi Hassine, Nadia Souayeh

We consider a coupled wave system with partial Kelvin-Voigt damping in the interval (-1,1), where one wave is dissipative and the other does not. When the damping is effective in the whole domain (-1,1) it was proven in H.Portillo Oquendo and P.Sanez Pacheco, optimal decay for coupled waves with Kelvin-voigt damping, Applied Mathematics Letters 67 (2017), 16

Xiang Li, Jiechao Ma, Hongwei Li

Early diagnosis of pathological invasiveness of pulmonary adenocarcinomas using computed tomography (CT) imaging would alter the course of treatment of adenocarcinomas and subsequently improve the prognosis. Most of the existing systems use either conventional radiomics features or deep-learning features alone to predict the invasiveness. In this study, we e

Abhishek Dhar, Herbert Spohn

While Fourier's law is empirically confirmed for many substances and over an extremely wide range of thermodynamic parameters, a convincing microscopic derivation still poses difficulties. With current machines the solution of Newton's equations of motion can be obtained with high precision and for a reasonably large number of particles. For simplifi

Umberto Guarnotta, Salvatore A. Marano, Dumitru Motreanu

In this paper, the existence of smooth positive solutions to a Robin boundary-value problem with non-homogeneous differential operator and reaction given by a nonlinear convection term plus a singular one is established. Proofs chiefly exploit sub-super-solution and truncation techniques, set-valued analysis, recursive methods, nonlinear regularity theory, a

Juntao Du, Songxiao Li

The boundedness and compactness of Toeplitz operator from $A_ω^p$ to $A_ω^q$ with doubling weights $ω$ are studied in this paper. The characterizations of Schatten class Toeplitz operators and Volterra operators on $A_ω^2$ are also investigated.

Manuel Gerken, Binh Tran, Stephan Häfner, Eberhard Tiemann

We report on the observation of dipolar splitting in 6Li p-wave Feshbach resonances by highresolution atom-loss spectroscopy. The Feshbach resonances at 159 G and 215 G exhibit a doublet structure of 10 mG and 13 mG, respectively, associated with different projections of the orbital angular momentum. The observed splittings agree very well with coupled-chann

Shidong Jiang

We present a fast Gauss transform in one dimension using nearly optimal sum-of-exponentials approximations of the Gaussian kernel. For up to about ten-digit accuracy, the approximations are obtained via best rational approximations of the exponential function on the negative real axis. As compared with existing fast Gauss transforms, the algorithm is straigh

Zhi Chen, Jingjing Li, Yadan Luo, Zi Huang

Existing methods using generative adversarial approaches for Zero-Shot Learning (ZSL) aim to generate realistic visual features from class semantics by a single generative network, which is highly under-constrained. As a result, the previous methods cannot guarantee that the generated visual features can truthfully reflect the corresponding semantics. To add

C. O. Edet, P. O Okoi, S. O Chima

This study presents the solutions of Schrödinger equation for the Non-Central Generalized Inverse Quadratic Yukawa Potential within the framework of Nikiforov-Uvarov. The radial and angular part of the Schrödinger equation are obtained using the method of variable separation. More so, the bound states energy eigenvalues and corresponding eigenfunctions are o

Beyrem Khalfaoui, Joseph Boyd, Jean-Philippe Vert

Dropout is a regularisation technique in neural network training where unit activations are randomly set to zero with a given probability \emph{independently}. In this work, we propose a generalisation of dropout and other multiplicative noise injection schemes for shallow and deep neural networks, where the random noise applied to different units is not ind

Luma Omar, Ioannis Ivrissimtzis

Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value. The various measures of classifier performance assume, explicitly or implicitly, probability distributions $P_s$ and $P_n$ of the response belonging to either class, probability distributions for the cost of each type of misclassification,

Jayesh Badwaik, Praveen Chandrashekar, Christian Klingenberg

We propose an explicit, single step discontinuous Galerkin (DG) method on moving grids using the arbitrary Lagrangian-Eulerian (ALE) approach for one dimensional Euler equations. The grid is moved with the local fluid velocity modified by some smoothing, which is found to considerably reduce the numerical dissipation introduced by Riemann solvers. The scheme

D. A. Tumakov, Dmitry A. Telnov, G. Plunien, V. M. Shabaev

We calculate two-dimensional photoelectron momentum distributions and energy spectra after multiphoton ionization of Li atoms subject to intense laser fields in one-photon Rabi-flopping regime. The time-dependent Schrödinger equation is solved within the single-active-electron approximation using a model potential which reproduces accurately the binding ener

P. Kaklamanos, K. Uldall Kristiansen

In this work, we study the dynamics of piecewise smooth systems on a codimension-2 transverse intersection of two codimension-1 discontinuity sets. The Filippov convention can be extended to such intersections, but this approach does not provide a unique sliding vector and, as opposed to the classical sliding vector-field on codimension-1 discontinuity manif

Raj Kishore, Asmita Mukherjee, Sangem Rajesh

We present a recent calculation of the single spin asymmetry in inclusive photoproducton of $J/ψ$ at the future EIC, that can be used to probe the gluon Sivers function.

Hiba Abu Ahmad, Hongzhi Wang

Data repairing is a key problem in data cleaning which aims to uncover and rectify data errors. Traditional methods depend on data dependencies to check the existence of errors in data, but they fail to rectify the errors. To overcome this limitation, recent methods define repairing rules on which they depend to detect and fix errors. However, all existing d

Mengyao Zheng, Dixing Xu, Linshan Jiang, Chaojie Gu

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches developed in the context of cloud computing and