July 2019 arXiv papers — page 107

Aharon Davidson

We postulate a Planck scale horizon unit area, with no bits of information locally attached to it, connected but otherwise of free form, and let $n$ such geometric units compactly tile the black hole horizon. Associated with each topologically distinct tiling configuration is then a simple, connected, undirected, unlabeled, planar, chordal graph. The asympto

Neil K. Chada, Sari Lasanen, Lassi Roininen

This paper is concerned with the theoretical understanding of $α$-stable sheets $U$ on $\mathbb{R}^d$. Our motivation for this is in the context of Bayesian inverse problems, where we consider these processes as prior distributions, aiming to quantify information of the posterior. We derive convergence results referring to finite-dimensional approximations o

Dongfang Xu, Xianghao Yu, Yan Sun, Derrick Wing Kwan Ng

In this paper, we study resource allocation design for secure communication in intelligent reflecting surface (IRS)-assisted multiuser multiple-input single-output (MISO) communication systems. To enhance physical layer security, artificial noise (AN) is transmitted from the base station (BS) to deliberately impair the channel of an eavesdropper. In particul

Yuexin Zhang, Askar B. Abdikamalov, Dimitry Ayzenberg, Cosimo Bambi

In a previous paper, we tried to test the Kerr nature of the stellar-mass black hole in GRS 1915+105 by analyzing NuSTAR data of 2012 with our reflection model RELXILL_NK. We found that the choice of the intensity profile of the reflection component is crucial and eventually we were not able to get any constraint on the spacetime metric around the black hole

Risheng Liu, Long Ma, Xiaoming Yuan, Shangzhi Zeng

This paper firstly proposes a convex bilevel optimization paradigm to formulate and optimize popular learning and vision problems in real-world scenarios. Different from conventional approaches, which directly design their iteration schemes based on given problem formulation, we introduce a task-oriented energy as our latent constraint which integrates riche

Colton Powell, Christopher Desiniotis, Behnam Dezfouli

With the rise of the Internet of Things (IoT), fog computing has emerged to help traditional cloud computing in meeting scalability demands. Fog computing makes it possible to fulfill real-time requirements of applications by bringing more processing, storage, and control power geographically closer to end-devices. However, since fog computing is a relativel

Mehdi Neshat, Ehsan Abbasnejad, Qinfeng Shi, Bradley Alexander

The installed amount of renewable energy has expanded massively in recent years. Wave energy, with its high capacity factors has great potential to complement established sources of solar and wind energy. This study explores the problem of optimising the layout of advanced, three-tether wave energy converters in a size-constrained farm in a numerically model

Garrett Alston, Erkao Bao

We define Lagrangian Floer cohomology over $\mathbb Z_2$-coefficients by counting pearly trajectories for graded, exact Lagrangian immersions that satisfy certain positivity condition on the index of the non-embedded points, and show that it is an invariant of the Lagrangian immersion under Hamiltonian deformations. We also show that it is naturally isomorph

Nan Liu, Boling Guo, Deng-Shan Wang, Yufeng Wang

We investigate an integrable extended modified Korteweg-de Vries equation on the line with the initial value belonging to the Schwartz space. By performing the nonlinear steepest descent analysis of an associated matrix Riemann--Hilbert problem, we obtain the explicit leading-order asymptotics of the solution of this initial value problem as time $t$ goes to

Yaohua Xie

Super-resolution microscopes (such as STED) illuminate samples with a tiny spot, and achieve very high resolution. But structures smaller than the spot cannot be resolved in this way. Therefore, we propose a technique to solve this problem. It is termed "Deconvolution after Dense Scan (DDS)". First, a preprocessing stage is introduced to eliminate th

Matthew Groh, Ziv Epstein, Nick Obradovich, Manuel Cebrian

Recent advances in neural networks for content generation enable artificial intelligence (AI) models to generate high-quality media manipulations. Here we report on a randomized experiment designed to study the effect of exposure to media manipulations on over 15,000 individuals' ability to discern machine-manipulated media. We engineer a neural network

Junbo Wang, Peng Yan, Leiting Dong, Satya N. Atluri

An explicit solution, considering the interface bending resistance as described by the Steigmann-Ogden interface model, is derived for the problem of a spherical nano-inhomogeneity (nanoscale void/inclusion) embedded in an infinite linear-elastic matrix under a general uniform far-field-stress (including tensile and shear stresses). The Papkovich-Neuber (P-N

Xin Fang, Jihong Wen, Dianlong Yu

Material band structure is key foundation for various modern technologies, but it was regarded as a space-time invariant feature. Acoustic metamaterials show extraordinary properties for processing elastic waves, but conventional realizations suffer from narrow bandgaps. Here we first report a nonlinear acoustic metamaterial whose band structure self-adapts

Barak Shoshany

These lecture notes were prepared for a 25-hour course for advanced undergraduate students participating in Perimeter Institute's Undergraduate Summer Program. The lectures cover some of what is currently known about the possibility of superluminal travel and time travel within the context of established science, that is, general relativity and quantum f

George Trimponias, Yan Xiao, Xiaorui Wu, Hong Xu

Traffic engineering (TE) is a fundamental task in networking. Conventionally, traffic can take any path connecting the source and destination. Emerging technologies such as segment routing, however, use logical paths going through a predetermined set of middlepoints. Inspired by this, in this work we introduce the problem of node-constrained TE, where traffi

Aurel Bulgac

In reactions the wave packets of the emerging products typically are not eigenstates of particle number operators or any other conserved quantities and their properties are entangled. I describe a particle projection technique in parts of space, which eschews the need to evaluate Pfaffians in the case of overlap of generalized Slater determinants or Hartree-

Viktor V. Nikitin, Geser A. Dugarov, Anton A. Duchkov, Mihail I. Fokin

We present the results of dynamic in-situ 3D X-ray imaging of methane hydrates microstructure during methane hydrate formation and decomposition in sand samples. Short scanning times and high resolution provided by synchrotron X-rays allowed for better understanding of water movement and different types of gas-hydrate formation. Complementing previous observ

Yue Yang, Linshuang Long, Sheng Meng, Nicholas Denisuk

Through passively emitting excess heat to the outer space, radiative cooling has been demonstrated as an efficient way for energy saving applications. Selective surface with unity emittance only within the atmospheric window as well as zero absorption within the solar spectrum is sought to achieve the best sub-ambient radiative cooling performance during the

Hansi Zhang, Christopher Wheldon, Adam G. Dunn, Cui Tao

Objectives To test the feasibility of using Twitter data to assess determinants of consumers' health behavior towards Human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). Methods We used three Twitter datasets spanning from 2014 to 2018. We preprocessed and geocoded the tweets, and then built a rule-based model that cla

Zainab Alalawi, The Anh Han, Yifeng Zeng, Aiman Elragig

Spending by the UK's National Health Service (NHS) on independent healthcare treatment has been increased in recent years and is predicted to sustain its upward trend with the forecast of population growth. Some have viewed this increase as an attempt not to expand the patients' choices but to privatize public healthcare. This debate poses a social d

Pierre Baudot

This paper presents the computational methods of information cohomology applied to genetic expression in and in the companion paper and proposes its interpretations in terms of statistical physics and machine learning. In order to further underline the Hochschild cohomological nature af information functions and chain rules, following, the computation of the

Estelle Maudet, Oralie Cattan, Maureen de Seyssel, Christophe Servan

This paper reports on Qwant Research contribution to tasks 2 and 3 of the DEFT 2019's challenge, focusing on French clinical cases analysis. Task 2 is a task on semantic similarity between clinical cases and discussions. For this task, we propose an approach based on language models and evaluate the impact on the results of different preprocessings and m

Yu Bao, Hao Zhou, Shujian Huang, Lei Li

Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous latent space does not explicitly model the syntactic information. In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces. Our proposed method

Letao Liu, Martin Saerbeck, Justin Dauwels

Autonomous vehicles (AV) have progressed rapidly with the advancements in computer vision algorithms. The deep convolutional neural network as the main contributor to this advancement has boosted the classification accuracy dramatically. However, the discovery of adversarial examples reveals the generalization gap between dataset and the real world. Furtherm

Mohammed Senoussaoui, Patrick Cardinal, Alessandro Lameiras Koerich

In this paper we present a novel approach for extracting a Bag-of-Words (BoW) representation based on a Neural Network codebook. The conventional BoW model is based on a dictionary (codebook) built from elementary representations which are selected randomly or by using a clustering algorithm on a training dataset. A metric is then used to assign unseen eleme

Pierre Baudot, Monica Tapia, Daniel Bennequin, Jean-Marc Goaillard

This paper presents methods that quantify the structure of statistical interactions within a given data set, and was first used in \cite{Tapia2018}. It establishes new results on the k-multivariate mutual-informations (I_k) inspired by the topological formulation of Information introduced in. In particular we show that the vanishing of all I_k for 2\leq k \l

Jeremy Kepner, Vijay Gadepally, Lauren Milechin, Siddharth Samsi

The Dynamic Distributed Dimensional Data Model (D4M) library implements associative arrays in a variety of languages (Python, Julia, and Matlab/Octave) and provides a lightweight in-memory database implementation of hypersparse arrays that are ideal for analyzing many types of network data. D4M relies on associative arrays which combine properties of spreads

Jia-Yang Chen, Zhao-hui Ma, Yong Meng Sua, Yu-Ping Huang

We demonstrate efficient second harmonic generation in a quasi-phase-matched, high quality factor ($Q_0 \approx 5.3\times 10^5$) racetrack microresonator. The observed normalized conversion efficiency is about $3.8\%~mW^{-1}$.

Joydeep Singha, Neelima Gupte

We study the existence of chimera states, i.e. mixed states, in a globally coupled sine circle map lattice, with different strengths of inter-group and intra-group coupling. We find that at specific values of the parameters of the CML, a completely random initial condition evolves to chimera states, having a phase synchronised and a phase desynchronised grou

Radoslav Paulen, Miroslav Fikar

This paper studies a dynamic real-time optimization in the context of model-based time-optimal operation of batch processes under parametric model mismatch. In order to tackle the model-mismatch issue, a receding-horizon policy is usually followed with frequent re-optimization. The main problem addressed in this study is the high computational burden that is

Koichi Tojo, Taro Yoshino

Exponential family plays an important role in information geometry. In arXiv:1811.01394, we introduced a method to construct an exponential family $\mathcal{P}=\{p_θ\}_{θ\inΘ}$ on a homogeneous space $G/H$ from a pair $(V,v_0)$. Here $V$ is a representation of $G$ and $v_0$ is an $H$-fixed vector in $V$. Then the following questions naturally arise: (Q1) whe

Sergio J. Martinez, Ivan M. Mendoza, Genaro J. Martinez, Shigeru Ninagawa

Universality in cellular automata theory is a central problem studied and developed from their origins by John von Neumann. In this paper, we present an algorithm where any Turing machine can be converted to one-dimensional cellular automaton with a 2-linear time and display its spatial dynamics. Three particular Turing machines are converted in three univer

Shailin Ji, Haodong Liu

In this paper, we study the maximum principle for stochastic optimal control problems of forward-backward stochastic difference systems (FBSΔSs) where the uncertainty is modeled by a discrete time, finite state process, rather than white noises. Two types of FBSΔSs are investigated. The first one is described by a partially coupled forward-backward stochasti

Laura Jehl, Carolin Lawrence, Stefan Riezler

In many machine learning scenarios, supervision by gold labels is not available and consequently neural models cannot be trained directly by maximum likelihood estimation (MLE). In a weak supervision scenario, metric-augmented objectives can be employed to assign feedback to model outputs, which can be used to extract a supervision signal for training. We pr

Benjamin Morrison, Adam Groce

We study the relationship between problems solvable by quantum algorithms in polynomial time and those for which zero-knowledge proofs exist. In prior work, Aaronson [arxiv:quant-ph/0111102] showed an oracle separation between BQP and SZK, i.e. an oracle $A$ such that $\mathrm{SZK}^A \not\subseteq \mathrm{BQP}^A$. In this paper we give a simple extension of

J. K. Joseph, W. M. T. Chathurika, A. Nugaliyadde, Y. Mallawarachchi

Machine Translation (MT) is an area in natural language processing, which focus on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language ha

Isaac F. Silvera, Ranga Dias

Loubeyre, Occelli, and Dumas (LOD) [1] claim to have produced metallic hydrogen (MH) at a pressure of 425 GPa, without the necessary supporting evidence of an insulator to metal transition. The paper is much ado about nothing. Most of the results have been reported earlier. Zha, Liu, and Hemley [2] studied hydrogen at low temperature up to 360 GPa in 2012; t

Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei

``Composable core-sets'' are an efficient framework for solving optimization problems in massive data models. In this work, we consider efficient construction of composable core-sets for the determinant maximization problem. This can also be cast as the MAP inference task for determinantal point processes, that have recently gained a lot of interest

Juan D. S. Ortega, Mohammed Senoussaoui, Eric Granger, Marco Pedersoli

This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition. The proposed DNN architecture has independent and shared layers which aim to learn the representation for each modality, as well as the best combined representation to achieve the best prediction. Experimental results on th

Chansup Byun, Jeremy Kepner, William Arcand, David Bestor

The Intel Xeon Phi manycore processor is designed to provide high performance matrix computations of the type often performed in data analysis. Common data analysis environments include Matlab, GNU Octave, Julia, Python, and R. Achieving optimal performance of matrix operations within data analysis environments requires tuning the Xeon Phi OpenMP settings, p

Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld

There has been significant study on the sample complexity of testing properties of distributions over large domains. For many properties, it is known that the sample complexity can be substantially smaller than the domain size. For example, over a domain of size $n$, distinguishing the uniform distribution from distributions that are far from uniform in $\el

Ghazaleh Beigi, Kai Shu, Ruocheng Guo, Suhang Wang

Online users generate tremendous amounts of textual information by participating in different activities, such as writing reviews and sharing tweets. This textual data provides opportunities for researchers and business partners to study and understand individuals. However, this user-generated textual data not only can reveal the identity of the user but als

J. Sesma

A doubly infinite set of series expansion for $1/π$ are reported. They follow trivially from a formal expansion for the quotient of the values taken by the gamma function for two (complex) arguments differing by an integer plus one half, obtained by an alternative computation of the Wronskian of the modified Bessel functions. The same formal expansion allows

Bobak Farzin, Piotr Czapla, Jeremy Howard

Our entry into the HAHA 2019 Challenge placed $3^{rd}$ in the classification task and $2^{nd}$ in the regression task. We describe our system and innovations, as well as comparing our results to a Naive Bayes baseline. A large Twitter based corpus allowed us to train a language model from scratch focused on Spanish and transfer that knowledge to our competit

Junxian Geng, Wei Shi, Guanyu Hu

Intensity estimation is a common problem in statistical analysis of spatial point pattern data. This paper proposes a nonparametric Bayesian method for estimating the spatial point process intensity based on mixture of finite mixture (MFM) model. MFM approach leads to a consistent estimate of the intensity of spatial point patterns in different areas while c

William R. Milner, John M. Robinson, Colin J. Kennedy, Tobias Bothwell

We demonstrate a time scale based on a phase stable optical carrier that accumulates an estimated time error of $48\pm94$ ps over 34 days of operation. This all-optical time scale is formed with a cryogenic silicon cavity exhibiting improved long-term stability and an accurate $^{87}$Sr lattice clock. We show that this new time scale architecture outperforms

X. J. Liu, M. J. Keith, C. Bassa, B. W. Stappers

We investigate the impact of noise processes on high-precision pulsar timing. Our analysis focuses on the measurability of the second spin frequency derivative $\ddotν$. This $\ddotν$ can be induced by several factors including the radial velocity of a pulsar. We use Bayesian methods to model the pulsar times-of-arrival in the presence of red timing noise an

Maryam Aliakbarpour, Themis Gouleakis, John Peebles, Ronitt Rubinfeld

In this work, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution $p$ over a poset is monotone if, for any pair of domain elements $x$ and $y$ such that $x \preceq y$, $p(x) \leq p(y)$. To understand the sample complexity of this problem, we introduce a new property called bigness over

Meng Qu, Jian Tang, Yoshua Bengio

This paper studies aligning knowledge graphs from different sources or languages. Most existing methods train supervised methods for the alignment, which usually require a large number of aligned knowledge triplets. However, such a large number of aligned knowledge triplets may not be available or are expensive to obtain in many domains. Therefore, in this p

N. E. Firsova, S. A. Ktitorov

A recently derived formula for complex conductivity of the monolayer graphene is analyzed. We show that the real and imaginary parts in this formula obey the Kramers and Kronig dispersion relations which are a good test for validity of the formula for complex conductivity of monolayer graphene. We consider also an additional test for this formula, sensitive

Tuyen Vu

In this paper, we present an optimization algorithm based on an alternating projection method to solve the large-scale security constraint optimal power flow (SCOPF) problem in power systems. The SCOPF is first partitioned into sub-problems, which share common power components. The proposed algorithm is fundamentally a distributed computing algorithm, which

Nana Cabo Bizet, César Damián Ascencio, Octavio Obregón, Roberto Santos-Silva

Exploring the analogy between quantum mechanics and statistical mechanics we formulate an integrated version of the Quantropy functional [1]. With this prescription we compute the propagator associated to Boltzmann-Gibbs statistics in the semiclassical approximation as $K=F(T) \exp\left(i S_{cl}/\hbar\right)$. We determine also propagators associated to diff

Xuekai Ma, Bernd Berger, Marc Assmann, Rodislav Driben

Vortices are topological objects representing the circular motion of a fluid. With their additional degree of freedom, the 'vorticity', they have been widely investigated in many physical systems and different materials for fundamental interest and for applications in data storage and information processing. Vortices have also been observed in non-eq

Constant Schouder, Adam S. Chatterley, Florent Calvo, Lars Christiansen

Dimers of tetracene molecules are formed inside helium nanodroplets and identified through covariance analysis of the emission directions of kinetic tetracene cations stemming from femtosecond laser-induced Coulomb explosion. Next, the dimers are aligned in either one or three dimensions under field-free conditions by a nonresonant, moderately intense laser

Jingcheng Du, Chongliang Luo, Qiang Wei, Yong Chen

In this study, we proposed a convolutional neural network model for gender prediction using English Twitter text as input. Ensemble of proposed model achieved an accuracy at 0.8237 on gender prediction and compared favorably with the state-of-the-art performance in a recent author profiling task. We further leveraged the trained models to predict the gender

Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell

We propose a self-supervised approach to deep surface deformation. Given a pair of shapes, our algorithm directly predicts a parametric transformation from one shape to the other respecting correspondences. Our insight is to use cycle-consistency to define a notion of good correspondences in groups of objects and use it as a supervisory signal to train our n

Bilal Soomro, Anssi Kanervisto, Trung Ngo Trong, Ville Hautamäki

Deep neural networks (DNN) are able to successfully process and classify speech utterances. However, understanding the reason behind a classification by DNN is difficult. One such debugging method used with image classification DNNs is activation maximization, which generates example-images that are classified as one of the classes. In this work, we evaluate

Wei-Kun Chen, Yu-Hong Dai

It is important to design separation algorithms of low computational complexity in mixed integer programming. We study the separation problems of the two continuous knapsack polyhedra with divisible capacities. The two polyhedra are the convex hulls of the sets which consist of $ n $ nonnegative integer variables, one unbounded continuous, $ m $ bounded cont

Brajesh Kumar, Chakali Eswaraiah, Avinash Singh, D. K. Sahu

We present the results based on photometric ($Swift$ UVOT), broad-band polarimetric ($V$ and $R$-band) and optical spectroscopic observations of the Type IIn supernova (SN) 2017hcc. Our study is supplemented with spectropolarimetric data available in literature for this event. The post-peak light curve evolution is slow ($\sim$0.2 mag 100 d$^{-1}$ in $b$-ban

Avinash Singh, Brajesh Kumar, Takashi J. Moriya, G. C. Anupama

The optical and ultra-violet broadband photometric and spectroscopic observations of the Type II supernova (SN) 2016gfy are presented. The $V$-band light curve (LC) shows a distinct plateau phase with a slope, $s_2$ $\sim$ 0.12 mag / 100 d and a duration of 90 $\pm$ 5 d. Detailed analysis of SN 2016gfy provided a mean $\rm^{56}Ni$ mass of 0.033 $\pm$ 0.003 $

Stephen Finbow, Christopher M. van Bommel

For a graph $G = (V, E)$, the $γ$-graph of $G$, denoted $G(γ) = (V(γ), E(γ))$, is the graph whose vertex set is the collection of minimum dominating sets, or $γ$-sets of $G$, and two $γ$-sets are adjacent in $G(γ)$ if they differ by a single vertex and the two different vertices are adjacent in $G$. In this paper, we consider $γ$-graphs of trees. We develop

Amilcar Branquinho, Ana Foulquié Moreno, Manuel Mañas

In this paper the Riemann-Hilbert problem, with jump supported on a appropriate curve on the complex plane with a finite endpoint at the origin, is used for the study of corresponding matrix biorthogonal polynomials associated with Laguerre type matrices of weights ---which are constructed in terms of a given matrix Pearson equation. First and second order d

Helton Graziadei, Hedibert F. Lopes, Paulo C. Marques F

We develop a Bayesian hierarchical semiparametric model for phenomena related to time series of counts. The main feature of the model is its capability to learn a latent pattern of heterogeneity in the distribution of the process innovation rates, which are softly clustered through time with the help of a Dirichlet process placed at the top of the model hier

Anne Gégout-Petit, Aurélie Gueudin-Muller, Clémence Karmann

We consider the problem of variable selection in regression models. In particular, we are interested in selecting explanatory covariates linked with the response variable and we want to determine which covariates are relevant, that is which covariates are involved in the model. In this framework, we deal with L1-penalised regression models. To handle the cho

Dan-Andrei Geba, Evan Witz

This article studies the global well-posedness for a class of defocusing, generalized sixth-order Boussinesq equations, extending a previous result obtained by Wang and Esfahani for the case when the nonlinear term is cubic.

Sean M. Devine, Nathaniel D. Bastian

The use of machine learning and intelligent systems has become an established practice in the realm of malware detection and cyber threat prevention. In an environment characterized by widespread accessibility and big data, the feasibility of malware classification without the use of artificial intelligence-based techniques has been diminished exponentially.

Kirill A. Cherednichenko, Vladimir L. Solozhenko

Thermal expansion of α-rhombohedral boron (α-B12) and two isostructural boron-rich pnictides (B12P2 and B12As2) has been studied between 298 and 1280 K by high-temperature synchrotron X-ray diffraction. For all studied phases no temperature-induced phase transitions have been observed. The observed temperature dependencies of the lattice parameters and unit

Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, Pascal Poupart

Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones-a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been

Radek Honzik, Sarka Stejskalova

In the first part of the paper, we show that if $ω\le κ< λ$ are cardinals, $κ^{<κ} = κ$, and $λ$ is weakly compact, then in $V[\M(κ,λ)]$ the tree property at $λ= κ^{++V[\M(κ,λ)]}$ is indestructible under all $κ^+$-cc forcing notions which live in $V[\Add(κ,λ)]$, where $\Add(κ,λ)$ is the Cohen forcing for adding $λ$-many subsets of $κ$ and $\M(κ,λ)$ is the st

Tuyen Vu, Bang Le

In this paper, we present a novel distributed state estimation approach in networked DC microgrids to detect the false data injection in the microgrid control network. Each microgrid monitored by a distributed state estimator will detect if there is manipulated data received from their neighboring microgrids for control purposes. A dynamic model supporting t

Bang L. H. Nguyen, Tuyen V. Vu, Tuan A. Ngo

This paper proposes a decentralized dynamic state estimation scheme for microgrids. The approach employs the voltage and current measurements in the dq0 reference frame through phasor synchronization to be able to exclude orthogonal functions from their relationship formulas. Based on that premise, we utilize a Kalman filter to dynamically estimate states of

Mohamed Grissa, Attila A. Yavuz, Bechir Hamdaoui

As part of its ongoing efforts to meet the increased spectrum demand, the Federal Communications Commission (FCC) has recently opened up 150 MHz in the 3.5 GHz band for shared wireless broadband use. Access and operations in this band, aka Citizens Broadband Radio Service (CBRS), will be managed by a dynamic spectrum access system (SAS) to enable seamless sp

Bangti Jin, Zehui Zhou, Jun Zou

In this work, we analyze the regularizing property of the stochastic gradient descent for the efficient numerical solution of a class of nonlinear ill-posed inverse problems in Hilbert spaces. At each step of the iteration, the method randomly chooses one equation from the nonlinear system to obtain an unbiased stochastic estimate of the gradient, and then p

Kangsan Kim, Yongho Shin, Hyung-Chan An

Facility location is a prominent optimization problem that has inspired a large quantity of both theoretical and practical studies in combinatorial optimization. Although the problem has been investigated under various settings reflecting typical structures within the optimization problems of practical interest, little is known on how the problem behaves in

Pengju Liu, Hongzhi Zhang, Wei Lian, Wangmeng Zuo

In computer vision, convolutional networks (CNNs) often adopts pooling to enlarge receptive field which has the advantage of low computational complexity. However, pooling can cause information loss and thus is detrimental to further operations such as features extraction and analysis. Recently, dilated filter has been proposed to trade off between receptive

J. B. R. Oonk, E. L. Alexander, J. W. Broderick, M. Sokolowski

The Engineering Development Array (EDA) is a single test station for Square Kilometre Array (SKA) precursor technology. We have used the EDA to detect low-frequency radio recombination lines (RRLs) from the Galactic Centre region. Low-frequency RRLs are an area of interest for future low-frequency SKA work as these lines provide important information on the

Irina A. Bilenko, Kseniya S. Tavastsherna

Regularities of formation of coronal holes (CH) at the rising phase of cycle 23 are investigated. The period from 01.01.1997 to 01.03.2000 (Carrington rotations (CRs) 1918-2059) is considered in detail. The evolution of the global magnetic field (GMF) of the Sun from the zonal to the sectorial structure is analyzed. It is shown that the zonal structure is qu

Weishuo Liu, Jian Fang, Stefano Rolfo, Lipeng Lu

This work presents a converged framework of Machine-Learning Assisted Turbulence Modeling (MLATM). Our objective is to develop a turbulence model directly learning from high fidelity data (DNS/LES) with eddy-viscosity hypothesis induced. First, the target machine-learning quantity is discussed in order to avoid the ill-conditioning problem of RANS equations.

Gergana M. Radulova, Tatiana G. Slavova, Peter A. Kralchevsky, Elka S. Basheva

Colloidosomes provide a possibility to encapsulate oily substances in water in the form of core-in-shell structures. In this study, we produced microcapsules with shell from colloidal particles, where the interparticle openings are blocked by mixed layers from polymer and surfactant that prevent the leakage of cargo molecules. The particles and polymer play

Bicky A. Marquez, Jose Suarez-Vargas, Bhavin J. Shastri

We describe a new technique which minimizes the amount of neurons in the hidden layer of a random recurrent neural network (rRNN) for time series prediction. Merging Takens-based attractor reconstruction methods with machine learning, we identify a mechanism for feature extraction that can be leveraged to lower the network size. We obtain criteria specific t

Jia-Jing Lu, Zeng-Hua Li, G. F. Burgio, H. -J Schulze

We study the properties of hot beta-stable nuclear matter using equations of state derived within the Brueckner-Hartree-Fock approach at finite temperature including consistent three-body forces. Simple and accurate parametrizations of the finite-temperature equations of state are provided. The properties of hot neutron stars are then investigated within thi

Pavlos Kolias, Alexandra Papadopoulou

DNA segments and sequences have been studied thoroughly during the past decades. One of the main problems in computational biology is the identification of exon-intron structures inside genes using mathematical techniques. Previous studies have used different methods, such as Fourier analysis and hidden-Markov models, in order to be able to predict which par

Jie An, Haoyi Xiong, Jiebo Luo, Jun Huan

Universal style transfer is an image editing task that renders an input content image using the visual style of arbitrary reference images, including both artistic and photorealistic stylization. Given a pair of images as the source of content and the reference of style, existing solutions usually first train an auto-encoder (AE) to reconstruct the image usi

Dipti R. Pattanayak, Vivek K. Dwivedi, Vikram Karwal, Imran Shafique Ansari

In this letter, the secrecy performance of a mixed free space optics (FSO) and radio frequency (RF) system is analyzed from physical layer security (PHY) perspective. In this scenario, one or more eavesdroppers are trying to intercept the confidential signal in a mixed FSO/RF system. The faded FSO links are modeled by Malaga ($\mathcal{M} $) distribution and

JaeWon Choi, Sung-eui Yoon

At an early age, human infants are able to learn and build a model of the world very quickly by constantly observing and interacting with objects around them. One of the most fundamental intuitions human infants acquire is intuitive physics. Human infants learn and develop these models, which later serve as prior knowledge for further learning. Inspired by s

Boling Guo, Guoquan Qin

We prove the existence of time periodic solution to the 3D Ginzburg-Landau equation in weighted Sobolev spaces. We consider the cubic Ginzburg-Landau equation with an external force $g$ satisfying the oddness condition $g(-x,t)=-g(x,t)$. The existence of the periodic solution is proved for small time-periodic external force. The stability of the time periodi

Loris Arnold

We discuss the notion of $γ$-$H^{\infty}$-bounded calculus, strong $γ$-$m$-$H^{\infty}$-bounded calculus on half-plane and weak-$γ$-Gomilko-Shi-Feng condition and give a connection between them. Then we state a characterization of generation of $γ$-bounded $C_0$-semigroup in $K$-convex space, which leads to a version of Gearhart-Prüss on $K$-convex space.

Lena Shakurova, Beata Nyari, Chao Li, Mihai Rotaru

Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages. They are a crucial component for scaling tasks to multiple languages by transferring knowledge from languages with rich resources to low-resource languages. A common approach to learning cross-lingual embeddings

Mohamed Seghir Hadj Ameur, Farid Meziane, Ahmed Guessoum

In this paper, we make freely accessible ANETAC our English-Arabic named entity transliteration and classification dataset that we built from freely available parallel translation corpora. The dataset contains 79,924 instances, each instance is a triplet (e, a, c), where e is the English named entity, a is its Arabic transliteration and c is its class that c

Saba Heidari Gheshlaghi, Amin Ranjbar, Amir Abolfazl Suratgar, Mohammad Bagher Menhaj

A Superpixel Segmentation Based Technique for Multiple Sclerosis Lesion Detection

Lillian B. Pierce, Junyan Xu

In this work we establish a Burgess bound for short multiplicative character sums in arbitrary dimensions, in which the character is evaluated at a homogeneous form that belongs to a very general class of &#34;admissible&#34; forms. This $n$-dimensional Burgess bound is nontrivial for sums over boxes of sidelength at least $q^β$, with $β> 1/2 - 1/(2(n+1))$.

Emmanuel Klinger, Hrayr Azizbekyan, Armen Sargsyan, Claude Leroy

We present an experimental scheme performing scalar magnetometry based on the fitting of Rb D$_2$ line spectra recorded by derivative selective reflection spectroscopy from an optical nanometric-thick cell. To demonstrate its efficiency, the magnetometer is used to measure the inhomogeneous magnetic field produced by a permanent neodimuim-iron-boron alloy ri

Peter-Michael Osera

We explore an approach to type-directed program synthesis rooted in constraint-based type inference techniques. By doing this, we aim to more efficiently synthesize polymorphic code while also tackling advanced typing features such as GADTs that build upon polymorphism. Along the way, we also present an implementation of these techniques in Scythe, a prototy

Igor Shevkunov, Vladimir Katkovnik, Daniel Claus, Giancarlo Pedrini

A new denoising algorithm for hyperspectral complex domain data has been developed and studied. This algorithm is based on the complex domain block-matching 3D filter including the 3D Wiener filtering stage. The developed algorithm is applied and tuned to work in the singular value decomposition (SVD) eigenspace of reduced dimension. The accuracy and quantit

Reinhard Heckel

Deep convolutional neural networks trained on large datsets have emerged as an intriguing alternative for compressing images and solving inverse problems such as denoising and compressive sensing. However, it has only recently been realized that even without training, convolutional networks can function as concise image models, and thus regularize inverse pr

Santosh Pathak

In this paper, we reprove the principal result of a paper by H-O Kreiss and Jens Lorenz from a different approach than the method proposed in their paper. More precisely, we consider the Cauchy problem for the incompressible Navier-Stokes equations in $\mathbb{R}^n$ for $n \ge 3$ with non-decaying initial data and derive a priori estimates of the maximum nor

Elit Cenk Alp, Mehmet Serdar Guzel

Flappy Bird, which has a very high popularity, has been trained in many algorithms. Some of these studies were trained from raw pixel values of game and some from specific attributes. In this study, the model was trained with raw game images, which had not been seen before. The trained model has learned as reinforcement when to make which decision. As an inp

Shikun He, Qing Qin, Tiejun Zhou, Christos Panagopoulos

Broadband ferromagnetic resonance is a useful technique to determine the magnetic anisotropy and study the magnetization dynamics of magnetic thin films. We report a spring-loaded sample loading manipulator for reliable sample mounting and rotation. The manipulator enables maximum signal, enhances system stability and is particularly useful for fully automat

George Trenins, Michael J. Willatt, Stuart C. Althorpe

We develop a path-integral dynamics method for water that resembles centroid molecular dynamics (CMD), except that the centroids are averages of curvilinear, rather than cartesian, bead coordinates. The curvilinear coordinates are used explicitly only when computing the potential of mean force, the components of which are re-expressed in terms of cartesian &

Qianqian Yang, Qiangqiang Yuan, Linwei Yue, Huanfeng Shen

Satellite-based retrieval has become a popular PM2.5 monitoring method currently. To improve the retrieval performance, multiple variables are usually introduced as auxiliary variable in addition to aerosol optical depth (AOD). Different kinds of variables are usually at different resolutions varying from sub-kilometers to dozens of kilometers. Generally, wh