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Discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. A discrete Lawson correspondence
The main result of this paper is a discrete Lawson correspondence between discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. This is a correspondence between two discrete isothermic surfaces. We show that this correspondence is an isometry in the following sense: it preserves the metric coefficients i...
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Network Model Selection Using Task-Focused Minimum Description Length
Networks are fundamental models for data used in practically every application domain. In most instances, several implicit or explicit choices about the network definition impact the translation of underlying data to a network representation, and the subsequent question(s) about the underlying system being represente...
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Concentration of curvature and Lipschitz invariants of holomorphic functions of two variables
By combining analytic and geometric viewpoints on the concentration of the curvature of the Milnor fibre, we prove that Lipschitz homeomorphisms preserve the zones of multi-scale curvature concentration as well as the gradient canyon structure of holomorphic functions of two variables. This yields the first new Lipsc...
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Beam tuning and bunch length measurement in the bunch compression operation at the cERL
Realization of a short bunch beam by manipulating the longitudinal phase space distribution with a finite longitudinal dispersion following an off-crest accelera- tion is a widely used technique. The technique was applied in a compact test accelerator of an energy-recovery linac scheme for compressing the bunch lengt...
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Long-Lived Ultracold Molecules with Electric and Magnetic Dipole Moments
We create fermionic dipolar $^{23}$Na$^6$Li molecules in their triplet ground state from an ultracold mixture of $^{23}$Na and $^6$Li. Using magneto-association across a narrow Feshbach resonance followed by a two-photon STIRAP transfer to the triplet ground state, we produce $3\,{\times}\,10^4$ ground state molecule...
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Bilinear generalized Radon transforms in the plane
Let $\sigma$ be arc-length measure on $S^1\subset \mathbb R^2$ and $\Theta$ denote rotation by an angle $\theta \in (0, \pi]$. Define a model bilinear generalized Radon transform, $$B_{\theta}(f,g)(x)=\int_{S^1} f(x-y)g(x-\Theta y)\, d\sigma(y),$$ an analogue of the linear generalized Radon transforms of Guillemin an...
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Diffusion of new products with recovering consumers
We consider the diffusion of new products in the discrete Bass-SIR model, in which consumers who adopt the product can later "recover" and stop influencing their peers to adopt the product. To gain insight into the effect of the social network structure on the diffusion, we focus on two extreme cases. In the "most-co...
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Answering Complex Questions Using Open Information Extraction
While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date suc...
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On Testing Quantum Programs
A quantum computer (QC) can solve many computational problems more efficiently than a classic one. The field of QCs is growing: companies (such as DWave, IBM, Google, and Microsoft) are building QC offerings. We position that software engineers should look into defining a set of software engineering practices that ap...
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Degree weighted recurrence networks for the analysis of time series data
Recurrence networks are powerful tools used effectively in the nonlinear analysis of time series data. The analysis in this context is done mostly with unweighted and undirected complex networks constructed with specific criteria from the time series. In this work, we propose a novel method to construct "weighted rec...
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Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age
As Ocean General Circulation Models (OGCMs) move into the petascale age, where the output from global high-resolution model runs can be of the order of hundreds of terabytes in size, tools to analyse the output of these models will need to scale up too. Lagrangian Ocean Analysis, where virtual particles are tracked t...
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TFLMS: Large Model Support in TensorFlow by Graph Rewriting
While accelerators such as GPUs have limited memory, deep neural networks are becoming larger and will not fit with the memory limitation of accelerators for training. We propose an approach to tackle this problem by rewriting the computational graph of a neural network, in which swap-out and swap-in operations are i...
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Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties
Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy t...
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Multi-Player Bandits: A Trekking Approach
We study stochastic multi-armed bandits with many players. The players do not know the number of players, cannot communicate with each other and if multiple players select a common arm they collide and none of them receive any reward. We consider the static scenario, where the number of players remains fixed, and the...
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High-resolution investigation of spinal cord and spine
High-resolution non-invasive 3D study of intact spine and spinal cord morphology on the level of complex vascular and neuronal organization is a crucial issue for the development of treatments for the injuries and pathologies of central nervous system (CNS). X-ray phase contrast tomography enables high quality 3D vis...
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Bohr--Rogosinski radius for analytic functions
There are a number of articles which deal with Bohr's phenomenon whereas only a few papers appeared in the literature on Rogosinski's radii for analytic functions defined on the unit disk $|z|<1$. In this article, we introduce and investigate Bohr-Rogosinski's radii for analytic functions defined for $|z|<1$. Also, w...
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Methods to locate Saddle Points in Complex Landscapes
We present a class of simple algorithms that allows to find the reaction path in systems with a complex potential energy landscape. The approach does not need any knowledge on the product state and does not require the calculation of any second derivatives. The underlying idea is to use two nearby points in configura...
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On the Complexity of Opinions and Online Discussions
In an increasingly polarized world, demagogues who reduce complexity down to simple arguments based on emotion are gaining in popularity. Are opinions and online discussions falling into demagoguery? In this work, we aim to provide computational tools to investigate this question and, by doing so, explore the nature ...
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Free energy distribution of the stationary O'Connell-Yor directed random polymer model
We study the semi-discrete directed polymer model introduced by O'Connell-Yor in its stationary regime, based on our previous work on the stationary $q$-totally asymmetric simple exclusion process ($q$-TASEP) using a two-sided $q$-Whittaker process. We give a formula for the free energy distribution of the polymer mo...
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Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- using data from each patient's previous visits, and testing on future (held-out) data. We start by learning ...
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Analysis and Control of a Non-Standard Hyperbolic PDE Traffic Flow Model
The paper provides results for a non-standard, hyperbolic, 1-D, nonlinear traffic flow model on a bounded domain. The model consists of two first-order PDEs with a dynamic boundary condition that involves the time derivative of the velocity. The proposed model has features that are important from a traffic-theoretic ...
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Learning Robust Representations for Computer Vision
Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in the data can frustrate these approaches by obscuring the latent spaces. Our main goal is deeper understanding and new development of robust ...
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Detection Estimation and Grid matching of Multiple Targets with Single Snapshot Measurements
In this work, we explore the problems of detecting the number of narrow-band, far-field targets and estimating their corresponding directions from single snapshot measurements. The principles of sparse signal recovery (SSR) are used for the single snapshot detection and estimation of multiple targets. In the SSR fram...
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Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels
The latent feature relational model (LFRM) is a generative model for graph-structured data to learn a binary vector representation for each node in the graph. The binary vector denotes the node's membership in one or more communities. At its core, the LFRM miller2009nonparametric is an overlapping stochastic blockmod...
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Towards a Science of Mind
The ancient mind/body problem continues to be one of deepest mysteries of science and of the human spirit. Despite major advances in many fields, there is still no plausible link between subjective experience (qualia) and its realization in the body. This paper outlines some of the elements of a rigorous science of m...
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Resistance distance criterion for optimal slack bus selection
We investigate the dependence of transmission losses on the choice of a slack bus in high voltage AC transmission networks. We formulate a transmission loss minimization problem in terms of slack variables representing the additional power injection that each generator provides to compensate the transmission losses. ...
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Interpolation in the Presence of Domain Inhomogeneity
Standard interpolation techniques are implicitly based on the assumption that the signal lies on a homogeneous domain. In this letter, the proposed interpolation method instead exploits prior information about domain inhomogeneity, characterized by different, potentially overlapping, subdomains. By introducing a doma...
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Lectures on the mean values of functionals -- An elementary introduction to infinite-dimensional probability
This is an elementary introduction to infinite-dimensional probability. In the lectures, we compute the exact mean values of some functionals on C[0,1] and L[0,1] by considering these functionals as infinite-dimensional random variables. The results show that there exist the complete concentration of measure phenomen...
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Synthesis of Optimal Resilient Control Strategies
Repair mechanisms are important within resilient systems to maintain the system in an operational state after an error occurred. Usually, constraints on the repair mechanisms are imposed, e.g., concerning the time or resources required (such as energy consumption or other kinds of costs). For systems modeled by Marko...
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Limits of the Kucera-Gacs coding method
Every real is computable from a Martin-Loef random real. This well known result in algorithmic randomness was proved by Kucera and Gacs. In this survey article we discuss various approaches to the problem of coding an arbitrary real into a Martin-Loef random real,and also describe new results concerning optimal metho...
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Subspace Tracking Algorithms for Millimeter Wave MIMO Channel Estimation with Hybrid Beamforming
This paper proposes the use of subspace tracking algorithms for performing MIMO channel estimation at millimeter wave (mmWave) frequencies. Using a subspace approach, we develop a protocol enabling the estimation of the right (resp. left) singular vectors at the transmitter (resp. receiver) side; then, we adapt the p...
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Thomas Precession for Dressed Particles
We consider a particle dressed with boundary gravitons in three-dimensional Minkowski space. The existence of BMS transformations implies that the particle's wavefunction picks up a Berry phase when subjected to changes of reference frames that trace a closed path in the asymptotic symmetry group. We evaluate this ph...
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Exponential random graphs behave like mixtures of stochastic block models
We study the behavior of exponential random graphs in both the sparse and the dense regime. We show that exponential random graphs are approximate mixtures of graphs with independent edges whose probability matrices are critical points of an associated functional, thereby satisfying a certain matrix equation. In the ...
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Certificate Enhanced Data-Flow Analysis
Proof-carrying-code was proposed as a solution to ensure a trust relationship between two parties: a (heavyweight) analyzer and a (lightweight) checker. The analyzer verifies the conformance of a given application to a specified property and generates a certificate attesting the validity of the analysis result. It su...
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Small cells in a Poisson hyperplane tessellation
Until now, little was known about properties of small cells in a Poisson hyperplane tessellation. The few existing results were either heuristic or applying only to the two dimensional case and for very specific size functionals and directional distributions. This paper fills this gap by providing a systematic study ...
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Privacy-Aware Guessing Efficiency
We investigate the problem of guessing a discrete random variable $Y$ under a privacy constraint dictated by another correlated discrete random variable $X$, where both guessing efficiency and privacy are assessed in terms of the probability of correct guessing. We define $h(P_{XY}, \epsilon)$ as the maximum probabil...
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Positioning services of a travel agency in social networks
In this paper the methods of forming a travel company customer base by means of social networks are observed. These methods are made to involve web-users of the social networks (VK.com and Facebook) for positioning of the service of the travel agency "New Europe" on the Internet. The methods of applying the maintenan...
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Inverse Mapping for Rainfall-Runoff Models using History Matching Approach
In this paper, we consider two rainfall-runoff computer models. The first model is Matlab-Simulink model which simulates runoff from windrow compost pad (located at the Bioconversion Center in Athens, GA) over a period of time based on rainfall events. The second model is Soil Water Assessment Tool (SWAT) which estim...
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Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE
Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating artificial data for the minority class is a more general approach compared to algorithmic modifications. SMOTE algorithm and its variations generate sy...
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Liveness Verification and Synthesis: New Algorithms for Recursive Programs
We consider the problems of liveness verification and liveness synthesis for recursive programs. The liveness verification problem (LVP) is to decide whether a given omega-context-free language is contained in a given omega-regular language. The liveness synthesis problem (LSP) is to compute a strategy so that a give...
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A semiparametric approach for bivariate extreme exceedances
Inference over tails is performed by applying only the results of extreme value theory. Whilst such theory is well defined and flexible enough in the univariate case, multivariate inferential methods often require the imposition of arbitrary constraints not fully justifed by the underlying theory. In contrast, our ap...
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Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network
The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedule. Utilizing a state-of-the-art time-series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts ...
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Note on Green Function Formalism and Topological Invariants
It has been discovered previously that the topological order parameter could be identified from the topological data of the Green function, namely the (generalized) TKNN invariant in general dimensions, for both non-interacting and interacting systems. In this note, we show that this phenomena has a clear geometric d...
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Unsupervised Document Embedding With CNNs
We propose a new model for unsupervised document embedding. Leading existing approaches either require complex inference or use recurrent neural networks (RNN) that are difficult to parallelize. We take a different route and develop a convolutional neural network (CNN) embedding model. Our CNN architecture is fully p...
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A metric model for the functional architecture of the visual cortex
The purpose of this work is to construct a model for the functional architecture of the primary visual cortex (V1), based on a structure of metric measure space induced by the underlying organization of receptive profiles (RPs) of visual cells. In order to account for the horizontal connectivity of V1 in such a conte...
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Bayesian parameter identification in Cahn-Hilliard models for biological growth
We consider the inverse problem of parameter estimation in a diffuse interface model for tumour growth. The model consists of a fourth-order Cahn--Hilliard system and contains three phenomenological parameters: the tumour proliferation rate, the nutrient consumption rate, and the chemotactic sensitivity. We study the...
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Transport Phase Diagram and Anderson Localization in Hyperuniform Disordered Photonic Materials
Hyperuniform disordered photonic materials (HDPM) are spatially correlated dielectric structures with unconventional optical properties. They can be transparent to long-wavelength radiation while at the same time have isotropic band gaps in another frequency range. This phenomenon raises fundamental questions concern...
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Nearly Maximally Predictive Features and Their Dimensions
Scientific explanation often requires inferring maximally predictive features from a given data set. Unfortunately, the collection of minimal maximally predictive features for most stochastic processes is uncountably infinite. In such cases, one compromises and instead seeks nearly maximally predictive features. Here...
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A Survey Of Cross-lingual Word Embedding Models
Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding ...
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Space-efficient classical and quantum algorithms for the shortest vector problem
A lattice is the integer span of some linearly independent vectors. Lattice problems have many significant applications in coding theory and cryptographic systems for their conjectured hardness. The Shortest Vector Problem (SVP), which is to find the shortest non-zero vector in a lattice, is one of the well-known pro...
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Low Power SI Class E Power Amplifier and RF Switch For Health Care
This research was to design a 2.4 GHz class E Power Amplifier (PA) for health care, with 0.18um Semiconductor Manufacturing International Corporation CMOS technology by using Cadence software. And also RF switch was designed at cadence software with power Jazz 180nm SOI process. The ultimate goal for such application...
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Improved approximation algorithm for the Dense-3-Subhypergraph Problem
The study of Dense-$3$-Subhypergraph problem was initiated in Chlamt{á}c et al. [Approx'16]. The input is a universe $U$ and collection ${\cal S}$ of subsets of $U$, each of size $3$, and a number $k$. The goal is to choose a set $W$ of $k$ elements from the universe, and maximize the number of sets, $S\in {\cal S}$ ...
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A Survey of Security Assessment Ontologies
A literature survey on ontologies concerning the Security Assessment domain has been carried out to uncover initiatives that aim at formalizing concepts from the Security Assessment field of research. A preliminary analysis and a discussion on the selected works are presented. Our main contribution is an updated lite...
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Distributed Online Learning of Event Definitions
Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Induc...
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Solving Graph Isomorphism Problem for a Special case
Graph isomorphism is an important computer science problem. The problem for the general case is unknown to be in polynomial time. The base algorithm for the general case works in quasi-polynomial time. The solutions in polynomial time for some special type of classes are known. In this work, we have worked with a spe...
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Selective inference for effect modification via the lasso
Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modific...
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Is Climate Change Controversial? Modeling Controversy as Contention Within Populations
A growing body of research focuses on computationally detecting controversial topics and understanding the stances people hold on them. Yet gaps remain in our theoretical and practical understanding of how to define controversy, how it manifests, and how to measure it. In this paper, we introduce a novel measure we c...
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Defending Against Adversarial Attacks by Leveraging an Entire GAN
Recent work has shown that state-of-the-art models are highly vulnerable to adversarial perturbations of the input. We propose cowboy, an approach to detecting and defending against adversarial attacks by using both the discriminator and generator of a GAN trained on the same dataset. We show that the discriminator c...
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Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous gra...
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An independence system as knot invariant
An independence system (with respect to the unknotting number) is defined for a classical knot diagram. It is proved that the independence system is a knot invariant for alternating knots. The exchange property for minimal unknotting sets are also discussed. It is shown that there exists an infinite family of knot di...
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A cavity-induced artificial gauge field in a Bose-Hubbard ladder
We consider theoretically ultracold interacting bosonic atoms confined to quasi-one-dimensional ladder structures formed by optical lattices and coupled to the field of an optical cavity. The atoms can collect a spatial phase imprint during a cavity-assisted tunneling along a rung via Raman transitions employing a ca...
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A Review of Laser-Plasma Ion Acceleration
An overview of research on laser-plasma based acceleration of ions is given. The experimental state of the art is summarized and recent progress is discussed. The basic acceleration processes are briefly reviewed with an outlook on hybrid mechanisms and novel concepts. Finally, we put focus on the development of engi...
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Minimax Optimal Estimators for Additive Scalar Functionals of Discrete Distributions
In this paper, we consider estimators for an additive functional of $\phi$, which is defined as $\theta(P;\phi)=\sum_{i=1}^k\phi(p_i)$, from $n$ i.i.d. random samples drawn from a discrete distribution $P=(p_1,...,p_k)$ with alphabet size $k$. We propose a minimax optimal estimator for the estimation problem of the a...
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Van der Waals Heterostructures Based on Allotropes of Phosphorene and MoSe2
The van der Waals heterostructures of allotropes of phosphorene (${\alpha}$- and $\beta-P$) with MoSe2 (H-, T-, ZT- and SO-MoSe2) are investigated in the framework of state-of-the-art density functional theory. The semiconducting heterostructures, $\beta$-P /H-MoSe2 and ${\alpha}$-P / H-MoSe2, forms anti-type structu...
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On separated solutions of logistic population equation with harvesting
We provide a surprising answer to a question raised in S. Ahmad and A.C. Lazer [2], and extend the results of that paper.
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Nonreciprocal Electromagnetic Scattering from a Periodically Space-Time Modulated Slab and Application to a Quasisonic Isolator
Scattering of obliquely incident electromagnetic waves from periodically space-time modulated slabs is investigated. It is shown that such structures operate as nonreciprocal harmonic generators and spatial-frequency filters. For oblique incidences, low-frequency harmonics are filtered out in the form of surface wave...
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Story Cloze Ending Selection Baselines and Data Examination
This paper describes two supervised baseline systems for the Story Cloze Test Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using features based on word embeddings and semantic similarity computation. We further implement a neural LSTM system with different encoding strategies that try to mode...
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Linear Quadratic Optimal Control Problems with Fixed Terminal States and Integral Quadratic Constraints
This paper is concerned with a linear quadratic (LQ, for short) optimal control problem with fixed terminal states and integral quadratic constraints. A Riccati equation with infinite terminal value is introduced, which is uniquely solvable and whose solution can be approximated by the solution for a suitable unconst...
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A new sampling density condition for shift-invariant spaces
Let $X=\{x_i:i\in\mathbb{Z}\}$, $\dots<x_{i-1}<x_i<x_{i+1}<\dots$, be a sampling set which is separated by a constant $\gamma>0$. Under certain conditions on $\phi$, it is proved that if there exists a positive integer $\nu$ such that $$\delta_\nu:=\sup\limits_{i\in\mathbb{Z}}(x_{i+\nu}-x_i)<\dfrac{\nu}{2\pi}\left(\d...
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Experimental Design via Generalized Mean Objective Cost of Uncertainty
The mean objective cost of uncertainty (MOCU) quantifies the performance cost of using an operator that is optimal across an uncertainty class of systems as opposed to using an operator that is optimal for a particular system. MOCU-based experimental design selects an experiment to maximally reduce MOCU, thereby gain...
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Parallelizing Over Artificial Neural Network Training Runs with Multigrid
Artificial neural networks are a popular and effective machine learning technique. Great progress has been made parallelizing the expensive training phase of an individual network, leading to highly specialized pieces of hardware, many based on GPU-type architectures, and more concurrent algorithms such as synthetic ...
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Learning the Structure of Generative Models without Labeled Data
Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale from weak supervision sources. The generative model's dependency structure directly affects the quality of the estimated labels, but sele...
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Robust Loss Functions under Label Noise for Deep Neural Networks
In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks under label noise focus on modifying the network architecture a...
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A quality model for evaluating and choosing a stream processing framework architecture
Today, we have to deal with many data (Big data) and we need to make decisions by choosing an architectural framework to analyze these data coming from different area. Due to this, it become problematic when we want to process these data, and even more, when it is continuous data. When you want to process some data, ...
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On the Hardness of Inventory Management with Censored Demand Data
We consider a repeated newsvendor problem where the inventory manager has no prior information about the demand, and can access only censored/sales data. In analogy to multi-armed bandit problems, the manager needs to simultaneously "explore" and "exploit" with her inventory decisions, in order to minimize the cumula...
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Social Media Analysis For Organizations: Us Northeastern Public And State Libraries Case Study
Social networking sites such as Twitter have provided a great opportunity for organizations such as public libraries to disseminate information for public relations purposes. However, there is a need to analyze vast amounts of social media data. This study presents a computational approach to explore the content of t...
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Weakly tripotent rings
We study the class of rings $R$ with the property that for $x\in R$ at least one of the elements $x$ and $1+x$ are tripotent.
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ML for Flood Forecasting at Scale
Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount of data for a specific location, and the computational difficulty of building continent/global level models that are sufficiently accurate....
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Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis
We consider the frequency domain form of proper orthogonal decomposition (POD) called spectral proper orthogonal decomposition (SPOD). Spectral POD is derived from a space-time POD problem for statistically stationary flows and leads to modes that each oscillate at a single frequency. This form of POD goes back to th...
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An optimal transportation approach for assessing almost stochastic order
When stochastic dominance $F\leq_{st}G$ does not hold, we can improve agreement to stochastic order by suitably trimming both distributions. In this work we consider the $L_2-$Wasserstein distance, $\mathcal W_2$, to stochastic order of these trimmed versions. Our characterization for that distance naturally leads to...
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The Effect of Electron Lens as Landau Damping Device on Single Particle Dynamics in HL-LHC
An electron lens can serve as an effective mechanism for suppressing coherent instabilities in high intensity storage rings through nonlinear amplitude dependent betatron tune shift. However, the addition of a strong localized nonlinear focusing element to the accelerator lattice may lead to undesired effects in part...
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Search for sterile neutrinos in holographic dark energy cosmology: Reconciling Planck observation with the local measurement of the Hubble constant
We search for sterile neutrinos in the holographic dark energy cosmology by using the latest observational data. To perform the analysis, we employ the current cosmological observations, including the cosmic microwave background temperature power spectrum data from the Planck mission, the baryon acoustic oscillation ...
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Simulation of high temperature superconductors and experimental validation
In this work, we present a parallel, fully-distributed finite element numerical framework to simulate the low-frequency electromagnetic response of superconducting devices, which allows to efficiently exploit HPC platforms. We select the so-called H-formulation, which uses the magnetic field as a state variable. Nédé...
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Hardware Translation Coherence for Virtualized Systems
To improve system performance, modern operating systems (OSes) often undertake activities that require modification of virtual-to-physical page translation mappings. For example, the OS may migrate data between physical frames to defragment memory and enable superpages. The OS may migrate pages of data between hetero...
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MotifMark: Finding Regulatory Motifs in DNA Sequences
The interaction between proteins and DNA is a key driving force in a significant number of biological processes such as transcriptional regulation, repair, recombination, splicing, and DNA modification. The identification of DNA-binding sites and the specificity of target proteins in binding to these regions are two ...
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Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
This paper presents KeypointNet, an end-to-end geometric reasoning framework to learn an optimal set of category-specific 3D keypoints, along with their detectors. Given a single image, KeypointNet extracts 3D keypoints that are optimized for a downstream task. We demonstrate this framework on 3D pose estimation by p...
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Multi-State Trajectory Approach to Non-Adiabatic Dynamics: General Formalism and the Active State Trajectory Approximation
A general theoretical framework is derived for the recently developed multi-state trajectory (MST) approach from the time dependent Schrödinger equation, resulting in equations of motion for coupled nuclear-electronic dynamics equivalent to Hamilton dynamics or Heisenberg equation based on a new multistate Meyer-Mill...
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Combining Homotopy Methods and Numerical Optimal Control to Solve Motion Planning Problems
This paper presents a systematic approach for computing local solutions to motion planning problems in non-convex environments using numerical optimal control techniques. It extends the range of use of state-of-the-art numerical optimal control tools to problem classes where these tools have previously not been appli...
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High quality atomically thin PtSe2 films grown by molecular beam epitaxy
Atomically thin PtSe2 films have attracted extensive research interests for potential applications in high-speed electronics, spintronics and photodetectors. Obtaining high quality, single crystalline thin films with large size is critical. Here we report the first successful layer-by-layer growth of high quality PtS...
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NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images
We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable reward function is designed keeping in mind the cost and weight constraints for micro drone with minimum number of sensing modalities. Collection of small number ...
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Fast, Accurate and Fully Parallelizable Digital Image Correlation
Digital image correlation (DIC) is a widely used optical metrology for surface deformation measurements. DIC relies on nonlinear optimization method. Thus an initial guess is quite important due to its influence on the converge characteristics of the algorithm. In order to obtain a reliable, accurate initial guess, a...
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Discovering the effect of nonlocal payoff calculation on the stabilty of ESS: Spatial patterns of Hawk-Dove game in metapopulations
The classical idea of evolutionarily stable strategy (ESS) modeling animal behavior does not involve any spatial dependence. We considered a spatial Hawk-Dove game played by animals in a patchy environment with wrap around boundaries. We posit that each site contains the same number of individuals. An evolution equat...
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Analysis of the measurements of anisotropic a.c. vortex resistivity in tilted magnetic fields
Measurements of the high-frequency complex resistivity in superconductors are a tool often used to obtain the vortex parameters, such as the vortex viscosity, the pinning constant and the depinning frequency. In anisotropic superconductors, the extraction of these quantities from the measurements faces new difficulti...
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Deep Convolutional Neural Network to Detect J-UNIWARD
This paper presents an empirical study on applying convolutional neural networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG steganographic method. Experiments guiding the architectural design of the CNNs have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of size 512x512. Resu...
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Observing Power-Law Dynamics of Position-Velocity Correlation in Anomalous Diffusion
In this letter we present a measurement of the phase-space density distribution (PSDD) of ultra-cold \Rb atoms performing 1D anomalous diffusion. The PSDD is imaged using a direct tomographic method based on Raman velocity selection. It reveals that the position-velocity correlation function $C_{xv}(t)$ builds up on ...
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Modular curves, invariant theory and $E_8$
The $E_8$ root lattice can be constructed from the modular curve $X(13)$ by the invariant theory for the simple group $\text{PSL}(2, 13)$. This gives a different construction of the $E_8$ root lattice. It also gives an explicit construction of the modular curve $X(13)$.
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Analysis of Approximate Stochastic Gradient Using Quadratic Constraints and Sequential Semidefinite Programs
We present convergence rate analysis for the approximate stochastic gradient method, where individual gradient updates are corrupted by computation errors. We develop stochastic quadratic constraints to formulate a small linear matrix inequality (LMI) whose feasible set characterizes convergence properties of the app...
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Invariant holomorphic discs in some non-convex domains
We give a description of complex geodesics and we study the structure of stationary discs in some non-convex domains for which complex geodesics are not unique.
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MIMIX: a Bayesian Mixed-Effects Model for Microbiome Data from Designed Experiments
Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Cont...
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SpatEntropy: Spatial Entropy Measures in R
This article illustrates how to measure the heterogeneity of spatial data presenting a finite number of categories via computation of spatial entropy. The R package SpatEntropy contains functions for the computation of entropy and spatial entropy measures. The extension to spatial entropy measures is a unique feature...
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