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Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images
This paper describes a new algorithm for solar energy forecasting from a sequence of Cloud Optical Depth (COD) images. The algorithm is based on the following simple observation: the dynamics of clouds represented by COD images resembles the motion (transport) of a density in a fluid flow. This suggests that, to fore...
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On Mimura's extension problem
We determine the group strucure of the $23$-rd homotopy group $\pi_{23}(G_2 : 2)$, where $G_2$ is the Lie group of exceptional type, which hasn't been determined for $50$ years.
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On critical and supercritical pseudo-relativistic nonlinear Schrödinger equations
In this paper, we investigate existence and non-existence of a nontrivial solution to the pseudo-relativistic nonlinear Schrödinger equation $$\left( \sqrt{-c^2\Delta + m^2 c^4}-mc^2\right) u + \mu u = |u|^{p-1}u\quad \textrm{in}~\mathbb{R}^n~(n \geq 2)$$ involving an $H^{1/2}$-critical/supercritical power-type nonli...
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Eye Tracker Accuracy: Quantitative Evaluation of the Invisible Eye Center Location
Purpose. We present a new method to evaluate the accuracy of an eye tracker based eye localization system. Measuring the accuracy of an eye tracker's primary intention, the estimated point of gaze, is usually done with volunteers and a set of fixation points used as ground truth. However, verifying the accuracy of th...
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Theoretical Foundations of Forward Feature Selection Methods based on Mutual Information
Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various classes of methods, forward feature selection methods based o...
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Robust quantum switch with Rydberg excitations
We develop an approach to realize a quantum switch for Rydberg excitation in atoms with $Y$-typed level configuration. We find that the steady population on two different Rydberg states can be reversibly exchanged in a controllable way by properly tuning the Rydberg-Rydberg interaction. Moreover, our numerical simula...
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Non-robust phase transitions in the generalized clock model on trees
Pemantle and Steif provided a sharp threshold for the existence of a RPT (robust phase transition) for the continuous rotator model and the Potts model in terms of the branching number and the second eigenvalue of the transfer operator, where a robust phase transition is said to occur if an arbitrarily weak coupling ...
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The Role of Gender in Social Network Organization
The digital traces we leave behind when engaging with the modern world offer an interesting lens through which we study behavioral patterns as expression of gender. Although gender differentiation has been observed in a number of settings, the majority of studies focus on a single data stream in isolation. Here we us...
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The Schur Lie-Multiplier of Leibinz Algebras
For a free presentation $0 \to R \to F \to G \to 0$ of a Leibniz algebra $G$, the Baer invariant ${\cal M}^{\sf Lie}(G) = \frac{R \cap [F, F]_{Lie}}{[F, R]_{Lie}}$ is called the Schur multiplier of $G$ relative to the Liezation functor or Schur Lie-multiplier. For a two-sided ideal $N$ of a Leibniz algebra $G$, we co...
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Did we learn from LLC Side Channel Attacks? A Cache Leakage Detection Tool for Crypto Libraries
This work presents a new tool to verify the correctness of cryptographic implementations with respect to cache attacks. Our methodology discovers vulnerabilities that are hard to find with other techniques, observed as exploitable leakage. The methodology works by identifying secret dependent memory and introducing f...
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Effects of anisotropy in spin molecular-orbital coupling on effective spin models of trinuclear organometallic complexes
We consider layered decorated honeycomb lattices at two-thirds filling, as realized in some trinuclear organometallic complexes. Localized $S=1$ moments with a single-spin anisotropy emerge from the interplay of Coulomb repulsion and spin molecular-orbit coupling (SMOC). Magnetic anisotropies with bond dependent exch...
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Design and experimental test of an optical vortex coronagraph
The optical vortex coronagraph (OVC) is one of the promising ways for direct imaging exoplanets because of its small inner working angle and high throughput. This paper presents the design and laboratory demonstration performance at 633nm and 1520nm of the OVC based on liquid crystal polymers (LCP). Two LCPs has been...
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Symbolic Computation via Program Transformation
Symbolic computation is an important approach in automated program analysis. Most state-of-the-art tools perform symbolic computation as interpreters and directly maintain symbolic data. In this paper, we show that it is feasible, and in fact practical, to use a compiler-based strategy instead. Using compiler tooling...
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Simulating Dirac models with ultracold atoms in optical lattices
We present a general model allowing "quantum simulation" of one-dimensional Dirac models with 2- and 4-component spinors using ultracold atoms in driven 1D tilted optical latices. The resulting Dirac physics is illustrated by one of its well-known manifestations, Zitterbewegung. This general model can be extended and...
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Network analysis of Japanese global business using quasi-exhaustive micro-data for Japanese overseas subsidiaries
Network analysis techniques remain rarely used for understanding international management strategies. Our paper highlights their value as research tool in this field of social science using a large set of micro-data (20,000) to investigate the presence of networks of subsidiaries overseas. The research question is th...
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A Privacy-preserving Community-based P2P OSNs Using Broadcast Encryption Supporting Recommendation Mechanism
Online Social Networks (OSNs) have become one of the most important activities on the Internet, such as Facebook and Google+. However, security and privacy have become major concerns in existing C/S based OSNs. In this paper, we propose a novel scheme called a Privacy-preserving Community-based P2P OSNs Using Broadca...
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Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop
The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few years due to their superior theoretical properties and empirical behaviour on training supervised machine learning models via the empirical ...
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Hierarchical Temporal Representation in Linear Reservoir Computing
Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the s...
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Bombieri-Vinogradov for multiplicative functions, and beyond the $x^{1/2}$-barrier
Part-and-parcel of the study of "multiplicative number theory" is the study of the distribution of multiplicative functions in arithmetic progressions. Although appropriate analogies to the Bombieri-Vingradov Theorem have been proved for particular examples of multiplicative functions, there has not previously been h...
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Three-component fermions with surface Fermi arcs in topological semimetal tungsten carbide
Topological Dirac and Weyl semimetals not only host quasiparticles analogous to the elementary fermionic particles in high-energy physics, but also have nontrivial band topology manifested by exotic Fermi arcs on the surface. Recent advances suggest new types of topological semimetals, in which spatial symmetries pro...
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Detecting stochastic inclusions in electrical impedance tomography
This work considers the inclusion detection problem of electrical impedance tomography with stochastic conductivities. It is shown that a conductivity anomaly with a random conductivity can be identified by applying the Factorization Method or the Monotonicity Method to the mean value of the corresponding Neumann-to-...
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On compact Hermitian manifolds with flat Gauduchon connections
Given a Hermitian manifold $(M^n,g)$, the Gauduchon connections are the one parameter family of Hermitian connections joining the Chern connection and the Bismut connection. We will call $\nabla^s = (1-\frac{s}{2})\nabla^c + \frac{s}{2}\nabla^b$ the $s$-Gauduchon connection of $M$, where $\nabla^c$ and $\nabla^b$ are...
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A Support Tensor Train Machine
There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. An example is the support tensor machine (STM) that utilizes a rank-one tensor to capture the data structure, thereby alleviating the overfitting and curse of dimensionality problems in the convent...
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Diffusion under confinement: hydrodynamic finite-size effects in simulation
We investigate finite-size effects on diffusion in confined fluids using molecular dynamics simulations and hydrodynamic calculations. Specifically, we consider a Lennard-Jones fluid in slit pores without slip at the interface and show that the use of periodic boundary conditions in the directions along the surfaces ...
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SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning
It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting algori...
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Influence of thermal boundary conditions on the current-driven resistive transition in $\mathbf{VO_2}$ microbridges
We investigate the resistive switching behaviour of $\mathrm{VO_2}$ microbridges under current bias as a function of temperature and thermal coupling with the heat bath. Upon increasing the electrical current bias, the formation of the metallic phase can progress smoothly or through sharp jumps. The magnitude and thr...
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Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-spac...
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The ZX calculus is a language for surface code lattice surgery
Quantum computing is moving rapidly to the point of deployment of technology. Functional quantum devices will require the ability to correct error in order to be scalable and effective. A leading choice of error correction, in particular for modular or distributed architectures, is the surface code with logical two-q...
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Learning One-hidden-layer Neural Networks with Landscape Design
We consider the problem of learning a one-hidden-layer neural network: we assume the input $x\in \mathbb{R}^d$ is from Gaussian distribution and the label $y = a^\top \sigma(Bx) + \xi$, where $a$ is a nonnegative vector in $\mathbb{R}^m$ with $m\le d$, $B\in \mathbb{R}^{m\times d}$ is a full-rank weight matrix, and $...
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Growth and electronic structure of graphene on semiconducting Ge(110)
The direct growth of graphene on semiconducting or insulating substrates might help to overcome main drawbacks of metal-based synthesis, like metal-atom contaminations of graphene, transfer issues, etc. Here we present the growth of graphene on n-doped semiconducting Ge(110) by using an atomic carbon source and the s...
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Alternative Lagrangians obtained by scalar deformations
We study non-conservative like SODEs admitting explicit Lagrangian descriptions. Such systems are equivalent to the system of Lagrange equations of some Lagrangian $L$, including a covariant force field which represents non-conservative forces. We find necessary and sufficient conditions for the existence of a differ...
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Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning
Constrained Markov Decision Process (CMDP) is a natural framework for reinforcement learning tasks with safety constraints, where agents learn a policy that maximizes the long-term reward while satisfying the constraints on the long-term cost. A canonical approach for solving CMDPs is the primal-dual method which upd...
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Toward Common Components for Open Workflow Systems
The role of scalable high-performance workflows and flexible workflow management systems that can support multiple simulations will continue to increase in importance. For example, with the end of Dennard scaling, there is a need to substitute a single long running simulation with multiple repeats of shorter simulati...
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On the Sampling Problem for Kernel Quadrature
The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode the smoothness and dimension of the integrand. However, an empirical investigation ...
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Zeroth order regular approximation approach to electric dipole moment interactions of the electron
A quasi-relativistic two-component approach for an efficient calculation of $\mathcal{P,T}$-odd interactions caused by a permanent electric dipole moment of the electron (eEDM) is presented. The approach uses a (two-component) complex generalized Hartree-Fock (cGHF) and a complex generalized Kohn-Sham (cGKS) scheme w...
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Trace your sources in large-scale data: one ring to find them all
An important preprocessing step in most data analysis pipelines aims to extract a small set of sources that explain most of the data. Currently used algorithms for blind source separation (BSS), however, often fail to extract the desired sources and need extensive cross-validation. In contrast, their rarely used prob...
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The rigorous derivation of the linear Landau equation from a particle system in a weak-coupling limit
We consider a system of N particles interacting via a short-range smooth potential, in a intermediate regime between the weak-coupling and the low-density. We provide a rigorous derivation of the Linear Landau equation from this particle system. The strategy of the proof consists in showing the asymptotic equivalence...
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Fractal dimension and lower bounds for geometric problems
We study the complexity of geometric problems on spaces of low fractal dimension. It was recently shown by [Sidiropoulos & Sridhar, SoCG 2017] that several problems admit improved solutions when the input is a pointset in Euclidean space with fractal dimension smaller than the ambient dimension. In this paper we prov...
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Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
We present a unified framework to analyze the global convergence of Langevin dynamics based algorithms for nonconvex finite-sum optimization with $n$ component functions. At the core of our analysis is a direct analysis of the ergodicity of the numerical approximations to Langevin dynamics, which leads to faster conv...
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Eigenvalue Analysis via Kernel Density Estimation
In this paper, we propose an eigenvalue analysis -- of system dynamics models -- based on the Mutual Information measure, which in turn will be estimated via the Kernel Density Estimation method. We postulate that the proposed approach represents a novel and efficient multivariate eigenvalue sensitivity analysis.
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The Saga of KPR: Theoretical and Experimental developments
In this article, we present a brief narration of the origin and the overview of the recent developments done on the Kolkata Paise Restaurant (KPR) problem, which can serve as a prototype for a broader class of resource allocation problems in the presence of a large number of competing agents, typically studied using ...
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The computational complexity of integer programming with alternations
We prove that integer programming with three quantifier alternations is $NP$-complete, even for a fixed number of variables. This complements earlier results by Lenstra and Kannan, which together say that integer programming with at most two quantifier alternations can be done in polynomial time for a fixed number of...
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Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling
Dictionary learning and component analysis are part of one of the most well-studied and active research fields, at the intersection of signal and image processing, computer vision, and statistical machine learning. In dictionary learning, the current methods of choice are arguably K-SVD and its variants, which learn ...
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Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks
Despite the wide use of machine learning in adversarial settings including computer security, recent studies have demonstrated vulnerabilities to evasion attacks---carefully crafted adversarial samples that closely resemble legitimate instances, but cause misclassification. In this paper, we examine the adequacy of t...
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The way to uncover community structure with core and diversity
Communities are ubiquitous in nature and society. Individuals that share common properties often self-organize to form communities. Avoiding the shortages of computation complexity, pre-given information and unstable results in different run, in this paper, we propose a simple and effcient method to deepen our unders...
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Stochastic comparisons of the largest claim amounts from two sets of interdependent heterogeneous portfolios
Let $ X_{\lambda_1},\ldots,X_{\lambda_n}$ be dependent non-negative random variables and $Y_i=I_{p_i} X_{\lambda_i}$, $i=1,\ldots,n$, where $I_{p_1},\ldots,I_{p_n}$ are independent Bernoulli random variables independent of $X_{\lambda_i}$'s, with ${\rm E}[I_{p_i}]=p_i$, $i=1,\ldots,n$. In actuarial sciences, $Y_i$ co...
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An Achilles' Heel of Term-Resolution
Term-resolution provides an elegant mechanism to prove that a quantified Boolean formula (QBF) is true. It is a dual to Q-resolution (also referred to as clause-resolution) and is practically highly important as it enables certifying answers of DPLL-based QBF solvers. While term-resolution and Q-resolution are very s...
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Tunneling estimates and approximate controllability for hypoelliptic equations
This article is concerned with quantitative unique continuation estimates for equations involving a "sum of squares" operator $\mathcal{L}$ on a compact manifold $\mathcal{M}$ assuming: $(i)$ the Chow-Rashevski-Hörmander condition ensuring the hypoellipticity of $\mathcal{L}$, and $(ii)$ the analyticity of $\mathcal{...
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Optimal Resonant Beam Charging for Electronic Vehicles in Internet of Intelligent Vehicles
To enable electric vehicles (EVs) to access to the internet of intelligent vehicles (IoIV), charging EVs wirelessly anytime and anywhere becomes an urgent need. The resonant beam charging (RBC) technology can provide high-power and long-range wireless energy for EVs. However, the RBC system is unefficient. To improve...
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An Adaptive Version of Brandes' Algorithm for Betweenness Centrality
Betweenness centrality---measuring how many shortest paths pass through a vertex---is one of the most important network analysis concepts for assessing the relative importance of a vertex. The well-known algorithm of Brandes [2001] computes, on an $n$-vertex and $m$-edge graph, the betweenness centrality of all verti...
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Kinetic cascade in solar-wind turbulence: 3D3V hybrid-kinetic simulations with electron inertia
Understanding the nature of the turbulent fluctuations below the ion gyroradius in solar-wind turbulence is a great challenge. Recent studies have been mostly in favor of kinetic Alfvén wave (KAW) type of fluctuations, but other kinds of fluctuations with characteristics typical of magnetosonic, whistler and ion Bern...
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Gamma-Band Correlations in Primary Visual Cortex
Neural field theory is used to quantitatively analyze the two-dimensional spatiotemporal correlation properties of gamma-band (30 -- 70 Hz) oscillations evoked by stimuli arriving at the primary visual cortex (V1), and modulated by patchy connectivities that depend on orientation preference (OP). Correlation function...
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A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration
We address the problem of camera-to-laser-scanner calibration using a checkerboard and multiple image-laser scan pairs. Distinguishing which laser points measure the checkerboard and which lie on the background is essential to any such system. We formulate the checkerboard extraction as a combinatorial optimization p...
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On the mapping of Points of Interest through StreetView imagery and paid crowdsourcing
The use of volunteers has emerged as low-cost alternative to generate accurate geographical information, an approach known as Volunteered Geographic Information (VGI). However, VGI is limited by the number and availability of volunteers in the area to be mapped, hindering scalability for large areas and making diffic...
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Complex tensor factorisation with PARAFAC2 for the estimation of brain connectivity from the EEG
Objective: The coupling between neuronal populations and its magnitude have been shown to be informative for various clinical applications. One method to estimate brain connectivity is with electroencephalography (EEG) from which the cross-spectrum between different sensor locations is derived. We wish to test the ef...
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A New Approach of Exploiting Self-Adjoint Matrix Polynomials of Large Random Matrices for Anomaly Detection and Fault Location
Synchronized measurements of a large power grid enable an unprecedented opportunity to study the spatialtemporal correlations. Statistical analytics for those massive datasets start with high-dimensional data matrices. Uncertainty is ubiquitous in a future's power grid. These data matrices are recognized as random ma...
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Bayesian Pool-based Active Learning With Abstention Feedbacks
We study pool-based active learning with abstention feedbacks, where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the...
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Multiconfigurational Short-Range Density-Functional Theory for Open-Shell Systems
Many chemical systems cannot be described by quantum chemistry methods based on a singlereference wave function. Accurate predictions of energetic and spectroscopic properties require a delicate balance between describing the most important configurations (static correlation) and obtaining dynamical correlation effic...
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Shape recognition of volcanic ash by simple convolutional neural network
Shape analyses of tephra grains result in understanding eruption mechanism of volcanoes. However, we have to define and select parameter set such as convexity for the precise discrimination of tephra grains. Selection of the best parameter set for the recognition of tephra shapes is complicated. Actually, many shape ...
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The Uranie platform: an Open-source software for optimisation, meta-modelling and uncertainty analysis
The high-performance computing resources and the constant improvement of both numerical simulation accuracy and the experimental measurements with which they are confronted, bring a new compulsory step to strengthen the credence given to the simulation results: uncertainty quantification. This can have different mean...
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Learning Convolutional Text Representations for Visual Question Answering
Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for ...
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Cross-stream migration of active particles
For natural microswimmers, the interplay of swimming activity and external flow can promote robust motion, e.g. propulsion against ("upstream rheotaxis") or perpendicular to the direction of flow. These effects are generally attributed to their complex body shapes and flagellar beat patterns. Here, using catalytic Ja...
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Analysis of the Polya-Gamma block Gibbs sampler for Bayesian logistic linear mixed models
In this article, we construct a two-block Gibbs sampler using Polson et al. (2013) data augmentation technique with Polya-Gamma latent variables for Bayesian logistic linear mixed models under proper priors. Furthermore, we prove the uniform ergodicity of this Gibbs sampler, which guarantees the existence of the cent...
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Direct and Simultaneous Observation of Ultrafast Electron and Hole Dynamics in Germanium
Understanding excited carrier dynamics in semiconductors is crucial for the development of photovoltaics and efficient photonic devices. However, overlapping spectral features in optical/NIR pump-probe spectroscopy often render assignments of separate electron and hole carrier dynamics ambiguous. Here, ultrafast elec...
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Introducing the Robot Security Framework (RSF), a standardized methodology to perform security assessments in robotics
Robots have gained relevance in society, increasingly performing critical tasks. Nonetheless, robot security is being underestimated. Robotics security is a complex landscape, which often requires a cross-disciplinar perspective to which classical security lags behind. To address this issue, we present the Robot Secu...
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Statistical Verification of Computational Rapport Model
Rapport plays an important role during communication because it can help people understand each other's feelings or ideas and leads to a smooth communication. Computational rapport model has been proposed based on theory in previous work. But there lacks solid verification. In this paper, we apply structural equation...
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Least models of second-order set theories
The main theorems of this paper are (1) there is no least transitive model of Kelley--Morse set theory $\mathsf{KM}$ and (2) there is a least $\beta$-model---that is, a transitive model which is correct about which of its classes are well-founded---of Gödel--Bernays set theory $\mathsf{GBC}$ + Elementary Transfinite ...
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Characterizing K2 Candidate Planetary Systems Orbiting Low-Mass Stars I: Classifying Low-mass Host Stars Observed During Campaigns 1-7
We present near-infrared spectra for 144 candidate planetary systems identified during Campaigns 1-7 of the NASA K2 Mission. The goal of the survey was to characterize planets orbiting low-mass stars, but our IRTF/SpeX and Palomar/TripleSpec spectroscopic observations revealed that 49% of our targets were actually gi...
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Binary orbits from combined astrometric and spectroscopic data
An efficient Bayesian technique for estimation problems in fundamental stellar astronomy is tested on simulated data for a binary observed both astrometrically and spectroscopically. Posterior distributions are computed for the components' masses and for the binary's parallax. One thousand independent repetitions of ...
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Gyrotropic Zener tunneling and nonlinear IV curves in the zero-energy Landau level of graphene in a strong magnetic field
We have investigated tunneling current through a suspended graphene Corbino disk in high magnetic fields at the Dirac point, i.e. at filling factor $\nu$ = 0. At the onset of the dielectric breakdown the current through the disk grows exponentially before ohmic behaviour, but in a manner distinct from thermal activat...
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Accurate, Efficient and Scalable Graph Embedding
The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs. However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs --- state-of the art technique...
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A principled methodology for comparing relatedness measures for clustering publications
There are many different relatedness measures, based for instance on citation relations or textual similarity, that can be used to cluster scientific publications. We propose a principled methodology for evaluating the accuracy of clustering solutions obtained using these relatedness measures. We formally show that t...
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A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce
Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server...
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Holographic Butterfly Effect and Diffusion in Quantum Critical Region
We investigate the butterfly effect and charge diffusion near the quantum phase transition in holographic approach. We argue that their criticality is controlled by the holographic scaling geometry with deformations induced by a relevant operator at finite temperature. Specifically, in the quantum critical region con...
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High-temperature charge density wave correlations in La$_{1.875}$Ba$_{0.125}$CuO$_{4}$ without spin-charge locking
Although all superconducting cuprates display charge-ordering tendencies, their low-temperature properties are distinct, impeding efforts to understand the phenomena within a single conceptual framework. While some systems exhibit stripes of charge and spin, with a locked periodicity, others host charge density waves...
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A unified deep artificial neural network approach to partial differential equations in complex geometries
In this paper we use deep feedforward artificial neural networks to approximate solutions to partial differential equations in complex geometries. We show how to modify the backpropagation algorithm to compute the partial derivatives of the network output with respect to the space variables which is needed to approxi...
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Gradient Flows in Uncertainty Propagation and Filtering of Linear Gaussian Systems
The purpose of this work is mostly expository and aims to elucidate the Jordan-Kinderlehrer-Otto (JKO) scheme for uncertainty propagation, and a variant, the Laugesen-Mehta-Meyn-Raginsky (LMMR) scheme for filtering. We point out that these variational schemes can be understood as proximal operators in the space of de...
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Investigating Simulation-Based Metrics for Characterizing Linear Iterative Reconstruction in Digital Breast Tomosynthesis
Simulation-based image quality metrics are adapted and investigated for characterizing the parameter dependences of linear iterative image reconstruction for DBT. Three metrics based on 2D DBT simulation are investigated: (1) a root-mean-square-error (RMSE) between the test phantom and reconstructed image, (2) a grad...
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Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the simple case of on-policy evaluation. To increase our understanding of the problem, we investigate the issue of approximation errors in areas ...
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First Order Theories of Some Lattices of Open Sets
We show that the first order theory of the lattice of open sets in some natural topological spaces is $m$-equivalent to second order arithmetic. We also show that for many natural computable metric spaces and computable domains the first order theory of the lattice of effectively open sets is undecidable. Moreover, f...
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Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation
A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant...
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Lagrangian Statistics for Navier-Stokes Turbulence under Fourier-mode reduction: Fractal and Homogeneous Decimations
We study small-scale and high-frequency turbulent fluctuations in three-dimensional flows under Fourier-mode reduction. The Navier-Stokes equations are evolved on a restricted set of modes, obtained as a projection on a fractal or homogeneous Fourier set. We find a strong sensitivity (reduction) of the high-frequency...
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Conjunctive management of surface and groundwater under severe drought: A case study in southern Iran
Hormozgan Province, located in the south of Iran, faces several challenges regarding water resources management. The first one is the discharge of a massive volume of water to the Persian Gulf because of the concentration of the annual rainfalls in a short period of time and the narrow distance between the headwater ...
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Classification in biological networks with hypergraphlet kernels
Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins, drugs) and edges represent relational ties among these objects (binds-to, interacts-with, regulates). This approach has been highly successful owing to the theory, methodology and software tha...
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Pair Correlation and Gap Distributions for Substitution Tilings and Generalized Ulam Sets in the Plane
We study empirical statistical and gap distributions of several important tilings of the plane. In particular, we consider the slope distributions, the angle distributions, pair correlation, squared-distance pair correlation, angle gap distributions, and slope gap distributions for the Ammann Chair tiling, the recent...
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Pay-with-a-Selfie, a human-centred digital payment system
Mobile payment systems are increasingly used to simplify the way in which money transfers and transactions can be performed. We argue that, to achieve their full potential as economic boosters in developing countries, mobile payment systems need to rely on new metaphors suitable for the business models, lifestyle, an...
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LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
Deep convolutional Neural Networks (CNN) are the state-of-the-art performers for object detection task. It is well known that object detection requires more computation and memory than image classification. Thus the consolidation of a CNN-based object detection for an embedded system is more challenging. In this work...
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Phase transitions of a 2D deformed-AKLT model
We study spin-2 deformed-AKLT models on the square lattice, specifically a two-parameter family of $O(2)$-symmetric ground-state wavefunctions as defined by Niggemann, Klümper, and Zittartz, who found previously that the phase diagram consists of a Néel-ordered phase and a disordered phase which contains the AKLT poi...
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Statistical estimation of superhedging prices
We consider statistical estimation of superhedging prices using historical stock returns in a frictionless market with d traded assets. We introduce a simple plugin estimator based on empirical measures, show it is consistent but lacks suitable robustness. This is addressed by our improved estimators which use a larg...
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Ore's theorem on subfactor planar algebras
This paper proves that an irreducible subfactor planar algebra with a distributive biprojection lattice admits a minimal 2-box projection generating the identity biprojection. It is a generalization of a theorem of Ore on intervals of finite groups, conjectured by the author since 2013. We deduce a link between combi...
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Machine Learning CICY Threefolds
The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate geometric properties of Complete Intersection Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model building. An advanced neural network classifier and SVM are employed to (1) learn Hodge num...
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Weak lensing deflection of three-point correlation functions
Weak gravitational lensing alters the apparent separations between observed sources, potentially affecting clustering statistics. We derive a general expression for the lensing deflection which is valid for any three-point statistic, and investigate its effect on the three-point clustering correlation function. We fi...
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Solitonic dynamics and excitations of the nonlinear Schrodinger equation with third-order dispersion in non-Hermitian PT-symmetric potentials
Solitons are of the important significant in many fields of nonlinear science such as nonlinear optics, Bose-Einstein condensates, plamas physics, biology, fluid mechanics, and etc.. The stable solitons have been captured not only theoretically and experimentally in both linear and nonlinear Schrodinger (NLS) equatio...
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Supporting Ruled Polygons
We explore several problems related to ruled polygons. Given a ruling of a polygon $P$, we consider the Reeb graph of $P$ induced by the ruling. We define the Reeb complexity of $P$, which roughly equates to the minimum number of points necessary to support $P$. We give asymptotically tight bounds on the Reeb complex...
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Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of scienc...
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Self-doping effect arising from electron correlations in multi-layer cuprates
A self-doping effect between outer and inner CuO$_2$ planes (OPs and IPs) in multi-layer cuprate superconductors is studied. When one considers a three-layer tight-binding model of the Hg-based three-layer cuprate derived from the first principle calculations, the electron concentration gets to be large in the OP com...
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Increased Prediction Accuracy in the Game of Cricket using Machine Learning
Player selection is one the most important tasks for any sport and cricket is no exception. The performance of the players depends on various factors such as the opposition team, the venue, his current form etc. The team management, the coach and the captain select 11 players for each match from a squad of 15 to 20 p...
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Learning Probabilistic Programs Using Backpropagation
Probabilistic modeling enables combining domain knowledge with learning from data, thereby supporting learning from fewer training instances than purely data-driven methods. However, learning probabilistic models is difficult and has not achieved the level of performance of methods such as deep neural networks on man...
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Turbulent gas accretion between supermassive black holes and star-forming rings in the circumnuclear disk
While supermassive black holes are known to co-evolve with their host galaxy, the precise nature and origin of this co-evolution is not clear. We here explore the possible connection between star formation and black hole growth in the circumnuclear disk (CND) to probe this connection in the vicinity close to the blac...
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Grayscale Image Authentication using Neural Hashing
Many different approaches for neural network based hash functions have been proposed. Statistical analysis must correlate security of them. This paper proposes novel neural hashing approach for gray scale image authentication. The suggested system is rapid, robust, useful and secure. Proposed hash function generates ...
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