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Vector valued maximal Carleson type operators on the weighted Lorentz spaces
In this paper, by using the idea of linearizing maximal op-erators originated by Charles Fefferman and the TT* method of Stein-Wainger, we establish a weighted inequality for vector valued maximal Carleson type operators with singular kernels proposed by Andersen and John on the weighted Lorentz spaces with vector-va...
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Rigidity of square-tiled interval exchange transformations
We look at interval exchange transformations defined as first return maps on the set of diagonals of a flow of direction $\theta$ on a square-tiled surface: using a combinatorial approach, we show that, when the surface has at least one true singularity both the flow and the interval exchange are rigid if and only if...
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Global Marcinkiewicz estimates for nonlinear parabolic equations with nonsmooth coefficients
Consider the parabolic equation with measure data \begin{equation*} \left\{ \begin{aligned} &u_t-{\rm div} \mathbf{a}(D u,x,t)=\mu&\text{in}& \quad \Omega_T, &u=0 \quad &\text{on}& \quad \partial_p\Omega_T, \end{aligned}\right. \end{equation*} where $\Omega$ is a bounded domain in $\mathbb{R}^n$, $\Omega_T=\Omega\tim...
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Can Who-Edits-What Predict Edit Survival?
As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project. Existing solutions either rely on a user reputation system or consist of a highly specialized predictor that is tailored t...
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Introduction to intelligent computing unit 1
This brief note highlights some basic concepts required toward understanding the evolution of machine learning and deep learning models. The note starts with an overview of artificial intelligence and its relationship to biological neuron that ultimately led to the evolution of todays intelligent models.
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Spatial heterogeneities shape collective behavior of signaling amoeboid cells
We present novel experimental results on pattern formation of signaling Dictyostelium discoideum amoeba in the presence of a periodic array of millimeter-sized pillars. We observe concentric cAMP waves that initiate almost synchronously at the pillars and propagate outwards. These waves have higher frequency than the...
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Yamabe Solitons on three-dimensional normal almost paracontact metric manifolds
The purpose of the paper is to study Yamabe solitons on three-dimensional para-Sasakian, paracosymplectic and para-Kenmotsu manifolds. Mainly, we proved that *If the semi-Riemannian metric of a three-dimensional para-Sasakian manifold is a Yamabe soliton, then it is of constant scalar curvature, and the flow vector f...
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Clustering and Hitting Times of Threshold Exceedances and Applications
We investigate exceedances of the process over a sufficiently high threshold. The exceedances determine the risk of hazardous events like climate catastrophes, huge insurance claims, the loss and delay in telecommunication networks. Due to dependence such exceedances tend to occur in clusters. The cluster structure o...
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Classification of Local Field Potentials using Gaussian Sequence Model
A problem of classification of local field potentials (LFPs), recorded from the prefrontal cortex of a macaque monkey, is considered. An adult macaque monkey is trained to perform a memory-based saccade. The objective is to decode the eye movement goals from the LFP collected during a memory period. The LFP classific...
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A few explicit examples of complex dynamics of inertia groups on surfaces - a question of Professor Igor Dolgachev
We give a few explicit examples which answer an open minded question of Professor Igor Dolgachev on complex dynamics of the inertia group of a smooth rational curve on a projective K3 surface and its variants for a rational surface and a non-projective K3 surface.
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Ancestral inference from haplotypes and mutations
We consider inference about the history of a sample of DNA sequences, conditional upon the haplotype counts and the number of segregating sites observed at the present time. After deriving some theoretical results in the coalescent setting, we implement rejection sampling and importance sampling schemes to perform th...
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Ellipsoid Method for Linear Programming made simple
In this paper, ellipsoid method for linear programming is derived using only minimal knowledge of algebra and matrices. Unfortunately, most authors first describe the algorithm, then later prove its correctness, which requires a good knowledge of linear algebra.
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Collective strong coupling of cold atoms to an all-fiber ring cavity
We experimentally demonstrate a ring geometry all-fiber cavity system for cavity quantum electrodynamics with an ensemble of cold atoms. The fiber cavity contains a nanofiber section which mediates atom-light interactions through an evanescent field. We observe well-resolved, vacuum Rabi splitting of the cavity trans...
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Robust Model-Based Clustering of Voting Records
We explore the possibility of discovering extreme voting patterns in the U.S. Congressional voting records by drawing ideas from the mixture of contaminated normal distributions. A mixture of latent trait models via contaminated normal distributions is proposed. We assume that the low dimensional continuous latent va...
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The Quest for Solvable Multistate Landau-Zener Models
Recently, integrability conditions (ICs) in mutistate Landau-Zener (MLZ) theory were proposed [1]. They describe common properties of all known solved systems with linearly time-dependent Hamiltonians. Here we show that ICs enable efficient computer assisted search for new solvable MLZ models that span complexity ran...
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Bayesian Inference of the Multi-Period Optimal Portfolio for an Exponential Utility
We consider the estimation of the multi-period optimal portfolio obtained by maximizing an exponential utility. Employing Jeffreys' non-informative prior and the conjugate informative prior, we derive stochastic representations for the optimal portfolio weights at each time point of portfolio reallocation. This provi...
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Reconstructing the gravitational field of the local universe
Tests of gravity at the galaxy scale are in their infancy. As a first step to systematically uncovering the gravitational significance of galaxies, we map three fundamental gravitational variables -- the Newtonian potential, acceleration and curvature -- over the galaxy environments of the local universe to a distanc...
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Hierarchical organization of H. Eugene Stanley scientific collaboration community in weighted network representation
By mapping the most advanced elements of the contemporary social interactions, the world scientific collaboration network develops an extremely involved and heterogeneous organization. Selected characteristics of this heterogeneity are studied here and identified by focusing on the scientific collaboration community ...
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Rational links and DT invariants of quivers
We prove that the generating functions for the colored HOMFLY-PT polynomials of rational links are specializations of the generating functions of the motivic Donaldson-Thomas invariants of appropriate quivers that we naturally associate with these links. This shows that the conjectural links-quivers correspondence of...
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G2-structures for N=1 supersymmetric AdS4 solutions of M-theory
We study the N=1 supersymmetric solutions of D=11 supergravity obtained as a warped product of four-dimensional anti-de-Sitter space with a seven-dimensional Riemannian manifold M. Using the octonion bundle structure on M we reformulate the Killing spinor equations in terms of sections of the octonion bundle on M. Th...
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A Neural Stochastic Volatility Model
In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of compleme...
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A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
This paper proposes a segmentation-free, automatic and efficient procedure to detect general geometric quadric forms in point clouds, where clutter and occlusions are inevitable. Our everyday world is dominated by man-made objects which are designed using 3D primitives (such as planes, cones, spheres, cylinders, etc....
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Mass-to-Light versus Color Relations for Dwarf Irregular Galaxies
We have determined new relations between $UBV$ colors and mass-to-light ratios ($M/L$) for dwarf irregular (dIrr) galaxies, as well as for transformed $g^\prime - r^\prime$. These $M/L$ to color relations (MLCRs) are based on stellar mass density profiles determined for 34 LITTLE THINGS dwarfs from spectral energy di...
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Insight into High-order Harmonic Generation from Solids: The Contributions of the Bloch Wave-packets Moving on the Group and Phase Velocities
We study numerically the Bloch electron wavepacket dynamics in periodic potentials to simulate laser-solid interactions. We introduce a new perspective in the coordinate space combined with the motion of the Bloch electron wavepackets moving at group and phase velocities under the laser fields. This model interprets ...
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Using deterministic approximations to accelerate SMC for posterior sampling
Sequential Monte Carlo has become a standard tool for Bayesian Inference of complex models. This approach can be computationally demanding, especially when initialized from the prior distribution. On the other hand, deter-ministic approximations of the posterior distribution are often available with no theoretical gu...
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Evaluating (and improving) the correspondence between deep neural networks and human representations
Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying obje...
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An approach to nonsolvable base change and descent
We present a collection of conjectural trace identities and explain why they are equivalent to base change and descent of automorphic representations of $\mathrm{GL}_n(\mathbb{A}_F)$ along nonsolvable extensions (under some simplifying hypotheses). The case $n=2$ is treated in more detail and applications towards the...
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Towards Attack-Tolerant Networks: Concurrent Multipath Routing and the Butterfly Network
Targeted attacks against network infrastructure are notoriously difficult to guard against. In the case of communication networks, such attacks can leave users vulnerable to censorship and surveillance, even when cryptography is used. Much of the existing work on network fault-tolerance focuses on random faults and d...
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PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making
Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which ma...
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Structure of Native Two-dimensional Oxides on III--Nitride Surfaces
When pristine material surfaces are exposed to air, highly reactive broken bonds can promote the formation of surface oxides with structures and properties differing greatly from bulk. Determination of the oxide structure, however, is often elusive through the use of indirect diffraction methods or techniques that pr...
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Hydrodynamic charge and heat transport on inhomogeneous curved spaces
We develop the theory of hydrodynamic charge and heat transport in strongly interacting quasi-relativistic systems on manifolds with inhomogeneous spatial curvature. In solid-state physics, this is analogous to strain disorder in the underlying lattice. In the hydrodynamic limit, we find that the thermal and electric...
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Simulation assisted machine learning
Predicting how a proposed cancer treatment will affect a given tumor can be cast as a machine learning problem, but the complexity of biological systems, the number of potentially relevant genomic and clinical features, and the lack of very large scale patient data repositories make this a unique challenge. "Pure dat...
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Rechargeable redox flow batteries: Maximum current density with electrolyte flow reactant penetration in a serpentine flow structure
Rechargeable redox flow batteries with serpentine flow field designs have been demonstrated to deliver higher current density and power density in medium and large-scale stationary energy storage applications. Nevertheless, the fundamental mechanisms involved with improved current density in flow batteries with flow ...
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Open problems in mathematical physics
We present a list of open questions in mathematical physics. After a historical introduction, a number of problems in a variety of different fields are discussed, with the intention of giving an overall impression of the current status of mathematical physics, particularly in the topical fields of classical general r...
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Stochastic Bandit Models for Delayed Conversions
Online advertising and product recommendation are important domains of applications for multi-armed bandit methods. In these fields, the reward that is immediately available is most often only a proxy for the actual outcome of interest, which we refer to as a conversion. For instance, in web advertising, clicks can b...
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Fitting Analysis using Differential Evolution Optimization (FADO): Spectral population synthesis through genetic optimization under self-consistency boundary conditions
The goal of population spectral synthesis (PSS) is to decipher from the spectrum of a galaxy the mass, age and metallicity of its constituent stellar populations. This technique has been established as a fundamental tool in extragalactic research. It has been extensively applied to large spectroscopic data sets, nota...
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A Combinatoric Shortcut to Evaluate CHY-forms
In \cite{Chen:2016fgi} we proposed a differential operator for the evaluation of the multi-dimensional residues on isolated (zero-dimensional) poles.In this paper we discuss some new insight on evaluating the (generalized) Cachazo-He-Yuan (CHY) forms of the scattering amplitudes using this differential operator. We i...
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ART: adaptive residual--time restarting for Krylov subspace matrix exponential evaluations
In this paper a new restarting method for Krylov subspace matrix exponential evaluations is proposed. Since our restarting technique essentially employs the residual, some convergence results for the residual are given. We also discuss how the restart length can be adjusted after each restart cycle, which leads to an...
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Nil extensions of simple regular ordered semigroup
In this paper, nil extensions of some special type of ordered semigroups, such as, simple regular ordered semigroups, left simple and right regular ordered semigroup. Moreover, we have characterized complete semilattice decomposition of all ordered semigroups which are nil extension of ordered semigroup.
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The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation. For both the Johnson-Lindenstrauss transform and the angular kernel, we show that we can select matrices yie...
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The Bayesian optimist's guide to adaptive immune receptor repertoire analysis
Probabilistic modeling is fundamental to the statistical analysis of complex data. In addition to forming a coherent description of the data-generating process, probabilistic models enable parameter inference about given data sets. This procedure is well-developed in the Bayesian perspective, in which one infers prob...
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Predictive Indexing
There has been considerable research on automated index tuning in database management systems (DBMSs). But the majority of these solutions tune the index configuration by retrospectively making computationally expensive physical design changes all at once. Such changes degrade the DBMS's performance during the proces...
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Making Deep Q-learning methods robust to time discretization
Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust to hyperparameterization, implementation details, or small environment changes (Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key to making DRL applicable to real world problems. In this paper, we identify sensit...
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Anomalous current in diffusive ferromagnetic Josephson junctions
We demonstrate that in diffusive superconductor/ferromagnet/superconductor (S/F/S) junctions a finite, {\it anomalous}, Josephson current can flow even at zero phase difference between the S electrodes. The conditions for the observation of this effect are non-coplanar magnetization distribution and a broken magnetiz...
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Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates
Although a majority of the theoretical literature in high-dimensional statistics has focused on settings which involve fully-observed data, settings with missing values and corruptions are common in practice. We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse hi...
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Applications of an algorithm for solving Fredholm equations of the first kind
In this paper we use an iterative algorithm for solving Fredholm equations of the first kind. The basic algorithm is known and is based on an EM algorithm when involved functions are non-negative and integrable. With this algorithm we demonstrate two examples involving the estimation of a mixing density and a first p...
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Fully symmetric kernel quadrature
Kernel quadratures and other kernel-based approximation methods typically suffer from prohibitive cubic time and quadratic space complexity in the number of function evaluations. The problem arises because a system of linear equations needs to be solved. In this article we show that the weights of a kernel quadrature...
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Conditional Accelerated Lazy Stochastic Gradient Descent
In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of Hazan ...
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MMD GAN: Towards Deeper Understanding of Moment Matching Network
Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performa...
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Multipartite entanglement after a quantum quench
We study the multipartite entanglement of a quantum many-body system undergoing a quantum quench. We quantify multipartite entanglement through the quantum Fisher information (QFI) density and we are able to express it after a quench in terms of a generalized response function. For pure state initial conditions and i...
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Thermal properties of graphene from path-integral simulations
Thermal properties of graphene monolayers are studied by path-integral molecular dynamics (PIMD) simulations, which take into account the quantization of vibrational modes in the crystalline membrane, and allow one to consider anharmonic effects in these properties. This system was studied at temperatures in the rang...
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Measuring Affectiveness and Effectiveness in Software Systems
The summary presented in this paper highlights the results obtained in a four-years project aiming at analyzing the development process of software artifacts from two points of view: Effectiveness and Affectiveness. The first attribute is meant to analyze the productivity of the Open Source Communities by measuring t...
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Intertangled stochastic motifs in networks of excitatory-inhibitory units
A stochastic model of excitatory and inhibitory interactions which bears universality traits is introduced and studied. The endogenous component of noise, stemming from finite size corrections, drives robust inter-nodes correlations, that persist at large large distances. Anti-phase synchrony at small frequencies is ...
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Accurate Computation of the Distribution of Sums of Dependent Log-Normals with Applications to the Black-Scholes Model
We present a new Monte Carlo methodology for the accurate estimation of the distribution of the sum of dependent log-normal random variables. The methodology delivers statistically unbiased estimators for three distributional quantities of significant interest in finance and risk management: the left tail, or cumulat...
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The complete unitary dual of non-compact Lie superalgebra su(p,q|m) via the generalised oscillator formalism, and non-compact Young diagrams
We study the unitary representations of the non-compact real forms of the complex Lie superalgebra sl(n|m). Among them, only the real form su(p,q|m) (p+q=n) admits nontrivial unitary representations, and all such representations are of the highest-weight type (or the lowest-weight type). We extend the standard oscill...
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DeepPainter: Painter Classification Using Deep Convolutional Autoencoders
In this paper we describe the problem of painter classification, and propose a novel approach based on deep convolutional autoencoder neural networks. While previous approaches relied on image processing and manual feature extraction from paintings, our approach operates on the raw pixel level, without any preprocess...
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Sharp gradient estimate for heat kernels on $RCD^*(K,N)$ metric measure spaces
In this paper, we will establish an elliptic local Li-Yau gradient estimate for weak solutions of the heat equation on metric measure spaces with generalized Ricci curvature bounded from below. One of its main applications is a sharp gradient estimate for the logarithm of heat kernels. These results seem new even for...
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Thermodynamic properties of diatomic molecules systems under anharmonic Eckart potential
Due to one of the most representative contributions to the energy in diatomic molecules being the vibrational, we consider the generalized Morse potential (GMP) as one of the typical potential of interaction for one-dimensional microscopic systems, which describes local anharmonic effects. From Eckart potential (EP) ...
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Effects of ultrasound waves intensity on the removal of Congo red color from the textile industry wastewater by Fe3O4@TiO2 core-shell nanospheres
Environmental pollutants, such as colors from the textile industry, affect water quality indicators like color, smell, and taste. These substances in the water cause the obstruction of filters and membranes and thereby reduce the efficiency of advanced water treatment processes. In addition, they are harmful to human...
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Enumeration of Tree-like Maps with Arbitrary Number of Vertices
This paper provides the generating series for the embedding of tree-like graphs of arbitrary number of vertices, accourding to their genus. It applies and extends the techniques of Chan, where it was used to give an alternate proof of the Goulden and Slofstra formula. Furthermore, this greatly generalizes the famous ...
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Depth resolved chemical speciation of a superlattice structure
We report results of simultaneous x-ray reflectivity and grazing incidence x-ray fluorescence measurements in combination with x-ray standing wave assisted depth resolved near edge x-ray absorption measurements to reveal new insights on chemical speciation of W in a W-B4C superlattice structure. Interestingly, our re...
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Optospintronics in graphene via proximity coupling
The observation of micron size spin relaxation makes graphene a promising material for applications in spintronics requiring long distance spin communication. However, spin dependent scatterings at the contact/graphene interfaces affect the spin injection efficiencies and hence prevent the material from achieving its...
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Control strategy to limit duty cycle impact of earthquakes on the LIGO gravitational-wave detectors
Advanced gravitational-wave detectors such as the Laser Interferometer Gravitational-Wave Observatories (LIGO) require an unprecedented level of isolation from the ground. When in operation, they are expected to observe changes in the space-time continuum of less than one thousandth of the diameter of a proton. Stron...
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Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of ...
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Types and unitary representations of reductive p-adic groups
We prove that for every Bushnell-Kutzko type that satisfies a certain rigidity assumption, the equivalence of categories between the corresponding Bernstein component and the category of modules for the Hecke algebra of the type induces a bijection between irreducible unitary representations in the two categories. Th...
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Average values of L-functions in even characteristic
Let $k = \mathbb{F}_{q}(T)$ be the rational function field over a finite field $\mathbb{F}_{q}$, where $q$ is a power of $2$. In this paper we solve the problem of averaging the quadratic $L$-functions $L(s, \chi_{u})$ over fundamental discriminants. Any separable quadratic extension $K$ of $k$ is of the form $K = k(...
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Decoupled molecules with binding polynomials of bidegree (n,2)
We present a result on the number of decoupled molecules for systems binding two different types of ligands. In the case of $n$ and $2$ binding sites respectively, we show that, generically, there are $2(n!)^{2}$ decoupled molecules with the same binding polynomial. For molecules with more binding sites for the secon...
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Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
Learning to remember long sequences remains a challenging task for recurrent neural networks. Register memory and attention mechanisms were both proposed to resolve the issue with either high computational cost to retain memory differentiability, or by discounting the RNN representation learning towards encoding shor...
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McDiarmid Drift Detection Methods for Evolving Data Streams
Increasingly, Internet of Things (IoT) domains, such as sensor networks, smart cities, and social networks, generate vast amounts of data. Such data are not only unbounded and rapidly evolving. Rather, the content thereof dynamically evolves over time, often in unforeseen ways. These variations are due to so-called c...
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Yield in Amorphous Solids: The Ant in the Energy Landscape Labyrinth
It has recently been shown that yield in amorphous solids under oscillatory shear is a dynamical transition from asymptotically periodic to asymptotically chaotic, diffusive dynamics. However, the type and universality class of this transition are still undecided. Here we show that the diffusive behavior of the vecto...
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Statistical methods in astronomy
We present a review of data types and statistical methods often encountered in astronomy. The aim is to provide an introduction to statistical applications in astronomy for statisticians and computer scientists. We highlight the complex, often hierarchical, nature of many astronomy inference problems and advocate for...
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A note on some algebraic trapdoors for block ciphers
We provide sufficient conditions to guarantee that a translation based cipher is not vulnerable with respect to the partition-based trapdoor. This trapdoor has been introduced, recently, by Bannier et al. (2016) and it generalizes that introduced by Paterson in 1999. Moreover, we discuss the fact that studying the gr...
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Bi-Demographic Changes and Current Account using SVAR Modeling
The paper, as a new contribution, aims to explore the impacts of bi-demographic structure on the current account and growth. By using a SVAR modeling, we track the dynamic impacts between the underlying variables of the Saudi economy. New insights have been developed to study the interrelations between population gro...
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Supervised Machine Learning for Signals Having RRC Shaped Pulses
Classification performances of the supervised machine learning techniques such as support vector machines, neural networks and logistic regression are compared for modulation recognition purposes. The simple and robust features are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals having root-rai...
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End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks
We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find...
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Effective difference elimination and Nullstellensatz
We prove effective Nullstellensatz and elimination theorems for difference equations in sequence rings. More precisely, we compute an explicit function of geometric quantities associated to a system of difference equations (and these geometric quantities may themselves be bounded by a function of the number of variab...
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A note on primitive $1-$normal elements over finite fields
Let $q$ be a prime power of a prime $p$, $n$ a positive integer and $\mathbb F_{q^n}$ the finite field with $q^n$ elements. The $k-$normal elements over finite fields were introduced and characterized by Huczynska et al (2013). Under the condition that $n$ is not divisible by $p$, they obtained an existence result on...
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Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons
Zebrafish pretectal neurons exhibit specificities for large-field optic flow patterns associated with rotatory or translatory body motion. We investigate the hypothesis that these specificities reflect the input statistics of natural optic flow. Realistic motion sequences were generated using computer graphics simula...
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Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard a...
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Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations
We train multi-task autoencoders on linguistic tasks and analyze the learned hidden sentence representations. The representations change significantly when translation and part-of-speech decoders are added. The more decoders a model employs, the better it clusters sentences according to their syntactic similarity, as...
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Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, velocity and variety of the data collected in these applications pose significant and unique challenges that must be carefully addressed for eac...
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Extended Bose Hubbard model for two leg ladder systems in artificial magnetic fields
We investigate the ground state properties of ultracold atoms with long range interactions trapped in a two leg ladder configuration in the presence of an artificial magnetic field. Using a Gross-Pitaevskii approach and a mean field Gutzwiller variational method, we explore both the weakly interacting and strongly in...
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Insensitivity of The Distance Ladder Hubble Constant Determination to Cepheid Calibration Modeling Choices
Recent determination of the Hubble constant via Cepheid-calibrated supernovae by \citet{riess_2.4_2016} (R16) find $\sim 3\sigma$ tension with inferences based on cosmic microwave background temperature and polarization measurements from $Planck$. This tension could be an indication of inadequacies in the concordance...
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Phrase-based Image Captioning with Hierarchical LSTM Model
Automatic generation of caption to describe the content of an image has been gaining a lot of research interests recently, where most of the existing works treat the image caption as pure sequential data. Natural language, however possess a temporal hierarchy structure, with complex dependencies between each subseque...
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The three-dimensional structure of swirl-switching in bent pipe flow
Swirl-switching is a low-frequency oscillatory phenomenon which affects the Dean vortices in bent pipes and may cause fatigue in piping systems. Despite thirty years worth of research, the mechanism that causes these oscillations and the frequencies that characterise them remain unclear. Here we show that a three-dim...
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InfoVAE: Information Maximizing Variational Autoencoders
A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models. We find that existing training objectives for variational autoencoders can lead to inaccurate amortized inference distributions and, in some cases, improving the objective provably deg...
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Between-class Learning for Image Classification
In this paper, we propose a novel learning method for image classification called Between-Class learning (BC learning). We generate between-class images by mixing two images belonging to different classes with a random ratio. We then input the mixed image to the model and train the model to output the mixing ratio. B...
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Girsanov reweighting for path ensembles and Markov state models
The sensitivity of molecular dynamics on changes in the potential energy function plays an important role in understanding the dynamics and function of complex molecules.We present a method to obtain path ensemble averages of a perturbed dynamics from a set of paths generated by a reference dynamics. It is based on t...
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Coordination of multi-agent systems via asynchronous cloud communication
In this work we study a multi-agent coordination problem in which agents are only able to communicate with each other intermittently through a cloud server. To reduce the amount of required communication, we develop a self-triggered algorithm that allows agents to communicate with the cloud only when necessary rather...
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PatternListener: Cracking Android Pattern Lock Using Acoustic Signals
Pattern lock has been widely used for authentication to protect user privacy on mobile devices (e.g., smartphones and tablets). Given its pervasive usage, the compromise of pattern lock could lead to serious consequences. Several attacks have been constructed to crack the lock. However, these approaches require the a...
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Marginal Release Under Local Differential Privacy
Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper...
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A possible flyby anomaly for Juno at Jupiter
In the last decades there have been an increasing interest in improving the accuracy of spacecraft navigation and trajectory data. In the course of this plan some anomalies have been found that cannot, in principle, be explained in the context of the most accurate orbital models including all known effects from class...
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Crossover between various initial conditions in KPZ growth: flat to stationary
We conjecture the universal probability distribution at large time for the one-point height in the 1D Kardar-Parisi-Zhang (KPZ) stochastic growth universality class, with initial conditions interpolating from any one of the three main classes (droplet, flat, stationary) on the left, to another on the right, allowing ...
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Multimodel Response Assessment for Monthly Rainfall Distribution in Some Selected Indian Cities Using Best Fit Probability as a Tool
We carry out a study of the statistical distribution of rainfall precipitation data for 20 cites in India. We have determined the best-fit probability distribution for these cities from the monthly precipitation data spanning 100 years of observations from 1901 to 2002. To fit the observed data, we considered 10 diff...
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Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing
Bin Packing problems have been widely studied because of their broad applications in different domains. Known as a set of NP-hard problems, they have different vari- ations and many heuristics have been proposed for obtaining approximate solutions. Specifically, for the 1D variable sized bin packing problem, the two ...
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Surges of collective human activity emerge from simple pairwise correlations
Human populations exhibit complex behaviors---characterized by long-range correlations and surges in activity---across a range of social, political, and technological contexts. Yet it remains unclear where these collective behaviors come from, or if there even exists a set of unifying principles. Indeed, existing exp...
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Semantically Enhanced Dynamic Bayesian Network for Detecting Sepsis Mortality Risk in ICU Patients with Infection
Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impeded by non-specific signs of infection and failure to detect signs of sepsis-induced organ dysfunction in a constellation of dynamically c...
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Hölder regularity of viscosity solutions of some fully nonlinear equations in the Heisenberg group
In this paper we prove the Hölder regularity of bounded, uniformly continuous, viscosity solutions of some degenerate fully nonlinear equations in the Heisenberg group.
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Normal form for transverse instability of the line soliton with a nearly critical speed of propagation
There exists a critical speed of propagation of the line solitons in the Zakharov-Kuznetsov (ZK) equation such that small transversely periodic perturbations are unstable for line solitons with larger-than-critical speeds and orbitally stable for those with smaller-than-critical speeds. The normal form for transverse...
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The CLaC Discourse Parser at CoNLL-2016
This paper describes our submission "CLaC" to the CoNLL-2016 shared task on shallow discourse parsing. We used two complementary approaches for the task. A standard machine learning approach for the parsing of explicit relations, and a deep learning approach for non-explicit relations. Overall, our parser achieves an...
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