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Objective Bayesian Analysis for Change Point Problems
In this paper we present a loss-based approach to change point analysis. In particular, we look at the problem from two perspectives. The first focuses on the definition of a prior when the number of change points is known a priori. The second contribution aims to estimate the number of change points by using a loss-...
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On the Mechanism of Large Amplitude Flapping of Inverted Foil in a Uniform Flow
An elastic foil interacting with a uniform flow with its trailing edge clamped, also known as the inverted foil, exhibits a wide range of complex self-induced flapping regimes such as large amplitude flapping (LAF), deformed and flipped flapping. Here, we perform three-dimensional numerical experiments to examine the...
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Ultrafast Epitaxial Growth of Metre-Sized Single-Crystal Graphene on Industrial Cu Foil
A foundation of the modern technology that uses single-crystal silicon has been the growth of high-quality single-crystal Si ingots with diameters up to 12 inches or larger. For many applications of graphene, large-area high-quality (ideally of single-crystal) material will be enabling. Since the first growth on copp...
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Multidimensional Sampling of Isotropically Bandlimited Signals
A new lower bound on the average reconstruction error variance of multidimensional sampling and reconstruction is presented. It applies to sampling on arbitrary lattices in arbitrary dimensions, assuming a stochastic process with constant, isotropically bandlimited spectrum and reconstruction by the best linear inter...
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Asymptotic structure of almost eigenfunctions of drift Laplacians on conical ends
We use a weighted variant of the frequency functions introduced by Almgren to prove sharp asymptotic estimates for almost eigenfunctions of the drift Laplacian associated to the Gaussian weight on an asymptotically conical end. As a consequence, we obtain a purely elliptic proof of a result of L. Wang on the uniquene...
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Palomar Optical Spectrum of Hyperbolic Near-Earth Object A/2017 U1
We present optical spectroscopy of the recently discovered hyperbolic near-Earth object A/2017 U1, taken on 25 Oct 2017 at Palomar Observatory. Although our data are at a very low signal-to-noise, they indicate a very red surface at optical wavelengths without significant absorption features.
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Electrically driven quantum light emission in electromechanically-tuneable photonic crystal cavities
A single quantum dot deterministically coupled to a photonic crystal environment constitutes an indispensable elementary unit to both generate and manipulate single-photons in next-generation quantum photonic circuits. To date, the scaling of the number of these quantum nodes on a fully-integrated chip has been preve...
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The Odyssey Approach for Optimizing Federated SPARQL Queries
Answering queries over a federation of SPARQL endpoints requires combining data from more than one data source. Optimizing queries in such scenarios is particularly challenging not only because of (i) the large variety of possible query execution plans that correctly answer the query but also because (ii) there is on...
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A variant of Gromov's problem on Hölder equivalence of Carnot groups
It is unknown if there exists a locally $\alpha$-Hölder homeomorphism $f:\mathbb{R}^3\to \mathbb{H}^1$ for any $\frac{1}{2}< \alpha\le \frac{2}{3}$, although the identity map $\mathbb{R}^3\to \mathbb{H}^1$ is locally $\frac{1}{2}$-Hölder. More generally, Gromov asked: Given $k$ and a Carnot group $G$, for which $\alp...
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The Trees of Hanoi
The game of the Towers of Hanoi is generalized to binary trees. First, a straightforward solution of the game is discussed. Second, a shorter solution is presented, which is then shown to be optimal.
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On the risk of convex-constrained least squares estimators under misspecification
We consider the problem of estimating the mean of a noisy vector. When the mean lies in a convex constraint set, the least squares projection of the random vector onto the set is a natural estimator. Properties of the risk of this estimator, such as its asymptotic behavior as the noise tends to zero, have been well s...
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Benchmarks for Image Classification and Other High-dimensional Pattern Recognition Problems
A good classification method should yield more accurate results than simple heuristics. But there are classification problems, especially high-dimensional ones like the ones based on image/video data, for which simple heuristics can work quite accurately; the structure of the data in such problems is easy to uncover ...
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On the Binary Lossless Many-Help-One Problem with Independently Degraded Helpers
Although the rate region for the lossless many-help-one problem with independently degraded helpers is already "solved", its solution is given in terms of a convex closure over a set of auxiliary random variables. Thus, for any such a problem in particular, an optimization over the set of auxiliary random variables i...
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Efficient Correlated Topic Modeling with Topic Embedding
Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient i...
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Structure-aware error bounds for linear classification with the zero-one loss
We prove risk bounds for binary classification in high-dimensional settings when the sample size is allowed to be smaller than the dimensionality of the training set observations. In particular, we prove upper bounds for both 'compressive learning' by empirical risk minimization (ERM) (that is when the ERM classifier...
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Dimensional reduction and the equivariant Chern character
We propose a dimensional reduction procedure in the Stolz--Teichner framework of supersymmetric Euclidean field theories (EFTs) that is well-suited in the presence of a finite gauge group or, more generally, for field theories over an orbifold. As an illustration, we give a geometric interpretation of the Chern chara...
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Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions
We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent neural network. We demonstrate the effectiveness of the approach in a...
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Formal Privacy for Functional Data with Gaussian Perturbations
Motivated by the rapid rise in statistical tools in Functional Data Analysis, we consider the Gaussian mechanism for achieving differential privacy with parameter estimates taking values in a, potentially infinite-dimensional, separable Banach space. Using classic results from probability theory, we show how densitie...
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An algorithm to reconstruct convex polyhedra from their face normals and areas
A well-known result in the study of convex polyhedra, due to Minkowski, is that a convex polyhedron is uniquely determined (up to translation) by the directions and areas of its faces. The theorem guarantees existence of the polyhedron associated to given face normals and areas, but does not provide a constructive wa...
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Electron affinities of water clusters from density-functional and many-body-perturbation theory
In this work, we assess the accuracy of dielectric-dependent hybrid density functionals and many-body perturbation theory methods for the calculation of electron affinities of small water clusters, including hydrogen-bonded water dimer and water hexamer isomers. We show that many-body perturbation theory in the G$_0$...
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Grid-converged Solution and Analysis of the Unsteady Viscous Flow in a Two-dimensional Shock Tube
The flow in a shock tube is extremely complex with dynamic multi-scale structures of sharp fronts, flow separation, and vortices due to the interaction of the shock wave, the contact surface, and the boundary layer over the side wall of the tube. Prediction and understanding of the complex fluid dynamics is of theore...
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Exact zero modes in twisted Kitaev chains
We study the Kitaev chain under generalized twisted boundary conditions, for which both the amplitudes and the phases of the boundary couplings can be tuned at will. We explicitly show the presence of exact zero modes for large chains belonging to the topological phase in the most general case, in spite of the absenc...
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Generative Temporal Models with Memory
We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations. A sufficiently powerful temporal model should separate predictable elements of the sequence from unpredictable elements, e...
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Global research collaboration: Networks and partners in South East Asia
This is an empirical paper that addresses the role of bilateral and multilateral international co-authorships in the six leading science systems among the ASEAN group of countries (ASEAN6). The paper highlights the different ways that bilateral and multilateral co-authorships structure global networks and the collabo...
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Spontaneous and stimulus-induced coherent states of dynamically balanced neuronal networks
How the information microscopically processed by individual neurons is integrated and used in organising the macroscopic behaviour of an animal is a central question in neuroscience. Coherence of dynamics over different scales has been suggested as a clue to the mechanisms underlying this integration. Balanced excita...
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Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors
Pairwise association measure is an important operation in data analytics. Kendall's tau coefficient is one widely used correlation coefficient identifying non-linear relationships between ordinal variables. In this paper, we investigated a parallel algorithm accelerating all-pairs Kendall's tau coefficient computatio...
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Effect of Blast Exposure on Gene-Gene Interactions
Repeated exposure to low-level blast may initiate a range of adverse health problem such as traumatic brain injury (TBI). Although many studies successfully identified genes associated with TBI, yet the cellular mechanisms underpinning TBI are not fully elucidated. In this study, we investigated underlying relationsh...
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A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields
Gaussian Markov random fields are used in a large number of disciplines in machine vision and spatial statistics. The models take advantage of sparsity in matrices introduced through the Markov assumptions, and all operations in inference and prediction use sparse linear algebra operations that scale well with dimens...
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Rational motivic path spaces and Kim's relative unipotent section conjecture
We initiate a study of path spaces in the nascent context of "motivic dga's", under development in doctoral work by Gabriella Guzman. This enables us to reconstruct the unipotent fundamental group of a pointed scheme from the associated augmented motivic dga, and provides us with a factorization of Kim's relative uni...
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Convergence Rates of Latent Topic Models Under Relaxed Identifiability Conditions
In this paper we study the frequentist convergence rate for the Latent Dirichlet Allocation (Blei et al., 2003) topic models. We show that the maximum likelihood estimator converges to one of the finitely many equivalent parameters in Wasserstein's distance metric at a rate of $n^{-1/4}$ without assuming separability...
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Subspace Clustering of Very Sparse High-Dimensional Data
In this paper we consider the problem of clustering collections of very short texts using subspace clustering. This problem arises in many applications such as product categorisation, fraud detection, and sentiment analysis. The main challenge lies in the fact that the vectorial representation of short texts is both ...
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Quarnet inference rules for level-1 networks
An important problem in phylogenetics is the construction of phylogenetic trees. One way to approach this problem, known as the supertree method, involves inferring a phylogenetic tree with leaves consisting of a set $X$ of species from a collection of trees, each having leaf-set some subset of $X$. In the 1980's cha...
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21 cm Angular Power Spectrum from Minihalos as a Probe of Primordial Spectral Runnings
Measurements of 21 cm line fluctuations from minihalos have been discussed as a powerful probe of a wide range of cosmological models. However, previous studies have taken into account only the pixel variance, where contributions from different scales are integrated. In order to sort out information from different sc...
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The IRX-Beta Dust Attenuation Relation in Cosmological Galaxy Formation Simulations
We utilise a series of high-resolution cosmological zoom simulations of galaxy formation to investigate the relationship between the ultraviolet (UV) slope, beta, and the ratio of the infrared luminosity to UV luminosity (IRX) in the spectral energy distributions (SEDs) of galaxies. We employ dust radiative transfer ...
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Strict convexity of the Mabuchi functional for energy minimizers
There are two parts of this paper. First, we discovered an explicit formula for the complex Hessian of the weighted log-Bergman kernel on a parallelogram domain, and utilised this formula to give a new proof about the strict convexity of the Mabuchi functional along a smooth geodesic. Second, when a C^{1,1}-geodesic ...
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Anonymous Variables in Imperative Languages
In this paper, we bring anonymous variables into imperative languages. Anonymous variables represent don't-care values and have proven useful in logic programming. To bring the same level of benefits into imperative languages, we describe an extension to C wth anonymous variables.
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Simultaneously constraining the astrophysics of reionisation and the epoch of heating with 21CMMC
The cosmic 21 cm signal is set to revolutionise our understanding of the early Universe, allowing us to probe the 3D temperature and ionisation structure of the intergalactic medium (IGM). It will open a window onto the unseen first galaxies, showing us how their UV and X-ray photons drove the cosmic milestones of th...
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Non-penalized variable selection in high-dimensional linear model settings via generalized fiducial inference
Standard penalized methods of variable selection and parameter estimation rely on the magnitude of coefficient estimates to decide which variables to include in the final model. However, coefficient estimates are unreliable when the design matrix is collinear. To overcome this challenge an entirely new perspective on...
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DNN Filter Bank Cepstral Coefficients for Spoofing Detection
With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DN...
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The existence of positive least energy solutions for a class of Schrodinger-Poisson systems involving critical nonlocal term with general nonlinearity
The present study is concerned with the following Schrödinger-Poisson system involving critical nonlocal term with general nonlinearity: $$ \left\{ \begin{array}{ll} -\Delta u+V(x)u- \phi |u|^3u= f(u), & x\in\mathbb{R}^3, -\Delta \phi= |u|^5, & x\in\mathbb{R}^3,\\ \end{array} \right. $$ Under certain assumptions on n...
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Up-down colorings of virtual-link diagrams and the necessity of Reidemeister moves of type II
We introduce an up-down coloring of a virtual-link diagram. The colorabilities give a lower bound of the minimum number of Reidemeister moves of type II which are needed between two 2-component virtual-link diagrams. By using the notion of a quandle cocycle invariant, we determine the necessity of Reidemeister moves ...
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Comparison of SMT and RBMT; The Requirement of Hybridization for Marathi-Hindi MT
We present in this paper our work on comparison between Statistical Machine Translation (SMT) and Rule-based machine translation for translation from Marathi to Hindi. Rule Based systems although robust take lots of time to build. On the other hand statistical machine translation systems are easier to create, maintai...
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Introduction to the Special Issue on Approaches to Control Biological and Biologically Inspired Networks
The emerging field at the intersection of quantitative biology, network modeling, and control theory has enjoyed significant progress in recent years. This Special Issue brings together a selection of papers on complementary approaches to observe, identify, and control biological and biologically inspired networks. T...
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Towards Classification of Web ontologies using the Horizontal and Vertical Segmentation
The new era of the Web is known as the semantic Web or the Web of data. The semantic Web depends on ontologies that are seen as one of its pillars. The bigger these ontologies, the greater their exploitation. However, when these ontologies become too big other problems may appear, such as the complexity to charge big...
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An Efficient Version of the Bombieri-Vaaler Lemma
In their celebrated paper "On Siegel's Lemma", Bombieri and Vaaler found an upper bound on the height of integer solutions of systems of linear Diophantine equations. Calculating the bound directly, however, requires exponential time. In this paper, we present the bound in a different form that can be computed in pol...
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Personalized and Private Peer-to-Peer Machine Learning
The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a ful...
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Interacting Chaplygin gas revisited
The implications of considering interaction between Chaplygin gas and a barotropic fluid with constant equation of state have been explored. The unique feature of this work is that assuming an interaction $Q \propto H\rho_d$, analytic expressions for the energy density and pressure have been derived in terms of the H...
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GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations
We present GALARIO, a computational library that exploits the power of modern graphical processing units (GPUs) to accelerate the analysis of observations from radio interferometers like ALMA or the VLA. GALARIO speeds up the computation of synthetic visibilities from a generic 2D model image or a radial brightness p...
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Focused time-lapse inversion of radio and audio magnetotelluric data
Geoelectrical techniques are widely used to monitor groundwater processes, while surprisingly few studies have considered audio (AMT) and radio (RMT) magnetotellurics for such purposes. In this numerical investigation, we analyze to what extent inversion results based on AMT and RMT monitoring data can be improved by...
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Deep Exploration via Randomized Value Functions
We study the use of randomized value functions to guide deep exploration in reinforcement learning. This offers an elegant means for synthesizing statistically and computationally efficient exploration with common practical approaches to value function learning. We present several reinforcement learning algorithms th...
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Spectral Norm Regularization for Improving the Generalizability of Deep Learning
We investigate the generalizability of deep learning based on the sensitivity to input perturbation. We hypothesize that the high sensitivity to the perturbation of data degrades the performance on it. To reduce the sensitivity to perturbation, we propose a simple and effective regularization method, referred to as s...
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Estimating the chromospheric magnetic field from a revised NLTE modeling: the case of HR7428
In this work we use the semi-empirical atmospheric modeling method to obtain the chro-mospheric temperature, pressure, density and magnetic field distribution versus height in the K2 primary component of the RS CVn binary system HR 7428. While temperature, pressure, density are the standard output of the semi-empiric...
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An accurate approximation formula for gamma function
In this paper, we present a very accurate approximation for gamma function: \begin{equation*} \Gamma \left( x+1\right) \thicksim \sqrt{2\pi x}\left( \dfrac{x}{e}\right) ^{x}\left( x\sinh \frac{1}{x}\right) ^{x/2}\exp \left( \frac{7}{324}\frac{1}{ x^{3}\left( 35x^{2}+33\right) }\right) =W_{2}\left( x\right) \end{equat...
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Reconciling Bayesian and Total Variation Methods for Binary Inversion
A central theme in classical algorithms for the reconstruction of discontinuous functions from observational data is perimeter regularization. On the other hand, sparse or noisy data often demands a probabilistic approach to the reconstruction of images, to enable uncertainty quantification; the Bayesian approach to ...
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Perception Driven Texture Generation
This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attri...
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Computational modeling approaches in gonadotropin signaling
Follicle-stimulating hormone (FSH) and luteinizing hormone (LH) play essential roles in animal reproduction. They exert their function through binding to their cognate receptors, which belong to the large family of G protein-coupled receptors (GPCRs). This recognition at the plasma membrane triggers a plethora of cel...
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SEP-Nets: Small and Effective Pattern Networks
While going deeper has been witnessed to improve the performance of convolutional neural networks (CNN), going smaller for CNN has received increasing attention recently due to its attractiveness for mobile/embedded applications. It remains an active and important topic how to design a small network while retaining t...
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Fundamental Limitations of Cavity-assisted Atom Interferometry
Atom interferometers employing optical cavities to enhance the beam splitter pulses promise significant advances in science and technology, notably for future gravitational wave detectors. Long cavities, on the scale of hundreds of meters, have been proposed in experiments aiming to observe gravitational waves with f...
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Markov chain aggregation and its application to rule-based modelling
Rule-based modelling allows to represent molecular interactions in a compact and natural way. The underlying molecular dynamics, by the laws of stochastic chemical kinetics, behaves as a continuous-time Markov chain. However, this Markov chain enumerates all possible reaction mixtures, rendering the analysis of the c...
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Shape and Positional Geometry of Multi-Object Configurations
In previous work, we introduced a method for modeling a configuration of objects in 2D and 3D images using a mathematical "medial/skeletal linking structure." In this paper, we show how these structures allow us to capture positional properties of a multi-object configuration in addition to the shape properties of th...
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Interface mediated mechanisms of plastic strain recovery in AgCu alloy
Through the combination of transmission electron microscopy analysis of the deformed microstructure and molecular dynamics computer simulations of the deformation processes, the mechanisms of plastic strain recovery in bulk AgCu eutectic with either incoherent twin or cube-on-cube interfaces between the Ag and Cu lay...
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Refounding legitimacy towards Aethogenesis
The fusion of humans and technology takes us into an unknown world described by some authors as populated by quasi living species that would relegate us - ordinary humans - to the rank of alienated agents emptied of our identity and consciousness. I argue instead that our world is woven of simple though invisible per...
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FELIX-2.0: New version of the finite element solver for the time dependent generator coordinate method with the Gaussian overlap approximation
The time-dependent generator coordinate method (TDGCM) is a powerful method to study the large amplitude collective motion of quantum many-body systems such as atomic nuclei. Under the Gaussian Overlap Approximation (GOA), the TDGCM leads to a local, time-dependent Schrödinger equation in a multi-dimensional collecti...
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Scattering in the energy space for Boussinesq equations
In this note we show that all small solutions in the energy space of the generalized 1D Boussinesq equation must decay to zero as time tends to infinity, strongly on slightly proper subsets of the space-time light cone. Our result does not require any assumption on the power of the nonlinearity, working even for the ...
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A Data-Driven Supply-Side Approach for Measuring Cross-Border Internet Purchases
The digital economy is a highly relevant item on the European Union's policy agenda. Cross-border internet purchases are part of the digital economy, but their total value can currently not be accurately measured or estimated. Traditional approaches based on consumer surveys or business surveys are shown to be inadeq...
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Low Rank Magnetic Resonance Fingerprinting
Purpose: Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI measures using randomized acquisition. Extraction of physical quantitative tissue parameters is performed off-line, without the need of patient presence, based on acquisition with varying parameters and a dict...
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SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH), as well as its practical variant SARAH+, as a novel approach to the finite-sum minimization problems. Different from the vanilla SGD and other modern stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple recursive framew...
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A criterion related to the Riemann Hypothesis
A crucial role in the Nyman-Beurling-Báez-Duarte approach to the Riemann Hypothesis is played by the distance \[ d_N^2:=\inf_{A_N}\frac{1}{2\pi}\int_{-\infty}^\infty\left|1-\zeta A_N\left(\frac{1}{2}+it\right)\right|^2\frac{dt}{\frac{1}{4}+t^2}\:, \] where the infimum is over all Dirichlet polynomials $$A_N(s)=\sum_{...
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Non-Stationary Spectral Kernels
We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of no...
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Spectral and Energy Efficiency of Uplink D2D Underlaid Massive MIMO Cellular Networks
One of key 5G scenarios is that device-to-device (D2D) and massive multiple-input multiple-output (MIMO) will be co-existed. However, interference in the uplink D2D underlaid massive MIMO cellular networks needs to be coordinated, due to the vast cellular and D2D transmissions. To this end, this paper introduces a sp...
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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when conditions in training differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the ...
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The ANTARES Collaboration: Contributions to ICRC 2017 Part II: The multi-messenger program
Papers on the ANTARES multi-messenger program, prepared for the 35th International Cosmic Ray Conference (ICRC 2017, Busan, South Korea) by the ANTARES Collaboration
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Hamiltonian structure of peakons as weak solutions for the modified Camassa-Holm equation
The modified Camassa-Holm (mCH) equation is a bi-Hamiltonian system possessing $N$-peakon weak solutions, for all $N\geq 1$, in the setting of an integral formulation which is used in analysis for studying local well-posedness, global existence, and wave breaking for non-peakon solutions. Unlike the original Camassa-...
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Orbital-dependent correlations in PuCoGa$_5$
We investigate the normal state of the superconducting compound PuCoGa$_5$ using the combination of density functional theory (DFT) and dynamical mean field theory (DMFT), with the continuous time quantum Monte Carlo (CTQMC) and the vertex-corrected one-crossing approximation (OCA) as the impurity solvers. Our DFT+DM...
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A variational derivation of the nonequilibrium thermodynamics of a moist atmosphere with rain process and its pseudoincompressible approximation
Irreversible processes play a major role in the description and prediction of atmospheric dynamics. In this paper, we present a variational derivation of the evolution equations for a moist atmosphere with rain process and subject to the irreversible processes of viscosity, heat conduction, diffusion, and phase trans...
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On certain weighted 7-colored partitions
Inspired by Andrews' 2-colored generalized Frobenius partitions, we consider certain weighted 7-colored partition functions and establish some interesting Ramanujan-type identities and congruences. Moreover, we provide combinatorial interpretations of some congruences modulo 5 and 7. Finally, we study the properties ...
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Employee turnover prediction and retention policies design: a case study
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention d...
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Do You Want Your Autonomous Car To Drive Like You?
With progress in enabling autonomous cars to drive safely on the road, it is time to start asking how they should be driving. A common answer is that they should be adopting their users' driving style. This makes the assumption that users want their autonomous cars to drive like they drive - aggressive drivers want a...
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Now Playing: Continuous low-power music recognition
Existing music recognition applications require a connection to a server that performs the actual recognition. In this paper we present a low-power music recognizer that runs entirely on a mobile device and automatically recognizes music without user interaction. To reduce battery consumption, a small music detector ...
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COLA: Decentralized Linear Learning
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over many user devices, and the learning algorithm must run on-device...
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Improving the Expected Improvement Algorithm
The expected improvement (EI) algorithm is a popular strategy for information collection in optimization under uncertainty. The algorithm is widely known to be too greedy, but nevertheless enjoys wide use due to its simplicity and ability to handle uncertainty and noise in a coherent decision theoretic framework. To ...
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Performance Limits of Solutions to Network Utility Maximization Problems
We study performance limits of solutions to utility maximization problems (e.g., max-min problems) in wireless networks as a function of the power budget $\bar{p}$ available to transmitters. Special focus is devoted to the utility and the transmit energy efficiency (i.e., utility over transmit power) of the solution....
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Toeplitz Order
A new approach to problems of the Uncertainty Principle in Harmonic Analysis, based on the use of Toeplitz operators, has brought progress to some of the classical problems in the area. The goal of this paper is to develop and systematize the function theoretic component of the Toeplitz approach by introducing a part...
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Definably compact groups definable in real closed fields. I
We study definably compact definably connected groups definable in a sufficiently saturated real closed field $R$. We introduce the notion of group-generic point for $\bigvee$-definable groups and show the existence of group-generic points for definably compact groups definable in a sufficiently saturated o-minimal e...
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The unreasonable effectiveness of small neural ensembles in high-dimensional brain
Despite the widely-spread consensus on the brain complexity, sprouts of the single neuron revolution emerged in neuroscience in the 1970s. They brought many unexpected discoveries, including grandmother or concept cells and sparse coding of information in the brain. In machine learning for a long time, the famous cur...
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Explicit cocycle formulas on finite abelian groups with applications to braided linear Gr-categories and Dijkgraaf-Witten invariants
We provide explicit and unified formulas for the cocycles of all degrees on the normalized bar resolutions of finite abelian groups. This is achieved by constructing a chain map from the normalized bar resolution to a Koszul-like resolution for any given finite abelian group. With a help of the obtained cocycle formu...
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Threshold Selection for Multivariate Heavy-Tailed Data
Regular variation is often used as the starting point for modeling multivariate heavy-tailed data. A random vector is regularly varying if and only if its radial part $R$ is regularly varying and is asymptotically independent of the angular part $\Theta$ as $R$ goes to infinity. The conditional limiting distribution ...
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An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms
Deep learning (DL) has recently achieved tremendous success in a variety of cutting-edge applications, e.g., image recognition, speech and natural language processing, and autonomous driving. Besides the available big data and hardware evolution, DL frameworks and platforms play a key role to catalyze the research, d...
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On the complexity of topological conjugacy of compact metrizable $G$-ambits
In this note, we analyze the classification problem for compact metrizable $G$-ambits for a countable discrete group $G$ from the point of view of descriptive set theory. More precisely, we prove that the topological conjugacy relation on the standard Borel space of compact metrizable $G$-ambits is Borel for every co...
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Classical affine W-superalgebras via generalized Drinfeld-Sokolov reductions and related integrable systems
The purpose of this article is to investigate relations between W-superalgebras and integrable super-Hamiltonian systems. To this end, we introduce the generalized Drinfel'd-Sokolov (D-S) reduction associated to a Lie superalgebra $g$ and its even nilpotent element $f$, and we find a new definition of the classical a...
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Temporally Identity-Aware SSD with Attentional LSTM
Temporal object detection has attracted significant attention, but most popular detection methods can not leverage the rich temporal information in videos. Very recently, many different algorithms have been developed for video detection task, but real-time online approaches are frequently deficient. In this paper, ba...
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Carleman Estimate for Surface in Euclidean Space at Infinity
This paper develops a Carleman type estimate for immersed surface in Euclidean space at infinity. With this estimate, we obtain an unique continuation property for harmonic functions on immersed surfaces vanishing at infinity, which leads to rigidity results in geometry.
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Formalizing Timing Diagram Requirements in Discrete Duration Calulus
Several temporal logics have been proposed to formalise timing diagram requirements over hardware and embedded controllers. These include LTL, discrete time MTL and the recent industry standard PSL. However, succintness and visual structure of a timing diagram are not adequately captured by their formulae. Interval t...
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Cubical Covers of Sets in $\mathbb{R}^n$
Wild sets in $\mathbb{R}^n$ can be tamed through the use of various representations though sometimes this taming removes features considered important. Finding the wildest sets for which it is still true that the representations faithfully inform us about the original set is the focus of this rather playful, exposito...
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Mitochondrial network fragmentation modulates mutant mtDNA accumulation independently of absolute fission-fusion rates
Mitochondrial DNA (mtDNA) mutations cause severe congenital diseases but may also be associated with healthy aging. MtDNA is stochastically replicated and degraded, and exists within organelles which undergo dynamic fusion and fission. The role of the resulting mitochondrial networks in determining the time evolution...
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Depth Separation for Neural Networks
Let $f:\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}\to\mathbb{S}$ be a function of the form $f(\mathbf{x},\mathbf{x}') = g(\langle\mathbf{x},\mathbf{x}'\rangle)$ for $g:[-1,1]\to \mathbb{R}$. We give a simple proof that shows that poly-size depth two neural networks with (exponentially) bounded weights cannot approximate ...
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An Accurate Interconnect Test Structure for Parasitic Validation in On-Chip Machine Learning Accelerators
For nanotechnology nodes, the feature size is shrunk rapidly, the wire becomes narrow and thin, it leads to high RC parasitic, especially for resistance. The overall system performance are dominated by interconnect rather than device. As such, it is imperative to accurately measure and model interconnect parasitic in...
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A New Compton-thick AGN in our Cosmic Backyard: Unveiling the Buried Nucleus in NGC 1448 with NuSTAR
NGC 1448 is one of the nearest luminous galaxies ($L_{8-1000\mu m} >$ 10$^{9} L_{\odot}$) to ours ($z$ $=$ 0.00390), and yet the active galactic nucleus (AGN) it hosts was only recently discovered, in 2009. In this paper, we present an analysis of the nuclear source across three wavebands: mid-infrared (MIR) continuu...
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Blowup constructions for Lie groupoids and a Boutet de Monvel type calculus
We present natural and general ways of building Lie groupoids, by using the classical procedures of blowups and of deformations to the normal cone. Our constructions are seen to recover many known ones involved in index theory. The deformation and blowup groupoids obtained give rise to several extensions of $C^*$-alg...
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Possible spin gapless semiconductor type behaviour in CoFeMnSi epitaxial thin films
Spin-gapless semiconductors with their unique band structures have recently attracted much attention due to their interesting transport properties that can be utilized in spintronics applications. We have successfully deposited the thin films of quaternary spin-gapless semiconductor CoFeMnSi Heusler alloy on MgO (001...
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