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Trigonometric integrators for quasilinear wave equations
Trigonometric time integrators are introduced as a class of explicit numerical methods for quasilinear wave equations. Second-order convergence for the semi-discretization in time with these integrators is shown for a sufficiently regular exact solution. The time integrators are also combined with a Fourier spectral ...
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La leggenda del quanto centenario
Around year 2000 the centenary of Planck's thermal radiation formula awakened interest in the origins of quantum theory, traditionally traced back to the Planck's conference on 14 December 1900 at the Berlin Academy of Sciences. A lot of more accurate historical reconstructions, conducted under the stimulus of that r...
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A Highly Efficient Polarization-Independent Metamaterial-Based RF Energy-Harvesting Rectenna for Low-Power Applications
A highly-efficient multi-resonant RF energy-harvesting rectenna based on a metamaterial perfect absorber featuring closely-spaced polarization-independent absorption modes is presented. Its effective area is larger than its physical area, and so efficiencies of 230% and 130% are measured at power densities of 10 uW/c...
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Mixed Graphical Models for Causal Analysis of Multi-modal Variables
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for classification, feature selection and hypothesis generation, while reve...
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Estimation of quantile oriented sensitivity indices
The paper concerns quantile oriented sensitivity analysis. We rewrite the corresponding indices using the Conditional Tail Expectation risk measure. Then, we use this new expression to built estimators.
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Electromagnetically Induced Transparency (EIT) Amplitude Noise Spectroscopy
Intensity noise cross-correlation of the polarization eigenstates of light emerging from an atomic vapor cell in the Hanle configuration allows one to perform high resolution spectroscopy with free- running semiconductor lasers. Such an approach has shown promise as an inexpensive, simpler approach to magnetometry an...
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Deep Robust Framework for Protein Function Prediction using Variable-Length Protein Sequences
Amino acid sequence portrays most intrinsic form of a protein and expresses primary structure of protein. The order of amino acids in a sequence enables a protein to acquire a particular stable conformation that is responsible for the functions of the protein. This relationship between a sequence and its function mot...
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Helicity of convective flows from localized heat source in a rotating layer
Experimental and numerical study of the steady-state cyclonic vortex from isolated heat source in a rotating fluid layer is described. The structure of laboratory cyclonic vortex is similar to the typical structure of tropical cyclones from observational data and numerical modelling including secondary flows in the b...
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Tunable $φ$-Josephson junction with a quantum anomalous Hall insulator
We theoretically study the Josephson current in a superconductor/quantum anomalous Hall insulator/superconductor junction by using the lattice Green function technique. When an in-plane external Zeeman field is applied to the quantum anomalous Hall insulator, the Josephson current $J$ flows without a phase difference...
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What kind of content are you prone to tweet? Multi-topic Preference Model for Tweeters
According to tastes, a person could show preference for a given category of content to a greater or lesser extent. However, quantifying people's amount of interest in a certain topic is a challenging task, especially considering the massive digital information they are exposed to. For example, in the context of Twitt...
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Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference
The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the number of covariates can be larger than the sample size. Despite strong motivation from biostatistics applications, high-dimensional Fine-Gray...
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The Godunov Method for a 2-Phase Model
We consider the Godunov numerical method to the phase-transition traffic model, proposed in [6], by Colombo, Marcellini, and Rascle. Numerical tests are shown to prove the validity of the method. Moreover we highlight the differences between such model and the one proposed in [1], by Blandin, Work, Goatin, Piccoli, a...
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Cartan's Conjecture for Moving Hypersurfaces
Let $f$ be a holomorphic curve in $\mathbb{P}^n({\mathbb{C}})$ and let $\mathcal{D}=\{D_1,\ldots,D_q\}$ be a family of moving hypersurfaces defined by a set of homogeneous polynomials $\mathcal{Q}=\{Q_1,\ldots,Q_q\}$. For $j=1,\ldots,q$, denote by $Q_j=\sum\limits_{i_0+\cdots+i_n=d_j}a_{j,I}(z)x_0^{i_0}\cdots x_n^{i_...
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Safe Active Feature Selection for Sparse Learning
We present safe active incremental feature selection~(SAIF) to scale up the computation of LASSO solutions. SAIF does not require a solution from a heavier penalty parameter as in sequential screening or updating the full model for each iteration as in dynamic screening. Different from these existing screening method...
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Cobwebs from the Past and Present: Extracting Large Social Networks using Internet Archive Data
Social graph construction from various sources has been of interest to researchers due to its application potential and the broad range of technical challenges involved. The World Wide Web provides a huge amount of continuously updated data and information on a wide range of topics created by a variety of content pro...
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A Fluid-Flow Interpretation of SCED Scheduling
We show that a fluid-flow interpretation of Service Curve Earliest Deadline First (SCED) scheduling simplifies deadline derivations for this scheduler. By exploiting the recently reported isomorphism between min-plus and max-plus network calculus, and expressing deadlines in a max-plus algebra, deadline computations ...
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Emergence of Topological Nodal Lines and Type II Weyl Nodes in Strong Spin--Orbit Coupling System InNbX2(X=S,Se)
Using first--principles density functional calculations, we systematically investigate electronic structures and topological properties of InNbX2 (X=S, Se). In the absence of spin--orbit coupling (SOC), both compounds show nodal lines protected by mirror symmetry. Including SOC, the Dirac rings in InNbS2 split into t...
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Number-conserving interacting fermion models with exact topological superconducting ground states
We present a method to construct number-conserving Hamiltonians whose ground states exactly reproduce an arbitrarily chosen BCS-type mean-field state. Such parent Hamiltonians can be constructed not only for the usual $s$-wave BCS state, but also for more exotic states of this form, including the ground states of Kit...
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JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for developing and evaluating grammatical error correction (GEC). Unlike other corpora, it represents a broad range of language proficiency levels and uses holistic fluency edits to not only correct grammatical errors but also make the original...
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Scheduling with regular performance measures and optional job rejection on a single machine
We address single machine problems with optional jobs - rejection, studied recently in Zhang et al. [21] and Cao et al. [2]. In these papers, the authors focus on minimizing regular performance measures, i.e., functions that are non-decreasing in the jobs completion time, subject to the constraint that the total reje...
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Data-Driven Stochastic Robust Optimization: A General Computational Framework and Algorithm for Optimization under Uncertainty in the Big Data Era
A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dir...
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Algebraic multiscale method for flow in heterogeneous porous media with embedded discrete fractures (F-AMS)
This paper introduces an Algebraic MultiScale method for simulation of flow in heterogeneous porous media with embedded discrete Fractures (F-AMS). First, multiscale coarse grids are independently constructed for both porous matrix and fracture networks. Then, a map between coarse- and fine-scale is obtained by algeb...
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From Pragmatic to Systematic Software Process Improvement: An Evaluated Approach
Software processes improvement (SPI) is a challenging task, as many different stakeholders, project settings, and contexts and goals need to be considered. SPI projects are often operated in a complex and volatile environment and, thus, require a sound management that is resource-intensive requiring many stakeholders...
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A Bayesian Mixture Model for Clustering on the Stiefel Manifold
Analysis of a Bayesian mixture model for the Matrix Langevin distribution on the Stiefel manifold is presented. The model exploits a particular parametrization of the Matrix Langevin distribution, various aspects of which are elaborated on. A general, and novel, family of conjugate priors, and an efficient Markov cha...
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Discrete Cycloids from Convex Symmetric Polygons
Cycloids, hipocycloids and epicycloids have an often forgotten common property: they are homothetic to their evolutes. But what if use convex symmetric polygons as unit balls, can we define evolutes and cycloids which are genuinely discrete? Indeed, we can! We define discrete cycloids as eigenvectors of a discrete do...
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Multi-color image compression-encryption algorithm based on chaotic system and fuzzy transform
In this paper an algorithm for multi-color image compression-encryption is introduced. For compression step fuzzy transform based on exponential b-spline function is used. In encryption step, a novel combination chaotic system based on Sine and Tent systems is proposed. Also in the encryption algorithm, 3D shift base...
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Gaussian Graphical Models: An Algebraic and Geometric Perspective
Gaussian graphical models are used throughout the natural sciences, social sciences, and economics to model the statistical relationships between variables of interest in the form of a graph. We here provide a pedagogic introduction to Gaussian graphical models and review recent results on maximum likelihood estimati...
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The Dynamical History of Chariklo and its Rings
Chariklo is the only small Solar system body confirmed to have rings. Given the instability of its orbit, the presence of rings is surprising, and their origin remains poorly understood. In this work, we study the dynamical history of the Chariklo system by integrating almost 36,000 Chariklo clones backwards in time ...
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THAP: A Matlab Toolkit for Learning with Hawkes Processes
As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields. Among various point process models, Hawkes process and its variants attract many researchers in statistics and computer science these years because they ca...
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Studies of the Response of the SiD Silicon-Tungsten ECal
Studies of the response of the SiD silicon-tungsten electromagnetic calorimeter (ECal) are presented. Layers of highly granular (13 mm^2 pixels) silicon detectors embedded in thin gaps (~ 1 mm) between tungsten alloy plates give the SiD ECal the ability to separate electromagnetic showers in a crowded environment. A ...
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Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views
In this study, a multiple hypothesis tracking (MHT) algorithm for multi-target multi-camera tracking (MCT) with disjoint views is proposed. Our method forms track-hypothesis trees, and each branch of them represents a multi-camera track of a target that may move within a camera as well as move across cameras. Further...
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Smooth positon solutions of the focusing modified Korteweg-de Vries equation
The $n$-fold Darboux transformation $T_{n}$ of the focusing real mo\-di\-fied Kor\-te\-weg-de Vries (mKdV) equation is expressed in terms of the determinant representation. Using this representation, the $n$-soliton solutions of the mKdV equation are also expressed by determinants whose elements consist of the eigenv...
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On discrimination between two close distribution tails
The goodness-of-fit test for discrimination of two tail distribution using higher order statistics is proposed. The consistency of proposed test is proved for two different alternatives. We do not assume belonging the corresponding distribution function to a maximum domain of attraction.
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Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model, Belief Propagation is a heuristic which is quite successful in practice, even though its empirical success, typically, lacks theoretical ...
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Sharper and Simpler Nonlinear Interpolants for Program Verification
Interpolation of jointly infeasible predicates plays important roles in various program verification techniques such as invariant synthesis and CEGAR. Intrigued by the recent result by Dai et al.\ that combines real algebraic geometry and SDP optimization in synthesis of polynomial interpolants, the current paper con...
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Quantized Laplacian growth, III: On conformal field theories of Laplacian growth
A one-parametric stochastic dynamics of the interface in the quantized Laplacian growth with zero surface tension is introduced. The quantization procedure regularizes the growth by preventing the formation of cusps at the interface, and makes the interface dynamics chaotic. In a long time asymptotic, by coupling a c...
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Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images
Lesion segmentation is the first step in most automatic melanoma recognition systems. Deficiencies and difficulties in dermoscopic images such as color inconstancy, hair occlusion, dark corners and color charts make lesion segmentation an intricate task. In order to detect the lesion in the presence of these problems...
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The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description
Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD cla...
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Understanding the Impact of Label Granularity on CNN-based Image Classification
In recent years, supervised learning using Convolutional Neural Networks (CNNs) has achieved great success in image classification tasks, and large scale labeled datasets have contributed significantly to this achievement. However, the definition of a label is often application dependent. For example, an image of a c...
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Monte Carlo modified profile likelihood in models for clustered data
The main focus of the analysts who deal with clustered data is usually not on the clustering variables, and hence the group-specific parameters are treated as nuisance. If a fixed effects formulation is preferred and the total number of clusters is large relative to the single-group sizes, classical frequentist techn...
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Anisotropic spin-density distribution and magnetic anisotropy of strained La$_{1-x}$Sr$_x$MnO$_3$ thin films: Angle-dependent x-ray magnetic circular dichroism
Magnetic anisotropies of ferromagnetic thin films are induced by epitaxial strain from the substrate via strain-induced anisotropy in the orbital magnetic moment and that in the spatial distribution of spin-polarized electrons. However, the preferential orbital occupation in ferromagnetic metallic La$_{1-x}$Sr$_x$MnO...
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An Annotated Corpus of Relational Strategies in Customer Service
We create and release the first publicly available commercial customer service corpus with annotated relational segments. Human-computer data from three live customer service Intelligent Virtual Agents (IVAs) in the domains of travel and telecommunications were collected, and reviewers marked all text that was deemed...
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Putting gravity in control
The aim of the present manuscript is to present a novel proposal in Geometric Control Theory inspired in the principles of General Relativity and energy-shaping control.
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Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
Sometimes it is not enough for a DNN to produce an outcome. For example, in applications such as healthcare, users need to understand the rationale of the decisions. Therefore, it is imperative to develop algorithms to learn models with good interpretability (Doshi-Velez 2017). An important factor that leads to the l...
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Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion. Through the use of skip-connections, our model can successfully learn and infer a latent, hierarchical representation of objects. Furthermore, realistic 3D objects...
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Maximally rotating waves in AdS and on spheres
We study the cubic wave equation in AdS_(d+1) (and a closely related cubic wave equation on S^3) in a weakly nonlinear regime. Via time-averaging, these systems are accurately described by simplified infinite-dimensional quartic Hamiltonian systems, whose structure is mandated by the fully resonant spectrum of linear...
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Taming Wild High Dimensional Text Data with a Fuzzy Lash
The bag of words (BOW) represents a corpus in a matrix whose elements are the frequency of words. However, each row in the matrix is a very high-dimensional sparse vector. Dimension reduction (DR) is a popular method to address sparsity and high-dimensionality issues. Among different strategies to develop DR method, ...
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Spatial structure of shock formation
The formation of a singularity in a compressible gas, as described by the Euler equation, is characterized by the steepening, and eventual overturning of a wave. Using a self-similar description in two space dimensions, we show that the spatial structure of this process, which starts at a point, is equivalent to the ...
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How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for ...
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Inhabitants of interesting subsets of the Bousfield lattice
The set of Bousfield classes has some important subsets such as the distributive lattice $\mathbf{DL}$ of all classes $\langle E\rangle$ which are smash idempotent and the complete Boolean algebra $\mathbf{cBA}$ of closed classes. We provide examples of spectra that are in $\mathbf{DL}$, but not in $\mathbf{cBA}$; in...
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A Framework for Implementing Machine Learning on Omics Data
The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics of these data are not always well suited to machine learning techniques. These data are often generated across different technologies in d...
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Actively Calibrated Line Mountable Capacitive Voltage Transducer For Power Systems Applications
A class of Actively Calibrated Line Mounted Capacitive Voltage Transducers (LMCVT) are introduced as a viable line mountable instrumentation option for deploying large numbers of voltage transducers onto the medium and high voltage systems. Active Calibration is shown to reduce the error of line mounted voltage measu...
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AirCode: Unobtrusive Physical Tags for Digital Fabrication
We present AirCode, a technique that allows the user to tag physically fabricated objects with given information. An AirCode tag consists of a group of carefully designed air pockets placed beneath the object surface. These air pockets are easily produced during the fabrication process of the object, without any addi...
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Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features
This paper advances the state of the art in text understanding of medical guidelines by releasing two new annotated clinical guidelines datasets, and establishing baselines for using machine learning to extract condition-action pairs. In contrast to prior work that relies on manually created rules, we report experime...
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Adversarial Learning for Neural Dialogue Generation
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) problem where we jointly t...
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On the impact origin of Phobos and Deimos III: resulting composition from different impactors
The origin of Phobos and Deimos in a giant impact generated disk is gaining larger attention. Although this scenario has been the subject of many studies, an evaluation of the chemical composition of the Mars' moons in this framework is missing. The chemical composition of Phobos and Deimos is unconstrained. The larg...
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Dealing with the Dimensionality Curse in Dynamic Pricing Competition: Using Frequent Repricing to Compensate Imperfect Market Anticipations
Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustmen...
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Interleaved Group Convolutions for Deep Neural Networks
In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two gr...
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Lower Bounding Diffusion Constant by the Curvature of Drude Weight
We establish a general connection between ballistic and diffusive transport in systems where the ballistic contribution in canonical ensemble vanishes. A lower bound on the Green-Kubo diffusion constant is derived in terms of the curvature of the ideal transport coefficient, the Drude weight, with respect to the fill...
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Face Detection using Deep Learning: An Improved Faster RCNN Approach
In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark evaluation. In particular, we improve the state-of-the-art faster RCNN framework by combining a number of strategies, including feature conca...
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Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task
End-to-end control for robot manipulation and grasping is emerging as an attractive alternative to traditional pipelined approaches. However, end-to-end methods tend to either be slow to train, exhibit little or no generalisability, or lack the ability to accomplish long-horizon or multi-stage tasks. In this paper, w...
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The cosmic spiderweb: equivalence of cosmic, architectural, and origami tessellations
For over twenty years, the term 'cosmic web' has guided our understanding of the large-scale arrangement of matter in the cosmos, accurately evoking the concept of a network of galaxies linked by filaments. But the physical correspondence between the cosmic web and structural-engineering or textile 'spiderwebs' is ev...
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Data-driven polynomial chaos expansion for machine learning regression
We present a regression technique for data driven problems based on polynomial chaos expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ), where it is typically used to replace a runnable but expensive computational model subject to random inputs with an inexpensive-to-evaluate ...
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Robust Implicit Backpropagation
Arguably the biggest challenge in applying neural networks is tuning the hyperparameters, in particular the learning rate. The sensitivity to the learning rate is due to the reliance on backpropagation to train the network. In this paper we present the first application of Implicit Stochastic Gradient Descent (ISGD) ...
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Experimental and Theoretical Study of Magnetohydrodynamic Ship Models
Magnetohydrodynamic (MHD) ships represent a clear demonstration of the Lorentz force in fluids, which explains the number of students practicals or exercises described on the web. However, the related literature is rather specific and no complete comparison between theory and typical small scale experiments is curren...
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Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection
With a rapidly increasing number of devices connected to the internet, big data has been applied to various domains of human life. Nevertheless, it has also opened new venues for breaching users' privacy. Hence it is highly required to develop techniques that enable data owners to privatize their data while keeping i...
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Time-optimal control strategies in SIR epidemic models
We investigate the time-optimal control problem in SIR (Susceptible-Infected-Recovered) epidemic models, focusing on different control policies: vaccination, isolation, culling, and reduction of transmission. Applying the Pontryagin's Minimum Principle (PMP) to the unconstrained control problems (i.e. without costs o...
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On the validity of the formal Edgeworth expansion for posterior densities
We consider a fundamental open problem in parametric Bayesian theory, namely the validity of the formal Edgeworth expansion of the posterior density. While the study of valid asymptotic expansions for posterior distributions constitutes a rich literature, the validity of the formal Edgeworth expansion has not been ri...
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DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization
Adaptive gradient-based optimization methods such as ADAGRAD, RMSPROP, and ADAM are widely used in solving large-scale machine learning problems including deep learning. A number of schemes have been proposed in the literature aiming at parallelizing them, based on communications of peripheral nodes with a central no...
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Paramagnetic Meissner effect in ZrB12 single crystal with non-monotonic vortex-vortex interactions
The magnetic response related to paramagnetic Meissner effect (PME) is studied in a high quality single crystal ZrB12 with non-monotonic vortex-vortex interactions. We observe the expulsion and penetration of magnetic flux in the form of vortex clusters with increasing temperature. A vortex phase diagram is construct...
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Credal Networks under Epistemic Irrelevance
A credal network under epistemic irrelevance is a generalised type of Bayesian network that relaxes its two main building blocks. On the one hand, the local probabilities are allowed to be partially specified. On the other hand, the assessments of independence do not have to hold exactly. Conceptually, these two feat...
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Resonance fluorescence in the resolvent operator formalism
The Mollow spectrum for the light scattered by a driven two-level atom is derived in the resolvent operator formalism. The derivation is based on the construction of a master equation from the resolvent operator of the atom-field system. We show that the natural linewidth of the excited atomic level remains essential...
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On the the Berge Conjecture for tunnel number one knots
In this paper we use an approach based on dynamics to prove that if $K\subset S^3$ is a tunnel number one knot which admits a Dehn filling resulting in a lens space $L$ then $K$ is either a Berge knot, or $K\subset S^3$ is $(1,1)$-knot.
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EMRIs and the relativistic loss-cone: The curious case of the fortunate coincidence
Extreme mass ratio inspiral (EMRI) events are vulnerable to perturbations by the stellar background, which can abort them prematurely by deflecting EMRI orbits to plunging ones that fall directly into the massive black hole (MBH), or to less eccentric ones that no longer interact strongly with the MBH. A coincidental...
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Investigating the configurations in cross-shareholding: a joint copula-entropy approach
--- the companies populating a Stock market, along with their connections, can be effectively modeled through a directed network, where the nodes represent the companies, and the links indicate the ownership. This paper deals with this theme and discusses the concentration of a market. A cross-shareholding matrix is ...
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The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering' Language Embeddings
Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyberhate) has been frequently posted and widely circulated viathe World Wide Web. This can be considered as a key risk factor for individual and societal tension li...
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Linear Programming Formulations of Deterministic Infinite Horizon Optimal Control Problems in Discrete Time
This paper is devoted to a study of infinite horizon optimal control problems with time discounting and time averaging criteria in discrete time. We establish that these problems are related to certain infinite-dimensional linear programming (IDLP) problems. We also establish asymptotic relationships between the opti...
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Exact diagonalization of cubic lattice models in commensurate Abelian magnetic fluxes and translational invariant non-Abelian potentials
We present a general analytical formalism to determine the energy spectrum of a quantum particle in a cubic lattice subject to translationally invariant commensurate magnetic fluxes and in the presence of a general space-independent non-Abelian gauge potential. We first review and analyze the case of purely Abelian p...
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Non-normality, reactivity, and intrinsic stochasticity in neural dynamics: a non-equilibrium potential approach
Intrinsic stochasticity can induce highly non-trivial effects on dynamical systems, including stochastic and coherence resonance, noise induced bistability, noise-induced oscillations, to name but a few. In this paper we revisit a mechanism first investigated in the context of neuroscience by which relatively small d...
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Latent Gaussian Mixture Models for Nationwide Kidney Transplant Center Evaluation
Five year post-transplant survival rate is an important indicator on quality of care delivered by kidney transplant centers in the United States. To provide a fair assessment of each transplant center, an effect that represents the center-specific care quality, along with patient level risk factors, is often included...
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Small nonlinearities in activation functions create bad local minima in neural networks
We investigate the loss surface of neural networks. We prove that even for one-hidden-layer networks with "slightest" nonlinearity, the empirical risks have spurious local minima in most cases. Our results thus indicate that in general "no spurious local minima" is a property limited to deep linear networks, and insi...
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Poisson brackets with prescribed family of functions in involution
It is well known that functions in involution with respect to Poisson brackets have a privileged role in the theory of completely integrable systems. Finding functionally independent functions in involution with a given function $h$ on a Poisson manifold is a fundamental problem of this theory and is very useful for ...
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No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, whi...
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Stabilization Bounds for Linear Finite Dynamical Systems
A common problem to all applications of linear finite dynamical systems is analyzing the dynamics without enumerating every possible state transition. Of particular interest is the long term dynamical behaviour. In this paper, we study the number of iterations needed for a system to settle on a fixed set of elements....
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Magneto-thermopower in the Weak Ferromagnetic Oxide CaRu0.8Sc0.2O3: An Experimental Test for the Kelvin Formula in a Magnetic Material
We have measured the resistivity, the thermopower, and the specific heat of the weak ferromagnetic oxide CaRu0.8Sc0.2O3 in external magnetic fields up to 140 kOe below 80 K. We have observed that the thermopower Q is significantly suppressed by magnetic fields at around the ferromagnetic transition temperature of 30 ...
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Experimental Two-dimensional Quantum Walk on a Photonic Chip
Quantum walks, in virtue of the coherent superposition and quantum interference, possess exponential superiority over its classical counterpart in applications of quantum searching and quantum simulation. The quantum enhanced power is highly related to the state space of quantum walks, which can be expanded by enlarg...
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Dispersive Magnetic and Electronic Excitations in Iridate Perovskites Probed with Oxygen $K$-Edge Resonant Inelastic X-ray Scattering
Resonant inelastic X-ray scattering (RIXS) experiments performed at the oxygen-$K$ edge on the iridate perovskites {\SIOS} and {\SION} reveal a sequence of well-defined dispersive modes over the energy range up to $\sim 0.8$ eV. The momentum dependence of these modes and their variation with the experimental geometry...
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Reaction-Diffusion Models for Glioma Tumor Growth
Mathematical modelling of tumor growth is one of the most useful and inexpensive approaches to determine and predict the stage, size and progression of tumors in realistic geometries. Moreover, these models has been used to get an insight into cancer growth and invasion and in the analysis of tumor size and geometry ...
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On reducing the communication cost of the diffusion LMS algorithm
The rise of digital and mobile communications has recently made the world more connected and networked, resulting in an unprecedented volume of data flowing between sources, data centers, or processes. While these data may be processed in a centralized manner, it is often more suitable to consider distributed strateg...
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Teaching computer code at school
In today's education systems, there is a deep concern about the importance of teaching code and computer programming in schools. Moving digital learning from a simple use of tools to understanding the processes of the internal functioning of these tools is an old / new debate originated with the digital laboratories ...
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Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method
We propose statistical inferential procedures for panel data models with interactive fixed effects in a kernel ridge regression framework.Compared with traditional sieve methods, our method is automatic in the sense that it does not require the choice of basis functions and truncation parameters.Model complexity is c...
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Nonconvex penalties with analytical solutions for one-bit compressive sensing
One-bit measurements widely exist in the real world, and they can be used to recover sparse signals. This task is known as the problem of learning halfspaces in learning theory and one-bit compressive sensing (1bit-CS) in signal processing. In this paper, we propose novel algorithms based on both convex and nonconvex...
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How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets
The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems. We argue that this barrier can be effectively overcome. In particular, we develop methods to scale up kernel models to successfully tackle large-scale learning problems that are so far onl...
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Benchmarking Automatic Machine Learning Frameworks
AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process. A wide range of techniques is taken to address this, however there does not exist an objective comparison of these techniques. We present a benchmark of current open source A...
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Layered Based Augmented Complex Kalman Filter for Fast Forecasting-Aided State Estimation of Distribution Networks
In the presence of renewable resources, distribution networks have become extremely complex to monitor, operate and control. Furthermore, for the real time applications, active distribution networks require fast real time distribution state estimation (DSE). Forecasting aided state estimator (FASE), deploys measured ...
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Multiscale mixing patterns in networks
Assortative mixing in networks is the tendency for nodes with the same attributes, or metadata, to link to each other. It is a property often found in social networks manifesting as a higher tendency of links occurring between people with the same age, race, or political belief. Quantifying the level of assortativity...
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Single-Queue Decoding for Neural Machine Translation
Neural machine translation models rely on the beam search algorithm for decoding. In practice, we found that the quality of hypotheses in the search space is negatively affected owing to the fixed beam size. To mitigate this problem, we store all hypotheses in a single priority queue and use a universal score functio...
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Simulation of Parabolic Flow on an Eye-Shaped Domain with Moving Boundary
During the upstroke of a normal eye blink, the upper lid moves and paints a thin tear film over the exposed corneal and conjunctival surfaces. This thin tear film may be modeled by a nonlinear fourth-order PDE derived from lubrication theory. A challenge in the numerical simulation of this model is to include both th...
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Faster algorithms for 1-mappability of a sequence
In the k-mappability problem, we are given a string x of length n and integers m and k, and we are asked to count, for each length-m factor y of x, the number of other factors of length m of x that are at Hamming distance at most k from y. We focus here on the version of the problem where k = 1. The fastest known alg...
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PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network
Music creation is typically composed of two parts: composing the musical score, and then performing the score with instruments to make sounds. While recent work has made much progress in automatic music generation in the symbolic domain, few attempts have been made to build an AI model that can render realistic music...
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