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Pitfalls and Best Practices in Algorithm Configuration
Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning). Automated algorithm configuration methods have recently received much attention in t...
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Critical behaviors in contagion dynamics
We study the critical behavior of a general contagion model where nodes are either active (e.g. with opinion A, or functioning) or inactive (e.g. with opinion B, or damaged). The transitions between these two states are determined by (i) spontaneous transitions independent of the neighborhood, (ii) transitions induce...
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Machine Learning in Appearance-based Robot Self-localization
An appearance-based robot self-localization problem is considered in the machine learning framework. The appearance space is composed of all possible images, which can be captured by a robot's visual system under all robot localizations. Using recent manifold learning and deep learning techniques, we propose a new ge...
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Towards Communication-Aware Robust Topologies
We currently witness the emergence of interesting new network topologies optimized towards the traffic matrices they serve, such as demand-aware datacenter interconnects (e.g., ProjecToR) and demand-aware overlay networks (e.g., SplayNets). This paper introduces a formal framework and approach to reason about and des...
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Zero-shot Domain Adaptation without Domain Semantic Descriptors
We propose a method to infer domain-specific models such as classifiers for unseen domains, from which no data are given in the training phase, without domain semantic descriptors. When training and test distributions are different, standard supervised learning methods perform poorly. Zero-shot domain adaptation atte...
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Network modelling of topological domains using Hi-C data
Genome-wide chromosome conformation capture techniques such as Hi-C enable the generation of 3D genome contact maps and offer new pathways toward understanding the spatial organization of genome. One specific feature of the 3D organization is known as topologically associating domains (TADs), which are densely intera...
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On a Possible Giant Impact Origin for the Colorado Plateau
It is proposed and substantiated that an extraterrestrial object of the approximate size and mass of Planet Mars, impacting the Earth in an oblique angle along an approximately NE-SW route (with respect to the current orientation of the North America continent) around 750 million years ago (750 Ma), is likely to be t...
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Reversing Parallel Programs with Blocks and Procedures
We show how to reverse a while language extended with blocks, local variables, procedures and the interleaving parallel composition. Annotation is defined along with a set of operational semantics capable of storing necessary reversal information, and identifiers are introduced to capture the interleaving order of an...
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Detection of the Stellar Intracluster Medium in Perseus (Abell 426)
Hubble Space Telescope photometry from the ACS/WFC and WFPC2 cameras is used to detect and measure globular clusters (GCs) in the central region of the rich Perseus cluster of galaxies. A detectable population of Intragalactic GCs is found extending out to at least 500 kpc from the cluster center. These objects displ...
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Binets: fundamental building blocks for phylogenetic networks
Phylogenetic networks are a generalization of evolutionary trees that are used by biologists to represent the evolution of organisms which have undergone reticulate evolution. Essentially, a phylogenetic network is a directed acyclic graph having a unique root in which the leaves are labelled by a given set of specie...
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G-Deformations of maps into projective space
$G$-deformability of maps into projective space is characterised by the existence of certain Lie algebra valued 1-forms. This characterisation gives a unified way to obtain well known results regarding deformability in different geometries.
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Cloudless atmospheres for young low-gravity substellar objects
Atmospheric modeling of low-gravity (VL-G) young brown dwarfs remains a challenge. The presence of very thick clouds has been suggested because of their extremely red near-infrared (NIR) spectra, but no cloud models provide a good fit to the data with a radius compatible with evolutionary models for these objects. We...
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On the State of the Art of Evaluation in Neural Language Models
Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing code bases and limited computational resources, which represent uncontrolled sources of experimental v...
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Weakly Supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning
Separating audio mixtures into individual instrument tracks has been a long standing challenging task. We introduce a novel weakly supervised audio source separation approach based on deep adversarial learning. Specifically, our loss function adopts the Wasserstein distance which directly measures the distribution di...
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Proof of a conjecture of Kløve on permutation codes under the Chebychev distance
Let $d$ be a positive integer and $x$ a real number. Let $A_{d, x}$ be a $d\times 2d$ matrix with its entries $$ a_{i,j}=\left\{ \begin{array}{ll} x\ \ & \mbox{for} \ 1\leqslant j\leqslant d+1-i, 1\ \ & \mbox{for} \ d+2-i\leqslant j\leqslant d+i, 0\ \ & \mbox{for} \ d+1+i\leqslant j\leqslant 2d. \end{array} \right. $...
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Herschel observations of the Galactic HII region RCW 79
Triggered star formation around HII regions could be an important process. The Galactic HII region RCW 79 is a prototypical object for triggered high-mass star formation. We take advantage of Herschel data from the surveys HOBYS, "Evolution of Interstellar Dust", and Hi-Gal to extract compact sources in this region, ...
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Software-Defined Robotics -- Idea & Approach
The methodology of Software-Defined Robotics hierarchical-based and stand-alone framework can be designed and implemented to program and control different sets of robots, regardless of their manufacturers' parameters and specifications, with unified commands and communications. This framework approach will increase t...
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Stability interchanges in a curved Sitnikov problem
We consider a curved Sitnikov problem, in which an infinitesimal particle moves on a circle under the gravitational influence of two equal masses in Keplerian motion within a plane perpendicular to that circle. There are two equilibrium points, whose stability we are studying. We show that one of the equilibrium poin...
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Hardy Spaces over Half-strip Domains
We define Hardy spaces $H^p(\Omega_\pm)$ on half-strip domain~$\Omega_+$ and $\Omega_-= \mathbb{C}\setminus\overline{\Omega_+}$, where $0<p<\infty$, and prove that functions in $H^p(\Omega_\pm)$ has non-tangential boundary limit a.e. on $\Gamma$, the common boundary of $\Omega_\pm$. We then prove that Cauchy integral...
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An Optimization Framework with Flexible Inexact Inner Iterations for Nonconvex and Nonsmooth Programming
In recent years, numerous vision and learning tasks have been (re)formulated as nonconvex and nonsmooth programmings(NNPs). Although some algorithms have been proposed for particular problems, designing fast and flexible optimization schemes with theoretical guarantee is a challenging task for general NNPs. It has be...
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Attitude and angular velocity tracking for a rigid body using geometric methods on the two-sphere
The control task of tracking a reference pointing direction (the attitude about the pointing direction is irrelevant) while obtaining a desired angular velocity (PDAV) around the pointing direction using geometric techniques is addressed here. Existing geometric controllers developed on the two-sphere only address th...
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Miscomputation in software: Learning to live with errors
Computer programs do not always work as expected. In fact, ominous warnings about the desperate state of the software industry continue to be released with almost ritualistic regularity. In this paper, we look at the 60 years history of programming and at the different practical methods that software community develo...
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The Combinatorics of Weighted Vector Compositions
A vector composition of a vector $\mathbf{\ell}$ is a matrix $\mathbf{A}$ whose rows sum to $\mathbf{\ell}$. We define a weighted vector composition as a vector composition in which the column values of $\mathbf{A}$ may appear in different colors. We study vector compositions from different viewpoints: (1) We show ho...
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Fast Linear Model for Knowledge Graph Embeddings
This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-o...
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Deep learning for plasma tomography using the bolometer system at JET
Deep learning is having a profound impact in many fields, especially those that involve some form of image processing. Deep neural networks excel in turning an input image into a set of high-level features. On the other hand, tomography deals with the inverse problem of recreating an image from a number of projection...
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Assembly Bias and Splashback in Galaxy Clusters
We use publicly available data for the Millennium Simulation to explore the implications of the recent detection of assembly bias and splashback signatures in a large sample of galaxy clusters. These were identified in the SDSS/DR8 photometric data by the redMaPPer algorithm and split into high- and low-concentration...
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Accelerating Kernel Classifiers Through Borders Mapping
Support vector machines (SVM) and other kernel techniques represent a family of powerful statistical classification methods with high accuracy and broad applicability. Because they use all or a significant portion of the training data, however, they can be slow, especially for large problems. Piecewise linear classif...
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Information Criterion for Minimum Cross-Entropy Model Selection
This paper considers the problem of approximating a density when it can be evaluated up to a normalizing constant at a finite number of points. This density approximation problem is ubiquitous in machine learning, such as approximating a posterior density for Bayesian inference and estimating an optimal density for i...
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Scaling relations in large-Prandtl-number natural thermal convection
In this study we follow Grossmann and Lohse, Phys. Rev. Lett. 86 (2001), who derived various scalings regimes for the dependence of the Nusselt number $Nu$ and the Reynolds number $Re$ on the Rayleigh number $Ra$ and the Prandtl number $Pr$. We focus on theoretical arguments as well as on numerical simulations for th...
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The renormalization method from continuous to discrete dynamical systems: asymptotic solutions, reductions and invariant manifolds
The renormalization method based on the Taylor expansion for asymptotic analysis of differential equations is generalized to difference equations. The proposed renormalization method is based on the Newton-Maclaurin expansion. Several basic theorems on the renormalization method are proven. Some interesting applicati...
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A Stress/Displacement Virtual Element Method for Plane Elasticity Problems
The numerical approximation of 2D elasticity problems is considered, in the framework of the small strain theory and in connection with the mixed Hellinger-Reissner variational formulation. A low-order Virtual Element Method (VEM) with a-priori symmetric stresses is proposed. Several numerical tests are provided, alo...
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Estimation of the lead-lag parameter between two stochastic processes driven by fractional Brownian motions
In this paper, we consider the problem of estimating the lead-lag parameter between two stochastic processes driven by fractional Brownian motions (fBMs) of the Hurst parameter greater than 1/2. First we propose a lead-lag model between two stochastic processes involving fBMs, and then construct a consistent estimato...
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Multi-resolution polymer Brownian dynamics with hydrodynamic interactions
A polymer model given in terms of beads, interacting through Hookean springs and hydrodynamic forces, is studied. Brownian dynamics description of this bead-spring polymer model is extended to multiple resolutions. Using this multiscale approach, a modeller can efficiently look at different regions of the polymer in ...
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Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression algorithm on each of them, and then simply average the output functions learnt from these blocks. Since the average process will decrease ...
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Cluster-based Kriging Approximation Algorithms for Complexity Reduction
Kriging or Gaussian Process Regression is applied in many fields as a non-linear regression model as well as a surrogate model in the field of evolutionary computation. However, the computational and space complexity of Kriging, that is cubic and quadratic in the number of data points respectively, becomes a major bo...
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How to model fake news
Over the past three years it has become evident that fake news is a danger to democracy. However, until now there has been no clear understanding of how to define fake news, much less how to model it. This paper addresses both these issues. A definition of fake news is given, and two approaches for the modelling of f...
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Analysis of Dropout in Online Learning
Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers and a huge number of units and connections. Therefore, overfitting is a serious problem with it, and the dropout which is a kind of regularization tool is used. However...
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TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroim...
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A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian ...
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Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network. The encoder is a deep Convolutional Neural Network (CNN) based on the VGG network. The CTC ne...
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The Social and Work Structure of an Afterschool Math Club
This study focuses on the social structure and interpersonal dynamics of an afterschool math club for middle schoolers. Using social network analysis, two networks were formed and analyzed: The network of friendship relationships and the network of working relationships. The interconnections and correlations between ...
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Fourier Transform of Schwartz Algebras on Groups in the Harish-Chandra class
It is well-known that the Harish-Chandra transform, $f\mapsto\mathcal{H}f,$ is a topological isomorphism of the spherical (Schwartz) convolution algebra $\mathcal{C}^{p}(G//K)$ (where $K$ is a maximal compact subgroup of any arbitrarily chosen group $G$ in the Harish-Chandra class and $0<p\leq2$) onto the (Schwartz) ...
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Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation
We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projections. A few examples where this problem is relevant are compressed sensing, sparse superposition codes, and code division multiple access. There has been a number of works considering the mutual information for this p...
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A Memristor-Based Optimization Framework for AI Applications
Memristors have recently received significant attention as ubiquitous device-level components for building a novel generation of computing systems. These devices have many promising features, such as non-volatility, low power consumption, high density, and excellent scalability. The ability to control and modify bias...
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Estimating parameters of a directed weighted graph model with beta-distributed edge-weights
We introduce a directed, weighted random graph model, where the edge-weights are independent and beta-distributed with parameters depending on their endpoints. We will show that the row- and column-sums of the transformed edge-weight matrix are sufficient statistics for the parameters, and use the theory of exponenti...
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On the Performance of Reduced-Complexity Transmit/Receive Diversity Systems over MIMO-V2V Channel Model
In this letter, we investigate the performance of multiple-input multiple-output techniques in a vehicle-to-vehicle communication system. We consider both transmit antenna selection with maximal-ratio combining and transmit antenna selection with selection combining. The channel propagation model between two vehicles...
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Internal migration and education: A cross-national comparison
Migration the main process shaping patterns of human settlement within and between countries. It is widely acknowledged to be integral to the process of human development as it plays a significant role in enhancing educational outcomes. At regional and national levels, internal migration underpins the efficient funct...
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Free differential Lie Rota-Baxter algebras and Gröbner-Shirshov bases
We establish the Gröbner-Shirshov bases theory for differential Lie $\Omega$-algebras. As an application, we give a linear basis of a free differential Lie Rota-Baxter algebra on a set.
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Conversion Rate Optimization through Evolutionary Computation
Conversion optimization means designing a web interface so that as many users as possible take a desired action on it, such as register or purchase. Such design is usually done by hand, testing one change at a time through A/B testing, or a limited number of combinations through multivariate testing, making it possib...
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Emergence of spatial curvature
This paper investigates the phenomenon of emergence of spatial curvature. This phenomenon is absent in the Standard Cosmological Model, which has a flat and fixed spatial curvature (small perturbations are considered in the Standard Cosmological Model but their global average vanishes, leading to spatial flatness at ...
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Asymptotic generalized bivariate extreme with random index
In many biological, agricultural, military activity problems and in some quality control problems, it is almost impossible to have a fixed sample size, because some observations are always lost for various reasons. Therefore, the sample size itself is considered frequently to be a random variable (rv). The class of l...
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On problems in the calculus of variations in increasingly elongated domains
We consider minimization problems in the calculus of variations set in a sequence of domains the size of which tends to infinity in certain directions and such that the data only depend on the coordinates in the directions that remain constant. We study the asymptotic behavior of minimizers in various situations and ...
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Inequalities related to Symmetrized Harmonic Convex Functions
In this paper, we extend the Hermite-Hadamard type $\dot{I}$scan inequality to the class of symmetrized harmonic convex functions. The corresponding version for harmonic h-convex functions is also investigated. Furthermore, we establish Hermite-Hadamard type inequalites for the product of a harmonic convex function w...
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Parameter estimation for fractional Ornstein-Uhlenbeck processes of general Hurst parameter
This paper provides several statistical estimators for the drift and volatility parameters of an Ornstein-Uhlenbeck process driven by fractional Brownian motion, whose observations can be made either continuously or at discrete time instants. First and higher order power variations are used to estimate the volatility...
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E-polynomials of $PGL(2,\mathbb{C})$-character varieties of surface groups
In this paper, we compute the E-polynomials of the $PGL(2,\mathbb{C})$-character varieties associated to surfaces of genus $g$ with one puncture, for any holonomy around it, and compare it with its Langlands dual case, $SL(2,\mathbb{C})$. The study is based on the stratification of the space of representations and on...
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Security Analysis of Cache Replacement Policies
Modern computer architectures share physical resources between different programs in order to increase area-, energy-, and cost-efficiency. Unfortunately, sharing often gives rise to side channels that can be exploited for extracting or transmitting sensitive information. We currently lack techniques for systematic r...
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Supercongruences related to ${}_3F_2(1)$ involving harmonic numbers
We show various supercongruences for truncated series which involve central binomial coefficients and harmonic numbers. The corresponding infinite series are also evaluated.
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Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition
Layer normalization is a recently introduced technique for normalizing the activities of neurons in deep neural networks to improve the training speed and stability. In this paper, we introduce a new layer normalization technique called Dynamic Layer Normalization (DLN) for adaptive neural acoustic modeling in speech...
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First-Order vs. Second-Order Encodings for LTLf-to-Automata Translation
Translating formulas of Linear Temporal Logic (LTL) over finite traces, or LTLf, to symbolic Deterministic Finite Automata (DFA) plays an important role not only in LTLf synthesis, but also in synthesis for Safety LTL formulas. The translation is enabled by using MONA, a powerful tool for symbolic, BDD-based, DFA con...
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Regularity results and parametrices of semi-linear boundary problems of product type
This short note describes the benefit one obtains from a specific construction of a family of parametrices for a class of elliptic boundary value problems perturbed by non-linear terms of product type. The construction is based on the Boutet de Monvel calculus of pseudo-differential boundary operators for the linear ...
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$\left( β, \varpi \right)$-stability for cross-validation and the choice of the number of folds
In this paper, we introduce a new concept of stability for cross-validation, called the $\left( \beta, \varpi \right)$-stability, and use it as a new perspective to build the general theory for cross-validation. The $\left( \beta, \varpi \right)$-stability mathematically connects the generalization ability and the st...
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Directional convexity of harmonic mappings
The convolution properties are discussed for the complex-valued harmonic functions in the unit disk $\mathbb{D}$ constructed from the harmonic shearing of the analytic function $\phi(z):=\int_0^z (1/(1-2\xi\textit{e}^{\textit{i}\mu}\cos\nu+\xi^2\textit{e}^{2\textit{i}\mu}))\textit{d}\xi$, where $\mu$ and $\nu$ are re...
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Optimal Non-uniform Deployments in Ultra-Dense Finite-Area Cellular Networks
Network densification and heterogenisation through the deployment of small cellular access points (picocells and femtocells) are seen as key mechanisms in handling the exponential increase in cellular data traffic. Modelling such networks by leveraging tools from Stochastic Geometry has proven particularly useful in ...
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Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder
We investigate the integration of a planning mechanism into an encoder-decoder architecture with an explicit alignment for character-level machine translation. We develop a model that plans ahead when it computes alignments between the source and target sequences, constructing a matrix of proposed future alignments a...
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Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding
In this paper, we propose a novel recurrent neural network architecture for speech separation. This architecture is constructed by unfolding the iterations of a sequential iterative soft-thresholding algorithm (ISTA) that solves the optimization problem for sparse nonnegative matrix factorization (NMF) of spectrogram...
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Birth of isolated nested cylinders and limit cycles in 3D piecewise smooth vector fields with symmetry
Our start point is a 3D piecewise smooth vector field defined in two zones and presenting a shared fold curve for the two smooth vector fields considered. Moreover, these smooth vector fields are symmetric relative to the fold curve, giving raise to a continuum of nested topological cylinders such that each orthogona...
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The difficulty of folding self-folding origami
Why is it difficult to refold a previously folded sheet of paper? We show that even crease patterns with only one designed folding motion inevitably contain an exponential number of `distractor' folding branches accessible from a bifurcation at the flat state. Consequently, refolding a sheet requires finding the grou...
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Integrable Trotterization: Local Conservation Laws and Boundary Driving
We discuss a general procedure to construct an integrable real-time trotterization of interacting lattice models. As an illustrative example we consider a spin-$1/2$ chain, with continuous time dynamics described by the isotropic ($XXX$) Heisenberg Hamiltonian. For periodic boundary conditions local conservation laws...
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The Multivariate Hawkes Process in High Dimensions: Beyond Mutual Excitation
The Hawkes process is a class of point processes whose future depends on its own history. Previous theoretical work on the Hawkes process is limited to the case of a mutually-exciting process, in which a past event can only increase the occurrence of future events. However, in neuronal networks and other real-world a...
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Aerial-Ground collaborative sensing: Third-Person view for teleoperation
Rapid deployment and operation are key requirements in time critical application, such as Search and Rescue (SaR). Efficiently teleoperated ground robots can support first-responders in such situations. However, first-person view teleoperation is sub-optimal in difficult terrains, while a third-person perspective can...
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A universal thin film model for Ginzburg-Landau energy with dipolar interaction
We present an analytical treatment of a three-dimensional variational model of a system that exhibits a second-order phase transition in the presence of dipolar interactions. Within the framework of Ginzburg-Landau theory, we concentrate on the case in which the domain occupied by the sample has the shape of a flat t...
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LOCATA challenge: speaker localization with a planar array
This document describes our submission to the 2018 LOCalization And TrAcking (LOCATA) challenge (Tasks 1, 3, 5). We estimate the 3D position of a speaker using the Global Coherence Field (GCF) computed from multiple microphone pairs of a DICIT planar array. One of the main challenges when using such an array with omn...
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Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers
The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of existing batch and online ensemble classifiers. There are only a limited number of studies on the en...
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Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better empirical performance. However, the resulting optimization problem is much more challeng...
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Computational Eco-Systems for Handwritten Digits Recognition
Inspired by the importance of diversity in biological system, we built an heterogeneous system that could achieve this goal. Our architecture could be summarized in two basic steps. First, we generate a diverse set of classification hypothesis using both Convolutional Neural Networks, currently the state-of-the-art t...
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Compressing networks with super nodes
Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network to be partitioned into a smaller network of 'super nodes', each super node comprising one or more nodes in the...
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Mode specific electronic friction in dissociative chemisorption on metal surfaces: H$_2$ on Ag(111)
Electronic friction and the ensuing nonadiabatic energy loss play an important role in chemical reaction dynamics at metal surfaces. Using molecular dynamics with electronic friction evaluated on-the-fly from Density Functional Theory, we find strong mode dependence and a dominance of nonadiabatic energy loss along t...
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The quantum auxiliary linear problem & quantum Darboux-Backlund transformations
We explore the notion of the quantum auxiliary linear problem and the associated problem of quantum Backlund transformations (BT). In this context we systematically construct the analogue of the classical formula that provides the whole hierarchy of the time components of Lax pairs at the quantum level for both close...
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Simplified Minimal Gated Unit Variations for Recurrent Neural Networks
Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we i...
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Parallel G-duplex and C-duplex DNA with Uninterrupted Spines of AgI-Mediated Base Pairs
Hydrogen bonding between nucleobases produces diverse DNA structural motifs, including canonical duplexes, guanine (G) quadruplexes and cytosine (C) i-motifs. Incorporating metal-mediated base pairs into nucleic acid structures can introduce new functionalities and enhanced stabilities. Here we demonstrate, using mas...
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Constraints on Vacuum Energy from Structure Formation and Nucleosynthesis
This paper derives an upper limit on the density $\rho_{\scriptstyle\Lambda}$ of dark energy based on the requirement that cosmological structure forms before being frozen out by the eventual acceleration of the universe. By allowing for variations in both the cosmological parameters and the strength of gravity, the ...
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Dynamic Mobile Edge Caching with Location Differentiation
Mobile edge caching enables content delivery directly within the radio access network, which effectively alleviates the backhaul burden and reduces round-trip latency. To fully exploit the edge resources, the most popular contents should be identified and cached. Observing that content popularity varies greatly at di...
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Online Learning for Distribution-Free Prediction
We develop an online learning method for prediction, which is important in problems with large and/or streaming data sets. We formulate the learning approach using a covariance-fitting methodology, and show that the resulting predictor has desirable computational and distribution-free properties: It is implemented on...
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Thermal transitions, pseudogap behavior and BCS-BEC crossover in Fermi-Fermi mixtures
We study the mass imbalanced Fermi-Fermi mixture within the framework of a two-dimensional lattice fermion model. Based on the thermodynamic and species dependent quasiparticle behavior we map out the finite temperature phase diagram of this system and show that unlike the balanced Fermi superfluid there are now two ...
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Spectral Approximation for Ergodic CMV Operators with an Application to Quantum Walks
We establish concrete criteria for fully supported absolutely continuous spectrum for ergodic CMV matrices and purely absolutely continuous spectrum for limit-periodic CMV matrices. We proceed by proving several variational estimates on the measure of the spectrum and the vanishing set of the Lyapunov exponent for CM...
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Quantitative Photoacoustic Imaging in the Acoustic Regime using SPIM
While in standard photoacoustic imaging the propagation of sound waves is modeled by the standard wave equation, our approach is based on a generalized wave equation with variable sound speed and material density, respectively. In this paper we present an approach for photoacoustic imaging, which in addition to recov...
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Integrated Deep and Shallow Networks for Salient Object Detection
Deep convolutional neural network (CNN) based salient object detection methods have achieved state-of-the-art performance and outperform those unsupervised methods with a wide margin. In this paper, we propose to integrate deep and unsupervised saliency for salient object detection under a unified framework. Specific...
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Feedback Capacity over Networks
In this paper, we investigate the fundamental limitations of feedback mechanism in dealing with uncertainties for network systems. The study of maximum capability of feedback control was pioneered in Xie and Guo (2000) for scalar systems with nonparametric nonlinear uncertainty. In a network setting, nodes with unkno...
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Flexural phonons in supported graphene: from pinning to localization
We identify graphene layer on a disordered substrate as a possible system where Anderson localization of phonons can be observed. Generally, observation of localization for scattering waves is not simple, because the Rayleigh scattering is inversely proportional to a high power of wavelength. The situation is radical...
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Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack
Many approaches for testing configurable software systems start from the same assumption: it is impossible to test all configurations. This motivated the definition of variability-aware abstractions and sampling techniques to cope with large configuration spaces. Yet, there is no theoretical barrier that prevents the...
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Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells
We propose a reinforcement learning (RL) based closed loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells. The main contributions of our paper are to 1) use RL to solve performance tuning problems in an indoor cellular network for vo...
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Ancient shrinking spherical interfaces in the Allen-Cahn flow
We consider the parabolic Allen-Cahn equation in $\mathbb{R}^n$, $n\ge 2$, $$u_t= \Delta u + (1-u^2)u \quad \hbox{ in } \mathbb{R}^n \times (-\infty, 0].$$ We construct an ancient radially symmetric solution $u(x,t)$ with any given number $k$ of transition layers between $-1$ and $+1$. At main order they consist of $...
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Measuring the Eccentricity of Items
The long-tail phenomenon tells us that there are many items in the tail. However, not all tail items are the same. Each item acquires different kinds of users. Some items are loved by the general public, while some items are consumed by eccentric fans. In this paper, we propose a novel metric, item eccentricity, to i...
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Dataset: Rare Event Classification in Multivariate Time Series
A real-world dataset is provided from a pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x's) and the event label (y). The p...
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Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues
An experimental setup for consecutive measurement of ion and x-ray absorption in tissue or other materials is introduced. With this setup using a 3D-printed sample container, the reference stopping-power ratio (SPR) of materials can be measured with an uncertainty of below 0.1%. A total of 65 porcine and bovine tissu...
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How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks
In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi Oreskes and Erik Conway describe the "tobacco strategy", which was used by the tobacco industry to influence policy makers regarding the health risks of tobacco products. The strategy involved two parts, consisting of (1) promoting and sharing ...
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A complete and partial integrability technique of the Lorenz system
In this paper we deal with the well-known nonlinear Lorenz system that describes the deterministic chaos phenomenon. We consider an interesting problem with time-varying phenomena in quantum optics. Then we establish from the motion equations the passage to the Lorenz system. Furthermore, we show that the reduction t...
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Comparision of the definitions of generalized solution of the Cauchy problem for quasi-linear equation
In preprint we consider and compare different definitions of generalized solution of the Cauchy problem for 1d-scalar quasilinear equation (conservation law). We start from the classical approaches goes back to I.M. Gelfand, O.A. Oleinik, S.N. Kruzhkov and move to the modern finite-difference approximations approache...
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On The Complexity of Sparse Label Propagation
This paper investigates the computational complexity of sparse label propagation which has been proposed recently for processing network structured data. Sparse label propagation amounts to a convex optimization problem and might be considered as an extension of basis pursuit from sparse vectors to network structured...
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Detecting Qualia in Natural and Artificial Agents
The Hard Problem of consciousness has been dismissed as an illusion. By showing that computers are capable of experiencing, we show that they are at least rudimentarily conscious with potential to eventually reach superconsciousness. The main contribution of the paper is a test for confirming certain subjective exper...
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