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High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental diffe...
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Adaptive posterior contraction rates for the horseshoe
We investigate the frequentist properties of Bayesian procedures for estimation based on the horseshoe prior in the sparse multivariate normal means model. Previous theoretical results assumed that the sparsity level, that is, the number of signals, was known. We drop this assumption and characterize the behavior of ...
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A Convex Optimization Approach to Dynamic Programming in Continuous State and Action Spaces
A convex optimization-based method is proposed to numerically solve dynamic programs in continuous state and action spaces. This approach using a discretization of the state space has the following salient features. First, by introducing an auxiliary optimization variable that assigns the contribution of each grid po...
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Investigating Enactive Learning for Autonomous Intelligent Agents
The enactive approach to cognition is typically proposed as a viable alternative to traditional cognitive science. Enactive cognition displaces the explanatory focus from the internal representations of the agent to the direct sensorimotor interaction with its environment. In this paper, we investigate enactive learn...
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Computability of semicomputable manifolds in computable topological spaces
We study computable topological spaces and semicomputable and computable sets in these spaces. In particular, we investigate conditions under which semicomputable sets are computable. We prove that a semicomputable compact manifold $M$ is computable if its boundary $\partial M$ is computable. We also show how this re...
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Acoustic streaming and its suppression in inhomogeneous fluids
We present a theoretical and experimental study of boundary-driven acoustic streaming in an inhomogeneous fluid with variations in density and compressibility. In a homogeneous fluid this streaming results from dissipation in the boundary layers (Rayleigh streaming). We show that in an inhomogeneous fluid, an additio...
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Predicting Pulsar Scintillation from Refractive Plasma Sheets
The dynamic and secondary spectra of many pulsars show evidence for long-lived, aligned images of the pulsar that are stationary on a thin scattering sheet. One explanation for this phenomenon considers the effects of wave crests along sheets in the ionized interstellar medium, such as those due to Alfvén waves propa...
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Disruptive Behavior Disorder (DBD) Rating Scale for Georgian Population
In the presented study Parent/Teacher Disruptive Behavior Disorder (DBD) rating scale based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR [APA, 2000]) which was developed by Pelham and his colleagues (Pelham et al., 1992) was translated and adopted for assessment of childhood behavioral abno...
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A GPU Poisson-Fermi Solver for Ion Channel Simulations
The Poisson-Fermi model is an extension of the classical Poisson-Boltzmann model to include the steric and correlation effects of ions and water treated as nonuniform spheres in aqueous solutions. Poisson-Boltzmann electrostatic calculations are essential but computationally very demanding for molecular dynamics or c...
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Quantum Monte Carlo with variable spins: fixed-phase and fixed-node approximations
We study several aspects of the recently introduced fixed-phase spin-orbit diffusion Monte Carlo (FPSODMC) method, in particular, its relation to the fixed-node method and its potential use as a general approach for electronic structure calculations. We illustrate constructions of spinor-based wave functions with the...
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Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding. It extends the ordinary ICA model in a theoretic...
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Diversification-Based Learning in Computing and Optimization
Diversification-Based Learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization. We show that the DBL framework goes significantly beyond that of the more recent Opposition-based learning (OBL) framework intr...
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A Tutorial on Canonical Correlation Methods
Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has for instance been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the d...
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Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces
The field of brain-computer interfaces is poised to advance from the traditional goal of controlling prosthetic devices using brain signals to combining neural decoding and encoding within a single neuroprosthetic device. Such a device acts as a "co-processor" for the brain, with applications ranging from inducing He...
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Enhanced conservation properties of Vlasov codes through coupling with conservative fluid models
Many phenomena in collisionless plasma physics require a kinetic description. The evolution of the phase space density can be modeled by means of the Vlasov equation, which has to be solved numerically in most of the relevant cases. One of the problems that often arise in such simulations is the violation of importan...
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A note on $p^λ$-convex set in a complete Riemannian manifold
In this paper we have generalized the notion of $\lambda$-radial contraction in complete Riemannian manifold and developed the concept of $p^\lambda$-convex function. We have also given a counter example proving the fact that in general $\lambda$-radial contraction of a geodesic is not necessarily a geodesic. We have...
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Bounded cohomology and virtually free hyperbolically embedded subgroups
Using a probabilistic argument we show that the second bounded cohomology of an acylindrically hyperbolic group $G$ (e.g., a non-elementary hyperbolic or relatively hyperbolic group, non-exceptional mapping class group, ${\rm Out}(F_n)$, \dots) embeds via the natural restriction maps into the inverse limit of the sec...
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Transpiling Programmable Computable Functions to Answer Set Programs
Programming Computable Functions (PCF) is a simplified programming language which provides the theoretical basis of modern functional programming languages. Answer set programming (ASP) is a programming paradigm focused on solving search problems. In this paper we provide a translation from PCF to ASP. Using this tra...
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Scattering polarization of the $d$-states of ions and solar magnetic field: Effects of isotropic collisions
Analysis of solar magnetic fields using observations as well as theoretical interpretations of the scattering polarization is commonly designated as a high priority area of the solar research. The interpretation of the observed polarization raises a serious theoretical challenge to the researchers involved in this fi...
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Persistence Codebooks for Topological Data Analysis
Topological data analysis, such as persistent homology has shown beneficial properties for machine learning in many tasks. Topological representations, such as the persistence diagram (PD), however, have a complex structure (multiset of intervals) which makes it difficult to combine with typical machine learning work...
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Multivariate Analysis for Computing Maxima in High Dimensions
We study the problem of computing the \textsc{Maxima} of a set of $n$ $d$-dimensional points. For dimensions 2 and 3, there are algorithms to solve the problem with order-oblivious instance-optimal running time. However, in higher dimensions there is still room for improvements. We present an algorithm sensitive to t...
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Generalized multi-Galileons, covariantized new terms, and the no-go theorem for non-singular cosmologies
It has been pointed out that non-singular cosmological solutions in second-order scalar-tensor theories generically suffer from gradient instabilities. We extend this no-go result to second-order gravitational theories with an arbitrary number of interacting scalar fields. Our proof follows directly from the action o...
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Tonic activation of extrasynaptic NMDA receptors decreases intrinsic excitability and promotes bistability in a model of neuronal activity
NMDA receptors (NMDA-R) typically contribute to excitatory synaptic transmission in the central nervous system. While calcium influx through NMDA-R plays a critical role in synaptic plasticity, indirect experimental evidence also exists demonstrating actions of NMDAR-mediated calcium influx on neuronal excitability t...
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Using Minimum Path Cover to Boost Dynamic Programming on DAGs: Co-Linear Chaining Extended
Aligning sequencing reads on graph representations of genomes is an important ingredient of pan-genomics. Such approaches typically find a set of local anchors that indicate plausible matches between substrings of a read to subpaths of the graph. These anchor matches are then combined to form a (semi-local) alignment...
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Visual Integration of Data and Model Space in Ensemble Learning
Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classificat...
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Steady States of Rotating Stars and Galaxies
A rotating continuum of particles attracted to each other by gravity may be modeled by the Euler-Poisson system. The existence of solutions is a very classical problem. Here it is proven that a curve of solutions exists, parametrized by the rotation speed, with a fixed mass independent of the speed. The rotation is a...
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Critical properties of the contact process with quenched dilution
We have studied the critical properties of the contact process on a square lattice with quenched site dilution by Monte Carlo simulations. This was achieved by generating in advance the percolating cluster, through the use of an appropriate epidemic model, and then by the simulation of the contact process on the top ...
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A Unified Strouhal-Reynolds Number Relationship for Laminar Vortex Streets Generated by Different Shaped Obstacles
A new Strouhal-Reynolds number relationship, $St=1/(A+B/Re)$, has been recently proposed based on observations of laminar vortex shedding from circular cylinders in a flowing soap film. Since the new $St$-$Re$ relation was derived from a general physical consideration, it raises the possibility that it may be applica...
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On Data-Dependent Random Features for Improved Generalization in Supervised Learning
The randomized-feature approach has been successfully employed in large-scale kernel approximation and supervised learning. The distribution from which the random features are drawn impacts the number of features required to efficiently perform a learning task. Recently, it has been shown that employing data-dependen...
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Anomalous electron spectrum and its relation to peak structure of electron scattering rate in cuprate superconductors
The recent discovery of a direct link between the sharp peak in the electron quasiparticle scattering rate of cuprate superconductors and the well-known peak-dip-hump structure in the electron quasiparticle excitation spectrum is calling for an explanation. Within the framework of the kinetic-energy driven supercondu...
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New goodness-of-fit diagnostics for conditional discrete response models
This paper proposes new specification tests for conditional models with discrete responses, which are key to apply efficient maximum likelihood methods, to obtain consistent estimates of partial effects and to get appropriate predictions of the probability of future events. In particular, we test the static and dynam...
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Tailoring Heterovalent Interface Formation with Light
Integrating different semiconductor materials into an epitaxial device structure offers additional degrees of freedom to select for optimal material properties in each layer. However, interface between materials with different valences (i.e. III-V, II-VI and IV semiconductors) can be difficult to form with high quali...
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Looking backward: From Euler to Riemann
We survey the main ideas in the early history of the subjects on which Riemann worked and that led to some of his most important discoveries. The subjects discussed include the theory of functions of a complex variable, elliptic and Abelian integrals, the hypergeometric series, the zeta function, topology, differenti...
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Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper
In this paper we investigate an emerging application, 3D scene understanding, likely to be significant in the mobile space in the near future. The goal of this exploration is to reduce execution time while meeting our quality of result objectives. In previous work we showed for the first time that it is possible to m...
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A comparison theorem for MW-motivic cohomology
We prove that for a finitely generated field over an infinite perfect field k, and for any integer n, the (n,n)-th MW-motivic cohomology group identifies with the n-th Milnor-Witt K-theory group of that field
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Wiener Filtering for Passive Linear Quantum Systems
This paper considers a version of the Wiener filtering problem for equalization of passive quantum linear quantum systems. We demonstrate that taking into consideration the quantum nature of the signals involved leads to features typically not encountered in classical equalization problems. Most significantly, findin...
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Robust Navigation In GNSS Degraded Environment Using Graph Optimization
Robust navigation in urban environments has received a considerable amount of both academic and commercial interest over recent years. This is primarily due to large commercial organizations such as Google and Uber stepping into the autonomous navigation market. Most of this research has shied away from Global Naviga...
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On reproduction of On the regularization of Wasserstein GANs
This report has several purposes. First, our report is written to investigate the reproducibility of the submitted paper On the regularization of Wasserstein GANs (2018). Second, among the experiments performed in the submitted paper, five aspects were emphasized and reproduced: learning speed, stability, robustness ...
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Density Estimation with Contaminated Data: Minimax Rates and Theory of Adaptation
This paper studies density estimation under pointwise loss in the setting of contamination model. The goal is to estimate $f(x_0)$ at some $x_0\in\mathbb{R}$ with i.i.d. observations, $$ X_1,\dots,X_n\sim (1-\epsilon)f+\epsilon g, $$ where $g$ stands for a contamination distribution. In the context of multiple testin...
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A uniform bound on the Brauer groups of certain log K3 surfaces
Let U be the complement of a smooth anticanonical divisor in a del Pezzo surface of degree at most 7 over a number field k. We show that there is an effective uniform bound for the size of the Brauer group of U in terms of the degree of k.
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Composable Deep Reinforcement Learning for Robotic Manipulation
Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model-free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real-world ...
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Stimulated Raman Scattering Imposes Fundamental Limits to the Duration and Bandwidth of Temporal Cavity Solitons
Temporal cavity solitons (CS) are optical pulses that can persist in passive resonators, and they play a key role in the generation of coherent microresonator frequency combs. In resonators made of amorphous materials, such as fused silica, they can exhibit a spectral red-shift due to stimulated Raman scattering. Her...
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Investigation of Using VAE for i-Vector Speaker Verification
New system for i-vector speaker recognition based on variational autoencoder (VAE) is investigated. VAE is a promising approach for developing accurate deep nonlinear generative models of complex data. Experiments show that VAE provides speaker embedding and can be effectively trained in an unsupervised manner. LLR e...
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A Unifying Framework for Convergence Analysis of Approximate Newton Methods
Many machine learning models are reformulated as optimization problems. Thus, it is important to solve a large-scale optimization problem in big data applications. Recently, subsampled Newton methods have emerged to attract much attention for optimization due to their efficiency at each iteration, rectified a weaknes...
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A Newman property for BLD-mappings
We define a Newman property for BLD-mappings and study its connections to the porosity of the branch set in the setting of generalized manifolds equipped with complete path metrics.
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Learning to Use Learners' Advice
In this paper, we study a variant of the framework of online learning using expert advice with limited/bandit feedback. We consider each expert as a learning entity, seeking to more accurately reflecting certain real-world applications. In our setting, the feedback at any time $t$ is limited in a sense that it is onl...
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Fast and Accurate Time Series Classification with WEASEL
Time series (TS) occur in many scientific and commercial applications, ranging from earth surveillance to industry automation to the smart grids. An important type of TS analysis is classification, which can, for instance, improve energy load forecasting in smart grids by detecting the types of electronic devices bas...
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Deep Neural Network Architectures for Modulation Classification
In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections in a real wireless channel, and uses 10 different modulation types. Further, ...
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Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security
Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Anecdotes report that software developers copy and paste code snippets from those information sourc...
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United Nations Digital Blue Helmets as a Starting Point for Cyber Peacekeeping
Prior works, such as the Tallinn manual on the international law applicable to cyber warfare, focus on the circumstances of cyber warfare. Many organizations are considering how to conduct cyber warfare, but few have discussed methods to reduce, or even prevent, cyber conflict. A recent series of publications started...
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Motives of derived equivalent K3 surfaces
We observe that derived equivalent K3 surfaces have isomorphic Chow motives.
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Robust Dual View Deep Agent
Motivated by recent advance of machine learning using Deep Reinforcement Learning this paper proposes a modified architecture that produces more robust agents and speeds up the training process. Our architecture is based on Asynchronous Advantage Actor-Critic (A3C) algorithm where the total input dimensionality is ha...
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Microplasma generation by slow microwave in an electromagnetically induced transparency-like metasurface
Microplasma generation using microwaves in an electromagnetically induced transparency (EIT)-like metasurface composed of two types of radiatively coupled cut-wire resonators with slightly different resonance frequencies is investigated. Microplasma is generated in either of the gaps of the cut-wire resonators as a r...
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K-theory of group Banach algebras and Banach property RD
We investigate Banach algebras of convolution operators on the $L^p$ spaces of a locally compact group, and their K-theory. We show that for a discrete group, the corresponding K-theory groups depend continuously on $p$ in an inductive sense. Via a Banach version of property RD, we show that for a large class of grou...
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A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can basically be categorized into two classes, backpropagation-like training methods and...
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Learning from various labeling strategies for suicide-related messages on social media: An experimental study
Suicide is an important but often misunderstood problem, one that researchers are now seeking to better understand through social media. Due in large part to the fuzzy nature of what constitutes suicidal risks, most supervised approaches for learning to automatically detect suicide-related activity in social media re...
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Modeling Information Flow Through Deep Neural Networks
This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e.g. convolutional neural networks (CNN). The output of convolutional filters is modeled as a random variable Y conditioned on the object class C and network filter bank F. The conditional entropy (CE...
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Scalable Twin Neural Networks for Classification of Unbalanced Data
Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative to Support Vector Machines (SVM) for learning from imbalanced datasets. The TWSVM learns two non-parallel classifying hyperplanes by solving a couple of smaller sized problems. However, it is unsuitable for large datasets, as it involves matr...
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Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition i...
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Deep Learning applied to Road Traffic Speed forecasting
In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to forecast a regression model for time dependent data. These algorithm's are designed to handle Floating Car Data (FCD) historic speeds to predict road traffic data. For this we aggregate the speeds into the network inputs in an innovative way...
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Wait For It: Identifying "On-Hold" Self-Admitted Technical Debt
Self-admitted technical debt refers to situations where a software developer knows that their current implementation is not optimal and indicates this using a source code comment. In this work, we hypothesize that it is possible to develop automated techniques to understand a subset of these comments in more detail, ...
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Real time observation of granular rock analogue material deformation and failure using nonlinear laser interferometry
A better understanding and anticipation of natural processes such as landsliding or seismic fault activity requires detailed theoretical and experimental analysis of rock mechanics and geomaterial dynamics. These last decades, considerable progress has been made towards understanding deformation and fracture process ...
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Virtual link and knot invariants from non-abelian Yang-Baxter 2-cocycle pairs
For a given $(X,S,\beta)$, where $S,\beta\colon X\times X\to X\times X$ are set theoretical solutions of Yang-Baxter equation with a compatibility condition, we define an invariant for virtual (or classical) knots/links using non commutative 2-cocycles pairs $(f,g)$ that generalizes the one defined in [FG2]. We also ...
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An extensible cluster-graph taxonomy for open set sound scene analysis
We present a new extensible and divisible taxonomy for open set sound scene analysis. This new model allows complex scene analysis with tangible descriptors and perception labels. Its novel structure is a cluster graph such that each cluster (or subset) can stand alone for targeted analyses such as office sound event...
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CREATE: Multimodal Dataset for Unsupervised Learning, Generative Modeling and Prediction of Sensory Data from a Mobile Robot in Indoor Environments
The CREATE database is composed of 14 hours of multimodal recordings from a mobile robotic platform based on the iRobot Create. The various sensors cover vision, audition, motors and proprioception. The dataset has been designed in the context of a mobile robot that can learn multimodal representations of its environ...
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Deep learning enhanced mobile-phone microscopy
Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile-phones are not designed for microscopy and produce spatial and spectral distortions in imaging microsc...
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On convergence of the sample correlation matrices in high-dimensional data
In this paper, we consider an estimation problem concerning the matrix of correlation coefficients in context of high dimensional data settings. In particular, we revisit some results in Li and Rolsalsky [Li, D. and Rolsalsky, A. (2006). Some strong limit theorems for the largest entries of sample correlation matrice...
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Minimal Controllability of Conjunctive Boolean Networks is NP-Complete
Given a conjunctive Boolean network (CBN) with $n$ state-variables, we consider the problem of finding a minimal set of state-variables to directly affect with an input so that the resulting conjunctive Boolean control network (CBCN) is controllable. We give a necessary and sufficient condition for controllability of...
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Consensus report on 25 years of searches for damped Ly$α$ galaxies in emission: Confirming their metallicity-luminosity relation at $z \gtrsim 2$
Starting from a summary of detection statistics of our recent X-shooter campaign, we review the major surveys, both space and ground based, for emission counterparts of high-redshift damped Ly$\alpha$ absorbers (DLAs) carried out since the first detection 25 years ago. We show that the detection rates of all surveys ...
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D-optimal designs for complex Ornstein-Uhlenbeck processes
Complex Ornstein-Uhlenbeck (OU) processes have various applications in statistical modelling. They play role e.g. in the description of the motion of a charged test particle in a constant magnetic field or in the study of rotating waves in time-dependent reaction diffusion systems, whereas Kolmogorov used such a proc...
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On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models
We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional and non-Gaussian setting. Such models form a rich class of non-linear models with applications to signal processing, machine learning and statistics. Our estimators leverage the score function based first a...
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Self-adjointness and spectral properties of Dirac operators with magnetic links
We define Dirac operators on $\mathbb{S}^3$ (and $\mathbb{R}^3$) with magnetic fields supported on smooth, oriented links and prove self-adjointness of certain (natural) extensions. We then analyze their spectral properties and show, among other things, that these operators have discrete spectrum. Certain examples, s...
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Latent tree models
Latent tree models are graphical models defined on trees, in which only a subset of variables is observed. They were first discussed by Judea Pearl as tree-decomposable distributions to generalise star-decomposable distributions such as the latent class model. Latent tree models, or their submodels, are widely used i...
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Program Synthesis from Visual Specification
Program synthesis is the process of automatically translating a specification into computer code. Traditional synthesis settings require a formal, precise specification. Motivated by computer education applications where a student learns to code simple turtle-style drawing programs, we study a novel synthesis setting...
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Towards a Knowledge Graph based Speech Interface
Applications which use human speech as an input require a speech interface with high recognition accuracy. The words or phrases in the recognised text are annotated with a machine-understandable meaning and linked to knowledge graphs for further processing by the target application. These semantic annotations of reco...
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An asymptotic equipartition property for measures on model spaces
Let $G$ be a sofic group, and let $\Sigma = (\sigma_n)_{n\geq 1}$ be a sofic approximation to it. For a probability-preserving $G$-system, a variant of the sofic entropy relative to $\Sigma$ has recently been defined in terms of sequences of measures on its model spaces that `converge' to the system in a certain sens...
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Dynamic Objects Segmentation for Visual Localization in Urban Environments
Visual localization and mapping is a crucial capability to address many challenges in mobile robotics. It constitutes a robust, accurate and cost-effective approach for local and global pose estimation within prior maps. Yet, in highly dynamic environments, like crowded city streets, problems arise as major parts of ...
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Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size
A key problem in research on adversarial examples is that vulnerability to adversarial examples is usually measured by running attack algorithms. Because the attack algorithms are not optimal, the attack algorithms are prone to overestimating the size of perturbation needed to fool the target model. In other words, t...
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Gaussian process regression for forest attribute estimation from airborne laser scanning data
While the analysis of airborne laser scanning (ALS) data often provides reliable estimates for certain forest stand attributes -- such as total volume or basal area -- there is still room for improvement, especially in estimating species-specific attributes. Moreover, while information on the estimate uncertainty wou...
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Quantum capacitance of double-layer graphene
We study the ground-state properties of a double layer graphene system with the Coulomb interlayer electron-electron interaction modeled within the random phase approximation. We first obtain an expression of the quantum capacitance of a two layer system. In addition, we calculate the many-body exchange-correlation e...
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DeepTriangle: A Deep Learning Approach to Loss Reserving
We propose a novel approach for loss reserving based on deep neural networks. The approach allows for jointly modeling of paid losses and claims outstanding, and incorporation of heterogenous inputs. We validate the models on loss reserving data across lines of business, and show that they attain or exceed the predic...
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Distributed Time Synchronization for Networks with Random Delays and Measurement Noise
In this paper a new distributed asynchronous algorithm is proposed for time synchronization in networks with random communication delays, measurement noise and communication dropouts. Three different types of the drift correction algorithm are introduced, based on different kinds of local time increments. Under nonre...
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Using High-Rising Cities to Visualize Performance in Real-Time
For developers concerned with a performance drop or improvement in their software, a profiler allows a developer to quickly search and identify bottlenecks and leaks that consume much execution time. Non real-time profilers analyze the history of already executed stack traces, while a real-time profiler outputs the r...
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Controller Synthesis for Discrete-time Hybrid Polynomial Systems via Occupation Measures
We present a novel controller synthesis approach for discrete-time hybrid polynomial systems, a class of systems that can model a wide variety of interactions between robots and their environment. The approach is rooted in recently developed techniques that use occupation measures to formulate the controller synthesi...
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Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning
Recently, research on accelerated stochastic gradient descent methods (e.g., SVRG) has made exciting progress (e.g., linear convergence for strongly convex problems). However, the best-known methods (e.g., Katyusha) requires at least two auxiliary variables and two momentum parameters. In this paper, we propose a fas...
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A vertex-weighted-Least-Squares gradient reconstruction
Gradient reconstruction is a key process for the spatial accuracy and robustness of finite volume method, especially in industrial aerodynamic applications in which grid quality affects reconstruction methods significantly. A novel gradient reconstruction method for cell-centered finite volume scheme is introduced. T...
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The reverse mathematics of theorems of Jordan and Lebesgue
The Jordan decomposition theorem states that every function $f \colon [0,1] \to \mathbb{R}$ of bounded variation can be written as the difference of two non-decreasing functions. Combining this fact with a result of Lebesgue, every function of bounded variation is differentiable almost everywhere in the sense of Lebe...
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Semiflat Orbifold Projections
We compute the semiflat positive cone $K_0^{+SF}(A_\theta^\sigma)$ of the $K_0$-group of the irrational rotation orbifold $A_\theta^\sigma$ under the noncommutative Fourier transform $\sigma$ and show that it is determined by classes of positive trace and the vanishing of two topological invariants. The semiflat orbi...
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Magnetization process of the S = 1/2 two-leg organic spin-ladder compound BIP-BNO
We have measured the magnetization of the organic compound BIP-BNO (3,5'-bis(N-tert-butylaminoxyl)-3',5-dibromobiphenyl) up to 76 T where the magnetization is saturated. The S = 1/2 antiferromagnetic Heisenberg two-leg spin-ladder model accounts for the obtained experimental data regarding the magnetization curve, wh...
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Deep Over-sampling Framework for Classifying Imbalanced Data
Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose...
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Failsafe Mechanism Design of Multicopters Based on Supervisory Control Theory
In order to handle undesirable failures of a multicopter which occur in either the pre-flight process or the in-flight process, a failsafe mechanism design method based on supervisory control theory is proposed for the semi-autonomous control mode. Failsafe mechanism is a control logic that guides what subsequent act...
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Inverse Design of Single- and Multi-Rotor Horizontal Axis Wind Turbine Blades using Computational Fluid Dynamics
A method for inverse design of horizontal axis wind turbines (HAWTs) is presented in this paper. The direct solver for aerodynamic analysis solves the Reynolds Averaged Navier Stokes (RANS) equations, where the effect of the turbine rotor is modeled as momentum sources using the actuator disk model (ADM); this approa...
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The Double Galaxy Cluster Abell 2465 III. X-ray and Weak-lensing Observations
We report Chandra X-ray observations and optical weak-lensing measurements from Subaru/Suprime-Cam images of the double galaxy cluster Abell 2465 (z=0.245). The X-ray brightness data are fit to a beta-model to obtain the radial gas density profiles of the northeast (NE) and southwest (SW) sub-components, which are se...
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Superdensity Operators for Spacetime Quantum Mechanics
We introduce superdensity operators as a tool for analyzing quantum information in spacetime. Superdensity operators encode spacetime correlation functions in an operator framework, and support a natural generalization of Hilbert space techniques and Dirac's transformation theory as traditionally applied to standard ...
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Monte Carlo study of magnetic nanoparticles adsorbed on halloysite $Al_2Si_2O_5(OH)_4$ nanotubes
We study properties of magnetic nanoparticles adsorbed on the halloysite surface. For that a distinct magnetic Hamiltonian with random distribution of spins on a cylindrical surface was solved by using a nonequilibrium Monte Carlo method. The parameters for our simulations: anisotropy constant, nanoparticle size dist...
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Inference in high-dimensional linear regression models
We introduce an asymptotically unbiased estimator for the full high-dimensional parameter vector in linear regression models where the number of variables exceeds the number of available observations. The estimator is accompanied by a closed-form expression for the covariance matrix of the estimates that is free of t...
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Counterfactual Learning for Machine Translation: Degeneracies and Solutions
Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions. In order to avoid the risk of showing inferior translations to users, in such scenarios mostly exploration-free deterministic logging policies are in place...
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Conversion of Mersenne Twister to double-precision floating-point numbers
The 32-bit Mersenne Twister generator MT19937 is a widely used random number generator. To generate numbers with more than 32 bits in bit length, and particularly when converting into 53-bit double-precision floating-point numbers in $[0,1)$ in the IEEE 754 format, the typical implementation concatenates two successi...
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BaHaMAS: A Bash Handler to Monitor and Administrate Simulations
Numerical QCD is often extremely resource demanding and it is not rare to run hundreds of simulations at the same time. Each of these can last for days or even months and it typically requires a job-script file as well as an input file with the physical parameters for the application to be run. Moreover, some monitor...
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Computational Study of Halide Perovskite-Derived A$_2$BX$_6$ Inorganic Compounds: Chemical Trends in Electronic Structure and Structural Stability
The electronic structure and energetic stability of A$_2$BX$_6$ halide compounds with the cubic and tetragonal variants of the perovskite-derived K$_2$PtCl$_6$ prototype structure are investigated computationally within the frameworks of density-functional-theory (DFT) and hybrid (HSE06) functionals. The HSE06 calcul...
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