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A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models
For high-dimensional sparse linear models, how to construct confidence intervals for coefficients remains a difficult question. The main reason is the complicated limiting distributions of common estimators such as the Lasso. Several confidence interval construction methods have been developed, and Bootstrap Lasso+OL...
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The Music Streaming Sessions Dataset
At the core of many important machine learning problems faced by online streaming services is a need to model how users interact with the content. These problems can often be reduced to a combination of 1) sequentially recommending items to the user, and 2) exploiting the user's interactions with the items as feedbac...
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CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. How...
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Finding events in temporal networks: Segmentation meets densest-subgraph discovery
In this paper we study the problem of discovering a timeline of events in a temporal network. We model events as dense subgraphs that occur within intervals of network activity. We formulate the event-discovery task as an optimization problem, where we search for a partition of the network timeline into k non-overlap...
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On the area of constrained polygonal linkages
We study configuration spaces of linkages whose underlying graph are polygons with diagonal constrains, or more general, partial two-trees. We show that (with an appropriate definition) the oriented area is a Bott-Morse function on the configuration space. Its critical points are described and Bott-Morse indices are ...
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Effects of geometrical frustration on ferromagnetism in the Hubbard model on the Shastry-Sutherland lattice
The small-cluster exact-diagonalization calculations and the projector quantum Monte Carlo method are used to examine the competing effects of geometrical frustration and interaction on ferromagnetism in the Hubbard model on the Shastry-Sutherland lattice. It is shown that the geometrical frustration stabilizes the f...
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Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence
Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining a...
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El Lenguaje Natural como Lenguaje Formal
Formal languages theory is useful for the study of natural language. In particular, it is of interest to study the adequacy of the grammatical formalisms to express syntactic phenomena present in natural language. First, it helps to draw hypothesis about the nature and complexity of the speaker-hearer linguistic comp...
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Bounds for the completely positive rank of a symmetric matrix over a tropical semiring
In this paper, we find an upper bound for the CP-rank of a matrix over a tropical semiring, according to the vertex clique cover of the graph prescribed by the pattern of the matrix. We study the graphs that beget the patterns of matrices with the lowest possible CP-ranks and prove that any such graph must have its d...
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Gaussian process classification using posterior linearisation
This paper proposes a new algorithm for Gaussian process classification based on posterior linearisation (PL). In PL, a Gaussian approximation to the posterior density is obtained iteratively using the best possible linearisation of the conditional mean of the labels and accounting for the linearisation error. Consid...
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On Inconsistency Indices and Inconsistency Axioms in Pairwise Comparisons
Pairwise comparisons are an important tool of modern (multiple criteria) decision making. Since human judgments are often inconsistent, many studies focused on the ways how to express and measure this inconsistency, and several inconsistency indices were proposed as an alternative to Saaty inconsistency index and inc...
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Heteroclinic traveling fronts for a generalized Fisher-Burgers equation with saturating diffusion
We study the existence of monotone heteroclinic traveling waves for a general Fisher-Burgers equation with nonlinear and possibly density-dependent diffusion. Such a model arises, for instance, in physical phenomena where a saturation effect appears for large values of the gradient. We give an estimate for the critic...
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Computer Algebra for Microhydrodynamics
I describe a method for computer algebra that helps with laborious calculations typically encountered in theoretical microhydrodynamics. The program mimics how humans calculate by matching patterns and making replacements according to the rules of algebra and calculus. This note gives an overview and walks through an...
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Rapid micro fluorescence in situ hybridization in tissue sections
This paper describes a micro fluorescence in situ hybridization ({\mu}FISH)-based rapid detection of cytogenetic biomarkers on formalin-fixed paraffin embedded (FFPE) tissue sections. We demonstrated this method in the context of detecting human epidermal growth factor 2 (HER2) in breast tissue sections. This method ...
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Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Neural time-series data contain a wide variety of prototypical signal waveforms (atoms) that are of significant importance in clinical and cognitive research. One of the goals for analyzing such data is hence to extract such 'shift-invariant' atoms. Even though some success has been reported with existing algorithms,...
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Machine Learning for Drug Overdose Surveillance
We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan enables early detection of emerging patterns in spatio-temporal data, accounting for both the non-iid nature of the data and the fact that detecting subtle...
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A Capsule based Approach for Polyphonic Sound Event Detection
Polyphonic sound event detection (polyphonic SED) is an interesting but challenging task due to the concurrence of multiple sound events. Recently, SED methods based on convolutional neural networks (CNN) and recurrent neural networks (RNN) have shown promising performance. Generally, CNN are designed for local featu...
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Linear simulation of ion temperature gradient driven instabilities in W7-X and LHD stellarators using GTC
The global gyrokinetic toroidal code (GTC) has been recently upgraded to do simulations in non-axisymmetric equilibrium configuration, such as stellarators. Linear simulation of ion temperature gradient (ITG) driven instabilities has been done in Wendelstein7-X (W7-X) and Large Helical Device (LHD) stellarators using...
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Advanced engineering of single-crystal gold nanoantennas
A nanofabrication process for realizing optical nanoantennas carved from a single-crystal gold plate is presented in this communication. The method relies on synthesizing two-dimensional micron-size gold crystals followed by the dry etching of a desired antenna layout. The fabrication of single-crystal optical nanoan...
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Stick-breaking processes, clumping, and Markov chain occupation laws
We consider the connections among `clumped' residual allocation models (RAMs), a general class of stick-breaking processes including Dirichlet processes, and the occupation laws of certain discrete space time-inhomogeneous Markov chains related to simulated annealing and other applications. An intermediate structure ...
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Scaling laws of Rydberg excitons
Rydberg atoms have attracted considerable interest due to their huge interaction among each other and with external fields. They demonstrate characteristic scaling laws in dependence on the principal quantum number $n$ for features such as the magnetic field for level crossing. While bearing striking similarities to ...
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Variable selection in multivariate linear models with high-dimensional covariance matrix estimation
In this paper, we propose a novel variable selection approach in the framework of multivariate linear models taking into account the dependence that may exist between the responses. It consists in estimating beforehand the covariance matrix of the responses and to plug this estimator in a Lasso criterion, in order to...
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A Novel Model of Cancer-Induced Peripheral Neuropathy and the Role of TRPA1 in Pain Transduction
Background. Models of cancer-induced neuropathy are designed by injecting cancer cells near the peripheral nerves. The interference of tissue-resident immune cells does not allow a direct contact with nerve fibres which affects the tumor microenvironment and the invasion process. Methods. Anaplastic tumor-1 (AT-1) ce...
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Statistical inference using SGD
We present a novel method for frequentist statistical inference in $M$-estimation problems, based on stochastic gradient descent (SGD) with a fixed step size: we demonstrate that the average of such SGD sequences can be used for statistical inference, after proper scaling. An intuitive analysis using the Ornstein-Uhl...
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Core Discovery in Hidden Graphs
Massive network exploration is an important research direction with many applications. In such a setting, the network is, usually, modeled as a graph $G$, whereas any structural information of interest is extracted by inspecting the way nodes are connected together. In the case where the adjacency matrix or the adjac...
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Some exact Bradlow vortex solutions
We consider the Bradlow equation for vortices which was recently found by Manton and find a two-parameter class of analytic solutions in closed form on nontrivial geometries with non-constant curvature. The general solution to our class of metrics is given by a hypergeometric function and the area of the vortex domai...
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Saturating sets in projective planes and hypergraph covers
Let $\Pi_q$ be an arbitrary finite projective plane of order $q$. A subset $S$ of its points is called saturating if any point outside $S$ is collinear with a pair of points from $S$. Applying probabilistic tools we improve the upper bound on the smallest possible size of the saturating set to $\lceil\sqrt{3q\ln{q}}\...
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Accelerated Optimization in the PDE Framework: Formulations for the Active Contour Case
Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient-based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical. Not only does accelerated gradient descent con...
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Limitations on Variance-Reduction and Acceleration Schemes for Finite Sum Optimization
We study the conditions under which one is able to efficiently apply variance-reduction and acceleration schemes on finite sum optimization problems. First, we show that, perhaps surprisingly, the finite sum structure by itself, is not sufficient for obtaining a complexity bound of $\tilde{\cO}((n+L/\mu)\ln(1/\epsilo...
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Stability Enhanced Large-Margin Classifier Selection
Stability is an important aspect of a classification procedure because unstable predictions can potentially reduce users' trust in a classification system and also harm the reproducibility of scientific conclusions. The major goal of our work is to introduce a novel concept of classification instability, i.e., decisi...
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Some Sphere Theorems in Linear Potential Theory
In this paper we analyze the capacitary potential due to a charged body in order to deduce sharp analytic and geometric inequalities, whose equality cases are saturated by domains with spherical symmetry. In particular, for a regular bounded domain $\Omega \subset \mathbb{R}^n$, $n\geq 3$, we prove that if the mean c...
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Non-Gaussian Component Analysis using Entropy Methods
Non-Gaussian component analysis (NGCA) is a problem in multidimensional data analysis which, since its formulation in 2006, has attracted considerable attention in statistics and machine learning. In this problem, we have a random variable $X$ in $n$-dimensional Euclidean space. There is an unknown subspace $\Gamma$ ...
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Positive Geometries and Canonical Forms
Recent years have seen a surprising connection between the physics of scattering amplitudes and a class of mathematical objects--the positive Grassmannian, positive loop Grassmannians, tree and loop Amplituhedra--which have been loosely referred to as "positive geometries". The connection between the geometry and phy...
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Computational Thinking in Education: Where does it Fit? A systematic literary review
Computational Thinking (CT) has been described as an essential skill which everyone should learn and can therefore include in their skill set. Seymour Papert is credited as concretising Computational Thinking in 1980 but since Wing popularised the term in 2006 and brought it to the international community's attention...
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Spincaloritronic signal generation in non-degenerate Si
Spincaloritronic signal generation due to thermal spin injection and spin transport is demonstrated in a non-degenerate Si spin valve. The spin-dependent Seebeck effect is used for the spincaloritronic signal generation, and the thermal gradient of about 200 mK at an interface of Fe and Si enables generating a spin v...
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Optimal fidelity multi-level Monte Carlo for quantification of uncertainty in simulations of cloud cavitation collapse
We quantify uncertainties in the location and magnitude of extreme pressure spots revealed from large scale multi-phase flow simulations of cloud cavitation collapse. We examine clouds containing 500 cavities and quantify uncertainties related to their initial spatial arrangement. The resulting 2000-dimensional space...
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A New Take on Protecting Cyclists in Smart Cities
Pollution in urban centres is becoming a major societal problem. While pollution is a concern for all urban dwellers, cyclists are one of the most exposed groups due to their proximity to vehicle tailpipes. Consequently, new solutions are required to help protect citizens, especially cyclists, from the harmful effect...
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The effect upon neutrinos of core-collapse supernova accretion phase turbulence
During the accretion phase of a core-collapse supernovae, large amplitude turbulence is generated by the combination of the standing accretion shock instability and convection driven by neutrino heating. The turbulence directly affects the dynamics of the explosion, but there is also the possibility of an additional,...
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Positive-Unlabeled Learning with Non-Negative Risk Estimator
From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning. However, if its model is very flexible, empirical risks on training data will go negative, and we will suffer from serious overfitting. In this paper, we propose...
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PSZ2LenS. Weak lensing analysis of the Planck clusters in the CFHTLenS and in the RCSLenS
The possibly unbiased selection process in surveys of the Sunyaev Zel'dovich effect can unveil new populations of galaxy clusters. We performed a weak lensing analysis of the PSZ2LenS sample, i.e. the PSZ2 galaxy clusters detected by the Planck mission in the sky portion covered by the lensing surveys CFHTLenS and RC...
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GP CaKe: Effective brain connectivity with causal kernels
A fundamental goal in network neuroscience is to understand how activity in one region drives activity elsewhere, a process referred to as effective connectivity. Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connec...
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On boundary behavior of mappings on Riemannian manifolds in terms of prime ends
A boundary behavior of ring mappings on Riemannian manifolds, which are generalization of quasiconformal mappings by Gehring, is investigated. In terms of prime ends, there are obtained theorems about continuous extension to a boundary of classes mentioned above. In the terms mentioned above, there are obtained resul...
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Privacy Preserving Face Retrieval in the Cloud for Mobile Users
Recently, cloud storage and processing have been widely adopted. Mobile users in one family or one team may automatically backup their photos to the same shared cloud storage space. The powerful face detector trained and provided by a 3rd party may be used to retrieve the photo collection which contains a specific gr...
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A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)
This work provides a simplified proof of the statistical minimax optimality of (iterate averaged) stochastic gradient descent (SGD), for the special case of least squares. This result is obtained by analyzing SGD as a stochastic process and by sharply characterizing the stationary covariance matrix of this process. T...
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Averages of Unlabeled Networks: Geometric Characterization and Asymptotic Behavior
It is becoming increasingly common to see large collections of network data objects -- that is, data sets in which a network is viewed as a fundamental unit of observation. As a result, there is a pressing need to develop network-based analogues of even many of the most basic tools already standard for scalar and vec...
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The Abelian distribution
We define the Abelian distribution and study its basic properties. Abelian distributions arise in the context of neural modeling and describe the size of neural avalanches in fully-connected integrate-and-fire models of self-organized criticality in neural systems.
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PULSEDYN - A dynamical simulation tool for studying strongly nonlinear chains
We introduce PULSEDYN, a particle dynamics program in $C++$, to solve many-body nonlinear systems in one dimension. PULSEDYN is designed to make computing accessible to non-specialists in the field of nonlinear dynamics of many-body systems and to ensure transparency and easy benchmarking of numerical results for an ...
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Stabilization Control of the Differential Mobile Robot Using Lyapunov Function and Extended Kalman Filter
This paper presents the design of a control model to navigate the differential mobile robot to reach the desired destination from an arbitrary initial pose. The designed model is divided into two stages: the state estimation and the stabilization control. In the state estimation, an extended Kalman filter is employed...
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A Random Sample Partition Data Model for Big Data Analysis
Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data block...
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A family of transformed copulas with singular component
In this paper, we present a family of bivariate copulas by transforming a given copula function with two increasing functions, named as transformed copula. One distinctive characteristic of the transformed copula is its singular component along the main diagonal. Conditions guaranteeing the transformed function to be...
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Deep Learning for micro-Electrocorticographic (μECoG) Data
Machine learning can extract information from neural recordings, e.g., surface EEG, ECoG and {\mu}ECoG, and therefore plays an important role in many research and clinical applications. Deep learning with artificial neural networks has recently seen increasing attention as a new approach in brain signal decoding. Her...
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Clipped Matrix Completion: A Remedy for Ceiling Effects
We consider the problem of recovering a low-rank matrix from its clipped observations. Clipping is conceivable in many scientific areas that obstructs statistical analyses. On the other hand, matrix completion (MC) methods can recover a low-rank matrix from various information deficits by using the principle of low-r...
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End-to-End Multi-Task Denoising for joint SDR and PESQ Optimization
Supervised learning based on a deep neural network recently has achieved substantial improvement on speech enhancement. Denoising networks learn mapping from noisy speech to clean one directly, or to a spectra mask which is the ratio between clean and noisy spectrum. In either case, the network is optimized by minimi...
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Scalable Importance Tempering and Bayesian Variable Selection
We propose a Monte Carlo algorithm to sample from high-dimensional probability distributions that combines Markov chain Monte Carlo (MCMC) and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high-dimensionality, explicit comparison with standard MCMC and illustrat...
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Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals
Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to multidimensional data. CDL has proven useful for image denoising or inpainting, as well as for pattern discovery on multivariate signals. As estimated patterns can be positioned anywhere in signals or images, optimization techniques fa...
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Performance Analysis of Robust Stable PID Controllers Using Dominant Pole Placement for SOPTD Process Models
This paper derives new formulations for designing dominant pole placement based proportional-integral-derivative (PID) controllers to handle second order processes with time delays (SOPTD). Previously, similar attempts have been made for pole placement in delay-free systems. The presence of the time delay term manife...
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On Conjugates and Adjoint Descent
In this note we present an $\infty$-categorical framework for descent along adjunctions and a general formula for counting conjugates up to equivalence which unifies several known formulae from different fields.
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Learning to Parse and Translate Improves Neural Machine Translation
There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RNNG, that learns to parse and translate by combining t...
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Boundary feedback stabilization of a flexible wing model under unsteady aerodynamic loads
This paper addresses the boundary stabilization of a flexible wing model, both in bending and twisting displacements, under unsteady aerodynamic loads, and in presence of a store. The wing dynamics is captured by a distributed parameter system as a coupled Euler-Bernoulli and Timoshenko beam model. The problem is tac...
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Out-colourings of Digraphs
We study vertex colourings of digraphs so that no out-neighbourhood is monochromatic and call such a colouring an {\bf out-colouring}. The problem of deciding whether a given digraph has an out-colouring with only two colours (called a 2-out-colouring) is ${\cal NP}$-complete. We show that for every choice of positiv...
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An Improved Video Analysis using Context based Extension of LSH
Locality Sensitive Hashing (LSH) based algorithms have already shown their promise in finding approximate nearest neighbors in high dimen- sional data space. However, there are certain scenarios, as in sequential data, where the proximity of a pair of points cannot be captured without considering their surroundings o...
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Commuting graphs on Coxeter groups, Dynkin diagrams and finite subgroups of $SL(2,\mathbb{C})$
For a group $H$ and a non empty subset $\Gamma\subseteq H$, the commuting graph $G=\mathcal{C}(H,\Gamma)$ is the graph with $\Gamma$ as the node set and where any $x,y \in \Gamma$ are joined by an edge if $x$ and $y$ commute in $H$. We prove that any simple graph can be obtained as a commuting graph of a Coxeter grou...
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Exact energy stability of Bénard-Marangoni convection at infinite Prandtl number
Using the energy method we investigate the stability of pure conduction in Pearson's model for Bénard-Marangoni convection in a layer of fluid at infinite Prandtl number. Upon extending the space of admissible perturbations to the conductive state, we find an exact solution to the energy stability variational problem...
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A sharpening of a problem on Bernstein polynomials and convex functions
We present an elementary proof of a conjecture proposed by I. Rasa in 2017 which is an inequality involving Bernstein basis polynomials and convex functions. It was affirmed in positive by A. Komisarski and T. Rajba very recently by the use of stochastic convex orderings.
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Local Estimate on Convexity Radius and decay of injectivity radius in a Riemannian manifold
In this paper we prove the following pointwise and curvature-free estimates on convexity radius, injectivity radius and local behavior of geodesics in a complete Riemannian manifold $M$: 1) the convexity radius of $p$, $\operatorname{conv}(p)\ge \min\{\frac{1}{2}\operatorname{inj}(p),\operatorname{foc}(B_{\operatorna...
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Magnetic properties of the spin-1 chain compound NiCl$_3$C$_6$H$_5$CH$_2$CH$_2$NH$_3$
We report experimental results of the static magnetization, ESR and NMR spectroscopic measurements of the Ni-hybrid compound NiCl$_3$C$_6$H$_5$CH$_2$CH$_2$NH$_3$. In this material NiCl$_3$ octahedra are structurally arranged in chains along the crystallographic $a$-axis. According to the static susceptibility and ESR...
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Multiple core hole formation by free-electron laser radiation in molecular nitrogen
We investigate the formation of multiple-core-hole states of molecular nitrogen interacting with a free-electron laser pulse. We obtain bound and continuum molecular orbitals in the single-center expansion scheme and use these orbitals to calculate photo-ionization and Auger decay rates. Using these rates, we compute...
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An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional neural networks may be appropriate. In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate t...
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Uniformly recurrent subgroups and the ideal structure of reduced crossed products
We study the ideal structure of reduced crossed product of topological dynamical systems of a countable discrete group. More concretely, for a compact Hausdorff space $X$ with an action of a countable discrete group $\Gamma$, we consider the absence of a non-zero ideals in the reduced crossed product $C(X) \rtimes_r ...
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TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks
Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet-a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS trac...
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Modularity Matters: Learning Invariant Relational Reasoning Tasks
We focus on two supervised visual reasoning tasks whose labels encode a semantic relational rule between two or more objects in an image: the MNIST Parity task and the colorized Pentomino task. The objects in the images undergo random translation, scaling, rotation and coloring transformations. Thus these tasks invol...
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Proofs of some Propositions of the semi-Intuitionistic Logic with Strong Negation
We offer the proofs that complete our article introducing the propositional calculus called semi-intuitionistic logic with strong negation.
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Entanglement in topological systems
These lecture notes on entanglement in topological systems are part of the 48th IFF Spring School 2017 on Topological Matter: Topological Insulators, Skyrmions and Majoranas at the Forschungszentrum Juelich, Germany. They cover a short discussion on topologically ordered phases and review the two main tools available...
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Equivalence between Differential Inclusions Involving Prox-regular sets and maximal monotone operators
In this paper, we study the existence and the stability in the sense of Lyapunov of solutions for\ differential inclusions governed by the normal cone to a prox-regular set and subject to a Lipschitzian perturbation. We prove that such, apparently, more general nonsmooth dynamics can be indeed remodelled into the cla...
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Effect of iron oxide loading on magnetoferritin structure in solution as revealed by SAXS and SANS
Synthetic biological macromolecule of magnetoferritin containing an iron oxide core inside a protein shell (apoferritin) is prepared with different content of iron. Its structure in aqueous solution is analyzed by small-angle synchrotron X-ray (SAXS) and neutron (SANS) scattering. The loading factor (LF) defined as t...
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Volumetric Super-Resolution of Multispectral Data
Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7 ETM+) provide low-spatial high-spectral resolution multispectral (MS) or high-spatial low-spectral resolution panchromatic (PAN) images, separately. In order to reconstruct a high-spatial/high-spectral resolution multispectral image volume, eith...
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Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowl...
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Random characters under the $L$-measure, I : Dirichlet characters
We define the $L$-measure on the set of Dirichlet characters as an analogue of the Plancherel measure, once considered as a measure on the irreducible characters of the symmetric group. We compare the two measures and study the limit in distribution of characters evaluations when the size of the underlying group grow...
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The Effects of Superheating Treatment on Distribution of Eutectic Silicon Particles in A357-Continuous Stainless Steel Composite
In the present study, superheating treatment has been applied on A357 reinforced with 0.5 wt. % (Composite 1) and 1.0 wt.% (Composite 2) continuous stainless steel composite. In Composite 1 the microstructure displayed poor bonding between matrix and reinforcement interface. Poor bonding associated with large voids a...
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Cycle-of-Learning for Autonomous Systems from Human Interaction
We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms. We provide a taxonomy to categorize the types of human interaction and present our Cycle-of-Learning framework for autonomous systems that combines different human-interaction modal...
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Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework
Background: Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison. But some aspects have received little attention. First, most comparison studies treat prediction performance and variab...
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How the notion of ACCESS guides the organization of a European research infrastructure: the example of DARIAH
This contribution will show how Access play a strong role in the creation and structuring of DARIAH, a European Digital Research Infrastructure in Arts and Humanities.To achieve this goal, this contribution will develop the concept of Access from five examples: Interdisciplinarity point of view, Manage contradiction ...
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Soliton-potential interactions for nonlinear Schrödinger equation in $\mathbb{R}^3$
In this work we mainly consider the dynamics and scattering of a narrow soliton of NLS equation with a potential in $\mathbb{R}^3$, where the asymptotic state of the system can be far from the initial state in parameter space. Specifically, if we let a narrow soliton state with initial velocity $\upsilon_{0}$ to inte...
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Incommensurately modulated twin structure of nyerereite Na1.64K0.36Ca(CO3)2
Incommensurately modulated twin structure of nyerereite Na1.64K0.36Ca(CO3)2 has been first determined in the (3+1)D symmetry group Cmcm({\alpha}00)00s with modulation vector q = 0.383a*. Unit-cell values are a = 5.062(1), b = 8.790(1), c = 12.744(1) {\AA}. Three orthorhombic components are related by threefold rotati...
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Jensen's force and the statistical mechanics of cortical asynchronous states
The cortex exhibits self-sustained highly-irregular activity even under resting conditions, whose origin and function need to be fully understood. It is believed that this can be described as an "asynchronous state" stemming from the balance between excitation and inhibition, with important consequences for informati...
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Notes on relative normalizations of ruled surfaces in the three-dimensional Euclidean space
This paper deals with relative normalizations of skew ruled surfaces in the Euclidean space $\mathbb{E}^{3}$. In section 2 we investigate some new formulae concerning the Pick invariant, the relative curvature, the relative mean curvature and the curvature of the relative metric of a relatively normalized ruled surfa...
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Ferroelectric control of the giant Rashba spin orbit coupling in GeTe(111)/InP(111) superlattice
GeTe wins the renewed research interest due to its giant bulk Rashba spin orbit coupling (SOC), and becomes the father of a new multifunctional material, i.e., ferroelectric Rashba semiconductor. In the present work, we investigate Rashba SOC at the interface of the ferroelectric semiconductor superlattice GeTe(111)/...
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Topological phase of the interlayer exchange coupling with application to magnetic switching
We show, theoretically, that the phase of the interlayer exchange coupling (IEC) undergoes a topological change of approximately $2\pi$ as the chemical potential of the ferromagnetic (FM) lead moves across a hybridization gap (HG). The effect is largely independent of the detailed parameters of the system, in particu...
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New constraints on time-dependent variations of fundamental constants using Planck data
Observations of the CMB today allow us to answer detailed questions about the properties of our Universe, targeting both standard and non-standard physics. In this paper, we study the effects of varying fundamental constants (i.e., the fine-structure constant, $\alpha_{\rm EM}$, and electron rest mass, $m_{\rm e}$) a...
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ForestClaw: A parallel algorithm for patch-based adaptive mesh refinement on a forest of quadtrees
We describe a parallel, adaptive, multi-block algorithm for explicit integration of time dependent partial differential equations on two-dimensional Cartesian grids. The grid layout we consider consists of a nested hierarchy of fixed size, non-overlapping, logically Cartesian grids stored as leaves in a quadtree. Dyn...
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Distributed Triangle Counting in the Graphulo Matrix Math Library
Triangle counting is a key algorithm for large graph analysis. The Graphulo library provides a framework for implementing graph algorithms on the Apache Accumulo distributed database. In this work we adapt two algorithms for counting triangles, one that uses the adjacency matrix and another that also uses the inciden...
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Ensemble Classifier for Eye State Classification using EEG Signals
The growing importance and utilization of measuring brain waves (e.g. EEG signals of eye state) in brain-computer interface (BCI) applications highlighted the need for suitable classification methods. In this paper, a comparison between three of well-known classification methods (i.e. support vector machine (SVM), hi...
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Nutritionally recommended food for semi- to strict vegetarian diets based on large-scale nutrient composition data
Diet design for vegetarian health is challenging due to the limited food repertoire of vegetarians. This challenge can be partially overcome by quantitative, data-driven approaches that utilise massive nutritional information collected for many different foods. Based on large-scale data of foods' nutrient composition...
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Does data interpolation contradict statistical optimality?
We show that learning methods interpolating the training data can achieve optimal rates for the problems of nonparametric regression and prediction with square loss.
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Spin-Frustrated Pyrochlore Chains in the Volcanic Mineral Kamchatkite (KCu3OCl(SO4)2)
Search of new frustrated magnetic systems is of a significant importance for physics studying the condensed matter. The platform for geometric frustration of magnetic systems can be provided by copper oxocentric tetrahedra (OCu4) forming the base of crystalline structures of copper minerals from Tolbachik volcanos in...
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Detail-revealing Deep Video Super-resolution
Previous CNN-based video super-resolution approaches need to align multiple frames to the reference. In this paper, we show that proper frame alignment and motion compensation is crucial for achieving high quality results. We accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN framework. Analy...
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MON: Mission-optimized Overlay Networks
Large organizations often have users in multiple sites which are connected over the Internet. Since resources are limited, communication between these sites needs to be carefully orchestrated for the most benefit to the organization. We present a Mission-optimized Overlay Network (MON), a hybrid overlay network archi...
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Generalized Results on Monoids as Memory
We show that some results from the theory of group automata and monoid automata still hold for more general classes of monoids and models. Extending previous work for finite automata over commutative groups, we demonstrate a context-free language that can not be recognized by any rational monoid automaton over a fini...
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Radio Resource Allocation for Multicarrier-Low Density Spreading Multiple Access
Multicarrier-low density spreading multiple access (MC-LDSMA) is a promising multiple access technique that enables near optimum multiuser detection. In MC-LDSMA, each user's symbol spread on a small set of subcarriers, and each subcarrier is shared by multiple users. The unique structure of MC-LDSMA makes the radio ...
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Estimating the Spectral Density of Large Implicit Matrices
Many important problems are characterized by the eigenvalues of a large matrix. For example, the difficulty of many optimization problems, such as those arising from the fitting of large models in statistics and machine learning, can be investigated via the spectrum of the Hessian of the empirical loss function. Netw...
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