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Parametric Inference for Discretely Observed Subordinate Diffusions
Subordinate diffusions are constructed by time changing diffusion processes with an independent Lévy subordinator. This is a rich family of Markovian jump processes which exhibit a variety of jump behavior and have found many applications. This paper studies parametric inference of discretely observed ergodic subordi...
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Adversarially Regularized Graph Autoencoder for Graph Embedding
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the lat...
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Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
We introduce a novel approach to perform first-order optimization with orthogonal and unitary constraints. This approach is based on a parametrization stemming from Lie group theory through the exponential map. The parametrization transforms the constrained optimization problem into an unconstrained one over a Euclid...
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Training Big Random Forests with Little Resources
Without access to large compute clusters, building random forests on large datasets is still a challenging problem. This is, in particular, the case if fully-grown trees are desired. We propose a simple yet effective framework that allows to efficiently construct ensembles of huge trees for hundreds of millions or ev...
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Epitaxy of Advanced Nanowire Quantum Devices
Semiconductor nanowires provide an ideal platform for various low-dimensional quantum devices. In particular, topological phases of matter hosting non-Abelian quasi-particles can emerge when a semiconductor nanowire with strong spin-orbit coupling is brought in contact with a superconductor. To fully exploit the pote...
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Toward universality in degree 2 of the Kricker lift of the Kontsevich integral and the Lescop equivariant invariant
In the setting of finite type invariants for null-homologous knots in rational homology 3-spheres with respect to null Lagrangian-preserving surgeries, there are two candidates to be universal invariants, defined respectively by Kricker and Lescop. In a previous paper, the second author defined maps between spaces of...
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Automatic segmentation of MR brain images with a convolutional neural network
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtai...
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Integrable Structure of Multispecies Zero Range Process
We present a brief review on integrability of multispecies zero range process in one dimension introduced recently. The topics range over stochastic $R$ matrices of quantum affine algebra $U_q (A^{(1)}_n)$, matrix product construction of stationary states for periodic systems, $q$-boson representation of Zamolodchiko...
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A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates
Clinical trial registries can be used to monitor the production of trial evidence and signal when systematic reviews become out of date. However, this use has been limited to date due to the extensive manual review required to search for and screen relevant trial registrations. Our aim was to evaluate a new method th...
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Spectra of Magnetic Operators on the Diamond Lattice Fractal
We adapt the well-known spectral decimation technique for computing spectra of Laplacians on certain symmetric self-similar sets to the case of magnetic Schrodinger operators and work through this method completely for the diamond lattice fractal. This connects results of physicists from the 1980's, who used similar ...
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Arrow calculus for welded and classical links
We develop a calculus for diagrams of knotted objects. We define Arrow presentations, which encode the crossing informations of a diagram into arrows in a way somewhat similar to Gauss diagrams, and more generally w-tree presentations, which can be seen as `higher order Gauss diagrams'. This Arrow calculus is used to...
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Determinacy of Schmidt's Game and Other Intersection Games
Schmidt's game, and other similar intersection games have played an important role in recent years in applications to number theory, dynamics, and Diophantine approximation theory. These games are real games, that is, games in which the players make moves from a complete separable metric space. The determinacy of the...
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Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
Kernel $k$-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear $k$-means clustering algorithm. However, kernel $k$-means clustering is computationally expensive when the non-linear feature map is high-dimensional and there are many input points. Kerne...
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Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data
The classification of time series data is a challenge common to all data-driven fields. However, there is no agreement about which are the most efficient techniques to group unlabeled time-ordered data. This is because a successful classification of time series patterns depends on the goal and the domain of interest,...
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On the bound states of magnetic Laplacians on wedges
This paper is mainly inspired by the conjecture about the existence of bound states for magnetic Neumann Laplacians on planar wedges of any aperture $\phi\in (0,\pi)$. So far, a proof was only obtained for apertures $\phi\lesssim 0.511\pi$. The conviction in the validity of this conjecture for apertures $\phi\gtrsim ...
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Finite-Time Distributed Linear Equation Solver for Minimum $l_1$ Norm Solutions
This paper proposes distributed algorithms for multi-agent networks to achieve a solution in finite time to a linear equation $Ax=b$ where $A$ has full row rank, and with the minimum $l_1$-norm in the underdetermined case (where $A$ has more columns than rows). The underlying network is assumed to be undirected and f...
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High Throughput Probabilistic Shaping with Product Distribution Matching
Product distribution matching (PDM) is proposed to generate target distributions over large alphabets by combining the output of several parallel distribution matchers (DMs) with smaller output alphabets. The parallel architecture of PDM enables low-complexity and high-throughput implementation. PDM is used as a shap...
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Minimax Estimation of Large Precision Matrices with Bandable Cholesky Factor
Last decade witnesses significant methodological and theoretical advances in estimating large precision matrices. In particular, there are scientific applications such as longitudinal data, meteorology and spectroscopy in which the ordering of the variables can be interpreted through a bandable structure on the Chole...
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Machine learning in protein engineering
Machine learning-guided protein engineering is a new paradigm that enables the optimization of complex protein functions. Machine-learning methods use data to predict protein function without requiring a detailed model of the underlying physics or biological pathways. They accelerate protein engineering by learning f...
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Advanced Steel Microstructural Classification by Deep Learning Methods
The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise...
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Categorizing Hirsch Index Variants
Utilizing the Hirsch index h and some of its variants for an exploratory factor analysis we discuss whether one of the most important Hirsch-type indices, namely the g-index comprises information about not only the size of the productive core but also the impact of the papers in the core. We also study the effect of ...
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Why a Population Genetics Framework is Inappropriate for Cultural Evolution
Although Darwinian models are rampant in the social sciences, social scientists do not face the problem that motivated Darwin's theory of natural selection: the problem of explaining how lineages evolve despite that any traits they acquire are regularly discarded at the end of the lifetime of the individuals that acq...
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Is together better? Examining scientific collaborations across multiple authors, institutions, and departments
Collaborations are an integral part of scientific research and publishing. In the past, access to large-scale corpora has limited the ways in which questions about collaborations could be investigated. However, with improvements in data/metadata quality and access, it is possible to explore the idea of research colla...
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A Lichnerowicz estimate for the spectral gap of the sub-Laplacian
For a second order operator on a compact manifold satisfying the strong Hörmander condition, we give a bound for the spectral gap analogous to the Lichnerowicz estimate for the Laplacian of a Riemannian manifold. We consider a wide class of such operators which includes horizontal lifts of the Laplacian on Riemannian...
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Quasiclassical theory of spin dynamics in superfluid $^3$He: kinetic equations in the bulk and spin response of surface Majorana states
We develop a theory based on the formalism of quasiclassical Green's functions to study the spin dynamics in superfluid $^3$He. First, we derive kinetic equations for the spin-dependent distribution function in the bulk superfluid reproducing the results obtained earlier without quasiclassical approximation. Then we ...
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The RBO Dataset of Articulated Objects and Interactions
We present a dataset with models of 14 articulated objects commonly found in human environments and with RGB-D video sequences and wrenches recorded of human interactions with them. The 358 interaction sequences total 67 minutes of human manipulation under varying experimental conditions (type of interaction, lightin...
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Training Generative Adversarial Networks via Primal-Dual Subgradient Methods: A Lagrangian Perspective on GAN
We relate the minimax game of generative adversarial networks (GANs) to finding the saddle points of the Lagrangian function for a convex optimization problem, where the discriminator outputs and the distribution of generator outputs play the roles of primal variables and dual variables, respectively. This formulatio...
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A Detailed Observational Analysis of V1324 Sco, the Most Gamma-Ray Luminous Classical Nova to Date
It has recently been discovered that some, if not all, classical novae emit GeV gamma rays during outburst, but the mechanisms involved in the production of the gamma rays are still not well understood. We present here a comprehensive multi-wavelength dataset---from radio to X-rays---for the most gamma-ray luminous c...
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The California-Kepler Survey. III. A Gap in the Radius Distribution of Small Planets
The size of a planet is an observable property directly connected to the physics of its formation and evolution. We used precise radius measurements from the California-Kepler Survey (CKS) to study the size distribution of 2025 $\textit{Kepler}$ planets in fine detail. We detect a factor of $\geq$2 deficit in the occ...
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Large sets avoiding linear patterns
We prove that for any dimension function $h$ with $h \prec x^d$ and for any countable set of linear patterns, there exists a compact set $E$ with $\mathcal{H}^h(E)>0$ avoiding all the given patterns. We also give several applications and recover results of Keleti, Maga, and Máthé.
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Faster Algorithms for Weighted Recursive State Machines
Pushdown systems (PDSs) and recursive state machines (RSMs), which are linearly equivalent, are standard models for interprocedural analysis. Yet RSMs are more convenient as they (a) explicitly model function calls and returns, and (b) specify many natural parameters for algorithmic analysis, e.g., the number of entr...
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Local Density Approximation for Almost-Bosonic Anyons
We discuss the average-field approximation for a trapped gas of non-interacting anyons in the quasi-bosonic regime. In the homogeneous case, i.e., for a confinement to a bounded region, we prove that the energy in the regime of large statistics parameter, i.e., for "less-bosonic" anyons, is independent of boundary co...
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Chemical dynamics between wells across a time-dependent barrier: Self-similarity in the Lagrangian descriptor and reactive basins
In chemical or physical reaction dynamics, it is essential to distinguish precisely between reactants and products for all time. This task is especially demanding in time-dependent or driven systems because therein the dividing surface (DS) between these states often exhibits a nontrivial time-dependence. The so-call...
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Dust-trapping vortices and a potentially planet-triggered spiral wake in the pre-transitional disk of V1247 Orionis
The radial drift problem constitutes one of the most fundamental problems in planet formation theory, as it predicts particles to drift into the star before they are able to grow to planetesimal size. Dust-trapping vortices have been proposed as a possible solution to this problem, as they might be able to trap parti...
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Weak and smooth solutions for a fractional Yamabe flow: the case of general compact and locally conformally flat manifolds
As a counterpart of the classical Yamabe problem, a fractional Yamabe flow has been introduced by Jin and Xiong (2014) on the sphere. Here we pursue its study in the context of general compact smooth manifolds with positive fractional curvature. First, we prove that the flow is locally well posed in the weak sense on...
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Lipschitz Properties for Deep Convolutional Networks
In this paper we discuss the stability properties of convolutional neural networks. Convolutional neural networks are widely used in machine learning. In classification they are mainly used as feature extractors. Ideally, we expect similar features when the inputs are from the same class. That is, we hope to see a sm...
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Quadrature Compound: An approximating family of distributions
Compound distributions allow construction of a rich set of distributions. Typically they involve an intractable integral. Here we use a quadrature approximation to that integral to define the quadrature compound family. Special care is taken that this approximation is suitable for computation of gradients with respec...
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Centrality in Modular Networks
Identifying influential nodes in a network is a fundamental issue due to its wide applications, such as accelerating information diffusion or halting virus spreading. Many measures based on the network topology have emerged over the years to identify influential nodes such as Betweenness, Closeness, and Eigenvalue ce...
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Yield Trajectory Tracking for Hyperbolic Age-Structured Population Systems
For population systems modeled by age-structured hyperbolic partial differential equations (PDEs) that are bilinear in the input and evolve with a positive-valued infinite-dimensional state, global stabilization of constant yield set points was achieved in prior work. Seasonal demands in biotechnological production p...
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A sixteen-relator presentation of an infinite hyperbolic Kazhdan group
We provide an explicit presentation of an infinite hyperbolic Kazhdan group with $4$ generators and $16$ relators of length at most $73$. That group acts properly and cocompactly on a hyperbolic triangle building of type $(3,4,4)$. We also point out a variation of the construction that yields examples of lattices in ...
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Fast and accurate Bayesian model criticism and conflict diagnostics using R-INLA
Bayesian hierarchical models are increasingly popular for realistic modelling and analysis of complex data. This trend is accompanied by the need for flexible, general, and computationally efficient methods for model criticism and conflict detection. Usually, a Bayesian hierarchical model incorporates a grouping of t...
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Optimizing deep video representation to match brain activity
The comparison of observed brain activity with the statistics generated by artificial intelligence systems is useful to probe brain functional organization under ecological conditions. Here we study fMRI activity in ten subjects watching color natural movies and compute deep representations of these movies with an ar...
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Non-Abelian Fermionization and Fractional Quantum Hall Transitions
There has been a recent surge of interest in dualities relating theories of Chern-Simons gauge fields coupled to either bosons or fermions within the condensed matter community, particularly in the context of topological insulators and the half-filled Landau level. Here, we study the application of one such duality t...
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Periodic solutions and regularization of a Kepler problem with time-dependent perturbation
We consider a Kepler problem in dimension two or three, with a time-dependent $T$-periodic perturbation. We prove that for any prescribed positive integer $N$, there exist at least $N$ periodic solutions (with period $T$) as long as the perturbation is small enough. Here the solutions are understood in a general sens...
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Ultra-wide-band slow light in photonic crystal coupled-cavity waveguides
Slow light propagation in structured materials is a highly promising approach for realizing on-chip integrated photonic devices based on enhanced optical nonlinearities. One of the most successful research avenues consists in engineering the band dispersion of light-guiding photonic crystal (PC) structures. The prima...
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Sketch Layer Separation in Multi-Spectral Historical Document Images
High-resolution imaging has delivered new prospects for detecting the material composition and structure of cultural treasures. Despite the various techniques for analysis, a significant diagnostic gap remained in the range of available research capabilities for works on paper. Old master drawings were mostly compose...
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Comment on "Spatial optical solitons in highly nonlocal media" and related papers
In a recent paper [A. Alberucci, C. Jisha, N. Smyth, and G. Assanto, Phys. Rev. A 91, 013841 (2015)], Alberucci et al. have studied the propagation of bright spatial solitary waves in highly nonlocal media. We find that the main results in that and related papers, concerning soliton shape and dynamics, based on the a...
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Local Descriptor for Robust Place Recognition using LiDAR Intensity
Place recognition is a challenging problem in mobile robotics, especially in unstructured environments or under viewpoint and illumination changes. Most LiDAR-based methods rely on geometrical features to overcome such challenges, as generally scene geometry is invariant to these changes, but tend to affect camera-ba...
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Assessing the performance of self-consistent hybrid functional for band gap calculation in oxide semiconductors
In this paper we assess the predictive power of the self-consistent hybrid functional scPBE0 in calculating the band gap of oxide semiconductors. The computational procedure is based on the self-consistent evaluation of the mixing parameter $\alpha$ by means of an iterative calculation of the static dielectric consta...
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Local Synchronization of Sampled-Data Systems on Lie Groups
We present a smooth distributed nonlinear control law for local synchronization of identical driftless kinematic agents on a Cartesian product of matrix Lie groups with a connected communication graph. If the agents are initialized sufficiently close to one another, then synchronization is achieved exponentially fast...
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On the orbits that generate the X-shape in the Milky Way bulge
The Milky Way bulge shows a box/peanut or X-shaped bulge (hereafter BP/X) when viewed in infrared or microwave bands. We examine orbits in an N-body model of a barred disk galaxy that is scaled to match the kinematics of the Milky Way (MW) bulge. We generate maps of projected stellar surface density, unsharp masked i...
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How Peer Effects Influence Energy Consumption
This paper analyzes the impact of peer effects on electricity consumption of a network of rational, utility-maximizing users. Users derive utility from consuming electricity as well as consuming less energy than their neighbors. However, a disutility is incurred for consuming more than their neighbors. To maximize th...
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Physical Origins of Gas Motions in Galaxy Cluster Cores: Interpreting Hitomi Observations of the Perseus Cluster
The Hitomi X-ray satellite has provided the first direct measurements of the plasma velocity dispersion in a galaxy cluster. It finds a relatively "quiescent" gas with a line-of-sight velocity dispersion ~ 160 km/s, at 30 kpc to 60 kpc from the cluster center. This is surprising given the presence of jets and X-ray c...
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Variational Approaches for Auto-Encoding Generative Adversarial Networks
Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given by an auto-encoder. Such models aim to prevent mode collapse in the learned generative model by ensuring that it is grounded in all the a...
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Verification of the anecdote about Edwin Hubble and the Nobel Prize
Edwin Powel Hubble is regarded as one of the most important astronomers of 20th century. In despite of his great contributions to the field of astronomy, he never received the Nobel Prize because astronomy was not considered as the field of the Nobel Prize in Physics at that era. There is an anecdote about the relati...
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LSTM Fully Convolutional Networks for Time Series Classification
Fully convolutional neural networks (FCN) have been shown to achieve state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Ou...
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Flat $F$-manifolds, Miura invariants and integrable systems of conservation laws
We extend some of the results proved for scalar equations in [3,4], to the case of systems of integrable conservation laws. In particular, for such systems we prove that the eigenvalues of a matrix obtained from the quasilinear part of the system are invariants under Miura transformations and we show how these invari...
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Analysis and Design of Cost-Effective, High-Throughput LDPC Decoders
This paper introduces a new approach to cost-effective, high-throughput hardware designs for Low Density Parity Check (LDPC) decoders. The proposed approach, called Non-Surjective Finite Alphabet Iterative Decoders (NS-FAIDs), exploits the robustness of message-passing LDPC decoders to inaccuracies in the calculation...
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TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible building blocks for probabilistic computation. Distributions provide fast, numerically stable met...
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A generalised Davydov-Scott model for polarons in linear peptide chains
We present a one-parameter family of mathematical models describing the dynamics of polarons in linear periodic structures such as polypeptides. By tuning the parameter, we are able to recover the Davydov and the Scott models. We describe the physical significance of this parameter. In the continuum limit, we derive ...
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Wide Binaries in Tycho-{\it Gaia}: Search Method and the Distribution of Orbital Separations
We mine the Tycho-{\it Gaia} astrometric solution (TGAS) catalog for wide stellar binaries by matching positions, proper motions, and astrometric parallaxes. We separate genuine binaries from unassociated stellar pairs through a Bayesian formulation that includes correlated uncertainties in the proper motions and par...
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Classification on Large Networks: A Quantitative Bound via Motifs and Graphons
When each data point is a large graph, graph statistics such as densities of certain subgraphs (motifs) can be used as feature vectors for machine learning. While intuitive, motif counts are expensive to compute and difficult to work with theoretically. Via graphon theory, we give an explicit quantitative bound for t...
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Predicting Future Machine Failure from Machine State Using Logistic Regression
Accurately predicting machine failures in advance can decrease maintenance cost and help allocate maintenance resources more efficiently. Logistic regression was applied to predict machine state 24 hours in the future given the current machine state.
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Design and Optimisation of the FlyFast Front-end for Attribute-based Coordination
Collective Adaptive Systems (CAS) consist of a large number of interacting objects. The design of such systems requires scalable analysis tools and methods, which have necessarily to rely on some form of approximation of the system's actual behaviour. Promising techniques are those based on mean-field approximation. ...
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Distributed rank-1 dictionary learning: Towards fast and scalable solutions for fMRI big data analytics
The use of functional brain imaging for research and diagnosis has benefitted greatly from the recent advancements in neuroimaging technologies, as well as the explosive growth in size and availability of fMRI data. While it has been shown in literature that using multiple and large scale fMRI datasets can improve re...
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HARPO: 1.7 - 74 MeV gamma-ray beam validation of a high angular resolutio n, high linear polarisation dilution, gas time projection chamber telescope and polarimeter
A presentation at the SciNeGHE conference of the past achievements, of the present activities and of the perspectives for the future of the HARPO project, the development of a time projection chamber as a high-performance gamma-ray telescope and linear polarimeter in the e+e- pair creation regime.
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One-to-one composant mappings of $[0,\infty)$ and $(-\infty,\infty)$
Knaster continua and solenoids are well-known examples of indecomposable continua whose composants (maximal arcwise-connected subsets) are one-to-one images of lines. We show that essentially all non-trivial one-to-one composant images of (half-)lines are indecomposable. And if $f$ is a one-to-one mapping of $[0,\inf...
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Valued fields, Metastable groups
We introduce a class of theories called metastable, including the theory of algebraically closed valued fields (ACVF) as a motivating example. The key local notion is that of definable types dominated by their stable part. A theory is metastable (over a sort $\Gamma$) if every type over a sufficiently rich base struc...
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Radio Galaxy Zoo: Cosmological Alignment of Radio Sources
We study the mutual alignment of radio sources within two surveys, FIRST and TGSS. This is done by producing two position angle catalogues containing the preferential directions of respectively $30\,059$ and $11\,674$ extended sources distributed over more than $7\,000$ and $17\,000$ square degrees. The identificatio...
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Non-iterative Label Propagation in Optimal Leading Forest
Graph based semi-supervised learning (GSSL) has intuitive representation and can be improved by exploiting the matrix calculation. However, it has to perform iterative optimization to achieve a preset objective, which usually leads to low efficiency. Another inconvenience lying in GSSL is that when new data come, the...
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PeerHunter: Detecting Peer-to-Peer Botnets through Community Behavior Analysis
Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this paper...
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An X-ray/SDSS sample (II): outflowing gas plasma properties
Galaxy-scale outflows are nowadays observed in many active galactic nuclei (AGNs); however, their characterisation in terms of (multi-) phase nature, amount of flowing material, effects on the host galaxy, is still unsettled. In particular, ionized gas mass outflow rate and related energetics are still affected by ma...
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ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern...
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Existence of Noise Induced Order, a Computer Aided Proof
We prove, by a computer aided proof, the existence of noise induced order in the model of chaotic chemical reactions where it was first discovered numerically by Matsumoto and Tsuda in 1983. We prove that in this random dynamical system the increase in amplitude of the noise causes the Lyapunov exponent to decrease f...
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Residual Unfairness in Fair Machine Learning from Prejudiced Data
Recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historical prejudices may lead to unfair decision policies. We connect these lines of work and study the residual unfairness that arises when a fai...
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Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an...
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Metriplectic formalism: friction and much more
The metriplectic formalism couples Poisson brackets of the Hamiltonian description with metric brackets for describing systems with both Hamiltonian and dissipative components. The construction builds in asymptotic convergence to a preselected equilibrium state. Phenomena such as friction, electric resistivity, therm...
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Hierarchical Video Understanding
We introduce a hierarchical architecture for video understanding that exploits the structure of real world actions by capturing targets at different levels of granularity. We design the model such that it first learns simpler coarse-grained tasks, and then moves on to learn more fine-grained targets. The model is tra...
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When Hashes Met Wedges: A Distributed Algorithm for Finding High Similarity Vectors
Finding similar user pairs is a fundamental task in social networks, with numerous applications in ranking and personalization tasks such as link prediction and tie strength detection. A common manifestation of user similarity is based upon network structure: each user is represented by a vector that represents the u...
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Hysteretic vortex matching effects in high-$T_c$ superconductors with nanoscale periodic pinning landscapes fabricated by He ion beam projection technique
Square arrays of sub-micrometer columnar defects in thin YBa$_{2}$Cu$_{3}$O$_{7-\delta}$ (YBCO) films with spacings down to 300 nm have been fabricated by a He ion beam projection technique. Pronounced peaks in the critical current and corresponding minima in the resistance demonstrate the commensurate arrangement of...
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Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping
Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next generation collection initiatives of remotely sensed Light Detection and Ranging (LiDAR) data are specificall...
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Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction
Recently, many studies have demonstrated deep neural network (DNN) classifiers can be fooled by the adversarial example, which is crafted via introducing some perturbations into an original sample. Accordingly, some powerful defense techniques were proposed. However, existing defense techniques often require modifyin...
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Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion Lexicon
Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored more nuanced ways to capture emotions that go beyond polarity. For these method...
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Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
We address the statistical and optimization impacts of the classical sketch and Hessian sketch used to approximately solve the Matrix Ridge Regression (MRR) problem. Prior research has quantified the effects of classical sketch on the strictly simpler least squares regression (LSR) problem. We establish that classica...
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Ballistic magnon heat conduction and possible Poiseuille flow in the helimagnetic insulator Cu$_2$OSeO$_3$
We report on the observation of magnon thermal conductivity $\kappa_m\sim$ 70 W/mK near 5 K in the helimagnetic insulator Cu$_2$OSeO$_3$, exceeding that measured in any other ferromagnet by almost two orders of magnitude. Ballistic, boundary-limited transport for both magnons and phonons is established below 1 K, and...
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On Alzer's inequality
Extensions and generalizations of Alzer's inequality; which is of Wirtinger type are proved. As applications, sharp trapezoid type inequality and sharp bound for the geometric mean are deduced.
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Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs
We present Oncilla robot, a novel mobile, quadruped legged locomotion machine. This large-cat sized, 5.1 robot is one of a kind of a recent, bioinspired legged robot class designed with the capability of model-free locomotion control. Animal legged locomotion in rough terrain is clearly shaped by sensor feedback syst...
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A generative model for sparse, evolving digraphs
Generating graphs that are similar to real ones is an open problem, while the similarity notion is quite elusive and hard to formalize. In this paper, we focus on sparse digraphs and propose SDG, an algorithm that aims at generating graphs similar to real ones. Since real graphs are evolving and this evolution is imp...
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Penalized Interaction Estimation for Ultrahigh Dimensional Quadratic Regression
Quadratic regression goes beyond the linear model by simultaneously including main effects and interactions between the covariates. The problem of interaction estimation in high dimensional quadratic regression has received extensive attention in the past decade. In this article we introduce a novel method which allo...
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4D limit of melting crystal model and its integrable structure
This paper addresses the problems of quantum spectral curves and 4D limit for the melting crystal model of 5D SUSY $U(1)$ Yang-Mills theory on $\mathbb{R}^4\times S^1$. The partition function $Z(\mathbf{t})$ deformed by an infinite number of external potentials is a tau function of the KP hierarchy with respect to th...
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On constraints and dividing in ternary homogeneous structures
Let M be ternary, homogeneous and simple. We prove that if M is finitely constrained, then it is supersimple with finite SU-rank and dependence is $k$-trivial for some $k < \omega$ and for finite sets of real elements. Now suppose that, in addition, M is supersimple with SU-rank 1. If M is finitely constrained then a...
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Changes in lipid membranes may trigger amyloid toxicity in Alzheimer's disease
Amyloid beta peptides (A\b{eta}), implicated in Alzheimers disease (AD), interact with the cellular membrane and induce amyloid toxicity. The composition of cellular membranes changes in aging and AD. We designed multi component lipid models to mimic healthy and diseased states of the neuronal membrane. Using atomic ...
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Transcendency Degree One Function Fields Over a Finite Field with Many Automorphisms
Let $\mathbb{K}$ be the algebraic closure of a finite field $\mathbb{F}_q$ of odd characteristic $p$. For a positive integer $m$ prime to $p$, let $F=\mathbb{K}(x,y)$ be the transcendency degree $1$ function field defined by $y^q+y=x^m+x^{-m}$. Let $t=x^{m(q-1)}$ and $H=\mathbb{K}(t)$. The extension $F|H$ is a non-Ga...
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The phase transitions between $Z_n\times Z_n$ bosonic topological phases in 1+1 D, and a constraint on the central charge for the critical points between bosonic symmetry protected topological phases
The study of continuous phase transitions triggered by spontaneous symmetry breaking has brought revolutionary ideas to physics. Recently, through the discovery of symmetry protected topological phases, it is realized that continuous quantum phase transition can also occur between states with the same symmetry but di...
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Universal Reinforcement Learning Algorithms: Survey and Experiments
Many state-of-the-art reinforcement learning (RL) algorithms typically assume that the environment is an ergodic Markov Decision Process (MDP). In contrast, the field of universal reinforcement learning (URL) is concerned with algorithms that make as few assumptions as possible about the environment. The universal Ba...
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Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks
Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on ...
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Full replica symmetry breaking in p-spin-glass-like systems
It is shown that continuously changing the effective number of interacting particles in p-spin-glass-like model allows to describe the transition from the full replica symmetry breaking glass solution to stable first replica symmetry breaking glass solution in the case of non-reflective symmetry diagonal operators us...
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Formation and condensation of excitonic bound states in the generalized Falicov-Kimball model
The density-matrix-renormalization-group (DMRG) method and the Hartree-Fock (HF) approximation with the charge-density-wave (CDW) instability are used to study a formation and condensation of excitonic bound states in the generalized Falicov-Kimball model. In particular, we examine effects of various factors, like th...
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Tunable Emergent Heterostructures in a Prototypical Correlated Metal
At the interface between two distinct materials desirable properties, such as superconductivity, can be greatly enhanced, or entirely new functionalities may emerge. Similar to in artificially engineered heterostructures, clean functional interfaces alternatively exist in electronically textured bulk materials. Elect...
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Weak Label Supervision for Monaural Source Separation Using Non-negative Denoising Variational Autoencoders
Deep learning models are very effective in source separation when there are large amounts of labeled data available. However it is not always possible to have carefully labeled datasets. In this paper, we propose a weak supervision method that only uses class information rather than source signals for learning to sep...
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