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Rectangular Photonic Crystal Nanobeam Cavities in Bulk Diamond
We demonstrate the fabrication of photonic crystal nanobeam cavities with rectangular cross section into bulk diamond. In simulation, these cavities have an unloaded quality factor (Q) of over 1 million. Measured cavity resonances show fundamental modes with spectrometer-limited quality factors larger than 14,000 wit...
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Accelerated Primal-Dual Proximal Block Coordinate Updating Methods for Constrained Convex Optimization
Block Coordinate Update (BCU) methods enjoy low per-update computational complexity because every time only one or a few block variables would need to be updated among possibly a large number of blocks. They are also easily parallelized and thus have been particularly popular for solving problems involving large-scal...
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Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields
Overhead depth map measurements capture sufficient amount of information to enable human experts to track pedestrians accurately. However, fully automating this process using image analysis algorithms can be challenging. Even though hand-crafted image analysis algorithms are successful in many common cases, they fail...
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Absence of magnetic long range order in Ba$_3$ZnRu$_2$O$_9$: A spin-liquid candidate in the $S=3/2$ dimer lattice
We have discovered a novel candidate for a spin liquid state in a ruthenium oxide composed of dimers of $S = $ 3/2 spins of Ru$^{5+}$,Ba$_3$ZnRu$_2$O$_9$. This compound lacks a long range order down to 37 mK, which is a temperature 5000-times lower than the magnetic interaction scale of around 200 K. Partial substitu...
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Deforming Representations of SL(2,R)
The spherical principal series representations $\pi(\nu)$ of SL(2,$\mathbb R$) is a family of infinite dimensional representations parametrized by $\nu\in\mathbb C$. The representation $\pi(\nu)$ is irreducible unless $\nu$ is an odd integer, in which case it is indecomposable. We find a new continuous family of repr...
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3D Reconstruction & Assessment Framework based on affordable 2D Lidar
Lidar is extensively used in the industry and mass-market. Due to its measurement accuracy and insensitivity to illumination compared to cameras, It is applied onto a broad range of applications, like geodetic engineering, self driving cars or virtual reality. But the 3D Lidar with multi-beam is very expensive, and t...
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Probabilistic Relational Reasoning via Metrics
The Fuzz programming language [Reed and Pierce, 2010] uses an elegant linear type system combined with a monad-like type to express and reason about probabilistic sensitivity properties, most notably $\epsilon$-differential privacy. We show how to extend Fuzz to capture more general relational properties of probabili...
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Upper bounds for constant slope $p$-adic families of modular forms
We study $p$-adic families of eigenforms for which the $p$-th Hecke eigenvalue $a_p$ has constant $p$-adic valuation ("constant slope families"). We prove two separate upper bounds for the size of such families. The first is in terms of the logarithmic derivative of $a_p$ while the second depends only on the slope of...
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Size dependence of the surface tension of a free surface of an isotropic fluid
We report on the size dependence of the surface tension of a free surface of an isotropic fluid. The size dependence of the surface tension is evaluated based on the Gibbs-Tolman-Koenig-Buff equation for positive and negative values of curvatures and the Tolman lengths. For all combinations of positive and negative s...
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Bayesian Belief Updating of Spatiotemporal Seizure Dynamics
Epileptic seizure activity shows complicated dynamics in both space and time. To understand the evolution and propagation of seizures spatially extended sets of data need to be analysed. We have previously described an efficient filtering scheme using variational Laplace that can be used in the Dynamic Causal Modelli...
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Split, Send, Reassemble: A Formal Specification of a CAN Bus Protocol Stack
We present a formal model for a fragmentation and a reassembly protocol running on top of the standardised CAN bus, which is widely used in automotive and aerospace applications. Although the CAN bus comes with an in-built mechanism for prioritisation, we argue that this is not sufficient and provide another protocol...
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An extinction free AGN selection by 18-band SED fitting in mid-infrared in the AKARI NEP deep field
We have developed an efficient Active Galactic Nucleus (AGN) selection method using 18-band Spectral Energy Distribution (SED) fitting in mid-infrared (mid-IR). AGNs are often obscured by gas and dust, and those obscured AGNs tend to be missed in optical, UV and soft X-ray observations. Mid-IR light can help us to re...
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Spatial point processes intensity estimation with a diverging number of covariates
Feature selection procedures for spatial point processes parametric intensity estimation have been recently developed since more and more applications involve a large number of covariates. In this paper, we investigate the setting where the number of covariates diverges as the domain of observation increases. In part...
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A novel methodology on distributed representations of proteins using their interacting ligands
The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand ba...
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Distributed Robust Set-Invariance for Interconnected Linear Systems
We introduce a class of distributed control policies for networks of discrete-time linear systems with polytopic additive disturbances. The objective is to restrict the network-level state and controls to user-specified polyhedral sets for all times. This problem arises in many safety-critical applications. We consid...
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Liouville's theorem and comparison results for solutions of degenerate elliptic equations in exterior domains
A version of Liouville's theorem is proved for solutions of some degenerate elliptic equations defined in $\mathbb{R}^n\backslash K$, where $K$ is a compact set, provided the structure of this equation and the dimension $n$ are related. This result is a correction of a previous one established by Serrin, since some a...
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An Optimal Control Problem for the Steady Nonhomogeneous Asymmetric Fluids
We study an optimal boundary control problem for the two-dimensional stationary micropolar fluids system with variable density. We control the system by considering boundary controls, for the velocity vector and angular velocity of rotation of particles, on parts of the boundary of the flow domain. On the remaining p...
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A thermodynamic view of dusty protoplanetary disks
Small solids embedded in gaseous protoplanetary disks are subject to strong dust-gas friction. Consequently, tightly-coupled dust particles almost follow the gas flow. This near conservation of dust-to-gas ratio along streamlines is analogous to the near conservation of entropy along flows of (dust-free) gas with wea...
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Stable determination of a Lamé coefficient by one internal measurement of displacement
In this paper we show that the shear modulus $\mu$ of an isotropic elastic body can be stably recovered by the knowledge of one single displacement field whose boundary data can be assigned independently of the unknown elasticity tensor.
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Mordell-Weil Groups of Linear Systems and the Hitchin Fibration
In this paper, we study rational sections of the relative Picard scheme of a linear system on a smooth projective variety. We prove that if the linear system is basepoint-free and the locus of non-integral divisors has codimension at least two, then all rational sections of the relative Picard scheme come from restri...
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Subgradients of Minimal Time Functions without Calmness
In recent years there has been great interest in variational analysis of a class of nonsmooth functions called the minimal time function. In this paper we continue this line of research by providing new results on generalized differentiation of this class of functions, relaxing assumptions imposed on the functions an...
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Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data
Alzheimer's disease is a major cause of dementia. Its diagnosis requires accurate biomarkers that are sensitive to disease stages. In this respect, we regard probabilistic classification as a method of designing a probabilistic biomarker for disease staging. Probabilistic biomarkers naturally support the interpretati...
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Optimal bounds and extremal trajectories for time averages in nonlinear dynamical systems
For any quantity of interest in a system governed by ordinary differential equations, it is natural to seek the largest (or smallest) long-time average among solution trajectories, as well as the extremal trajectories themselves. Upper bounds on time averages can be proved a priori using auxiliary functions, the opti...
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Unsupervised Learning with Stein's Unbiased Risk Estimator
Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this work, we revisit a classical idea: Stein's Unbiased Risk Estimator (SURE). We show that, in the context of image recovery, SURE and its gen...
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Cellular function given parametric variation: excitability in the Hodgkin-Huxley model
How is reliable physiological function maintained in cells despite considerable variability in the values of key parameters of multiple interacting processes that govern that function? Here we use the classic Hodgkin-Huxley formulation of the squid giant axon action potential to propose a possible approach to this pr...
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P4K: A Formal Semantics of P4 and Applications
Programmable packet processors and P4 as a programming language for such devices have gained significant interest, because their flexibility enables rapid development of a diverse set of applications that work at line rate. However, this flexibility, combined with the complexity of devices and networks, increases the...
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Global aspects of polarization optics and coset space geometry
We use group theoretic ideas and coset space methods to deal with problems in polarization optics of a global nature. These include the possibility of a globally smooth phase convention for electric fields for all points on the Poincaré sphere, and a similar possibility of real or complex bases of transverse electric...
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Nonlinear Large Deviations: Beyond the Hypercube
We present a framework to calculate large deviations for nonlinear functions of independent random variables supported on compact sets in Banach spaces, by extending the result in Chatterjee and Dembo [6]. Previous research on nonlinear large deviations has only focused on random variables supported on $\{-1,+1\}^{n}...
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Force and torque of a string on a pulley
Every university introductory physics course considers the problem of Atwood's machine taking into account the mass of the pulley. In the usual treatment the tensions at the two ends of the string are offhandedly taken to act on the pulley and be responsible for its rotation. However such a free-body diagram of the f...
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Imagining Probabilistic Belief Change as Imaging (Technical Report)
Imaging is a form of probabilistic belief change which could be employed for both revision and update. In this paper, we propose a new framework for probabilistic belief change based on imaging, called Expected Distance Imaging (EDI). EDI is sufficiently general to define Bayesian conditioning and other forms of imag...
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Sparse Rational Function Interpolation with Finitely Many Values for the Coefficients
In this paper, we give new sparse interpolation algorithms for black box univariate and multivariate rational functions h=f/g whose coefficients are integers with an upper bound. The main idea is as follows: choose a proper integer beta and let h(beta) = a/b with gcd(a,b)=1. Then f and g can be computed by solving th...
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Research Opportunities and Visions for Smart and Pervasive Health
Improving the health of the nation's population and increasing the capabilities of the US healthcare system to support diagnosis, treatment, and prevention of disease is a critical national and societal priority. In the past decade, tremendous advances in expanding computing capabilities--sensors, data analytics, net...
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Training L1-Regularized Models with Orthant-Wise Passive Descent Algorithms
The $L_1$-regularized models are widely used for sparse regression or classification tasks. In this paper, we propose the orthant-wise passive descent algorithm (OPDA) for optimizing $L_1$-regularized models, as an improved substitute of proximal algorithms, which are the standard tools for optimizing the models nowa...
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Zero-Delay Source-Channel Coding with a One-Bit ADC Front End and Correlated Side Information at the Receiver
Zero-delay transmission of a Gaussian source over an additive white Gaussian noise (AWGN) channel is considered with a one-bit analog-to-digital converter (ADC) front end and a correlated side information at the receiver. The design of the optimal encoder and decoder is studied for two performance criteria, namely, t...
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Non-Oscillatory Pattern Learning for Non-Stationary Signals
This paper proposes a novel non-oscillatory pattern (NOP) learning scheme for several oscillatory data analysis problems including signal decomposition, super-resolution, and signal sub-sampling. To the best of our knowledge, the proposed NOP is the first algorithm for these problems with fully non-stationary oscilla...
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Analysis of Set-Valued Stochastic Approximations: Applications to Noisy Approximate Value and Fixed point Iterations
The main aim of this paper is the development of Lyapunov function based sufficient conditions for stability (almost sure boundedness) and convergence of stochastic approximation algorithms (SAAs) with set-valued mean-fields, a class of model-free algorithms that have become important in recent times. In this paper w...
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Predicting Surgery Duration with Neural Heteroscedastic Regression
Scheduling surgeries is a challenging task due to the fundamental uncertainty of the clinical environment, as well as the risks and costs associated with under- and over-booking. We investigate neural regression algorithms to estimate the parameters of surgery case durations, focusing on the issue of heteroscedastici...
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Multichannel End-to-end Speech Recognition
The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology. Using an attention mechanism in a recurrent encoder-decoder architecture solves the dynamic time alignment problem, allowing joint end-to-end trai...
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Universal Rules for Fooling Deep Neural Networks based Text Classification
Recently, deep learning based natural language processing techniques are being extensively used to deal with spam mail, censorship evaluation in social networks, among others. However, there is only a couple of works evaluating the vulnerabilities of such deep neural networks. Here, we go beyond attacks to investigat...
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Synthetic Observations of 21cm HI Line Profiles from Inhomogeneous Turbulent Interstellar HI Gas with Magnetic Field
We carried out synthetic observations of interstellar atomic hydrogen at 21cm wavelength by utilizing the magneto-hydrodynamical numerical simulations of the inhomogeneous turbulent interstellar medium (ISM) Inoue and Inutsuka (2012). The cold neutral medium (CNM) shows significantly clumpy distribution with a small ...
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Clarifying Trust in Social Internet of Things
A social approach can be exploited for the Internet of Things (IoT) to manage a large number of connected objects. These objects operate as autonomous agents to request and provide information and services to users. Establishing trustworthy relationships among the objects greatly improves the effectiveness of node in...
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The Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis
In this paper, we introduce a powerful technique, Leave-One-Out, to the analysis of low-rank matrix completion problems. Using this technique, we develop a general approach for obtaining fine-grained, entry-wise bounds on iterative stochastic procedures. We demonstrate the power of this approach in analyzing two of t...
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Weyl calculus with respect to the Gaussian measure and restricted $L^p$-$L^q$ boundedness of the Ornstein-Uhlenbeck semigroup in complex time
In this paper, we introduce a Weyl functional calculus $a \mapsto a(Q,P)$ for the position and momentum operators $Q$ and $P$ associated with the Ornstein-Uhlenbeck operator $ L = -\Delta + x\cdot \nabla$, and give a simple criterion for restricted $L^p$-$L^q$ boundedness of operators in this functional calculus. The...
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Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning
Recent model-free reinforcement learning algorithms have proposed incorporating learned dynamics models as a source of additional data with the intention of reducing sample complexity. Such methods hold the promise of incorporating imagined data coupled with a notion of model uncertainty to accelerate the learning of...
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A new method of joint nonparametric estimation of probability density and its support
In this paper we propose a new method of joint nonparametric estimation of probability density and its support. As is well known, nonparametric kernel density estimator has "boundary bias problem" when the support of the population density is not the whole real line. To avoid the unknown boundary effects, our estimat...
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An OpenCL(TM) Deep Learning Accelerator on Arria 10
Convolutional neural nets (CNNs) have become a practical means to perform vision tasks, particularly in the area of image classification. FPGAs are well known to be able to perform convolutions efficiently, however, most recent efforts to run CNNs on FPGAs have shown limited advantages over other devices such as GPUs...
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Two-channel conduction in YbPtBi
We investigated transport, magnetotransport, and broadband optical properties of the half-Heusler compound YbPtBi. Hall measurements evidence two types of charge carriers: highly mobile electrons with a temperature-dependent concentration and low-mobile holes; their concentration stays almost constant within the inve...
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Cosmological constraints on scalar-tensor gravity and the variation of the gravitational constant
We present cosmological constraints on the scalar-tensor theory of gravity by analyzing the angular power spectrum data of the cosmic microwave background obtained from the Planck 2015 results together with the baryon acoustic oscillations (BAO) data. We find that the inclusion of the BAO data improves the constraint...
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Extending Bayesian structural time-series estimates of causal impact to many-household conservation initiatives
Government agencies offer economic incentives to citizens for conservation actions, such as rebates for installing efficient appliances and compensation for modifications to homes. The intention of these conservation actions is frequently to reduce the consumption of a utility. Measuring the conservation impact of in...
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Absorption probabilities for Gaussian polytopes, and regular spherical simplices
The Gaussian polytope $\mathcal P_{n,d}$ is the convex hull of $n$ independent standard normally distributed points in $\mathbb R^d$. We derive explicit expressions for the probability that $\mathcal P_{n,d}$ contains a fixed point $x\in\mathbb R^d$ as a function of the Euclidean norm of $x$, and the probability that...
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Modeling news spread as an SIR process over temporal networks
News spread in internet media outlets can be seen as a contagious process generating temporal networks representing the influence between published articles. In this article we propose a methodology based on the application of natural language analysis of the articles to reconstruct the spread network. From the recon...
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Price dynamics on a risk-averse market with asymmetric information
A market with asymmetric information can be viewed as a repeated exchange game between the informed sector and the uninformed one. In a market with risk-neutral agents, De Meyer [2010] proves that the price process should be a particular kind of Brownian martingale called CMMV. This type of dynamics is due to the str...
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Learning to Generalize: Meta-Learning for Domain Generalization
Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. {Domain Generalization} (DG) techniques attempt to alleviate this issue by producing models which by design generalize well to novel testing domains. We pr...
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Divide and Conquer: Recovering Contextual Information of Behaviors in Android Apps around Limited-quantity Audit Logs
Android users are now suffering serious threats from various unwanted apps. The analysis of apps' audit logs is one of the critical methods for some device manufactures to unveil the underlying malice of apps. We propose and implement DroidHolmes, a novel system that recovers contextual information around limited-qua...
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Homotopy Theoretic Classification of Symmetry Protected Phases
We classify a number of symmetry protected phases using Freed-Hopkins' homotopy theoretic classification. Along the way we compute the low-dimensional homotopy groups of a number of novel cobordism spectra.
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Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation meth...
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Self-adjoint approximations of degenerate Schrodinger operator
The problem of construction a quantum mechanical evolution for the Schrodinger equation with a degenerate Hamiltonian which is a symmetric operator that does not have self-adjoint extensions is considered. Self-adjoint regularization of the Hamiltonian does not lead to a preserving probability limiting evolution for ...
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Inelastic deformation during sill and laccolith emplacement: Insights from an analytic elastoplastic model
Numerous geological observations evidence that inelastic deformation occurs during sills and laccoliths emplacement. However, most models of sill and laccolith emplacement neglect inelastic processes by assuming purely elastic deformation of the host rock. This assumption has never been tested, so that the role of in...
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On the magnetic shield for a Vlasov-Poisson plasma
We study the screening of a bounded body $\Gamma$ against the effect of a wind of charged particles, by means of a shield produced by a magnetic field which becomes infinite on the border of $\Gamma$. The charged wind is modeled by a Vlasov-Poisson plasma, the bounded body by a torus, and the external magnetic field ...
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Judicious partitions of uniform hypergraphs
The vertices of any graph with $m$ edges may be partitioned into two parts so that each part meets at least $\frac{2m}{3}$ edges. Bollobás and Thomason conjectured that the vertices of any $r$-uniform hypergraph with $m$ edges may likewise be partitioned into $r$ classes such that each part meets at least $\frac{r}{2...
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Full and maximal squashed flat antichains of minimum weight
A full squashed flat antichain (FSFA) in the Boolean lattice $B_n$ is a family $\mathcal{A}\cup\mathcal{B}$ of subsets of $[n]=\{1,2,\dots,n\}$ such that, for some $k\in [n]$ and $0\le m\le \binom n k$, $\mathcal{A}$ is the family of the first $m$ $k$-sets in squashed (reverse-lexicographic) order and $\mathcal{B}$ c...
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On the phantom barrier crossing and the bounds on the speed of sound in non-minimal derivative coupling theories
In this paper we investigate the so called "phantom barrier crossing" issue in a cosmological model based in the scalar-tensor theory with non-minimal derivative coupling to the Einstein's tensor. Special attention will be paid to the physical bounds on the squared sound speed. The numeric results are geometrically i...
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Detecting tropical defects of polynomial equations
We introduce the notion of tropical defects, certificates that a system of polynomial equations is not a tropical basis, and provide algorithms for finding them around affine spaces of complementary dimension to the zero set. We use these techniques to solve open problems regarding del Pezzo surfaces of degree 3 and ...
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Wasserstein Variational Inference
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and the Wasserstein distance as special cases. The gradients of the Wasserstein v...
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Comptage probabiliste sur la frontière de Furstenberg
Let $G$ be a real linear semisimple algebraic group without compact factors and $\Gamma$ a Zariski dense subgroup of $G$. In this paper, we use a probabilistic counting in order to study the asymptotic properties of $\Gamma$ acting on the Furstenberg boundary of $G$. First, we show that the $K$ components of the elem...
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Bures-Hall Ensemble: Spectral Densities and Average Entropies
We consider an ensemble of random density matrices distributed according to the Bures measure. The corresponding joint probability density of eigenvalues is described by the fixed trace Bures-Hall ensemble of random matrices which, in turn, is related to its unrestricted trace counterpart via a Laplace transform. We ...
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Electron and Nucleon Localization Functions of Oganesson: Approaching the Thomas-Fermi Limit
Fermion localization functions are used to discuss electronic and nucleonic shell structure effects in the superheavy element oganesson, the heaviest element discovered to date. Spin-orbit splitting in the $7p$ electronic shell becomes so large ($\sim$ 10 eV) that Og is expected to show uniform-gas-like behavior in t...
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Constrained Bayesian Optimization with Noisy Experiments
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high variance, resulting in potentially large measurement error. Bayesian optimization is a promising technique for efficiently optimizing mul...
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On synthetic data with predetermined subject partitioning and cluster profiling, and pre-specified categorical variable marginal dependence structure
A standard approach for assessing the performance of partition or mixture models is to create synthetic data sets with a pre-specified clustering structure, and assess how well the model reveals this structure. A common format is that subjects are assigned to different clusters, with variable observations simulated s...
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Multi-Agent Diverse Generative Adversarial Networks
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different gen...
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Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data
The aetiology of polygenic obesity is multifactorial, which indicates that life-style and environmental factors may influence multiples genes to aggravate this disorder. Several low-risk single nucleotide polymorphisms (SNPs) have been associated with BMI. However, identified loci only explain a small proportion of t...
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Packet Throughput Analysis of Static and Dynamic TDD in Small Cell Networks
We develop an analytical framework for the perfor- mance comparison of small cell networks operating under static time division duplexing (S-TDD) and dynamic TDD (D-TDD). While in S-TDD downlink/uplink (DL/UL) cell transmissions are synchronized, in D-TDD each cell dynamically allocates resources to the most demandin...
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False Discovery Rate Control via Debiased Lasso
We consider the problem of variable selection in high-dimensional statistical models where the goal is to report a set of variables, out of many predictors $X_1, \dotsc, X_p$, that are relevant to a response of interest. For linear high-dimensional model, where the number of parameters exceeds the number of samples $...
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Relativistic Astronomy
The "Breakthrough Starshot" aims at sending near-speed-of-light cameras to nearby stellar systems in the future. Due to the relativistic effects, a trans-relativistic camera naturally serves as a spectrograph, a lens, and a wide-field camera. We demonstrate this through a simulation of the optical-band image of the n...
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Connection between Fermi contours of zero-field electrons and $ν=\frac12$ composite fermions in two-dimensional systems
We investigate the relation between the Fermi sea (FS) of zero-field carriers in two-dimensional systems and the FS of the corresponding composite fermions which emerge in a high magnetic field at filling $\nu = \frac{1}{2}$, as the kinetic energy dispersion is varied. We study cases both with and without rotational ...
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An instrumental intelligibility metric based on information theory
We propose a monaural intrusive instrumental intelligibility metric called speech intelligibility in bits (SIIB). SIIB is an estimate of the amount of information shared between a talker and a listener in bits per second. Unlike existing information theoretic intelligibility metrics, SIIB accounts for talker variabil...
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Scattering of kinks in a non-polynomial model
We study a model described by a single real scalar field in the two-dimensional space-time. The model is specified by a potential which is non-polynomial and supports analytical kink-like solutions that are similar to the standard kink-like solutions that appear in the $\varphi^4$ model when it develops spontaneous s...
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On the Specification of Constraints for Dynamic Architectures
In dynamic architectures, component activation and connections between components may vary over time. With the emergence of mobile computing such architectures became increasingly important and several techniques emerged to support in their specification. These techniques usually allow for the specification of concre...
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Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional Neural Networks
Forward-looking sonar can capture high resolution images of underwater scenes, but their interpretation is complex. Generic object detection in such images has not been solved, specially in cases of small and unknown objects. In comparison, detection proposal algorithms have produced top performing object detectors i...
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Rgtsvm: Support Vector Machines on a GPU in R
Rgtsvm provides a fast and flexible support vector machine (SVM) implementation for the R language. The distinguishing feature of Rgtsvm is that support vector classification and support vector regression tasks are implemented on a graphical processing unit (GPU), allowing the libraries to scale to millions of exampl...
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Geometric Bijections for Regular Matroids, Zonotopes, and Ehrhart Theory
Let $M$ be a regular matroid. The Jacobian group ${\rm Jac}(M)$ of $M$ is a finite abelian group whose cardinality is equal to the number of bases of $M$. This group generalizes the definition of the Jacobian group (also known as the critical group or sandpile group) ${\rm Jac}(G)$ of a graph $G$ (in which case bases...
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Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping
Collaborative Filtering (CF) is a widely adopted technique in recommender systems. Traditional CF models mainly focus on predicting a user's preference to the items in a single domain such as the movie domain or the music domain. A major challenge for such models is the data sparsity problem, and especially, CF canno...
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Deep Interest Network for Click-Through Rate Prediction
Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding\&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors,...
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Nonlinear Dirac Cones
Physics arising from two-dimensional~(2D) Dirac cones has been a topic of great theoretical and experimental interest to studies of gapless topological phases and to simulations of relativistic systems. Such $2$D Dirac cones are often characterized by a $\pi$ Berry phase and are destroyed by a perturbative mass term....
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Portfolio Optimization in Fractional and Rough Heston Models
We consider a fractional version of the Heston volatility model which is inspired by [16]. Within this model we treat portfolio optimization problems for power utility functions. Using a suitable representation of the fractional part, followed by a reasonable approximation we show that it is possible to cast the prob...
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Vanishing in stable motivic homotopy sheaves
We determine systematic regions in which the bigraded homotopy sheaves of the motivic sphere spectrum vanish.
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Classifying Character Degree Graphs With 6 Vertices
We investigate prime character degree graphs of solvable groups that have six vertices. There are one hundred twelve non-isomorphic connected graphs with six vertices, of which all except nine are classified in this paper. We also completely classify the disconnected graphs with six vertices.
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On the Table of Marks of a Direct Product of Finite Groups
We present a method for computing the table of marks of a direct product of finite groups. In contrast to the character table of a direct product of two finite groups, its table of marks is not simply the Kronecker product of the tables of marks of the two groups. Based on a decomposition of the inclusion order on th...
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An Adiabatic Decomposition of the Hodge Cohomology of Manifolds Fibred over Graphs
In this article we use the combinatorial and geometric structure of manifolds with embedded cylinders in order to develop an adiabatic decomposition of the Hodge cohomology of these manifolds. We will on the one hand describe the adiabatic behaviour of spaces of harmonic forms by means of a certain Čech-de Rham compl...
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Search Intelligence: Deep Learning For Dominant Category Prediction
Deep Neural Networks, and specifically fully-connected convolutional neural networks are achieving remarkable results across a wide variety of domains. They have been trained to achieve state-of-the-art performance when applied to problems such as speech recognition, image classification, natural language processing ...
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Grouped Convolutional Neural Networks for Multivariate Time Series
Analyzing multivariate time series data is important for many applications such as automated control, fault diagnosis and anomaly detection. One of the key challenges is to learn latent features automatically from dynamically changing multivariate input. In visual recognition tasks, convolutional neural networks (CNN...
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A mathematical bridge between discretized gauge theories in quantum physics and approximate reasoning in pairwise comparisons
We describe a mathematical link between aspects of information theory, called pairwise comparisons, and discretized gauge theories. The link is made by the notion of holonomy along the edges of a simplex. This correspondance leads to open questions in both field.
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Ten Simple Rules for Reproducible Research in Jupyter Notebooks
Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biol...
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No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search Results
Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this pape...
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Avalanches and Plastic Flow in Crystal Plasticity: An Overview
Crystal plasticity is mediated through dislocations, which form knotted configurations in a complex energy landscape. Once they disentangle and move, they may also be impeded by permanent obstacles with finite energy barriers or frustrating long-range interactions. The outcome of such complexity is the emergence of d...
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Explicit expression for the stationary distribution of reflected brownian motion in a wedge
For Brownian motion in a (two-dimensional) wedge with negative drift and oblique reflection on the axes, we derive an explicit formula for the Laplace transform of its stationary distribution (when it exists), in terms of Cauchy integrals and generalized Chebyshev polyno-mials. To that purpose we solve a Carleman-typ...
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A Data-Driven Framework for Assessing Cold Load Pick-up Demand in Service Restoration
Cold load pick-up (CLPU) has been a critical concern to utilities. Researchers and industry practitioners have underlined the impact of CLPU on distribution system design and service restoration. The recent large-scale deployment of smart meters has provided the industry with a huge amount of data that is highly gran...
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On the topological complexity of aspherical spaces
The well-known theorem of Eilenberg and Ganea expresses the Lusternik - Schnirelmann category of an aspherical space as the cohomological dimension of its fundamental group. In this paper we study a similar problem of determining algebraically the topological complexity of the Eilenberg-MacLane spaces. One of our mai...
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Computable Isomorphisms for Certain Classes of Infinite Graphs
We investigate (2,1):1 structures, which consist of a countable set $A$ together with a function $f: A \to A$ such that for every element $x$ in $A$, $f$ maps either exactly one element or exactly two elements of $A$ to $x$. These structures extend the notions of injection structures, 2:1 structures, and (2,0):1 stru...
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Tug-of-War: Observations on Unified Content Handling
Modern applications and Operating Systems vary greatly with respect to how they register and identify different types of content. These discrepancies lead to exploits and inconsistencies in user experience. In this paper, we highlight the issues arising in the modern content handling ecosystem, and examine how the op...
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