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Riemann-Langevin Particle Filtering in Track-Before-Detect
Track-before-detect (TBD) is a powerful approach that consists in providing the tracker with sensor measurements directly without pre-detection. Due to the measurement model non-linearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorpo...
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The universal connection for principal bundles over homogeneous spaces and twistor space of coadjoint orbits
Given a holomorphic principal bundle $Q\, \longrightarrow\, X$, the universal space of holomorphic connections is a torsor $C_1(Q)$ for $\text{ad} Q \otimes T^*X$ such that the pullback of $Q$ to $C_1(Q)$ has a tautological holomorphic connection. When $X\,=\, G/P$, where $P$ is a parabolic subgroup of a complex simp...
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Equivariance Through Parameter-Sharing
We propose to study equivariance in deep neural networks through parameter symmetries. In particular, given a group $\mathcal{G}$ that acts discretely on the input and output of a standard neural network layer $\phi_{W}: \Re^{M} \to \Re^{N}$, we show that $\phi_{W}$ is equivariant with respect to $\mathcal{G}$-action...
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Quantum light in curved low dimensional hexagonal boron nitride systems
Low-dimensional wide bandgap semiconductors open a new playing field in quantum optics using sub-bandgap excitation. In this field, hexagonal boron nitride (h-BN) has been reported to host single quantum emitters (QEs), linking QE density to perimeters. Furthermore, curvature/perimeters in transition metal dichalcoge...
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Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the $\textit{de facto}$ evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in ...
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Exact time-dependent exchange-correlation potential in electron scattering processes
We identify peak and valley structures in the exact exchange-correlation potential of time-dependent density functional theory that are crucial for time-resolved electron scattering in a model one-dimensional system. These structures are completely missed by adiabatic approximations which consequently significantly u...
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Polynomial-Time Algorithms for Sliding Tokens on Cactus Graphs and Block Graphs
Given two independent sets $I, J$ of a graph $G$, and imagine that a token (coin) is placed at each vertex of $I$. The Sliding Token problem asks if one could transform $I$ to $J$ via a sequence of elementary steps, where each step requires sliding a token from one vertex to one of its neighbors so that the resulting...
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Summarized Network Behavior Prediction
This work studies the entity-wise topical behavior from massive network logs. Both the temporal and the spatial relationships of the behavior are explored with the learning architectures combing the recurrent neural network (RNN) and the convolutional neural network (CNN). To make the behavioral data appropriate for ...
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Stabiliser states are efficiently PAC-learnable
The exponential scaling of the wave function is a fundamental property of quantum systems with far reaching implications in our ability to process quantum information. A problem where these are particularly relevant is quantum state tomography. State tomography, whose objective is to obtain a full description of a qu...
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A bound for the shortest reset words for semisimple synchronizing automata via the packing number
We show that if a semisimple synchronizing automaton with $n$ states has a minimal reachable non-unary subset of cardinality $r\ge 2$, then there is a reset word of length at most $(n-1)D(2,r,n)$, where $D(2,r,n)$ is the $2$-packing number for families of $r$-subsets of $[1,n]$.
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HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT
Recent developments within memory-augmented neural networks have solved sequential problems requiring long-term memory, which are intractable for traditional neural networks. However, current approaches still struggle to scale to large memory sizes and sequence lengths. In this paper we show how access to memory can ...
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X-Ray and Gamma-Ray Emission from Middle-aged Supernova Remnants in Cavities. I. Spherical Symmetry
We present analytical and numerical studies of models of supernova-remnant (SNR) blast waves expanding into uniform media and interacting with a denser cavity wall, in one spatial dimension. We predict the nonthermal emission from such blast waves: synchrotron emission at radio and X-ray energies, and bremsstrahlung,...
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An adverse selection approach to power pricing
We study the optimal design of electricity contracts among a population of consumers with different needs. This question is tackled within the framework of Principal-Agent problems in presence of adverse selection. The particular features of electricity induce an unusual structure on the production cost, with no decr...
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Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains
We consider the minimization of an objective function given access to unbiased estimates of its gradient through stochastic gradient descent (SGD) with constant step-size. While the detailed analysis was only performed for quadratic functions, we provide an explicit asymptotic expansion of the moments of the averaged...
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Learning Overcomplete HMMs
We study the problem of learning overcomplete HMMs---those that have many hidden states but a small output alphabet. Despite having significant practical importance, such HMMs are poorly understood with no known positive or negative results for efficient learning. In this paper, we present several new results---both ...
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Migration barriers for surface diffusion on a rigid lattice: challenges and solutions
Atomistic rigid lattice Kinetic Monte Carlo is an efficient method for simulating nano-objects and surfaces at timescales much longer than those accessible by molecular dynamics. A laborious part of constructing any Kinetic Monte Carlo model is, however, to calculate all migration barriers that are needed to give the...
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On the Quest for an Acyclic Graph
The paper aims at finding acyclic graphs under a given set of constraints. More specifically, given a propositional formula {\phi} over edges of a fixed-size graph, the objective is to find a model of {\phi} that corresponds to a graph that is acyclic. The paper proposes several encodings of the problem and compares ...
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Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning
This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to disti...
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C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset
Visual Question Answering (VQA) has received a lot of attention over the past couple of years. A number of deep learning models have been proposed for this task. However, it has been shown that these models are heavily driven by superficial correlations in the training data and lack compositionality -- the ability to...
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Spherical Planetary Robot for Rugged Terrain Traversal
Wheeled planetary rovers such as the Mars Exploration Rovers (MERs) and Mars Science Laboratory (MSL) have provided unprecedented, detailed images of the Mars surface. However, these rovers are large and are of high-cost as they need to carry sophisticated instruments and science laboratories. We propose the developm...
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Active Learning for Accurate Estimation of Linear Models
We explore the sequential decision making problem where the goal is to estimate uniformly well a number of linear models, given a shared budget of random contexts independently sampled from a known distribution. The decision maker must query one of the linear models for each incoming context, and receives an observat...
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Revisiting Frequency Reuse towards Supporting Ultra-Reliable Ubiquitous-Rate Communication
One of the goals of 5G wireless systems stated by the NGMN alliance is to provide moderate rates (50+ Mbps) everywhere and with very high reliability. We term this service Ultra-Reliable Ubiquitous-Rate Communication (UR2C). This paper investigates the role of frequency reuse in supporting UR2C in the downlink. To th...
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Local isometric immersions of pseudo-spherical surfaces and k-th order evolution equations
We consider the class of evolution equations that describe pseudo-spherical surfaces of the form u\_t = F (u, $\partial$u/$\partial$x, ..., $\partial$^k u/$\partial$x^k), k $\ge$ 2 classified by Chern-Tenenblat. This class of equations is characterized by the property that to each solution of a differential equation ...
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Integrating Flexible Normalization into Mid-Level Representations of Deep Convolutional Neural Networks
Deep convolutional neural networks (CNNs) are becoming increasingly popular models to predict neural responses in visual cortex. However, contextual effects, which are prevalent in neural processing and in perception, are not explicitly handled by current CNNs, including those used for neural prediction. In primary v...
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Moments and non-vanishing of Hecke $L$-functions with quadratic characters in $\mathbb{Q}(i)$ at the central point
In this paper, we study the moments of central values of Hecke $L$-functions associated with quadratic characters in $\mq(i)$, and establish quantitative non-vanishing result for the $L$-values.
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Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning that any graph used to represent them is inherently temporal. However, many of the machine learning models designed to capture knowledge ...
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Decentralized Tube-based Model Predictive Control of Uncertain Nonlinear Multi-Agent Systems
This paper addresses the problem of decentralized tube-based nonlinear Model Predictive Control (NMPC) for a class of uncertain nonlinear continuous-time multi-agent systems with additive and bounded disturbance. In particular, the problem of robust navigation of a multi-agent system to predefined states of the works...
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Cognition of the circle in ancient India
We discuss the understanding of geometry of the circle in ancient India, in terms of enunciation of various principles, constructions, applications etc. during various phases of history and cultural contexts.
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Estimates for $π(x)$ for large values of $x$ and Ramanujan's prime counting inequality
In this paper we use refined approximations for Chebyshev's $\vartheta$-function to establish new explicit estimates for the prime counting function $\pi(x)$, which improve the current best estimates for large values of $x$. As an application we find an upper bound for the number $H_0$ which is defined to be the smal...
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Unveiled electric profiles within hydrogen bonds suggest DNA base pairs with similar bond strengths
Electrical forces are the background of all the interactions occurring in biochemical systems. From here and by using a combination of ab-initio and ad-hoc models, we introduce the first description of electric field profiles with intrabond resolution to support a characterization of single bond forces attending to i...
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Mimetization of the elastic properties of cancellous bone via a parameterized cellular material
Bone tissue mechanical properties and trabecular microarchitecture are the main factors that determine the biomechanical properties of cancellous bone. Artificial cancellous microstructures, typically described by a reduced number of geometrical parameters, can be designed to obtain a mechanical behavior mimicking th...
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Extended Vertical Lists for Temporal Pattern Mining from Multivariate Time Series
Temporal Pattern Mining (TPM) is the problem of mining predictive complex temporal patterns from multivariate time series in a supervised setting. We develop a new method called the Fast Temporal Pattern Mining with Extended Vertical Lists. This method utilizes an extension of the Apriori property which requires a mo...
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PowerAlert: An Integrity Checker using Power Measurement
We propose PowerAlert, an efficient external integrity checker for untrusted hosts. Current attestation systems suffer from shortcomings in requiring complete checksum of the code segment, being static, use of timing information sourced from the untrusted machine, or use of timing information with high error (network...
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Extension complexities of Cartesian products involving a pyramid
It is an open question whether the linear extension complexity of the Cartesian product of two polytopes P, Q is the sum of the extension complexities of P and Q. We give an affirmative answer to this question for the case that one of the two polytopes is a pyramid.
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Speech recognition for medical conversations
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations ($14,000$ hr), designed the task to represent the real word scenario, and explored several alignment approaches to iteratively improve data q...
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Changing users' security behaviour towards security questions: A game based learning approach
Fallback authentication is used to retrieve forgotten passwords. Security questions are one of the main techniques used to conduct fallback authentication. In this paper, we propose a serious game design that uses system-generated security questions with the aim of improving the usability of fallback authentication. ...
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Understanding Web Archiving Services and Their (Mis)Use on Social Media
Web archiving services play an increasingly important role in today's information ecosystem, by ensuring the continuing availability of information, or by deliberately caching content that might get deleted or removed. Among these, the Wayback Machine has been proactively archiving, since 2001, versions of a large nu...
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Mining a Sub-Matrix of Maximal Sum
Biclustering techniques have been widely used to identify homogeneous subgroups within large data matrices, such as subsets of genes similarly expressed across subsets of patients. Mining a max-sum sub-matrix is a related but distinct problem for which one looks for a (non-necessarily contiguous) rectangular sub-matr...
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Training Deep Networks without Learning Rates Through Coin Betting
Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks...
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Joint Beamforming and Antenna Selection for Sum Rate Maximization in Cognitive Radio Networks
This letter studies joint transmit beamforming and antenna selection at a secondary base station (BS) with multiple primary users (PUs) in an underlay cognitive radio multiple-input single-output broadcast channel. The objective is to maximize the sum rate subject to the secondary BS transmit power, minimum required ...
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Multi-sequence segmentation via score and higher-criticism tests
We propose local segmentation of multiple sequences sharing a common time- or location-index, building upon the single sequence local segmentation methods of Niu and Zhang (2012) and Fang, Li and Siegmund (2016). We also propose reverse segmentation of multiple sequences that is new even in the single sequence contex...
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Evidence for structural transition in crystalline tantalum pentoxide films grown by RF magnetron sputtering
We investigate the effect of annealing temperature on the crystalline structure and physical properties of tantalum-pentoxide films grown by radio frequency magnetron sputtering. For this purpose, several tantalum films were deposited and the Ta$_2$O$_5$ crystalline phase was induced by exposing the samples to heat t...
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Emergence and Reductionism: an awkward Baconian alliance
This article discusses the relationship between emergence and reductionism from the perspective of a condensed matter physicist. Reductionism and emergence play an intertwined role in the everyday life of the physicist, yet we rarely stop to contemplate their relationship: indeed, the two are often regarded as confli...
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Pipelined Parallel FFT Architecture
In this paper, an optimized efficient VLSI architecture of a pipeline Fast Fourier transform (FFT) processor capable of producing the reverse output order sequence is presented. Paper presents Radix-2 multipath delay architecture for FFT calculation. The implementation of FFT in hardware is very critical because for ...
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Global smoothing of a subanalytic set
We give rather simple answers to two long-standing questions in real-analytic geometry, on global smoothing of a subanalytic set, and on transformation of a proper real-analytic mapping to a mapping with equidimensional fibres by global blowings-up of the target. These questions are related: a positive answer to the ...
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Coupled Compound Poisson Factorization
We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. We derive a stochastic variational inference algorithm for the resulting mode...
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A model bridging chimera state and explosive synchronization
Global and partial synchronization are the two distinctive forms of synchronization in coupled oscillators and have been well studied in the past decades. Recent attention on synchronization is focused on the chimera state (CS) and explosive synchronization (ES), but little attention has been paid to their relationsh...
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Spatial modeling of shot conversion in soccer to single out goalscoring ability
Goals are results of pin-point shots and it is a pivotal decision in soccer when, how and where to shoot. The main contribution of this study is two-fold. At first, after showing that there exists high spatial correlation in the data of shots across games, we introduce a spatial process in the error structure to mode...
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Metrics for Formal Structures, with an Application to Kripke Models and their Dynamics
This report introduces and investigates a family of metrics on sets of pointed Kripke models. The metrics are generalizations of the Hamming distance applicable to countably infinite binary strings and, by extension, logical theories or semantic structures. We first study the topological properties of the resulting m...
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Hyperbolic Dispersion Dominant Regime Identified through Spontaneous Emission Variations near Metamaterial Interfaces
Surface plasmon polariton, hyberbolic dispersion of energy and momentum, and emission interference provide opportunities to control photoluminescence properties. However, the interplays between these regimes need to be understood to take advantage of them in optoelectronic applications. Here, we investigate broadband...
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Legendrian Satellites and Decomposable Concordances
We investigate the ramifications of the Legendrian satellite construction on the relation of Lagrangian cobordism between Legendrian knots. Under a simple hypothesis, we construct a Lagrangian concordance between two Legendrian satellites by stacking up a sequence of elementary cobordisms. This construction narrows t...
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Plausible Deniability for Privacy-Preserving Data Synthesis
Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechan...
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The Co-Evolution of Test Maintenance and Code Maintenance through the lens of Fine-Grained Semantic Changes
Automatic testing is a widely adopted technique for improving software quality. Software developers add, remove and update test methods and test classes as part of the software development process as well as during the evolution phase, following the initial release. In this work we conduct a large scale study of 61 p...
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Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning
Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly. Such prediction should be probabilistic, to address the uncertainties in human behavior. Such prediction should also be ...
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Fast-slow asymptotics for a Markov chain model of fast sodium current
We explore the feasibility of using fast-slow asymptotic to eliminate the computational stiffness of the discrete-state, continuous-time deterministic Markov chain models of ionic channels underlying cardiac excitability. We focus on a Markov chain model of the fast sodium current, and investigate its asymptotic beha...
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Beating the bookies with their own numbers - and how the online sports betting market is rigged
The online sports gambling industry employs teams of data analysts to build forecast models that turn the odds at sports games in their favour. While several betting strategies have been proposed to beat bookmakers, from expert prediction models and arbitrage strategies to odds bias exploitation, their returns have b...
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Fixation probabilities for the Moran process in evolutionary games with two strategies: graph shapes and large population asymptotics
This paper is based on the complete classification of evolutionary scenarios for the Moran process with two strategies given by Taylor et al. (B. Math. Biol. 66(6): 1621--1644, 2004). Their classification is based on whether each strategy is a Nash equilibrium and whether the fixation probability for a single individ...
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FPGA Design Techniques for Stable Cryogenic Operation
In this paper we show how a deep-submicron FPGA can be modified to operate at extremely low temperatures through modifications in the supporting hardware and in the firmware programming it. Though FPGAs are not designed to operate at a few Kelvin, it is possible to do so on virtue of the extremely high doping levels ...
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Curvature-aided Incremental Aggregated Gradient Method
We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method. Motivated by the problem of training a classifier for a d-dimensional problem, where the number of training data is $m$ and $m \gg d \gg 1$, the CIAG method seeks to accelerate incre...
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The composition of Solar system asteroids and Earth/Mars moons, and the Earth-Moon composition similarity
[abridged] In the typical giant-impact scenario for the Moon formation most of the Moon's material originates from the impactor. Any Earth-impactor composition difference should, therefore, correspond to a comparable Earth-Moon composition difference. Analysis of Moon rocks shows a close Earth-Moon composition simila...
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Maximum Margin Interval Trees
Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task a...
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Provable and practical approximations for the degree distribution using sublinear graph samples
The degree distribution is one of the most fundamental properties used in the analysis of massive graphs. There is a large literature on graph sampling, where the goal is to estimate properties (especially the degree distribution) of a large graph through a small, random sample. The degree distribution estimation pos...
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Efficient and Robust Polylinear Analysis of Noisy Time Series
A method is proposed to generate an optimal fit of a number of connected linear trend segments onto time-series data. To be able to efficiently handle many lines, the method employs a stochastic search procedure to determine optimal transition point locations. Traditional methods use exhaustive grid searches, which s...
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On the optimal design of wall-to-wall heat transport
We consider the problem of optimizing heat transport through an incompressible fluid layer. Modeling passive scalar transport by advection-diffusion, we maximize the mean rate of total transport by a divergence-free velocity field. Subject to various boundary conditions and intensity constraints, we prove that the ma...
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On the decay rate for the wave equation with viscoelastic boundary damping
We consider the wave equation with a boundary condition of memory type. Under natural conditions on the acoustic impedance $\hat{k}$ of the boundary one can define a corresponding semigroup of contractions (Desch, Fasangova, Milota, Probst 2010). With the help of Tauberian theorems we establish energy decay rates via...
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Code Reuse With Transformation Objects
We present an approach for a lightweight datatype-generic programming in Objective Caml programming language aimed at better code reuse. We show, that a large class of transformations usually expressed via recursive functions with pattern matching can be implemented using the single per-type traversal function and th...
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Fixed effects testing in high-dimensional linear mixed models
Many scientific and engineering challenges -- ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations -- require an understanding of the unobserved heterogeneity in order to develop the best decision making-processes. In this paper, we develop a hypothesis ...
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Ubiquitous quasi-Fuchsian surfaces in cusped hyperbolic 3-manifolds
This paper proves that every finite volume hyperbolic 3-manifold M contains a ubiquitous collection of closed, immersed, quasi-Fuchsian surfaces. These surfaces are ubiquitous in the sense that their preimages in the universal cover separate any pair of disjoint, non-asymptotic geodesic planes. The proof relies in a ...
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Generic Cospark of a Matrix Can Be Computed in Polynomial Time
The cospark of a matrix is the cardinality of the sparsest vector in the column space of the matrix. Computing the cospark of a matrix is well known to be an NP hard problem. Given the sparsity pattern (i.e., the locations of the non-zero entries) of a matrix, if the non-zero entries are drawn from independently dist...
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Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder
Neural networks based vocoders, typically the WaveNet, have achieved spectacular performance for text-to-speech (TTS) in recent years. Although state-of-the-art parallel WaveNet has addressed the issue of real-time waveform generation, there remains problems. Firstly, due to the noisy input signal of the model, there...
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CitizenGrid: An Online Middleware for Crowdsourcing Scientific Research
In the last few years, contributions of the general public in scientific projects has increased due to the advancement of communication and computing technologies. Internet played an important role in connecting scientists and volunteers who are interested in participating in their scientific projects. However, despi...
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Singular sensitivity in a Keller-Segel-fluid system
In bounded smooth domains $\Omega\subset\mathbb{R}^N$, $N\in\{2,3\}$, considering the chemotaxis--fluid system \[ \begin{cases} \begin{split} & n_t + u\cdot \nabla n &= \Delta n - \chi \nabla \cdot(\frac{n}{c}\nabla c) &\\ & c_t + u\cdot \nabla c &= \Delta c - c + n &\\ & u_t + \kappa (u\cdot \nabla) u &= \Delta u + ...
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Asymptotic theory of multiple-set linear canonical analysis
This paper deals with asymptotics for multiple-set linear canonical analysis (MSLCA). A definition of this analysis, that adapts the classical one to the context of Euclidean random variables, is given and properties of the related canonical coefficients are derived. Then, estimators of the MSLCA's elements, based on...
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Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography
The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screening mammograms using an "end-to-end" training approach that efficientl...
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Nviz - A General Purpse Visualization tool for Wireless Sensor Networks
In a Wireless Sensor Network (WSN), data manipulation and representation is a crucial part and can take a lot of time to be developed from scratch. Although various visualization tools have been created for certain projects so far, these tools can only be used in certain scenarios, due to their hard-coded packet form...
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Transient photon echoes from donor-bound excitons in ZnO epitaxial layers
The coherent optical response from 140~nm and 65~nm thick ZnO epitaxial layers is studied using transient four-wave-mixing spectroscopy with picosecond temporal resolution. Resonant excitation of neutral donor-bound excitons results in two-pulse and three-pulse photon echoes. For the donor-bound A exciton (D$^0$X$_\t...
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The Quasar Luminosity Function at Redshift 4 with Hyper Suprime-Cam Wide Survey
We present the luminosity function of z=4 quasars based on the Hyper Suprime-Cam Subaru Strategic Program Wide layer imaging data in the g, r, i, z, and y bands covering 339.8 deg^2. From stellar objects, 1666 z~4 quasar candidates are selected by the g-dropout selection down to i=24.0 mag. Their photometric redshift...
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Towards Industry 4.0: Gap Analysis between Current Automotive MES and Industry Standards using Model-Based Requirement Engineering
The dawn of the fourth industrial revolution, Industry 4.0 has created great enthusiasm among companies and researchers by giving them an opportunity to pave the path towards the vision of a connected smart factory ecosystem. However, in context of automotive industry there is an evident gap between the requirements ...
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A Constrained Conditional Likelihood Approach for Estimating the Means of Selected Populations
Given p independent normal populations, we consider the problem of estimating the mean of those populations, that based on the observed data, give the strongest signals. We explicitly condition on the ranking of the sample means, and consider a constrained conditional maximum likelihood (CCMLE) approach, avoiding the...
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Phase transition in the spiked random tensor with Rademacher prior
We consider the problem of detecting a deformation from a symmetric Gaussian random $p$-tensor $(p\geq 3)$ with a rank-one spike sampled from the Rademacher prior. Recently in Lesieur et al. (2017), it was proved that there exists a critical threshold $\beta_p$ so that when the signal-to-noise ratio exceeds $\beta_p$...
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Extended depth-range profilometry using the phase-difference and phase-sum of two close-sensitivity projected fringes
We propose a high signal-to-noise extended depth-range three-dimensional (3D) profilometer projecting two linear-fringes with close phase-sensitivity. We use temporal phase-shifting algorithms (PSAs) to phase-demodulate the two close sensitivity phases. Then we calculate their phase-difference and their phase-sum. If...
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A Language for Probabilistically Oblivious Computation
An oblivious computation is one that is free of direct and indirect information leaks, e.g., due to observable differences in timing and memory access patterns. This paper presents Lobliv, a core language whose type system enforces obliviousness. Prior work on type-enforced oblivious computation has focused on determ...
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Single shot, double differential spectral measurements of inverse Compton scattering in linear and nonlinear regimes
Inverse Compton scattering (ICS) is a unique mechanism for producing fast pulses - picosecond and below - of bright X- to gamma-rays. These nominally narrow spectral bandwidth electromagnetic radiation pulses are efficiently produced in the interaction between intense, well-focused electron and laser beams. The spect...
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Yarkovsky Drift Detections for 159 Near-Earth Asteroids
The Yarkovsky effect is a thermal process acting upon the orbits of small celestial bodies, which can cause these orbits to slowly expand or contract with time. The effect is subtle -- typical drift rates lie near $10^{-4}$ au/My for a $\sim$1 km diameter object -- and is thus generally difficult to measure. However,...
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A short note on Godbersen's Conjecture
In this short note we improve the best to date bound in Godbersen's conjecture, and show some implications for unbalanced difference bodies.
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The derivative NLS equation: global existence with solitons
We extend the global existence result for the derivative NLS equation to the case when the initial datum includes a finite number of solitons. This is achieved by an application of the Bäcklund transformation that removes a finite number of zeros of the scattering coefficient. By means of this transformation, the Rie...
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Missing dust signature in the cosmic microwave background
I examine a possible spectral distortion of the Cosmic Microwave Background (CMB) due to its absorption by galactic and intergalactic dust. I show that even subtle intergalactic opacity of $1 \times 10^{-7}\, \mathrm{mag}\, h\, \mathrm{Gpc}^{-1}$ at the CMB wavelengths in the local Universe causes non-negligible CMB ...
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Option market (in)efficiency and implied volatility dynamics after return jumps
In informationally efficient financial markets, option prices and this implied volatility should immediately be adjusted to new information that arrives along with a jump in underlying's return, whereas gradual changes in implied volatility would indicate market inefficiency. Using minute-by-minute data on S&P 500 in...
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Combining Alchemical Transformation with Physical Pathway to Accurately Compute Absolute Binding Free Energy
We present a new method that combines alchemical transformation with physical pathway to accurately and efficiently compute the absolute binding free energy of receptor-ligand complex. Currently, the double decoupling method (DDM) and the potential of mean force approach (PMF) methods are widely used to compute the a...
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Embedding Feature Selection for Large-scale Hierarchical Classification
Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-...
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Orientably-regular maps on twisted linear fractional groups
We present an enumeration of orientably-regular maps with automorphism group isomorphic to the twisted linear fractional group $M(q^2)$ for any odd prime power $q$.
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Deep Mean Functions for Meta-Learning in Gaussian Processes
Fitting machine learning models in the low-data limit is challenging. The main challenge is to obtain suitable prior knowledge and encode it into the model, for instance in the form of a Gaussian process prior. Recent advances in meta-learning offer powerful methods for extracting such prior knowledge from data acqui...
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Projectors separating spectra for $L^2$ on symmetric spaces $GL(n,\C)/GL(n,\R)$
The Plancherel decomposition of $L^2$ on a pseudo-Riemannian symmetric space $GL(n,C)/GL(n,R)$ has spectrum of $[n/2]$ types. We write explicitly orthogonal projectors separating spectrum into uniform pieces
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On the continued fraction expansion of absolutely normal numbers
We construct an absolutely normal number whose continued fraction expansion is normal in the sense that it contains all finite patterns of partial quotients with the expected asymptotic frequency as given by the Gauss-Kuzmin measure. The construction is based on ideas of Sierpinski and uses a large deviations theorem...
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Talking Open Data
Enticing users into exploring Open Data remains an important challenge for the whole Open Data paradigm. Standard stock interfaces often used by Open Data portals are anything but inspiring even for tech-savvy users, let alone those without an articulated interest in data science. To address a broader range of citize...
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Static non-reciprocity in mechanical metamaterials
Reciprocity is a fundamental principle governing various physical systems, which ensures that the transfer function between any two points in space is identical, regardless of geometrical or material asymmetries. Breaking this transmission symmetry offers enhanced control over signal transport, isolation and source p...
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A forward-adjoint operator pair based on the elastic wave equation for use in transcranial photoacoustic tomography
Photoacoustic computed tomography (PACT) is an emerging imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to an inverse source problem in which the...
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Sewing Riemannian Manifolds with Positive Scalar Curvature
We explore to what extent one may hope to preserve geometric properties of three dimensional manifolds with lower scalar curvature bounds under Gromov-Hausdorff and Intrinsic Flat limits. We introduce a new construction, called sewing, of three dimensional manifolds that preserves positive scalar curvature. We then u...
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Concentration of quadratic forms under a Bernstein moment assumption
A concentration result for quadratic form of independent subgaussian random variables is derived. If the moments of the random variables satisfy a "Bernstein condition", then the variance term of the Hanson-Wright inequality can be improved. The Bernstein condition is satisfied, for instance, by all log-concave subga...
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Short DNA persistence length in a mesoscopic helical model
The flexibility of short DNA chains is investigated via computation of the average correlation function between dimers which defines the persistence length. Path integration techniques have been applied to confine the phase space available to base pair fluctuations and derive the partition function. The apparent pers...
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