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Morphology of PbTe crystal surface sputtered by argon plasma under Secondary Neutral Mass Spectrometry conditions
We have investigated morphology of the lateral surfaces of PbTe crystal samples grown from melt by the Bridgman method sputtered by Ar+ plasma with ion energy of 50-550 eV for 5-50 minutes under Secondary Neutral Mass Spectrometry (SNMS) conditions. The sputtered PbTe crystal surface was found to be simultaneously bo...
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Indefinite Integrals of Spherical Bessel Functions
Highly oscillatory integrals, such as those involving Bessel functions, are best evaluated analytically as much as possible, as numerical errors can be difficult to control. We investigate indefinite integrals involving monomials in $x$ multiplying one or two spherical Bessel functions of the first kind $j_l(x)$ with...
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Proceedings 14th International Workshop on the ACL2 Theorem Prover and its Applications
This volume contains the proceedings of the Fourteenth International Workshop on the ACL2 Theorem Prover and Its Applications, ACL2 2017, a two-day workshop held in Austin, Texas, USA, on May 22-23, 2017. ACL2 workshops occur at approximately 18-month intervals, and they provide a technical forum for researchers to p...
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Mean Field Stochastic Games with Binary Action Spaces and Monotone Costs
This paper considers mean field games in a multi-agent Markov decision process (MDP) framework. Each player has a continuum state and binary action. By active control, a player can bring its state to a resetting point. All players are coupled through their cost functions. The structural property of the individual str...
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Heterogeneous Cellular Networks with LoS and NLoS Transmissions--The Role of Massive MIMO and Small Cells
We develop a framework for downlink heterogeneous cellular networks with line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Using stochastic geometry, we derive tight approximation of achievable downlink rate that enables us to compare the performance between densifying small cells and expanding BS anten...
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Gould's Belt: Local Large Scale Structure in the Milky Way
Gould's Belt is a flat local system composed of young OB stars, molecular clouds and neutral hydrogen within 500 pc from the Sun. It is inclined about 20 degrees to the galactic plane and its velocity field significantly deviates from rotation around the distant center of the Milky Way. We discuss possible models of ...
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Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise
In recent years, correntropy and its applications in machine learning have been drawing continuous attention owing to its merits in dealing with non-Gaussian noise and outliers. However, theoretical understanding of correntropy, especially in the statistical learning context, is still limited. In this study, within t...
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The Suppression and Promotion of Magnetic Flux Emergence in Fully Convective Stars
Evidence of surface magnetism is now observed on an increasing number of cool stars. The detailed manner by which dynamo-generated magnetic fields giving rise to starspots traverse the convection zone still remains unclear. Some insight into this flux emergence mechanism has been gained by assuming bundles of magneti...
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Opportunistic Content Delivery in Fading Broadcast Channels
We consider content delivery over fading broadcast channels. A server wants to transmit K files to K users, each equipped with a cache of finite size. Using the coded caching scheme of Maddah-Ali and Niesen, we design an opportunistic delivery scheme where the long-term sum content delivery rate scales with K the num...
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Conservation Laws With Random and Deterministic Data
The dynamics of nonlinear conservation laws have long posed fascinating problems. With the introduction of some nonlinearity, e.g. Burgers' equation, discontinuous behavior in the solutions is exhibited, even for smooth initial data. The introduction of randomness in any of several forms into the initial condition ma...
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Theoretical study of HfF$^+$ cation to search for the T,P-odd interactions
The combined all-electron and two-step approach is applied to calculate the molecular parameters which are required to interpret the ongoing experiment to search for the effects of manifestation of the T,P-odd fundamental interactions in the HfF$^+$ cation by Cornell/Ye group [Science 342, 1220 (2013); J. Mol. Spectr...
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Adaptive Information Gathering via Imitation Learning
In the adaptive information gathering problem, a policy is required to select an informative sensing location using the history of measurements acquired thus far. While there is an extensive amount of prior work investigating effective practical approximations using variants of Shannon's entropy, the efficacy of such...
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Demonstration of dispersive rarefaction shocks in hollow elliptical cylinder chains
We report an experimental and numerical demonstration of dispersive rarefaction shocks (DRS) in a 3D-printed soft chain of hollow elliptical cylinders. We find that, in contrast to conventional nonlinear waves, these DRS have their lower amplitude components travel faster, while the higher amplitude ones propagate sl...
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Gaussian Process Regression for Arctic Coastal Erosion Forecasting
Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shorefast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern, since the majority o...
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Calibration for Stratified Classification Models
In classification problems, sampling bias between training data and testing data is critical to the ranking performance of classification scores. Such bias can be both unintentionally introduced by data collection and intentionally introduced by the algorithm, such as under-sampling or weighting techniques applied to...
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Two classes of fast-declining type Ia supernovae
Fast-declining Type Ia supernovae (SN Ia) separate into two categories based on their bolometric and near-infrared (NIR) properties. The peak bolometric luminosity ($\mathrm{L_{max}}$), the phase of the first maximum relative to the optical, the NIR peak luminosity and the occurrence of a second maximum in the NIR di...
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What Sets the Radial Locations of Warm Debris Disks?
The architectures of debris disks encode the history of planet formation in these systems. Studies of debris disks via their spectral energy distributions (SEDs) have found infrared excesses arising from cold dust, warm dust, or a combination of the two. The cold outer belts of many systems have been imaged, facilita...
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Nonlinear Modulational Instability of Dispersive PDE Models
We prove nonlinear modulational instability for both periodic and localized perturbations of periodic traveling waves for several dispersive PDEs, including the KDV type equations (e.g. the Whitham equation, the generalized KDV equation, the Benjamin-Ono equation), the nonlinear Schrödinger equation and the BBM equat...
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Unsupervised Learning of Disentangled Representations from Video
We present a new model DrNET that learns disentangled image representations from video. Our approach leverages the temporal coherence of video and a novel adversarial loss to learn a representation that factorizes each frame into a stationary part and a temporally varying component. The disentangled representation ca...
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Control of Ultracold Photodissociation with Magnetic Fields
Photodissociation of a molecule produces a spatial distribution of photofragments determined by the molecular structure and the characteristics of the dissociating light. Performing this basic chemical reaction at ultracold temperatures allows its quantum mechanical features to dominate. In this regime, weak applied ...
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JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains). This is achieved by learning to sample from conditional di...
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A lightweight MapReduce framework for secure processing with SGX
MapReduce is a programming model used extensively for parallel data processing in distributed environments. A wide range of algorithms were implemented using MapReduce, from simple tasks like sorting and searching up to complex clustering and machine learning operations. Many of these implementations are part of serv...
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Formation of wide-orbit gas giants near the stability limit in multi-stellar systems
We have investigated the formation of a circumstellar wide-orbit gas giant planet in a multiple stellar system. We consider a model of orbital circularization for the core of a giant planet after it is scattered from an inner disk region by a more massive planet, which was proposed by Kikuchi et al (2014). We extend ...
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Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint
Symmetric nonnegative matrix factorization has found abundant applications in various domains by providing a symmetric low-rank decomposition of nonnegative matrices. In this paper we propose a Frank-Wolfe (FW) solver to optimize the symmetric nonnegative matrix factorization problem under a simplicial constraint, wh...
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A Fourier Disparity Layer representation for Light Fields
In this paper, we present a new Light Field representation for efficient Light Field processing and rendering called Fourier Disparity Layers (FDL). The proposed FDL representation samples the Light Field in the depth (or equivalently the disparity) dimension by decomposing the scene as a discrete sum of layers. The ...
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Narrating Networks
Networks have become the de facto diagram of the Big Data age (try searching Google Images for [big data AND visualisation] and see). The concept of networks has become central to many fields of human inquiry and is said to revolutionise everything from medicine to markets to military intelligence. While the mathemat...
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Policy Evaluation and Optimization with Continuous Treatments
We study the problem of policy evaluation and learning from batched contextual bandit data when treatments are continuous, going beyond previous work on discrete treatments. Previous work for discrete treatment/action spaces focuses on inverse probability weighting (IPW) and doubly robust (DR) methods that use a reje...
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Geometry of Factored Nuclear Norm Regularization
This work investigates the geometry of a nonconvex reformulation of minimizing a general convex loss function $f(X)$ regularized by the matrix nuclear norm $\|X\|_*$. Nuclear-norm regularized matrix inverse problems are at the heart of many applications in machine learning, signal processing, and control. The statist...
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Effect of Scrape-Off-Layer Current on Reconstructed Tokamak Equilibrium
Methods are described that extend fields from reconstructed equilibria to include scrape-off-layer current through extrapolated parametrized and experimental fits. The extrapolation includes both the effects of the toroidal-field and pressure gradients which produce scrape-off-layer current after recomputation of the...
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Holomorphy of Osborn loops
Let $(L,\cdot)$ be any loop and let $A(L)$ be a group of automorphisms of $(L,\cdot)$ such that $\alpha$ and $\phi$ are elements of $A(L)$. It is shown that, for all $x,y,z\in L$, the $A(L)$-holomorph $(H,\circ)=H(L)$ of $(L,\cdot)$ is an Osborn loop if and only if $x\alpha (yz\cdot x\phi^{-1})= x\alpha (yx^\lambda\c...
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A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders
Multi-objective recommender systems address the difficult task of recommending items that are relevant to multiple, possibly conflicting, criteria. However these systems are most often designed to address the objective of one single stakeholder, typically, in online commerce, the consumers whose input and purchasing ...
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Interactive Discovery System for Direct Democracy
Decide Madrid is the civic technology of Madrid City Council which allows users to create and support online petitions. Despite the initial success, the platform is encountering problems with the growth of petition signing because petitions are far from the minimum number of supporting votes they must gather. Previou...
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Deep Learning to Attend to Risk in ICU
Modeling physiological time-series in ICU is of high clinical importance. However, data collected within ICU are irregular in time and often contain missing measurements. Since absence of a measure would signify its lack of importance, the missingness is indeed informative and might reflect the decision making by the...
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Steklov problem on differential forms
In this paper we study spectral properties of Dirichlet-to-Neumann map on differential forms obtained by a slight modification of the definition due to Belishev and Sharafutdinov. The resulting operator $\Lambda$ is shown to be self-adjoint on the subspace of coclosed forms and to have purely discrete spectrum there....
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Constraining a dark matter and dark energy interaction scenario with a dynamical equation of state
In this work we have used the recent cosmic chronometers data along with the latest estimation of the local Hubble parameter value, $H_0$ at 2.4\% precision as well as the standard dark energy probes, such as the Supernovae Type Ia, baryon acoustic oscillation distance measurements, and cosmic microwave background me...
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Comprehensive evaluation of statistical speech waveform synthesis
Statistical TTS systems that directly predict the speech waveform have recently reported improvements in synthesis quality. This investigation evaluates Amazon's statistical speech waveform synthesis (SSWS) system. An in-depth evaluation of SSWS is conducted across a number of domains to better understand the consist...
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Component response rate variation drives stability in large complex systems
The stability of a complex system generally decreases with increasing system size and interconnectivity, a counterintuitive result of widespread importance across the physical, life, and social sciences. Despite recent interest in the relationship between system properties and stability, the effect of variation in th...
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Time-resolved ultrafast x-ray scattering from an incoherent electronic mixture
Time-resolved ultrafast x-ray scattering from photo-excited matter is an emerging method to image ultrafast dynamics in matter with atomic-scale spatial and temporal resolutions. For a correct and rigorous understanding of current and upcoming imaging experiments, we present the theory of time-resolved x-ray scatteri...
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Calculation of the critical overdensity in the spherical-collapse approximation
Critical overdensity $\delta_c$ is a key concept in estimating the number count of halos for different redshift and halo-mass bins, and therefore, it is a powerful tool to compare cosmological models to observations. There are currently two different prescriptions in the literature for its calculation, namely, the di...
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Point distributions in compact metric spaces, II
We consider finite point subsets (distributions) in compact metric spaces. In the case of general rectifiable metric spaces, non-trivial bounds for sums of distances between points of distributions and for discrepancies of distributions in metric balls are given (Theorem 1.1). We generalize Stolarsky's invariance pri...
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A Variational Projection Scheme for Nonmatching Surface-to-Line Coupling between 3D Flexible Multibody System and Incompressible Turbulent Flow
This paper is concerned with the partitioned iterative formulation to simulate the fluid-structure interaction of a nonlinear multibody system in an incompressible turbulent flow. The proposed formulation relies on a three-dimensional (3D) incompressible turbulent flow solver, a nonlinear monolithic elastic structura...
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The Hubble Catalog of Variables
The Hubble Catalog of Variables (HCV) is a 3 year ESA funded project that aims to develop a set of algorithms to identify variables among the sources included in the Hubble Source Catalog (HSC) and produce the HCV. We will process all HSC sources with more than a predefined number of measurements in a single filter/i...
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A Loop-Based Methodology for Reducing Computational Redundancy in Workload Sets
The design of general purpose processors relies heavily on a workload gathering step in which representative programs are collected from various application domains. Processor performance, when running the workload set, is profiled using simulators that model the targeted processor architecture. However, simulating t...
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Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization
Most traditional video summarization methods are designed to generate effective summaries for single-view videos, and thus they cannot fully exploit the complicated intra and inter-view correlations in summarizing multi-view videos in a camera network. In this paper, with the aim of summarizing multi-view videos, we ...
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A Logical Approach to Cloud Federation
Federated clouds raise a variety of challenges for managing identity, resource access, naming, connectivity, and object access control. This paper shows how to address these challenges in a comprehensive and uniform way using a data-centric approach. The foundation of our approach is a trust logic in which participan...
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Deep Affordance-grounded Sensorimotor Object Recognition
It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them. This fact has r...
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Liquid crystal induced elasto-capillary suppression of crack formation in thin colloidal films
Drying of colloidal droplets on solid, rigid substrates is associated with a capillary pressure developing within the droplet. In due course of time, the capillary pressure builds up due to droplet evaporation resulting in the formation of a colloidal thin film that is prone to crack formation. In this study, we show...
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Autonomous Reactive Mission Scheduling and Task-Path Planning Architecture for Autonomous Underwater Vehicle
An Autonomous Underwater Vehicle (AUV) should carry out complex tasks in a limited time interval. Since existing AUVs have limited battery capacity and restricted endurance, they should autonomously manage mission time and the resources to perform effective persistent deployment in longer missions. Task assignment re...
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Stealth Attacks on the Smart Grid
Random attacks that jointly minimize the amount of information acquired by the operator about the state of the grid and the probability of attack detection are presented. The attacks minimize the information acquired by the operator by minimizing the mutual information between the observations and the state variables...
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Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network
In this paper we present an alternative strategy for fine-tuning the parameters of a network. We named the technique Gradual Tuning. Once trained on a first task, the network is fine-tuned on a second task by modifying a progressively larger set of the network's parameters. We test Gradual Tuning on different transfe...
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Bayesian power-spectrum inference with foreground and target contamination treatment
This work presents a joint and self-consistent Bayesian treatment of various foreground and target contaminations when inferring cosmological power-spectra and three dimensional density fields from galaxy redshift surveys. This is achieved by introducing additional block sampling procedures for unknown coefficients o...
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3-Lie bialgebras and 3-Lie classical Yang-Baxter equations in low dimensions
In this paper, we give some low-dimensional examples of local cocycle 3-Lie bialgebras and double construction 3-Lie bialgebras which were introduced in the study of the classical Yang-Baxter equation and Manin triples for 3-Lie algebras. We give an explicit and practical formula to compute the skew-symmetric solutio...
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Adaptive pixel-super-resolved lensfree holography for wide-field on-chip microscopy
High-resolution wide field-of-view (FOV) microscopic imaging plays an essential role in various fields of biomedicine, engineering, and physical sciences. As an alternative to conventional lens-based scanning techniques, lensfree holography provides a new way to effectively bypass the intrinsical trade-off between th...
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Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the user's active vocabularies in previous documents. As applications of personalized image captioning, we...
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Hardness of almost embedding simplicial complexes in $\mathbb R^d$
A map $f\colon K\to \mathbb R^d$ of a simplicial complex is an almost embedding if $f(\sigma)\cap f(\tau)=\emptyset$ whenever $\sigma,\tau$ are disjoint simplices of $K$. Theorem. Fix integers $d,k\ge2$ such that $d=\frac{3k}2+1$. (a) Assume that $P\ne NP$. Then there exists a finite $k$-dimensional complex $K$ that ...
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Asymmetry of short-term control of spatio-temporal gait parameters during treadmill walking
Optimization of energy cost determines average values of spatio-temporal gait parameters such as step duration, step length or step speed. However, during walking, humans need to adapt these parameters at every step to respond to exogenous and/or endogenic perturbations. While some neurological mechanisms that trigge...
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Edge N-Level Sparse Visibility Graphs: Fast Optimal Any-Angle Pathfinding Using Hierarchical Taut Paths
In the Any-Angle Pathfinding problem, the goal is to find the shortest path between a pair of vertices on a uniform square grid, that is not constrained to any fixed number of possible directions over the grid. Visibility Graphs are a known optimal algorithm for solving the problem with the use of pre-processing. How...
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Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing me...
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Higher-genus quasimap wall-crossing via localization
We give a new proof of Ciocan-Fontanine and Kim's wall-crossing formula relating the virtual classes of the moduli spaces of $\epsilon$-stable quasimaps for different $\epsilon$ in any genus, whenever the target is a complete intersection in projective space and there is at least one marked point. Our techniques invo...
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Uncertainty in Cyber Security Investments
When undertaking cyber security risk assessments, we must assign numeric values to metrics to compute the final expected loss that represents the risk that an organization is exposed to due to cyber threats. Even if risk assessment is motivated from real-world observations and data, there is always a high chance of a...
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ELT Linear Algebra II
This paper is a continuation of [arXiv:1603.02204]. Exploded layered tropical (ELT) algebra is an extension of tropical algebra with a structure of layers. These layers allow us to use classical algebraic results in order to easily prove analogous tropical results. Specifically we prove and use an ELT version of the ...
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Inner-Scene Similarities as a Contextual Cue for Object Detection
Using image context is an effective approach for improving object detection. Previously proposed methods used contextual cues that rely on semantic or spatial information. In this work, we explore a different kind of contextual information: inner-scene similarity. We present the CISS (Context by Inner Scene Similarit...
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Time-dependent spectral renormalization method
The spectral renormalization method was introduced by Ablowitz and Musslimani in 2005, [Opt. Lett. 30, pp. 2140-2142] as an effective way to numerically compute (time-independent) bound states for certain nonlinear boundary value problems. % of the nonlinear Schrödinger (NLS), Gross-Pitaevskii and water wave type equ...
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Cloth Manipulation Using Random-Forest-Based Imitation Learning
We present a novel approach for robust manipulation of high-DOF deformable objects such as cloth. Our approach uses a random forest-based controller that maps the observed visual features of the cloth to an optimal control action of the manipulator. The topological structure of this random forest-based controller is ...
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Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations
We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the coefficients (e.g. material parameters) exhibit random, fine scale variability. The direct solution to such problems requires grids that are small enough to resolve this fine scale variability which unavoidably requires the repeated solution ...
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A practical guide to the simultaneous determination of protein structure and dynamics using metainference
Accurate protein structural ensembles can be determined with metainference, a Bayesian inference method that integrates experimental information with prior knowledge of the system and deals with all sources of uncertainty and errors as well as with system heterogeneity. Furthermore, metainference can be implemented u...
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Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods
The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a "library" of candidate prediction models. The SL is not restricted to a single prediction model, but use...
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Transmission spectroscopy of the hot Jupiter TrES-3 b: Disproof of an overly large Rayleigh-like feature
Context. Transit events of extrasolar planets offer the opportunity to study the composition of their atmospheres. Previous work on transmission spectroscopy of the close-in gas giant TrES-3 b revealed an increase in absorption towards blue wavelengths of very large amplitude in terms of atmospheric pressure scale he...
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Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters
Markov decision processes (MDPs) are a popular model for performance analysis and optimization of stochastic systems. The parameters of stochastic behavior of MDPs are estimates from empirical observations of a system; their values are not known precisely. Different types of MDPs with uncertain, imprecise or bounded ...
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Certificates for triangular equivalence and rank profiles
In this paper, we give novel certificates for triangular equivalence and rank profiles. These certificates enable to verify the row or column rank profiles or the whole rank profile matrix faster than recomputing them, with a negligible overall overhead. We first provide quadratic time and space non-interactive certi...
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TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting
TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed training of gradient boosted trees. It is based on TensorFlow, and its distinguishing features include a novel architecture, automatic loss differentiation, layer-by-layer boosting that results in smaller ensembles and faster prediction, pri...
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Circuit Treewidth, Sentential Decision, and Query Compilation
The evaluation of a query over a probabilistic database boils down to computing the probability of a suitable Boolean function, the lineage of the query over the database. The method of query compilation approaches the task in two stages: first, the query lineage is implemented (compiled) in a circuit form where prob...
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Improvements in the Small Sample Efficiency of the Minimum $S$-Divergence Estimators under Discrete Models
This paper considers the problem of inliers and empty cells and the resulting issue of relative inefficiency in estimation under pure samples from a discrete population when the sample size is small. Many minimum divergence estimators in the $S$-divergence family, although possessing very strong outlier stability pro...
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Homogeneous Kobayashi-hyperbolic manifolds with automorphism group of subcritical dimension
We determine all connected homogeneous Kobayashi-hyperbolic manifolds of dimension $n\ge 2$ whose holomorphic automorphism group has dimension $n^2-3$. This result complements existing classifications for automorphism group dimension $n^2-2$ (which is in some sense critical) and greater.
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Centroidal localization game
One important problem in a network is to locate an (invisible) moving entity by using distance-detectors placed at strategical locations. For instance, the metric dimension of a graph $G$ is the minimum number $k$ of detectors placed in some vertices $\{v_1,\cdots,v_k\}$ such that the vector $(d_1,\cdots,d_k)$ of the...
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Improving the Burgess bound via Polya-Vinogradov
We show that even mild improvements of the Polya-Vinogradov inequality would imply significant improvements of Burgess' bound on character sums. Our main ingredients are a lower bound on certain types of character sums (coming from works of the second author joint with J. Bober and Y. Lamzouri) and a quantitative rel...
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Processes accompanying stimulated recombination of atoms
The phenomenon of polarization of nuclei in the process of stimulated recombination of atoms in the field of circularly polarized laser radiation is considered. This effect is considered for the case of the proton-electron beams used in the method of electron cooling. An estimate is obtained for the maximum degree of...
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The Complexity of Factors of Multivariate Polynomials
The existence of string functions, which are not polynomial time computable, but whose graph is checkable in polynomial time, is a basic assumption in cryptography. We prove that in the framework of algebraic complexity, there are no such families of polynomial functions of polynomially bounded degree over fields of ...
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Chatbots as Conversational Recommender Systems in Urban Contexts
In this paper, we outline the vision of chatbots that facilitate the interaction between citizens and policy-makers at the city scale. We report the results of a co-design session attended by more than 60 participants. We give an outlook of how some challenges associated with such chatbot systems could be addressed i...
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An estimate of the first non-zero eigenvalue of the Laplacian by the Ricci curvature on edges of graphs
We define the distance between edges of graphs and study the coarse Ricci curvature on edges. We consider the Laplacian on edges based on the Jost-Horak's definition of the Laplacian on simplicial complexes. As one of our main results, we obtain an estimate of the first non-zero eigenvalue of the Laplacian by the Ric...
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A Deep Reinforcement Learning Chatbot
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural lan...
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Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance operation cost and conditional value-at-risk (CVaR) of device and...
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Deep Learning for Real-Time Crime Forecasting and its Ternarization
Real-time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this w...
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Thermal Modeling of Comet-Like Objects from AKARI Observation
We investigated the physical properties of the comet-like objects 107P/(4015) Wilson--Harrington (4015WH) and P/2006 HR30 (Siding Spring; HR30) by applying a simple thermophysical model (TPM) to the near-infrared spectroscopy and broadband observation data obtained by AKARI satellite of JAXA when they showed no detec...
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Improvement to the Prediction of Fuel Cost Distributions Using ARIMA Model
Availability of a validated, realistic fuel cost model is a prerequisite to the development and validation of new optimization methods and control tools. This paper uses an autoregressive integrated moving average (ARIMA) model with historical fuel cost data in development of a three-step-ahead fuel cost distribution...
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Timed Discrete-Event Systems are Synchronous Product Structures
In this work, we show that the model of timed discrete-event systems (TDES) proposed by Brandin and Wonham is essentially a synchronous product structure. This resolves an open problem that has remained unaddressed for the past 25 years and has its application in developing a more efficient timed state-tree structure...
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Maturation Trajectories of Cortical Resting-State Networks Depend on the Mediating Frequency Band
The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whe...
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High-power closed-cycle $^4$He cryostat with top-loading sample exchange
We report on the development of a versatile cryogen-free laboratory cryostat based upon a commercial pulse tube cryocooler. It provides enough cooling power for continuous recondensation of circulating $^4$He gas at a condensation pressure of approximately 250~mbar. Moreover, the cryostat allows for exchange of diffe...
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A new proof of Kirchberg's $\mathcal O_2$-stable classification
I present a new proof of Kirchberg's $\mathcal O_2$-stable classification theorem: two separable, nuclear, stable/unital, $\mathcal O_2$-stable $C^\ast$-algebras are isomorphic if and only if their ideal lattices are order isomorphic, or equivalently, their primitive ideal spaces are homeomorphic. Many intermediate r...
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Scaling evidence of the homothetic nature of cities
In this paper we analyse the profile of land use and population density with respect to the distance to the city centre for the European city. In addition to providing the radial population density and soil-sealing profiles for a large set of cities, we demonstrate a remarkable constancy of the profiles across city s...
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Subconvex bounds for Hecke-Maass forms on compact arithmetic quotients of semisimple Lie groups
Let $H$ be a semisimple algebraic group, $K$ a maximal compact subgroup of $G:=H(\mathbb{R})$, and $\Gamma\subset H(\mathbb{Q})$ a congruence arithmetic subgroup. In this paper, we generalize existing subconvex bounds for Hecke-Maass forms on the locally symmetric space $\Gamma \backslash G/K$ to corresponding bounds...
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Studying Positive Speech on Twitter
We present results of empirical studies on positive speech on Twitter. By positive speech we understand speech that works for the betterment of a given situation, in this case relations between different communities in a conflict-prone country. We worked with four Twitter data sets. Through semi-manual opinion mining...
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Geostatistical inference in the presence of geomasking: a composite-likelihood approach
In almost any geostatistical analysis, one of the underlying, often implicit, modelling assump- tions is that the spatial locations, where measurements are taken, are recorded without error. In this study we develop geostatistical inference when this assumption is not valid. This is often the case when, for example, ...
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Large sample analysis of the median heuristic
In kernel methods, the median heuristic has been widely used as a way of setting the bandwidth of RBF kernels. While its empirical performances make it a safe choice under many circumstances, there is little theoretical understanding of why this is the case. Our aim in this paper is to advance our understanding of th...
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Divergence Framework for EEG based Multiclass Motor Imagery Brain Computer Interface
Similar to most of the real world data, the ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this letter, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimiz...
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Accelerated Stochastic Power Iteration
Principal component analysis (PCA) is one of the most powerful tools in machine learning. The simplest method for PCA, the power iteration, requires $\mathcal O(1/\Delta)$ full-data passes to recover the principal component of a matrix with eigen-gap $\Delta$. Lanczos, a significantly more complex method, achieves an...
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Generalized phase mixing: Turbulence-like behaviour from unidirectionally propagating MHD waves
We present the results of three-dimensional (3D) ideal magnetohydrodynamics (MHD) simulations on the dynamics of a perpendicularly inhomogeneous plasma disturbed by propagating Alfvénic waves. Simpler versions of this scenario have been extensively studied as the phenomenon of phase mixing. We show that, by generaliz...
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Discovering Bayesian Market Views for Intelligent Asset Allocation
Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction. However, how market participants' behavior is affected by public mood has been rarely discussed. Consequently, there has been little progress in leveraging public mood for the asset allocat...
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Bayesian Static Parameter Estimation for Partially Observed Diffusions via Multilevel Monte Carlo
In this article we consider static Bayesian parameter estimation for partially observed diffusions that are discretely observed. We work under the assumption that one must resort to discretizing the underlying diffusion process, for instance using the Euler-Maruyama method. Given this assumption, we show how one can ...
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EigenNetworks
In many applications, the interdependencies among a set of $N$ time series $\{ x_{nk}, k>0 \}_{n=1}^{N}$ are well captured by a graph or network $G$. The network itself may change over time as well (i.e., as $G_k$). We expect the network changes to be at a much slower rate than that of the time series. This paper int...
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