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A Review on Quantile Regression for Stochastic Computer Experiments
We report on an empirical study of the main strategies for conditional quantile estimation in the context of stochastic computer experiments. To ensure adequate diversity, six metamodels are presented, divided into three categories based on order statistics, functional approaches, and those of Bayesian inspiration. T...
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Scalability of Voltage-Controlled Filamentary and Nanometallic Resistance Memories
Much effort has been devoted to device and materials engineering to realize nanoscale resistance random access memory (RRAM) for practical applications, but there still lacks a rational physical basis to be relied on to design scalable devices spanning many length scales. In particular, the critical switching criteri...
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Functional geometry of protein-protein interaction networks
Motivation: Protein-protein interactions (PPIs) are usually modelled as networks. These networks have extensively been studied using graphlets, small induced subgraphs capturing the local wiring patterns around nodes in networks. They revealed that proteins involved in similar functions tend to be similarly wired. Ho...
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A Two-Layer Component-Based Allocation for Embedded Systems with GPUs
Component-based development is a software engineering paradigm that can facilitate the construction of embedded systems and tackle its complexities. The modern embedded systems have more and more demanding requirements. One way to cope with such versatile and growing set of requirements is to employ heterogeneous pro...
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Sales Forecast in E-commerce using Convolutional Neural Network
Sales forecast is an essential task in E-commerce and has a crucial impact on making informed business decisions. It can help us to manage the workforce, cash flow and resources such as optimizing the supply chain of manufacturers etc. Sales forecast is a challenging problem in that sales is affected by many factors ...
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On normalization of inconsistency indicators in pairwise comparisons
In this study, we provide mathematical and practice-driven justification for using $[0,1]$ normalization of inconsistency indicators in pairwise comparisons. The need for normalization, as well as problems with the lack of normalization, are presented. A new type of paradox of infinity is described.
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Gamma factors of intertwining periods and distinction for inner forms of $\GL(n)$
Let $F$ be a $p$-adic fied, $E$ be a quadratic extension of $F$, and $D$ be an $F$-division algebra of odd index. Set $H=\mathrm{GL}m,D)$ and $G=\mathrm{GL}(m,D\otimes_F E)$, we carry out a fine study of local intertwining open periods attached to $H$-distinguished induced representations of inner forms of $G$. These...
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Spin-flip scattering selection in a controlled molecular junction
A simple double-decker molecule with magnetic anisotropy, nickelocene, is attached to the metallic tip of a low-temperature scanning tunneling microscope. In the presence of a Cu(100) surface, the conductance around the Fermi energy is governed by spin-flip scattering, the nature of which is determined by the tunneli...
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Mean-Field Sparse Jurdjevic--Quinn Control
We consider nonlinear transport equations with non-local velocity, describing the time-evolution of a measure, which in practice may represent the density of a crowd. Such equations often appear by taking the mean-field limit of finite-dimensional systems modelling collective dynamics. We first give a sense to dissip...
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Sampling from Social Networks with Attributes
Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networ...
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Exploiting Investors Social Network for Stock Prediction in China's Market
Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail i...
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Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks
Can we make a famous rap singer like Eminem sing whatever our favorite song? Singing style transfer attempts to make this possible, by replacing the vocal of a song from the source singer to the target singer. This paper presents a method that learns from unpaired data for singing style transfer using generative adve...
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A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots
We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots. Our system mainly focuses on localization of endoscopic capsule robots inside the GI tract using only visual information captured by a mono camera integrated to the robot. The proposed system is a 23-...
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On a cross-diffusion system arising in image denosing
We study a generalization of a cross-diffusion problem deduced from a nonlinear complex-variable diffusion model for signal and image denoising. We prove the existence of weak solutions of the time-independent problem with fidelity terms under mild conditions on the data problem. Then, we show that this translates on...
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Locally Repairable Codes with Multiple $(r_{i}, δ_{i})$-Localities
In distributed storage systems, locally repairable codes (LRCs) are introduced to realize low disk I/O and repair cost. In order to tolerate multiple node failures, the LRCs with \emph{$(r, \delta)$-locality} are further proposed. Since hot data is not uncommon in a distributed storage system, both Zeh \emph{et al.} ...
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Reconciling Enumerative and Symbolic Search in Syntax-Guided Synthesis
Syntax-guided synthesis aims to find a program satisfying semantic specification as well as user-provided structural hypothesis. For syntax-guided synthesis there are two main search strategies: concrete search, which systematically or stochastically enumerates all possible solutions, and symbolic search, which inter...
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Periodic solutions of semilinear Duffing equations with impulsive effects
In this paper we are concerned with the existence of periodic solutions for semilinear Duffing equations with impulsive effects. Firstly for the autonomous one, basing on Poincaré-Birkhoff twist theorem, we prove the existence of infinitely many periodic solutions. Secondly, as for the nonautonomous case, the impulse...
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Spectral and scattering theory for perturbed block Toeplitz operators
We analyse spectral properties of a class of compact perturbations of block Toeplitz operators associated with analytic symbols. In particular, a limiting absorption principle and the absence of singular continuous spectrum are shown. The existence and the completeness of wave operators are also obtained. Our study i...
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Robust adaptive droop control for DC microgrids
There are tradeoffs between current sharing among distributed resources and DC bus voltage stability when conventional droop control is used in DC microgrids. As current sharing approaches the setpoint, bus voltage deviation increases. Previous studies have suggested using secondary control utilizing linear controlle...
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Off-diagonal asymptotic properties of Bergman kernels associated to analytic Kähler potentials
We prove a new off-diagonal asymptotic of the Bergman kernels associated to tensor powers of a positive line bundle on a compact Kähler manifold. We show that if the Kähler potential is real analytic, then the Bergman kernel accepts a complete asymptotic expansion in a neighborhood of the diagonal of shrinking size $...
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Network Backboning with Noisy Data
Networks are powerful instruments to study complex phenomena, but they become hard to analyze in data that contain noise. Network backbones provide a tool to extract the latent structure from noisy networks by pruning non-salient edges. We describe a new approach to extract such backbones. We assume that edge weights...
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The 2017 DAVIS Challenge on Video Object Segmentation
We present the 2017 DAVIS Challenge on Video Object Segmentation, a public dataset, benchmark, and competition specifically designed for the task of video object segmentation. Following the footsteps of other successful initiatives, such as ILSVRC and PASCAL VOC, which established the avenue of research in the fields...
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How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow
Context: The success of Stack Overflow and other community-based question-and-answer (Q&A) sites depends mainly on the will of their members to answer others' questions. In fact, when formulating requests on Q&A sites, we are not simply seeking for information. Instead, we are also asking for other people's help and ...
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Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays
Massive multiple-input multiple-output (MIMO) systems, which utilize a large number of antennas at the base station, are expected to enhance network throughput by enabling improved multiuser MIMO techniques. To deploy many antennas in reasonable form factors, base stations are expected to employ antenna arrays in bot...
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A Novel Stretch Energy Minimization Algorithm for Equiareal Parameterizations
Surface parameterizations have been widely applied to computer graphics and digital geometry processing. In this paper, we propose a novel stretch energy minimization (SEM) algorithm for the computation of equiareal parameterizations of simply connected open surfaces with a very small area distortion and a highly imp...
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Time Complexity Analysis of a Distributed Stochastic Optimization in a Non-Stationary Environment
In this paper, we consider a distributed stochastic optimization problem where the goal is to minimize the time average of a cost function subject to a set of constraints on the time averages of related stochastic processes called penalties. We assume that the state of the system is evolving in an independent and non...
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Robust Counterfactual Inferences using Feature Learning and their Applications
In a wide variety of applications, including personalization, we want to measure the difference in outcome due to an intervention and thus have to deal with counterfactual inference. The feedback from a customer in any of these situations is only 'bandit feedback' - that is, a partial feedback based on whether we cho...
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Regularity of symbolic powers and Arboricity of matroids
Let $\Delta$ be a simplicial complex of a matroid $M$. In this paper, we explicitly compute the regularity of all the symbolic powers of a Stanley-Reisner ideal $I_\Delta$ in terms of combinatorial data of the matroid $M$. In order to do that, we provide a sharp bound between the arboricity of $M$ and the circumferen...
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Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smooth...
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On Packet Scheduling with Adversarial Jamming and Speedup
In Packet Scheduling with Adversarial Jamming packets of arbitrary sizes arrive over time to be transmitted over a channel in which instantaneous jamming errors occur at times chosen by the adversary and not known to the algorithm. The transmission taking place at the time of jamming is corrupt, and the algorithm lea...
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Coarse-Grid Computational Fluid Dynamic (CG-CFD) Error Prediction using Machine Learning
Despite the progress in high performance computing, Computational Fluid Dynamics (CFD) simulations are still computationally expensive for many practical engineering applications such as simulating large computational domains and highly turbulent flows. One of the major reasons of the high expense of CFD is the need ...
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EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection
In this paper, we propose a deep learning based approach for facial action unit detection by enhancing and cropping the regions of interest. The approach is implemented by adding two novel nets (layers): the enhancing layers and the cropping layers, to a pretrained CNN model. For the enhancing layers, we designed an ...
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Influence of surface and bulk water ice on the reactivity of a water-forming reaction
On the surface of icy dust grains in the dense regions of the interstellar medium a rich chemistry can take place. Due to the low temperature, reactions that proceed via a barrier can only take place through tunneling. The reaction H + H$_2$O$_2$ $\rightarrow$ H$_2$O + OH is such a case with a gas-phase barrier of $\...
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Impact of energy dissipation on interface shapes and on rates for dewetting from liquid substrates
We revisit the fundamental problem of liquid-liquid dewetting and perform a detailed comparison of theoretical predictions based on thin-film models with experimental measurements obtained by atomic force microscopy (AFM). Specifically, we consider the dewetting of a liquid polystyrene (PS) layer from a liquid polyme...
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Resonance control of graphene drum resonator in nonlinear regime by standing wave of light
We demonstrate the control of resonance characteristics of a drum type graphene mechanical resonator in nonlinear oscillation regime by the photothermal effect, which is induced by a standing wave of light between a graphene and a substrate. Unlike the conventional Duffing type nonlinearity, the resonance characteris...
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Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos
A surrogate model approximates a computationally expensive solver. Polynomial Chaos is a method to construct surrogate models by summing combinations of carefully chosen polynomials. The polynomials are chosen to respect the probability distributions of the uncertain input variables (parameters); this allows for both...
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Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards
The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. However, the current algorithms lack an eff...
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Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification
This work explores the trade-off between the number of samples required to accurately build models of dynamical systems and the degradation of performance in various control objectives due to a coarse approximation. In particular, we show that simple models can be easily fit from input/output data and are sufficient ...
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Construction,sensitivity index, and synchronization speed of optimal networks
The stability (or instability) of synchronization is important in a number of real world systems, including the power grid, the human brain and biological cells. For identical synchronization, the synchronizability of a network, which can be measured by the range of coupling strength that admits stable synchronizatio...
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A Vorticity-Preserving Hydrodynamical Scheme for Modeling Accretion Disk Flows
Vortices, turbulence, and unsteady non-laminar flows are likely both prominent and dynamically important features of astrophysical disks. Such strongly nonlinear phenomena are often difficult, however, to simulate accurately, and are generally amenable to analytic treatment only in idealized form. In this paper, we e...
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Direct simulation of liquid-gas-solid flow with a free surface lattice Boltzmann method
Direct numerical simulation of liquid-gas-solid flows is uncommon due to the considerable computational cost. As the grid spacing is determined by the smallest involved length scale, large grid sizes become necessary -- in particular if the bubble-particle aspect ratio is on the order of 10 or larger. Hence, it arise...
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TALL: Temporal Activity Localization via Language Query
This paper focuses on temporal localization of actions in untrimmed videos. Existing methods typically train classifiers for a pre-defined list of actions and apply them in a sliding window fashion. However, activities in the wild consist of a wide combination of actors, actions and objects; it is difficult to design...
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2D granular flows with the $μ(I)$ rheology and side walls friction: a well balanced multilayer discretization
We present here numerical modelling of granular flows with the $\mu(I)$ rheology in confined channels. The contribution is twofold: (i) a model to approximate the Navier-Stokes equations with the $\mu(I)$ rheology through an asymptotic analysis. Under the hypothesis of a one-dimensional flow, this model takes into ac...
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A scientists' view of scientometrics: Not everything that counts can be counted
Like it or not, attempts to evaluate and monitor the quality of academic research have become increasingly prevalent worldwide. Performance reviews range from at the level of individuals, through research groups and departments, to entire universities. Many of these are informed by, or functions of, simple scientomet...
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On analyzing and evaluating privacy measures for social networks under active attack
Widespread usage of complex interconnected social networks such as Facebook, Twitter and LinkedIn in modern internet era has also unfortunately opened the door for privacy violation of users of such networks by malicious entities. In this article we investigate, both theoretically and empirically, privacy violation m...
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Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise
Recent developments have established the vulnerability of deep reinforcement learning to policy manipulation attacks via intentionally perturbed inputs, known as adversarial examples. In this work, we propose a technique for mitigation of such attacks based on addition of noise to the parameter space of deep reinforc...
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Multi-Observation Elicitation
We study loss functions that measure the accuracy of a prediction based on multiple data points simultaneously. To our knowledge, such loss functions have not been studied before in the area of property elicitation or in machine learning more broadly. As compared to traditional loss functions that take only a single ...
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Next Stop "NoOps": Enabling Cross-System Diagnostics Through Graph-based Composition of Logs and Metrics
Performing diagnostics in IT systems is an increasingly complicated task, and it is not doable in satisfactory time by even the most skillful operators. Systems and their architecture change very rapidly in response to business and user demand. Many organizations see value in the maintenance and management model of N...
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Davenport-Heilbronn Theorems for Quotients of Class Groups
We prove a generalization of the Davenport-Heilbronn theorem to quotients of ideal class groups of quadratic fields by the primes lying above a fixed set of rational primes $S$. Additionally, we obtain average sizes for the relaxed Selmer group $\mathrm{Sel}_3^S(K)$ and for $\mathcal{O}_{K,S}^\times/(\mathcal{O}_{K,S...
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Learning Universal Adversarial Perturbations with Generative Models
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. It was recently shown that given a dataset and classifier, there exists so called universal adversarial perturbations, a sing...
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Large-Scale Classification using Multinomial Regression and ADMM
We present a novel method for learning the weights in multinomial logistic regression based on the alternating direction method of multipliers (ADMM). In each iteration, our algorithm decomposes the training into three steps; a linear least-squares problem for the weights, a global variable update involving a separab...
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First order dipolar phase transition in the Dicke model with infinitely coordinated frustrating interaction
We found analytically a first order quantum phase transition in the Cooper pair box array of $N$ low-capacitance Josephson junctions capacitively coupled to a resonant photon in a microwave cavity. The Hamiltonian of the system maps on the extended Dicke Hamiltonian of $N$ spins one-half with infinitely coordinated a...
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Parsimonious Bayesian deep networks
Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that infer capacity-regularized network architectures from the data and require neither cross-validation nor fine-tuning when training the model. One of the two essential components of a ...
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Learning to Play Othello with Deep Neural Networks
Achieving superhuman playing level by AlphaGo corroborated the capabilities of convolutional neural architectures (CNNs) for capturing complex spatial patterns. This result was to a great extent due to several analogies between Go board states and 2D images CNNs have been designed for, in particular translational inv...
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A causal modelling framework for reference-based imputation and tipping point analysis
We consider estimating the "de facto" or effectiveness estimand in a randomised placebo-controlled or standard-of-care-controlled drug trial with quantitative outcome, where participants who discontinue an investigational treatment are not followed up thereafter. Carpenter et al (2013) proposed reference-based imputa...
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The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design...
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Weakly-Supervised Spatial Context Networks
We explore the power of spatial context as a self-supervisory signal for learning visual representations. In particular, we propose spatial context networks that learn to predict a representation of one image patch from another image patch, within the same image, conditioned on their real-valued relative spatial offs...
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Variation of ionizing continuum: the main driver of Broad Absorption Line Variability
We present a statistical analysis of the variability of broad absorption lines (BALs) in quasars using the large multi-epoch spectroscopic dataset of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We divide the sample into two groups according to the pattern of the variation of C iv BAL with respect to tha...
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Low Dimensional Atomic Norm Representations in Line Spectral Estimation
The line spectral estimation problem consists in recovering the frequencies of a complex valued time signal that is assumed to be sparse in the spectral domain from its discrete observations. Unlike the gridding required by the classical compressed sensing framework, line spectral estimation reconstructs signals whos...
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The Trio Identity for Quasi-Monte Carlo Error
Monte Carlo methods approximate integrals by sample averages of integrand values. The error of Monte Carlo methods may be expressed as a trio identity: the product of the variation of the integrand, the discrepancy of the sampling measure, and the confounding. The trio identity has different versions, depending on wh...
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Weak type operator Lipschitz and commutator estimates for commuting tuples
Let $f: \mathbb{R}^d \to\mathbb{R}$ be a Lipschitz function. If $B$ is a bounded self-adjoint operator and if $\{A_k\}_{k=1}^d$ are commuting bounded self-adjoint operators such that $[A_k,B]\in L_1(H),$ then $$\|[f(A_1,\cdots,A_d),B]\|_{1,\infty}\leq c(d)\|\nabla(f)\|_{\infty}\max_{1\leq k\leq d}\|[A_k,B]\|_1,$$ whe...
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Fast Spectral Ranking for Similarity Search
Despite the success of deep learning on representing images for particular object retrieval, recent studies show that the learned representations still lie on manifolds in a high dimensional space. This makes the Euclidean nearest neighbor search biased for this task. Exploring the manifolds online remains expensive ...
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Transition of multi-diffusive states in a biased periodic potential
We study a frequency-dependent damping model of hyper-diffusion within the generalized Langevin equation. The model allows for the colored noise defined by its spectral density, assumed to be proportional to $\omega^{\delta-1}$ at low frequencies with $0<\delta<1$ (sub-Ohmic damping) or $1<\delta<2$ (super-Ohmic damp...
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Deformable Classifiers
Geometric variations of objects, which do not modify the object class, pose a major challenge for object recognition. These variations could be rigid as well as non-rigid transformations. In this paper, we design a framework for training deformable classifiers, where latent transformation variables are introduced, an...
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Energy Dissipation in Hamiltonian Chains of Rotators
We discuss, in the context of energy flow in high-dimensional systems and Kolmogorov-Arnol'd-Moser (KAM) theory, the behavior of a chain of rotators (rotors) which is purely Hamiltonian, apart from dissipation at just one end. We derive bounds on the dissipation rate which become arbitrarily small in certain physical...
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Computational complexity, torsion-freeness of homoclinic Floer homology, and homoclinic Morse inequalities
Floer theory was originally devised to estimate the number of 1-periodic orbits of Hamiltonian systems. In earlier works, we constructed Floer homology for homoclinic orbits on two dimensional manifolds using combinatorial techniques. In the present paper, we study theoretic aspects of computational complexity of hom...
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Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models
Bayesian inference for stochastic volatility models using MCMC methods highly depends on actual parameter values in terms of sampling efficiency. While draws from the posterior utilizing the standard centered parameterization break down when the volatility of volatility parameter in the latent state equation is small...
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Simple root flows for Hitchin representations
We study simple root flows and Liouville currents for Hitchin representations. We show that the Liouville current is associated to the measure of maximal entropy for a simple root flow, derive a Liouville volume rigidity result, and construct a Liouville pressure metric on the Hitchin component.
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On the Origin of Deep Learning
This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep learning like convolutional neural networks, deep belief networks, and recurre...
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Control of Asynchronous Imitation Dynamics on Networks
Imitation is widely observed in populations of decision-making agents. Using our recent convergence results for asynchronous imitation dynamics on networks, we consider how such networks can be efficiently driven to a desired equilibrium state by offering payoff incentives for using a certain strategy, either uniform...
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Finsler structures on holomorphic Lie algebroids
Complex Finsler vector bundles have been studied mainly by T. Aikou, who defined complex Finsler structures on holomorphic vector bundles. In this paper, we consider the more general case of a holomorphic Lie algebroid E and we introduce Finsler structures, partial and Chern-Finsler connections on it. First, we recal...
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The role of complex analysis in modeling economic growth
Development and growth are complex and tumultuous processes. Modern economic growth theories identify some key determinants of economic growth. However, the relative importance of the determinants remains unknown, and additional variables may help clarify the directions and dimensions of the interactions. The novel s...
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Inputs from Hell: Generating Uncommon Inputs from Common Samples
Generating structured input files to test programs can be performed by techniques that produce them from a grammar that serves as the specification for syntactically correct input files. Two interesting scenarios then arise for effective testing. In the first scenario, software engineers would like to generate inputs...
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On equivariant formal deformation theory
Using the set-up of deformation categories of Talpo and Vistoli, we re-interpret and generalize, in the context of cartesian morphisms in abstract categories, some results of Rim concerning obstructions against extensions of group actions in infinitesimal deformations. Furthermore, we observe that finite étale coveri...
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A self-consistent cloud model for brown dwarfs and young giant exoplanets: comparison with photometric and spectroscopic observations
We developed a simple, physical and self-consistent cloud model for brown dwarfs and young giant exoplanets. We compared different parametrisations for the cloud particle size, by either fixing particle radii, or fixing the mixing efficiency (parameter fsed) or estimating particle radii from simple microphysics. The ...
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Consistency Between the Luminosity Function of Resolved Millisecond Pulsars and the Galactic Center Excess
Fermi Large Area Telescope data reveal an excess of GeV gamma rays from the direction of the Galactic Center and bulge. Several explanations have been proposed for this excess including an unresolved population of millisecond pulsars (MSPs) and self-annihilating dark matter. It has been claimed that a key discriminan...
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First functionality tests of a 64 x 64 pixel DSSC sensor module connected to the complete ladder readout
The European X-ray Free Electron Laser (XFEL.EU) will provide every 0.1 s a train of 2700 spatially coherent ultrashort X-ray pulses at 4.5 MHz repetition rate. The Small Quantum Systems (SQS) instrument and the Spectroscopy and Coherent Scattering instrument (SCS) operate with soft X-rays between 0.5 keV - 6keV. The...
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Label Propagation on K-partite Graphs with Heterophily
In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption. Homophily label propagation (i.e., two connected nodes share similar labels) in homogeneous graph (with same types of vertices and relations) has been extensively studied before. Unfortunately, real-lif...
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On LoRaWAN Scalability: Empirical Evaluation of Susceptibility to Inter-Network Interference
Appearing on the stage quite recently, the Low Power Wide Area Networks (LPWANs) are currently getting much of attention. In the current paper we study the susceptibility of one LPWAN technology, namely LoRaWAN, to the inter-network interferences. By means of excessive empirical measurements employing the certified c...
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DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downs...
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Statistical analysis of the ambiguities in the asteroid period determinations
Among asteroids there exist ambiguities in their rotation period determinations. They are due to incomplete coverage of the rotation, noise and/or aliases resulting from gaps between separate lightcurves. To help to remove such uncertainties, basic characteristic of the lightcurves resulting from constraints imposed ...
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The Assistive Multi-Armed Bandit
Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they ...
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Localized Manifold Harmonics for Spectral Shape Analysis
The use of Laplacian eigenfunctions is ubiquitous in a wide range of computer graphics and geometry processing applications. In particular, Laplacian eigenbases allow generalizing the classical Fourier analysis to manifolds. A key drawback of such bases is their inherently global nature, as the Laplacian eigenfunctio...
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How to Search the Internet Archive Without Indexing It
Significant parts of cultural heritage are produced on the web during the last decades. While easy accessibility to the current web is a good baseline, optimal access to the past web faces several challenges. This includes dealing with large-scale web archive collections and lacking of usage logs that contain implici...
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Quantum Dot at a Luttinger liquid edge - Exact solution via Bethe Ansatz
We study a system consisting of a Luttinger liquid coupled to a quantum dot on the boundary. The Luttinger liquid is expressed in terms of fermions interacting via density-density coupling and the dot is modeled as an interacting resonant level on to which the bulk fermions can tunnel. We solve the Hamiltonian exactl...
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Optimal Jittered Sampling for two Points in the Unit Square
Jittered Sampling is a refinement of the classical Monte Carlo sampling method. Instead of picking $n$ points randomly from $[0,1]^2$, one partitions the unit square into $n$ regions of equal measure and then chooses a point randomly from each partition. Currently, no good rules for how to partition the space are ava...
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Brain structural connectivity atrophy in Alzheimer's disease
Analysis and quantification of brain structural changes, using Magnetic resonance imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Network-based models of the brain have shown that both local and global topological properties can reveal patterns o...
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Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry
This work proposes a visual odometry method that combines points and plane primitives, extracted from a noisy depth camera. Depth measurement uncertainty is modelled and propagated through the extraction of geometric primitives to the frame-to-frame motion estimation, where pose is optimized by weighting the residual...
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Autoignition of Butanol Isomers at Low to Intermediate Temperature and Elevated Pressure
Autoignition delay experiments for the isomers of butanol, including n-, sec-, tert-, and iso-butanol, have been performed using a heated rapid compression machine. For a compressed pressure of 15 bar, the compressed temperatures have been varied in the range of 725-855 K for all the stoichiometric fuel/oxidizer mixt...
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On the $k$-abelian complexity of the Cantor sequence
In this paper, we prove that for every integer $k \geq 1$, the $k$-abelian complexity function of the Cantor sequence $\mathbf{c} = 101000101\cdots$ is a $3$-regular sequence.
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Sharp rates of convergence for accumulated spectrograms
We investigate an inverse problem in time-frequency localization: the approximation of the symbol of a time-frequency localization operator from partial spectral information by the method of accumulated spectrograms (the sum of the spectrograms corresponding to large eigenvalues). We derive a sharp bound for the rate...
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Putting a Face to the Voice: Fusing Audio and Visual Signals Across a Video to Determine Speakers
In this paper, we present a system that associates faces with voices in a video by fusing information from the audio and visual signals. The thesis underlying our work is that an extremely simple approach to generating (weak) speech clusters can be combined with visual signals to effectively associate faces and voice...
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Topological Interplay between Knots and Entangled Vortex-Membranes
In this paper, the Kelvin wave and knot dynamics are studied on three dimensional smoothly deformed entangled vortex-membranes in five dimensional space. Owing to the existence of local Lorentz invariance and diffeomorphism invariance, in continuum limit gravity becomes an emergent phenomenon on 3+1 dimensional zero-...
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The first and second fundamental theorems of invariant theory for the quantum general linear supergroup
We develop the non-commutative polynomial version of the invariant theory for the quantum general linear supergroup ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$. A non-commutative ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-module superalgebra $\mathcal{P}^{k|l}_{\,r|s}$ is constructed, which is the quantum analogue of the supersymmetr...
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Poly-Spline Finite Element Method
We introduce an integrated meshing and finite element method pipeline enabling black-box solution of partial differential equations in the volume enclosed by a boundary representation. We construct a hybrid hexahedral-dominant mesh, which contains a small number of star-shaped polyhedra, and build a set of high-order...
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Turing Completeness of Finite, Epistemic Programs
In this note, we show the class of finite, epistemic programs to be Turing complete. Epistemic programs is a widely used update mechanism used in epistemic logic, where it such are a special type of action models: One which does not contain postconditions.
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Spontaneous domain formation in disordered copolymers as a mechanism for chromosome structuring
Motivated by the problem of domain formation in chromosomes, we studied a co--polymer model where only a subset of the monomers feel attractive interactions. These monomers are displaced randomly from a regularly-spaced pattern, thus introducing some quenched disorder in the system. Previous work has shown that in th...
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Reconstruction of Correlated Sources with Energy Harvesting Constraints in Delay-constrained and Delay-tolerant Communication Scenarios
In this paper, we investigate the reconstruction of time-correlated sources in a point-to-point communications scenario comprising an energy-harvesting sensor and a Fusion Center (FC). Our goal is to minimize the average distortion in the reconstructed observations by using data from previously encoded sources as sid...
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Future of Flexible Robotic Endoscopy Systems
Robotics enables a variety of unconventional actuation strategies to be used for endoscopes, resulting in reduced trauma to the GI tract. For transmission of force to distally mounted endoscopic instruments, robotically actuated tendon sheath mechanisms are the current state of the art. Robotics in surgical endoscopy...
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Shear-driven parametric instability in a precessing sphere
The present numerical study aims at shedding light on the mechanism underlying the precessional instability in a sphere. Precessional instabilities in the form of parametric resonance due to topographic coupling have been reported in a spheroidal geometry both analytically and numerically. We show that such parametri...
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