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Learning Filter Functions in Regularisers by Minimising Quotients
Learning approaches have recently become very popular in the field of inverse problems. A large variety of methods has been established in recent years, ranging from bi-level learning to high-dimensional machine learning techniques. Most learning approaches, however, only aim at fitting parametrised models to favoura...
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Riemann-Theta Boltzmann Machine
A general Boltzmann machine with continuous visible and discrete integer valued hidden states is introduced. Under mild assumptions about the connection matrices, the probability density function of the visible units can be solved for analytically, yielding a novel parametric density function involving a ratio of Rie...
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A Joint Quantile and Expected Shortfall Regression Framework
We introduce a novel regression framework which simultaneously models the quantile and the Expected Shortfall (ES) of a response variable given a set of covariates. This regression is based on a strictly consistent loss function for the pair quantile and ES, which allows for M- and Z-estimation of the joint regressio...
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Poincaré Embeddings for Learning Hierarchical Representations
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this prope...
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Randomly cross-linked polymer models
Polymer models are used to describe chromatin, which can be folded at different spatial scales by binding molecules. By folding, chromatin generates loops of various sizes. We present here a randomly cross-linked (RCL) polymer model, where monomer pairs are connected randomly. We obtain asymptotic formulas for the st...
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Multi-Stage Complex Contagions in Random Multiplex Networks
Complex contagion models have been developed to understand a wide range of social phenomena such as adoption of cultural fads, the diffusion of belief, norms, and innovations in social networks, and the rise of collective action to join a riot. Most existing works focus on contagions where individuals' states are rep...
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Secrecy and Robustness for Active Attack in Secure Network Coding and its Application to Network Quantum Key Distribution
In network coding, we discuss the effect of sequential error injection on information leakage. We show that there is no improvement when the operations in the network are linear operations. However, when the operations in the network contains non-linear operations, we find a counterexample to improve Eve's obtained i...
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SLAMBooster: An Application-aware Controller for Approximation in SLAM
Simultaneous Localization and Mapping (SLAM) is the problem of constructing a map of an agent's environment while localizing or tracking the mobile agent's position and orientation within the map. Algorithms for SLAM have high computational requirements, which has hindered their use on embedded devices. Approximation...
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Modeling sepsis progression using hidden Markov models
Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis diagnostic criteria fail to account for significant underlying heterogeneity, both between patients as well as over time in a single patient....
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Optimized Bacteria are Environmental Prediction Engines
Experimentalists have observed phenotypic variability in isogenic bacteria populations. We explore the hypothesis that in fluctuating environments this variability is tuned to maximize a bacterium's expected log growth rate, potentially aided by epigenetic markers that store information about past environments. We sh...
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Photo-realistic Facial Texture Transfer
Style transfer methods have achieved significant success in recent years with the use of convolutional neural networks. However, many of these methods concentrate on artistic style transfer with few constraints on the output image appearance. We address the challenging problem of transferring face texture from a styl...
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Segmentation of skin lesions based on fuzzy classification of pixels and histogram thresholding
This paper proposes an innovative method for segmentation of skin lesions in dermoscopy images developed by the authors, based on fuzzy classification of pixels and histogram thresholding.
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Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been successfully applied to a range of challenging environments, but the proliferation of algorithms makes it difficult to discern which particular approach would be bes...
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Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification
The key issues pertaining to collection of epidemic disease data for our analysis purposes are that it is a labour intensive, time consuming and expensive process resulting in availability of sparse sample data which we use to develop prediction models. To address this sparse data issue, we present novel Incremental ...
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Millimeter-scale layered MoSe2 grown on sapphire and evidence for negative magnetoresistance
Molecular beam epitaxy technique has been used to deposit a single layer and a bilayer of MoSe 2 on sapphire. Extensive characterizations including in-situ and ex-situ measurements show that the layered MoSe 2 grows in a scalable manner on the substrate and reveals characteristics of a stoichiometric 2H-phase. The la...
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Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars
We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar - Dynamic Syntax and Type Theory with Records (DS-TTR) - with Reinforcement Learning (RL), where language generation and dialogue mana...
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Learning Deep Latent Spaces for Multi-Label Classification
Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming...
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Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive task. We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-reso...
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Improper Filter Reduction
Combinatorial filters have been the subject of increasing interest from the robotics community in recent years. This paper considers automatic reduction of combinatorial filters to a given size, even if that reduction necessitates changes to the filter's behavior. We introduce an algorithmic problem called improper f...
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Collective Dynamics of Self-propelled Semiflexible Filaments
The collective behavior of active semiflexible filaments is studied with a model of tangentially driven self-propelled worm-like chains. The combination of excluded-volume interactions and self-propulsion leads to several distinct dynamic phases as a function of bending rigidity, activity, and aspect ratio of individ...
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Two sources of poor coverage of confidence intervals after model selection
We compare the following two sources of poor coverage of post-model-selection confidence intervals: the preliminary data-based model selection sometimes chooses the wrong model and the data used to choose the model is re-used for the construction of the confidence interval.
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On the ERM Principle with Networked Data
Networked data, in which every training example involves two objects and may share some common objects with others, is used in many machine learning tasks such as learning to rank and link prediction. A challenge of learning from networked examples is that target values are not known for some pairs of objects. In thi...
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Front interaction induces excitable behavior
Spatially extended systems can support local transient excitations in which just a part of the system is excited. The mechanisms reported so far are local excitability and excitation of a localized structure. Here we introduce an alternative mechanism based on the coexistence of two homogeneous stable states and spat...
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Characterization of optimal carbon nanotubes under stretching and validation of the Cauchy-Born rule
Carbon nanotubes are modeled as point configurations and investigated by minimizing configurational energies including two-and three-body interactions. Optimal configurations are identified with local minima and their fine geometry is fully characterized in terms of lower-dimensional problems. Under moderate tension,...
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The Closer the Better: Similarity of Publication Pairs at Different Co-Citation Levels
We investigate the similarities of pairs of articles which are co-cited at the different co-citation levels of the journal, article, section, paragraph, sentence and bracket. Our results indicate that textual similarity, intellectual overlap (shared references), author overlap (shared authors), proximity in publicati...
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MP2-F12 Basis Set Convergence for the S66 Noncovalent Interactions Benchmark: Transferability of the Complementary Auxiliary Basis Set (CABS)
Complementary auxiliary basis sets for F12 explicitly correlated calculations appear to be more transferable between orbital basis sets than has been generally assumed. We also find that aVnZ-F12 basis sets, originally developed with anionic systems in mind, appear to be superior for noncovalent interactions as well,...
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Quantized Minimum Error Entropy Criterion
Comparing with traditional learning criteria, such as mean square error (MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and non-Gaussian signal processing and machine learning. The argument of the logarithm in Renyis entropy estimator, called information potential (IP), is a popular MEE cost...
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Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor
A novel predictor for traffic flow forecasting, namely spatio-temporal Bayesian network predictor, is proposed. Unlike existing methods, our approach incorporates all the spatial and temporal information available in a transportation network to carry our traffic flow forecasting of the current site. The Pearson corre...
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AlteregoNets: a way to human augmentation
A person dependent network, called an AlterEgo net, is proposed for development. The networks are created per person. It receives at input an object descriptions and outputs a simulation of the internal person's representation of the objects. The network generates a textual stream resembling the narrative stream of c...
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Modalities in homotopy type theory
Univalent homotopy type theory (HoTT) may be seen as a language for the category of $\infty$-groupoids. It is being developed as a new foundation for mathematics and as an internal language for (elementary) higher toposes. We develop the theory of factorization systems, reflective subuniverses, and modalities in homo...
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Exponentially Slow Heating in Short and Long-range Interacting Floquet Systems
We analyze the dynamics of periodically-driven (Floquet) Hamiltonians with short- and long-range interactions, finding clear evidence for a thermalization time, $\tau^*$, that increases exponentially with the drive frequency. We observe this behavior, both in systems with short-ranged interactions, where our results ...
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High moments of the Estermann function
For $a/q\in\mathbb{Q}$ the Estermann function is defined as $D(s,a/q):=\sum_{n\geq1}d(n)n^{-s}\operatorname{e}(n\frac aq)$ if $\Re(s)>1$ and by meromorphic continuation otherwise. For $q$ prime, we compute the moments of $D(s,a/q)$ at the central point $s=1/2$, when averaging over $1\leq a<q$. As a consequence we ded...
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On Game-Theoretic Risk Management (Part Three) - Modeling and Applications
The game-theoretic risk management framework put forth in the precursor reports "Towards a Theory of Games with Payoffs that are Probability-Distributions" (arXiv:1506.07368 [q-fin.EC]) and "Algorithms to Compute Nash-Equilibria in Games with Distributions as Payoffs" (arXiv:1511.08591v1 [q-fin.EC]) is herein conclud...
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PKS 1954-388: RadioAstron Detection on 80,000 km Baselines and Multiwavelength Observations
We present results from a multiwavelength study of the blazar PKS 1954-388 at radio, UV, X-ray, and gamma-ray energies. A RadioAstron observation at 1.66 GHz in June 2012 resulted in the detection of interferometric fringes on baselines of 6.2 Earth-diameters. This suggests a source frame brightness temperature of gr...
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Smart Assessment of and Tutoring for Computational Thinking MOOC Assignments using MindReader
One of the major hurdles toward automatic semantic understanding of computer programs is the lack of knowledge about what constitutes functional equivalence of code segments. We postulate that a sound knowledgebase can be used to deductively understand code segments in a hierarchical fashion by first de-constructing ...
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EasyInterface: A toolkit for rapid development of GUIs for research prototype tools
In this paper we describe EasyInterface, an open-source toolkit for rapid development of web-based graphical user interfaces (GUIs). This toolkit addresses the need of researchers to make their research prototype tools available to the community, and integrating them in a common environment, rapidly and without being...
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The Cramér-Rao inequality on singular statistical models I
We introduce the notion of the essential tangent bundle of a parametrized measure model and the notion of reduced Fisher metric on a (possibly singular) 2-integrable measure model. Using these notions and a new characterization of $k$-integrable parametrized measure models, we extend the Cramér-Rao inequality to $2$-...
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Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning
Machine learning applications often require hyperparameter tuning. The hyperparameters usually drive both the efficiency of the model training process and the resulting model quality. For hyperparameter tuning, machine learning algorithms are complex black-boxes. This creates a class of challenging optimization probl...
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Reduced fusion systems over $p$-groups with abelian subgroup of index $p$: III
We finish the classification, begun in two earlier papers, of all simple fusion systems over finite nonabelian $p$-groups with an abelian subgroup of index $p$. In particular, this gives many new examples illustrating the enormous variety of exotic examples that can arise. In addition, we classify all simple fusion s...
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Deep learning for studies of galaxy morphology
Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning...
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A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit
Multi-start algorithms are a common and effective tool for metaheuristic searches. In this paper we amplify multi-start capabilities by employing the parallel processing power of the graphics processer unit (GPU) to quickly generate a diverse starting set of solutions for the Unconstrained Binary Quadratic Optimizati...
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Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images
Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e., the derivation of the spectral signatures of the endmembers (endmembers' extraction) and the ...
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On the Necessity of Structured Codes for Communications over MAC with Feedback
The problem of three-user multiple-access channel (MAC) with noiseless feedback is investigated. A new coding strategy is presented. The coding scheme builds upon the natural extension of the Cover-Leung (CL) scheme; and uses quasi-linear codes. A new single-letter achievable rate region is derived. The new achievabl...
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Historical Review of Recurrence Plots
In the last two decades recurrence plots (RPs) were introduced in many different scientific disciplines. It turned out how powerful this method is. After introducing approaches of quantification of RPs and by the study of relationships between RPs and fundamental properties of dynamical systems, this method attracted...
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The Meta Distribution of the SIR for Cellular Networks with Power Control
The meta distribution of the signal-to-interference ratio (SIR) provides fine-grained information about the performance of individual links in a wireless network. This paper focuses on the analysis of the meta distribution of the SIR for both the cellular network uplink and downlink with fractional power control. For...
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Temperature induced transition from p-n to n-n electronic behavior in Ni0.07Zn0.93O/Mg0.21Zn0.79O heterojunction
The transport characteristics across the pulsed laser deposited Ni0.07Zn0.93O/Mg0.21Zn0.79O heterojunction exhibits p-n type semiconducting properties at 10 K while at 100 K, its characteristics become similar to that of an n-n junction. The reason for the same is attributed to the role of larger electronegativity of...
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N-body simulations of gravitational redshifts and other relativistic distortions of galaxy clustering
Large redshift surveys of galaxies and clusters are providing the first opportunities to search for distortions in the observed pattern of large-scale structure due to such effects as gravitational redshift. We focus on non-linear scales and apply a quasi-Newtonian approach using N-body simulations to predict the sma...
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Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architecture where class representations are conditioned for each few-shot trial based on a target image. We also deviate from traditional metric-...
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Gradient Coding from Cyclic MDS Codes and Expander Graphs
Gradient coding is a technique for straggler mitigation in distributed learning. In this paper we design novel gradient codes using tools from classical coding theory, namely, cyclic MDS codes, which compare favourably with existing solutions, both in the applicable range of parameters and in the complexity of the in...
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Commissioning of te China-ADS injector-I testing facility
The 10 MeV accelerator-driven subcritical system (ADS) Injector-I test stand at Institute of High Energy Physics (IHEP) is a testing facility dedicated to demonstrate one of the two injector design schemes [Injector Scheme-I, which works at 325 MHz], for the ADS project in China. The Injector adopted a four vane copp...
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Impact of the Global Crisis on SME Internal vs. External Financing in China
Changes in the capital structure before and after the global financial crisis for SMEs are studied, emphasizing their financing problems, distinguishing between internal financing and external financing determinants. The empirical research bears upon 158 small and medium-sized firms listed on Shenzhen and Shanghai St...
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Dipole force free optical control and cooling of nanofiber trapped atoms
The evanescent field surrounding nano-scale optical waveguides offers an efficient interface between light and mesoscopic ensembles of neutral atoms. However, the thermal motion of trapped atoms, combined with the strong radial gradients of the guided light, leads to a time-modulated coupling between atoms and the li...
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Control refinement for discrete-time descriptor systems: a behavioural approach via simulation relations
The analysis of industrial processes, modelled as descriptor systems, is often computationally hard due to the presence of both algebraic couplings and difference equations of high order. In this paper, we introduce a control refinement notion for these descriptor systems that enables analysis and control design over...
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Beam Based RF Voltage Measurements and Longitudinal Beam Tomography at the Fermilab Booster
Increasing proton beam power on neutrino production targets is one of the major goals of the Fermilab long term accelerator programs. In this effort, the Fermilab 8 GeV Booster synchrotron plays a critical role for at least the next two decades. Therefore, understanding the Booster in great detail is important as we ...
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A framework for quantitative modeling and analysis of highly (re)configurable systems
This paper presents our approach to the quantitative modeling and analysis of highly (re)configurable systems, such as software product lines. Different combinations of the optional features of such a system give rise to combinatorially many individual system variants. We use a formal modeling language that allows us...
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Generating large misalignments in gapped and binary discs
Many protostellar gapped and binary discs show misalignments between their inner and outer discs; in some cases, $\sim70$ degree misalignments have been observed. Here we show that these misalignments can be generated through a "secular precession resonance" between the nodal precession of the inner disc and the prec...
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Bilinear approach to the supersymmetric Gardner equation
We study a supersymmetric version of the Gardner equation (both focusing and defocusing) using the superbilinear formalism. This equation is new and cannot be obtained from supersymmetric modified Korteweg-de Vries equation with a nonzero boundary condition. We construct supersymmetric solitons and then by passing to...
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The Effect of Focal Distance, Age, and Brightness on Near-Field Augmented Reality Depth Matching
Many augmented reality (AR) applications operate within near-field reaching distances, and require matching the depth of a virtual object with a real object. The accuracy of this matching was measured in three experiments, which examined the effect of focal distance, age, and brightness, within distances of 33.3 to 5...
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Small-amplitude steady water waves with critical layers: non-symmetric waves
The problem for two-dimensional steady water waves with vorticity is considered. Using methods of spatial dynamics, we reduce the problem to a finite dimensional Hamiltonian system. As an application, we prove the existence of non-symmetric steady water waves when the number of roots of the dispersion equation is gre...
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Emergence of Leadership in Communication
We study a neuro-inspired model that mimics a discussion (or information dissemination) process in a network of agents. During their interaction, agents redistribute activity and network weights, resulting in emergence of leader(s). The model is able to reproduce the basic scenarios of leadership known in nature and ...
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Throughput-Improving Control of Highways Facing Stochastic Perturbations
In this article, we study the problem of controlling a highway segment facing stochastic perturbations, such as recurrent incidents and moving bottlenecks. To model traffic flow under perturbations, we use the cell-transmission model with Markovian capacities. The control inputs are: (i) the inflows that are sent to ...
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Deep Learning for Accelerated Ultrasound Imaging
In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an increasing demand to reconstruct high quality images from limited number of data. However, the existing solutions require either hardware changes or computationally expansive algorithms. To overcome these limitations, here we propose a novel...
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Adaptive Estimation for Nonlinear Systems using Reproducing Kernel Hilbert Spaces
This paper extends a conventional, general framework for online adaptive estimation problems for systems governed by unknown nonlinear ordinary differential equations. The central feature of the theory introduced in this paper represents the unknown function as a member of a reproducing kernel Hilbert space (RKHS) an...
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Weak saturation and weak amalgamation property
The two model-theoretic concepts of weak saturation and weak amalgamation property are studied in the context of accessible categories. We relate these two concepts providing sufficient conditions for existence and uniqueness of weakly saturated objects of an accessible category K. We discuss the implications of this...
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Perturbation problems in homogenization of hamilton-jacobi equations
This paper is concerned with the behavior of the ergodic constant associated with convex and superlinear Hamilton-Jacobi equation in a periodic environment which is perturbed either by medium with increasing period or by a random Bernoulli perturbation with small parameter. We find a first order Taylor's expansion fo...
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Chimera states: Effects of different coupling topologies
Collective behavior among coupled dynamical units can emerge in various forms as a result of different coupling topologies as well as different types of coupling functions. Chimera states have recently received ample attention as a fascinating manifestation of collective behavior, in particular describing a symmetry ...
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Human Activity Recognition using Recurrent Neural Networks
Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learn...
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Eigenvalues of elliptic operators with density
We consider eigenvalue problems for elliptic operators of arbitrary order $2m$ subject to Neumann boundary conditions on bounded domains of the Euclidean $N$-dimensional space. We study the dependence of the eigenvalues upon variations of mass density and in particular we discuss the existence and characterization of...
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A projection pursuit framework for testing general high-dimensional hypothesis
This article develops a framework for testing general hypothesis in high-dimensional models where the number of variables may far exceed the number of observations. Existing literature has considered less than a handful of hypotheses, such as testing individual coordinates of the model parameter. However, the problem...
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On the impact of quantum computing technology on future developments in high-performance scientific computing
Quantum computing technologies have become a hot topic in academia and industry receiving much attention and financial support from all sides. Building a quantum computer that can be used practically is in itself an outstanding challenge that has become the 'new race to the moon'. Next to researchers and vendors of f...
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Local Monotonic Attention Mechanism for End-to-End Speech and Language Processing
Recently, encoder-decoder neural networks have shown impressive performance on many sequence-related tasks. The architecture commonly uses an attentional mechanism which allows the model to learn alignments between the source and the target sequence. Most attentional mechanisms used today is based on a global attenti...
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Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming
Approximate dynamic programming algorithms, such as approximate value iteration, have been successfully applied to many complex reinforcement learning tasks, and a better approximate dynamic programming algorithm is expected to further extend the applicability of reinforcement learning to various tasks. In this paper...
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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and...
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Machine Learning for Networking: Workflow, Advances and Opportunities
Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the net-working and distributed computing system is the key infrastructure to provide efficient computational resource for machine learning. Networking itself can also benefit from this promising technolog...
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The WAGGS project - I. The WiFeS Atlas of Galactic Globular cluster Spectra
We present the WiFeS Atlas of Galactic Globular cluster Spectra, a library of integrated spectra of Milky Way and Local Group globular clusters. We used the WiFeS integral field spectrograph on the Australian National University 2.3 m telescope to observe the central regions of 64 Milky Way globular clusters and 22 g...
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Going Viral: Stability of Consensus-Driven Adoptive Spread
The spread of new products in a networked population is often modeled as an epidemic. However, in the case of "complex" contagion, these models are insufficient to properly model adoption behavior. In this paper, we investigate a model of complex contagion which allows a coevolutionary interplay between adoption, mod...
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What's In A Patch, I: Tensors, Differential Geometry and Statistical Shading Analysis
We develop a linear algebraic framework for the shape-from-shading problem, because tensors arise when scalar (e.g. image) and vector (e.g. surface normal) fields are differentiated multiple times. The work is in two parts. In this first part we investigate when image derivatives exhibit invariance to changing illumi...
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Counterexample to Gronwall's Conjecture
We present a projectively invariant description of planar linear 3-webs and construct a counterexample to Gronwall's conjecture.
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Urban Swarms: A new approach for autonomous waste management
Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past ...
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An Algorithm of Parking Planning for Smart Parking System
There are so many vehicles in the world and the number of vehicles is increasing rapidly. To alleviate the parking problems caused by that, the smart parking system has been developed. The parking planning is one of the most important parts of it. An effective parking planning strategy makes the better use of parking...
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Classical Control, Quantum Circuits and Linear Logic in Enriched Category Theory
We describe categorical models of a circuit-based (quantum) functional pro- gramming language. We show that enriched categories play a crucial role. Following earlier work on QWire by Paykin et al., we consider both a simple first-order linear language for circuits, and a more powerful host language, such that the ci...
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Unifying Map and Landmark Based Representations for Visual Navigation
This works presents a formulation for visual navigation that unifies map based spatial reasoning and path planning, with landmark based robust plan execution in noisy environments. Our proposed formulation is learned from data and is thus able to leverage statistical regularities of the world. This allows it to effic...
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Accounting for Uncertainty About Past Values In Probabilistic Projections of the Total Fertility Rate for All Countries
Since the 1940s, population projections have in most cases been produced using the deterministic cohort component method. However, in 2015, for the first time, in a major advance, the United Nations issued official probabilistic population projections for all countries based on Bayesian hierarchical models for total ...
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Periodic solutions of a perturbed Kepler problem in the plane: from existence to stability
The existence of elliptic periodic solutions of a perturbed Kepler problem is proved. The equations are in the plane and the perturbation depends periodically on time. The proof is based on a local description of the symplectic group in two degrees of freedom.
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Removal of Narrowband Interference (PLI in ECG Signal) Using Ramanujan Periodic Transform (RPT)
Suppression of interference from narrowband frequency signals play vital role in many signal processing and communication applications. A transform based method for suppression of narrow band interference in a biomedical signal is proposed. As a specific example Electrocardiogram (ECG) is considered for the analysis....
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Global regularity and fast small scale formation for Euler patch equation in a disk
It is well known that the Euler vortex patch in $\mathbb{R}^{2}$ will remain regular if it is regular enough initially. In bounded domains, the regularity theory for patch solutions is less complete. We study here the Euler vortex patch in a disk. We prove global in time regularity by providing the upper bound of the...
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Effective interaction in a non-Fermi liquid conductor and spin correlations in under-doped cuprates
The effective interaction between the itinerant spin degrees of freedom in the paramagnetic phases of hole doped quantum Heisenberg antiferromagnets is investigated theoretically, based on the single-band t-J model on 1D lattice, at zero temperature. The effective spin-spin interaction for this model in the strong co...
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Planning with Verbal Communication for Human-Robot Collaboration
Human collaborators coordinate effectively their actions through both verbal and non-verbal communication. We believe that the the same should hold for human-robot teams. We propose a formalism that enables a robot to decide optimally between doing a task and issuing an utterance. We focus on two types of utterances:...
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Magnon Condensation and Spin Superfluidity
We consider the phenomenon of Bose-Einstein condensation of quasi-equilibrium magnons which leads to a spin superfluidity, the coherent quantum transfer of magnetization in magnetic materials. These phenomena are beyond the classical Landau-Lifshitz-Gilbert paradigm. The critical conditions for excited magnon density...
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Electroweak Vacuum Metastability and Low-scale Inflation
We study the stability of the electroweak vacuum in low-scale inflation models whose Hubble parameter is much smaller than the instability scale of the Higgs potential. In general, couplings between the inflaton and Higgs are present, and hence we study effects of these couplings during and after inflation. We derive...
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Spectral selectivity in capillary dye lasers
We explore the spectral properties of a capillary dye laser in the highly multimode regime. Our experiments indicate that the spectral behavior of the laser does not conform with a simple Fabry-Perot analysis; rather, it is strongly dictated by a Vernier resonant mechanism involving multiple modes, which propagate wi...
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The Mechanism of Electrolyte Gating on High-Tc Cuprates: The Role of Oxygen Migration and Electrostatics
Electrolyte gating is widely used to induce large carrier density modulation on solid surfaces to explore various properties. Most of past works have attributed the charge modulation to electrostatic field effect. However, some recent reports have argued that the electrolyte gating effect in VO2, TiO2 and SrTiO3 orig...
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Invariant theory of a special group action on irreducible polynomials over finite fields
In the past few years, an action of $\mathrm{PGL}_2(\mathbb F_q)$ on the set of irreducible polynomials in $\mathbb F_q[x]$ has been introduced and many questions have been discussed, such as the characterization and number of invariant elements. In this paper, we analyze some recent works on this action and provide ...
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TIP: Typifying the Interpretability of Procedures
We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for compar...
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Differences in 1D electron plasma wake field acceleration in MeV versus GeV and linear versus blowout regimes
In some laboratory and most astrophysical situations plasma wake-field acceleration of electrons is one dimensional, i.e. variation transverse to the beam's motion can be ignored. Thus, one dimensional (1D), particle-in-cell (PIC), fully electromagnetic simulations of electron plasma wake field acceleration are condu...
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Estimating network memberships by simplex vertex hunting
Consider an undirected mixed membership network with $n$ nodes and $K$ communities. For each node $1 \leq i \leq n$, we model the membership by $\pi_{i} = (\pi_{i}(1), \pi_{i}(2), \ldots$, $\pi_{i}(K))'$, where $\pi_{i}(k)$ is the probability that node $i$ belongs to community $k$, $1 \leq k \leq K$. We call node $i$...
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Non-singular Green's functions for the unbounded Poisson equation in one, two and three dimensions
In this paper, we derive the non-singular Green's functions for the unbounded Poisson equation in two and three dimensions using a spectral approach to regularize the homogeneous equation. The resulting Green's functions are relevant to applications which are restricted to a minimum resolved length scale (e.g. a mesh...
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Evolutionary phases of gas-rich galaxies in a galaxy cluster at z=1.46
We report a survey of molecular gas in galaxies in the XMMXCS J2215.9-1738 cluster at $z=1.46$. We have detected emission lines from 17 galaxies within a radius of $R_{200}$ from the cluster center, in Band 3 data of the Atacama Large Millimeter/submillimeter Array (ALMA) with a coverage of 93 -- 95 GHz in frequency ...
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An iterative ensemble Kalman filter in presence of additive model error
The iterative ensemble Kalman filter (IEnKF) in a deterministic framework was introduced in Sakov et al. (2012) to extend the ensemble Kalman filter (EnKF) and improve its performance in mildly up to strongly nonlinear cases. However, the IEnKF assumes that the model is perfect. This assumption simplified the update ...
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A Bayesian algorithm for detecting identity matches and fraud in image databases
A statistical algorithm for categorizing different types of matches and fraud in image databases is presented. The approach is based on a generative model of a graph representing images and connections between pairs of identities, trained using properties of a matching algorithm between images.
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