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On Characters of a Class of P-polynomial table algebras and applicationsJul 10 2019In this paper, we study the characters of homogeneous monotonic P-polynomial table algebras with finite dimension d>=5. We then apply them to association schemes. To this end, we develop some methods using tridiagonal matrices and Z-transform. Moreover, ... More

The Application of Tridiagonal Matrices in P-polynomial Table AlgebrasJun 14 2019In this paper, we study the characters of two classes of P-polynomial table algebras using tridiagonal matrices. To this end, we obtain some results about the eigen-structure of special tridiagonal matrices. We also find a recursive relation for the characteristic ... More

Differentiation Matrices for Univariate PolynomialsSep 15 2018We collect here elementary properties of differentiation matrices for univariate polynomials expressed in various bases, including orthogonal polynomial bases and non-degree-graded bases such as Bernstein bases and Lagrange \& Hermite interpolational ... More

Solving Linear Systems over Idempotent Semifields through $LU$-factorizationApr 30 2019In this paper, we introduce and analyze a generalized $LU$-factorization method for square matrices over idempotent semifields. We use this $LU$-factorization to propose a technique for solving linear systems of equations as an extension of similar techniques ... More

Analysis of linear systems over idempotent semifieldsJun 11 2019Jun 21 2019In this paper, we present and analyze methods for solving a system of linear equations over idempotent semifields. The first method is based on the pseudo-inverse of the system matrix. We then present a specific version of Cramer's rule which is also ... More

Analysis of linear systems over idempotent semifieldsJun 11 2019In this paper, we present and analyze methods for solving a system of linear equations over idempotent semifields. The first method is based on the pseudo-inverse of the system matrix. We then present a specific version of Cramer's rule which is also ... More

On the Maximal Solution of A Linear System over Tropical SemiringsApr 30 2019In this paper, we present methods for solving a system of linear equations, $ AX=b $, over tropical semirings. To this end, if possible, we first reduce the order of the system through some row-column analysis, and obtain a new system with fewer equations ... More

Solving Linear Systems over Tropical Semirings through Normalization Method and its ApplicationsApr 30 2019In this paper, we introduce and analyze a normalization method for solving a system of linear equations over tropical semirings. We use a normalization method to construct an associated normalized matrix, which gives a technique for solving the system. ... More

Measuring Systemic Risk: Robust Ranking Techniques ApproachMar 21 2015The recent economic crisis has raised a wide awareness that the financial system should be considered as a complex network with financial institutions and financial dependencies respectively as nodes and links between these nodes. Systemic risk is defined ... More

Measuring Systemic Risk: Robust Ranking Techniques ApproachMar 21 2015Feb 22 2017In this research, we introduce a robust metric to identify Systemically Important Financial Institution (SIFI) in a financial network by taking into account both common idiosyncratic shocks and contagion through counterparty exposures. We develop an efficient ... More

Geometric Modality and Weak ExponentialsNov 06 2017The intuitionistic implication and hence the notion of function space in constructive disciplines is both non-geometric and impredicative. In this paper we try to solve both of these problems by first introducing weak exponential objects as a formalization ... More

Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech SignalsJun 10 2017Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which ... More

Russellian Propositional Logic and the BHK InterpretationApr 20 2017The BHK interpretation interprets propositional statements as descriptions of the world of proofs; a world which is hierarchical in nature. It consists of different layers of the concept of proof; the proofs, the proofs about proofs and so on. To describe ... More

Full-Duplex GFDM Radio Transceivers in the Presence of Phase Noise, CFO and IQ ImbalanceMar 29 2019This paper addresses the performance of a full-duplex (FD) generalized frequency division multiplexing (GFDM) transceiver in the presence of radio frequency (RF) impairments including phase noise, carrier frequency offset (CFO) and in-phase (I) and quadrature ... More

Forecasting the Colorado River Discharge Using an Artificial Neural Network (ANN) ApproachNov 27 2014Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management ... More

A Fast and Memory Efficient Sparse Solver with Applications to Finite-Element MatricesOct 10 2014Apr 22 2015In this article, we introduce a fast and memory efficient solver for sparse matrices arising from the finite element discretization of elliptic partial differential equations (PDEs). We use a fast direct (but approximate) multifrontal solver as a preconditioner, ... More

Latency Analysis of Coded Computation Schemes over Wireless NetworksJun 30 2017Large-scale distributed computing systems face two major bottlenecks that limit their scalability: straggler delay caused by the variability of computation times at different worker nodes and communication bottlenecks caused by shuffling data across many ... More

Provability Logics of HierarchiesApr 20 2017The branch of provability logic investigates the provability-based behavior of the mathematical theories. In a more precise way, it studies the relation between a mathematical theory $T$ and a modal logic $L$ via the provability interpretation which interprets ... More

Computational Flows in ArithmeticNov 06 2017A computational flow is a pair consisting of a sequence of computational problems of a certain sort and a sequence of computational reductions among them. In this paper we will develop a theory for these computational flows and we will use it to make ... More

Equalization Enhanced Phase Noise in Coherent Receivers: DSP-Aware Analysis and Shaped ConstellationsApr 25 2019We revisit the analysis of equalization-enhanced phase noise (EEPN) arising in coherent receivers from the interaction between the chromatic dispersion compensation by an electronic equalizer and the phase noise of the local oscillator. Through numerical ... More

A Numerical Method for Pricing Discrete Double Barrier Option by Lagrange Interpolation on Jacobi NodeDec 01 2017Feb 02 2018In this paper, a rapid and high accurate numerical method for pricing discrete single and double barrier knock-out call options is presented. According to the well-known Black-Scholes framework, the price of option in each monitoring date could be calculate ... More

2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANsFeb 19 2019We provide a framework to approximate the 2-Wasserstein distance and the optimal transport map, amenable to efficient training as well as statistical and geometric analysis. With the quadratic cost and considering the Kantorovich dual form of the optimal ... More

Provability Interpretation of Propositional and Modal LogicsApr 20 2017Aug 31 2017In 1933, G\"odel introduced a provability interpretation of the propositional intuitionistic logic to establish a formalization for the BHK interpretation. He used the modal system, $\mathbf{S4}$, as a formalization of the intuitive concept of provability ... More

A Numerical Method for Pricing Discrete Double Barrier Option by Legendre MultiwaveletMar 27 2017Mar 28 2017In this Article, a fast numerical numerical algorithm for pricing discrete double barrier option is presented. According to Black-Scholes model, the price of option in each monitoring date can be evaluated by a recursive formula upon the heat equation ... More

Training a Hidden Markov Model with a Bayesian Spiking Neural NetworkJun 02 2016Jul 20 2016It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. This paper explores a novel hybrid approach for classifying segments of sequential ... More

Emergence of localized plasticity and failure through shear banding during microcompression of a nanocrystalline alloyJul 22 2014Microcompression testing is used to probe the uniaxial stress-strain response of a nanocrystalline alloy, with an emphasis on exploring how grain size and grain boundary relaxation state impact the complete flow curve and failure behavior. The yield strength, ... More

Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP LearningNov 09 2016Jun 24 2017Hierarchical feature discovery using non-spiking convolutional neural networks (CNNs) has attracted much recent interest in machine learning and computer vision. However, it is still not well understood how to create a biologically plausible network of ... More

Universal Proof Theory: Semi-analytic Rules and Uniform InterpolationAug 19 2018In [7] and [8], Iemhoff introduced a connection between the existence of a terminating sequent calculi of a certain kind and the uniform interpolation property of the super-intuitionistic logic that the calculus captures. In this paper, we will generalize ... More

Universal Proof Theory: Semi-analytic Rules and Craig InterpolationAug 19 2018In [6], Iemhoff introduced the notion of a focused axiom and a focused rule as the building blocks for a certain form of sequent calculus which she calls a focused proof system. She then showed how the existence of a terminating focused system implies ... More

On Higher Order Positive Differential Energy OperatorJan 09 2017The higher order differential energy operator (DEO), denoted via $\Upsilon_k(x)$, is an extension to the second order famous Teager-Kaiser operator. The DEO helps measuring the higher order gauge of energy of a signal which is useful for AM-FM demodulation. ... More

Gain Function Approximation in the Feedback Particle FilterMar 17 2016This paper is concerned with numerical algorithms for gain function approximation in the feedback particle filter. The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian. The problem is to approximate ... More

BP-STDP: Approximating Backpropagation using Spike Timing Dependent PlasticityNov 12 2017Mar 09 2018The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has shown that supervised learning in multi-layer SNNs enables bio-inspired ... More

Accelerated Flow for Probability DistributionsJan 10 2019Jan 11 2019This paper presents a methodology and numerical algorithms for constructing accelerated gradient flows on the space of probability distributions. In particular, we extend the recent variational formulation of accelerated gradient methods in (wibisono, ... More

Counting Homomorphisms Modulo a Prime NumberMay 25 2019Counting problems in general and counting graph homomorphisms in particular have numerous applications in combinatorics, computer science, statistical physics, and elsewhere. One of the most well studied problems in this area is #GraphHom(H) --- the problem ... More

A Spiking Network that Learns to Extract Spike Signatures from Speech SignalsJun 02 2016Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks. Speech recognition, as an application involving audio coding and dynamic learning, provides a good test problem to study SNN functionality. We ... More

Error Analysis for the Linear Feedback Particle FilterOct 30 2017This paper is concerned with the convergence and the error analysis for the feedback particle filter (FPF) algorithm. The FPF is a controlled interacting particle system where the control law is designed to solve the nonlinear filtering problem. For the ... More

Prediction of polymer mixture compatibility by Monte Carlo simulation of intermolecular binary interactionsMay 28 2009We have evaluated conformational and orientational averages of binary interaction integrals for pairs of chains constituting atomistic representations of short polymer molecules. By considering A-A, B-B and A-B pairs, we relate these results with the ... More

A Spiking Network that Learns to Extract Spike Signatures from Speech SignalsJun 02 2016Oct 11 2016Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks. Speech recognition, as an application involving audio coding and dynamic learning, provides a good test problem to study SNN functionality. We ... More

High-temperature stability and grain boundary complexion formation in a nanocrystalline Cu-Zr alloyJul 10 2015Aug 28 2015Nanocrystalline Cu-3 at.% Zr powders with ~20 nm average grain size were created with mechanical alloying and their thermal stability was studied from 550-950 {\deg}C. Annealing drove Zr segregation to the grain boundaries, which led to the formation ... More

An Optimal Transport Formulation of the Linear Feedback Particle FilterOct 07 2015Feedback particle filter (FPF) is an algorithm to numerically approximate the solution of the nonlinear filtering problem in continuous time. The algorithm implements a feedback control law for a system of particles such that the empirical distribution ... More

An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labelsJun 18 2016Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments ... More

Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP LearningNov 09 2016Hierarchical feature discovery using non-spiking convolutional neural networks (CNNs) has attracted much recent interest in machine learning and computer vision. However, it is still not well understood how to create spiking deep networks with multi-layer, ... More

Disruption of Thermally-Stable Nanoscale Grain Structures by Strain LocalizationFeb 03 2015Apr 03 2015Nanocrystalline metals with average grain sizes of only a few nanometers have recently been observed to fail through the formation of shear bands. Here, we investigate this phenomenon in nanocrystalline Ni which has had its grain structure stabilized ... More

Error Analysis of the Stochastic Linear Feedback Particle FilterSep 20 2018This paper is concerned with the convergence and long-term stability analysis of the feedback particle filter (FPF) algorithm. The FPF is an interacting system of $N$ particles where the interaction is designed such that the empirical distribution of ... More

Molecular Dynamics Simulation of Miscibility in Several Polymer BlendsMay 28 2009The miscibility in several polymer blend mixtures (polymethylmethacrylate/polystyrene, (1,4-cis) polyisoprene/polystyrene, and polymethylmethacrylate/polyoxyethylene) has been investigated using Molecular Dynamics simulations for atomistic representations ... More

An Effective Payload Attribution Scheme for Cybercriminal Detection Using Compressed Bitmap Index Tables and Traffic DownsamplingJun 12 2019Payload attribution systems (PAS) are one of the most important tools of network forensics for detecting an offender after the occurrence of a cybercrime. A PAS stores the network traffic history in order to detect the source and destination pair of a ... More

A Convex Similarity Index for Sparse Recovery of Missing Image SamplesJan 25 2017Oct 17 2017This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted especially for image signals. Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality measure is used as the similarity ... More

An orthogonal basis expansion method for solving path-independent stochastic differential equationsMar 28 2017Nov 10 2017In this article, we present an orthogonal basis expansion method for solving stochastic differential equations with a path-independent solution of the form $X_{t}=\phi(t,W_{t})$. For this purpose, we define a Hilbert space and construct an orthogonal ... More

Non-invasive Blood Pressure Estimation Using PhonocardiogramMay 07 2019This paper presents a novel approach based on pulse transit time (PTT) for the estimation of blood pressure (BP). In order to achieve this goal, a data acquisition hardware is designed for high-resolution sampling of phonocardiogram (PCG) and photoplethysmogram ... More

A Conformal Collider for Holographic CFTsMay 18 2018Dec 14 2018We develop a formalism to study the implications of causality on OPE coefficients in conformal field theories with large central charge and a sparse spectrum of higher spin operators. The formalism has the interpretation of a new conformal collider-type ... More

Motion Planning for Global Localization in Non-Gaussian Belief SpacesNov 14 2015Feb 27 2016This paper presents a method for motion planning under uncertainty to deal with situations where ambiguous data associations result in a multimodal hypothesis on the robot state. In the global localization problem, sometimes referred to as the "lost or ... More

Decentralized State Estimation via a Hybrid of Consensus and Covariance intersectionMar 03 2016This paper presents a new recursive information consensus filter for decentralized dynamic-state estimation. No structure is assumed about the topology of the network and local estimators are assumed to have access only to local information. The network ... More

Towards Collaborative Intelligence Friendly Architectures for Deep LearningFeb 01 2019Modern mobile devices are equipped with high-performance hardware resources such as graphics processing units (GPUs), making the end-side intelligent services more feasible. Even recently, specialized silicons as neural engines are being used for mobile ... More

Representation Learning using Event-based STDPJun 20 2017Mar 09 2018Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a challenging problem. This paper proposes an event-based method to train a ... More

Modeling Processor Idle Times in MPSoC Platforms to Enable Integrated DPM, DVFS, and Task Scheduling Subject to a Hard DeadlineDec 19 2018Energy efficiency is one of the most critical design criteria for modern embedded systems such as multiprocessor system-on-chips (MPSoCs). Dynamic voltage and frequency scaling (DVFS) and dynamic power management (DPM) are two major techniques for reducing ... More

Fast Conformal Bootstrap and Constraints on 3d GravityMar 14 2019The crossing equations of a conformal field theory can be systematically truncated to a finite, closed system of polynomial equations. In certain cases, solutions of the truncated equations place strict bounds on the space of all unitary CFTs. We describe ... More

Noninvasive Blockade of Action Potential by Electromagnetic InductionAug 29 2018Conventional anesthesia methods such as injective anesthetic agents may cause various side effects such as injuries, allergies, and infections. We aim to investigate a noninvasive scheme of an electromagnetic radiator system to block action potential ... More

A Fast Block Low-Rank Dense Solver with Applications to Finite-Element MatricesMar 21 2014Mar 18 2015This article presents a fast solver for the dense "frontal" matrices that arise from the multifrontal sparse elimination process of 3D elliptic PDEs. The solver relies on the fact that these matrices can be efficiently represented as a hierarchically ... More

Nonlinear Cuff-less Blood Pressure Estimation of Healthy Subjects Using Pulse Transit Time and Arrival TimeDec 03 2018May 06 2019This paper presents a novel blood pressure (BP) estimation method based on pulse transit time (PTT) and pulse arrival time (PAT) to estimate the systolic BP (SBP) and the diastolic BP (DBP). A data acquisition hardware is designed for high-resolution ... More

Averaged Null Energy Condition from CausalityOct 17 2016Unitary, Lorentz-invariant quantum field theories in flat spacetime obey microcausality: commutators vanish at spacelike separation. For interacting theories in more than two dimensions, we show that this implies that the averaged null energy, $\int du ... More

Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent PlasticityJun 03 2016This paper explores modifications to a feedforward five-layer spiking convolutional network (SCN) of the ventral visual stream [Masquelier, T., Thorpe, S., Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Computational ... More

Hypergraph Automata: A Theoretical Model for Patterned Self-assemblyFeb 12 2013Patterned self-assembly is a process whereby coloured tiles self-assemble to build a rectangular coloured pattern. We propose self-assembly (SA) hypergraph automata as an automata-theoretic model for patterned self-assembly. We investigate the computational ... More

Calculation of Band Structure Using Local Sampling and Green's FunctionsOct 05 2009Feb 06 2010A new method for calculation of band structure has been proposed based on the Green's function theory and local sampling. Potential energy in the Hamiltonian of Schrodinger's equation is approximated with a series of sampled Dirac delta functions weighted ... More

A Controlled Particle Filter for Global OptimizationJan 10 2017A particle filter is introduced to numerically approximate a solution of the global optimization problem. The theoretical significance of this work comes from its variational aspects: (i) the proposed particle filter is a controlled interacting particle ... More

Attitude Estimation with Feedback Particle FilterApr 05 2016This paper presents theory, application, and comparisons of the feedback particle filter (FPF) algorithm for the problem of attitude estimation. The paper builds upon our recent work on the exact FPF solution of the continuous-time nonlinear filtering ... More

Feedback Particle Filter on Matrix Lie GroupsOct 05 2015This paper is concerned with the problem of continuous-time nonlinear filtering for stochastic processes on a compact and connected matrix Lie group without boundary, e.g. SO(n) and SE(n), in the presence of real-valued observations. This problem is important ... More

Adversarial Delays in Online Strongly-Convex OptimizationMay 20 2016We consider the problem of strongly-convex online optimization in presence of adversarial delays; in a T-iteration online game, the feedback of the player's query at time t is arbitrarily delayed by an adversary for d_t rounds and delivered before the ... More

Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of PatchesFeb 07 2018The variation of pose, illumination and expression makes face recognition still a challenging problem. As a pre-processing in holistic approaches, faces are usually aligned by eyes. The proposed method tries to perform a pixel alignment rather than eye-alignment ... More

Nonlinear Cuff-less Blood Pressure Estimation of Healthy Subjects Using Pulse Transit Time and Arrival TimeDec 03 2018This paper presents a novel blood pressure (BP) estimation method based on pulse transit time (PTT) and pulse arrival time (PAT) to estimate the systolic BP (SBP) and the diastolic BP (DBP). A data acquisition hardware is designed for high-resolution ... More

Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity MeasureJun 28 2017In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. ... More

Motion Planning in Non-Gaussian Belief Spaces (M3P): The Case of a Kidnapped RobotJun 05 2015Planning under uncertainty is a key requirement for physical systems due to the noisy nature of actuators and sensors. Using a belief space approach, planning solutions tend to generate actions that result in information seeking behavior which reduce ... More

A Bound on Massive Higher Spin ParticlesNov 05 2018Dec 27 2018According to common lore, massive elementary higher spin particles lead to inconsistencies when coupled to gravity. However, this scenario was not completely ruled out by previous arguments. In this paper, we show that in a theory where the low energy ... More

Energy-Aware Scheduling of Task Graphs with Imprecise Computations and End-to-End DeadlinesMay 10 2019Imprecise computations provide an avenue for scheduling algorithms developed for energy-constrained computing devices by trading off output quality with the utilization of system resources. This work proposes a method for scheduling task graphs with potentially ... More

Feedback Particle Filter on Matrix Lie GroupsJan 10 2017This paper is concerned with the problem of continuous-time nonlinear filtering for stochastic processes on a connected matrix Lie group. The main contribution of this paper is to derive the feedback particle filter (FPF) algorithm for this problem. In ... More

BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing ServicesFeb 04 2019Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. This paper introduces a new deep learning architecture, called BottleNet, ... More

Einstein gravity 3-point functions from conformal field theoryOct 28 2016We study stress tensor correlation functions in four-dimensional conformal field theories with large $N$ and a sparse spectrum. Theories in this class are expected to have local holographic duals, so effective field theory in anti-de Sitter suggests that ... More

VideoStory Embeddings Recognize Events when Examples are ScarceNov 08 2015This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their ... More

Impulsive Noise Robust Sparse Recovery via Continuous Mixed NormApr 12 2018This paper investigates the problem of sparse signal recovery in the presence of additive impulsive noise. The heavytailed impulsive noise is well modelled with stable distributions. Since there is no explicit formulation for the probability density function ... More

Spectral Analysis of GFDM Modulated Signal under Nonlinear Behavior of Power AmplifierMar 06 2018General frequency division multiplexing (GFDM) is a flexible non-orthogonal waveform candidate for 5G which can offer some advantages such as low out-of-band (OOB) emission and high spectral efficiency. In this paper, the effects of nonlinear behavior ... More

Representation Independent Proximity and Similarity SearchAug 15 2015Aug 25 2016Finding similar or strongly related entities in a graph database is a fundamental problem in data management and analytics with applications in similarity query processing, entity resolution, and pattern matching. Similarity search algorithms usually ... More

Coded Computing for Distributed Graph AnalyticsJan 17 2018Jul 04 2018To combat the growing demands for efficient processing of large scale graph-structured datasets, many distributed graph computing systems have been developed recently. As these systems require many messages to be exchanged among computing machines at ... More

Piezoresistivity and Strain-induced Band Gap Tuning in Atomically Thin MoS2Jul 21 2015The bandgap of MoS2 is highly strain-tunable which results in the modulation of its electrical conductivity and manifests itself as the piezoresistive effect while a piezoelectric effect was also observed in odd-layered MoS2 with broken inversion symmetry. ... More

Shockwaves from the Operator Product ExpansionSep 11 2017We clarify and further explore the CFT dual of shockwave geometries in Anti-de Sitter. The shockwave is dual to a CFT state produced by a heavy local operator inserted at a complex point. It can also be created by light operators, smeared over complex ... More

Structure Learning in Coupled Dynamical Systems and Dynamic Causal ModellingMar 28 2019Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In ... More

Deep Neural Networks Meet CSI-Based AuthenticationNov 26 2018The first step of a secure communication is authenticating legible users and detecting the malicious ones. In the last recent years, some promising schemes proposed using wireless medium network's features, in particular, channel state information (CSI) ... More

Gain function approximation in the Feedback Particle FilterFeb 19 2019This paper is concerned with numerical algorithms for the problem of gain function approximation in the feedback particle filter. The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian. The numerical problem ... More

Derivation and Extensions of the Linear Feedback Particle Filter based on Duality FormalismsApr 11 2018This paper is concerned with a duality-based approach to derive the linear feedback particle filter (FPF). The FPF is a controlled interacting particle system where the control law is designed to provide an exact solution for the nonlinear filtering problem. ... More

How regularization affects the critical points in linear networksSep 27 2017This paper is concerned with the problem of representing and learning a linear transformation using a linear neural network. In recent years, there has been a growing interest in the study of such networks in part due to the successes of deep learning. ... More

Error Estimates for the Kernel Gain Function Approximation in the Feedback Particle FilterDec 16 2016This paper is concerned with the analysis of the kernel-based algorithm for gain function approximation in the feedback particle filter. The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian. The kernel-based ... More

Feedback Motion Planning Under Non-Gaussian Uncertainty and Non-Convex State ConstraintsNov 16 2015Jan 12 2016Planning under process and measurement uncertainties is a challenging problem. In its most general form it can be modeled as a Partially Observed Markov Decision Process (POMDP) problem. However POMDPs are generally difficult to solve when the underlying ... More

Einstein gravity 3-point functions from conformal field theoryOct 28 2016Nov 10 2017We study stress tensor correlation functions in four-dimensional conformal field theories with large $N$ and a sparse spectrum. Theories in this class are expected to have local holographic duals, so effective field theory in anti-de Sitter suggests that ... More

Kalman Filter and its Modern Extensions for the Continuous-time Nonlinear Filtering ProblemFeb 21 2017Dec 21 2017This paper is concerned with the filtering problem in continuous-time. Three algorithmic solution approaches for this problem are reviewed: (i) the classical Kalman-Bucy filter which provides an exact solution for the linear Gaussian problem, (ii) the ... More

Coupling between time series: a network viewJan 06 2013Recently, the visibility graph has been introduced as a novel view for analyzing time series, which maps it to a complex network. In this paper, we introduce new algorithm of visibility, "cross-visibility", which reveals the conjugation of two coupled ... More

Elastic, dielectric and piezoelectric anomalies and Raman spectroscopy of 0.5Ba(Ti0.8Zr0.2)O3-0.5(Ba0.7Ca0.3)TiO3Mar 25 2012The solid solution 0.5Ba(Ti0.8Zr0.2)O3-0.5(Ba0.7Ca0.3)TiO3 (BCZT) is a promising lead-free piezoelectric material with exceptionally high piezoelectric coefficients. The strong response is related to structural instabilities close to ambient temperature. ... More

Holography at finite cutoff with a $T^2$ deformationJul 30 2018We generalize the $T\overline{T}$ deformation of CFT$_2$ to higher-dimensional large-$N$ CFTs, and show that in holographic theories, the resulting effective field theory matches semiclassical gravity in AdS with a finite radial cutoff. We also derive ... More

Grain boundary character distributions in nanocrystalline metals produced by different processing routesMay 21 2015Nov 09 2015Nanocrystalline materials are defined by their fine grain size, but details of the grain boundary character distribution should also be important. Grain boundary character distributions are reported for ball milled, sputter deposited, and electrodeposited ... More

Middleware Technologies for Cloud of Things - a surveyApr 30 2017The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, ... More

Coded Computation over Heterogeneous ClustersJan 21 2017Oct 09 2018In large-scale distributed computing clusters, such as Amazon EC2, there are several types of "system noise" that can result in major degradation of performance: bottlenecks due to limited communication bandwidth, latency due to straggler nodes, etc. ... More

Coded Computing for Distributed Graph AnalyticsJan 17 2018Jun 20 2019To combat the growing demands for efficient processing of large scale graph-structured datasets, many distributed graph computing systems have been developed recently. As these systems require many messages to be exchanged among computing machines at ... More

HGR-Net: A Fusion Network for Hand Gesture Segmentation and RecognitionJun 14 2018Dec 15 2018Robust recognition of hand gestures in real-world applications is still an unaccomplished goal due to many remaining challenges, such as cluttered backgrounds and unconstrained environmental factors. In most existing methods in this field, hand segmentation ... More