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Lower Bounds for the Fair Resource Allocation ProblemFeb 08 2018The $\alpha$-fair resource allocation problem has received remarkable attention and has been studied in numerous application fields. Several algorithms have been proposed in the context of $\alpha$-fair resource sharing to distributively compute its value. ... More

Virtual Function Placement for Service Chaining with Partial Orders and Anti-Affinity RulesMay 30 2017Aug 08 2017Software Defined Networking and Network Function Virtualization are two paradigms that offer flexible software-based network management. Service providers are instantiating Virtualized Network Functions - e.g., firewalls, DPIs, gateways - to highly facilitate ... More

Real-Time Fair Resource Allocation in Distributed Software Defined NetworksNov 27 2017The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time.To address this issue, bandwidth sharing techniques that quickly ... More

Multi-Path Alpha-Fair Resource Allocation at Scale in Distributed Software Defined NetworksSep 04 2018Sep 12 2018The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly ... More

An Improved One-to-All Broadcasting in Higher Dimensional Eisenstein-Jacobi NetworksDec 06 2016Recently, a higher dimensional Eisenstein-Jacobi (EJ) networks, has been proposed in [22], which is shown that they have better average distance with more number of nodes than a single dimensional EJ net- works. Some communication algorithms such as one-to-all ... More

An Improved One-to-All Broadcasting in Higher Dimensional Eisenstein-Jacobi NetworksDec 06 2016Dec 07 2016Recently, a higher dimensional Eisenstein-Jacobi networks, has been proposed in [22], which is shown that they have better average distance with more number of nodes than a single dimensional EJ networks. Some communication algorithms such as one-to-all ... More

Survey of Parallel Computing with MATLABJul 25 2014Matlab is one of the most widely used mathematical computing environments in technical computing. It has an interactive environment which provides high performance computing (HPC) procedures and easy to use. Parallel computing with Matlab has been an ... More

Resource Allocation in Cellular Systems for Applications with Random ParametersJul 27 2015In this paper, we conduct a study to optimize resource allocation for adaptive real-time and delay-tolerant applications in cellular systems. To represent the user applications via several devices and equipment, sigmoidal-like and logarithm utility functions ... More

Efficient First-Order Algorithms for Adaptive Signal DenoisingMar 29 2018Jun 12 2018We consider the problem of discrete-time signal denoising, focusing on a specific family of non-linear convolution-type estimators. Each such estimator is associated with a time-invariant filter which is obtained adaptively, by solving a certain convex ... More

Semi-proximal Mirror-Prox for Nonsmooth Composite MinimizationJul 06 2015We propose a new first-order optimisation algorithm to solve high-dimensional non-smooth composite minimisation problems. Typical examples of such problems have an objective that decomposes into a non-smooth empirical risk part and a non-smooth regularisation ... More

High-dimensional change-point detection with sparse alternativesDec 06 2013Feb 26 2014We consider the problem of detecting a change in mean in a sequence of Gaussian vectors. Under the alternative hypothesis, the change occurs only in some subset of the components of the vector. We propose a test of the presence of a change-point that ... More

A RobustICA Based Algorithm for Blind Separation of Convolutive MixturesAug 01 2014We propose a frequency domain method based on robust independent component analysis (RICA) to address the multichannel Blind Source Separation (BSS) problem of convolutive speech mixtures in highly reverberant environments. We impose regularization processes ... More

Convex Cauchy Schwarz Independent Component Analysis for Blind Source SeparationAug 01 2014We present a new high performance Convex Cauchy Schwarz Divergence (CCS DIV) measure for Independent Component Analysis (ICA) and Blind Source Separation (BSS). The CCS DIV measure is developed by integrating convex functions into the Cauchy Schwarz inequality. ... More

A Survey of Multibiometric SystemsOct 02 2012Most biometric systems deployed in real-world applications are unimodal. Using unimodal biometric systems have to contend with a variety of problems such as: Noise in sensed data; Intra-class variations; Inter-class similarities; Non-universality; Spoof ... More

A Statistical Investigation of Long Memory in Language and MusicApr 08 2019Representation and learning of long-range dependencies is a central challenge confronted in modern applications of machine learning to sequence data. Yet despite the prominence of this issue, the basic problem of measuring long-range dependence, either ... More

A Blind Adaptive CDMA Receiver Based on State Space StructuresAug 01 2014Jan 14 2016Code Division Multiple Access (CDMA) is a channel access method, based on spread-spectrum technology, used by various radio technologies world-wide. In general, CDMA is used as an access method in many mobile standards such as CDMA2000 and WCDMA. We address ... More

Two Pairwise Iterative Schemes For High Dimensional Blind Source SeparationApr 16 2016This paper addresses the high dimensionality problem in blind source separation (BSS), where the number of sources is greater than two. Two pairwise iterative schemes are proposed to tackle this high dimensionality problem. The two pairwise schemes realize ... More

A Convex Cauchy-Schwarz DivergenceMeasure for Blind Source SeparationApr 16 2016We propose a new class of divergence measures for Independent Component Analysis (ICA) for the demixing of multiple source mixtures. We call it the Convex Cauchy-Schwarz Divergence (CCS-DIV), and it is formed by integrating convex functions into the Cauchy-Schwarz ... More

Noisy swimming at low Reynolds numbersMar 25 2009Jul 06 2009Small organisms (e.g., bacteria) and artificial microswimmers move due to a combination of active swimming and passive Brownian motion. Considering a simplified linear three-sphere swimmer, we study how the swimmer size regulates the interplay between ... More

Optimization along Families of Periodic and Quasiperiodic Orbits in Dynamical Systems with DelayJan 26 2019This paper generalizes a previously-conceived, continuation-based optimization technique for scalar objective functions on constraint manifolds to cases of periodic and quasiperiodic solutions of delay-differential equations. A Lagrange formalism is used ... More

A Universal Catalyst for First-Order OptimizationJun 06 2015Oct 25 2015We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds upon a new analysis of the accelerated proximal point algorithm. Our approach consists of minimizing a convex objective by approximately ... More

de Sitter Spacetime as a Natural SuperconductorFeb 16 2016Oct 19 2016Motivated by the studies done on magnetically induced superconduc- tivity on QCD vaccum we propose that de Sitter spacetime is a natural superconductor. This is due to the occurrence of spinor condensation. We provide a framework for joining the curvature ... More

Learning to track for spatio-temporal action localizationJun 05 2015Sep 27 2015We propose an effective approach for spatio-temporal action localization in realistic videos. The approach first detects proposals at the frame-level and scores them with a combination of static and motion CNN features. It then tracks high-scoring proposals ... More

Conditional Gradient Algorithms for Norm-Regularized Smooth Convex OptimizationFeb 10 2013Mar 28 2013Motivated by some applications in signal processing and machine learning, we consider two convex optimization problems where, given a cone $K$, a norm $\|\cdot\|$ and a smooth convex function $f$, we want either 1) to minimize the norm over the intersection ... More

Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised AlgorithmJul 02 2016Jan 25 2018We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing $\kappa$ predominant ... More

Communication without Interception: Defense against Deep-Learning-based Modulation DetectionFeb 27 2019We consider a communication scenario, in which an intruder, employing a deep neural network (DNN), tries to determine the modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the intruder, while guaranteeing that the intended ... More

Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised AlgorithmJul 02 2016Jul 29 2016We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing $\kappa$ predominant ... More

Learning Features of Music from ScratchNov 29 2016We introduce a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written ... More

Fast and Robust Archetypal Analysis for Representation LearningMay 26 2014We revisit a pioneer unsupervised learning technique called archetypal analysis, which is related to successful data analysis methods such as sparse coding and non-negative matrix factorization. Since it was proposed, archetypal analysis did not gain ... More

Coherent Minimisation: Towards efficient tamper-proof compilationDec 17 2012Automata representing game-semantic models of programs are meant to operate in environments whose input-output behaviour is constrained by the rules of a game. This can lead to a notion of equivalence between states which is weaker than the conventional ... More

A Kernel Multiple Change-point Algorithm via Model SelectionFeb 17 2012Mar 14 2019We tackle the change-point problem with data belonging to a general set. We build a penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Capp{\'e} (2007). This penalty generalizes the one proposed by Lebarbier (2005) ... More

Catalyst Acceleration for First-order Convex Optimization: from Theory to PracticeDec 15 2017Jun 19 2018We introduce a generic scheme for accelerating gradient-based optimization methods in the sense of Nesterov. The approach, called Catalyst, builds upon the inexact accelerated proximal point algorithm for minimizing a convex objective function, and consists ... More

Stability and Performance Limits of Adaptive Primal-Dual NetworksAug 16 2014May 13 2015This work studies distributed primal-dual strategies for adaptation and learning over networks from streaming data. Two first-order methods are considered based on the Arrow-Hurwicz (AH) and augmented Lagrangian (AL) techniques. Several revealing results ... More

A kernel multiple change-point algorithm via model selectionFeb 17 2012Mar 24 2016We tackle the change-point problem with data belonging to a general set. We build a penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Capp{\'e} (2007). This penalty generalizes the one proposed by Lebarbier (2005) ... More

QuickeNing: A Generic Quasi-Newton Algorithm for Faster Gradient-Based Optimization *Oct 04 2016We propose an approach to accelerate gradient-based optimization algorithms by giving them the ability to exploit curvature information using quasi-Newton update rules. The proposed scheme, called QuickeNing, is generic and can be applied to a large class ... More

Adaptive Penalty-Based Distributed Stochastic Convex OptimizationDec 16 2013In this work, we study the task of distributed optimization over a network of learners in which each learner possesses a convex cost function, a set of affine equality constraints, and a set of convex inequality constraints. We propose a fully-distributed ... More

Testing for Homogeneity with Kernel Fisher Discriminant AnalysisApr 07 2008We propose to investigate test statistics for testing homogeneity in reproducing kernel Hilbert spaces. Asymptotic null distributions under null hypothesis are derived, and consistency against fixed and local alternatives is assessed. Finally, experimental ... More

An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton AccelerationOct 04 2016Jan 29 2019We propose an inexact variable-metric proximal point algorithm to accelerate gradient-based optimization algorithms. The proposed scheme, called QNing can be notably applied to incremental first-order methods such as the stochastic variance-reduced gradient ... More

Node-Independent Spanning Trees in Gaussian NetworksJan 11 2017Message broadcasting in networks could be carried over spanning trees. A set of spanning trees in the same network is node independent if two conditions are satisfied. First, all trees are rooted at node $r$. Second, for every node $u$ in the network, ... More

Kernel-based Translations of Convolutional NetworksMar 19 2019Convolutional Neural Networks, as most artificial neural networks, are commonly viewed as methods different in essence from kernel-based methods. We provide a systematic translation of Convolutional Neural Networks (ConvNets) into their kernel-based counterparts, ... More

Excess-Risk of Distributed Stochastic LearnersFeb 05 2013Jul 17 2016This work studies the learning ability of consensus and diffusion distributed learners from continuous streams of data arising from different but related statistical distributions. Four distinctive features for diffusion learners are revealed in relation ... More

Modelling of a compact anisotropic star as an anisotropic fluid sphere in $f(T)$ gravityNov 10 2016In this paper, we have studied the new exact model of anisotropic star in $f(T)$ theory of gravity. The dynamical equations in $f(T)$ theory with the anisotropic fluid have been solved by using Krori-Barua solution. We have determined that all the obtained ... More

Invariances and Data Augmentation for Supervised Music TranscriptionNov 13 2017This paper explores a variety of models for frame-based music transcription, with an emphasis on the methods needed to reach state-of-the-art on human recordings. The translation-invariant network discussed in this paper, which combines a traditional ... More

Short Distance Modification of a Gravitational System and its Optical AnalogFeb 24 2018Motivated by developments in string theory, such as T-duality, it has been proposed that the geometry of spacetime should have an intrinsic minimal length associated with it. This would modify the short distance behavior of quantum systems studied on ... More

A hierarchical Bayesian setting for an inverse problem in linear parabolic PDEs with noisy boundary conditionsJan 20 2015Jan 28 2015In this work we develop a Bayesian setting to infer unknown parameters in initial-boundary value problems related to linear parabolic partial differential equations. We realistically assume that the boundary data are noisy, for a given prescribed initial ... More

Modelling of a compact anisotropic star as an anisotropic fluid sphere in $f(T)$ gravityNov 10 2016Jan 09 2018In this paper, we have studied the new exact model of anisotropic star in $f(T)$ theory of gravity. The dynamical equations in $f(T)$ theory with the anisotropic fluid have been solved by using Krori-Barua solution. We have determined that all the obtained ... More

Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: Application to heat transfer in building wallsNov 26 2017Apr 08 2018In this work, we present the ensemble-marginalized Kalman filter (EnMKF), a sequential algorithm analogous to our previously proposed approach [1,2], for estimating the state and parameters of linear parabolic partial differential equations in initial-boundary ... More

Bayesian inference and model comparison for metallic fatigue dataDec 06 2015In this work, we present a statistical treatment of stress-life (S-N) data drawn from a collection of records of fatigue experiments that were performed on 75S-T6 aluminum alloys. Our main objective is to predict the fatigue life of materials by providing ... More

Adaptive Denoising of Signals with Shift-Invariant StructureJun 11 2018We study the problem of discrete-time signal denoising, following the line of research initiated by [Nem91] and further developed in [JN09, JN10, HJNO15, OHJN16]. Previous papers considered the following setup: the signal is assumed to admit a convolution-type ... More

Convolutional Kernel NetworksJun 12 2014Nov 14 2014An important goal in visual recognition is to devise image representations that are invariant to particular transformations. In this paper, we address this goal with a new type of convolutional neural network (CNN) whose invariance is encoded by a reproducing ... More

Bounds on Slow Roll at the Boundary of the LandscapeOct 22 2018Apr 03 2019We present strong evidence that the tree level slow roll bounds of arXiv:1807.05193 and arXiv:1810.05506 are valid, even when the tachyon has overlap with the volume of the cycle wrapped by the orientifold. This extends our previous results in the volume-dilaton ... More

Detection of Syntactic Aspect Interaction in UML State Diagrams Using Critical Pair Analysis in Graph TransformationDec 25 2013Aspect Oriented Modeling separates crosscutting concerns by defining Aspects and composition mechanisms at the model level. Composition of multiple Aspects will most likely result in more than one Aspect matching the same join points. Consequently, Aspects ... More

Parallelize Bubble Sort Algorithm Using OpenMPJul 24 2014Sorting has been a profound area for the algorithmic researchers and many resources are invested to suggest more works for sorting algorithms. For this purpose, many existing sorting algorithms were observed in terms of the efficiency of the algorithmic ... More

Object Discovery in Videos as Foreground Motion ClusteringDec 06 2018Apr 05 2019We consider the problem of providing dense segmentation masks for object discovery in videos. We formulate the object discovery problem as foreground motion clustering, where the goal is to cluster foreground pixels in videos into different objects. We ... More

Edge-Disjoint Node-Independent Spanning Trees in Dense Gaussian NetworksJan 26 2016Jan 27 2016Independent trees are used in building secure and/or fault-tolerant network communication protocols. They have been investigated for different network topologies including tori. Dense Gaussian networks are potential alternatives for 2-dimensional tori. ... More

Bayesian inferences of the thermal properties of a wall using temperature and heat flux measurementsAug 12 2016We develop a hierarchical Bayesian inference method to estimate the thermal resistance and the volumetric heat capacity of a wall. These thermal properties are essential for accurate building energy simulations that are needed to make effective energy-saving ... More

EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical FlowJan 12 2015May 19 2015We propose a novel approach for optical flow estimation , targeted at large displacements with significant oc-clusions. It consists of two steps: i) dense matching by edge-preserving interpolation from a sparse set of matches; ii) variational energy minimization ... More

Structure-Blind Signal RecoveryJul 19 2016We consider the problem of recovering a signal observed in Gaussian noise. If the set of signals is convex and compact, and can be specified beforehand, one can use classical linear estimators that achieve a risk within a constant factor of the minimax ... More

Dictionary Learning over Distributed ModelsFeb 06 2014Dec 07 2014In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in Big Data scenarios where large dictionary models may be spread ... More

On Distributed Online Classification in the Midst of Concept DriftsJan 01 2013In this work, we analyze the generalization ability of distributed online learning algorithms under stationary and non-stationary environments. We derive bounds for the excess-risk attained by each node in a connected network of learners and study the ... More

Bounds on Slow Roll at the Boundary of the LandscapeOct 22 2018Dec 31 2018We present strong evidence that the tree level slow roll bounds of arXiv:1807.05193 and arXiv:1810.05506 are valid, even when the tachyon has overlap with the volume of the cycle wrapped by the orientifold. This extends our previous results in the volume-dilaton ... More

Label-Embedding for Image ClassificationMar 30 2015Oct 01 2015Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space ... More

Subdiffusion and weak ergodicity breaking in the presence of a reactive boundaryApr 13 2007May 10 2007We derive the boundary condition for a subdiffusive particle interacting with a reactive boundary with finite reaction rate. Molecular crowding conditions, that are found to cause subdiffusion of larger molecules in biological cells, are shown to effect ... More

Skin Texture Recognition Using Neural NetworksNov 23 2013Skin recognition is used in many applications ranging from algorithms for face detection, hand gesture analysis, and to objectionable image filtering. In this work a skin recognition system was developed and tested. While many skin segmentation algorithms ... More

Beat-Event Detection in Action Movie FranchisesAug 15 2015While important advances were recently made towards temporally localizing and recognizing specific human actions or activities in videos, efficient detection and classification of long video chunks belonging to semantically defined categories such as ... More

Quantum Fluctuations of a BTZ Black Hole in Massive GravitySep 12 2017In this work, we shall analyze the effects of quantum fluctuations on the properties of a BTZ black hole, in a massive theory of gravity. We will analyze this for a charged BTZ black hole in asymptotically AdS and dS space-times. The quantum fluctuations ... More

Object Discovery in Videos as Foreground Motion ClusteringDec 06 2018We consider the problem of providing dense segmentation masks for object discovery in videos. We formulate the object discovery problem as foreground motion clustering, where the goal is to cluster foreground pixels in videos into different objects. We ... More

Catalyst Acceleration for Gradient-Based Non-Convex OptimizationMar 31 2017Dec 31 2018We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions. Even though these methods may originally require convexity to operate, the proposed approach allows ... More

Fast and Simple Optimization for Poisson Likelihood ModelsAug 03 2016Poisson likelihood models have been prevalently used in imaging, social networks, and time series analysis. We propose fast, simple, theoretically-grounded, and versatile, optimization algorithms for Poisson likelihood modeling. The Poisson log-likelihood ... More

Analysis of evoked EMG using wavelet transformationMay 29 2019Evoked EMG M-responses obtained from the thenar muscle in the palm by electrical stimulation of the median nerve demonstrate a well-established smooth bipolar shape for normal healthy subjects while kinks are observed in certain neurological disorders, ... More

Lévy Fluctuations and Tracer Diffusion in Dilute Suspensions of Algae and BacteriaSep 20 2010Swimming microorganisms rely on effective mixing strategies to achieve efficient nutrient influx. Recent experiments, probing the mixing capability of unicellular biflagellates, revealed that passive tracer particles exhibit anomalous non-Gaussian diffusion ... More

DeepMatching: Hierarchical Deformable Dense MatchingJun 25 2015Oct 08 2015We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a hierarchical, multi-layer, correlational architecture designed for matching images and was inspired by deep convolutional ... More

Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: Application to heat transfer in building wallsNov 26 2017In this work, we present the ensemble-marginalized Kalman filter (EnMKF), a sequential algorithm analogous to our previously proposed approach [1,2], for estimating the state and parameters of linear parabolic partial differential equations in initial-boundary ... More

Coupled Recurrent Models for Polyphonic Music CompositionNov 20 2018This work describes a novel recurrent model for music composition, which accounts for the rich statistical structure of polyphonic music. There are many ways to factor the probability distribution over musical scores; we consider the merits of various ... More

Parallelize Bubble and Merge Sort Algorithms Using Message Passing Interface (MPI)Nov 19 2014Sorting has been a profound area for the algorithmic researchers and many resources are invested to suggest more works for sorting algorithms. For this purpose, many existing sorting algorithms were observed in terms of the efficiency of the algorithmic ... More

A Smoother Way to Train Structured Prediction ModelsFeb 08 2019We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon. Smoothing overcomes the non-smoothness inherent to the maximum margin structured prediction objective, and paves the way for ... More

Bayesian inferences of the thermal properties of a wall using temperature and heat flux measurementsAug 12 2016Sep 12 2017The assessment of the thermal properties of walls is essential for accurate building energy simulations that are needed to make effective energy-saving policies. These properties are usually investigated through in-situ measurements of temperature and ... More

Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered EnvironmentsJul 22 2018Rapidly-exploring Random Tree star (RRT*) has recently gained immense popularity in the motion planning community as it provides a probabilistically complete and asymptotically optimal solution without requiring the complete information of the obstacle ... More

Spatial Poisson processes for fatigue crack initiationMay 09 2018Oct 07 2018In this work we propose a stochastic model for estimating the occurrence of crack initiations on the surface of metallic specimens in fatigue problems that can be applied to a general class of geometries. The stochastic model is based on spatial Poisson ... More

Boundary Effects in Super-Yang-Mills TheoryAug 31 2017In this paper, we shall analyse a three dimensional supersymmetry theory with $\mathcal{N} = 2$. The effective Lagrangian will be given by the sum of the gauge fixing term and the ghost term with the original classical Lagrangian. In presence of a boundary ... More

Multi-level Dynamic Optimization of Intelligent LEACH with Cost Effective Deep Belief NetworkApr 12 2019Energy utilization is a key attribute for energy constrained wireless sensor networks (WSN) that directly impacts the life time of the network. LEACH (and its variants) are considered to be the most common energy efficient routing protocols for WSN. In ... More

Measuring device suitable for linear distancesNov 24 2014Measuring device is proposed for determining a linear dimension. The device comprises three associated longitudinally moving parts one of which is a scale. The integer part of the device reading is being taken from the standard millimeter or inches scale, ... More

Elasticizing Linux via Joint Disaggregation of Memory and ComputationJun 03 2018In this paper, we propose a set of operating system primitives which provides a scaling abstraction to cloud applications in which they can transparently be enabled to support scaled execution across multiple physical nodes as resource needs go beyond ... More

A Neuron as a Signal Processing DeviceMay 12 2014A neuron is a basic physiological and computational unit of the brain. While much is known about the physiological properties of a neuron, its computational role is poorly understood. Here we propose to view a neuron as a signal processing device that ... More

Convolutional Patch Representations for Image Retrieval: an Unsupervised ApproachMar 01 2016Convolutional neural networks (CNNs) have recently received a lot of attention due to their ability to model local stationary structures in natural images in a multi-scale fashion, when learning all model parameters with supervision. While excellent performance ... More

Time-Dependent Strain in GrapheneFeb 17 2018Apr 23 2018We will analyse the effect of time-dependent strain on a sheet of graphene by using the field theory approach. It will be demonstrated that in the continuum limit, such a strain will induce a non-abelian gauge field in graphene. We will analyse the effective ... More

On learning to localize objects with minimal supervisionMar 05 2014May 15 2014Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper, we propose a new method that achieves this goal with only image-level ... More

Effective surface motion on a reactive cylinder of particles that perform intermittent bulk diffusionFeb 17 2011In many biological and small scale technological applications particles may transiently bind to a cylindrical surface. In between two binding events the particles diffuse in the bulk, thus producing an effective translation on the cylinder surface. We ... More

Bulk-mediated surface diffusion on a cylinder: propagators and crossoversDec 18 2008We consider the effective surface motion of a particle that freely diffuses in the bulk and intermittently binds to that surface. From an exact approach we derive various regimes of the effective surface motion characterized by physical rates for binding/unbinding ... More

Dynamic Pricing in Smart Grids under Thresholding Policies: Algorithms and HeuristicsOct 24 2016Minimizing the peak power consumption and matching demand to supply, under fixed threshold polices, are two key requirements for the success of the future electricity market. In this work, we consider dynamic pricing methods to minimize the peak load ... More

The Most General Form of Deformation of the Heisenberg Algebra from the Generalized Uncertainty PrincipleOct 29 2016In this paper, we will propose the most general form of the deformation of Heisenberg algebra motivated by the generalized uncertainty principle. This deformation of the Heisenberg algebra will deform all quantum mechanical systems. The form of the generalized ... More

Spectroscopic Analysis of the Double Lined Eclipsing Binary αVirJun 05 2014{\alpha}Vir is a well known double-lined spectroscopic binary with a B-type for both components. In the present paper we have analyzed a total of 90 spectra obtained through 1992-2000. Spectral analysis are based on two spectral lines H(alpha) and HeI ... More

Swimmer-tracer scattering at low Reynolds numberJul 02 2010Understanding the stochastic dynamics of tracer particles in active fluids is important for identifying the physical properties of flow generating objects such as colloids, bacteria or algae. Here, we study both analytically and numerically the scattering ... More

Bulk-mediated diffusion on a planar surface: full solutionMay 10 2012We consider the effective surface motion of a particle that intermittently unbinds from a planar surface and performs bulk excursions. Based on a random walk approach we derive the diffusion equations for surface and bulk diffusion including the surface-bulk ... More

Anisotropic electrical resistance in mesoscopic LaAlO$_3$/SrTiO$_3$ devices with individual domain wallsMar 21 2017The crystal structure of bulk SrTiO$_3$(STO) transitions from cubic to tetragonal at around 105K. Recent local scanning probe measurements of LaAlO$_3$/SrTiO$_3$ (LAO/STO) interfaces indicated the existence of spatially inhomogeneous electrical current ... More

BrainFrame: A heterogeneous accelerator platform for neuron simulationsDec 05 2016Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational ... More

Orbital solution and evolutionary state for the eclipsing binary 1SWASP J080150.03+471433.8Jun 24 2016We present an orbital solution study for the newly discovered system 1SWASP J080150.03+471433.8 by means of new CCD observations in VRI bands. Our observations were carried out on 25 Feb. 2013 using the Kottamia optical telescope at NRIAG, Egypt. 12 new ... More

Non-Local Deformation of a Supersymmetric Field TheoryOct 04 2017In this paper, we will analyse a supersymmetric field theory deformed by generalized uncertainty principle and Lifshitz scaling. It will be observed that this deformed supersymmetric field theory contains non-local fractional derivative terms. In order ... More

BrainFrame: A heterogeneous accelerator platform for neuron simulationsDec 05 2016Dec 06 2016Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational ... More

BrainFrame: A node-level heterogeneous accelerator platform for neuron simulationsDec 05 2016Aug 15 2017Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational ... More