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A characterization of nonnegativity relative to proper conesJan 30 2018May 21 2019Let $A$ be an $m \times n$ matrix with real entries. Given two proper cones $K_1$ and $K_2$ in $\mathbb{R}^n$ and $\mathbb{R}^m$, respectively, we say that $A$ is nonnegative if $A(K_1) \subseteq K_2$. $A$ is said to be semipositive if there exists a ... More

Distance Matrix of a Class of Completely Positive Graphs: Determinant and InverseJun 11 2019A real symmetric matrix $A$ is said to be completely positive if it can be written as $BB^t$ for some (not necessarily square) nonnegative matrix $B$. A simple graph $G$ is called a completely positive graph if every doubly nonnegative matrix realization ... More

Linear maps on $M_n(\mathbb{R})$ preserving Schur stable matricesFeb 15 2018An $n \times n$ matrix $A$ with real entries is said to be Schur stable if all the eigenvalues of $A$ are inside the open unit disc. We investigate the structure of linear maps on $M_n(\mathbb{R})$ that preserve the collection $\mathcal{S}$ of Schur stable ... More

A characterization of nonnegativity relative to proper conesJan 30 2018Feb 05 2019Let $A$ be an $m \times n$ matrix with real entries. Given two proper cones $K_1$ and $K_2$ in $\mathbb{R}^n$ and $\mathbb{R}^m$, respectively, we say that $A$ is nonnegative if $A(K_1) \subseteq K_2$. $A$ is said to be semipositive if there exists a ... More

A characterization of nonnegativity relative to proper conesJan 30 2018Feb 06 2018Let $A$ be an $m \times n$ matrix with real entries. Given two proper cones $K_1$ and $K_2$ in $\mathbb{R}^n$ and $\mathbb{R}^m$, respectively, we say that $A$ is nonnegative (relative to $K_1$ and $K_2$) if $A(K_1) \subseteq K_2$. $A$ is said to be semipositive ... More

Boundary Fermions, Coherent Sheaves and D-branes on Calabi-Yau manifoldsApr 14 2001Apr 19 2001We construct boundary conditions in the gauged linear sigma model for B-type D-branes on Calabi-Yau manifolds that correspond to coherent sheaves given by the cohomology of a monad. This necessarily involves the introduction of boundary fields, and in ... More

When Relaxations Go Bad: "Differentially-Private" Machine LearningFeb 24 2019Mar 01 2019Differential privacy is becoming a standard notion for performing privacy-preserving machine learning over sensitive data. It provides formal guarantees, in terms of the privacy budget, $\epsilon$, on how much information about individual training records ... More

Look-ahead before you leap: end-to-end active recognition by forecasting the effect of motionApr 30 2016Aug 05 2016Visual recognition systems mounted on autonomous moving agents face the challenge of unconstrained data, but simultaneously have the opportunity to improve their performance by moving to acquire new views of test data. In this work, we first show how ... More

A proposal for the geometry of W_n gravityMay 23 1994Oct 06 1994We relate the Teichmuller spaces obtained by Hitchin to the Teichmuller spaces of $WA_{n}$-gravity. The relationship of this space to $W$-gravity is obtained by identifying the flat $PSL(n+1,{\BR})$ connections of Hitchin to generalised vielbeins and ... More

Oscillations of a solid sphere falling through a wormlike micellar fluidMay 24 2002We present an experimental study of the motion of a solid sphere falling through a wormlike micellar fluid. While smaller or lighter spheres quickly reach a terminal velocity, larger or heavier spheres are found to oscillate in the direction of their ... More

When Relaxations Go Bad: "Differentially-Private" Machine LearningFeb 24 2019Differential privacy is becoming a standard notion for performing privacy-preserving machine learning over sensitive data. It provides formal guarantees, in terms of the privacy budget, $\epsilon$, on how much information about individual training records ... More

Interplay of Sensor Quantity, Placement and System Dimensionality on Energy Sparse Reconstruction of Fluid FlowsJun 21 2018Reconstruction of fine-scale information from sparse data is relevant to many practical fluid dynamic applications where the sensing is typically sparse. Fluid flows in an ideal sense are manifestations of nonlinear multiscale PDE dynamical systems with ... More

The impact of latent heating on the location, strength and structure of the Tropical Easterly Jet in the Community Atmosphere Model, version 3.1: Aqua-planet simulationsMay 03 2015Aug 05 2015The Tropical Easterly Jet (TEJ) is a prominent atmospheric circulation feature observed during the Asian Summer Monsoon (ASM). The simulation of TEJ by the Community Atmosphere Model, version 3.1 (CAM-3.1) has been discussed in detail. Although the simulated ... More

D-branes, Exceptional Sheaves and Quivers on Calabi-Yau manifolds: From Mukai to McKayOct 23 2000Nov 06 2000We present a method based on mutations of helices which leads to the construction (in the large volume limit) of exceptional coherent sheaves associated with the $(\sum_al_a=0)$ orbits in Gepner models. This is explicitly verified for a few examples including ... More

JavaTA: A Logic-based Debugger for JavaJan 17 2007This paper presents a logic based approach to debugging Java programs. In contrast with traditional debugging we propose a debugging methodology for Java programs using logical queries on individual execution states and also over the history of execution. ... More

On the Landau-Ginzburg description of Boundary CFTs and special Lagrangian submanifoldsMar 27 2000Mar 30 2000We consider Landau-Ginzburg (LG) models with boundary conditions preserving A-type N=2 supersymmetry. We show the equivalence of a linear class of boundary conditions in the LG model to a particular class of boundary states in the corresponding CFT by ... More

D-branes and Vector Bundles on Calabi-Yau Manifolds: a view from the HelixMay 22 2001We review some recent results on D-branes on Calabi-Yau (CY) manifolds. We show the existence of structures (helices and quivers) which enable one to make statements about large families of D-branes in various phases of the Gauged Linear Sigma Model (GLSM) ... More

Assessment of End-to-End and Sequential Data-driven Learning of Fluid FlowsJun 20 2018Nov 02 2018In this work we explore the advantages of end-to-end learning of multilayer maps offered by feed forward neural-networks (FFNN) for learning and predicting dynamics from transient fluid flow data.While machine learning in general depends on data quality ... More

A Generative Modeling Approach to Limited Channel ECG ClassificationFeb 18 2018Jun 14 2018Processing temporal sequences is central to a variety of applications in health care, and in particular multi-channel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling. While Recurrent Neural Networks ... More

Improved Deep Embeddings for Inferencing with Multi-Layered NetworksSep 20 2018Mar 01 2019Inferencing with network data necessitates the mapping of its nodes into a vector space, where the relationships are preserved. However, with multi-layered networks, where multiple types of relationships exist for the same set of nodes, it is crucial ... More

Amplitude Equations for Electrostatic Waves: multiple speciesJun 10 1997The amplitude equation for an unstable electrostatic wave is analyzed using an expansion in the mode amplitude $A(t)$. In the limit of weak instability, i.e. $\gamma\to 0^+$ where $\gamma$ is the linear growth rate, the nonlinear coefficients are singular ... More

First Principles Justification of a ``Single Wave Model'' for Electrostatic InstabilitiesApr 14 1998The nonlinear evolution of a unstable electrostatic wave is considered for a multi-species Vlasov plasma. From the singularity structure of the associated amplitude expansions, the asymptotic features of the electric field and distribution functions are ... More

Nonlinear saturation of electrostatic waves: mobile ions modify trapping scalingJun 24 1996The amplitude equation for an unstable electrostatic wave in a multi-species Vlasov plasma has been derived. The dynamics of the mode amplitude $\rho(t)$ is studied using an expansion in $\rho$; in particular, in the limit $\gamma\rightarrow0^+$, the ... More

The Surprising Transition in Atmospheric Boundary Layer Turbulence Structure from Neutral to Moderately Convective Stability States and Mechanisms Underlying Large-scale RollsJul 09 2018Dec 07 2018The vectoral wind structure of daytime atmospheric boundary layer (ABL) turbulence is strongly dependent on the balance between shear-driven turbulence production of horizontal fluctuations (driven by winds at the mesoscale), and buoyancy-driven turbulence ... More

Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder ClassificationApr 24 2017Oct 30 2018Using predictive models to identify patterns that can act as biomarkers for different neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the problem of Autism Spectrum Disorder (ASD) classification where previous work ... More

Decentralized Certificate AuthoritiesJun 11 2017Oct 10 2017The security of TLS depends on trust in certificate authorities, and that trust stems from their ability to protect and control the use of a private signing key. The signing key is the key asset of a certificate authority (CA), and its value is based ... More

Scoping Constructs in Logic Programming: Implementation Problems and their SolutionSep 10 1998The inclusion of universal quantification and a form of implication in goals in logic programming is considered. These additions provide a logical basis for scoping but they also raise new implementation problems. When universal and existential quantifiers ... More

On the Trajectory Dependence of Atmospheric Boundary Layer Turbulence Sensing using Small Unmanned VehiclesJul 11 2018Atmospheric turbulence, especially in the near-surface boundary layer is known to be under-sampled due to the need to capture a wide separation in length and time-scales and limitation in the number of sensors. Over the past decade, the use if Unmanned ... More

Fractional two-branes, toric orbifolds and the quantum McKay correspondenceJun 16 2006Sep 16 2006We systematically study and obtain the large-volume analogues of fractional two-branes on resolutions of orbifolds C^3/Z_n. We study a generalisation of the McKay correspondence proposed in hep-th/0504164 called the quantum McKay correspondence by constructing ... More

Worldsheet approaches to D-branes on supersymmetric cyclesJul 15 1999Mar 28 2000We consider D-branes wrapped around supersymmetric cycles of Calabi-Yau manifolds from the viewpoint of N=2 Landau-Ginzburg models with boundary as well as by consideration of boundary states in the corresponding Gepner models. The Landau-Ginzburg approach ... More

Monotone Solutions of a Nonautonomous Differential Equation for a Sedimenting SphereDec 14 2000May 24 2002We study a class of integrodifferential equations and related ordinary differential equations for the initial value problem of a rigid sphere falling through an infinite fluid medium. We prove that for creeping Newtonian flow, the motion of the sphere ... More

Correlation Functions and Multicritical Flows in $c<1$ String TheorySep 08 1993We compute all string tree level correlation functions of vertex operators in $c<1$ string theory. This is done by using the ring structure of the theory. In order to study the multicritical behaviour, we calculate the correlation functions after perturbation ... More

Genus Zero Correlation Functions in c<1 String TheoryAug 25 1992Sep 07 1992We compute N-point correlation functions of pure vertex operator states(DK states) for minimal models coupled to gravity. We obtain agreement with the matrix model results on analytically continuing in the numbers of cosmological constant operators and ... More

Disc Instantons in Linear Sigma ModelsAug 31 2001Sep 02 2001We construct a linear sigma model for open-strings ending on special Lagrangian cycles of a Calabi-Yau manifold. We illustrate the construction for the cases considered by Aganagic and Vafa in hep-th/0012041. This leads naturally to concrete models for ... More

Clustering of Complex Networks and Community Detection Using Group Search OptimizationJul 04 2013Aug 19 2013Group Search Optimizer(GSO) is one of the best algorithms, is very new in the field of Evolutionary Computing. It is very robust and efficient algorithm, which is inspired by animal searching behaviour. The paper describes an application of GSO to clustering ... More

Object-Centric Representation Learning from Unlabeled VideosDec 01 2016Supervised (pre-)training currently yields state-of-the-art performance for representation learning for visual recognition, yet it comes at the cost of (1) intensive manual annotations and (2) an inherent restriction in the scope of data relevant for ... More

Pano2Vid: Automatic Cinematography for Watching 360$^{\circ}$ VideosDec 07 2016We introduce the novel task of Pano2Vid $-$ automatic cinematography in panoramic 360$^{\circ}$ videos. Given a 360$^{\circ}$ video, the goal is to direct an imaginary camera to virtually capture natural-looking normal field-of-view (NFOV) video. By selecting ... More

Chiral Rings and Physical States in c<1 String TheoryJul 30 1992We show how the double cohomology of the String and Felder BRST charges naturally leads to the ring structure of $c<1$ strings. The chiral ring is a ring of polynomials in two variables modulo an equivalence relation of the form $x^p \simeq y^{p+1}$ for ... More

A quantum McKay correspondence for fractional 2p-branes on LG orbifoldsApr 20 2005Jun 27 2005We study fractional 2p-branes and their intersection numbers in non-compact orbifolds as well the continuation of these objects in Kahler moduli space to coherent sheaves in the corresponding smooth non-compact Calabi-Yau manifolds. We show that the restriction ... More

On D-branes from Gauged Linear Sigma ModelsJul 10 2000Jul 15 2000We study both A-type and B-type D-branes in the gauged linear sigma model by considering worldsheets with boundary. The boundary conditions on the matter and vector multiplet fields are first considered in the large-volume phase/non-linear sigma model ... More

Can Deep Clinical Models Handle Real-World Domain Shifts?Sep 20 2018The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare. Deep learning, in particular, has been a game changer in building predictive models, thereby leading to community-wide ... More

ShapeCodes: Self-Supervised Feature Learning by Lifting Views to ViewgridsSep 01 2017Jul 31 2018We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the ... More

Causal Confusion in Imitation LearningMay 28 2019Behavioral cloning reduces policy learning to supervised learning by training a discriminative model to predict expert actions given observations. Such discriminative models are non-causal: the training procedure is unaware of the causal structure of ... More

Learning Robust Representations for Computer VisionJul 31 2017Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in the data can frustrate these approaches by obscuring the latent spaces. Our ... More

Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDENOct 15 2013Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can ... More

Dynamic Configuration of Sensors Using Mobile Sensor Hub in Internet of Things ParadigmFeb 05 2013Internet of Things (IoT) envisions billions of sensors to be connected to the Internet. By deploying intelligent low-level computational devices such as mobile phones in-between sensors and cloud servers, we can reduce data communication with the use ... More

Unsupervised Dimension Selection using a Blue Noise SpectrumOct 31 2018Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics. While dimensionality reduction is commonly utilized to construct low-dimensional embeddings, they produce ... More

Ensemble Sparse Models for Image AnalysisFeb 27 2013Sparse representations with learned dictionaries have been successful in several image analysis applications. In this paper, we propose and analyze the framework of ensemble sparse models, and demonstrate their utility in image restoration and unsupervised ... More

Designing an Effective Metric Learning Pipeline for Speaker DiarizationNov 01 2018State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data. However, much of recent focus has been on choosing the ... More

Quad-rotor Flight Simulation in Realistic Atmospheric ConditionsFeb 04 2019In trajectory planning and control design for unmanned air vehicles, highly simplified models are typically used to represent the vehicle dynamics and the operating environment. The goal of this work is to perform real-time, but realistic flight simulations ... More

SALT: Subspace Alignment as an Auxiliary Learning Task for Domain AdaptationJun 11 2019Unsupervised domain adaptation aims to transfer and adapt knowledge learned from a labeled source domain to an unlabeled target domain. Key components of unsupervised domain adaptation include: (a) maximizing performance on the source, and (b) aligning ... More

States of non-zero ghost number in $c<1$ matter coupled to 2d gravityDec 14 1991We study $c<1$ matter coupled to gravity in the Coulomb gas formalism using the double cohomology of the string BRST and Felder BRST charges. We find that states outside the primary conformal grid are related to the states of non-zero ghost number by ... More

The Schema Editor of OpenIoT for Semantic Sensor NetworksJun 21 2016Ontologies provide conceptual abstractions over data, in domains such as the Internet of Things, in a way that sensor data can be harvested and interpreted by people and applications. The Semantic Sensor Network (SSN) ontology is the de-facto standard ... More

Robust Local Scaling using Conditional Quantiles of Graph SimilaritiesDec 14 2016Spectral analysis of neighborhood graphs is one of the most widely used techniques for exploratory data analysis, with applications ranging from machine learning to social sciences. In such applications, it is typical to first encode relationships between ... More

Recovering Non-negative and Combined Sparse RepresentationsMar 12 2013Sep 20 2013The non-negative solution to an underdetermined linear system can be uniquely recovered sometimes, even without imposing any additional sparsity constraints. In this paper, we derive conditions under which a unique non-negative solution for such a system ... More

Sparse Convolution-based Markov Models for Nonlinear Fluid FlowsMar 22 2018Mar 23 2018Data-driven modeling for nonlinear fluid flows using sparse convolution-based mapping into a feature space where the dynamics are Markov linear is explored in this article. The underlying principle of low-order models for fluid systems is identifying ... More

MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial DefenseNov 20 2018Solving inverse problems continues to be a central challenge in computer vision. Existing techniques either explicitly construct an inverse mapping using prior knowledge about the corruption, or learn the inverse directly using a large collection of examples. ... More

MARGIN: Uncovering Deep Neural Networks using Graph Signal AnalysisNov 15 2017Dec 04 2018Interpretability has emerged as a crucial aspect of machine learning, aimed at providing insights into the working of complex neural networks. However, existing solutions vary vastly based on the nature of the interpretability task, with each use case ... More

Characterization of a Canonical Helicopter Hub WakeJul 10 2018Nov 14 2018The current study investigates the long-age wake behind rotating helicopter hub models composed of geometrically simple, canonical bluff body shapes. The models consisted of a 4-arm rotor mounted on a shaft above a 2-arm (scissor) rotor with all the rotor ... More

A Look at the Effect of Sample Design on Generalization through the Lens of Spectral AnalysisJun 06 2019This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, ... More

Attention Models with Random Features for Multi-layered Graph EmbeddingsOct 02 2018Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent ... More

Optimizing Kernel Machines using Deep LearningNov 15 2017Building highly non-linear and non-parametric models is central to several state-of-the-art machine learning systems. Kernel methods form an important class of techniques that induce a reproducing kernel Hilbert space (RKHS) for inferring non-linear models ... More

Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning AlgorithmOct 11 2017Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based ... More

D-branes and the Conifold SingularityJul 16 1996We analyze in detail the description of type IIB theory on a Calabi-Yau three-fold near a conifold singularity in terms of intersecting D-branes. In particular we study the singularity structure of higher derivative $F$-terms of the form $F_g W^{2g}$ ... More

TreeView: Peeking into Deep Neural Networks Via Feature-Space PartitioningNov 22 2016With the advent of highly predictive but opaque deep learning models, it has become more important than ever to understand and explain the predictions of such models. Existing approaches define interpretability as the inverse of complexity and achieve ... More

Multiple Kernel Sparse Representations for Supervised and Unsupervised LearningMar 03 2013Oct 04 2013In complex visual recognition tasks it is typical to adopt multiple descriptors, that describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature ... More

An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial NetworksMay 18 2018Jun 04 2018Solving inverse problems continues to be a challenge in a wide array of applications ranging from deblurring, image inpainting, source separation etc. Most existing techniques solve such inverse problems by either explicitly or implicitly finding the ... More

Attend and Diagnose: Clinical Time Series Analysis using Attention ModelsNov 10 2017Nov 19 2017With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data. Powered by Recurrent Neural Network (RNN) architectures with Long Short-Term Memory (LSTM) ... More

GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention ModelsOct 02 2018Mar 27 2019Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent ... More

Universal Collaboration Strategies for Signal Detection: A Sparse Learning ApproachJan 22 2016Jun 28 2016This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with ... More

MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing ApplicationsMay 22 2014Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile ... More

Context-aware Dynamic Discovery and Configuration of 'Things' in Smart EnvironmentsNov 09 2013The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. ... More

D-branes on Calabi-Yau Manifolds and SuperpotentialsMar 19 2002Apr 12 2002We show how to compute terms in an expansion of the world-volume superpotential for fairly general D-branes on the quintic Calabi-Yau using linear sigma model techniques, and show in examples that this superpotential captures the geometry and obstruction ... More

Multiple Subspace Alignment Improves Domain AdaptationNov 11 2018We present a novel unsupervised domain adaptation (DA) method for cross-domain visual recognition. Though subspace methods have found success in DA, their performance is often limited due to the assumption of approximating an entire dataset using a single ... More

Beyond L2-Loss Functions for Learning Sparse ModelsMar 26 2014Incorporating sparsity priors in learning tasks can give rise to simple, and interpretable models for complex high dimensional data. Sparse models have found widespread use in structure discovery, recovering data from corruptions, and a variety of large ... More

Learning Stable Multilevel Dictionaries for Sparse RepresentationsMar 03 2013Sep 25 2013Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The availability of abundant training data necessitates the development of efficient, robust and provably ... More

Automatic Inference of the Quantile ParameterNov 12 2015Supervised learning is an active research area, with numerous applications in diverse fields such as data analytics, computer vision, speech and audio processing, and image understanding. In most cases, the loss functions used in machine learning assume ... More

Time-Agnostic Prediction: Predicting Predictable Video FramesAug 23 2018Oct 23 2018Prediction is arguably one of the most basic functions of an intelligent system. In general, the problem of predicting events in the future or between two waypoints is exceedingly difficult. However, most phenomena naturally pass through relatively predictable ... More

Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time SeriesNov 29 2016We present an approach to model time series data from resting state fMRI for autism spectrum disorder (ASD) severity classification. We propose to adopt kernel machines and employ graph kernels that define a kernel dot product between two graphs. This ... More

Understanding Deep Neural Networks through Input UncertaintiesOct 31 2018Nov 01 2018Techniques for understanding the functioning of complex machine learning models are becoming increasingly popular, not only to improve the validation process, but also to extract new insights about the data via exploratory analysis. Though a large class ... More

Improving Robustness of Attention Models on GraphsNov 01 2018Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention ... More

A Look at the Effect of Sample Design on Generalization through the Lens of Spectral AnalysisJun 06 2019Jun 08 2019This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, ... More

ARBAC Policy for a Large Multi-National BankOct 13 2011Administrative role-based access control (ARBAC) is the first comprehensive administrative model proposed for role-based access control (RBAC). ARBAC has several features for designing highly expressive policies, but current work has not highlighted the ... More

Hypertree Decompositions Revisited for PGMsJul 02 2018We revisit the classical problem of exact inference on probabilistic graphical models (PGMs). Our algorithm is based on recent \emph{worst-case optimal database join} algorithms, which can be asymptotically faster than traditional data processing methods. ... More

A 10,000 star spectroscopic survey of the thick disk-halo interface : Phase-space sub-structure in the thick diskFeb 20 2013We analyse a 10,000 star spectroscopic survey, focused on Galactic thick disk stars typically 2-5 kpc from the Sun, carried out using the AAOmega Spectrograph on the AAT. We develop methods for completeness-correction of the survey based on SDSS photometry, ... More

Triplet Network with Attention for Speaker DiarizationAug 04 2018In automatic speech processing systems, speaker diarization is a crucial front-end component to separate segments from different speakers. Inspired by the recent success of deep neural networks (DNNs) in semantic inferencing, triplet loss-based architectures ... More

Magnetic skyrmions and skyrmion clusters in the helical phase of Cu$_2$OSeO$_3$Mar 20 2017Sep 29 2017Skyrmions are nanometric spin whirls that can be stabilized in magnets lacking inversion symmetry. The properties of isolated skyrmions embedded in a ferromagnetic background have been intensively studied. We show that single skyrmions and clusters of ... More

City Data Fusion: Sensor Data Fusion in the Internet of ThingsJun 30 2015Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. ... More

Audio Source Separation via Multi-Scale Learning with Dilated Dense U-NetsApr 08 2019Modern audio source separation techniques rely on optimizing sequence model architectures such as, 1D-CNNs, on mixture recordings to generalize well to unseen mixtures. Specifically, recent focus is on time-domain based architectures such as Wave-U-Net ... More

Resource Usage Estimation of Data Stream Processing Workloads in Datacenter CloudsJan 28 2015Real-time computation of data streams over affordable virtualized infrastructure resources is an important form of data in motion processing architecture. However, processing such data streams while ensuring strict guarantees on quality of services is ... More

In situ Electric Field Skyrmion Creation in Magnetoelectric Cu$_2$OSeO$_3$Oct 25 2017Magnetic skyrmions are localized nanometric spin textures with quantized winding numbers as the topological invariant. Rapidly increasing attention has been paid to the investigations of skyrmions since their experimental discovery in 2009, due both to ... More

Fog Computing: Survey of Trends, Architectures, Requirements, and Research DirectionsJul 03 2018Emerging technologies like the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated ... More

Sensor Discovery and Configuration Framework for The Internet of Things ParadigmDec 23 2013Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. The data generated by the Internet of Things are valuable and have the potential to drive innovative and novel applications. The data ... More

Analytics-as-a-Service in a Multi-Cloud Environment through Semantically enabled Hierarchical Data ProcessingJun 25 2016A large number of cloud middleware platforms and tools are deployed to support a variety of Internet of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined ... More

Kernel Sparse Models for Automated Tumor SegmentationMar 11 2013In this paper, we propose sparse coding-based approaches for segmentation of tumor regions from MR images. Sparse coding with data-adapted dictionaries has been successfully employed in several image recovery and vision problems. The proposed approaches ... More

The Role of Big Data Analytics in Industrial Internet of ThingsApr 11 2019Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources ... More

Manipulation by Feel: Touch-Based Control with Deep Predictive ModelsMar 11 2019Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage ... More

A Novel Quantum N-Queens Solver Algorithm and its Simulation and Application to Satellite Communication Using IBM Quantum ExperienceJun 26 2018Jul 30 2018Quantum computers can potentially solve problems that are computationally intractable on a classical computer in polynomial time using quantum-mechanical effects such as superposition and entanglement. The N-Queens Problem is a notable example that falls ... More

Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram CompletionNov 28 2017Jul 11 2018Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180$^\circ$ view of the object. ... More

Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula ModelsNov 30 2016Influential node detection is a central research topic in social network analysis. Many existing methods rely on the assumption that the network structure is completely known \textit{a priori}. However, in many applications, network structure is unavailable ... More