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PixelNN: Example-based Image SynthesisAug 17 2017We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an "incomplete" signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models designed ... More

Active Testing: An Efficient and Robust Framework for Estimating AccuracyJul 02 2018Much recent work on visual recognition aims to scale up learning to massive, noisily-annotated datasets. We address the problem of scaling- up the evaluation of such models to large-scale datasets with noisy labels. Current protocols for doing so require ... More

Brute-Force Facial Landmark Analysis With A 140,000-Way ClassifierFeb 06 2018Feb 14 2018We propose a simple approach to visual alignment, focusing on the illustrative task of facial landmark estimation. While most prior work treats this as a regression problem, we instead formulate it as a discrete $K$-way classification task, where a classifier ... More

Recycle-GAN: Unsupervised Video RetargetingAug 15 2018We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver's speech were to be transferred to Stephen Colbert, ... More

Photo-Sketching: Inferring Contour Drawings from ImagesJan 02 2019Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision. On one hand, they are the 2D elements that convey 3D shapes, on the other hand, they are indicative of occlusion events and thus separation of ... More

DistInit: Learning Video Representations Without a Single Labeled VideoJan 26 2019Aug 20 2019Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models have not been ... More

Multi-scale recognition with DAG-CNNsMay 20 2015We explore multi-scale convolutional neural nets (CNNs) for image classification. Contemporary approaches extract features from a single output layer. By extracting features from multiple layers, one can simultaneously reason about high, mid, and low-level ... More

Finding Tiny FacesDec 13 2016Apr 15 2017Though tremendous strides have been made in object recognition, one of the remaining open challenges is detecting small objects. We explore three aspects of the problem in the context of finding small faces: the role of scale invariance, image resolution, ... More

Bottom-Up and Top-Down Reasoning with Hierarchical Rectified GaussiansJul 21 2015May 04 2016Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a unidirectional bottom-up feed-forward fashion. However, practical experience and biological evidence tells us that feedback ... More

Tinkering Under the Hood: Interactive Zero-Shot Learning with Net SurgeryDec 15 2016We consider the task of visual net surgery, in which a CNN can be reconfigured without extra data to recognize novel concepts that may be omitted from the training set. While most prior work make use of linguistic cues for such "zero-shot" learning, we ... More

Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection DatasetJul 22 2017Oct 26 2017Person detection from vehicles has made rapid progress recently with the advent of multiple highquality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here ... More

Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement LearningJul 17 2017We formulate tracking as an online decision-making process, where a tracking agent must follow an object despite ambiguous image frames and a limited computational budget. Crucially, the agent must decide where to look in the upcoming frames, when to ... More

Growing a Brain: Fine-Tuning by Increasing Model CapacityJul 18 2019CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically ... More

Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsMay 12 2019In most practical settings and theoretical analysis, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and models may violate such assumptions. Moreover, current approaches for hyper-parameter ... More

Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsMay 12 2019Jul 18 2019In most practical settings and theoretical analysis, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and models may violate such assumptions. Moreover, current approaches for hyper-parameter ... More

Shapes and Context: In-the-Wild Image Synthesis & ManipulationJun 11 2019We introduce a data-driven approach for interactively synthesizing in-the-wild images from semantic label maps. Our approach is dramatically different from recent work in this space, in that we make use of no learning. Instead, our approach uses simple ... More

Microsoft COCO: Common Objects in ContextMay 01 2014Feb 21 2015We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex ... More

Towards Segmenting Anything That MovesFeb 11 2019Apr 25 2019For many applications such as action detection or robotic interaction, segmenting all moving objects is a crucial first step. While this problem has been well-studied under the formulation of spatiotemporal video segmentation, virtually none of the prior ... More

Learning Policies for Adaptive Tracking with Deep Feature CascadesAug 09 2017Sep 13 2017Visual object tracking is a fundamental and time-critical vision task. Recent years have seen many shallow tracking methods based on real-time pixel-based correlation filters, as well as deep methods that have top performance but need a high-end GPU. ... More

Towards Segmenting Everything That MovesFeb 11 2019Video analysis is the task of perceiving the world as it changes. Often, though, most of the world doesn't change all that much: it's boring. For many applications such as action detection or robotic interaction, segmenting all moving objects is a crucial ... More

Predictive-Corrective Networks for Action DetectionApr 12 2017Dec 12 2017While deep feature learning has revolutionized techniques for static-image understanding, the same does not quite hold for video processing. Architectures and optimization techniques used for video are largely based off those for static images, potentially ... More

Active Learning with Partial FeedbackFeb 21 2018Sep 28 2018In the large-scale multiclass setting, assigning labels often consists of answering multiple questions to drill down through a hierarchy of classes. Here, the labor required per annotation scales with the number of questions asked. We propose active learning ... More

Active Learning with Partial FeedbackFeb 21 2018Jul 09 2019While many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one among many classes), and the form of an annotation (typically yes/no binary ... More

The Open World of Micro-VideosMar 31 2016Apr 01 2016Micro-videos are six-second videos popular on social media networks with several unique properties. Firstly, because of the authoring process, they contain significantly more diversity and narrative structure than existing collections of video "snippets". ... More

Weakly-supervised Action Localization with Background ModelingAug 19 2019We describe a latent approach that learns to detect actions in long sequences given training videos with only whole-video class labels. Our approach makes use of two innovations to attention-modeling in weakly-supervised learning. First, and most notably, ... More

Cross-Domain Image Matching with Deep Feature MapsApr 06 2018Oct 01 2018We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene. This recognition problem is made difficult by the variability in types of crime scene evidence (ranging from traces of dust or oil on ... More

DistInit: Learning Video Representations without a Single Labeled VideoJan 26 2019Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models has not been ... More

ActionVLAD: Learning spatio-temporal aggregation for action classificationApr 10 2017In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks with learnable ... More

PixelNet: Towards a General Pixel-level ArchitectureSep 21 2016We explore architectures for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fully-convolutional network (FCN), have ... More

Do We Need More Training Data?Mar 05 2015Datasets for training object recognition systems are steadily increasing in size. This paper investigates the question of whether existing detectors will continue to improve as data grows, or saturate in performance due to limited model complexity and ... More

Egocentric Pose Recognition in Four Lines of CodeNov 29 2014We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera. Importantly, we consider pose estimation during everyday interactions with objects. Past work shows that strong pose+viewpoint ... More

Patch Correspondences for Interpreting Pixel-level CNNsNov 29 2017Sep 04 2018We present compositional nearest neighbors (CompNN), a simple approach to visually interpreting distributed representations learned by a convolutional neural network (CNN) for pixel-level tasks (e.g., image synthesis and segmentation). It does so by reconstructing ... More

PixelNet: Representation of the pixels, by the pixels, and for the pixelsFeb 21 2017We explore design principles for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fully-convolutional network (FCN), ... More

Reflected diffusions defined via the extended Skorokhod mapOct 03 2006This work introduces the extended Skorokhod problem (ESP) and associated extended Skorokhod map (ESM) that enable a pathwise construction of reflected diffusions that are not necessarily semimartingales. Roughly speaking, given the closure G of an open ... More

Depth-based hand pose estimation: methods, data, and challengesApr 24 2015May 06 2015Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose ... More

A systematic framework for natural perturbations from videosJun 05 2019Aug 12 2019We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos. As part of this framework, we construct Imagenet-Video-Robust, a human-expert--reviewed dataset of 22,178 ... More

A systematic framework for natural perturbations from videosJun 05 2019We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos. As part of this framework, we construct Imagenet-Video-Robust, a human-expert--reviewed dataset of 22,178 ... More

3D Hand Pose Detection in Egocentric RGB-D ImagesNov 29 2014We focus on the task of everyday hand pose estimation from egocentric viewpoints. For this task, we show that depth sensors are particularly informative for extracting near-field interactions of the camera wearer with his/her environment. Despite the ... More

Need for Speed: A Benchmark for Higher Frame Rate Object TrackingMar 17 2017Mar 21 2017In this paper, we propose the first higher frame rate video dataset (called Need for Speed - NfS) and benchmark for visual object tracking. The dataset consists of 100 videos (380K frames) captured with now commonly available higher frame rate (240 FPS) ... More

Asymptotic approximations for stationary distributions of many-server queues with abandonmentMar 17 2010Apr 27 2012A many-server queueing system is considered in which customers arrive according to a renewal process and have service and patience times that are drawn from two independent sequences of independent, identically distributed random variables. Customers ... More

Bounds on Lifting Continuous Markov Chains to Speed Up MixingJun 10 2016Feb 27 2017It is often possible to speed up the mixing of a Markov chain $\{ X_{t} \}_{t \in \mathbb{N}}$ on a state space $\Omega$ by \textit{lifting}, that is, running a more efficient Markov chain $\{ \hat{X}_{t} \}_{t \in \mathbb{N}}$ on a larger state space ... More

Triplet Pairing in pure neutron matterJun 29 2016Oct 24 2016We study the zero temperature BCS gaps for the triplet channel in pure neutron matter using Similarity Renormalization Group (SRG) evolved interactions. We use the dependence of the results on the SRG resolution scale, as a tool to analyze medium and ... More

Characterization of stationary distributions of reflected diffusionsApr 23 2012Given a domain G, a reflection vector field d(.) on the boundary of G, and drift and dispersion coefficients b(.) and \sigma(.), let L be the usual second-order elliptic operator associated with b(.) and \sigma(.). Under suitable assumptions that, in ... More

Intertwinings of beta-Dyson Brownian motions of different dimensionsAug 04 2016We show that for all positive beta the semigroups of beta-Dyson Brownian motions of different dimensions are intertwined. The proof relates beta-Dyson Brownian motions directly to Jack symmetric polynomials and omits an approximation of the former by ... More

The hydrodynamic limit of a randomized load balancing networkJul 07 2017Oct 11 2017Randomized load balancing networks arise in a variety of applications, and allow for efficient sharing of resources, while being relatively easy to implement. We consider a network of parallel queues in which incoming jobs with independent and identically ... More

BEC-BCS Crossover in Neutron Matter with Renormalization Group based Effective InteractionsAug 05 2013Nov 07 2013We study pure neutron matter in the BEC-BCS crossover regime using renormalization group based low-momentum interactions within the Nozi\`eres-Schmitt-Rink framework. This is an attempt to go beyond the mean field description for low-density matter. We ... More

Bounds on Lifting Continuous Markov Chains to Speed Up MixingJun 10 2016It is often possible to speed up the mixing of a Markov chain $\{ X_{t} \}_{t \in \mathbb{N}}$ on a state space $\Omega$ by \textit{lifting}, that is, running a more efficient Markov chain $\{ \hat{X}_{t} \}_{t \in \mathbb{N}}$ on a larger state space ... More

SPDE Limits of Many Server QueuesOct 02 2010A many-server queueing system is considered in which customers with independent and identically distributed service times enter service in the order of arrival. The state of the system is represented by a process that describes the total number of customers ... More

Ergodicity of an SPDE Associated with a Many-Server QueueDec 09 2015Dec 04 2017We consider the so-called GI/GI/N queueing network in which a stream of jobs with independent and identically distributed service times arrive according to a renewal process to a common queue served by $N$ identical servers in a First-Come-First-Serve ... More

A Monte Carlo method for estimating sensitivities of reflected diffusions in convex polyhedral domainsNov 30 2017In this work we develop an effective Monte Carlo method for estimating sensitivities, or gradients of expectations of sufficiently smooth functionals, of a reflected diffusion in a convex polyhedral domain with respect to its defining parameters --- namely, ... More

On directional derivatives of Skorokhod maps in convex polyhedral domainsFeb 04 2016The study of both sensitivity analysis and differentiability of the stochastic flow of a reflected process in a convex polyhedral domain is challenging because the dynamics are discontinuous at the boundary of the domain and the boundary of the domain ... More

Ergodicity of an SPDE Associated with a Many-Server QueueDec 09 2015Oct 08 2016We introduce a two-component infinite-dimensional Markov process that serves as a diffusion model for a certain parallel server queue called the GI/GI/N queue, in the so-called Halfin-Whitt asymptotic regime. Under suitable assumptions on the service ... More

The Limit of Stationary Distributions of Many-Server Queues in the Halfin-Whitt RegimeOct 04 2016We consider the so-called GI/GI/N queue, in which a stream of jobs with independent and identically distributed service times arrive as a renewal process to a common queue that is served by $N$ identical parallel servers in a first-come-first-serve manner. ... More

New evidence for Green's conjecture on syzygies of canonical curvesJul 31 1997We prove that two weakened forms of Green's conjectures for canonical curves are equivalent when the genus $g$ is odd.

The Limit of Stationary Distributions of Many-Server Queues in the Halfin-Whitt RegimeOct 04 2016Dec 04 2017We consider the so-called GI/GI/N queue, in which a stream of jobs with independent and identically distributed service times arrive as a renewal process to a common queue that is served by $N$ identical parallel servers in a first-come-first-serve manner. ... More

On the submartingale problem for reflected diffusions in domains with piecewise smooth boundariesDec 01 2014Two frameworks that have been used to characterize reflected diffusions include stochastic differential equations with reflection and the so-called submartingale problem. We introduce a general formulation of the submartingale problem for (obliquely) ... More

Sensitivity analysis for the stationary distribution of reflected Brownian motion in a convex polyhedral coneJun 30 2019Reflected Brownian motion (RBM) in a convex polyhedral cone arises in a variety of applications ranging from the theory of stochastic networks to math finance, and under general stability conditions, it has a unique stationary distribution. In such applications, ... More

A duality for Spin Verlinde spaces and Prym theta functionsDec 13 1999We prove canonical isomorphisms between Spin Verlinde spaces, i.e, spaces of global sections of a determinant line bundle over the moduli space of semistable Spin-bundles over a smooth projective curve C, and the dual spaces of theta functions over Prym ... More

Constraining the low energy pion electromagnetic form factor with space-like dataJan 14 2008The pionic contribution to the g-2 of the muon involves a certain integral over the the modulus squared of F_\pi(t), the pion electromagnetic form factor. We extend techniques that use cut-plane analyticity properties of F_\pi(t) in order to account for ... More

Constraining the low energy Pion electromagnetic form factor with space-like and phase of time-like dataNov 04 2008Dec 15 2008The Taylor coefficients c and d of the Pion EM form factor are constrained using analyticity, knowledge of the phase of the form factor in the time-like region, 4 m_pi^2 \le t \le \tin and its value at one space-like point, using as input the (g-2) of ... More

Fluid limits of many-server queues with renegingNov 12 2010This work considers a many-server queueing system in which impatient customers with i.i.d., generally distributed service times and i.i.d., generally distributed patience times enter service in the order of arrival and abandon the queue if the time before ... More

A Dirichlet process characterization of a class of reflected diffusionsOct 11 2010For a class of stochastic differential equations with reflection for which a certain ${\mathbb{L}}^p$ continuity condition holds with $p>1$, it is shown that any weak solution that is a strong Markov process can be decomposed into the sum of a local martingale ... More

Concentration inequalities for dependent Random variables via the martingale methodSep 29 2006Jan 19 2009The martingale method is used to establish concentration inequalities for a class of dependent random sequences on a countable state space, with the constants in the inequalities expressed in terms of certain mixing coefficients. Along the way, bounds ... More

Uniqueness of Gibbs Measures for Continuous Hardcore ModelsAug 14 2017We formulate a continuous version of the well known discrete hardcore (or independent set) model on a locally finite graph, parameterized by the so-called activity parameter $\lambda > 0$. In this version, the state or "spin value" $x_u$ of any node $u$ ... More

Law of Large Numbers Limits for Many Server QueuesAug 07 2007This work considers a many-server queueing system in which customers with i.i.d., generally distributed service times enter service in the order of arrival. The dynamics of the system is represented in terms of a process that describes the total number ... More

Pathwise differentiability of reflected diffusions in convex polyhedral domainsMay 05 2017Reflected diffusions in convex polyhedral domains arise in a variety of applications, including interacting particle systems, queueing networks, biochemical reaction networks and mathematical finance. Under suitable conditions on the data, we establish ... More

Basis-independent methods for the two-Higgs-doublet model II. The significance of tan(beta)Feb 28 2006Sep 07 2006In the most general two-Higgs-doublet model (2HDM), there is no distinction between the two complex hypercharge-one SU(2) doublet scalar fields, Phi_a (a=1,2). Thus, any two orthonormal linear combinations of these two fields can serve as a basis for ... More

The heavy traffic limit of an unbalanced generalized processor sharing modelJan 21 2008This work considers a server that processes $J$ classes using the generalized processor sharing discipline with base weight vector $\alpha=(\alpha _1,...,\alpha_J)$ and redistribution weight vector $\beta=(\beta_1,...,\beta_J)$. The invariant manifold ... More

Involutions and higher order automorphisms of Higgs bundle moduli spacesMay 17 2016Mar 11 2019We consider the moduli space $\mathcal{M}(G)$ of $G$-Higgs bundles over a compact Riemann surface $X$, where $G$ is a complex semisimple Lie group. This is a hyperk\"ahler manifold homeomorphic to the moduli space $\mathcal{R}(G)$ of representations of ... More

Twisted Higgs bundles and the fundamental group of compact Kähler manifoldsSep 08 2000We study polystable Higgs bundles twisted by a line bundle over a compact K\"ahler manifold. These form a Tannakian category when the first and second Chern classes of the bundle are zero. In this paper we identify the corresponding Tannaka group in the ... More

Hecke curves and Hitchin discriminantSep 03 2003Mar 31 2004Let $C$ be a smooth projective curve of genus $g\geq 4$ over the complex numbers and ${\cal SU}^s_C(r,d)$ be the moduli space of stable vector bundles of rank $r$ with a fixed determinant of degree $d$. In the projectivized cotangent space at a general ... More

A conditional limit theorem for high-dimensional $\ell^{p}$ spheresSep 17 2015Jun 20 2018The study of high-dimensional distributions is of interest in probability theory, statistics and asymptotic convex geometry, where the object of interest is the uniform distribution on a convex set in high dimensions. The $\ell^p$ spaces and norms are ... More

Involutions of rank 2 Higgs bundle moduli spacesJan 29 2018We consider the moduli space of rank 2 Higgs bundles with fixed determinant over a smooth projective curve X of genus 2 over the complex numbers, and study involutions defined by tensoring the vector bundle with an element $\alpha$ of order 2 in the Jacobian ... More

Trigonal curves and Galois Spin(8)-bundlesJul 19 1999Let SU_C(2) denote the moduli variety of rank 2 semistable vector bundles with trivial determinant on an algebraic curve C. We prove that if C is trigonal then there exists a projective moduli variety N_C containing SU_C(2) as a subvariety and smooth ... More

Involutions and higher order automorphisms of Higgs bundle moduli spacesMay 17 2016Jul 29 2016We consider the moduli space ${\cal{M}}(G)$ of $G$-Higgs bundles over a compact Riemann surface $X$, where $G$ is a complex semisimple Lie group. This is a hyperk\"ahler manifold homeomorphic to the moduli space ${\cal{R}}(G)$ of representations of the ... More

Basis-independent methods for the two-Higgs-doublet model III: The CP-conserving limit, custodial symmetry, and the oblique parameters S, T, UNov 29 2010Dec 05 2010In the Standard Model, custodial symmetry is violated by the hypercharge U(1) gauge interactions and the Yukawa couplings, while being preserved by the Higgs scalar potential. In the two-Higgs doublet model (2HDM), the generic scalar potential introduces ... More

A Sanov-type theorem for empirical measures associated with the surface and cone measures on $\ell^{p}$ spheresSep 17 2015We prove a large deviations principle (LDP) for the empirical measure of the coordinates of a random vector distributed according to the surface measure on a suitably scaled $\ell^p$ sphere in $\mathbb{R}^n$, as $n\rightarrow\infty$. This LDP is established ... More

Particle Swarm Optimization Based Reactive Power OptimizationJan 20 2010Reactive power plays an important role in supporting the real power transfer by maintaining voltage stability and system reliability. It is a critical element for a transmission operator to ensure the reliability of an electric system while minimizing ... More

Simulating the Electroweak Phase Transition: Sonification of Bubble NucleationJun 03 2011As an applicaton of sonification, a simulation of the early universe was developed to portray a phase transition that occurred shortly after the Big Bang. The Standard Model of particle physics postulates that a hypothetical particle, the Higgs boson, ... More

Interactions between species introduce spurious associations in microbiome studiesAug 15 2017Jan 30 2018Microbiota contribute to many dimensions of host phenotype, including disease. To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples. Abundance differences, however, ... More

The Skorokhod problem in a time-dependent intervalDec 18 2007We consider the Skorokhod problem in a time-varying interval. We prove existence and uniqueness for the solution. We also express the solution in terms of an explicit formula. Moving boundaries may generate singularities when they touch. We establish ... More

Large deviations for random projections of $\ell^p$ ballsDec 15 2015Let $p\in[1,\infty]$. Consider the projection of a uniform random vector from a suitably normalized $\ell^p$ ball in $\mathbb{R}^n$ onto an independent random vector from the unit sphere. We show that sequences of such random projections, when suitably ... More

Large deviations for empirical measures generated by Gibbs measures with singular energy functionalsNov 21 2015We establish large deviation principles (LDPs) for empirical measures associated with a sequence of Gibbs distributions on $n$-particle configurations, each of which is defined in terms of an inverse temperature $\beta_n$ and an energy functional that ... More

Mean-field Dynamics of Load-Balancing Networks with General Service DistributionsDec 16 2015Dec 22 2015We introduce a general framework for the mean-field analysis of large-scale load-balancing networks with general service distributions. Specifically, we consider a parallel server network that consists of N queues and operates under the $SQ(d)$ load balancing ... More

From the master equation to mean field game limit theory: A central limit theoremApr 23 2018Mean field games (MFGs) describe the limit, as $n$ tends to infinity, of stochastic differential games with $n$ players interacting with one another through their common empirical distribution. Under suitable smoothness assumptions that guarantee uniqueness ... More

Large sparse networks of interacting diffusionsApr 04 2019We consider interacting particle systems on a large sparse, possibly random, interaction graph $G_n$, where each particle evolves infinitesimally like a d-dimensional diffusion whose drift coefficient depends on the histories of its own state and the ... More

Nonmonotonicity of phase transitions in a loss network with controlsOct 10 2006We consider a symmetric tree loss network that supports single-link (unicast) and multi-link (multicast) calls to nearest neighbors and has capacity $C$ on each link. The network operates a control so that the number of multicast calls centered at any ... More

Information Loss due to Finite Block Length in a Gaussian Line Network: An Improved BoundJan 26 2013A bound on the maximum information transmission rate through a cascade of Gaussian links is presented. The network model consists of a source node attempting to send a message drawn from a finite alphabet to a sink, through a cascade of Additive White ... More

New constraints on the Pion EM form factor using Pi'(-Q^2)Mar 25 2009Apr 15 2009We study the constraints arising on the expansion parameters c and d of the Pion electromagnetic form factor from the inclusion of pure space-like data and the phase of time-like data along with one space-like datum, using as input the first derivative ... More

Large Deviation Principle For Finite-State Mean Field Interacting Particle SystemsJan 23 2016We establish a large deviation principle for the empirical measure process associated with a general class of finite-state mean field interacting particle systems with Lipschitz continuous transition rates that satisfy a certain ergodicity condition. ... More

From the master equation to mean field game limit theory: Large deviations and concentration of measureApr 23 2018We study a sequence of symmetric $n$-player stochastic differential games driven by both idiosyncratic and common sources of noise, in which players interact with each other through their empirical distribution. The unique Nash equilibrium empirical measure ... More

Large Deviations for Weighted Sums of Stretched Exponential Random VariablesJan 18 2014Dec 24 2014We consider the probability that a weighted sum of $n$ i.i.d. random variables $X_j$, $j = 1, . . ., n$, with stretched exponential tails is larger than its expectation and determine the rate of its decay, under suitable conditions on the weights. We ... More

Cramér's theorem is atypicalAug 18 2015Oct 06 2015The empirical mean of $n$ independent and identically distributed (i.i.d.) random variables $(X_1,\dots,X_n)$ can be viewed as a suitably normalized scalar projection of the $n$-dimensional random vector $X^{(n)}\doteq(X_1,\dots,X_n)$ in the direction ... More

Unconstrained Face Detection and Open-Set Face Recognition ChallengeAug 08 2017Sep 15 2017Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. ... More

Unconstrained Face Detection and Open-Set Face Recognition ChallengeAug 08 2017Sep 25 2018Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. ... More

Weinberg Eigenvalues and Pairing with Low-Momentum PotentialsSep 05 2007The nonperturbative nature of nucleon-nucleon interactions evolved to low momentum has recently been investigated in free space and at finite density using Weinberg eigenvalues as a diagnostic. This analysis is extended here to the in-medium eigenvalues ... More

Low-momentum interactions with smooth cutoffsSep 01 2006Nucleon-nucleon potentials evolved to low momentum, which show great promise in few- and many-body calculations, have generally been formulated with a sharp cutoff on relative momenta. However, a sharp cutoff has technical disadvantages and can cause ... More

An Asynchronous, Decentralized Solution Framework for the Large Scale Unit Commitment ProblemApr 07 2019Apr 12 2019With increased reliance on cyber infrastructure, large scale power networks face new challenges owing to computational scalability. In this paper we focus on developing an asynchronous decentralized solution framework for the Unit Commitment(UC) problem ... More

Unveiling Regions in multi-scale Feynman Integrals using Singularities and Power GeometryOct 15 2018Jan 14 2019We introduce a novel approach for solving the problem of identifying regions in the framework of Method of Regions by considering singularities and the associated Landau equations given a multi-scale Feynman diagram. These equations are then analyzed ... More

Limits of relative entropies associated with weakly interacting particle systemsDec 17 2014Feb 13 2015The limits of scaled relative entropies between probability distributions associated with N-particle weakly interacting Markov processes are considered. The convergence of such scaled relative entropies is established in various settings. The analysis ... More