Publications

Large–scale Sparse Tensor Decomposition Using a Damped Gauss–Newton Method

CANDECOMP/PARAFAC (CP) tensor decomposition is a popular unsupervised machine learning method with numerous applications. This process involves modeling a high–dimensional, multi–modal array (a tensor) as the sum of several low–dimensional components. In order to decompose a tensor, one must solve an optimization problem, whose objective is often given by the

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HACCLE: An Ecosystem for Building Secure Multi-Party Computations

Cryptographic techniques have the potential to enable distrusting parties to collaborate in fundamentally new ways, but their practical implementation poses numerous challenges. An important class of such cryptographic techniques is known as secure multi-party computation (MPC). In an effort to provide an ecosystem for building secure MPC applications using higher

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Systems and methods for stencil amplification

In a sequence of major computational steps or in an iterative computation, a stencil amplifier can increase the number of data elements accessed from one or more data structures in a single major step or iteration, thereby decreasing the total number of computations and/or communication operations in the overall sequence

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On the Bottleneck Structure of Congestion-Controlled Networks

In this paper, we introduce the Theory of Bottleneck Ordering, a mathematical framework that reveals the bottleneck structure of data networks. This theoretical framework provides insights into the inherent topological properties of a network at least in three areas: (1) It identifies the regions of influence of each bottleneck; (2)

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Low-Frequency Electromagnetic Imaging Using Sensitivity Functions and Beamforming

We present a computational technique for low-frequency electromagnetic imaging in inhomogeneous media that provides superior three-dimensional resolution over existing techniques. The method is enabled through large-scale, fast (low-complexity) algorithms that we introduce for simulating electromagnetic wave propagation. We numerically study the performance of the technique on various problems including the

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System and method for generation of event driven, tuple-space based programs

In a system for automatic generation of event-driven, tuple-space based programs from a sequential specification, a hierarchical mapping solution can target different runtimes relying on event-driven tasks (EDTs). The solution uses loop types to encode short, transitive relations among EDTs that can be evaluated efficiently at runtime. Specifically, permutable loops

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Uniform Random Sampling in Polyhedra

We propose a method for generating uniform samples among a domain of integer points defined by a polyhedron in a multidimensional space. The method extends to domains defined by parametric polyhedra, in which a subset of the variables are symbolic. We motivate this work by a list of applications for

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