Sparse matrix-vector multiplication forms the heart of iterative linear solvers used widely in scientiic computations (e.g., nite element methods). In such solvers, the matrix-vector product is computed repeatedly, often thousands of times, with updated values of the vector until convergence is achieved. In an SIMD architecture, each processor has to fetch the updated oo-processor vector elemen...

Sparse matrix-vector multiplication forms the heart of iterative linear solvers used widely in scientific computations (e.g., finite element methods). In such solvers, the matrix-vector product is computed repeatedly, often thousands of times, with updated values of the vector until convergence is achieved. In an SIMD architecture, each processor has to fetch the updated off-processor vector el...

Convolutional Neural Networks (CNNs) have been successfully applied to many computer vision tasks, such as image classification. By performing linear combinations and element-wise nonlinear operations, these networks can be thought of as extracting solely first-order information from an input image. In the past, however, second-order statistics computed from handcrafted features, e.g., covarian...

SUMMARY The CluSTr database employs a fully automatic single-linkage hierarchical clustering method based on a similarity matrix. In order to compute the matrix, first all-against-all pair-wise comparisons between protein sequences are computed using the Smith-Waterman algorithm. The statistical significance of the similarity scores is then assessed using a Monte Carlo analysis, yielding Z-valu...

In this paper, a novel framework for automated liver segmentation via a level set formulation is presented. A sparse representation of both global (region-based) and local (voxel-wise) image information is embedded in a level set formulation to innovate a new cost function. Two dictionaries are build: A region-based feature dictionary and a voxel-wise dictionary. These dictionaries are learned,...

In this paper an approach is presented to estimate a linear multivariable model on the basis of (noisy) frequency domain data via a curve tting procedure. The multivariable model is parametrized in either a left or a right polynomial matrix fraction description and the parameters are computed by using a two-norm minimization of a multivariable output error. Additionally, input-output or element...

In this paper we propose an optimization-based framework to multiple graph matching. The framework takes as input maps computed between pairs of graphs, and outputs maps that 1) are consistent among all pairs of graphs, and 2) preserve edge connectivity between pairs of graphs. We show how to formulate this as solving a piece-wise low-rank matrix recovery problem using a generalized message pas...

Wind turbines have been one of the dependable sources renewable energy that due to its abundance, witnessed constant innovations and quest for design productivity optimization. The commonly used type is Horizontal Turbine also known as Axis (HAWT). Understanding imperative factors influencing functionality HAWT provides insight into optimal design. This study therefore x-rays numerical analysis...