Sample covariance matrices are a fundamental tool of multivariate statistics. After data collection, we get an n×p data matrix X. We will call n the number of observations and p the number of predictors. The rows of X are assumed to be realizations of a random variable whose covariance structure is Σ p. For practical applications, one often wishes to estimate Σp in order to understand the depen...

The rapid increase in population in the world has propelled pressure on arable land. Consequently, the food basket has continuously declined while global demand for food has grown twofold. There is need to monitor and update agriculture land-cover to support food security measures. This study develops a spatial-temporal approach using conditional random fields (CRF) to classify co-registered im...

Statistical comparison of dissolution profiles under a variety of conditions relating to formulation characteristics, lot-to-lot, and brand-to-brand variation attracts interest of pharmaceutical scientist. The objective of this work is to apply several profile comparison approaches to the dissolution data of five-marketed aceclofenac tablet formulations. Model-independent approaches including A...

(N × N)-matrix is called additive when its elements are pair-wise sums of N real numbers ai. For a quadratic binary functional with an additive connection matrix we succeeded in finding the global minimum expressing it through external parameters of the problem. Computer simulations show that energy surface of a quadratic binary functional with an additive matrix is complicate enough.

Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, recommendation. However, due to added complexity with coupling between tensor matrix data, existing N-CMTF algorithms exhibit poor computat...

Given a matrix A ∈ R, we present a randomized algorithm that sparsifies A by retaining some of its elements by sampling them according to a distribution that depends on both the square and the absolute value of the entries. We combine the ideas of [4, 1] and provide an elementary proof of the approximation accuracy of our algorithm following [4] without the truncation step.

Introduction: Iterative reconstruction techniques provide better image quality and have the potential for reconstructions with lower imaging dose than classical methods in computed tomography (CT). However, the computational speed is major concern for these iterative techniques. The system matrix calculation during the forward- and back projection is one of the most time- cons...