Data structures for efficient sampling from a set of weighted items are an important building block many applications. However, few parallel solutions known. We close these gaps. give efficient, fast, and practicable distributed algorithms data that support single (alias tables, compressed structures). This also yields simplified more space-efficient sequential algorithm alias table constructio...

We show that a simple, general, and easily reproducible method for generating non-uniform sampling (NUS) schedules preserves the benefits of random sampling, including inherently reduced sampling artifacts, while removing the pitfalls associated with choosing an arbitrary seed. Sampling schedules are generated from a discrete cumulative distribution function (CDF) that closely fits the continuo...

Compressed sensing and magnetic resonance imaging are hot topics in the field of signal processing. In this study we introduced in Lustig’s variable density sampling method, integrated it to compressed sensing, and applied it to brain MRI acquisition. The realistic experiment shows the variable density sampling recovery better than traditional random sampling method on a 256x256 brain magnetic ...

1. There is currently much interest in replacing the design-based component of conventional distance sampling methods by a modelling approach where animal densities are related to environmental covariates. These models allow identification of relationships between density and covariates. One of the uses of such models is to assess the effects of some intervention on numbers for species of conse...

In this paper, we develop an importance sampling method with the help of flexible control on the Lévy measure in the density transformation. The method has significant efficacy even on evaluating random variables with complex path-dependent structures. Numerical examples are presented to illustrate convergence acceleration through variance reduction with a view towards financial derivatives pri...

A method to improve l1 performance of the CS (Compressive Sampling) for signals with known spectral energy density is proposed. Instead of random sampling, the proposed method selects the location of samples to follow the distribution of the spectral energy. Samples collected from three different measurement methods; the uniform sampling, random sampling, and energy equipartition sampling, are ...