The global illumination or transport problems can also be considered as a sequence of integrals, while its MonteCarlo solutions as different sampling techniques. Multiple importance sampling takes advantage of different sampling strategies and combines the results obtained with them. In this paper we propose the combination of very different global illumination algorithms in a way that their st...

Exactly computing network reliability and performability measures are NP-hard problems, precluding their frequent use in design of large networks. Instead, Monte Carlo simulation has been frequently used by network designers to obtain accurate estimates. This paper focuses on simulation estimation of network reliability and performability. First, a literature survey of existing approaches is gi...

Here we describe an automated, pressure-driven, sampling device for harvesting 10 to 30 ml samples, in replicate, with intervals as short as 10 s. Correlation between biological replicate time courses measured by microarrays was extremely high. The sampler enables sampling at intervals within the range of many important biological processes.

This paper compares the trace-sampling techniques of set sampling and time sampling. Using the multi-billion-reference traces of Borg et al., we apply both techniques to multi-megabyte caches, where sampling is most valuable. We evaluate whether either technique meets a 10% sampling goal: a method meets this goal if, at least 90% of the time, it estimates the trace’s true misses per instruction...

Respondent driven sampling (RDS) is a method often used to estimate population properties (e.g. sexual risk behavior) in hard-to-reach populations. It combines an effective modified snowball sampling methodology with an estimation procedure that yields unbiased population estimates under the assumption that the sampling process behaves like a random walk on the social network of the population....

Respondent-driven sampling is a network-based technique to collect information and make estimation about behavior and composition of social groups in hidden population. The non-randomly selected samples prohibit the use of the sample mean as a statistically valid estimator. Researchers have proposed several asymptotically unbiased estimators, but many fail to realize that the high variance of t...

The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements. Motivated by the potential for an in-the-field cell sorting detector, we examine a Synechocystis sp. PCC 6803 dataset wherein cells are grown alternatively in nitrogen rich or deplete cultures. We use deep learning techniques to both successfully classify cells and generate a ma...

BACKGROUND Internationally wheelchair users are an emerging demographic phenomenon due to their rapidly increasing life-span coupled with accelerated general population ageing. While having significant healthcare and social implications, basic robust epidemiological information of wheelchair users is often lacking due in part to this population's "hidden" nature. Increasingly popular in epidemi...

In this paper we discuss some numerical procedures involved in dynamic data driven simulations (DDDAS). The main objective of this paper is the recovery of the initial data as well as the media properties based on dynamic data. We consider the contaminant transport in porous media with a number of sensors placed at some locations. Based on measured data we propose to recover the permeability fi...