Sandia National Laboratories

About: Sandia National Laboratories is a facility organization based out in Livermore, California, United States. It is known for research contribution in the topics: Laser & Thin film. The organization has 21501 authors who have published 46724 publications receiving 1484388 citations. The organization is also known as: SNL & Sandia National Labs.

Combination of multiple classifiers using local accuracy estimates

Abstract: Combination of multiple classifiers (CMC) has recently drawn attention as a method of improving classification accuracy. This paper presents a method for combining classifiers that use estimates of each individual classifier's local accuracy in small regions of feature space surrounding an unknown test sample. Only the output of the most locally accurate classifier is considered. We address issues of (1) optimization of individual classifiers, and (2) the effect of varying the sensitivity of the individual classifiers on the CMC algorithm. Our algorithm performs better on data from a real problem in mammogram image analysis than do other recently proposed CMC techniques.

Membrane bending by protein-protein crowding

Abstract: Membrane deformation is necessary to generate endocytic vesicles, but the molecular mechanisms proposed to drive membrane bending are controversial. Stachowiak and Schmid et al. report that crowding of proteins at the membrane is sufficient to induce curvature in vitro.

Radioactive Iodine Capture in Silver-Containing Mordenites through Nanoscale Silver Iodide Formation

Abstract: The effective capture and storage of radiological iodine (129I) remains a strong concern for safe nuclear waste storage and safe nuclear energy. Silver-containing mordenite (MOR) is a longstanding benchmark for iodine capture; however, the molecular level understanding of this process needed to develop more effective iodine getters has remained elusive. Here we probe the structure and distribution of iodine sorbed by silver-containing MOR using differential pair distribution function analysis. While iodine is distributed between γ-AgI nanoparticles on the zeolite surface and subnanometer α-AgI clusters within the pores for reduced silver MOR, in the case of unreduced silver-exchanged MOR, iodine is exclusively confined to the pores as subnanometer α-AgI. Consequently, unreduced silver-containing zeolites may offer a more secure route for radioactive iodine capture, with the potential to more effectively trap the iodine for long-term storage.

Laser-induced incandescence : recent trends and current questions

Abstract: This paper provides an overview of a workshop focused on fundamental experimental and theoretical aspects of soot measurements by laser-induced incandescence (LII). This workshop was held in Duisburg, Germany in September 2005. The goal of the workshop was to review the current understanding of the technique and identify gaps in this understanding associated with experimental implementation, model descriptions, and signal interpretation. The results of this workshop suggest that uncertainties in the understanding of this technique are sufficient to lead to large variability among model predictions from different LII models, among measurements using different experimental approaches, and between modeled and measured signals, even under well-defined conditions. This article summarizes the content and conclusions of the workshop, discusses controversial topics and areas of disagreement identified during the workshop, and highlights recent important references related to these topics. It clearly demonstrates that despite the widespread application of LII for soot-concentration and particle-size measurements there is still a significant lack in fundamental understanding for many of the underlying physical processes.

Algorithm 862: MATLAB tensor classes for fast algorithm prototyping

Abstract: Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to psychometrics. We describe four MATLAB classes for tensor manipulations that can be used for fast algorithm prototyping. The tensor class extends the functionality of MATLAB's multidimensional arrays by supporting additional operations such as tensor multiplication. The tensorlaslmatrix class supports the “matricization” of a tensor, that is, the conversion of a tensor to a matrix (and vice versa), a commonly used operation in many algorithms. Two additional classes represent tensors stored in decomposed formats: cpltensor and tuckerltensor. We describe all of these classes and then demonstrate their use by showing how to implement several tensor algorithms that have appeared in the literature.