Sampling in the multicanonical ensemble: Small He clusters in W

We carry out generalized-ensemble molecular dynamics simulations of the formation of small Helium (He) clusters in bulk Tungsten (W), a process of practical relevance for fusion energy production. We calculate formation free energies of small Helium clusters at temperatures up to the melting point of W, encompassing the whole range of interest for fusion-energy production. From this, parameters like cluster break-up or formation rates can be calculated, which help to refine models of microstructure evolution in He-irradiated Tungsten.Comment: 27th Annual CSP Workshop on Recent Developments in Computer Simulation Studies in Condensed Matter Physics, Athens, GA, 201

Nuclear fusion induced by X-rays in a crystal

The nuclei that constitute a crystalline lattice, oscillate relative to each other with a very low energy that is not sufficient to penetrate through the Coulomb barriers separating them. An additional energy, which is needed to tunnel through the barrier and fuse, can be supplied by external electromagnetic waves (X-rays or the synchrotron radiation). Exposing to the X-rays the solid compound LiD (lithium-deuteride) for the duration of 111 hours, we have detected 88 events of the nuclear fusion d+Li6 ---> Be8*. Our theoretical estimate agrees with what we observed. One of possible applications of the phenomenon we found, could be the measurements of the rates of various nuclear reactions (not necessarily fusion) at extremely low energies inaccessible in accelerator experiments.Comment: 27 pages, 12 figures; submitted to Phys. Rev. C on 28 October 201

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure

Enhancing the effectiveness of ligand-based virtual screening using data fusion

Data fusion is being increasingly used to combine the outputs of different types of sensor. This paper reviews the application of the approach to ligand-based virtual screening, where the sensors to be combined are functions that score molecules in a database on their likelihood of exhibiting some required biological activity. Much of the literature to date involves the combination of multiple similarity searches, although there is also increasing interest in the combination of multiple machine learning techniques. Both approaches are reviewed here, focusing on the extent to which fusion can improve the effectiveness of searching when compared with a single screening mechanism, and on the reasons that have been suggested for the observed performance enhancement