Thermal Bremsstrahlung Radiation in a Two-Temperature Plasma

In the normal one-temperature plasma the motion of ions is usually neglected when calculating the Bremsstrahlung radiation of the plasma. Here we calculate the Bremsstrahlung radiation of a two-temperature plasma by taking into account of the motion of ions. Our results show that the total radiation power is always lower if the motion of ions is considered. We also apply the two-temperature Bremsstrahlung radiation mechanism for an analytical Advection-Dominated Accretion Flow (ADAF) model; we find the two-temperature correction to the total Bremsstrahlung radiation for ADAF is negligible.Comment: 5 pages, 4 figures, accepted for publication in CHJAA. Some discussions and references adde

Fast pedestrian detection from a moving vehicle

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 69-71).This paper presents a method of real-time multi-modal pedestrian detection from a moving vehicle. The system uses both intensity and thermal images captured from cameras mounted at the front of the vehicle to train cascades of classifiers, which results in a detector that is able to detect a large percentage of pedestrians with very few false positives. The system has also been tested with inputs of high-resolution intensity images along with low-resolution thermal images, showing that the addition of even a low-resolution thermal camera may return better pedestrian detection results than using only intensity information alone.by Shuang You.M.Eng

A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

<p>Abstract</p> <p>Background</p> <p>Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design.</p> <p>Results</p> <p>In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) is constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in <it>S. cerevisiae </it>to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in <it>S. cerevisiae </it>(with a sensitivity of 92% and specificity of 91%). Noticeably, the SSL method is more efficient than SVM, especially for very small training sets and large test sets.</p> <p>Conclusions</p> <p>We developed a graph-based SSL classifier for predicting the SGI. The classifier employs topological properties of weighted FGN as input features and simultaneously employs information induced from labelled and unlabelled data. Our analysis indicates that the topological properties of weighted FGN can be employed to accurately predict SGI. Also, the graph-based SSL method outperforms the traditional standard supervised approach, especially when used with small training sets. The proposed method can alleviate experimental burden of exhaustive test and provide a useful guide for the biologist in narrowing down the candidate gene pairs with SGI. The data and source code implementing the method are available from the website: <url>http://home.ustc.edu.cn/~yzh33108/GeneticInterPred.htm</url></p