Multimedia Content Distribution in Hybrid Wireless Networks using Weighted Clustering

Fixed infrastructured networks naturally support centralized approaches for group management and information provisioning. Contrary to infrastructured networks, in multi-hop ad-hoc networks each node acts as a router as well as sender and receiver. Some applications, however, requires hierarchical arrangements that-for practical reasons-has to be done locally and self-organized. An additional challenge is to deal with mobility that causes permanent network partitioning and re-organizations. Technically, these problems can be tackled by providing additional uplinks to a backbone network, which can be used to access resources in the Internet as well as to inter-link multiple ad-hoc network partitions, creating a hybrid wireless network. In this paper, we present a prototypically implemented hybrid wireless network system optimized for multimedia content distribution. To efficiently manage the ad-hoc communicating devices a weighted clustering algorithm is introduced. The proposed localized algorithm deals with mobility, but does not require geographical information or distances.Comment: 2nd ACM Workshop on Wireless Multimedia Networking and Performance Modeling 2006 (ISBN 1-59593-485

Natural data structure extracted from neighborhood-similarity graphs

'Big' high-dimensional data are commonly analyzed in low-dimensions, after performing a dimensionality-reduction step that inherently distorts the data structure. For the same purpose, clustering methods are also often used. These methods also introduce a bias, either by starting from the assumption of a particular geometric form of the clusters, or by using iterative schemes to enhance cluster contours, with uncontrollable consequences. The goal of data analysis should, however, be to encode and detect structural data features at all scales and densities simultaneously, without assuming a parametric form of data point distances, or modifying them. We propose a novel approach that directly encodes data point neighborhood similarities as a sparse graph. Our non-iterative framework permits a transparent interpretation of data, without altering the original data dimension and metric. Several natural and synthetic data applications demonstrate the efficacy of our novel approach

Evidential Label Propagation Algorithm for Graphs

Community detection has attracted considerable attention crossing many areas as it can be used for discovering the structure and features of complex networks. With the increasing size of social networks in real world, community detection approaches should be fast and accurate. The Label Propagation Algorithm (LPA) is known to be one of the near-linear solutions and benefits of easy implementation, thus it forms a good basis for efficient community detection methods. In this paper, we extend the update rule and propagation criterion of LPA in the framework of belief functions. A new community detection approach, called Evidential Label Propagation (ELP), is proposed as an enhanced version of conventional LPA. The node influence is first defined to guide the propagation process. The plausibility is used to determine the domain label of each node. The update order of nodes is discussed to improve the robustness of the method. ELP algorithm will converge after the domain labels of all the nodes become unchanged. The mass assignments are calculated finally as memberships of nodes. The overlapping nodes and outliers can be detected simultaneously through the proposed method. The experimental results demonstrate the effectiveness of ELP.Comment: 19th International Conference on Information Fusion, Jul 2016, Heidelber, Franc

Local-scale patterns of genetic variability, outcrossing, and spatial structure in natural stands of Arabidopsis thaliana

As Arabidopsis thaliana is increasingly employed in evolutionary and ecological studies, it is essential to understand patterns of natural genetic variation and the forces that shape them. Previous work focusing mostly on global and regional scales has demonstrated the importance of historical events such as long-distance migration and colonization. Far less is known about the role of contemporary factors or environmental heterogeneity in generating diversity patterns at local scales. We sampled 1,005 individuals from 77 closely spaced stands in diverse settings around Tübingen, Germany. A set of 436 SNP markers was used to characterize genome-wide patterns of relatedness and recombination. Neighboring genotypes often shared mosaic blocks of alternating marker identity and divergence. We detected recent outcrossing as well as stretches of residual heterozygosity in largely homozygous recombinants. As has been observed for several other selfing species, there was considerable heterogeneity among sites in diversity and outcrossing, with rural stands exhibiting greater diversity and heterozygosity than urban stands. Fine-scale spatial structure was evident as well. Within stands, spatial structure correlated negatively with observed heterozygosity, suggesting that the high homozygosity of natural A. thaliana may be partially attributable to nearest-neighbor mating of related individuals. The large number of markers and extensive local sampling employed here afforded unusual power to characterize local genetic patterns. Contemporary processes such as ongoing outcrossing play an important role in determining distribution of genetic diversity at this scale. Local "outcrossing hotspots" appear to reshuffle genetic information at surprising rates, while other stands contribute comparatively little. Our findings have important implications for sampling and interpreting diversity among A. thaliana accessions.Financial support came from an NIH Ruth Kirschstein NRSA Postdoctoral Fellowship (KB), a Human Frontiers Science Program Postdoctoral Fellowship (RAL), grants DFG ERA-PG ARelatives and FP6 IP AGRON-OMICS (contract LSHG-CT-2006-037704), from a Gottfried Wilhelm Leibniz Award of the DFG, and the Max Planck Society (DW)