Distributed Private Online Learning for Social Big Data Computing over Data Center Networks

With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from distributed data centers. These data are so large-scale, high-dimension and widely distributed that we propose a distributed sparse online algorithm to handle them. Additionally, privacy-protection is an important point in social networks. We should not compromise the privacy of individuals in networks, while these social data are being learned for data mining. Thus we also consider the privacy problem in this article. Our simulations shows that the appropriate sparsity of data would enhance the performance of our algorithm and the privacy-preserving method does not significantly hurt the performance of the proposed algorithm.Comment: ICC201

Temporal and spatial stability of Anopheles gambiae larval habitat distribution in Western Kenya highlands.

BACKGROUND: Localized mosquito larval habitat management and the use of larvicides have been proposed as important control tools in integrated malaria vector management programs. In order to optimize the utility of these tools, detailed knowledge of the spatial distribution patterns of mosquito larval habitats is crucial. However, the spatial and temporal changes of habitat distribution patterns under different climatic conditions are rarely quantified and their implications to larval control are unknown. RESULTS: Using larval habitat data collected in western Kenya highlands during both dry and rainy seasons of 2003-2005, this study analyzed the seasonal and inter-annual changes in the spatial patterns in mosquito larval habitat distributions. We found that the spatial patterns of larval habitats had significant temporal variability both seasonally and inter-annually. CONCLUSIONS: The pattern of larval habitats is extremely important to the epidemiology of malaria because it results in spatial heterogeneity in the adult mosquito population and, subsequently, the spatial distribution of clinical malaria cases. Results from this study suggest that larval habitat management activities need to consider the dynamic nature of malaria vector habitats

Thanatomicrobiome composition profiling as a tool for forensic investigation.

Thanatomicrobiome, or the postmortem microbiome, has been recognized as a useful microbial marker of the time and location of host death. In this mini-review, we compare the experimental methods commonly applied to thanatomicrobiome studies to the state-of-the-art methodologies in the microbiome field. Then, we review present findings in thanatomicrobiome studies, focusing on the diversity of the thanatomicrobiome composition and prediction models that have been proposed. Finally, we discuss potential improvements and future directions of the field

KINEMATICS ANALYSIS ON HANDSPRING SIDEWAYS STRETCHED SIDEWARD SOMERSAULT WITH 1 3/4 TURNS IN HORSE-VAULTING OF ZEPENG LUO

By using the 3D video analysis method, we have tested on the handspring sideways stretched side-ward somersault with 1 3/4 turns in Horse-vaulting (also known as Kasamatsu 360°) of Chinese elite athletes Zepeng Luo at the game site; obtain the relevant kinematics’ parameters through analysis on the complete movement. The results show that: Zepeng Luo completed this action with high quality and advanced technology, but at the first flight, his legs were not fully close together