2010 TABLE OF CONTENTS

can be attributed largely to the power it has given to people to share and link documents. Growth of a much richer Web of the future could very well be driven by its ability to enable applications and people to share and link data and services over a broad range of devices. The World Wide Web

digital video | multimedia lab

of data and information. This explosive growth in stored data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge. This book explores the concepts and techniques of data mining

. Finally, we find that among the various network topologies we consider, uniform random graphs yield the bestperformance. 1 Introduction The computer science community has become accustomed to the Internet's continuing rapid growth, but even tosuch jaded observers the explosive increase in Peer

Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology

Chandlerian Business Model to Wintelism ” at the Research Unit Regulation of Work. I wish to thank

. CC-BY 4.0 International licensepeer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not. http://dx.doi.org/10.1101/015917doi: bioRxiv preprint first posted online Mar. 2, 2015; Much recent debate has focused on the role of rare variants in complex phenotypes. However, it is well known that rare alleles can only contribute a substantial propor-tion of the phenotypic variance when they have much larger e↵ect sizes than common variants, which is most easily explained by natural selection constraining trait-altering alleles to low frequency. It is also plausible that demographic events will influence the ge-netic architecture of complex traits. Unfortunately, most rare variant association tests do not explicitly model natural selection or non-equilibrium demography. Here, we develop a novel evolutionary model of complex traits. We perform numerical calculations and simulate phenotypes under this model using inferred human demographic and selection parameters. We show that rare variants only contribute substantially to complex traits under very strong assumptions about the relationship between e↵ect size and selection strength. We then assess the performance of state-of-the-art rare variant tests using our

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology

their corridors, and 31 cities were in initial planning. This impressive growth may be attributed in part to the successes of Curitiba, Bogotá, México City, Istanbul, Ahmedabad and Guangzhou. These cities show low cost, rapid implementation and high performance BRTs, with significant positive externalities