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We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals. For the first time at such a large scale, we study individual node arrival and edge creation processes that collectively lead to macroscopic properties of networks. Using a methodology based on the maximum-likelihood principle, we investigate a wide variety of network formation strategies, and show that edge locality plays a critical role in evolution of networks. Our findings supplement earlier network models based on the inherently non-local preferential attachment.Based on our observations, we develop a complete model of network evolution, where nodes arrive at a prespecified rate and select their lifetimes. Each node then independently initiates edges according to a "gap" process, selecting a destination for each edge according to a simple triangle-closing model free of any parameters. We show analytically that the combination of the gap distribution with the node lifetime leads to a power law out-degree distribution that accurately reflects the true network in all four cases. Finally, we give model parameter settings that allow automatic evolution and generation of realistic synthetic networks of arbitrary scale.

We consider the problem of estimating the size of a collection of documents using only a standard query interface. Our main idea is to construct an unbiased and low-variance estimator that can closely approximate the size of any set of documents defined by certain conditions, including that each document in the set must match at least one query from a uniformly sampleable query pool of known size, fixed in advance.Using this basic estimator, we propose two approaches to estimating corpus size. The first approach requires a uniform random sample of documents from the corpus. The second approach avoids this notoriously difficult sample generation problem, and instead uses two fairly uncorrelated sets of terms as query pools; the accuracy of the second approach depends on the degree of correlation among the two sets of terms.Experiments on a large TREC collection and on three major search engines demonstrates the effectiveness of our algorithms.

Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blocks induced by the row / column partitions are good clusters. Motivated by several applications in text mining, market-basket analysis, and bioinformatics, this problem has attracted severe attention in the past few years. Unfortunately, to date, most of the algorithmic work on this problem has been heuristic in nature. In this work we obtain the first approximation algorithms for the co-clustering problem. Our algorithms are simple and obtain constant-factor approximation solutions to the optimum. We also show that co-clustering is NP-hard, thereby complementing our algorithmic result. Copyright 2008 ACM

We formulate and study a new computational model for dynamic data. In this model, the data changes gradually and the goal of an algorithm is to compute the solution to some problem on the data at each time step, under the constraint that it only has limited access to the data each time. As the data is constantly changing and the algorithm might be unaware of these changes, it cannot be expected to always output the exact right solution; we are interested in algorithms that guarantee to output an approximate solution. In particular, we focus on the fundamental problems of sorting and selection, where the true ordering of the elements changes slowly. We provide algorithms with performance close to the optimal in expectation and with high probability. (C) 2010 Elsevier B.V. All rights reserved

Infection of the eye caused by Acanthamoeba species constitutes a burgeoning and unsolved problem. Of individuals with Acanthamoeba keratitis, 85% wear contact lenses; abrasion of the cornea is implicated. Corneal infection often can be prevented by good lens care and hygiene. Severe Acanthamoeba keratitis often can be very difficult to treat; surgery can be less than successful and may lead to further problems. The encysted stage in the life cycle of Acanthamoeba species appears to cause the most problems; many biocides are ineffective in killing the highly resistant cysts. Combination therapy--that is, use of 2 or 3 biocides, sometimes with antibacterial antibiotics--appears to work best. Recurrence is common if treatment is stopped prematurely. Immunologic methods are being investigated as a form of prevention, and oral immunization of animals recently has been successful in the prevention of Acanthamoeba keratitis by inducing immunity before infection occurs. Immunization thus may eventually become the best approach for reduction of the incidence of amebic infection in humans.

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Wastewater particularly from electroplating, paint, leather, metal and tanning industries contain enormous amount of heavy metals. Microorganisms including fungi have been reported to exclude heavy metals from wastewater through bioaccumulation and biosorption at low cost and in eco-friendly way. An attempt was, therefore, made to isolate fungi from sites contaminated with heavy metals for higher tolerance and removal of heavy metals from wastewater. Seventy-six fungal isolates tolerant to heavy metals like Pb, Cd, Cr and Ni were isolated from sewage, sludge and industrial effluents containing heavy metals. Four fungi (Phanerochaete chrysosporium, Aspegillus awamori, Aspergillus flavus, Trichoderma viride) also were included in this study. The majority of the fungal isolates were able to tolerate up to 400 ppm concentration of Pb, Cd, Cr and Ni. The most heavy metal tolerant fungi were studied for removal of heavy metals from liquid media at 50 ppm concentration. Results indicated removal of substantial amount of heavy metals by some of the fungi. With respect to Pb, Cd, Cr and Ni, maximum uptake of 59.67, 16.25, 0.55, and 0.55 mg/g was observed by fungi Pb3 (Aspergillus terreus), Trichoderma viride, Cr8 (Trichoderma longibrachiatum), and isolate Ni27 (A. niger) respectively. This indicated the potential of these fungi as biosorbent for removal of heavy metals from wastewater and industrial effluents containing higher concentration of heavy metals.