8,126 research outputs found
Clustering with diversity
We consider the {\em clustering with diversity} problem: given a set of
colored points in a metric space, partition them into clusters such that each
cluster has at least points, all of which have distinct colors.
We give a 2-approximation to this problem for any when the objective
is to minimize the maximum radius of any cluster. We show that the
approximation ratio is optimal unless , by providing a matching
lower bound. Several extensions to our algorithm have also been developed for
handling outliers. This problem is mainly motivated by applications in
privacy-preserving data publication.Comment: Extended abstract accepted in ICALP 2010. Keywords: Approximation
algorithm, k-center, k-anonymity, l-diversit
Randomized Algorithms for Tracking Distributed Count, Frequencies, and Ranks
We show that randomization can lead to significant improvements for a few
fundamental problems in distributed tracking. Our basis is the {\em
count-tracking} problem, where there are players, each holding a counter
that gets incremented over time, and the goal is to track an
\eps-approximation of their sum continuously at all times,
using minimum communication. While the deterministic communication complexity
of the problem is \Theta(k/\eps \cdot \log N), where is the final value
of when the tracking finishes, we show that with randomization, the
communication cost can be reduced to \Theta(\sqrt{k}/\eps \cdot \log N). Our
algorithm is simple and uses only O(1) space at each player, while the lower
bound holds even assuming each player has infinite computing power. Then, we
extend our techniques to two related distributed tracking problems: {\em
frequency-tracking} and {\em rank-tracking}, and obtain similar improvements
over previous deterministic algorithms. Both problems are of central importance
in large data monitoring and analysis, and have been extensively studied in the
literature.Comment: 19 pages, 1 figur
A practical approach to managing patients with HCV infection.
Hepatitis C virus (HCV) infection is a major worldwide public health concern. It is a common cause of chronic liver disease and hepatocellular carcinoma. HCV antibody and HCV RNA testing are available diagnostic studies that offer high degree of accuracy. Current standard therapy includes a combination of pegylated interferon and ribavirin. Response rate is approximately 40% for genotype 1 and 80% for genotypes 2 and 3, respectively. Successful treatment can stop the progression of chronic liver disease, reduce the need for liver transplantation, and possibly decrease the risk for Hepatocellular carcinoma (HCC). Evaluating for potential treatment candidacy is an important initial step in the management of chronic HCV infection as not all individuals may need or qualify for the treatment. Understanding the natural history, the different diagnostic modalities, the current therapeutic options and, the treatment response and adverse effect profiles can help the practitioners better manage chronic HCV infection
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