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Communication latencies constitute a significant factor in the performance of parallel applications. With techniques such as wormhole routing, the variation in no-load latencies became insignificant, i.e., the no-load latencies for far-away processors were not significantly higher (and too small to matter) than those for nearby processors. Contention in the(More)
BACKGROUND AND OBJECTIVES Complex regional pain syndrome (CRPS) is a poorly understood pain disorder with little information on the natural course of the disease. Changes in its diagnostic criteria have simplified the identification of this syndrome, but convincing epidemiological data regarding this disorder are still lacking. Here, we collected(More)
Recommender systems associated with social networks often use social explanations (e.g. "X, Y and 2 friends like this") to support the recommendations. We present a study of the effects of these social explanations in a music recommendation context. We start with an experiment with 237 users, in which we show explanations with varying levels of social(More)
The mission of the DNASU Plasmid Repository is to accelerate research by providing high-quality, annotated plasmid samples and online plasmid resources to the research community through the curated DNASU database, website and repository (http://dnasu.asu.edu or http://dnasu.org). The collection includes plasmids from grant-funded, high-throughput cloning(More)
People often rely on the collective intelligence of their social network for making choices, which in turn influences their preferences and decisions. However, traditional recommender systems largely ignore social context, and even network-aware recommenders don't explicitly support social goals and concerns such as shared consumption and identity(More)
Two main approaches to using social network information in recommendation have emerged: augmenting collaborative filtering with social data and algorithms that use only ego-centric data. We compare the two approaches using movie and music data from Facebook, and hashtag data from Twitter. We find that recommendation algorithms based only on friends perform(More)