Andrew S. Fast

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We use knowledge discovery techniques to guide the creation of efficient overlay networks for peer-to-peer file sharing. An overlay network specifies the logical connections among peers in a network and is distinct from the physical connections of the network. It determines the order in which peers will be queried when a user is searching for a specific(More)
We analyze publication patterns in theoretical high-energy physics using a relational learning approach. We focus on four related areas: understanding and identifying patterns of citations, examining publication patterns at the author level, predicting whether a paper will be accepted by specific journals, and identifying research communities from the(More)
Six separate experiments were conducted which examined the effects of long-term administration of anabolic-androgenic steroid (AAS) compounds on the sexual behavior of gonadally intact male rats. The six AAS compounds analyzed in this study were 17alpha-methyltestosterone, methandrostenolone, nandrolone decanoate, stanozolol, oxymetholone, and testosterone(More)
Collective classification techniques jointly infer all class labels of a relational data set, using the inferences about one class label to influence inferences about related class labels. Kou and Cohen recently introduced an efficient relational model based on stacking that, despite its simplicity, has equivalent accuracy to more sophisticated joint(More)
In a series of 3 experiments, adult male Long-Evans rats were castrated and treated with 1 of 3 different anabolic-androgenic steroid (AAS) compounds (17 alpha-methyltestosterone, methandrostenolone, or nandrolone decanoate) for 6 weeks. In each experiment, subjects received daily injections of a high, medium, or low dose of AAS or the oil vehicle. The AAS(More)
Researchers in the social and behavioral sciences routinely rely on quasi-experimental designs to discover knowledge from large data-bases. Quasi-experimental designs (QEDs) exploit fortuitous circumstances in non-experimental data to identify situations (sometimes called "natural experiments") that provide the equivalent of experimental control and(More)
Commercial datasets are often large, relational, and dynamic. They contain many records of people, places, things, events and their interactions over time. Such datasets are rarely structured appropriately for knowledge discovery, and they often contain variables whose meanings change across different subsets of the data. We describe how these challenges(More)
We apply statistical relational learning to a database of criminal and terrorist activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform(More)