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The MapReduce parallel programming model is designed for large-scale data processing, but its benefits, such as fault tolerance and automatic message routing, are also helpful for computationally-intensive algorithms. However, popular MapReduce frameworks such as Hadoop are slow for many scientific applications and are inconvenient on supercomputers and(More)
Probabilistic models are a useful means for analyzing large text corpora. Integrating such models with human interaction enables many new use cases. However, adding human interaction to probabilistic models requires inference algorithms which are both fast and accurate. We explore the use of Iterated Conditional Modes as a fast alternative to Gibbs sampling(More)
Mrs [1] is a lightweight Python-based MapReduce implementation designed to make MapReduce programs easy to write and quick to run, particularly useful for research and academia. A common set of algorithms that would benefit from Mrs are iterative algorithms, like those frequently found in machine learning; however, iterative algorithms typically perform(More)
The current literature on youth employment and its effects is mainly concerned with year round employment and its effect on school performance and activities. Very little research has been conducted solely on youth employment during the summer months and the resulting effects on long term employability skills and workforce participation. This study seeks to(More)
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