Abstract. Model selection is an important problem in statistics, machine learning, and data mining. In this paper, we investigate the problem of enabling multiple parties to perfor...
Recent advances in statistical inference and machine learning close the divide between simulation and classical optimization, thereby enabling more rigorous and robust microarchit...
We report on the experiences of Siemens Corporation in nine globally-distributed software development projects. These projects represent a range of collaboration models, from co-d...
James D. Herbsleb, Daniel J. Paulish, Matthew Bass
We investigated the relative merits of C++ and Erlang in the implementation of a parallel acoustic ray tracing algorithm for the U.S. Navy. We found a much smaller learning curve ...
Christian Convey, Andrew Fredricks, Christopher Ga...
The complexity of developing and deploying context-aware pervasive-computing applications calls for distributed software infrastructures that assist applications to collect, aggre...