Rehg, James M.

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    Segmental Switching Linear Dynamic Systems
    (Georgia Institute of Technology, 2005) Oh, Sang Min ; Rehg, James M. ; Dellaert, Frank
    We introduce Segmental Switching Linear Dynamic Systems (S-SLDS), which improve on standard SLDSs by explicitly incorporating duration modeling capabilities. We show that S-SLDSs can adopt arbitrary finite-sized duration models that describe data more accurately than the geometric distributions induced by standard SLDSs. We also show that we can convert an S-SLDS to an equivalent standard SLDS with sparse structure in the resulting transition matrix. This insight makes it possible to adopt existing inference and learning algorithms for the standard SLDS models to the S-SLDS framework. As a consequence, the more powerful S-SLDS model can be adopted with only modest additional effort in most cases where an SLDS model can be applied. The experimental results on honeybee dance decoding tasks demonstrate the robust inference capabilities of the proposed S-SLDS model.
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    Towards aspect-oriented programming support for cluster computing
    (Georgia Institute of Technology, 2002) Wolenetz, Matthew David ; Mandviwala, Hasnain A. ; Adhikari, Sameer ; Angelov, Yavor ; Ramachandran, Umakishore ; Mackenzie, Kenneth M. ; Rehg, James M.
    Interactive multimedia applications (such as audio/video processing) are good candidates for cluster computing. Such applications are best represented as coarse-grain dataflow graphs and are rich in pipelined, task, and data parallelism. Specification of the strategies for mapping computational abstractions to compute nodes, and their plumbing are two important issues in the design of such complex parallel and distributed applications. Due to the varieties of parallelism that are available in such applications, the space of strategies to be explored can be vast. We have developed an aspect-oriented programming language for cluster computing called STAGES. This language allows the algorithm design to be disentangled from the connection management and performance concerns. STAGES provides a simple syntax for specifying the connections among threads and data abstractions, and their mapping onto the nodes of the cluster. The current implementation targets the Stampede cluster programming library. However, the language is general and can be retargeted to a different set of abstractions. In this paper, we present STAGES, its implementation, and its utility for mapping complex applications onto a cluster. We also present performance results from exploring the parallelism space for two such applications on a 17-node cluster of 8-way SMPs (Intel Xeon processors) interconnected by Gigabit Ethernet.