Contest Winners!

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    • Congratulations to the following people who have each won $100.00 in the SCA Software Contest! Criteria included sharing the most unique, interesting, powerful and/or creative applications for which they used LINDA� or PARADISE�.

    LINDA in Computer Games

    Submitted by: Professor Paolo Ciancarini
    University of Bologna
    Computer Science Department
    Bologna, Italy

    �Using LINDA, we built a chess program consisting of several independent advising agents each using different chess knowledge to evaluate the position; each agent offers its advice to a coordinating process, which uses a selection policy to choose the move. We have built the advisors redistributing the knowledge included in a strong sequential program.

    LINDA allowed us to make thousands of experiments with several different knowledge distributions and move selection policies. Some programs we built are sensibly stronger than the original program even using seven workstations only."

    LINDA in Computational Chemistry

    Submitted by: Haibo Wang
    Supercomputer User Consultant
    University of Mississippi
    University, MS, U.S.A.
    �90% of MCSR users are computational chemistry related. LINDA gives us the capability to build parallel version computational chemistry software on our Linux Beowulf Cluster, so we can provide the cost efficient HPC systems to our user community. �

    LINDA in Medical Sciences

    Submitted by: Perry L. Miller, M.D., Ph.D.
    Director, Center for Medical Informatics
    Yale University School of Medicine
    New Haven, CT
    �We are developing a pilot pathology image database system, PathMaster, as a testbed to explore desired Next Generation Internet capabilities. The database contains images obtained by digitally imaging many cells, each indexed by computationally-derived descriptors. A digital image of a cell can be submitted to PathMaster. PathMaster automatically computes descriptors for the unknown cell and retrieve images of "similar" cells from its database.

    This analysis is performed in parallel using network-based workstations running Linda. The images can then be passed back to the user along with their diagnoses, as a "visual differential diagnosis" to help the user classify the unknown cell.�

    		 
     
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