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Filtering by: Creator McManus, M. A. Remove constraint Creator: McManus, M. A. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad File Format pdf Remove constraint File Format: pdf

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  • Supporting branch-and-bound computations on parallel machines

    Creator:
    Mannan, Mohammed A.
    Abstract:
    We have developed SearchPak, a machine independent parallel searching tool on shared and distributed memory machines. It can be used for combinatorial optimiza­tion problems and OR-parallel computations as well. Both depth-first and best-first search of the state space can be performed using the SearchPak. With SearchPak, a user just provides...
    Resource Type:
    Capstone Project
    Full Text:
    requesting child processors. If 'theRoot' processor Send a request to all 'm' CPs to send 'x' highest

  • Selling and buying in a distributed virtual market place

    Creator:
    Bella, Ravi
    Abstract:
    We present a model for a distributed virtual market place that can be con­structed on the Internet to support selling and buying requests, such as those found as classified advertisements. One requirement for a transaction to take place in the virtual market place is that a sell request and a...
    Resource Type:
    Capstone Project
    Full Text:
    Selling and Buying in a Distributed Virtual Market Place

  • Heuristic search in symbolic probability inference

    Creator:
    Pan, Yonghong
    Abstract:
    I present a new heuristic search approach to compute approximate answers for the probability query in belief nets. This approach can compute the 'best' bounds for a query in a period of any given time (if time permitted, it will get an exact value). It inherits the essence of Symbolic...
    Resource Type:
    Capstone Project
    Full Text:
    Y onghong Pan A research project submitted in partial fulfillment of the degree of Master of

  • Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

    Creator:
    Aziere, Nicolas
    Abstract:
    This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
    Resource Type:
    Dissertation
    Full Text:
    with a learnable diffusion. Our main contribution in (1) is a new model which integrates convolutional