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Full-Text Articles in Physical Sciences and Mathematics
Development Of A Reverse Engineered, Parameterized, And Structurally Validated Computational Model To Identify Design Parameters That Influence American Football Faceguard Performance, William Ferriell
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Traumatic brain injury (TBI) continues to have the greatest incidence among athletes participating in American football. The headgear design research community has focused on developing accurate computational and experimental analysis techniques to better assess the ability of headgear technology to attenuate impacts and protect athletes from TBI. Despite efforts to innovate the headgear system, minimal progress has been made to innovate the faceguard. Although the faceguard is not the primary component of the headgear system that contributes to impact attenuation, faceguard performance metrics, such as weight, structural stiffness, and visual field occlusions, have been linked to athlete safety. To improve …
Tempering The Adversary: An Exploration Into The Applications Of Game Theoretic Feature Selection And Regression, Stephen Mcgee
Tempering The Adversary: An Exploration Into The Applications Of Game Theoretic Feature Selection And Regression, Stephen Mcgee
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Most modern machine learning algorithms tend to focus on an "average-case" approach, where every data point contributes the same amount of influence towards calculating the fit of a model. This "per-data point" error (or loss) is averaged together into an overall loss and typically minimized with an objective function. However, this can be insensitive to valuable outliers. Inspired by game theory, the goal of this work is to explore the utility of incorporating an optimally-playing adversary into feature selection and regression frameworks. The adversary assigns weights to the data elements so as to degrade the modeler's performance in an optimal …
Automated, Parallel Optimization Algorithms For Stochastic Functions, Dheeraj Chahal
Automated, Parallel Optimization Algorithms For Stochastic Functions, Dheeraj Chahal
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The optimization algorithms for stochastic functions are desired specifically for real-world and simulation applications where results are obtained from sampling, and contain experimental error or random noise. We have developed a series of stochastic optimization algorithms based on the well-known classical down hill simplex algorithm. Our parallel implementation of these optimization algorithms, using a framework called MW, is based on a master-worker architecture where each worker runs a massively parallel program. This parallel implementation allows the sampling to proceed independently on many processors as demonstrated by scaling up to more than 100 vertices and 300 cores.
This framework is highly …