4,394 research outputs found

    Hybrid and Perennial Tetraploid Ryegrasses Are at least as Productive and Persistent as Perennial Diploids in Dryland Conditions in Northern Tasmania

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    Perennial ryegrass Lolium perenne is the preferred grass for fertile conditions and high rainfall areas or those with irrigation. Persistence of ryegrass can become a problem in drier and warmer areas (Fraser 1994). Even in high rainfall areas of south eastern Australia receiving between 550 and 750 mm of annual rainfall, loss of perennial ryegrass within a few years from sowing is a common problem (Waller and Sale 2001). This work aimed to examine the ability of a range of lines and cultivars of ryegrass to produce and persist under dryland conditions and rotational grazing by sheep in northern Tasmania, Australia

    Policy Point—Counterpoint: Do African American Athletes Have an Obligation to Fight Against Racial Injustice?

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    This policy point-counterpoint, authored by history Ph.D. candidates BJ Marach and J. Marcos Reynolds, frames black-athletic activism as a longstanding historical debate reaching back to the late 1800s. Both argue for the efficacy of such activism and ground their analysis in the “Revolt of the Black Athlete” of the late 1960s. From here they diverge. Marach contends that black athletes are under no obligation to protest racial injustice, while Reynolds concludes that their platform and position within the black community requires that they act

    Mutual Information for Explainable Deep Learning of Multiscale Systems

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    Timely completion of design cycles for complex systems ranging from consumer electronics to hypersonic vehicles relies on rapid simulation-based prototyping. The latter typically involves high-dimensional spaces of possibly correlated control variables (CVs) and quantities of interest (QoIs) with non-Gaussian and possibly multimodal distributions. We develop a model-agnostic, moment-independent global sensitivity analysis (GSA) that relies on differential mutual information to rank the effects of CVs on QoIs. The data requirements of this information-theoretic approach to GSA are met by replacing computationally intensive components of the physics-based model with a deep neural network surrogate. Subsequently, the GSA is used to explain the network predictions, and the surrogate is deployed to close design loops. Viewed as an uncertainty quantification method for interrogating the surrogate, this framework is compatible with a wide variety of black-box models. We demonstrate that the surrogate-driven mutual information GSA provides useful and distinguishable rankings on two applications of interest in energy storage. Consequently, our information-theoretic GSA provides an "outer loop" for accelerated product design by identifying the most and least sensitive input directions and performing subsequent optimization over appropriately reduced parameter subspaces.Comment: 27 pages, 8 figures. Added additional example
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