Tsunami effects on soil stability are evaluated by imposing hypothetical, but typical, tsunami loads on an idealised beach condition of a plane beach with homogeneous sediments. Under this model tsunami condition, the extent of sediment motion in the form of bed load and suspended load on the beach is demonstrated. Sediment motions are particularly significant in the nearshore area between the initial shoreline and the maximum tsunami drawdown. During the drawdown phase, pore-pressure gradients develop by rapid reduction in water pressure on the bed, creating soil instability, and triggering momentary liquefaction in some locations. Such effects of pore-pressure gradients are quantitatively evaluated with a proposed modified Shields parameter. The analysis used for the model tsunami case is further applied to laboratory data and real field data collected in Kesen-numa, Japan, during the 2011 East Japan tsunami.
We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML systems, primarily for the purpose of personalizing these systems. In recent years, support for an additional federated task has been added: federated learning (FL) of deep neural networks. To our knowledge, only one other system has been described in literature that supports FL at scale. We include comparisons to that system to help discuss design decisions and attached trade-offs. Finally, we describe two specific large scale personalization use cases in detail to showcase the applicability of federated tuning to on-device personalization and to highlight application specific solutions.
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