Yield-stress fluids, including gels and pastes, are effectively fluid at high stress and solid at low stress. In liquid-solid impacts, the fluid motion can be halted by the yield stress at different points of the event, and these fluids can therefore stick and accumulate where they impact, motivating several applications of these rheologically complex materials. Here, we use high-speed imaging to experimentally study liquid-solid impact of yield-stress fluids on pre-coated horizontal surfaces. With a pre-coating of the same material, we can observe large long-lifetime ejection sheets with redirected momentum which extend away from the impact location. Under critical splash conditions, sheet breakup occurs and ejected droplets can be nonspherical and threadlike due to the inability of capillary stresses to deform material above a certain lengthscale. By varying the droplet size, impact velocity, surface coating thickness, and rheological material properties, we develop appropriate dimensionless parameters and present a low-dimensional regime map of impact behaviors. C 2015 AIP Publishing LLC. [http://dx.

This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning. Starting from common output error formulations, similarities in update law modifications are examined. Concepts in stability, performance, and learning, common to both fields are then discussed. Building on the similarities in update laws and common concepts, new intersections and opportunities for improved algorithm analysis are provided. In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.

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