Electrically Enhanced Free Dendrite Growth in Polar and Non-polar Systems

We describe the electrically enhanced growth of needle crystals from the vapor phase, for which there exists a morphological instability above a threshold applied potential. Our improved theoretical treatment of this phenomenon shows that the instability is present in both polar and non-polar systems, and we provide an extension of solvability theory to include electrical effects. We present extensive experimental data for ice needle growth above the electrical threshold, where at $T=-5$C high-velocity shape-preserving growth is observed. These data indicate that the needle tip assumes an effective radius} $R^{\ast}$ which is nearly independent of both supersaturation and the applied potential. The small scale of $R^{\ast}$ and its response to chemical additives suggest that the needle growth rate is being limited primarily by structural instabilities, possibly related to surface melting. We also demonstrate experimentally that non-polar systems exhibit this same electrically induced morphological instability

An Astrophysical Laboratory: Understanding and Exploiting the Young Massive Cluster Westerlund 1

Westerlund 1 provides a unique opportunity to probe the physics of massive stars, from birth to death and beyond, as well as the formation and evolution of a super star cluster that appears destined to evolve into a globular cluster. We highlight the result of current studies of this cluster, its diverse stellar constituents and immediate environment, concluding with a summary of future research avenues enabled by ESO facilities

Autonomous Hybrid Ground/Aerial Mobility in Unknown Environments

Hybrid ground and aerial vehicles can possess distinct advantages over ground-only or flight-only designs in terms of energy savings and increased mobility. In this work we outline our unified framework for controls, planning, and autonomy of hybrid ground/air vehicles. Our contribution is three-fold: 1) We develop a control scheme for the control of passive two-wheeled hybrid ground/aerial vehicles. 2) We present a unified planner for both rolling and flying by leveraging differential flatness mappings. 3) We conduct experiments leveraging mapping and global planning for hybrid mobility in unknown environments, showing that hybrid mobility uses up to five times less energy than flying only

Robustness and Generalization

We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study generalization of learning algorithms. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property for learning algorithms to work