This application paper investigates the stability of hydrogen bonds (H-bonds), as characterized by the Quantum Theory of Atoms in Molecules (QTAIM). First, we contribute a database of 4544 electron densities associated to four isomers of water hexamers (the so-called Ring, Book, Cage and Prism), generated by distorting their equilibrium geometry under various structural perturbations, modeling the natural dynamic behavior of molecular systems. Second, we present a new stability measure, called bond occurrence rate, associating each bond path present at equilibrium with its rate of occurrence within the input ensemble. We also provide an algorithm, called BondMatcher, for its automatic computation, based on a tailored, geometry-aware partial isomorphism estimation between the extremum graphs of the considered electron densities. Our new stability measure allows for the automatic identification of densities lacking H-bond paths, enabling further visual inspections. Specifically, the topological analysis enabled by our framework corroborates experimental observations and provides refined geometrical criteria for characterizing the disappearance of H-bond paths. Our electron density database and our C++ implementation are available at this address: https://github.com/thom-dani/BondMatcher.

We develop a method to learn a bio-inspired motion control policy using data collected from hawkmoths navigating in a virtual forest. A Markov Decision Process (MDP) framework is introduced to model the dynamics of moths and sparse logistic regression is used to learn control policy parameters from the data. The results show that moths do not favor detailed obstacle location information in navigation, but rely heavily on optical flow. Using the policy learned from the moth data as a starting point, we propose an actor-critic learning algorithm to refine policy parameters and obtain a policy that can be used by an autonomous aerial vehicle operating in a cluttered environment. Compared with the moths' policy, the policy we obtain integrates both obstacle location and optical flow. We compare the performance of these two policies in terms of their ability to navigate in artificial forest areas. While the optimized policy can adjust its parameters to outperform the moth's policy in each different terrain, the moth's policy exhibits a high level of robustness across terrains.

There was a time - in the dim historical past - when foundries actually made ASICs with only 5000 to 50,000 logic gates. But FPGAs and CPLDs conquered those markets and pushed ASIC silicon toward opportunities with more logic, volume, and speed. Today's largest FPGAs approach the few-million-gate size of a typical ASIC design, and continue to sprout embedded cores, such as CPUs, memories, and interfaces. And given the risks of nonworking nanometer silicon, FPGA costs and time-to-market are looking awfully attractive. So, will FPGAs kill ASICs? ASIC technologists certainly think not. ASICs are themselves sprouting patches of programmable FPGA fabric, and pushing new realms of size and especially speed. New tools claim to have tamed the convergence problems of older ASIC flows. Is the future to be found in a market full of FPGAs with ASIC-like cores? ASICs with FPGA cores? Other exotic hybrids? Our panelists will share their disagreements on these prognostications.