Publications

This study firstly presents (i) a novel general cellular mapping scheme for two dimensional neuromorphic dynamical systems such as bio-inspired neuron models, and (ii) an efficient mixed analog-digital circuit, which can be conveniently implemented on a hybrid memristor-crossbar/CMOS platform, for hardware implementation of the scheme. This approach employs 4n memristors and no switch for implementing an n-cell system in comparison with 2n (2) memristors and 2n switches of a Cellular Memristive Dynamical System (CMDS). Moreover, this approach allows for dynamical variables with both analog and one-hot digital values opening a wide range of choices for interconnections and networking schemes. Dynamical response analyses show that this circuit exhibits various responses based on the underlying bifurcation scenarios which determine the main characteristics of the neuromorphic dynamical systems. Due to high programmability of the circuit, it can be applied to a variety of learning systems, real-time applications, and analytically indescribable dynamical systems. We simulate the FitzHugh-Nagumo (FHN), Adaptive Exponential (AdEx) integrate and fire, and Izhikevich neuron models on our platform, and investigate the dynamical behaviors of these circuits as case studies. Moreover, error analysis shows that our approach is suitably accurate. We also develop a simple hardware prototype for experimental demonstration of our approach.

The mechanical behavior of four rare earth (RE)‐Mg‐doped Si 3 N 4 ceramics (RE=La, Lu, Y, Yb) with varying grain‐boundary adhesion has been examined with emphasis on materials containing La and Lu (which represent the extremes of RE ionic radius). Fracture‐resistance curves ( R ‐curves) for all ceramics rose very steeply initially, giving them exceptional strength and relative insensitivity to flaw size. The highest strength was seen in the Lu‐doped material, which may be explained by its steeper initial R ‐curve; the highest “apparent” toughness (for fracture from millimeter‐scale micronotches) was seen in the lowest strength La‐doped material, which may be explained by its slowly rising R ‐curve at longer crack lengths. Excellent agreement was found between the predicted strengths from R ‐curves and the actual strengths for failures originating from natural flaws, a result attributed to careful estimation of the early part of the R ‐curve by deducing the intrinsic toughness, K 0 , and the fact that this portion of the R ‐curve is relatively insensitive to sample geometry. Finally, it was found that RE elements with relatively large ionic radius (e.g., La) tended to result in lower grain‐boundary adhesion. This implies that there is a small window of optimal grain‐boundary adhesion which can lead to the fastest rising R ‐curves (for short cracks) and the highest strengths. The importance of this work is that it reinforces the notion that factors which contribute to the early part of the R ‐curve are critical for the design of ceramic microstructures with both high strength and high toughness.

Abstract The Ru‐catalyzed intramolecular [5+2] cycloaddition of cyclopropylenynes is investigated with respect to the regio‐ and diastereoselectivity as well as the functional group compatibility of the reaction. Evidence for the mechanism as occurring through a ruthenacyclopentene intermediate is elucidated from 1) the study of the diastereoselectivity of the cycloaddition; 2) the effect of variation of substituents on the regioselectivity of cyclopropyl bond cleavage in 1,2‐ trans‐ and 1,2‐ cis ‐disubstituted cyclopropanes and 3) examples that clearly do not involve ruthenacyclohexene as intermediates as products still incorporate the cyclopropyl moiety. The scope and limitations of the Ru‐catalyzed cycloaddition are discussed and compared with the Rh‐catalyzed reaction. The potential power of this methodology towards natural product total synthesis is demonstrated by the formation of several polycyclic systems with the chosen reaction conditions and readily available cyclopropylenyne substrates.

Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the training environment and may not avoid rare, catastrophic events such as automotive accidents. A classical technique for improving the robustness of reinforcement learning algorithms is to train on a set of randomized environments, but this approach only guards against common situations. Recently, robust adversarial reinforcement learning (RARL) was developed, which allows efficient applications of random and systematic perturbations by a trained adversary. A limitation of RARL is that only the expected control objective is optimized; there is no explicit modeling or optimization of risk. Thus the agents do not consider the probability of catastrophic events (i.e., those inducing abnormally large negative reward), except through their effect on the expected objective. In this paper we introduce risk-averse robust adversarial reinforcement learning (RARARL), using a risk-averse protagonist and a risk-seeking adversary. We test our approach on a self-driving vehicle controller. We use an ensemble of policy networks to model risk as the variance of value functions. We show through experiments that a risk-averse agent is better equipped to handle a risk-seeking adversary, and experiences substantially fewer crashes compared to agents trained without an adversary. Supplementary materials are available at https://sites.google.com/view/rararl.

We report the regiospecific, rhodium-catalyzed borylation of saturated terminal C-H bonds in molecules with existing functionality. Moderate to good yields were obtained with the organic substrate in excess and as limiting reagent. The borylations of trialkylamines, protected alcohols, protected ketones, and fluoroalkanes occurred regiospecifically at the methyl group that is least sterically hindered. Reactions were also conducted that probed electronic effects on the alkyl borylation. These reactions showed that the borylation occurred preferentially at the methyl group that is most electron-deficient. Methods to conduct tandem borylation of C-H bonds and conversion of the resulting boronate esters to alcohols, alkylarenes, and alkyltrifluoroborates were also developed.

No abstract is provided for this article.

Until practical ad-hoc localization systems are developed, early deployments of wireless sensor networks will manually configure location information in network nodes in order to assign spatial context to sensor readings. In this paper, we argue that such deployments will use hierarchical location names (for example, a node in a habitat monitoring network might be said to be node number N in cluster C of region R), rather than positions in a two- or three-dimensional coordinate system. We show that these hierarchical location names can be used to design a scalable routing system called HLR. HLR provides a variety of primitives including unicast, scoped anycast and broadcast, as well as various forms of scalable rendezvous. These primitives can be used to implement most data-centric routing and storage schemes proposed in the literature; these schemes currently need precise position information and geographic routing in order to scale well. We evaluate HLR using simulations as well as an implementation on the Mica-2 motes.