Rate constants for the coupled two-electron/halide transfer self-exchange reactions of [Cp2MX]+ with Cp2M (M = Ru, Os; X = Cl, Br) have been measured in CD3CN at 298 K by 2D NMR spectroscopic techniques. Including data for the iodo complexes, the rates vary over 6 orders of magnitude depending on the metal and the halide, with the relative orderings I > Br > Cl and Ru > Os. The relative reactant state free energies for the ruthenium complexes have been determined from halide substitution equilibria and indicate that the reactant state energy ordering is Cl < Br < I. From these data and the free energies of activation for the self-exchange reactions, the relative transition state energies have been determined. A reaction coordinate and orbital correlation is proposed which rationalizes the observed data.

Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum. More recently, endto-end classification systems in image and auditory recognition systems have been developed to learn features jointly with classification and result in improved classification accuracy. In this paper, we propose a novel end-to-end deep neural network to map the raw waveform inputs to sound class labels. Our network includes an "inception nucleus" that optimizes the size of convolutional filters on the fly that results in reducing engineering efforts dramatically. Classification results compared favorably against current state-of-the-art approaches, besting them by 10.4 percentage points on the Ur-bansound8k dataset. Analyses of learned representations revealed that filters in the earlier hidden layers learned waveletlike transforms to extract features that were informative for classification.

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