The mutual influence of Y⋯N and H⋯H interactions in XHY⋯NCH⋯HM complexes (X = F, Cl, Br; Y = S, Se; M = Li, Na, BeH, MgH): Tuning of the chalcogen bond by dihydrogen bond interaction

The equilibrium structures, interaction energies, and bonding properties of ternary XHY⋯NCH⋯HM complexes are studied by ab initio calculations, where X = F, Cl, Br, Y = S, Se, and M = Li, Na, BeH, MgH. The ab initio calculations are carried out at the MP2/aug-cc-pVTZ level. The results indicate that all optimized Y⋯N and H⋯H binding distances in the ternary complexes are smaller than the corresponding values in the binary systems. The calculated cooperative energies (Ecoop) are between -0.20 kcal/mol in BrHS⋯NCH⋯HBeH and -3.29 kcal/mol in FHSe⋯NCH⋯HNa. For a given Y and M, the estimated Ecoop values increase as X = F > Cl > Br. In addition, the selenium-bonded complexes exibit larger Ecoop values than those of the sulfur-bonded counterparts. The cooperativity between Y⋯N and H⋯H interactions is further analyzed by quantum theory of atoms in molecules and natural bond orbital methods. Cooperative effects make an increase in the J(Y-N) and J(H-H) spin-spin coupling constants of the ternary complexes with respect to the binary systems. © 2016 Published by NRC Research Press

Interaction-aware Kalman Neural Networks for Trajectory Prediction

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for intelligent and autonomous vehicles. Complex scenes always yield great challenges in modeling the patterns of surrounding traffic. For example, one main challenge comes from the intractable interaction effects in a complex traffic system. In this paper, we propose a multi-layer architecture Interaction-aware Kalman Neural Networks (IaKNN) which involves an interaction layer for resolving high-dimensional traffic environmental observations as interaction-aware accelerations, a motion layer for transforming the accelerations to interaction aware trajectories, and a filter layer for estimating future trajectories with a Kalman filter network. Attributed to the multiple traffic data sources, our end-to-end trainable approach technically fuses dynamic and interaction-aware trajectories boosting the prediction performance. Experiments on the NGSIM dataset demonstrate that IaKNN outperforms the state-of-the-art methods in terms of effectiveness for traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium (IV) 202

The Vision of the Mystery of the Trinity in Thomas Aquinas

This essay discusses: (1) the nature of Aquinas’s approach to the mystery of the Trinity; (2) the centrality of the divine persons, and the understanding of the divine person as a subsisting relation; (3) the theme of the Word and Love; (4) Trinity and creation; (5) Trinity and grace (that is, the divine missions)