(4R,11R)-9-(1-hydroxypropan-2-yl)-4,11-diphenyl-1,3,5,7,9-pentaazatri­cyclo[5.3.1.04,11]undecane-2,6-dithione

The asymmetric unit of the title compound, C21H23N5OS2, contains two independent chiral mol­ecules. The two phenyl rings of one mol­ecule form a dihedral angle of 51.95 (7)° and the distance between their centroids is 4.345 (1) Å. In the other mol­ecule, the phenyl rings form a dihedral angle of 58.79 (8)° with a ring centroid–centroid distance of 4.435 (2) Å. An intra­molecular O—H⋯N hydrogen bond occurs in each independent mol­ecule. The crystal packing is stabilized by and inter­molecular N—H⋯O and N—H⋯S hydrogen bonds and C—H⋯S inter­actions

GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance trade-offs, and fast technological advancements. Although there has been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black-box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search, and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.Comment: Accepted to the 57th Design Automation Conference (DAC 2020); 6 pages, 8 figure

A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV). However, the heterogeneities of databases in size, structure and driving context make existing datasets practically ineffective due to a lack of uniform frameworks and searchable indexes. In order to overcome these limitations on existing public datasets, this paper proposes a data unification framework based on traffic primitives with ability to automatically unify and label heterogeneous traffic data. This is achieved by two steps: 1) Carefully arrange raw multidimensional time series driving data into a relational database and then 2) automatically extract labeled and indexed traffic primitives from traffic data through a Bayesian nonparametric learning method. Finally, we evaluate the effectiveness of our developed framework using the collected real vehicle data.Comment: 6 pages, 7 figures, 1 table, ITSC 201