Drivers have unique and rich driving behaviors when operating vehicles in traffic. This paper presents a novel driver behavior learning approach that captures the uniqueness richness of human realistic scenarios. A stochastic inverse reinforcement (SIRL) is proposed to learn distribution cost function, which represents with given set driver-specific demonstrations. Evaluations are conducted on ...

A large number of simulation models have been proposed over the years to mimic electrical behavior memristive devices. The are based either on sophisticated mathematical formulations that do not account for physical and chemical processes responsible actual switching dynamics or multi-physical spatially resolved approaches include inherent stochastic real-world devices but computationally very ...

We contrast a standard deterministic signaling game with one where the signal-generating mechanism is stochastic. With stochastic signals a unique equilibrium emerges that involves separation and has intuitive comparative-static properties as the degree of signaling depends on the prior type distribution. With deterministic signals both pooling and separating configurations occur. Laboratory da...

We show that noise-induced oscillations in a gene circuit model display stochastic coherence, that is, a maximum in the regularity of the oscillations as a function of noise amplitude. The effect is manifest as a system-size effect in a purely stochastic molecular reaction description of the circuit dynamics. We compare the molecular reaction model behavior with that predicted by a rate equatio...