Photothermal effects in plasmonic nanostructures have great potentials in applications for photothermal cancer therapy, optical storage, thermo-photovoltaics, etc. However, the transient temperature behavior of a nanoscale material system during an ultrafast photothermal process has rarely been accurately investigated. Here a heat transfer model is constructed to investigate the temporal and spatial variation of temperature in plasmonic gold nanostructures. First, as a benchmark scenario, we study the light-induced heating of a gold nanosphere in water and calculate the relaxation time of the nanosphere excited by a modulated light. Second, we investigate heating and reshaping of gold nanoparticles in a more complex metamaterial absorber structure induced by a nanosecond pulsed light. The model shows that the temperature of the gold nanoparticles can be raised from room temperature to >795 K in just a few nanoseconds with a low light luminance, owing to enhanced light absorption through strong plasmonic resonance. Such quantitative predication of temperature change, which is otherwise formidable to measure experimentally, can serve as an excellent guideline for designing devices for ultrafast photothermal applications.

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Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed. However, important real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, Temporal Recurrent Network (TRN), to model greater temporal context of a video frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS'14. The results show that TRN significantly outperforms the state-of-the-art.

Driving Scene understanding is a key ingredient for intelligent transportation systems. To achieve systems that can operate in a complex physical and social environment, they need to understand and learn how humans drive and interact with traffic scenes. We present the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior in real-life environments. The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors. We provide a detailed analysis of HDD with a comparison to other driving datasets. A novel annotation methodology is introduced to enable research on driver behavior understanding from untrimmed data sequences. As the first step, baseline algorithms for driver behavior detection are trained and tested to demonstrate the feasibility of the proposed task.