Recent advances achieved in triboelectric nanogenerators (TENG) focus on boosting power generation and conversion efficiency. Nevertheless, obstacles concerning economical and biocompatible utilization of TENGs continue to prevail. Being an abundant natural biopolymer from marine crustacean shells, chitosan enables exciting opportunities for low-cost, biodegradable TENG applications in related fields. Here, the development of biodegradable and flexible TENGs based on chitosan is presented for the first time. The physical and chemical properties of the chitosan nanocomposites are systematically studied and engineered for optimized triboelectric power generation, transforming the otherwise wasted natural materials into functional energy devices. The feasibility of laser processing of constituent materials is further explored for the first time for engineering the TENG performance. The laser treatment of biopolymer films offers a potentially promising scheme for surface engineering in polymer-based TENGs. The chitosan-based TENGs present efficient energy conversion performance and tunable biodegradation rate. Such a new class of TENGs derived from natural biomaterials may pave the way toward the economically viable and ecologically friendly production of flexible TENGs for self-powered nanosystems in biomedical and environmental applications.

Recent years have seen remarkable progress in semantic segmentation. Yet, it remains a challenging task to apply segmentation techniques to video-based applications. Specifically, the high throughput of video streams, the sheer cost of running fully convolutional networks, together with the low-latency requirements in many real-world applications, e.g. autonomous driving, present a significant challenge to the design of the video segmentation framework. To tackle this combined challenge, we develop a framework for video semantic segmentation, which incorporates two novel components: (1) a feature propagation module that adaptively fuses features over time via spatially variant convolution, thus reducing the cost of per-frame computation; and (2) an adaptive scheduler that dynamically allocate computation based on accuracy prediction. Both components work together to ensure low latency while maintaining high segmentation quality. On both Cityscapes and CamVid, the proposed framework obtained competitive performance compared to the state of the art, while substantially reducing the latency, from 360 ms to 119 ms. * This work is done when Yule Li is intern at CUHK Multimedia Lab 85 80 75 70 65 60 55 0 100 200 300 400 500 600 700 PSPNet Ours Clockwork PEARL DFF Deeplab NetWarp Latency(ms) Accuracy(mIoU%)

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