Characterization, Modification and Application of Biochar for Energy Storage and Catalysis: A Review

Biomass can be converted to biofuels and bioproducts via thermochemical processes. Biochar is one of the major products of thermochemical conversion of biomass. The efficient use of biochar is critical to improving the economic viability and environmental sustainability of biomass conversion technologies. Applications of biochar for both agricultural and environmental benefits (e.g. as soil amendment, for inorganic pollutant removal) have been studied and reviewed extensively. However, biochar for energy storage materials and catalytic applications has not been widely reviewed in the recent past. This review aims to present the more significant recent advances in several biochar utilizations such as catalysts and supercapacitors. Discussions on biochar production technologies, chemistry, properties, characteristics, and advanced functionalization techniques are provided. It also points out barriers to achieving improvements in the future. Citation: Xiu, S., Shahbazi, A., and Li, R. (2017). Characterization, Modification and Application of Biochar for Energy Storage and Catalysis: A Review. Trends in Renewable Energy, 3(1), 86-101. DOI: 10.17737/tre.2017.3.1.003

BoxSnake: Polygonal Instance Segmentation with Box Supervision

Box-supervised instance segmentation has gained much attention as it requires only simple box annotations instead of costly mask or polygon annotations. However, existing box-supervised instance segmentation models mainly focus on mask-based frameworks. We propose a new end-to-end training technique, termed BoxSnake, to achieve effective polygonal instance segmentation using only box annotations for the first time. Our method consists of two loss functions: (1) a point-based unary loss that constrains the bounding box of predicted polygons to achieve coarse-grained segmentation; and (2) a distance-aware pairwise loss that encourages the predicted polygons to fit the object boundaries. Compared with the mask-based weakly-supervised methods, BoxSnake further reduces the performance gap between the predicted segmentation and the bounding box, and shows significant superiority on the Cityscapes dataset. The code has been available publicly.Comment: ICCV 202

GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction

This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data. To address these challenges, we propose the GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools. It generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts. By using the Low-Rank Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs to solve a range of visual problems, including visual comprehension and image generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to use tools, which is performed in both zero-shot and fine-tuning ways. Extensive experiments demonstrate the effectiveness of our method on various language models, which not only significantly improves the accuracy of invoking seen tools, but also enables the zero-shot capacity for unseen tools. The code and demo are available at https://github.com/StevenGrove/GPT4Tools