Application of Fireproof Coating for New Energy Vehicle Battery Pack

In the development process of new energy vehicles, the battery pack is one of the key parts, and the safety of the battery pack has always been an important factor affecting the application range and market sales of new energy vehicles. In order to improve the safety of battery packs, fireproof coatings are widely used on the surface of battery packs. This paper introduces the application of fireproof coatings in new energy vehicles by analyzing the composition and function of fireproof coatings

Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages

Chain-of-thought (CoT) is capable of eliciting models to explicitly generate reasoning paths, thus promoting reasoning accuracy and attracting increasing attention. Specifically, zero-shot CoT achieves remarkable improvements in a wide range of reasoning tasks by simply instructing the LLM with the prompt "Let's think step by step!". Despite the success of zero-shot CoT, the existing zero-shot prompting techniques remain limited to a single language, making it challenging to generalize to other languages and hindering global development. In this work, we introduce cross-lingual prompting (CLP), aiming to improve zero-shot CoT reasoning across languages. Specifically, CLP consists of two main components: (1) cross-lingual alignment prompting and (2) task-specific solver prompting. The cross-lingual alignment prompting is responsible for aligning representations across different languages, whereas the task-specific solver prompting is used to generate the final chain of thoughts and results for the reasoning task. In addition, we further introduce cross-lingual self-consistent prompting (CLSP) to ensemble different reasoning paths across languages. Our experimental evaluations on several benchmarks demonstrate that CLP and CLSP significantly outperform the existing prompting methods and achieve state-of-the-art performance. We hope this work will inspire further breakthroughs in cross-lingual CoT.Comment: Accepted at EMNLP2023 Main Conferenc

A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding

Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention. In this work, we investigate the understanding ability of ChatGPT for zero-shot dialogue understanding tasks including spoken language understanding (SLU) and dialogue state tracking (DST). Experimental results on four popular benchmarks reveal the great potential of ChatGPT for zero-shot dialogue understanding. In addition, extensive analysis shows that ChatGPT benefits from the multi-turn interactive prompt in the DST task but struggles to perform slot filling for SLU. Finally, we summarize several unexpected behaviors of ChatGPT in dialogue understanding tasks, hoping to provide some insights for future research on building zero-shot dialogue understanding systems with Large Language Models (LLMs).Comment: Technical Repor

Mechanical Properties of Recycled Aggregate Concrete Modified by Nano-particles

In this study, different nano-particles were used to modify recycled aggregates concrete (RAC) containing recycled clay brick aggregates (RCBAs) to improve the RAC properties. Two stages of experimental works were performed. In the first stage, various nano-particle mixtures produced by different mixing methods, i.e. the use of surfactant and ultrasonication, were examined by optical microscope to evaluate the dispersion of the nano-particles in water liquid. The nano-particles modified cement mortar specimens were further evaluated by flexural tensile test to check how these mixing methods affect the properties of the nano-particle modified cement mortar. In the second experimental stage, the effects of four replacement ratios of recycled aggregates, three type of nano-particles, two mixing methods of RAC, additional surfactant and ultrasonication process used in the mix of nano-particle liquid, and the dosages of the nano-particles on the workability, compressive and split tensile properties of the nano-particle modified RAC were investigated

Text with Knowledge Graph Augmented Transformer for Video Captioning

Video captioning aims to describe the content of videos using natural language. Although significant progress has been made, there is still much room to improve the performance for real-world applications, mainly due to the long-tail words challenge. In this paper, we propose a text with knowledge graph augmented transformer (TextKG) for video captioning. Notably, TextKG is a two-stream transformer, formed by the external stream and internal stream. The external stream is designed to absorb additional knowledge, which models the interactions between the additional knowledge, e.g., pre-built knowledge graph, and the built-in information of videos, e.g., the salient object regions, speech transcripts, and video captions, to mitigate the long-tail words challenge. Meanwhile, the internal stream is designed to exploit the multi-modality information in videos (e.g., the appearance of video frames, speech transcripts, and video captions) to ensure the quality of caption results. In addition, the cross attention mechanism is also used in between the two streams for sharing information. In this way, the two streams can help each other for more accurate results. Extensive experiments conducted on four challenging video captioning datasets, i.e., YouCookII, ActivityNet Captions, MSRVTT, and MSVD, demonstrate that the proposed method performs favorably against the state-of-the-art methods. Specifically, the proposed TextKG method outperforms the best published results by improving 18.7% absolute CIDEr scores on the YouCookII dataset.Comment: Accepted by CVPR202