Zobrazeno 1 - 10
of 1 474
pro vyhledávání: '"LIU Siyang"'
Publikováno v:
Cailiao gongcheng, Vol 51, Iss 7, Pp 22-32 (2023)
Induction implant welding (IIW) of thermoplastic composites (TPC) is a new technology which uses the heat from the electromagnetic induction heating element implanted in the lap region of TPC laminate under the action of high-frequency alternating ma
Externí odkaz:
https://doaj.org/article/93444c2ee4e745d492734178f0a8c34b
Autor:
Chi Yanyan, Wang Xiahui, Bao Mingtao, Zhang Liping, Liu Siyang, Fu Le, Xu Kaipeng, Wang Jingjing
Publikováno v:
中国工程科学, Vol 24, Iss 1, Pp 104-112 (2022)
The Yellow River Basin is an important ecological barrier for China; the ecology there is sensitive and fragile and ecological problems are severe in some regions. Therefore, it is necessary to conduct a systematic planning of ecological management a
Externí odkaz:
https://doaj.org/article/0e3f3298919c416f999d1ae8b6d44c71
Autor:
Liu, Siyang, Pan, Jie
We describe the conjugation of the reddening sequence according to the formula of $c$-vectors with respect to changing of the initial seed. As applications, we extend the Rotation Lemma, the Target before Source Theorem, and the mutation invariant pr
Externí odkaz:
http://arxiv.org/abs/2410.09585
Publikováno v:
电力工程技术, Vol 3, Iss 45, Pp 39-47,91 (2022)
With the development of active distribution network and Internet of Things technology,the access of reactive power equipment is becoming more complicated and marginalized,and the related computing of voltage control is also developing towards edge co
Externí odkaz:
https://doaj.org/article/5a094a97819849df9ff1bf33fee11ce2
Autor:
Liu Siyang, Nie Yongjie, Li Bo, Zhu Mengyao, Li Zhengxing, Li Ting, Cao Min, Chang Yanping, Xu Hua, Yan Hongfeng, Jin Hui, Wang Hongyu
Publikováno v:
E3S Web of Conferences, Vol 406, p 03020 (2023)
In order to constantly improve city environmental air quality, it is necessary to accurately control the major pollutants emissions such as air fine particulate matter. By adopting the proposed iterative update framework of air pollutant emission inv
Externí odkaz:
https://doaj.org/article/b5193af974994ff9b9b92b912ce58ede
Publikováno v:
Jixie qiangdu, Vol 41, Pp 468-472 (2019)
In order to study the caving process of caving coal hydraulic support and the breaking law between immediate roof and basic roof,Taking the ZF15000/28/52 export caving hydraulic support as the research object, establish a three-dimensional simulation
Externí odkaz:
https://doaj.org/article/f871fbc2d4ee4756827cc86ecf2daf03
Publikováno v:
The 2024 Conference on Empirical Methods in Natural Language Processing
We explore the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen categories. Through a diverse set of prompts tailored to ensure response robustness, we find a ge
Externí odkaz:
http://arxiv.org/abs/2404.08760
Autor:
Sabour, Sahand, Liu, Siyang, Zhang, Zheyuan, Liu, June M., Zhou, Jinfeng, Sunaryo, Alvionna S., Li, Juanzi, Lee, Tatia M. C., Mihalcea, Rada, Huang, Minlie
Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelligence (EI) is considerably limited. Existing benchmarks have two major
Externí odkaz:
http://arxiv.org/abs/2402.12071
Implicit neural representation has demonstrated promising results in view synthesis for large and complex scenes. However, existing approaches either fail to capture the fast-moving objects or need to build the scene graph without camera ego-motions,
Externí odkaz:
http://arxiv.org/abs/2312.09076
Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond
Publikováno v:
The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)
We propose task-adaptive tokenization as a way to adapt the generation pipeline to the specifics of a downstream task and enhance long-form generation in mental health. Inspired by insights from cognitive science, our task-adaptive tokenizer samples
Externí odkaz:
http://arxiv.org/abs/2310.05317