Zobrazeno 1 - 10
of 6 353
pro vyhledávání: '"Song, Yun"'
Autor:
Liang, Jingcong, Wang, Junlong, Zhai, Xinyu, Zhuang, Yungui, Zheng, Yiyang, Xu, Xin, Ran, Xiandong, Dong, Xiaozheng, Rong, Honghui, Liu, Yanlun, Chen, Hao, Wei, Yuhan, Li, Donghai, Peng, Jiajie, Huang, Xuanjing, Shi, Chongde, Feng, Yansong, Song, Yun, Wei, Zhongyu
We give a detailed overview of the CAIL 2023 Argument Mining Track, one of the Chinese AI and Law Challenge (CAIL) 2023 tracks. The main goal of the track is to identify and extract interacting argument pairs in trial dialogs. It mainly uses summariz
Externí odkaz:
http://arxiv.org/abs/2406.14503
Autor:
Li, Zhi-Jun, Yuan, Ming-Kuan, Song, Yun-Xuan, Li, Yan-Gu, Li, Jing-Shu, Sun, Sheng-Sen, Wang, Xiao-Long, You, Zheng-Yun, Mao, Ya-Jun
Publikováno v:
Front. Phys. 19, 64201 (2024)
Modern particle physics experiments usually rely on highly complex and large-scale spectrometer devices. In high energy physics experiments, visualization helps detector design, data quality monitoring, offline data processing, and has great potentia
Externí odkaz:
http://arxiv.org/abs/2404.07951
Many biological studies involve inferring the genealogical history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive populati
Externí odkaz:
http://arxiv.org/abs/2402.17153
Autor:
Dohnt, Henriette C, Dowling, Mitchell J, Davenport, Tracey A, Lee, Grace, Cross, Shane P, Scott, Elizabeth M, Song, Yun Ju C, Hamilton, Blake, Hockey, Samuel J, Rohleder, Cathrin, LaMonica, Haley M, Hickie, Ian B
Publikováno v:
JMIR Research Protocols, Vol 10, Iss 6, p e24697 (2021)
BackgroundAustralia’s mental health care system has long been fragmented and under-resourced, with services falling well short of demand. In response, the World Economic Forum has recently called for the rapid deployment of smarter, digitally enhan
Externí odkaz:
https://doaj.org/article/4e3d77a4c8c34cbbb810c33ca44c40f5
Autor:
Patel, Shrujna, Boulton, Kelsie Ann, Redoblado-Hodge, Marie Antoinette, Papanicolaou, Angela, Barnett, Diana, Bennett, Beverley, Drevensek, Suzi, Cramsie, Jane, Ganesalingam, Kalaichelvi, Ong, Natalie, Rozsa, Magdalen, Sutherland, Rebecca, Williamsz, Marcia, Pokorski, Izabella, Song, Yun Ju Christine, Silove, Natalie, Guastella, Adam John
Publikováno v:
JMIR Formative Research, Vol 5, Iss 1, p e18214 (2021)
BackgroundThere is a growing need for cost-efficient and patient-centered approaches to support families in hospital- and community-based neurodevelopmental services. For such purposes, electronic data collection (EDC) may hold advantages over paper-
Externí odkaz:
https://doaj.org/article/6e5c0afbe7dd42e6a4695d49e22f39a5
Autor:
Niu, Yuekun, Ni, Yu, Zhang, Haishan, Qiu, Liang, Wang, Jianli, Chen, Leiming, Song, Yun, Feng, Shiping
We examine the orbital-selective Mott transition in the non-hybridized two-band Hubbard model using the dynamical mean-field theory. We find that the orbital-selective Mott transition could be depicted by the local quantum state fidelity. Additionall
Externí odkaz:
http://arxiv.org/abs/2312.05860
Large Pre-trained Transformers exhibit an intriguing capacity for in-context learning. Without gradient updates, these models can rapidly construct new predictors from demonstrations presented in the inputs. Recent works promote this ability in the v
Externí odkaz:
http://arxiv.org/abs/2307.07742
Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to construct the user and items representations. However, in this paper, we ar
Externí odkaz:
http://arxiv.org/abs/2306.16526
In this paper, we take the initiative to investigate the performance of LLMs on complex planning tasks that require LLMs to understand a virtual spatial environment simulated via natural language and act correspondingly in text. We propose a benchmar
Externí odkaz:
http://arxiv.org/abs/2305.10276
Neural abstractive summarization has been widely studied and achieved great success with large-scale corpora. However, the considerable cost of annotating data motivates the need for learning strategies under low-resource settings. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2303.14011