Zobrazeno 1 - 10
of 1 758
pro vyhledávání: '"Jin Qiao"'
Autor:
Jing Bai, Ya Bai, Xiaobing Li, Yaqian Mu, Xiaolong Sun, Bo Wang, Lei Shang, Zhengli Di, Wei Zhang, Jin Qiao, Rui Li, Xin Guo, Xinyao Liu, Yan Shi, Xuedong Liu
Publikováno v:
Trials, Vol 25, Iss 1, Pp 1-14 (2024)
Abstract Background Multiple system atrophy (MSA) is recognized as an atypical Parkinsonian syndrome, distinguished by a more rapid progression than that observed in Parkinson’s disease. Unfortunately, the prognosis for MSA remains poor, with a not
Externí odkaz:
https://doaj.org/article/b0f2eb9aef054da4b018ff5de1d76068
Autor:
Yu Peng, Yang Zheng, Ziwen Yuan, Jing Guo, Chunyang Fan, Chenxi Li, Jingyuan Deng, Siming Song, Jin Qiao, Jue Wang
Publikováno v:
Frontiers in Neuroscience, Vol 17 (2023)
ObjectivesPost-stroke depression (PSD) may be associated with the altered brain network property. This study aimed at exploring the brain network characteristics of PSD under the classic cognitive task, i.e., the oddball task, in order to promote our
Externí odkaz:
https://doaj.org/article/460b8786c1fe4ea490921bb0b252ede3
Publikováno v:
Frontiers in Astronomy and Space Sciences, Vol 9 (2023)
The examination of parity symmetry in gravitational interactions has drawn increasing attention. Although Einstein’s General Relativity is parity-conserved, numerous theories of parity-violating (PV) gravity in different frameworks have recently be
Externí odkaz:
https://doaj.org/article/1e33827bc16642a5bd46f4a7d65acc6b
Publikováno v:
Nanotechnology Reviews, Vol 10, Iss 1, Pp 1339-1348 (2021)
This study examines the rheological properties of shear thickening fluid (STF) enhanced by additives such as multi-walled carbon nanotubes (MWCNTs), polyvinylpyrrolidone (PVP), and nano-silica (SiO2) at different mass fraction ratios. The rheological
Externí odkaz:
https://doaj.org/article/d3be03a054a748b1b614103611d50aae
Autor:
Yang, Yifan, Jin, Qiao, Zhu, Qingqing, Wang, Zhizheng, Álvarez, Francisco Erramuspe, Wan, Nicholas, Hou, Benjamin, Lu, Zhiyong
Large Language Models (LLMs) have gained significant attention in the medical domain for their human-level capabilities, leading to increased efforts to explore their potential in various healthcare applications. However, despite such a promising fut
Externí odkaz:
http://arxiv.org/abs/2410.18460
Autor:
Jin, Qiao, Wan, Nicholas, Leaman, Robert, Tian, Shubo, Wang, Zhizheng, Yang, Yifan, Wang, Zifeng, Xiong, Guangzhi, Lai, Po-Ting, Zhu, Qingqing, Hou, Benjamin, Sarfo-Gyamfi, Maame, Zhang, Gongbo, Gilson, Aidan, Bhasuran, Balu, He, Zhe, Zhang, Aidong, Sun, Jimeng, Weng, Chunhua, Summers, Ronald M., Chen, Qingyu, Peng, Yifan, Lu, Zhiyong
Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Th
Externí odkaz:
http://arxiv.org/abs/2410.18856
Autor:
Choi, Songhee, Jin, Qiao, Zi, Xian, Rong, Dongke, Fang, Jie, Zhang, Jinfeng, Zhang, Qinghua, Li, Wei, Xu, Shuai, Chen, Shengru, Hong, Haitao, Ting, Cui, Wang, Qianying, Tang, Gang, Ge, Chen, Wang, Can, Chen, Zhiguo, Gu, Lin, Li, Qian, Wang, Lingfei, Wang, Shanmin, Hong, Jiawang, Jin, Kuijuan, Guo, Er-Jia
The integration of ferroelectrics with semiconductors is crucial for developing functional devices, such as field-effect transistors, tunnel junctions, and nonvolatile memories. However, the synthesis of high-quality single-crystalline ferroelectric
Externí odkaz:
http://arxiv.org/abs/2410.16987
Autor:
Xie, Yunfei, Wu, Juncheng, Tu, Haoqin, Yang, Siwei, Zhao, Bingchen, Zong, Yongshuo, Jin, Qiao, Xie, Cihang, Zhou, Yuyin
Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an internalized c
Externí odkaz:
http://arxiv.org/abs/2409.15277
Autor:
Gilson, Aidan, Ai, Xuguang, Arunachalam, Thilaka, Chen, Ziyou, Cheong, Ki Xiong, Dave, Amisha, Duic, Cameron, Kibe, Mercy, Kaminaka, Annette, Prasad, Minali, Siddig, Fares, Singer, Maxwell, Wong, Wendy, Jin, Qiao, Keenan, Tiarnan D. L., Hu, Xia, Chew, Emily Y., Lu, Zhiyong, Xu, Hua, Adelman, Ron A., Tham, Yih-Chung, Chen, Qingyu
Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address this issue, few studies imple
Externí odkaz:
http://arxiv.org/abs/2409.13902
The emergent abilities of large language models (LLMs) have demonstrated great potential in solving medical questions. They can possess considerable medical knowledge, but may still hallucinate and are inflexible in the knowledge updates. While Retri
Externí odkaz:
http://arxiv.org/abs/2408.00727