Zobrazeno 1 - 10
of 2 838
pro vyhledávání: '"ZHU Xiaofeng"'
Autor:
ZHU Xiaofeng, ZHAO Zhihong, TAN Rui, ZHOU Long, WANG Yichuan, LIU Wenjing, ZHANG Minghui, LIU Huabing
Publikováno v:
Chinese Journal of Magnetic Resonance, Vol 41, Iss 2, Pp 173-183 (2024)
Investigating the moisture migration during the wood drying process can help improve wood utilization. Single-sided nuclear magnetic resonance (NMR) technology facilitates such investigation with its advantage in conducting one-dimensional measuremen
Externí odkaz:
https://doaj.org/article/152932fe097b42529b8230487c441e6c
Publikováno v:
Stem Cell Research, Vol 75, Iss , Pp 103302- (2024)
CCL22 is a macrophage-derived immunosuppressive chemokine that recruits regulatory T cells through the CCL22:CCR4 axis, playing an important role in homeostatic and inflammatory responses. A CCL22-overexpressing human induced pluripotent stem cell li
Externí odkaz:
https://doaj.org/article/95d711c5d17b4a738d28f059771e6712
Prognosis prediction is crucial for determining optimal treatment plans for lung cancer patients. Traditionally, such predictions relied on models developed from retrospective patient data. Recently, large language models (LLMs) have gained attention
Externí odkaz:
http://arxiv.org/abs/2408.07971
Lymph node metastasis (LNM) is a crucial factor in determining the initial treatment for patients with lung cancer, yet accurate preoperative diagnosis of LNM remains challenging. Recently, large language models (LLMs) have garnered significant atten
Externí odkaz:
http://arxiv.org/abs/2407.17900
Autor:
Wang, Zhiyuan, Duan, Jinhao, Cheng, Lu, Zhang, Yue, Wang, Qingni, Shen, Hengtao, Zhu, Xiaofeng, Shi, Xiaoshuang, Xu, Kaidi
Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the intricate nature of the recent large language models (LLMs). This study investigates adapting conformal prediction (CP), which ca
Externí odkaz:
http://arxiv.org/abs/2407.00499
Large language models (LLMs) like ChatGPT show excellent capabilities in various natural language processing tasks, especially for text generation. The effectiveness of LLMs in summarizing radiology report impressions remains unclear. In this study,
Externí odkaz:
http://arxiv.org/abs/2406.02134
Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level op
Externí odkaz:
http://arxiv.org/abs/2404.17875
With the prevalence of social media, the connectedness between people has been greatly enhanced. Real-world relations between users on social media are often not limited to expressing positive ties such as friendship, trust, and agreement, but they a
Externí odkaz:
http://arxiv.org/abs/2402.15980
Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large models to a wide range of downstream tasks. Nonetheless, applying existing prompt learning methods for the diagnosis of neurological disorder still suffers fr
Externí odkaz:
http://arxiv.org/abs/2312.14574
Although current prompt learning methods have successfully been designed to effectively reuse the large pre-trained models without fine-tuning their large number of parameters, they still have limitations to be addressed, i.e., without considering th
Externí odkaz:
http://arxiv.org/abs/2312.00823