Zobrazeno 1 - 10
of 2 133
pro vyhledávání: '"LI Junyi"'
Publikováno v:
Guangxi Zhiwu, Vol 44, Iss 6, Pp 1091-1104 (2024)
Auxin response factor (ARF) is a transcription factor family that mediates auxin signaling and regulates various biological processes. To investigate the ARF gene family members and their roles in response to high temperature and drought stress, the
Externí odkaz:
https://doaj.org/article/1a1fc909291a4cad80005109a3a2e8eb
Publikováno v:
Fenmo yejin jishu, Vol 42, Iss 2, Pp 159-164 (2024)
Nickel nanopowders coated by magnesium oxide used for inner electrode of multilayer ceramic capacitor (MLCC) were prepared by coating thermal decomposition sintering method. The effects of reactant concentration and dispersant content on the particle
Externí odkaz:
https://doaj.org/article/b549c0fc129746efbcbcb15c24a2cbbb
Autor:
Li Junyi
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
The development mode relying on the traditional factor dividend has become unsustainable, and the integration and application of artificial intelligence technology can promote the high-quality development of science and technology industry incubation
Externí odkaz:
https://doaj.org/article/245b6c44d3f24e9da3466f4d9c1a9686
Autor:
Li, Junyi, Huang, Heng
Bilevel Optimization has experienced significant advancements recently with the introduction of new efficient algorithms. Mirroring the success in single-level optimization, stochastic gradient-based algorithms are widely used in bilevel optimization
Externí odkaz:
http://arxiv.org/abs/2411.05868
We conducted a series of pore-scale numerical simulations on convective flow in porous media, with a fixed Schmidt number of 400 and a wide range of Rayleigh numbers. The porous media are modeled using regularly arranged square obstacles in a Rayleig
Externí odkaz:
http://arxiv.org/abs/2409.19652
We present PartGLEE, a part-level foundation model for locating and identifying both objects and parts in images. Through a unified framework, PartGLEE accomplishes detection, segmentation, and grounding of instances at any granularity in the open wo
Externí odkaz:
http://arxiv.org/abs/2407.16696
Adapting general large language models (LLMs) to specialized domains presents great challenges due to varied data distributions. This adaptation typically requires continual pre-training on massive domain-specific corpora to facilitate knowledge memo
Externí odkaz:
http://arxiv.org/abs/2407.10804
Drug-target relationships may now be predicted computationally using bioinformatics data, which is a valuable tool for understanding pharmacological effects, enhancing drug development efficiency, and advancing related research. A number of structure
Externí odkaz:
http://arxiv.org/abs/2407.10055
Autor:
Tang, Tianyi, Hu, Yiwen, Li, Bingqian, Luo, Wenyang, Qin, Zijing, Sun, Haoxiang, Wang, Jiapeng, Xu, Shiyi, Cheng, Xiaoxue, Guo, Geyang, Peng, Han, Zheng, Bowen, Tang, Yiru, Min, Yingqian, Chen, Yushuo, Chen, Jie, Zhao, Yuanqian, Ding, Luran, Wang, Yuhao, Dong, Zican, Xia, Chunxuan, Li, Junyi, Zhou, Kun, Zhao, Wayne Xin, Wen, Ji-Rong
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. This library is featured with three main merits: (1) a unified data int
Externí odkaz:
http://arxiv.org/abs/2407.05563
Recent work has explored the capability of large language models (LLMs) to identify and correct errors in LLM-generated responses. These refinement approaches frequently evaluate what sizes of models are able to do refinement for what problems, but l
Externí odkaz:
http://arxiv.org/abs/2407.02397