Zobrazeno 1 - 10
of 773
pro vyhledávání: '"Li Jiaxi"'
Publikováno v:
Cailiao gongcheng, Vol 52, Iss 2, Pp 78-91 (2024)
Sweat contains many physiological information about the body, such as electrolytes, metabolites, hormones, temperature, etc. Sweat-based wearable sensors enable real-time, continuous, non-invasive monitoring of multimodal bio-metrics at the molecular
Externí odkaz:
https://doaj.org/article/59f308c86df24be886e7e4d03b14ca49
Autor:
Li Jialiang, Li Jiaxi, Yang Yubing, He Xijing, Wei Xinyu, Tan Qinghua, Wang Yiqun, Xu Siyue, Chang Sue, Liu Weiwei
Publikováno v:
Open Life Sciences, Vol 18, Iss 1, Pp 1703608-71 (2023)
Externí odkaz:
https://doaj.org/article/94f848ec455a448caddc16b90ad6870c
Publikováno v:
Zhenduanxue lilun yu shijian, Vol 21, Iss 06, Pp 710-718 (2022)
Objective: To investigate the effect of RAB25 on the sensitivity of erastin-induced ferroptosis in colorectal cancer(CRC). Methods: The GEPIA database was used to analyze the mRNA expression levels of RAB25 in CRC tissues and the co-expression betwee
Externí odkaz:
https://doaj.org/article/2c9c7f2e5e44411882b5eb19104f4c06
Autor:
Latif, Ehsan, Zhou, Yifan, Guo, Shuchen, Gao, Yizhu, Shi, Lehong, Nayaaba, Matthew, Lee, Gyeonggeon, Zhang, Liang, Bewersdorff, Arne, Fang, Luyang, Yang, Xiantong, Zhao, Huaqin, Jiang, Hanqi, Lu, Haoran, Li, Jiaxi, Yu, Jichao, You, Weihang, Liu, Zhengliang, Liu, Vincent Shung, Wang, Hui, Wu, Zihao, Lu, Jin, Dou, Fei, Ma, Ping, Liu, Ninghao, Liu, Tianming, Zhai, Xiaoming
As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perf
Externí odkaz:
http://arxiv.org/abs/2410.21287
Autor:
Zhong, Tianyang, Liu, Zhengliang, Pan, Yi, Zhang, Yutong, Zhou, Yifan, Liang, Shizhe, Wu, Zihao, Lyu, Yanjun, Shu, Peng, Yu, Xiaowei, Cao, Chao, Jiang, Hanqi, Chen, Hanxu, Li, Yiwei, Chen, Junhao, Hu, Huawen, Liu, Yihen, Zhao, Huaqin, Xu, Shaochen, Dai, Haixing, Zhao, Lin, Zhang, Ruidong, Zhao, Wei, Yang, Zhenyuan, Chen, Jingyuan, Wang, Peilong, Ruan, Wei, Wang, Hui, Zhao, Huan, Zhang, Jing, Ren, Yiming, Qin, Shihuan, Chen, Tong, Li, Jiaxi, Zidan, Arif Hassan, Jahin, Afrar, Chen, Minheng, Xia, Sichen, Holmes, Jason, Zhuang, Yan, Wang, Jiaqi, Xu, Bochen, Xia, Weiran, Yu, Jichao, Tang, Kaibo, Yang, Yaxuan, Sun, Bolun, Yang, Tao, Lu, Guoyu, Wang, Xianqiao, Chai, Lilong, Li, He, Lu, Jin, Sun, Lichao, Zhang, Xin, Ge, Bao, Hu, Xintao, Zhang, Lian, Zhou, Hua, Zhang, Lu, Zhang, Shu, Liu, Ninghao, Jiang, Bei, Kong, Linglong, Xiang, Zhen, Ren, Yudan, Liu, Jun, Jiang, Xi, Bao, Yu, Zhang, Wei, Li, Xiang, Li, Gang, Liu, Wei, Shen, Dinggang, Sikora, Andrea, Zhai, Xiaoming, Zhu, Dajiang, Liu, Tianming
This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguist
Externí odkaz:
http://arxiv.org/abs/2409.18486
Differentiable particle filters combine the flexibility of neural networks with the probabilistic nature of sequential Monte Carlo methods. However, traditional approaches rely on the availability of labelled data, i.e., the ground truth latent state
Externí odkaz:
http://arxiv.org/abs/2405.01251
Differentiable particle filters are an emerging class of sequential Bayesian inference techniques that use neural networks to construct components in state space models. Existing approaches are mostly based on offline supervised training strategies.
Externí odkaz:
http://arxiv.org/abs/2312.05955
The underlying mechanism of neural networks in capturing precise knowledge has been the subject of consistent research efforts. In this work, we propose a theoretical approach based on Neural Tangent Kernels (NTKs) to investigate such mechanisms. Spe
Externí odkaz:
http://arxiv.org/abs/2310.16350
While prompt tuning approaches have achieved competitive performance with high efficiency, we observe that they invariably employ the same initialization process, wherein the soft prompt is either randomly initialized or derived from an existing embe
Externí odkaz:
http://arxiv.org/abs/2310.10094
This work considers the problem of heterogeneous graph-level anomaly detection. Heterogeneous graphs are commonly used to represent behaviours between different types of entities in complex industrial systems for capturing as much information about t
Externí odkaz:
http://arxiv.org/abs/2308.14340