Zobrazeno 1 - 10
of 13 091 704
pro vyhledávání: '"A Wang"'
Publikováno v:
International Journal of Multiphysics, Vol 17, Iss 3, Pp 349-369 (2023)
Wearable technology has broad market prospects in the military, fire protection, medical and health, sports and other fields under its ability to effectively solve practical application needs. Self-powered energy systems with miniaturized, light-weig
Externí odkaz:
https://doaj.org/article/20f37201a2d24c77bb98fad61fe9e8d5
Publikováno v:
International Journal of Multiphysics, Vol 17, Iss 1, Pp 37-54 (2023)
Excellent temperature characteristics of space Li-ion batteries can effectively improve the on-orbit operation to reduce the temperature fluctuation range and improve reliability. This paper adopts the hardware-in-the-loop approach, which organically
Externí odkaz:
https://doaj.org/article/436c57e49b58475c9fa4ef5108935023
Publikováno v:
Continence, Vol 7, Iss , Pp 100746- (2023)
Externí odkaz:
https://doaj.org/article/e602b347a55142de87f3af796a2f56c4
Autor:
Tian, Runchu, Li, Yanghao, Fu, Yuepeng, Deng, Siyang, Luo, Qinyu, Qian, Cheng, Wang, Shuo, Cong, Xin, Zhang, Zhong, Wu, Yesai, Lin, Yankai, Wang, Huadong, Liu, Xiaojiang
Positional bias in large language models (LLMs) hinders their ability to effectively process long inputs. A prominent example is the "lost in the middle" phenomenon, where LLMs struggle to utilize relevant information situated in the middle of the in
Externí odkaz:
http://arxiv.org/abs/2410.14641
The pioneering work of Oono and Suzuki [ICLR, 2020] and Cai and Wang [arXiv:2006.13318] initializes the analysis of the smoothness of graph convolutional network (GCN) features. Their results reveal an intricate empirical correlation between node cla
Externí odkaz:
http://arxiv.org/abs/2410.14604
With the rapid advancements in wireless communication fields, including low-altitude economies, 6G, and Wi-Fi, the scale of wireless networks continues to expand, accompanied by increasing service quality demands. Traditional deep reinforcement learn
Externí odkaz:
http://arxiv.org/abs/2410.14481
Autor:
Yang, Enneng, Shen, Li, Wang, Zhenyi, Guo, Guibing, Wang, Xingwei, Cao, Xiaocun, Zhang, Jie, Tao, Dacheng
Model merging-based multitask learning (MTL) offers a promising approach for performing MTL by merging multiple expert models without requiring access to raw training data. However, in this paper, we examine the merged model's representation distribu
Externí odkaz:
http://arxiv.org/abs/2410.14389
Text documents with numerical values involved are widely used in various applications such as scientific research, economy, public health and journalism. However, it is difficult for readers to quickly interpret such data-involved texts and gain deep
Externí odkaz:
http://arxiv.org/abs/2410.14331
Autor:
Hu, Xiang, Fu, Hongyu, Wang, Jinge, Wang, Yifeng, Li, Zhikun, Xu, Renjun, Lu, Yu, Jin, Yaochu, Pan, Lili, Lan, Zhenzhong
Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability i
Externí odkaz:
http://arxiv.org/abs/2410.14255
Autor:
Liu, Zihan, Zeng, Ruinan, Wang, Dongxia, Peng, Gengyun, Wang, Jingyi, Liu, Qiang, Liu, Peiyu, Wang, Wenhai
In industrial control systems, the generation and verification of Programmable Logic Controller (PLC) code are critical for ensuring operational efficiency and safety. While Large Language Models (LLMs) have made strides in automated code generation,
Externí odkaz:
http://arxiv.org/abs/2410.14209