Zobrazeno 1 - 10
of 1 168
pro vyhledávání: '"Mo Fan"'
Publikováno v:
Redai dili, Vol 43, Iss 2, Pp 308-319 (2023)
Since the launch of the Rural Revitalization Strategy, China's rural construction in county seats and villages has made remarkable achievements. Meanwhile, towns have received insufficient attention as a traditionally important node connecting urban
Externí odkaz:
https://doaj.org/article/c1e9520bf82042a99a158bd12fcc01a5
Publikováno v:
Urban Science, Vol 8, Iss 1, p 6 (2024)
Contemporary urban development places a critical emphasis on pedestrian environments, especially in historic cities like George Town, which is a UNESCO World Heritage Site in Malaysia. Although survey questionnaires effectively captured public percep
Externí odkaz:
https://doaj.org/article/bd57434c052547cc8989d0262ea5f065
Autor:
Zhang, Yuxiang, Fan, Xin, Wang, Junjie, Chen, Chongxian, Mo, Fan, Sakai, Tetsuya, Yamana, Hayato
Recent advancements in large language models (LLMs) integrated with external tools and APIs have successfully addressed complex tasks by using in-context learning or fine-tuning. Despite this progress, the vast scale of tool retrieval remains challen
Externí odkaz:
http://arxiv.org/abs/2410.03212
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, although their susceptibility to hallucination poses significant challenges for their deployment in critical areas such as healthcare. To address this issu
Externí odkaz:
http://arxiv.org/abs/2405.06545
Publikováno v:
Frontiers in Energy Research, Vol 9 (2021)
In the carbon capture and storage (CCS) infrastructure, the risk of a high-pressure buried pipeline rupture possibly leads to catastrophic accidents due to the release of tremendous amounts of carbon dioxide (CO2). Therefore, a comprehensive understa
Externí odkaz:
https://doaj.org/article/f36037767c9e4b2c87f3f770676f3e42
Federated learning (FL) in multidevice environments creates new opportunities to learn from a vast and diverse amount of private data. Although personal devices capture valuable data, their memory, computing, connectivity, and battery resources are o
Externí odkaz:
http://arxiv.org/abs/2211.04175
Autor:
Lv, Shiya1,2 (AUTHOR), Mo, Fan1,2 (AUTHOR), Xu, Zhaojie1,2 (AUTHOR), Wang, Yu1,2 (AUTHOR), Yang, Gucheng1,2 (AUTHOR), Han, Meiqi1,2 (AUTHOR), Jing, Luyi1,2 (AUTHOR), Xu, Wei1,2 (AUTHOR), Duan, Yiming1,2 (AUTHOR), Liu, Yaoyao1,2 (AUTHOR), Li, Ming1,2 (AUTHOR), Liu, Juntao1,2 (AUTHOR), Luo, Jinping1,2 (AUTHOR), Wang, Mixia1,2 (AUTHOR), Song, Yilin1,2 (AUTHOR) ylsong@mail.ie.ac.cn, Wu, Yirong1,2 (AUTHOR) wyr@mail.ie.ac.cn, Cai, Xinxia1,2 (AUTHOR) xxcai@mail.ie.ac.cn
Publikováno v:
Advanced Science. 8/7/2024, Vol. 11 Issue 29, p1-13. 13p.
Privacy and security challenges in Machine Learning (ML) have become increasingly severe, along with ML's pervasive development and the recent demonstration of large attack surfaces. As a mature system-oriented approach, Confidential Computing has be
Externí odkaz:
http://arxiv.org/abs/2208.10134
Autor:
Brossard, Mathias, Bryant, Guilhem, Gaabouri, Basma El, Fan, Xinxin, Ferreira, Alexandre, Grimley-Evans, Edmund, Haster, Christopher, Johnson, Evan, Miller, Derek, Mo, Fan, Mulligan, Dominic P., Spinale, Nick, van Hensbergen, Eric, Vincent, Hugo J. M., Xiong, Shale
Sensitive computations are now routinely delegated to third-parties. In response, Confidential Computing technologies are being introduced to microprocessors, offering a protected processing environment, which we generically call an isolate, providin
Externí odkaz:
http://arxiv.org/abs/2205.03322
Autor:
Zhao, Yuchen, Afzal, Sayed Saad, Akbar, Waleed, Rodriguez, Osvy, Mo, Fan, Boyle, David, Adib, Fadel, Haddadi, Hamed
This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments? An affirmative answer to this question would have significant implications for a new gene
Externí odkaz:
http://arxiv.org/abs/2202.08174