Zobrazeno 1 - 10
of 751
pro vyhledávání: '"ZHANG Qizhi"'
Autor:
Sun, Qingfeng, Zhang, Qizhi
We compute the quantum variance of holomorphic cusp forms on the vertical geodesic for smooth compactly supported test functions. As an application we show that almost all holomorphic Hecke cusp forms, whose weights are in a short interval, satisfy Q
Externí odkaz:
http://arxiv.org/abs/2408.15259
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 6, Pp 142-149 (2024)
The health status of the planetary gears in the cutting section of shearer's rocker arm directly affects the cutting efficiency. The strong noise interference caused by multiple impacts during the cutting of coal and rock by the shearer, the complex
Externí odkaz:
https://doaj.org/article/07cd6e6488d74ddb92da8af338aec603
Publikováno v:
In Case Studies in Construction Materials December 2024 21
Publikováno v:
In Advanced Engineering Informatics October 2024 62 Part B
Autor:
Qian, Kang, Yang, Peng, Li, Yixian, Meng, Ran, Cheng, Yunlong, Zhou, Lingling, Wu, Jing, Xu, Shuting, Bao, Xiaoyan, Guo, Qian, Wang, Pengzhen, Xu, Minjun, Sheng, Dongyu, Zhang, Qizhi
Publikováno v:
In Asian Journal of Pharmaceutical Sciences August 2024 19(4)
Secure multi-party computation enables multiple mutually distrusting parties to perform computations on data without revealing the data itself, and has become one of the core technologies behind privacy-preserving machine learning. In this work, we p
Externí odkaz:
http://arxiv.org/abs/2109.11726
Autor:
Li, Yixian, Yang, Peng, Meng, Ran, Xu, Shuting, Zhou, Lingling, Qian, Kang, Wang, Pengzhen, Cheng, Yunlong, Sheng, Dongyu, Xu, Minjun, Wang, Tianying, Wu, Jing, Cao, Jinxu, Zhang, Qizhi
Publikováno v:
In Acta Pharmaceutica Sinica B March 2024 14(3):1380-1399
Publikováno v:
In Journal of Controlled Release March 2024 367:604-619
Secure comparison and secure selection are two fundamental MPC (secure Multi-Party Computation) protocols. One important application of these protocols is the secure ReLU and DReLU computation in privacy preserving deep learning. In this paper, we in
Externí odkaz:
http://arxiv.org/abs/2007.03975