Zobrazeno 1 - 10
of 276
pro vyhledávání: '"LI Xuran"'
Publikováno v:
Shanghai Jiaotong Daxue xuebao. Yixue ban, Vol 43, Iss 4, Pp 406-416 (2023)
Objective·To investigate the effects of small extracellular vesicles (sEVs) derived from human bone marrow mesenchymal stem cells (BMSCs) on the regulation of osteoclast differentiation and macrophage polarization in mice, and mouse model of osteopo
Externí odkaz:
https://doaj.org/article/56072696624c4be499b3eda81947cceb
Deep neural networks (DNNs) are known to be sensitive to adversarial input perturbations, leading to a reduction in either prediction accuracy or individual fairness. To jointly characterize the susceptibility of prediction accuracy and individual fa
Externí odkaz:
http://arxiv.org/abs/2404.01356
Publikováno v:
IEEE Wireless Communications Letters, vol. 12, no. 12, pp. 2033-2037
In this letter, we analyze the performance trade-off in distributed integrated sensing and communication (ISAC) networks. Specifically, with the aid of stochastic geometry theory, we derive the probability of detection of that of the coverage given u
Externí odkaz:
http://arxiv.org/abs/2308.06596
Integrated sensing and communication (ISAC) technology is one of the featuring technologies of the next-generation communication systems. When sensing capability becomes ubiquitous, more information can be collected, which can facilitate many applica
Externí odkaz:
http://arxiv.org/abs/2308.00253
Deep neural networks (DNNs) often face challenges due to their vulnerability to various adversarial perturbations, including false perturbations that undermine prediction accuracy and biased perturbations that cause biased predictions for similar inp
Externí odkaz:
http://arxiv.org/abs/2305.10906
The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In thi
Externí odkaz:
http://arxiv.org/abs/2210.02017
Accuracy and individual fairness are both crucial for trustworthy machine learning, but these two aspects are often incompatible with each other so that enhancing one aspect may sacrifice the other inevitably with side effects of true bias or false f
Externí odkaz:
http://arxiv.org/abs/2205.08704
Autor:
Han, Daoyuan a, 1, Zhao, Xianting a, 1, Li, Xuran a, Zhu, Mengqi a, ⁎, Dong, Guowen b, Weng, Jingzheng a, ⁎, Zhang, Jindan a
Publikováno v:
In Journal of Alloys and Compounds 15 November 2024 1005
Publikováno v:
In Journal of Energy Storage 1 November 2024 101 Part A
Publikováno v:
In Carbon November 2024 230