Zobrazeno 1 - 10
of 10 071
pro vyhledávání: '"Pengju An"'
Publikováno v:
地质科技通报, Vol 41, Iss 6, Pp 169-179 (2022)
Periodic fluctuations of reservoir water level lead to the variations in seepage stress inside landslide bodies. Dynamic seepage pressures can lead to deterioration in the structure and strength of the slide zone, which affects the stability of the l
Externí odkaz:
https://doaj.org/article/8f3a41ea9c5441f6a00bd500e9f8e275
Publikováno v:
Remote Sensing, Vol 15, Iss 19, p 4880 (2023)
The roughness of rock joints exerts a substantial influence on the mechanical behavior of rock masses. In order to identify potential failure mechanisms and to design effective protection measures, the accurate measurement of joint roughness is essen
Externí odkaz:
https://doaj.org/article/37137a843d184661bbc00917066c36b4
Camera calibration is a crucial step in photogrammetry and 3D vision applications. In practical scenarios with a long working distance to cover a wide area, target-based calibration methods become complicated and inflexible due to site limitations. T
Externí odkaz:
http://arxiv.org/abs/2409.20034
In practical scenarios, federated learning frequently necessitates training personalized models for each client using heterogeneous data. This paper proposes a backbone self-distillation approach to facilitate personalized federated learning. In this
Externí odkaz:
http://arxiv.org/abs/2409.15636
Federated learning provides a privacy-preserving manner to collaboratively train models on data distributed over multiple local clients via the coordination of a global server. In this paper, we focus on label distribution skew in federated learning,
Externí odkaz:
http://arxiv.org/abs/2409.13136
Autor:
Liu, Bochao, Lu, Jianghu, Wang, Pengju, Zhang, Junjie, Zeng, Dan, Qian, Zhenxing, Ge, Shiming
Deep learning models can achieve high inference accuracy by extracting rich knowledge from massive well-annotated data, but may pose the risk of data privacy leakage in practical deployment. In this paper, we present an effective teacher-student lear
Externí odkaz:
http://arxiv.org/abs/2409.12384
While deep models have proved successful in learning rich knowledge from massive well-annotated data, they may pose a privacy leakage risk in practical deployment. It is necessary to find an effective trade-off between high utility and strong privacy
Externí odkaz:
http://arxiv.org/abs/2409.02404
Autor:
Matselyukh, Danylo T., Rott, Florian, Schnappinger, Thomas, Zhang, Pengju, Li, Zheng, Richardson, Jeremy O., de Vivie-Riedle, Regina, Wörner, Hans Jakob
The transfer of population between two intersecting quantum states is the most fundamental dynamical event that governs a broad variety of processes in physics, chemistry, biology and material science. Whereas any two-state description implies that p
Externí odkaz:
http://arxiv.org/abs/2408.17402
While the success of deep learning relies on large amounts of training datasets, data is often limited in privacy-sensitive domains. To address this challenge, generative model learning with differential privacy has emerged as a solution to train pri
Externí odkaz:
http://arxiv.org/abs/2408.14738
Autor:
Ma, Jun, Zhang, Yao, Gu, Song, Ge, Cheng, Wang, Ershuai, Zhou, Qin, Huang, Ziyan, Lyu, Pengju, He, Jian, Wang, Bo
Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide c
Externí odkaz:
http://arxiv.org/abs/2408.12534