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Quantifying Heterogeneous Ecosystem Services With Multi-Label Soft Classification

Autor: Tian, Zhihui, Upchurch, John, Simon, G. Austin, Dubeux, José, Zare, Alina, Zhao, Chang, Harley, Joel B.

Understanding and quantifying ecosystem services are crucial for sustainable environmental management, conservation efforts, and policy-making. The advancement of remote sensing technology and machine learning techniques has greatly facilitated this

Externí odkaz: http://arxiv.org/abs/2406.17147

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PerLLM: Personalized Inference Scheduling with Edge-Cloud Collaboration for Diverse LLM Services

Autor: Yang, Zheming, Yang, Yuanhao, Zhao, Chang, Guo, Qi, He, Wenkai, Ji, Wen

With the rapid growth in the number of large language model (LLM) users, it is difficult for bandwidth-constrained cloud servers to simultaneously process massive LLM services in real-time. Recently, edge-cloud infrastructures have been used to impro

Externí odkaz: http://arxiv.org/abs/2405.14636

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Akademický článek

X‐ray ghost imaging with a specially developed beam splitter.

Autor: Zhao, Chang-Zhe^1,2,3 (AUTHOR), Zhang, Hai-Peng² (AUTHOR), Tang, Jie^1,2,3 (AUTHOR), Zhao, Ni-Xi^1,2,3 (AUTHOR), Li, Zhong-Liang^1,2,3 (AUTHOR) lizhongliang@sari.ac.cn, Xiao, Ti-Qiao² (AUTHOR) tqxiao@sari.ac.cn

Publikováno v: Journal of Synchrotron Radiation. Nov2024, Vol. 31 Issue 6, p1525-1533. 9p.

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Orlicz log-Aleksandrov-Fenchel inequality

Autor: Zhao, Chang-Jian

In this paper, we establish an Orlicz log-Aleksandrov-Fenchel inequality by introducing new concepts of mixed volume measure and Orlicz multiple mixed volume measure, and using the Orlicz-Aleksandrov-Fenchel inequality. The Orlicz log-Aleksandrov-Fen

Externí odkaz: http://arxiv.org/abs/2304.01361

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