C
Chang Xu
Researcher at University of Sydney
Publications - 467
Citations - 13012
Chang Xu is an academic researcher from University of Sydney. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 42, co-authored 260 publications receiving 7189 citations. Previous affiliations of Chang Xu include University of Melbourne & Information Technology University.
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Journal ArticleDOI
Degradation mechanism of the Ru wares unearthed from the Qingliangsi site in Henan, China
TL;DR: In this article , the degradation mechanism of the Ru glaze was investigated, showing that the heterogeneity of the glaze microstructure resulted in different degradation morphologies, and the weakest part determined the overall corrosion resistance.
Proceedings ArticleDOI
Crafting Efficient Neural Graph of Large Entropy
TL;DR: This work proposes to use graph entropy as the measurement, which shows useful properties to craft high-quality neural graphs and enables the proposed efficient algorithm to construct them as the initial network architecture.
Proceedings ArticleDOI
Independent Feature and Label Components for Multi-label Classification
TL;DR: Experimental results on real-world multi- label datasets demonstrate the necessity of exploring independence components from multi-label data and the effectiveness of the proposed algorithm.
Journal ArticleDOI
Greenhouse gas emissions from fed mollusk mariculture: A case study of a Sinonovacula constricta farming system
Wang‐Wang Ye,Heng Sun,Yuhong Li,Jiexia Zhang,Miming Zhang,Zhongyong Gao,Jinpei Yan,Jian Liu,Jianwen Wen,Hang Yang,Jun Shi,Shuhui Zhao,Man Wu,Suqing Xu,Chang Xu,Liyang Zhan +15 more
TL;DR: In this paper , the authors conducted two field campaigns in a constricted tagelus (Sinonovacula constricta) farming system (March 3-5 and April 21-23, 2021) to observe the changes in GHGs.
Journal ArticleDOI
FastMIM: Expediting Masked Image Modeling Pre-training for Vision
TL;DR: FastMIM as discussed by the authors proposes a simple and generic framework for expediting masked image modeling with the following two steps: (i) pre-training vision backbones with low-resolution input images; and (ii) reconstructing Histograms of Oriented Gradients (HOG) feature instead of original RGB values of the input images.