Browsing by Author "Li, Ming"
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Item Projected increase in carbon dioxide drawdown and acidification in large estuaries under climate change(Communications Earth & Environment, 2023-03-13) Li, Ming; Guo, Yijun; Cai, Wei-Jun; Testa, Jeremy M.; Shen, Chunqi; Li, Renjian; Su, JianzhongMost estuaries are substantial sources of carbon dioxide (CO2) to the atmosphere. The estimated estuarine CO2 degassing is about 17% of the total oceanic uptake, but the effect of rising atmospheric CO2 on estuarine carbon balance remains unclear. Here we use 3D hydrodynamic-biogeochemical models of a large eutrophic estuary and a box model of two generic, but contrasting estuaries to generalize how climate change affects estuarine carbonate chemistry and CO2 fluxes. We found that small estuaries with short flushing times remain a CO2 source to the atmosphere, but large estuaries with long flushing times may become a greater carbon sink and acidify. In particular, climate downscaling projections for Chesapeake Bay in the mid-21st century showed a near-doubling of CO2 uptake, a pH decline of 0.1–0.3, and >90% expansion of the acidic volume. Our findings suggest that large eutrophic estuaries will become carbon sinks and suffer from accelerated acidification in a changing climate.Item Region-aware Arbitrary-shaped Text Detection with Progressive Fusion(IEEE Transactions on Multimedia, 2022-08-04) Wang, Qitong; Fu, Bin; Li, Ming; He, Junjun; Peng, Xi; Qiao, YuSegmentation-based text detectors are flexible to capture arbitrary-shaped text regions. Due to large geometry variance, it is necessary to construct effective and robust representations to identify text regions with various shapes and scales. In this paper, we focus on designing effective multi-scale contextual features for locating text instances. Specially, we develop a Region Context Module (RCM) to summarize the semantic response and adaptively extract text-region-aware information in a limited local area. To construct complementary multi-scale contextual representations, multiple RCM branches with different scales are employed and integrated via Progressive Fusion Module (PFM). Our proposed RCM and PFM serve as the plug-and-play modules which can be incorporated into existing scene text detection platforms to further boost detection performance. Extensive experiments show that our methods achieve state-of-the-art performances on Total-Text, SCUT-CTW1500 and MSRA-TD500 datasets. The code with models will become publicly available at https://github.com/wqtwjt1996/RP-Text.