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Coastal wetlands play an important role in regulating atmospheric carbon dioxide (CO2) concentrations and contribute significantly to climate change mitigation. However, climate change, reclamation, and restoration have been causing substantial changes in coastal wetland areas and carbon exchange in China during recent decades. Here we compiled a carbon flux database consisting of 15 coastal wetland sites to assess the magnitude, patterns, and drivers of carbon fluxes and to compare fluxes among contrasting natural, disturbed, and restored wetlands. The natural coastal wetlands have the average net ecosystem exchange of CO2 (NEE) of −577 g C m−2 year−1, with −821 g C m−2 year−1 for mangrove forests and −430 g C m−2 year−1 for salt marshes. There are pronounced latitudinal patterns for carbon dioxide exchange of natural coastal wetlands: NEE increased whereas gross primary production (GPP) and respiration of ecosystem decreased with increasing latitude. Distinct environmental factors drive annual variations of GPP between mangroves and salt marshes; temperature was the dominant controlling factor in salt marshes, while temperature, precipitation, and solar radiation were co‐dominant in mangroves. Meanwhile, both anthropogenic reclamation and restoration had substantial effects on coastal wetland carbon fluxes, and the effect of the anthropogenic perturbation in mangroves was more extensive than that in salt marshes. Furthermore, from 1980 to 2020, anthropogenic reclamation of China's coastal wetlands caused a carbon loss of ~3720 Gg C, while the mangrove restoration project during the period of 2021–2025 may switch restored coastal wetlands from a carbon source to carbon sink with a net carbon gain of 73 Gg C. The comparison of carbon fluxes among these coastal wetlands can improve our understanding of how anthropogenic perturbation can affect the potentials of coastal blue carbon in China, which has implications for informing conservation and restoration strategies and efforts of coastal wetlands. [ABSTRACT FROM AUTHOR]

Wetlands play an important role in regulating the atmospheric carbon dioxide ( CO2) concentrations and thus affecting the climate. However, there is still lack of quantitative evaluation of such a role across different wetland types, especially at the global scale. Here, we conducted a meta-analysis to compare ecosystem CO2 fluxes among various types of wetlands using a global database compiled from the literature. This database consists of 143 site-years of eddy covariance data from 22 inland wetland and 21 coastal wetland sites across the globe. Coastal wetlands had higher annual gross primary productivity ( GPP), ecosystem respiration ( Re), and net ecosystem productivity ( NEP) than inland wetlands. On a per unit area basis, coastal wetlands provided large CO2 sinks, while inland wetlands provided small CO2 sinks or were nearly CO2 neutral. The annual CO2 sink strength was 93.15 and 208.37 g C m−2 for inland and coastal wetlands, respectively. Annual CO2 fluxes were mainly regulated by mean annual temperature ( MAT) and mean annual precipitation ( MAP). For coastal and inland wetlands combined, MAT and MAP explained 71%, 54%, and 57% of the variations in GPP, Re, and NEP, respectively. The CO2 fluxes of wetlands were also related to leaf area index ( LAI). The CO2 fluxes also varied with water table depth ( WTD), although the effects of WTD were not statistically significant. NEP was jointly determined by GPP and Re for both inland and coastal wetlands. However, the NEP/ Re and NEP/ GPP ratios exhibited little variability for inland wetlands and decreased for coastal wetlands with increasing latitude. The contrasting of CO2 fluxes between inland and coastal wetlands globally can improve our understanding of the roles of wetlands in the global C cycle. Our results also have implications for informing wetland management and climate change policymaking, for example, the efforts being made by international organizations and enterprises to restore coastal wetlands for enhancing blue carbon sinks. [ABSTRACT FROM AUTHOR]

Recently, ithas been observed that {0,±1}‐ternary codes, which are simply generated from deep features by hard thresholding, tend to outperform {−1,1}‐binary codes in image retrieval. To obtain better ternary codes, the authors for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non‐smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes. For the first time, the authors propose to generate ternary hash codes by jointly learning the codes with deep features via a continuation method. Experiments show that the proposed method outperforms existing methods. [ABSTRACT FROM AUTHOR]

Recently, the binary matrix with binary correlations between columns has attracted attention for its sparse structure and competitive performance. However, such kind of matrix tends to suffer an increasing coherence, i.e. the maximum absolute correlation between columns, as the compression ratio decreases. It is shown here that the binary matrix mentioned above allows to be duplicated in columns, while not degrading the coherence, if bipolar signs are introduced. The resulting ternary matrix is thus expected to present smaller coherence than the aforementioned binary matrix of same size. [ABSTRACT FROM AUTHOR]