The Asia-Pacific Economic Cooperation(APEC)is contemplating expanding its list of environmental goods(EG)for trade liberalization to fight climate change.In support of doing so,this study proposes that a long list tha...The Asia-Pacific Economic Cooperation(APEC)is contemplating expanding its list of environmental goods(EG)for trade liberalization to fight climate change.In support of doing so,this study proposes that a long list that retains controversies is better for carbon emission reduction than a short common list.This study examines four mechanisms of longer lists:enlarging market scales,enriching product mixes,enhancing product sophistication,and enriching trade patterns.Using China’s emerging EG trade during the 2001-2015 period as a case study,this study compares four EG lists with different EG.The results show that:(1)a longer list reduces carbon emissions from both imports and exports,making domestic regions with different advantages have better chances of improving carbon efficiencies.(2)Product sophistication reduces the emission gap between trading partners,regardless of the length of EG lists.(3)China’s EG exports contribute to carbon reduction in leading regions,while EG imports provide laggard regions with better chances of reducing carbon emissions.These findings provide three implications for future list-making:it is important to(1)seek a long and inclusive list rather than a short common list,(2)shift the focus from environmental end-use to the technological contents of products,and(3)balance the demand of laggard regions to import and the capacity of leading regions to export.展开更多
The end-to-end separation algorithm with superior performance in the field of speech separation has not been effectively used in music separation.Moreover,since music signals are often dual channel data with a high sa...The end-to-end separation algorithm with superior performance in the field of speech separation has not been effectively used in music separation.Moreover,since music signals are often dual channel data with a high sampling rate,how to model longsequence data and make rational use of the relevant information between channels is also an urgent problem to be solved.In order to solve the above problems,the performance of the end-to-end music separation algorithm is enhanced by improving the network structure.Our main contributions include the following:(1)A more reasonable densely connected U-Net is designed to capture the long-term characteristics of music,such as main melody,tone and so on.(2)On this basis,the multi-head attention and dualpath transformer are introduced in the separation module.Channel attention units are applied recursively on the feature map of each layer of the network,enabling the network to perform long-sequence separation.Experimental results show that after the introduction of the channel attention,the performance of the proposed algorithm has a stable improvement compared with the baseline system.On the MUSDB18 dataset,the average score of the separated audio exceeds that of the current best-performing music separation algorithm based on the time-frequency domain(T-F domain).展开更多
基金supported by the National Natural Science Foundation of China(Grants No.42271178 and 41801104).
文摘The Asia-Pacific Economic Cooperation(APEC)is contemplating expanding its list of environmental goods(EG)for trade liberalization to fight climate change.In support of doing so,this study proposes that a long list that retains controversies is better for carbon emission reduction than a short common list.This study examines four mechanisms of longer lists:enlarging market scales,enriching product mixes,enhancing product sophistication,and enriching trade patterns.Using China’s emerging EG trade during the 2001-2015 period as a case study,this study compares four EG lists with different EG.The results show that:(1)a longer list reduces carbon emissions from both imports and exports,making domestic regions with different advantages have better chances of improving carbon efficiencies.(2)Product sophistication reduces the emission gap between trading partners,regardless of the length of EG lists.(3)China’s EG exports contribute to carbon reduction in leading regions,while EG imports provide laggard regions with better chances of reducing carbon emissions.These findings provide three implications for future list-making:it is important to(1)seek a long and inclusive list rather than a short common list,(2)shift the focus from environmental end-use to the technological contents of products,and(3)balance the demand of laggard regions to import and the capacity of leading regions to export.
基金National Natural Science Foundation of China,Grant/Award Number:62071039Beijing Natural Science Foundation,Grant/Award Number:L223033。
文摘The end-to-end separation algorithm with superior performance in the field of speech separation has not been effectively used in music separation.Moreover,since music signals are often dual channel data with a high sampling rate,how to model longsequence data and make rational use of the relevant information between channels is also an urgent problem to be solved.In order to solve the above problems,the performance of the end-to-end music separation algorithm is enhanced by improving the network structure.Our main contributions include the following:(1)A more reasonable densely connected U-Net is designed to capture the long-term characteristics of music,such as main melody,tone and so on.(2)On this basis,the multi-head attention and dualpath transformer are introduced in the separation module.Channel attention units are applied recursively on the feature map of each layer of the network,enabling the network to perform long-sequence separation.Experimental results show that after the introduction of the channel attention,the performance of the proposed algorithm has a stable improvement compared with the baseline system.On the MUSDB18 dataset,the average score of the separated audio exceeds that of the current best-performing music separation algorithm based on the time-frequency domain(T-F domain).
