This paper conducts a scientometric analysis and systematic literature review to identify the trends in microfinance outcomes from the perspective of their recipients,specifically more vulnerable people,while also foc...This paper conducts a scientometric analysis and systematic literature review to identify the trends in microfinance outcomes from the perspective of their recipients,specifically more vulnerable people,while also focusing on the demand side.Applying the keywords“co-occurrence networks”and“citation networks,”we examined 524 studies indexed on the ISI Web of Science database between 2012 and March 2021.The subsequent content analysis of bibliometric-coupled articles concerns the main research topics in this field:the socioeconomic outcomes of microfinance,the dichotomy between social performance and the mission drift of microfinance institutions,and how entrepreneurship and financial innovation,specifically through crowdfunding,mitigate poverty and empower the more vulnerable.The findings reinforce the idea that microfinance constitutes a distinct field of development thinking,and indicate that a more holistic approach should be adopted to boost microfinance outcomes through a better understanding of their beneficiaries.The trends in this field will help policymakers,regulators,and academics to examine the nuts and bolts of microfinance and identify the most relevant areas of intervention.展开更多
Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of...Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps.However,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information.More high-level information can make the segmentationmore accurate.In this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level information.The MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive paths.The proposed MU-Net architecture is evaluated based on three different medical imaging datasets.Our experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different datasets.At the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.展开更多
Latent information is difficult to get from the text in speech synthesis.Studies show that features from speech can get more information to help text encoding.In the field of speech encoding,a lot of work has been con...Latent information is difficult to get from the text in speech synthesis.Studies show that features from speech can get more information to help text encoding.In the field of speech encoding,a lot of work has been conducted on two aspects.The first aspect is to encode speech frame by frame.The second aspect is to encode the whole speech to a vector.But the scale in these aspects is fixed.So,encoding speech with an adjustable scale for more latent information is worthy of investigation.But current alignment approaches only support frame-by-frame encoding and speech-to-vector encoding.It remains a challenge to propose a new alignment approach to support adjustable scale speech encoding.This paper presents the dynamic speech encoder with a new alignment approach in conjunction with frame-by-frame encoding and speech-to-vector encoding.The speech feature fromourmodel achieves three functions.First,the speech feature can reconstruct the origin speech while the length of the speech feature is equal to the text length.Second,our model can get text embedding fromspeech,and the encoded speech feature is similar to the text embedding result.Finally,it can transfer the style of synthesis speech and make it more similar to the given reference speech.展开更多
基金support of Fundação para a Ciência e a Tecnologia(UBI PTDC/EGE/OGE/31246/2017,UIDB/04630/2020,UIDB/04728/2020,UIDB/04105/2020)。
文摘This paper conducts a scientometric analysis and systematic literature review to identify the trends in microfinance outcomes from the perspective of their recipients,specifically more vulnerable people,while also focusing on the demand side.Applying the keywords“co-occurrence networks”and“citation networks,”we examined 524 studies indexed on the ISI Web of Science database between 2012 and March 2021.The subsequent content analysis of bibliometric-coupled articles concerns the main research topics in this field:the socioeconomic outcomes of microfinance,the dichotomy between social performance and the mission drift of microfinance institutions,and how entrepreneurship and financial innovation,specifically through crowdfunding,mitigate poverty and empower the more vulnerable.The findings reinforce the idea that microfinance constitutes a distinct field of development thinking,and indicate that a more holistic approach should be adopted to boost microfinance outcomes through a better understanding of their beneficiaries.The trends in this field will help policymakers,regulators,and academics to examine the nuts and bolts of microfinance and identify the most relevant areas of intervention.
基金The authors received Sichuan Science and Technology Program(No.18YYJC1917)funding for this study.
文摘Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps.However,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information.More high-level information can make the segmentationmore accurate.In this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level information.The MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive paths.The proposed MU-Net architecture is evaluated based on three different medical imaging datasets.Our experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different datasets.At the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.
基金supported by National Key R&D Program of China (2020AAA0107901).
文摘Latent information is difficult to get from the text in speech synthesis.Studies show that features from speech can get more information to help text encoding.In the field of speech encoding,a lot of work has been conducted on two aspects.The first aspect is to encode speech frame by frame.The second aspect is to encode the whole speech to a vector.But the scale in these aspects is fixed.So,encoding speech with an adjustable scale for more latent information is worthy of investigation.But current alignment approaches only support frame-by-frame encoding and speech-to-vector encoding.It remains a challenge to propose a new alignment approach to support adjustable scale speech encoding.This paper presents the dynamic speech encoder with a new alignment approach in conjunction with frame-by-frame encoding and speech-to-vector encoding.The speech feature fromourmodel achieves three functions.First,the speech feature can reconstruct the origin speech while the length of the speech feature is equal to the text length.Second,our model can get text embedding fromspeech,and the encoded speech feature is similar to the text embedding result.Finally,it can transfer the style of synthesis speech and make it more similar to the given reference speech.
