Research on the impact of green finance on carbon emissions: evidence from China

Green finance is an effective means adopted by the Chinese government to reduce carbon emissions. Does the development of green finance in Chinese provinces reduce carbon emissions to a certain extent? This study selects panel data of various provinces and cities in China from 2003 to 2019. Based on the Hansen threshold regression model, an empirical analysis is conducted with economic growth and industrial structure as threshold variables to test the impact of green finance on carbon emissions. The results show that green finance increases the speed of carbon emission mitigation when PGDP is the threshold. Taking industrial structure as the threshold, the impact of green finance development on carbon emissions presents an inverted N shape. At the same time, it is found that green finance has become an important means to reduce carbon emissions in the eastern region, the impact of green finance on carbon emissions in the central region presents an inverted U shape, and the driving force of green finance on carbon emission reduction in the western region is weak. Furthermore, it is pointed out that improving the quality of green finance and enhancing the level of green finance empowered by science and technology are the keys to realizing sustainable green development

TextScanner: Reading Characters in Order for Robust Scene Text Recognition

Driven by deep learning and the large volume of data, scene text recognition has evolved rapidly in recent years. Formerly, RNN-attention based methods have dominated this field, but suffer from the problem of \textit{attention drift} in certain situations. Lately, semantic segmentation based algorithms have proven effective at recognizing text of different forms (horizontal, oriented and curved). However, these methods may produce spurious characters or miss genuine characters, as they rely heavily on a thresholding procedure operated on segmentation maps. To tackle these challenges, we propose in this paper an alternative approach, called TextScanner, for scene text recognition. TextScanner bears three characteristics: (1) Basically, it belongs to the semantic segmentation family, as it generates pixel-wise, multi-channel segmentation maps for character class, position and order; (2) Meanwhile, akin to RNN-attention based methods, it also adopts RNN for context modeling; (3) Moreover, it performs paralleled prediction for character position and class, and ensures that characters are transcripted in correct order. The experiments on standard benchmark datasets demonstrate that TextScanner outperforms the state-of-the-art methods. Moreover, TextScanner shows its superiority in recognizing more difficult text such Chinese transcripts and aligning with target characters.Comment: Accepted by AAAI-202

Mechanical Design and Kinematic Modeling of a Cable-Driven Arm Exoskeleton Incorporating Inaccurate Human Limb Anthropomorphic Parameters

Compared with conventional exoskeletons with rigid links, cable-driven upper-limb exoskeletons are light weight and have simple structures. However, cable-driven exoskeletons rely heavily on the human skeletal system for support. Kinematic modeling and control thus becomes very challenging due to inaccurate anthropomorphic parameters and flexible attachments. In this paper, the mechanical design of a cable-driven arm rehabilitation exoskeleton is proposed to accommodate human limbs of different sizes and shapes. A novel arm cuff able to adapt to the contours of human upper limbs is designed. This has given rise to an exoskeleton which reduces the uncertainties caused by instabilities between the exoskeleton and the human arm. A kinematic model of the exoskeleton is further developed by considering the inaccuracies of human-arm skeleton kinematics and attachment errors of the exoskeleton. A parameter identification method is used to improve the accuracy of the kinematic model. The developed kinematic model is finally tested with a primary experiment with an exoskeleton prototype

Real-time Scene Text Detection with Differentiable Binarization

Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text. However, the post-processing of binarization is essential for segmentation-based detection, which converts probability maps produced by a segmentation method into bounding boxes/regions of text. In this paper, we propose a module named Differentiable Binarization (DB), which can perform the binarization process in a segmentation network. Optimized along with a DB module, a segmentation network can adaptively set the thresholds for binarization, which not only simplifies the post-processing but also enhances the performance of text detection. Based on a simple segmentation network, we validate the performance improvements of DB on five benchmark datasets, which consistently achieves state-of-the-art results, in terms of both detection accuracy and speed. In particular, with a light-weight backbone, the performance improvements by DB are significant so that we can look for an ideal tradeoff between detection accuracy and efficiency. Specifically, with a backbone of ResNet-18, our detector achieves an F-measure of 82.8, running at 62 FPS, on the MSRA-TD500 dataset. Code is available at: https://github.com/MhLiao/DBComment: Accepted to AAAI 202

Transparent hydrogel with enhanced water retention capacity by introducing highly hydratable salt

Polyacrylamide hydrogels containing salt as electrolyte have been used as highly stretchable transparent electrodes in flexible electronics, but those hydrogels are easy to dry out due to water evaporation. Targeted, we try to enhance water retention capacity of polyacrylamide hydrogel by introducing highly hydratable salts into the hydrogel. These hydrogels show enhanced water retention capacity in different level. Specially, polyacrylamide hydrogel containing high content of lithium chloride can retain over 70% of its initial water even in environment with relative humidity of only 10% RH. The excellent water retention capacities of these hydrogels will make more applications of hydrogels become possible.Engineering and Applied Science