Research on the Path to Achieving Equalization of Basic Public Services in Zhejiang Province

The current situation that the total supply of basic public services in rural areas of Zhejiang Province is insufficient and unbalanced with the demand determines the need to conduct an in-depth study on the equalization of basic services in rural areas. The article uses the entropy power method and the Thiel index method to analyze the level of basic public service equalization and the least squares method to conduct regression analysis, and finally proposes practical suggestions for the equalization of basic public services. The study finds that the overall level of basic public service equalization in Zhejiang Province during 2018-2021 is showing an upward trend, the level of basic public facility equalization is on the rise, basic public education maintains a relatively stable trend, while the level of basic medical and health equalization is affected by factors such as epidemics and the development is relatively stable, and the level of basic social security equalization is all increasing. Among the 11 cities in Zhejiang, Hangzhou has the highest level of equalization of basic services. Through the regression we can learn that both the economic level and the financial level have positive effects on the equalization of basic public services. Keywords: equalization of basic public services, entropy method, thiel's index method, least squares regression model DOI: 10.7176/EJBM/15-19-08 Publication date: December 31st 202

Digging Into Normal Incorporated Stereo Matching

Despite the remarkable progress facilitated by learning-based stereo-matching algorithms, disparity estimation in low-texture, occluded, and bordered regions still remains a bottleneck that limits the performance. To tackle these challenges, geometric guidance like plane information is necessary as it provides intuitive guidance about disparity consistency and affinity similarity. In this paper, we propose a normal incorporated joint learning framework consisting of two specific modules named non-local disparity propagation(NDP) and affinity-aware residual learning(ARL). The estimated normal map is first utilized for calculating a non-local affinity matrix and a non-local offset to perform spatial propagation at the disparity level. To enhance geometric consistency, especially in low-texture regions, the estimated normal map is then leveraged to calculate a local affinity matrix, providing the residual learning with information about where the correction should refer and thus improving the residual learning efficiency. Extensive experiments on several public datasets including Scene Flow, KITTI 2015, and Middlebury 2014 validate the effectiveness of our proposed method. By the time we finished this work, our approach ranked 1st for stereo matching across foreground pixels on the KITTI 2015 dataset and 3rd on the Scene Flow dataset among all the published works