Group 1 - The research team from Donghua University, Shanghai Jiao Tong University, and the Chinese Academy of Sciences has proposed two geometry-based homography decomposition methods that significantly reduce the computational load of solving homographies from four points by over 95% compared to conventional sparse linear equation methods [3][4]. - The paper titled "Fast and Interpretable 2D Homography Decomposition: Similarity-Kernel-Similarity and Affine-Core-Affine Transformations" has been accepted by the IEEE T-PAMI journal [5][4]. - The proposed methods are expected to be applicable in various visual applications, including QR code scanning, projective geometry, computer vision, and graphics problems [3]. Group 2 - The traditional Direct Linear Transformation (DLT) method constructs a sparse linear equation system for homography solving, which typically requires around 2000 floating-point operations [7]. - Improved methods have been developed, reducing the computational load to approximately 1800 operations for SVD decomposition and 220 operations for a customized Gaussian elimination method [7]. - The new methods, SKS and ACA, achieve a significant reduction in floating-point operations, with ACA requiring only 29 operations for specific cases like square templates [18][22]. Group 3 - The SKS transformation decomposes the homography matrix into multiple sub-transformations, leveraging the hierarchical nature of geometric transformations [9][10]. - The ACA transformation similarly computes affine transformations from three corresponding points, resulting in an efficient homography matrix decomposition [15]. - The average time for a single four-point homography calculation using the ACA method is reported to be only 17 nanoseconds, achieving acceleration factors of 29 times and 43 times compared to previous methods [22]. Group 4 - The methods can be integrated into various visual processing applications, replacing traditional homography algorithms, particularly in QR code scanning, which is estimated to reach billions of scans daily in China [24]. - The research team is also exploring further applications in deep learning for estimating geometric parameters, P3P pose estimation based on planar homography, and N-dimensional homography matrix decomposition [25].

Group 1 - The Shanghai Social Science Federation held its eighth representative assembly, where the seventh committee's achievements were reviewed under the leadership of the municipal party committee and the publicity department [1][3] - A total of 237 members were elected to the eighth committee, with Xu Jiong elected as the chairman [3] - The assembly emphasized the importance of aligning academic pursuits with national and ethnic development, focusing on theoretical and policy research [3][4] Group 2 - The assembly was marked by a strong sense of democracy and anticipation for future developments in the social sciences [4] - The Shanghai Social Science Federation has played a significant role in guiding research and promoting the innovative theories of the Communist Party, particularly focusing on Xi Jinping's thoughts [6] - Various activities, including annual meetings and forums, have been organized to consolidate the strength of the social science community and enhance research impact [6] Group 3 - The Shanghai social science community is committed to contributing to national strategies and urban development, with a focus on practical applications and international studies [10] - The federation has established a platform for academic exchange and recognition of outstanding contributions in the field, fostering a culture of rigorous research and innovation [10][11] - Future goals include optimizing award mechanisms and supporting young scholars to enhance their contributions to the field [11][14]