Harish-Chandra modules over the \Q Heisenberg-Virasoro Algebra

In this paper, it is proved that all irreducible Harish-Chandra modules over the \Q Heisenberg-Virasoro algebra are of intermediate series (all weight spaces are 1-dimensional)

Education Challenges and Coping Mechanisms for Artificial Intelligence in Primary and Secondary Schools

The application of artificial intelligence (AI) education at the primary and secondary levels has far-reaching implications for the universalization of AI education, the cultivation of AI talent for national growth, and the improvement of student intellectual competency. This article focuses on the history of AI education in China and the challenges it faces in terms of course standards, teaching materials, personnel availability, and instructional strategies. Suggestions for coping tactics include adjusting course standards; creating a scientific textbook structure; expanding the availability of high-quality AI instructors; and promoting fundamental AI education through multi-agency collaboration. It is hoped that this study will help improve how AI is taught in elementary and secondary schools

“Double-Teacher Classroom”: A Successful Practice of Digital Education in China

In the age of information, digital education has become a crucial avenue for educational transformation. The “double-teacher classroom” is an exemplary model of digital education, with the potential to optimize educational resource distribution and advance the development of balanced education. This article expounds on the fundamental components of the “double-teacher classroom” model and its advantages as a digital education paradigm, with the purpose of further popularizing its application

Retinal vessel segmentation:An efficient graph cut approach with Retinex and local phase

Our application concerns the automated detection of vessels in retinal images to improve understanding of the disease mechanism, diagnosis and treatment of retinal and a number of systemic diseases. We propose a new framework for segmenting retinal vasculatures with much improved accuracy and efficiency. The proposed framework consists of three technical components: Retinex-based image inhomogeneity correction, local phase-based vessel enhancement and graph cut-based active contour segmentation. These procedures are applied in the following order. Underpinned by the Retinex theory, the inhomogeneity correction step aims to address challenges presented by the image intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The local phase enhancement technique is employed to enhance vessels for its superiority in preserving the vessel edges. The graph cut-based active contour method is used for its efficiency and effectiveness in segmenting the vessels from the enhanced images using the local phase filter. We have demonstrated its performance by applying it to four public retinal image datasets (3 datasets of color fundus photography and 1 of fluorescein angiography). Statistical analysis demonstrates that each component of the framework can provide the level of performance expected. The proposed framework is compared with widely used unsupervised and supervised methods, showing that the overall framework outperforms its competitors. For example, the achieved sensitivity (0:744), specificity (0:978) and accuracy (0:953) for the DRIVE dataset are very close to those of the manual annotations obtained by the second observer