Intelligent English Teaching Based on the Pedagogy of Performing Another Culture and ChatGPT Technology

In the background of digital education, the intelligent capabilities of ChatGPT-based technology in teaching have emerged as a powerful tool for facilitating teachers’ preparation for lesson, improving students’ learning motivation and effectiveness, and promoting the development of intelligent curriculum. This study put forward a novel model of ChatGPT-based English teaching grounded in the “Pedagogy of Performing Another Culture” proposed by Galal Walker. From the three aspects of teaching design, application scenarios, and learning effect evaluation, this model explores the application of ChatGPT in the four fundamental language domains of listening, speaking, reading, and writing, with the objective of enhancing students’ language proficiency and cultural sensitivity, while also fostering their digital learning capabilities. To uphold academic integrity and ethical use of ChatGPT in English teaching, a C++-based ChatGPT application has been designed and tailored to the specific needs of English language learners. The school-based intelligent courses based on ChatGPT are available for students to carry out more diverse and effective English learning, promoting the rational use of ChatGPT in learning

Embroidering Guanyin: Constructions of the Divine through Hair

Hair embroidery was a particular technique practiced by lay Buddhist women to create devotional images. The embroiderers used their own hair as threads and applied them on silk to stitch figures. This paper will analyze the religious connotation of hair embroidery, the ritual process and the techniques for making hair embroidery in the Ming (1368-1644) and Qing (1644-1911) dynasties. By tracing its appearance in both literary texts and actual surviving objects, this essay will ask how and in what circumstances human hair was applied to embroidery? What was the significance of transferring one’s own hair onto an icon? How did hair embroidery combine women’s bodies (their hair) with a womanly skill (embroidery) to make a unique gendered practice in late imperial China

Diffusion Models for Probabilistic Deconvolution of Galaxy Images

Telescopes capture images with a particular point spread function (PSF). Inferring what an image would have looked like with a much sharper PSF, a problem known as PSF deconvolution, is ill-posed because PSF convolution is not an invertible transformation. Deep generative models are appealing for PSF deconvolution because they can infer a posterior distribution over candidate images that, if convolved with the PSF, could have generated the observation. However, classical deep generative models such as VAEs and GANs often provide inadequate sample diversity. As an alternative, we propose a classifier-free conditional diffusion model for PSF deconvolution of galaxy images. We demonstrate that this diffusion model captures a greater diversity of possible deconvolutions compared to a conditional VAE.Comment: Accepted to the ICML 2023 Workshop on Machine Learning for Astrophysic