Li Keke

Chen Ran, Yao Xueqi, Jiang Xuhui et al.

The field of human settlement construction encompasses a range of spatial designs and management tasks, including urban planning and landscape architecture design. These tasks involve a plethora of instructions and descriptions presented in natural language, which are essential for understanding design requirements and producing effective design solutions. Recent research has sought to integrate natural language processing (NLP) and generative artificial intelligence (AI) into human settlement construction tasks. Due to the efficient processing and analysis capabilities of AI with data, significant successes have been achieved in design within this domain. However, this task still faces several fundamental challenges. The semantic information involved includes complex spatial details, diverse data source formats, high sensitivity to regional culture, and demanding requirements for innovation and rigor in work scenarios. These factors lead to limitations when applying general generative AI in this field, further exacerbated by a lack of high-quality data for model training. To address these challenges, this paper first proposes HSC-GPT, a large-scale language model framework specifically designed for tasks in human settlement construction, considering the unique characteristics of this domain.

Li Keke, Yang Xinmin

This paper mainly conducts further research to alleviate the issue of limit cycling behavior in training generative adversarial networks (GANs) through the proposed predictive centripetal acceleration algorithm (PCAA). Specifically, we first derive the upper and lower bounds on the last-iterate convergence rates of PCAA for the general bilinear game, with the upper bound notably improving upon previous results. Then, we combine PCAA with the adaptive moment estimation algorithm (Adam) to propose PCAA-Adam, a practical approach for training GANs. Finally, we validate the effectiveness of the proposed algorithm through experiments conducted on bilinear games, multivariate Gaussian distributions, and the CelebA dataset, respectively.