This article studies event-triggered control for the prescribed-time bipartite consensus of first-order multiagent systems. For each agent, the new event-triggered control law and triggering condition are constructed without continuous interneighboring communication. Based on the Lyapunov stability theory and the algebraic graph theory, permissible value ranges of the designed parameters are established to guarantee that all agents reach bipartite consensus in a completely prespecified time. Moreover, a comprehensive theoretical discussion is provided to show that the Zeno behavior can be excluded during the whole time span except the prespecified settling time T. The simulation results demonstrate the feasibility of the provided methods.

Jundong Xu, Hao Fei, Liangming Pan, Qian Liu, Mong-Li Lee, Wynne Hsu. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Hao Fei, Bobo Li, Qian Liu, Lidong Bing, Fei Li, Tat-Seng Chua. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2023.

Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

In the text-to-image generation field, recent remarkable progress in Stable Diffusion makes it possible to generate rich kinds of novel photorealistic images. However, current models still face misalignment issues (e.g., problematic spatial relation understanding and numeration failure) in complex natural scenes, which impedes the high-faithfulness text-to-image generation. Although recent efforts have been made to improve controllability by giving fine-grained guidance (e.g., sketch and scribbles), this issue has not been fundamentally tackled since users have to provide such guidance information manually. In this work, we strive to synthesize high-fidelity images that are semantically aligned with a given textual prompt without any guidance. Toward this end, we propose a coarse-to-fine paradigm to achieve layout planning and image generation. Concretely, we first generate the coarse-grained layout conditioned on a given textual prompt via in-context learning based on Large Language Models. Afterward, we propose a fine-grained object-interaction diffusion method to synthesize high-faithfulness images conditioned on the prompt and the automatically generated layout. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art models in terms of layout and image generation. Our code and settings are available at https://layoutllm-t2i.github.io/.

Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i.e., aspect terms, opinion expressions, and correlated sentiment polarities. As a newly proposed task, ASTE depicts the complete sentiment picture from different perspectives to better facilitate real-world applications. Unfortunately, several major challenges, such as the overlapping issue and long-distance dependency, have not been addressed effectively by the existing ASTE methods, which limits the performance of the task. In this article, we present an innovative encoder-decoder framework for end-to-end ASTE. Specifically, the ASTE task is first modeled as an unordered triplet set prediction problem, which is satisfied with a nonautoregressive decoding paradigm with a pointer network. Second, a novel high-order aggregation mechanism is proposed for fully integrating the underlying interactions between the overlapping structure of aspect and opinion terms. Third, a bipartite matching loss is introduced for facilitating the training of our nonautoregressive system. Experimental results on benchmark datasets show that our proposed framework significantly outperforms the state-of-the-art methods. Further analysis demonstrates the advantages of the proposed framework in handling the overlapping issue, relieving long-distance dependency and decoding efficiency.