Merge trees are fundamental topological descriptors with broad theoretical and practical significance, yet their computation, particularly for augmented merge trees, incurs substantial computational and memory overhead. We present BiGMT, a highly scalable parallel algorithm for merge tree construction based on regions of the Morse-Smale segmentation. By exploiting boundary vertex characteristics and the dynamic merging of these regions, BiGMT preserves the essential topology of d-dimensional manifolds and represents the structure as a weighted bipartite graph. This formulation decomposes merge tree construction into fine-grained parallel tasks, significantly reducing traversal cost and memory consumption. Furthermore, BiGMT efficiently constructs augmented merge trees using reverse binary lifting search and parallel connection mechanisms, substantially improving augmentation performance. Extensive experiments on medium- to large-scale scalar field datasets demonstrate substantial GPU acceleration. BiGMT consistently outperforms ExTreeM, PPP, and FTM-Tree on RTX4090 and H200, achieving speedups of up to 23.95× and demonstrating superior parallel efficiency and scalability.

Facial Action Units (AUs) are of great significance in communication. Automatic AU detection can improve the understanding of psychological conditions and emotional status. Recently, several deep learning methods have been proposed to detect AUs automatically. However, several challenges, such as poor extraction of fine-grained and robust local AUs information, model overfitting on person-specific features, as well as the limitation of datasets with wrong labels, remain to be addressed. In this paper, we propose a joint strategy called PIAP-DF to solve these problems, which involves 1) a multi-stage Pixel-Interested learning method with pixel-level attention for each AU; 2) an Anti Person-Specific method aiming to eliminate features associated with any individual as much as possible; 3) a semi-supervised learning method with Discrete Feedback, designed to effectively utilize unlabeled data and mitigate the negative impacts of wrong labels. Experimental results on the two popular AU detection datasets BP4D and DISFA prove that PIAP-DF can be the new state-of-the-art method. Compared with the current best method, PIAP-DF improves the average F1 score by 3.2% on BP4D and by 0.5% on DISFA. All modules of PIAP-DF can be easily removed after training to obtain a lightweight model for practical application.

Internet-based taxi service not only facilitates passenger travel but also effectively improves the utilization of transportation resources. Autonomous electric vehicle (AEV), as a future-oriented form of transportation, is more environmentally friendly and intelligent as it does not require a driver and uses green energy to fulfill the trip. Using idle AEVs for taxi service is an effective way to realize smart city transportation in the future. With the aim of supporting AEVs to provide taxi services, AEV Taxi Management and Dispatching Module (ATMDM), is proposed to support AEV management and scheduling in the AEV Taxi Service (ATS) system. By efficiently maintaining AEV status and information, ATMDM is able to realize the management of multiple AEVs as well as can also provide taxi service by matching orders with appropriate AEVs and providing route information based on the received trip requests. Compared with traditional taxi dispatching solutions, ATMDM fully considers the operational characteristics of AEV with better adaptability, which shows insight for the future means of transportation.