Yuheng He

Hang Yin, Liyao Xiang, Dong Ding et al.

We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type of entity remains unlabeled. To address this challenge, we propose the framework \textit{Lambda} for dangling detection and then entity alignment. Lambda features a GNN-based encoder called KEESA with spectral contrastive learning for EA and a positive-unlabeled learning algorithm for dangling detection called iPULE. iPULE offers theoretical guarantees of unbiasedness, uniform deviation bounds, and convergence. Experimental results demonstrate that each component contributes to overall performances that are superior to baselines, even when baselines additionally exploit 30\% of dangling entities labeled for training.

He Cai, Shuping Guo, Yuheng He et al.

Most of the existing formation algorithms for multiagent systems are fully label-specified, i.e., the desired position for each agent in the formation is uniquely determined by its label, which would inevitably make the formation algorithms vulnerable to agent failures. To address this issue, in this paper, we propose a dynamic leader-follower approach to solving the line marching problem for a swarm of planar kinematic robots. In contrast to the existing results, the desired positions for the robots in the line are not fully label-specified, but determined in a dynamic way according to the current state of the robot swarm. By constantly forming a chain of leader-follower pairs, exact formation can be achieved by pairwise leader-following tracking. Since the order of the chain of leader-follower pairs is constantly updated, the proposed algorithm shows strong robustness against robot failures. Comprehensive numerical results are provided to evaluate the performance of the proposed algorithm.