The field of data analysis and computational methods is rapidly evolving, with a focus on developing innovative techniques to tackle complex problems. Recent research has emphasized the importance of capturing multiple solution modes in data association problems, particularly in scenarios with high ambiguity. This has led to the development of approximate Bayesian inference methods, which can effectively estimate the distribution of solutions and avoid premature commitment to a single solution. Another area of advancement is in the analysis of merge trees, with the introduction of novel heuristic algorithms for approximating the interleaving distance between labeled merge trees. These algorithms have the potential to provide practical and efficient alternatives for comparing merge trees, especially in cases involving unlabeled or structurally diverse data. Noteworthy papers in this area include the introduction of a data association framework that leverages approximate Bayesian inference, and the development of a novel heuristic algorithm for approximating the interleaving distance between labeled merge trees. Additionally, advancements in computational methods, such as the fast subdivision of Bézier curves and the development of piecewise functions for fast integral transforms, are also noteworthy. These developments have the potential to improve the efficiency and accuracy of various applications, including point cloud registration and robotic perception.