Predictive business process monitoring aims to forecast the future behaviors of ongoing process instances by analyzing historical event logs. Previous studies in this field have predominantly focused on one specific task, that is, to predict the next activity, the next resource that executes the next activity, the remaining execution time, etc., which ignore the mutual effects among various tasks. In this article, a framework that simultaneously performs multiple prediction tasks is proposed, which provides decision-makers with more comprehensive information and better adapts to complex business scenarios. Specifically, multi-view frequent patterns are firstly conducted from various perspectives to uncover explicit features of different prediction tasks, which serve as additional input for the prediction. Then, a multi-task learning model is designed to learn the implicit characteristics and relations among different prediction tasks as well as obtain accurate prediction results. Experiments on five real-life datasets prove that the proposed method outperforms state-of-the-art methods, and soft parameter sharing in multi-task learning is more effective than hard parameter sharing for PBPM tasks.

Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contrast, recent deep learning-based approaches can provide fast deformation estimation. These heuristic network architectures are fully data-driven and thus lack explicit geometric constraints which are indispensable to generate plausible deformations, e.g., topology-preserving. Moreover, these learning-based approaches typically pose hyper-parameter learning as a black-box problem and require considerable computational and human effort to perform many training runs. To tackle the aforementioned problems, we propose a new learning-based framework to optimize a diffeomorphic model via multi-scale propagation. Specifically, we introduce a generic optimization model to formulate diffeomorphic registration and develop a series of learnable architectures to obtain propagative updating in the coarse-to-fine feature space. Further, we propose a new bilevel self-tuned training strategy, allowing efficient search of task-specific hyper-parameters. This training strategy increases the flexibility to various types of data while reduces computational and human burdens. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data. Extensive results demonstrate the state-of-the-art performance of the proposed method with diffeomorphic guarantee and extreme efficiency. We also apply our framework to challenging multi-modal image registration, and investigate how our registration to support the down-streaming tasks for medical image analysis including multi-modal fusion and image segmentation.