Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model. However, the application of explanation supervision to higher-dimensional data, such as 3D medical images, remains an under-explored domain. Challenges associated with supervising visual explanations in the presence of an additional dimension include: 1) spatial correlation changed, 2) lack of direct 3D annotations, and 3) uncertainty varies across different parts of the explanation. To address these challenges, we propose a Dynamic Uncertainty-aware Explanation supervision (DUE) framework for 3D explanation supervision that ensures uncertainty-aware explanation guidance when dealing with sparsely annotated 3D data with diffusion-based 3D interpolation. Our proposed framework is validated through comprehensive experiments on diverse real-world medical imaging datasets. The results demonstrate the effectiveness of our framework in enhancing the predictability and explainability of deep learning models in the context of medical imaging diagnosis applications.

The development of Internet-of-Things (IoT) technologies in recent years brings us unprecedented opportunities for innovations in the power industry. This demo abstract introduces our research and practice with Pavatar - IoT for the power industry. Pavatar includes a series of system deployments in the core sections of Global Energy Internet (GEI), for the purposes of automatic surveillance and remote diagnosis of ultra-high-voltage converter stations (UHVCSs). Pavatar incorporates technologies like lower-power or battery-free sensing, cross-technology communication, edge computing, machine learning, and enhances the user experience with 3D virtual reality. The deployed system significantly reduces the manpower cost and enhances the operational efficiency of the UHVCS.