Regional Adaptive Hierarchical Transform (RAHT) is an effective point cloud attribute compression (PCAC) method. However, its application in deep learning lacks research. In this paper, we propose an end-to-end RAHT framework for lossy PCAC based on the sparse tensor, called DeepRAHT. The RAHT transform is performed within the learning reconstruction process, without requiring manual RAHT for pre-processing. We also introduce the predictive RAHT to reduce bitrates and design a learning-based prediction model to enhance the performance. Moreover, we devise a bitrate proxy that applies run-length coding to entropy model, achieving seamless variable-rate coding and improving the robustness. DeepRAHT is a reversible and distortion-controllable framework, ensuring its lower bound performance and offering significant application potential. The experiments demonstrate that DeepRAHT is a high-performance, faster, and more robust solution than the baseline methods.

Active fault diagnosis provides an efficient means for incipient fault detection by injecting auxiliary signal or modifying controller, which, however, usually infringes on system performance. In this paper, an amicable active fault diagnosis method based on controller modification is developed for incipient fault diagnosis of closed-loop control systems. As any stabilizing controller can be equivalently realized in the observer based form, a switching controller based on a bank of observers is firstly constructed. Then, by making a good tradeoff between the sensitivity of a specific actuator fault and the robustness of other faults and disturbances with the help of an iterative algorithm, the observers in the controller are obtained with high sensitivity to fault, wherein the difference of the outputs are also utilized to enhance incipient fault diagnosis ability. Further, a switching mechanism of the controller is developed, based on which actively switching the observers in the controller can not only detect and isolate the incipient fault timely but also guarantee the system stability. An heating, ventilation and air conditioning (HVAC) system example is finally adopted to demonstrate the effectiveness and superiority of the proposed method.

For mesh animation sequences with consistent topology, traditional compression methods are well established, but deep learning approaches are emerging. This paper presents a novel deep learning framework for vertex position reconstruction by encoding displacements. We propose LoD-Uniform Partitioning (LUP), which uses Levels-of-Detail (LoD) partitioning and uniform sampling to partition vertices into base and inference layers. Base layers use vertex-wise prediction and run-length coding, while inference layers use inter-layer prediction and a deep entropy model with a permutation-invariant transformer. By integrating vertex-based, face-based, and trajectory-based descriptors into our model context, we improve coding efficiency. Experiments on mesh animation datasets show that our method achieves a 25% bitrate reduction for lossless compression, a 15% BD-Rate improvement, and a 95% reduction in encoding time for lossy compression compared to MPEG Video-based Dynamic Mesh Compression (V-DMC), underscoring its strong potential for practical applications.

Spectral clustering is a fast and popular algorithm for finding clusters in networks. Recently, Chaudhuri et al. and Amini et al. proposed variations on the algorithm that artificially inflate the node degrees for improved statistical performance. The current paper extends the previous theoretical results to the more canonical spectral clustering algorithm in a way that removes any assumption on the minimum degree and provides guidance on the choice of tuning parameter. Moreover, our results show how the star shape" in the eigenvectors--which are consistently observed in empirical networks--can be explained by the Degree-Corrected Stochastic Blockmodel and the Extended Planted Partition model, two statistical model that allow for highly heterogeneous degrees. Throughout, the paper characterizes and justifies several of the variations of the spectral clustering algorithm in terms of these models. "