Hypergraph neural networks (HGNNs) have garnered considerable attention in hyperspectral image (HSI) classification for their ability to model higher-order nonlinear relationships. However, most existing HGNN-based HSI classification methods adopt simple hypergraph structures, which fail to fully leverage low-order and high-order information within HSIs. Moreover, these methods are constrained by rigid single-scale superpixel segmentations, which fail to fully capture rich spectral-spatial features of HSIs. To overcome these limitations, this paper proposes a progressive hybrid-order hypergraph framework (PHHF) that establishes a graph-hypergraph collaborative learning paradigm to learn multi-scale hybrid-order features across hierarchical graphs. Specifically, by synergistically modeling multiple structural information, we develop a progressive hypergraph neural network framework to enhance the extraction of hierarchical representations of HSIs. Second, at each level, a novel hybrid-order hyperedge generation strategy is designed to enhance spectral-spatial consistency across scales and overcome the limitations of existing hypergraph methods. Finally, a novel heterogeneous kernel-based convolution (HetConv) is introduced to enhance pixel-level feature extraction for the PHHF. Compared to conventional convolution, HetConv offers richer spatial-spectral details with lower computational cost. Extensive experiments on widely-used HSI datasets demonstrate that our proposed method achieves state-of-the-art HSI classification performance.

We present a novel learning-based method for predicting tooth movements in orthodontic treatment path planning (orthodontic staging). Recognizing the multi-solution nature of orthodontic staging, our approach involves generating the staging sequence progressively with a dedicated Transformer model. This model predicts teeth movements within a predefined number of steps (e.g., 10 or 20), targeting alignment in problematic dentition. The Transformer refines its predictions iteratively, building on previous outcomes until reaching a state that aligns with the target within an acceptable distance. This mirrors real-life scenarios where orthodontists dynamically adjust staging plans based on treatment outcomes. Our Transformer model is tailored to incorporate spatial and temporal attentions, addressing inter-tooth and inter-step interactions, respectively. These attentions are further refined with relative positional encoding. Recognizing the significant influence of tooth shape on the alignment process, we propose integrating a tooth-wise shape encoder to extract morphological features from the 3D teeth point cloud. These features are then fused into the Transformer, facilitating the capture of inter-tooth dynamics during staging, in collaboration with spatial attention. We validate the proposed method on a large-scale dataset that contains 10K real-life orthodontic cases. The results show that our method outperforms the state-of-the-art, and orthodontists favor its predictions.

This paper investigates event-triggered control for saturated switched systems with switching signals subject to dual-terminal denial-of-service (DoS) attacks. The original switching signals are sampled and transmitted to sub-system and sub-controller through networks, respectively. However, the openness of network may cause switching signal transmission to be affected by possible DoS attacks. Moreover, the existing dual-terminal mismatched network delays and attacks could lead to asynchronous phenomenon. Considering the impacts of the attacked switching signals and delays, a time-delay closed-loop switched system is developed. Subsequently, by utilizing piecewise Lyapunov functional technique, various cases caused by DoS attacks and different delays are analyzed. Then, novel conditions are derived to ensure the stability of closed-loop switched system, and sufficient conditions for the desired controller gains and event-triggering parameters are presented. Finally, simulation examples are given to verify the effectiveness of the proposed method. The simulation results show that in the case of single-terminal and dual-terminal attacks, switched systems both have good anti-attack characteristics.

This article investigates stabilization problems of networked event-triggered switched systems under multiasynchronous switching. Different from the existing asynchronous literature, a novel problem is considered in this article, i.e., the event-triggering scheme and controller both have independent switching delays relative to the system, which is called multiasynchronous switching. An adaptive event-triggering scheme with switching structure is utilized to achieve better adjustment in triggering frequency. A systematic framework for analyzing multiasynchronous switching stability and solving controller gains is established. Different Lyapunov functionals are employed, and new tight bound conditions on average dwell time are constructed. Besides, an active packet loss approach for handling packets disorder issues caused by large transmission delays is presented. Since the activation instants of the system, triggering scheme, and controller are mutually staggered with the data updating instants of the actuator, a novel analytical method aiming at the coupling effect is proposed. Finally, the validity of the adopted method in this article is demonstrated.