Recently, contrastive learning has emerged as a promising approach for multi-view clustering (MVC), as it enforces cross-view consistency and leverages complementary information from different views to enhance the analysis of heterogeneous data. However, traditional contrastive MVC methods suffer from an inherent limitation: their one-to-many contrast mechanism induces the False Negative Problem (FNP), where semantically similar intra-class instances are erroneously repelled. This phenomenon compromises intra-class consistency and ultimately degrades clustering performance. To overcome this issue, we propose a novel Pseudo lA bel gU ided univerS um lE arning (PAUSE) framework for robust multi-view clustering. Specifically, PAUSE operates in two synergistic stages: (1) A warm-up stage that employs dual contrastive learning to generate reliable pseudo-labels, establishing robust semantic relationships; (2) A fine-tuning stage that synthesizes universum samples via Mixup between anchor instances and out-of-class centroids, guided by the acquired pseudo-labels. This unique mechanism constructs generalized negative classes that expand inter-class margins while preserving intra-class cohesion. Crucially, the widened decision boundaries prevent misclassification of displaced intra-class instances, effectively circumventing FNP without requiring explicit negative pair correction. We further devise a robust universum contrastive loss that explicitly enforces cross-view consistency through adaptive boundary constraints. Extensive experiments on five multi-view benchmarks demonstrate that our PAUSE consistently outperforms 11 state-of-the-art multi-view learning methods. Our code is accessible at: https://github.com/xixi-555/PAUSE_main_code.

Reliable remaining useful life (RUL) prediction provides a reference for the secure operation of solid-state batteries (SSBs). However, the intricate potential relations of degradation mechanism and limited degradation data in SSBs bring tremendous challenge. Thus, a novel RUL prediction method named cured memory strategic term moments network with attention of degradation information combined progressive-topologia fusion health indicators (IDPHIs-CMSTM) is proposed for SSBs. It is designed to obtain the implicit relations from Euclidean space and non-Euclidean space and increase the predicted precision on limited degradation data while interpretability is guaranteed. Specifically, in IDPHIs, layer-by-layer progressive fusion method with back-connections assisted by learnable dot product attention mechanism is proposed to gain deep fusion degradation health indicators (HIs) in Euclidean space. It mitigates the risk on information loss during the deep fusion HIs construction process for SSBs. Besides, the topological relations of degradation HIs are presented by graph attention network with two-layers (GAT). The IDPHIs are fed into the developed novel CMSTM to realize RUL prediction. The motivation for CMSTM comes from the early phases of the Ebbinghaus forgetting process, in which recent historical information is utilized to mitigate the forgetting rate of recent degradation information while exploring implicit relations of different degradation information in limited samples. Experiment results on real SSBs dataset show that the IDPHIs-CMSTM achieves higher than 95% predicted precision with well interpretability.

Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to gen-eralize to new data distributions. In this paper, we propose EYOC, an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly, requiring no global pose labels. The core idea of EYOC is to train a feature extractor in a progressive fashion, where in each round, the feature extractor, trained with near point cloud pairs, can label slightly farther point cloud pairs, enabling self-supervision on such far point cloud pairs. This process continues until the derived ex-tractor can be used to register distant point clouds. Par-ticularly, to enable high-fidelity correspondence label gen-eration, we devise an effective spatial filtering scheme to select the most representative correspondences to register a point cloud pair, and then utilize the aligned point clouds to discover more correct correspondences. Experiments show that EYOC can achieve comparable performance with state-of-the-art supervised methods at a lower training cost. Moreover, it outwits supervised methods regarding generalization performance on new data distributions.

Point cloud registration (PCR) can significantly extend the visual field and enhance the point density on distant objects, thereby improving driving safety. However, it is very challenging for vehicles to perform online registration between long-range point clouds. In this paper, we propose an online long-range PCR scheme in VANETs, called LoRaPCR, where vehicles achieve long-range registration through multi-hop short-range highly-accurate registrations. Given the NP-hardness of the problem, a heuristic algorithm is developed to determine best registration paths while leveraging the reuse of registration results to reduce computation costs. Moreover, we utilize an optimized dynamic programming algorithm to determine the transmission routes while minimizing the communication overhead. Results of extensive simulations demonstrate that LoRaPCR can achieve high PCR accuracy with low relative translation and rotation errors of 0.55 meters and 1.43°, respectively, at a distance of over 100 meters, and reduce the computation overhead by more than 50% compared to the state-of-the-art method.

Point cloud registration (PCR) can significantly extend the visual field and enhance the point density on distant objects, thereby improving driving safety. However, it is very challenging for vehicles to perform online registration between long-range point clouds. In this paper, we propose an online long-range PCR scheme in VANETs, called LoRaPCR, where vehicles achieve long-range registration through multi-hop short-range highly-accurate registrations. Given the NP-hardness of the problem, a heuristic algorithm is developed to determine best registration paths while leveraging the reuse of registration results to reduce computation costs. Moreover, we utilize an optimized dynamic programming algorithm to determine the transmission routes while minimizing the communication overhead. To the best of our knowledge, LoRaPCR is the first solution to achieve multi-vehicle point cloud long-range registration. Results of extensive experiments demonstrate that LoRaPCR can achieve high PCR accuracy with low relative translation and rotation errors of 0.55 meters and 1.43${}^{\circ }$, respectively, at a distance of over 100 meters, and reduce the computation overhead by more than 50% compared to the state-of-the-art method.