Brain-computer Interface (BCI) technology enables direct communication between the brain and external devices via non-invasive methods and holds significant potential in neu roengineering, rehabilitation, and human-computer interaction. However, decoding motor imagery (MI) from electroencephalo gram (EEG) signals remains challenging due to these signals' non-stationary characteristics and the limited interpretability of existing deep learning models. In this paper, we propose a novel Hierarchical Collaborative Fuzzy Network (HCFN) for interpretable EEG-based MI decoding. We introduce an Adaptive Fuzzy Temporal Convolutional Network (AFTCN) that employs dynamic fuzzy kernels within causal convolutions to extract robust temporal features from EEG signals. Additionally, we design a fuzzy attention-guided Takagi–Sugeno–Kang (TSK) architecture that achieves a tighter integration between feature extraction and fuzzy inference through a novel fuzzy feedback loop, thereby improving the discriminability of extracted features. Extensive experiments on the BCI Competition IV-2a, IV-2b and OpenBMI datasets, under both subject-dependent and cross subject evaluation paradigms, demonstrate that the proposed model outperforms state-of-the-art methods in classification ac curacy and Cohen's kappa. Furthermore, we provide multi-level interpretability analyses, from macro to micro perspectives, elucidating the model's decision-making processes and highlighting the advantages of our collaborative reasoning framework over conventional cascaded approaches. The code is available at https://github.com/Pitiless-Quinn/HCFN.

Traditional single-center learning algorithms often face significant limitations in handling heterogeneous data integration, including insufficient generalization ability, weak privacy protection, and difficulties adapting to multi-center scenarios. To address these challenges, multi-center learning has emerged as a critical technological framework. Although our previously proposed MKTC-R0T algorithm partially addressed the integration and modeling of multi-center data through the knowledge transfer calibration strategy, it still exhibits notable shortcomings in terms of knowledge fusion stability, model interpretability and generalization, as well as the utilization of complementary information across centers. To overcome these limitations, we propose a Knowledge Calibration Fusion and Label Space Graph Regularization-based Multi-center TSK Fuzzy System (KCF-LSG-MTSK). Specifically, we introduce an enhanced knowledge calibration and fusion strategy to effectively integrate heterogeneous information between the base center (BC) and auxiliary center (AC). We also propose a novel label space graph regularization scheme that constructs both intracenter and intercenter graph structures, leveraging data consistency and complementarity to enhance the quality of knowledge sharing. Furthermore, building upon firstorder TSK fuzzy system optimization, our approach incorporates a projected maximum mean discrepancy (PMMD) transfer term to effectively reduce data distribution discrepancies between the BC and AC. Experimental results on thirteen benchmark datasets demonstrate that KCFLSGMTSK achieves an average accuracy of 88.7%, significantly outperforming stateoftheart singlecenter and multicenter methods, thereby validating the superiority of our approach in heterogeneous data integration, knowledge transfer, and interpretable classification.

To address the challenges of high uncertainty, inter-subject variability, and inefficiency multi-feature utilization in motor imagery electroencephalogram (MI-EEG) classification, this study proposes amultiviewtransferTakagi-Sugeno-Kang (TSK) fuzzy classifier withsoftvariable embedded anddiscriminativestructural preservation (MVT-TSK-SVDS). First, a transfer learning mechanism incorporating soft variable embedding in the consequent part is developed. This mechanism establishes cross-domain correlations via a shared consequent and representation matrix. Within this framework, soft variable embedding and low-rank constrained discriminative learning work in concert to effectively capture supervision information and cross-domain relationships. Second, a local-global structural preservation term incorporating graph embedding and low-rank constraint is implemented to maintain local discriminative information from source domain while integrating global geometric patterns across all data. Third, a multiview adaptive learning framework is designed to address feature representation diversity and information loss during knowledge transfer. MVT-TSK-SVDS dynamically optimizes view-specific contributions through entropy maximization criterion while ensuring collaborative decision via consistency constraints. Experimental results validate strong generalization between and across datasets. Our model achieves 62.16% and 72.71% accuracy in cross-subject tasks on BCI-IV 2a and OpenBMI, respectively. In cross-dataset evaluations, it attains 62.75% accuracy on BCI-IV 2a to OpenBMI and 65.08% accuracy on OpenBMI to BCI-IV 2a, respectively.

Hyperspectral imaging can capture the spectrum of each pixel in an image across various wavelengths, providing unparalleled opportunities for precise detection, classification, and analysis of transportation infrastructure. However, traditional methods often struggle with the curse of dimensionality, inter-class variability, and the spectral-spatial trade-off inherent in hyperspectral data. To address these challenges, we introduce a novel Multi-Source Patch Feature fusion based Neighborhood Flash Attention Transformer (MSPF-NFAT) for pixel-level vehicle and road recognition in hyperspectral images (HSIs). Our methodology hinges on the insight that the integration of complementary features from multiple sources and scales can significantly enhance classification performance. Specifically, the MSPF is designed to aggregate and harmonize features extracted from both spectral and spatial dimensions, as well as from different contextual scales within the image. This fusion process ensures a richer representation of the data, capturing both the fine-grained details and the broader contextual information essential for accurate classification. Building upon this enriched feature set, we employ the NFAT, a state-of-the-art attention mechanism that focuses on capturing local spatial relationships while efficiently scaling to accommodate the high-resolution characteristics of hyperspectral data. In addition, extensive experimental results on four widely used HSIs datasets show that our newly proposed method provides superior performance compared to other state-of-the-art methods.

Knowledge graph completion (KGC) aims to infer missing or incomplete parts in knowledge graph.The existing models are generally divided into structure-based and descriptionbased models, among description-based models often require longer training and inference times as well as increased memory usage.In this paper, we propose Pre-Encoded Masked Language Model (PEMLM) 1 to efficiently solve KGC problem.By encoding textual descriptions into semantic representations before training, the necessary resources are significantly reduced.Furthermore, we introduce a straightforward but effective fusion framework to integrate structural embedding with pre-encoded semantic description, which enhances the model's prediction performance on 1-N relations.The experimental results demonstrate that our proposed strategy attains state-of-the-art performance on the WN18RR (MRR+5.4% and Hits@1+6.4%)and UMLS datasets.Compared to existing models, we have increased inference speed by 30x and reduced training memory by approximately 60%.

The diagnosis and treatment of epilepsy necessitate the precise identification and classification of electroencephalogram (EEG) signals. However, EEG samples from different medical institutions often exhibit variability due to factors such as institutional characteristics, geographic locations, and the professional levels of physicians. This variability limits the widespread application of existing methods in small or single medical institutions, as they typically rely on large-scale and high-quality datasets. To rapidly assist small or single medical institutions in constructing models for the diagnosis and classification of epileptic EEG signals that are both highly generalizable and interpretable while ensuring patient privacy, this article proposes an innovative learning framework named Multicenter Knowledge Transfer Calibration with rapid zeroth-order TSK fuzzy system (MKTC-R0T). This method employs the zeroth-order TSK fuzzy system as the baseline model for each center and integrates a knowledge transfer calibration strategy within a multicenter learning framework, aiming to enhance the model's generalizability and classification accuracy in the face of inconsistent sample quality and sample heterogeneity. Specifically, MKTC-R0T first establishes a base center model in a large medical institution, and then, by imitating the forgetting mechanism of the human brain, a portion of the knowledge at the base center is randomly forgotten, while the remaining knowledge is utilized to assist the auxiliary centers in rapidly deploying models. Ultimately, through a knowledge integration strategy, all centers collectively guide the target center in building an efficient linear system for the diagnosis and classification of epileptic EEG signals. Extensive experiments conducted on 12 epilepsy EEG signal datasets have validated that MKTC-R0T outperforms other typical algorithms in terms of running time, deployment speed, rule complexity, and the model's generalization and robustness, which demonstrates the substantial potential of MKTC-R0T in the field of epilepsy EEG signal diagnosis and classification.

We attempt to intelligently identify violations of throwing waste from vehicles (TWV) in real-time traffic surveillance videos. In addition to polluting the environment, TWV easily causes injury to sanitation workers responsible for cleaning roads by passing vehicles. However, manual inspection is still the commonest way to recognize such uncivilized behavior in videos with very high time and labor-consuming. In answer to these challenges, we design a novel 20-layer residual network (Nov-ResNet-20) for training the vehicle-thrown-waste identification model (VTWIM). Then, incorporating Nov-ResNet-20, Selective Search, and Non-Maximum Suppression (NMS), we propose the deep-residual-network-leveraged vehicle-thrown-waste identification method (DRN-VTWI). Our method first splits one video frame into several regions matching suspected objects marked with location boxes via Selective Search. Then, in terms of the VTWIM trained by Nov-ResNet-20 our method identifies the regions containing TWV. Last, our method removes the redundant location boxes for each recognized, vehicle-thrown waste and only keeps the best one. The significance of our work is four-fold: 1) Nov-ResNet-20 has a moderate depth: 6 convolutional layers, 7 residual layers, and in total 20 weight layers. Due to the joint contribution of the residual, batch normalization, dropout, and cross-entropy loss, it is eligible to identify TWV using a small quantity of manually-annotated training samples. 2) Selective Search diversely marks all possible, suspected objects in video frames, whereas NMS keeps the best location box for each recognized vehicle-thrown waste, removing all redundancies. In this way, DRN-VTWI finds potential violations of TWV as many as possible and optimally annotates vehicle-thrown wastes in frames as well. 3) Combining the power of Nov-ResNet-20, Selective Search, and NMS, DRN-VTWI well solves the challenging, intelligent identification of vehicle-thrown wastes for real-time traffic surveillance. Experimental studies conducted on real-time traffic surveillance videos demonstrate the effectiveness as well as superiority of our efforts.