The accurate diagnosis of neurodegenerative diseases (NDDs), such as Amyotrophic Lateral Sclerosis (ALS), Huntington's Disease (HD), and Parkinson's Disease (PD), remains a clinical challenge due to the complexity and subtlety of gait abnormalities. This paper proposes the Dual-Branch Attention-Enhanced Residual Network (DAERN), a novel deep learning architecture that integrates Dilated Causal Convolutions (DCCBlock) for local gait pattern extraction and Multi-Head Self-Attention (MHSA) for long-range dependency modeling. A Cross-Attention Fusion module enhances feature integration, while SHapley Additive exPlanations (SHAP) and Integrated Gradients (IG) improve interpretability, providing clinically relevant insights into gait-based NDD classification. Uniform Manifold Approximation and Projection (UMAP) visualizations reveal well-separated clusters corresponding to distinct NDDs categories, demonstrating the model's ability to capture discriminative features. Comprehensive ablation studies validate the contributions of model components and preprocessing strategies, highlighting the significance of each in achieving state-of-the-art classification performance. Experimental evaluations on the Gait in Neurodegenerative Disease (GaitNDD) dataset demonstrate that DAERN achieves an accuracy of 99.64%, an F1-score of 99.65%, and an AUC of 0.9997, significantly outperforming conventional deep learning and machine learning baselines. These findings suggest that DAERN could be a valuable and interpretable tool for clinical gait assessment, aiding in early-stage monitoring and automated screening of NDDs, with potential applications in real-time wearable sensor-based gait analysis.

Abstract Precision agriculture benefits from point set registration, which can monitor plant health and growth in real time, promote the precise application of fertilizers and pesticides, and provide technical support for achieving sustainable development of agriculture. In this work, we propose a robust point set registration method for precision agriculture based on L*a*b* color guidance, bidirectional search and Cauchy distribution. First, the L*a*b* color guidance is applied to establish accurate correspondences between agricultural RGB‐D data. Second, the bidirectional nearest neighbor search strategy between point sets improves the reliability of establishing correspondences and broadens the convergence domain of the algorithm. Third, Cauchy distribution is utilized as an energy function for noise suppression, which further improves the robustness of the algorithm in dealing with complex vegetation scenes. Finally, results of ablation and simulation experiments indicate that the proposed registration algorithm can achieve more accurate and robust alignment results than other classic and state‐of‐the‐art point cloud registration algorithms to achieve monitoring and comparison of plant growth.

Summary SSL (semi‐supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo‐labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo‐labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug‐and‐play method named Accelerating semi‐supervised learning via contrastive learning (ASCL). This method combines contrastive learning with uncertainty‐based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo‐labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi‐supervised learning. Uncertainty‐based selection provides a robust mechanism for acquiring pseudo‐labels. The combination of the contrastive learning module and the uncertainty‐based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state‐of‐the‐art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR‐10 dataset, the application of ASCL to Pseudo‐label, UDA (unsupervised data augmentation for consistency training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%.

Distracted driving poses a serious threat to traffic safety and remains a widespread problem, highlighting the crucial need for effective recognition of distracted drivers. However, developing models that can generalize across diverse and changing real-world driving conditions is profoundly challenging. Variations in factors like lighting, weather, vehicle type, and drivers complicate generalization. Addressing this critical limitation is key to building recognition models with robust performance for practical deployment. This study presents a novel two-stage unsupervised domain adaptation framework to tackle the important challenge of recognizing distracted drivers across differing environments. The framework first constructs softly assigned class prototypes capturing underlying data structure by aggregating features locally and reweighting sample-prototype relationships globally, which increases the accuracy of class representations. The framework then aligns the probabilities between test samples and prototypes across source and target domains using soft distributional alignment, reducing domain gaps without explicit labeling of the target data. A growth control function balances prototype alignment with classification and adversarial losses. Experiments on distracted driver and object recognition datasets demonstrate this two-stage approach outperforms previous methods, especially under changing driving environments, which is an important problem distracted driving detection research must overcome to effectively enhance road safety.

In this paper, we present a new method to eliminate the effect of view angle for efficient gait recognition via deterministic learning theory. The width of the binarized silhouette models the periodic deformation of human gait silhouettes. It captures the spatio-temporal characteristics of each individual, represents the dynamics of gait motion, and can sensitively reflect the variance between gait patterns across various views. The gait recognition approach consists of two phases: a training phase and a recognition phase. In the training phase, the gait dynamics underlying different individuals' gaits from different view angles are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In order to address the problem of view change no matter the variation is small or significantly large, the training patters from different views constitute a uniform training dataset containing all kinds of gait dynamics of each individual observed across various views. In the recognition phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of human gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern whose view pattern contained in the prior training dataset, a set of recognition errors are generated. The average L1norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Finally, comprehensive experiments are carried out on the CASIA-B and CMU gait databases to demonstrate the effectiveness of the proposed approach.