Constraint-handling techniques and genetic operators are two crucial components in constrained multi-objective evolutionary algorithms (CMOEAs). Recent research in most of CMOEAs has primarily focused on adaptive designs of these components to address various constrained multi-objective optimization problems (CMOPs). However, the evolutionary process of solving a CMOP can involve various characteristics, such as continuity, discreteness, degeneracy, or some combination thereof, necessitating the tailored selection of constraint-handling techniques and genetic operators across different generations. This study conceptualizes these selections as a temporal sequence of constrained handling selection, where the time means the generation number. We argue that discovering the systematic patterns within the sequence based on the historical data of applying different selections significantly improves the performance of CMOEAs in finding Pareto optimal solutions. Based on this conceptualization, we propose a CMOEA with a deep reinforcement learning model for solving CMOPs. Specifically, the deep reinforcement learning model dynamically refines the selection of constraint-handling techniques and genetic operators for upcoming generations by learning from the performance of previous selections, thereby enhancing the predictive accuracy for subsequent selections. Experiments are conducted to validate the performance of the proposed algorithm against nine CMOEAs on thirty-seven benchmark problems and an unmanned aerial vehicle path planning problem. Experimental results show that the proposed algorithm substantially outperforms the compared algorithms regarding the obtained Pareto optimal solutions. Additionally, the results verify that discovering the systematic patterns within the sequence for CMOEAs has a positive impact on solving CMOPs in terms of objective optimization and constraint satisfaction.

Sound Event Detection (SED) aims to identify and locate specific sound events within audio streams. Despite recent advancements, current methodologies exhibit two fundamental limitations: (1) Insufficient modeling of discriminative temporal-spectral characteristics induces compromised differentiation for acoustically similar events. (2) Failing to address both inter-class and intra-class imbalance problems induces biased classifications toward dominant classes and inactive frames. To tackle these challenges, we propose Acoustic Clustering and Imbalance Learning-based Sound Event Detection (ACIL-SED), which consists of Cluster-Specialized Convolutional Recurrent Neural Network (CS-CRNN) and Dual-Objective Adaptive Balance Loss (DOABLoss). The CS-CRNN employs acoustic clustering-guided specialized sub-models, where each cluster’s sub-model focuses on discriminative feature learning in specific temporal-spectral characteristics, thereby resolving the compromised feature representation inherent in a shared single-model architecture. The DOABLoss adaptively combines mean squared error (MSE) and weighted binary cross-entropy (WBCE) to simultaneously address the issues of inter-class duration imbalance and intra-class active-inactive frame imbalance. Experimental results show that ACIL-SED outperforms state-of-the-art methods in complex acoustic environments.

As a privacy-preserving solution, federated learning (FL) demonstrates great potential in distributed model training, but limited bandwidth, particularly in near-field communication (NFC)-based systems, emerges as a key bottleneck by restricting the number of participating clients. To address this challenge, over-the-air FL leverages the superposition property of wireless multiple-access channels, enabling faster model training and accommodating more clients, even in bandwidth-constrained scenarios like NFC. However, due to its analog-integrated nature, the FL performance is also affected by other factors, such as channel noise. These motivate us to consider how the selected client set and channel noise affect FL performance. To explore this concern, in this article, we consider an over-the-air FL system with analog gradient aggregation and analyze the impact of the selected client set and channel noise on FL training performance. The theoretical analysis effectively shows the importance of the clients’ number and the power scaling factor to the FL training performance. Based on the theoretical analysis, we transform the global optimization problem into the client selection problem and propose a dynamic client selection scheme to optimize the training performance under the aggregation error constraint. Experimental results demonstrate that our proposed scheme can boost FL by speeding up the convergence of the global model (at least 35%) and saving energy consumption.

Graph contrastive learning (GCL) is emerging as a pivotal technique in graph representation learning. However, recent research indicates that GCL is vulnerable to adversarial attacks, while existing robust GCL methods against adversarial attacks are inefficient and lack scalability due to the significant computational expenses of explicit adversarial attacks on the graph structure. To address the shortcomings of existing approaches, we propose an efficient and robust GCL via multiadversarial views training framework, called ERMAV. Specifically, the ERMAV generates two adversarial views by attacking both node attributes and latent representations on randomly sampled subgraphs. The method conducts explicit adversarial attacks on node attributes by attacking node attributes and implicit adversarial attacks on the graph structure by attacking latent representations, which avoids the costly computation of explicit graph structure attacks. Moreover, two efficient attack methods are developed to construct adversarial perturbations, which can dynamically generate different adversarial views to enhance sample diversity in the training phase. Furthermore, to validate the effectiveness and robustness of the proposed framework, extensive experiments of node classification on seven real-world datasets are conducted. Experimental results show that our ERMAV outperforms state-of-the-art GCL methods on the original graphs and is consistently more robust than existing robust GCL methods on a variety of attacked graphs. This demonstrates the strong robustness and great potential of our ERMAV in real-world applications.

Parkinson's disease (PD) is a neurodegenerative disease with a high incidence rate. Effective early diagnosis of PD is critical to prevent further deterioration of a patient's condition, where gait abnormalities are important factors for doctors to diagnose PD. Deep learning (DL)-based methods for PD detection using gait information recorded by non-invasive sensors have emerged to assist doctors in accurate and efficient disease diagnosis. However, most existing DL-based PD detection models neglect information in the frequency domain and do not adaptively model the correlation of signals among sensors. Moreover, different people have different gait patterns. Therefore, the generalization capabilities of PD detection models on diversities of individuals' gaits are essential. This work proposes a novel robust frequency-domain-based graph adaptive network (RFdGAD) for PD detection from gait information (i.e., vertical ground reaction force signals recorded by foot sensors). Specifically, the RFdGAD first learns the frequency-domain features of signals from each foot sensor by a frequency representation learning block. Then, the RFdGAD utilizes a graph adaptive network block taking frequency-domain features as input to adaptively learn and exploit the interconnection between different sensor signals for accurate PD detection. Moreover, the RFdGAD is trained by minimizing the proposed Jensen-Shannon divergence-based localized generalization error to improve the generalization performance of RFdGAD on unseen subjects. Experimental results show that the RFdGAD outperforms existing DL-based models for PD detection on three widely used datasets in terms of three metrics, including accuracy, F1-score, and geometric mean.

The application of convolutional neural networks (CNNs) is sometimes limited by a large number of parameters and floating-point operations. Pruning methods have been proved to be effective to solve this problem. These methods improve the efficiency and storage occupancy of CNNs by removing weights connected with certain neurons/channels. The key issue is the selection of suitable neurons/channels to be pruned. Then, fine-tuning is usually applied to restore the performance of a pruned model to that before the pruning. However, existing neurons/channels selection methods do not explicitly consider the impact of the pruning on the model output. Moreover, the performance of a fine-tuned model may suffer from the information loss problem caused by the pruned neurons/channels. In this work, a stochastic sensitivity measure-based neurons/channels selection criterion is proposed to choose and prune insensitive neurons/channels, which effectively reduces the degradation of model performance. Moreover, a compensation operation followed by fine-tuning is proposed to relieve the information loss problem and restore model performance. Experimental results show that our method yields comparable compression and acceleration rates with less accuracy degradation compared with existing pruning methods for CNNs. For instance, the proposed method achieves more 6.8% FLOPs reduction and 0.25% accuracy improvement on VGG-16 compared with a recently proposed pruning method.