To support the rapidly increasing demand for low-latency content delivery from massive end-users, edge computing networks have emerged as a pivotal architecture by deploying computing and storage resources in close proximity to users. However, devising an effective caching strategy in such dynamic environments remains a critical challenge, primarily due to the fluctuating nature of content popularity and the constrained storage capacity of edge servers. In this paper, we propose PEPO, a Prediction-Enhanced Proactive Optimization framework for edge caching. PEPO models the content prediction problem as a time-series forecasting task and employs a BiLSTM-based algorithm to accurately predict future content popularity by learning from historical request patterns. For the caching strategy, PEPO formulates it as a multi-objective optimization problem, aiming to minimize a comprehensive system cost including data retrieval latency and quality of service penalty. Then, we design an evolutionary optimization mechanism that integrates a dynamic weighting scheme based on indicator-based evolutionary algorithm with the whale optimization algorithm to efficiently navigate the solution space and derive near-optimal caching decisions. Finally, comprehensive simulations under diverse system conditions demonstrate that PEPO consistently achieves the lowest system cost, showing particular strength in resource-constrained and high-load scenarios.

Self-supervised learning has been gaining attention in the field of sleep stage classification. It learns representations with unlabeled electroencephalography (EEG) signals, which alleviates the cost of labeling for specialists. However, most self-supervised approaches assume only the two augmented views from the same EEG sample is a positive pair, which suffers from the false negative problem. Therefore, we propose a new model named Temporal Augmentation and False Negative Suppression (TA-FNS) to solve the problem. Specifically, it first generates two augmented views for each EEG sample. Then the temporal augmentation module is proposed to learn temporal features during sleep from augmented views. Based on temporal features, intra-view and inter-view sample similarity matrices are calculated. Finally, the false negative suppression module identifies and eliminates potential false negatives according to the consistency between intra-view and inter-view similarity matrices. TA-FNS not only achieves state-of-the-art performances on Sleep-EDF and ISRUC datasets, but also learns semantic representation from EEG of different sleep stages, which demonstrates the effectiveness of it in mitigating the false negative problem.

Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. While supervised methods have demonstrated good performance in sleep stage classification, obtaining large-scale manually labeled datasets remains a challenge. Recently, self-supervised learning has received increasing attention in sleep stage classification. Self-supervised learning uses unlabeled EEG signals to learn representations, reducing the cost of expert labeling. However, the existing self-supervised learning methods often need to manually adjust the data augmentation strategy according to the characteristics of the data, and only learn the representation from the instance level. Therefore, we propose a self-supervised contrastive learning model FSA-TP for sleep stage classification. Firstly, we design a new feature augmentation module for disturbing the temporal features of EEG signals in the feature space to avoid the tedious operation of manually designing data augmentation strategies. Secondly, we propose a temporal prediction module to learn the temporal representation of EEG signals through a cross-view subsequence prediction task. Finally, we improve the quality of negative samples through the negative mixing module. We evaluate the performance of our proposed method on two publicly available sleep datasets. Experimental results show that FSA-TP not only learns meaningful representations but also produces superior performance.