The most anticipated particle physics result of recent years is here—but the real news came one week before: the “muon g–2 anomaly” might have never existed ...

Anomaly detection based on subspace learning has attracted much attention, in which the compactness of subspace is commonly considered as the core concern. Most related studies directly optimize the ...

CNN-based adversarial machine learning models are proposed to drive the innovation of anomaly detection techniques under Industry 5.0. However, the generalization inherent in the model is inadequate ...

An unsupervised autoencoder approach achieves moderate success for anomaly detection (accuracy = 0.881) but struggles with recall (0.070). These findings highlight the trade-off between detection ...

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