Sparse training has received an upsurging interest in machine learning due to its tantalizing saving potential for the entire training process as well as inference.

In this paper, we introduce a set of spatial-temporal logic rules as knowledge to explain human actions.

Unlike natural language processing and computer vision, the development of Foundation Models (FMs) for time series forecasting is blocked due to data scarcity.

However, SSA T suffers from catastrophic overfit-ting (CO), a phenomenon that leads to a severely distorted classifier, making it vulnerable to multi-step adversarial attacks. In this work, we observe that some adversarial examples generated on the SSA T -trained network exhibit anomalous behaviour, that is, although these training samples are generated by the inner maximization process, their associated loss decreases instead, which we named abnormal adversarial examples (AAEs).