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).

Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.

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Multi-distribution or collaborative learning involves learning a single predictor that works well across multiple data distributions, using samples from each during training.

Sharpness-A ware Minimization (SAM) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction performance of deep neural networks.