Social media platforms capture diverse attack sequence samples through both machine and manual screening processes. Investigating effective ways to leverage these adversarial samples to enhance robustness is imperative.

Weshowthatthelearned model exhibits strong performances interms ofimage and text generation and anomaly detection.The one-pagecode can be found in supplementarymaterials.

The datasets contain numerous grammatical and orthographic errors, poor pronunciation, limited vocabulary, and the content lacks cultural relevance to the language community.

While existing approaches only model finite, discrete fidelities, in practice, the feasible fidelity choice is often infinite, which can correspond to a continuous mesh spacing orfinite element length.

Wealsoshow thatacertified version ofthecomputationally tractable DOO algorithm matches these packing and integral bounds.

The diversity and dynamism of the real world require reinforcement learning (RL) agents that can quickly adapt and learn new behaviors when placed in novel situations.