跳出边界无用：NFA = FNFA
Thinking outside the box is useless NFA = FNFA
AI总结：
本文针对任意维度图像的非确定性图像行走自动机，证明了允许跳出图像的自动机（NFA）与不允许的自动机（FNFA）能力等价，解决了长期存在的开放性问题。
AI中文摘要：
我们研究图像行走自动机，即接受高维平行六面体/张量形状图像的有限状态自动机，允许其根据当前状态和读取符号向所有方向移动。长期存在的开放性问题是：若允许自动机跳出图像，非确定性此类自动机是否会变得更强？本文全面解决该问题：对于任意维度的图像，NFA = FNFA。
英文摘要：
We study picture-walking automata, namely finite-state automata that accept higher-dimensional parallelotope-/tensor-shaped pictures and are allowed to move in all directions depending on their current state and the currently read symbol. It is a long-standing open problem whether the nondeterministic such automata become stronger if automata are allowed to exit the picture. In this paper, we resolve the problem in full generality: NFA = FNFA, for pictures of any dimension.
