we assign a minterm id to each of these classes (e.g., 1 for letters, 0 for non-letters), and then compute derivatives based on these ids instead of characters. this is a huge win for performance and results in an absolutely enormous compression of memory, especially with large character classes like \w for word-characters in unicode, which would otherwise require tens of thousands of transitions alone (there’s a LOT of dotted umlauted squiggly characters in unicode). we show this in numbers as well, on the word counting \b\w{12,}\b benchmark, RE# is over 7x faster than the second-best engine thanks to minterm compressionremark here i’d like to correct, the second place already uses minterm compression, the rest are far behind. the reason we’re 7x faster than the second place is in the \b lookarounds :^).
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与此同时，vivo 顺理成章地将记录环境大场景的任务交给了超广角镜头，并让它承载日常随手拍视频的默认机位；高素质潜望长焦，则专心负责空间压缩与特写，并不断推进可用焦段。
我不知道大家对“动作片到底有多难拍”有多少实感，除了“一拳”、“一脚”、“后空翻”，你还能想到多少动作？