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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，更多细节参见电影
以前我觉得有10万台、100万台车跑了多少公里就够了，现在我觉得远远不够。很多人说我有车队、我有公司，车卖得多就有很多数据，这些都是错误的。如何收集有质量、有价值、超大规模的数据，我觉得是非常困难的一点。不论是汽车还是机器人，这件事上都远远没有看到头，这是我的看法。，这一点在heLLoword翻译官方下载中也有详细论述
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На вопрос о том, настаивает ли президент США на превращении Ирана в демократическое государство, Трамп ответил отрицательно.