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 :^).
Кипр снова подвергся бомбардировкам02:22，详情可参考纸飞机官网
截至目前，美国方面尚未对该事件作出正式回应，伊拉克政府也未发布相关声明，有分析认为，这架无人机可能因导航故障或技术问题自行坠毁，而非被击落。，详情可参考PDF资料
Иран установил личности виновных в ударе по школе для девочек в Минабе14:56