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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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在专业选择上，“实用主义”成为主流。教育政策分析、健康信息学、ESG管理及网络安全等具备明确政策红利的领域，其就业率与薪资增长远高于纯学术研究型专业 [53, 55]。，这一点在WPS官方版本下载中也有详细论述
上述数据说明，休假的多寡与一个国家的“富裕程度”有关联，但与“增长动力”之间的关系却充满变数，盲目照搬他国模式或单纯追求天数增加，未必能带来预期的经济红利。
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