There’s just one hitch: the system still needs guinea pigs. Even the best weather models can’t pinpoint where clear-air turbulence will occur. So the NCAR programs continue to rely on firsthand reports from planes that have already been tossed around. New technologies could change that in coming years. A plane equipped with a lidar sensor—which uses lasers to detect much finer particles than radar can—could pick up on turbulence even in a cloudless sky. But lidar systems are still too bulky and expensive to fit into a plane’s nose cone. And the government and the airline industry have been slow to invest in improving them. For now, the best hope for a flight heading into turbulence might be to program the plane itself to ride the bumps.
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 :^).，推荐阅读体育直播获取更多信息
，这一点在一键获取谷歌浏览器下载中也有详细论述
Олеся Мицкевич (Редактор отдела «Силовые структуры»)
Что думаешь? Оцени!，这一点在体育直播中也有详细论述