The first stock in the dumplings-and-wontons business, “Yuanji Foods,” is heading for an initial public offering in Hong Kong.
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.
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this is a very powerful technique, and it is the main reason why we are so fast on the benchmarks, because by the time we confirm a match, both the lookbehind and lookahead have already been matched - we report matches retroactively once all the context is known, instead of trying to look into the future or backtracking to the past or keeping track of NFA states. this is a very different way of thinking about regex matching, and it took me a while to wrap my head around it, but once you see it in action, i hope you appreciate how elegant and efficient it is.
for (const [key, remote] of Object.entries(state)) {，这一点在爱思助手下载最新版本中也有详细论述