There was public knowledge of a viral respiratory epidemic threat from China in January, yet serious nationwide public health responses in the U.S. and U.K. did not start until March 2020. Even once it became clear that wealthy countries were at risk, there was a widespread reluctance to learn from China and from other Asian countries. The American reaction focused instead on blaming China – consider President Trump’s use of the term “Chinese virus.” China was further criticized for using draconian measures when millions of people in Wuhan were put under lockdown – even though the countries of the West that denigrated such tactics might today be better off if they had acted similarly. Indeed, recent data suggests that the majority of cases in the United States came from New York City. Restricting travel out of the city, as was done in Wuhan, might have meant far fewer cases in the U.S.
A root CA cert from Certigo expired over the weekend and lots and lots of shit broke. SSL PKI is awful.
in science, you hold all your variables constant except one: keep the lid on your styrofoam cup and your china cup. That’s true, and if we were doing pure science — if we only cared about finding out what lockdown measures worked and which didn’t — then it would be simple: introduce measures one at a time, wait and see, do it slowly. But we’re not doing pure science. We’re also trying to make a country that works for its citizens, in conditions that change daily. “We’re trying to build a plane as we fly it,” my US epidemiologist told me. The most important thing, according to Javid, will be “nimbleness; being able to change policy in the light of new evidence”. If it turns out opening schools was wrong, then close them again. And we in the media need to be wary of shouting about mistakes and U-turns and instead say: when the facts change, you change your mind.
‘any orthogonal maze, with vertical walls protruding equal heights from a rectangular floor, can be folded efficiently from a rectangle of paper just a small factor larger than the floor. The design algorithm has been implemented as a freely available web application you can design a maze or generate one randomly, and the application produces a crease pattern, which you can print and fold into your design’
New processor behaviour means everything we know about performance optimization is wrong again:
We’ve seen how crucial it is to understand data dependencies in order to optimize code. Especially hidden memory dependencies between load and stores can greatly influence performance of work loops. Understanding the data dependency graph of code is often where the real performance gains lie, yet very little attention is given to it in the blogosphere. I’ve read many articles about the impact of branch mispredictions, importance of data locality and caches, but much less about data dependencies. I bet that a question like “why are linked lists slow?” is answered by many in terms of locality, caches or unpredictable random memory access. At least I’ve heard those reasons often, even Stroustrup says as much. Those reasons can play a part, but it’s not the main reason. Fundamentally iterating a linked list has a load-to-use on the critical path, making it 5 times slower than iterating a flat array. Furthermore accessing flat arrays allow loop unrolling which can further improve ILP.