Of course, idioms are preserved, and not everything is rewritten.

I quite ok with this style, it isn't grating. I expect this will be a style we'll see more of in science.

Instead, to me it looks like there is selection bias going on. Shorter males are more likely to get MPH prescribed is an equally valid explanation.

Luckily, the conclusion makes no assertions about height either, nor does the paper make conclusions about any causative relation.

Furthermore Europe can also choose to replant non-forest land or replant permanently afterwards.

Should science grow exponentially? Is there no value in a PhD graduate that isn't a researcher? Get everyone a PhD and chain them to a desk for research!

(1) Except for gender (and others not in scope) where past discrimination is still felt.

Though I think a more reasonable replacement rate is 10+ PhD graduates per professor. I do not think this problematic somehow, unlike what overproduction implies.

It would also make it impossible to have a decent non-zero check for

if ((x-y)!=0) progress((x-t)/(x-y));

* Every tile has two orientations 0 and 180 degrees which allows to cut a lot of possibilities for small fields. * Three tiles can be flipped together, meaning if you have one or two tiles unsolved, your idea won't work out. * Large fields have more solutions.

Sadly I found some bugs:

* World generation does not work correctly: there is an unmovable field (nice idea!) but during generation it gets moved. This makes some puzzles unsolvable. The randomizer should ignore this field. * The undo function has some issues undoing the wrong move or skipping a move. Fully undoing a puzzle does not always result in a solution. If this happens while you're playing you get locked.

"You train them by comparing the output to the original." ->

You train neural networks by producing output for known input, comparing the output with a cost-function to the expected output, and updating your system towards minimizing the cost, repeatedly, until it stops improving or you tire of waiting. Cost functions must have a minimal value when the output matches exactly the expected to work mathematically. Engineering-wise you can possibly fudge things and they probably do so ... now.

I don't agree with your critiques. It isn't an oversimplification, published code literally works as stated.

I say both can be a question in Statistics I where you calculate probabilities based on combinations.

So I'll say it depends on the level of education for how the question gets interpreted and saying those who never took Bayesian reasoning and consider n unknown are wrong gets elitist.

But also, it's not irrelevant because for two cases the probability stays equal. Calculating the exact chance is still an interesting problem and it could have been dumbed down for twitter only.

Instead: Make a sample and now you know that it is greater than a random number unknown to you. Can you derive any knowledge about your next sample?