shutdown: command

This is a bit misleading. You only need to treat the first generation of 2n watermelon with colchicine (which inhibits the movement of chromosomes during metaphase) to produce a 4n watermelon. Once you have a 4n watermelon, subsequent generations do not require colchicine treatment.

Deflationary periods may be helpful to those with large amounts of cash or cash equivalents, which generally isn’t the working class. Wage growth outpacing inflation helps the working class more.

There’s likely some cases of convergent evolution, but I’m not sure this is settled for all trees.

I’d suspect that at least some trees with last common ancestors that are shrubs have re-enabled genes that enable the tree phenotype rather than independently evolving the tree phenotype.

But still really cool (and maybe turns on exactly how much evolution your consider needs to happen before it’s convergent evolution.)

Ain’t nobody in the US got a fancy kitchen scale.

Lots of us have them. (Well, basic scales which weigh a tenth of a gram.) They’re useful when weighing compressible dry ingredients like flour and brown sugar, and viscous wet ingredients like molasses and corn syrup. They’re also helpful when you’re multiplying a recipe by a factor that doesn’t result in useful units; it’s annoying to figure out how to measure out fractional cups that involve teaspoons.

They also help with portion control if you’re watching calories.

Depends heavily on the kind (and intensity) of radiation. Beta (electron/positron) and gamma (photon) generally won’t, but neutron and alpha can. Many of the atoms that become radioactive will rapidly decay, and that’s one of the mechanisms behind the impact to structural integrity.

That didn’t work out so well.

Bonus points if your static site sends a 503 with a retry after header.

Usually that’s just for their version. Arxiv the version before it was accepted.

Exactly.

The general approach is to use interpretable models where you can understand how the model works and what features it uses to discriminate, but that doesn’t work for all ML approaches (and even when it does our understanding is incomplete.)

Maybe not the hardest, but still challenging. Unknown biases in training data are a challenge in any experimental design. Opaque ML frequently makes them more challenging to discover.

Love the cheese name! I think he looks like more like a Muenster to me, but Colby works! https://en.m.wikipedia.org/wiki/Muenster_cheese

Unless you’re in Tibet, Xinjiang, or another place observing UTC+8 with a significant offset from local solar time.

rRNA: typical. I do the work and everyone else takes credit.

There’s even an instructables on how to do it.

They also make NEMA plugs with threaded rings around the boot for applications in marine and other harsh environments.