eutectic

I think the idea is that some short peptides might survive the digestion and make it to the blood stream, but I'm doubtful there's any specific benefit to collagen.

Lidars use pulsed lasers with peak powers up to the kW range.

Silicon carbide fiber reinforced silicon carbide is also being increasingly used.

But 'i' is not a car, it is the index of a car.

I like einsum, it's concise and self-explanatory. Way better than multiple lines of nested for loops would be.

I got to level 8 by asking in rot13. I think I beat the bonus level too but I can't remember how.

Also, the problem is to find a global min-cut, which requires all-pairs max flow.

Different models have different inductive biases. There is no way you could build GPT4 with decision trees.

Is it not possible to compensate for inaccuracy, if you have a sufficiently precise measurement at the hand?

Your edit is confusing me. Which variant fails and which variant passes?

Here's a variant which gives 64-bit output:

  uint64_t x = 0, w = 1;
  uint64_t msws64(void) {
      x = x * x + (w *= 0xe9acc0f334e93bd5ULL);
      return (x = (x >> 32) | (x << 32)) ^ w;
  }

What if you don't truncate? I fell like it might still be OK.

One important component is adding significant damping, corresponding to a small probability of jumping to a random page. This makes sure that the graph is well connected and the power iteration converges fast.

I though finite difference differentiation was notoriously unstable.

I just did this exact thing today, writing a cloth simulator! I wanted to try updating each node position in turn using a Newton step, holding the other nodes fixed.

John Conway showed that if you generalise the coefficients of the Collatz conjecture then some instances are undecidable. So maybe there is no proof.

I would say the second law is more relevant; if you want to reduce entropy in one part of a system, you have to expend an equivalent or greater amount of free energy elsewhere.

Well, the derivations are different, and your comment seemed to imply that the maximum likelihood perspective was easier to understand.

Both the Bayesian perspective and the optimization perspectice are legitimate ways of understanding the Kalman filter. I like the Bayesian perspective better.

I think you need to recalibrate your perceptions; the world (including the US) is about as safe as it's ever been. And I don't think hypocritical is the right choice of word anyway.

But demand is lower in the summer, so this shouldn't effect renewable energy requirements.

I think even a bigram model would provide enough information.

Also, the parts of science that affect our everyday lives come together to form a coherent picture which explains many different observations. Religions don't have anything like that.

The generating function proof is also really beautiful! I think I maybe like it even more than the linear algebra proof.

How do you deal with materials in a spectral renderer? Use something like a gaussian for each rgb component?

I think convex hull is also closely related.

Obviously not the point of the post, but this doesn't seem like a very reasonable thing to calculate, statistically speaking.

Also, I wonder if there is some way to use branch-and-bound to look at fewer combinations.

Maximum Likelihood corresponds to Bayesian statistics with MAP estimation, which is not the typical way to use the posterior.

I find it hard to believe that some calcium ions constitute a health hazard.

Is it actual pollution or just water droplets?