273 karma · joined June 3, 2016
On the other hand, you see vector-matrix multiplication a lot in other places, for example, the Markov chain literature. There, the vector is a row vector and the resulting vector is formed by dot products of the columns of the matrix with the original vector.
Not even the Trump admin is alleging levels of indirect costs that high. See e.g.
https://grants.nih.gov/grants/guide/notice-files/NOT-OD-25-0...
"Yet the average indirect cost rate reported by NIH has averaged between 27% and 28% over time."
and a lot of that is simply because nobody wants to do the detailed accounting for things like: lab electricity usage, janitorial services, misc supplies.
> The result? 90%+ of academic science is fraud.
This is dramatic nonsense; a simple made up number.
If there is any use for LLMs in paper writing, I would think that it is for tedious but not well-defined tasks. For example, asking if an already written paper conforms to a journal's guidelines and style. I don't know about you, but I spend a meaningful amount of time [2] getting my papers into journal page limits. That involves rephrasing to trim overhangs, etc. "Rephrase the following paragraph to reduce the number of words by at least 2" is the kind of thing that LLMs really do seem to be able to do reliably.
1: As usual, the input data can be wrong, but that would be a problem for LLMs too. 2: I don't actually know how much time. It probably isn't all that long, but it's tedious and sure does feel like a long time while I'm doing it.
Oh boy, I hope that they missed a joke or misquoted.
Part of it is, I think, that "elegance" is flowery language that hides what mathematicians really want: not so much new proofs as new proof techniques and frameworks. An "elegant" proof can, with some modification, prove a lot more than its literal statement. That way, even if you don't care much about the specific result, you may still be interested because it can be altered to solve a problem you _were_ interested in.
1: It doesn't have to be as big of a deal as this.
It's just like if X~N(0,1), Y~N(0,1) and you want to know the distribution of X-Y. You need to know what the PDF of (X,Y) looks like. Well, you don't know. X and Y could be correlated or they might not be. e.g. if could be that (X,Y)~N( (0,0), [(1,0),(0,1)] ) or maybe (X,Y)~N( (0,0), [(1,1/2),(1/2,1)] ). The distribution of X-Y cares how correlated X and Y are.
This is off the cuff, but you might be able to fix this as follows: Interpret exponential smoothing as a ODE on the distance to the target. Call that distance D. Then exponential smoothing is the Euler update for dD/dt=-C*D. (the constant C>0 being a speed parameter) The issue you bring up is basically the fact that the solutions to the ODE are D(t)=A*exp(-C*t), which is asymptotic to zero as t->oo, but never reaches zero. Now, the fix is to replace the ODE with one that goes to zero in finite time. e.g. dD/dt=-C*sqrt(D). (Solutions are half-quadratic. i.e. they are quadratic for a bit then stay zero once you hit zero.) The Euler update for this is stateless like you wanted.
Any would be more formally: there is an element of the list which is true. But the list doesn't have any elements, so that's false.
Looks logical to me. Plus you get that `not any( map(lambda x: not x, l) )` is the same as `all(l)` for any list `l`.
What you want to care about here is preserving structures: How would you define addition on a disjoint union? e.g. If you have V⊔W you can add two things in V and you can add two things in W, but what about something in V with something in W? Which zero vector is "the" zero vector?
If you don't have all those properties that make a vector space, then it's just a set. Then, yes, if for some reason you want to do the sum of vector spaces V and W as sets, you will get the disjoint union of these sets. That would be a pretty odd thing to do though; in that case, why are they vector spaces?