moelf

I love https://frame.work/ though

the current show-stopper issue is that Typst can't insert PDF as figure[1], this makes a lot of scientific publication workflow impossible.

[1]: https://github.com/typst/typst/issues/145

>Einstein supposedly won for "especially his work on the photoelectric effect" rather than relativity.

just adding to this, this is because relativity wasn't experimentally verified (i.e. not sure if it's reality) at the time.

it already had, bottom-quark tagging has improved O(10)x in efficiency in the last decade without any new "physics" understanding, just from training with more low-level data and better ML arch (now using Transformers)

but we haven't found new physics with or without ML, making this prize a little weird.

what is so buggy, Julia the language or the deep learning libraries in Julia? in either case it would be good to have some examples.

More fragmented, calling-some-api based python code actually causes LLM to hallucinate and mix libraries more: https://www.stochasticlifestyle.com/chatgpt-performs-better-...

Apache arrow and parquet all work this way. Even HDF5 in column mode isn't completely bad.

TTree is succeeded by RNTuple, which is basically CERN's take on Apache Arrow, they're incredibly similar

Uproot already returns you Awkward array, so both things you mentioned are different ways of saying the same thing. The irreducible complexity of data analysis is there no matter how you do it, and "one-vector-at-a-time" sometimes feel like shoehorning (other terms people come up with include vector-style mental gymnastics).

For the record, vector-style programming is great when it works, I mean Julia even has a dedicated syntax for broadcasting. I'm saying when the irreducible complexity arrives, you don't want to NOT be able to just write a for-loop

Just a recent example, a double-for loop looks like this in Awkward array: https://github.com/Moelf/UnROOT_RDataFrame_MiniBenchmark/blo... -- the result looks "neat" as in a piece of art.

One common criticism of uproot is that it's not flexible when per-row computation gets complicated because for-loops in Python is too slow. For that one can either use Numba (when it works), or, here's the shameless plug, use Julia: https://github.com/JuliaHEP/UnROOT.jl

Past HN discussion on Julia for particle physics: https://news.ycombinator.com/item?id=38512793

it's very hard to argue anything is "better" than Wolfram/Mathematica when it comes to symbolic stuff, after all, most theoretical physicists use Mathematica for their professional work.

But for (entry-level) learning and possibly pivoting to application, Julia is delightful to use and can transit into some symbolics and numerical. Besides, it's free and open source.

> Firefox’s Enhanced Tracking Protection (Strict Mode) is known to cause issues on x.com

bye

I see. yeah I can see why it's confusing now -- we use "next-to-leading order" to mean a smaller effect than the leading order, but here, "next largest" means the one larger than natural numbers. Language is hard

two sets can both be infinite and yet one is "larger" than the other (c.f. https://en.wikipedia.org/wiki/Aleph_number).

You can establish two infinite sets are as large as one another by finding a bijection between them. These two sets would have the same "cardinality"

We know the real numbers has larger cardinality than natural numbers, but we don't know if there's anything in between -- can you construct an infinite set that has natural numbers < X < real numbers in terms of cardinality?

>and they don't also rebind <C-w>. Go from trying to delete a word (backwards) but end up losing a whole session

This is literally why Vim binding for Jupyter doesn't work for me and also why the "terminal" in Jupyter Lab is worse than not existing -- I can't help but press C-w

Earlier today: https://news.ycombinator.com/item?id=40142667

Specific to Particle physics and Peter Higgs, this book (https://www.basicbooks.com/titles/frank-close/elusive/978166...) by Frank Close is fantastic.

Same, same, and same, + currently working on one of the LHC detectors as a phd student -- but it can all be traced back to the lore of the golden age of particle physics and the discovery of Brout–Englert–Higgs boson in 2012.

There seems to be differentiable tree models now that perfor somewhat better than e.g. XGBoost https://github.com/Evovest/MLBenchmarks.jl?tab=readme-ov-fil...

come here to say Immich is absolutely great, the advanced search that landed recently is a game changer combined with semantic search: https://github.com/immich-app/immich/releases/tag/v1.96.0

I thought there's also the net they can use if, say, an aircraft lost tailhook

when I hit `F` the url density is too high that it's difficult to tell which is which, how do you navigate differently?

idk which is more cringeworth, Wolfram's computational reality https://www.scientificamerican.com/article/physicists-critic...

or Penrose's https://en.wikipedia.org/wiki/Orchestrated_objective_reducti...

1960s is only 10 years so if something went on in the public mind day on and day off for 5 years (like COVID did for 3 years), I think it's a good candidate for the decade-defining theme

My personal experience with ROOT REPL is that it's quite stateful, some error may make you want to restart the session I avoid it for anything longer than a few minutes of use

We had our first JuliaHEP workshop this year: https://indico.cern.ch/event/1292759/

your first two links are duplicates

For the replacing “Lee Hazlewood” with “Terry Allen” in the Wikipedia entry example, how is it possible that the Baseline has a >0 accuracy %?

Go hit 1.0 in 2012, Julia hit 1.0 in 2018

that term usually refers to one of the three people got Turing award in 2018: Geoffrey Hinton, Yoshua Bengio, and Yann LeCun,

any tutorial you would recommend? I found https://comfyanonymous.github.io/ComfyUI_examples/controlnet...