Is ggplot2 considered to be a nice interface to plot things compared to say matplotlib in Python? I'm asking out of curiosity, I haven't touched R much
I've switched form LaTeX to Typst for all my informal/semi-formal writing and it's a delight to work with. I hope I'll be able to use it for more formal documents in the future (conference/journal papers, slides for high stakes presentations)
I work on one of the projects featured in the PyTorch Ecosystem [1] and I really recommend it to anyone working on a PyTorch library. Their team is really responsive and they even offer promotion on their blog & social media.
I wonder how this relates to the Perez All-Weather and Preetham sky models. Not an expert about that but I managed to implement those in the past and it was quite a fun project!
Like other commenters the tone of this post threw me off but I was really impressed by the design of the website. Congrats for building it, it shows your hard work and taste!
Is it possible in practice to control the side effects of making changes in a huge legacy code base?
Maybe the software crashes when you write 42 in some field and you're able to tell it's due to a missing division-by-zero check deep down in the code base. Your gut tells you you should add the check but who knows if something relies on this bug somehow, plus you've never heard of anyone having issues with values other than 42.
At this point you decide to hard code the behavior you want for the value 42 specifically. It's nasty and it only makes the code base more complex, but at least you're not breaking anything.
Anyone has experience of this mindset of embracing the mess?
You're absolutely right! Diffusion models basically invert noise (random Gaussian samples that you add independently to every pixel) but they can also work with blur instead of noise.
Generally when you're dealing with a blurry image you're gonna be able to reduce the strength of the blur up to a point but there's always some amount of information that's impossible to recover. At this point you have two choices, either you leave it a bit blurry and call it a day or you can introduce (hallucinate) information that's not there in the image. Diffusion models generate images by hallucinating information at every stage to have crisp images at the end but in many deblurring applications you prefer to stay faithful to what's actually there and you leave the tiny amount of blur left at the end.
The missing piece of the puzzle is how to determine the blur kernel from the blurry image. There's a whole body of literature on that that's called blind deblurring.
Blur is perhaps surprisingly one of the degradations we know best how to undo. It's been studied extensively because there's just so many applications, for microscopes, telescopes, digital cameras. The usual tricks revolve around inverting blur kernels, and making educated guesses about what the blur kernel and underlying image might look like. My advisors and I were even able to train deep neural networks using only blurry images using a really mild assumption of approximate scale-invariance at the training dataset level [1].