jeremyscanvic

I'm reading singer song-writer Lily Allen's 2019 autobiography My Thoughts Exactly. I really liked her last album West End Girl (2025) and I wanted to know more about her and her creative process and inspirations. She tells about lots of different things having happened in her life, her relationship with her parents, how she grew up like. Really enjoyable read so far!

Neat! I wonder how they deal with people using possibly different map projections?

Worst case can mean two things. For fixed n, worst list content and worst k, which gives you the less fine-grained O(n). For fixed n and fixed k, worst list content, which gives you the fine-grained O(n - k).

Edit: Another example of that is the complexity of convolutional filtering, which is O(n min(log n, k)) for a signal of length n and a kernel of size k.

I'm interested in the context surrounding his statement but I could not find where it originates from. Do you have a link to it?

I'm only vaguely familiar with dithering/half-toning. What does "component" refer to here?

It's a somewhat active inverse problems research subfield https://github.com/MenghanXia/ReversibleHalftoning

Do you have tips to start using notebooks instead of marking books? I personally like to underline stuff in my books, when I start a new novel it's often people, places and events. I'm not sure what would be the notebook equivalent of that. Writing down what I would underline? It seems tedious and you loose context and the sense of proximity between words.

I was once telling a friend about a book I had just finished. They were curious about it and asked me to borrow it. I politely accepted knowing very well they could never read my copy for it was embarrassingly covered in the messy marks I had enjoyed making during my read. Fortunately neither of us ever brought the subject again and I now think twice before messing up my books!

Ah right this makes a lot of a sense - thanks!

> Also, from the fundamental theorem of algebra, once the Jacobian polynomial {\mathrm{det} DF} is non-zero, it must be constant.

I wouldn't have guessed this is true. I'm wondering what the proof looks like!

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 knew about .git/info/exclude and ~/.config/git/ignore but not about git-check-ignore(1). Neat!

That's wild I didn't expected the plagiarism to be that blatant. Extra shocked as a French who enjoyed listening to many of his talks

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)

In France some people do and some people don't but it's definitely a thing

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.

[1] https://github.com/deepinv/deepinv

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!

https://github.com/jscanvic/SkySim

It's very insightful how they explain the difference between dataframes and SQL tables / standard relational structures!

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!

What I usually do when I have to read large man pages like bash(1) is I read them as PDFs:

man -Tpdf bash | zathura -

Replace zathura with any PDF viewer reading from stdin or just save the PDF. Hope that can be useful to someone!

For those interested you can also look up for opto-electronic transfer functions (OETF) and electro-optical transfer functions (EOTF).

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?

How would you refer to it in French out of genuine curiosity?

Most of them do but it's not mandatory and deblurring can be used [1]

[1] Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise, Bansal et al., NeurIPS 2023

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.

For instance: https://deepinv.github.io/deepinv/auto_examples/blind-invers...

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].

[1] https://ieeexplore.ieee.org/document/11370202

Thanks!

Really cool! Any specific reason for the choice of Oklab instead of say HSL/HSV?

Any reference you can share on this? I'm genuinely curious speaking as a PhD student in image processing for computer vision