This is the paper that introduced concurrent Garbage Collection via the tri-color marking invariant. It forms the basis for most non-concurrent incremental collectors as well. No matter where you stand on garbage collection, I think it’s useful (and interesting!) to know how collectors work, and this is one paper you’ll be hard-pressed to avoid when delving into the matter; despite the claim in the paper’s introduction that “it has hardly been our purpose to contribute specifically to the art of garbage collection, and consequently no practical significance is claimed for our solution”, this is definitely one of the most important and influential papers on GC ever written.
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* From where it all started - Sun NFS
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Co-authored-by: Sean Broderick <hakutsuru@mac.com>
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Updating the link for "Internet Census via Insecure Routers"
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* Add statecharts paper in a new systems modeling category (#565)
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* self-similarity by Tom Leinster
Again on the topic of renormalisation. Dr Leinster has a nice, simple picture of self-similarity.
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Truncation of Wavelet Matrices
Understanding Deep Convolutional Networks
General self-similarity: an overview
cleanup url files (wrong repo format)
* what has sphere packing to do with compression?
• role of E8 & Leech lattice in optimal codes
• mathematically best compression was never used
• ikosahedron
* surfaces ∑
I show this paper to college freshmen because
• it’s pictorial
• it’s about an object you mightn’t have considered mathematical
• no calculus, crypto, ML, or pretentious notation
• it’s short
• it’s a classification proof: “How can it be that you know something about _all possible_ X, even the xϵX you haven’t seen yet?’
* good combinatorics
Programmers are used to counting boring things. Why not count something more interesting for a change?
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* graphs
Programmers work with graphs often (file system, greplin, trees, "graph isomorphism problem" (who cares) ). But have you ever tried to construct a simpler building-block (basis) with which graphs could be built? Or at least a different building block to build the same old things.
This <10-page paper also uses 𝔰𝔩₂(ℂ), a simple mathematical object you haven’t heard of, but which is a nice lead-in to an area of real mathematics—rep theory—that (1) contains actual insights (1a) that you aren’t using (2) is simple (3) isn’t pretentious.
* from dominoes to hexagons
why is this super-smart guy interested in such simple drawings?
* sorting
You do sorting all the time. Are there smart ways to organise sub-sorts?
* distributed robots!!
Robots! And varying your dimensionality across a space. But also — distributed robots!
* knitting
Get into knitting.
Learn a data structure that needs to be embedded in 3D to do its thing.
Break your mind a bit.
* female genius
* On “On Invariants of Manifolds”
2 pages about how notation and algorithms are inferior to clarity and simplicity.
* pretty robots
You’ll understand calculus better after looking at these pretty 75 pages.
* Farey
Have another look at ye olde Int class.
* renormalisation
Stéphane Mallat thinks renormalisation has something to do with why deep nets work.
* the torus trick, applied
In Simons Foundation’s interview by Michael Hartley Freedman of Robion Kirby, Freedman mentions this paper in which MHF applied RK’s “torus trick” to compression via wavelets.
* renormalisation
Here is a video of a master (https://press.princeton.edu/titles/5669.html) talking about renormalisation. Which S Mallat has suggested is key to why deep learning works.
* Cartan triality + Milnor fibre
This is a higher-level paper, but still a survey (so more readable). It ties together disparate areas like Platonic solids (A-D-E), Milnor’s exceptional fibre, and algebra.
It has pictures and you’ll get a better sense of what mathematics is like from skimming it.
* Create see.machine.learning
* tropical geometry
Recently there have been some papers posted about tropical geometry of neural nets. Tropical is also said to be derived from CS. This is a good introduction.
* self-similarity by Tom Leinster
Again on the topic of renormalisation. Dr Leinster has a nice, simple picture of self-similarity.
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* removed local copy and added link to Conway Zip Proof
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Co-authored-by: Zeeshan Lakhani <202820+zeeshanlakhani@users.noreply.github.com>
Co-authored-by: Wiktor Czajkowski <wiktor.czajkowski@gmail.com>
Co-authored-by: keddad <keddad@yandex.ru>
Co-authored-by: i <isomorphisms@sdf.org>
* created ReadMe
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* Updating Readme as per @hakutsuru's review
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* Adding Object Identification for Computer Vision Using Image Segmentation and Computer Vision Based Detection and Localization of Potholes in Asphalt Pavement Images
* Adding Object Identification for Computer Vision Using Image Segmentation and Computer Vision Based Detection and Localization of Potholes in Asphalt Pavement Images
