* Add gitter for community.
* Update CODE_OF_CONDUCT.md
* Add statecharts paper in a new systems modeling category (#565)
* Rename "paradigm" and "plt" folders for findability (#561)
* rename "language-paradigm" folder for findability
lang para pluralize
* rename PLT => languages-theory
* fixed formatting
* group pattern-* related papers (#564)
* combine clustering algo into pattern matching
* rename stringology with the pattern_ prefix
* improved the README header info for paper related to patterns
* consolidate org-sim and sw-eng dirs (#567)
* consolidate org-sim and sw-eng dirs
* typo and links
* Fixed link (#568)
* Update README.md
* Fixed A Unified Theory of Garbage Collection link
* Verification faults dirs (#566)
* consolidate program verificaiton and program fault detection listings.
* faults and validation gets header info
* self-similarity by Tom Leinster
Again on the topic of renormalisation. Dr Leinster has a nice, simple picture of self-similarity.
* added new papers in Machine Learning dir. fixed-up references
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?
* added comentaries from commit messages. more consistent formatting.
* 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.
* rename papers accordingly, and add descriptive info
remove dup maths papers
* fixed crappy explanations
* improved the annotations for papers in the Machine Learning readme
* remediated descriptive wording for papers in the mathematics section
* removed local copy and added link to Conway Zip Proof
* removed local copy and added link to Packing of Spheres - Sloane
* removed local copy and added link to Algebraic Topo - Hatcher
* removed local copy and added link to Topo of Numbers - Hatcher
* removed local copy and added link to Young Tableax - Yong
* removed local copy and added link to Elements of A Topo
* removed local copy and added link to Truncation of Wavlet Matrices
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
* Update and rename Brain–computer Interface/README.md to Brain-computer Interface/README.md
* Updating Readme as per @hakutsuru's review
* Update README.md
* 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
* combine clustering algo into pattern matching
* rename stringology with the pattern_ prefix
* improved the README header info for paper related to patterns
need, more than ever, for the software we write to work reliably in a
wide range of conditions--even, and especially, in unexpected
conditions. This paper, written by Robert Rasmussen from the Jet
Propulsion Laboratory in 2008, documents and explains some fundamental
principles about designing fault tolerant systems as learned through
the hard-won experience of designing Guidance, Navigation, and Control
(GN&C) systems for spacecraft. This paper is rich in principles,
examples, and advice, and has a lot to offer to our industry
generally--even for those of us who don't actively work on software for
spaceships!
