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Others have pointed out that this is Lights Out. If anyone's interested, there is a known algorithm (PDF):

https://linux.ime.usp.br/~renatolg/lights_out.pdf

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That's the clever solution. Here's a solution that is actually brute force, but tries to fake being clever: http://www.tzs.net/lights.html

PS: I have not updated that page in years, which is why it contains a link to a Dutch noodle seller. It was a link to an online game service back when I wrote the page.

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Wow. I worked on Millwheel as a summer intern the summer before last. At the time it was a team of about 11 people. I'm honestly pretty surprised to see this comment as I thought it was just a small internal research project.

Have you seen any references to it in the wild other than the Google Research paper?

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Oh maybe I'm wrong, I really thought I saw something that said it was used for the index creation. I'm just having a look over the papers I've read.

They do definitely seem to have switched from MapReduce though at least - http://www.theregister.co.uk/2010/09/09/google_caffeine_expl...

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Feature request: Could you strip the dots from user input if the email address is @gmail.com, and similarly strip the dots from the records of pwned email addresses? Gmail usernames are dot agnostic, and I sometimes use xyz@gmail.com, x.yz@gmail.com, etc. This makes it hard to use the tool to check of my Gmail has been pwned. (Also, I assume you don't do this already).

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I did exactly this for a school project about a year ago:

https://github.com/bcuccioli/neural-ocr

There's a paper in there that explains the design of the system and my results, which weren't great, probably due to the small size of training data.

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This doesn't load for me at the moment, so I can't compare, but I also made something similar a few months ago in node.js: http://texbin.bcuccioli.com

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I wrote a simple neural network about a year ago for doing optical character recognition as a class project. I think looking over the code could be good for learning, as it has a pretty simple OOP structure: https://github.com/bcuccioli/neural-ocr

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It's used in Cornell's intermediate-level, required, notoriously difficult functional programming course.

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It looks larger than the one I had in France. Happy to have read your comment.

offtopic ps: for a minute the staff pictures were CV segmented which was quite original.

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Another great tool (disclaimer: I've contributed a bit) is Phabricator (http://phabricator.com).

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There are two good correct equivalent ways to think of the determinant of varying generality. One is as the function from a ring of square matrices to the underlying field (e.g. from R^(n^2) -> R) that sends identity to identity, is alternating (swapping two rows or columns negates the function) and is multilinear (is a linear function in each of the columns independently). These properties are all useful and important on their own, so there is motivation to study a function which has all of them. It's not obvious that such a function exists, but you can prove that. As it turns out, these three properties uniquely determine such a function, which makes it seem like that function might be really important!

There's a more general definition too, which is based around the wedge product, a quintessential object in algebra and calculus. There's a good exposition here: http://codeblank.com/~int/det.pdf .

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The difference is calc 1 is not higher level math and the derivative of a real function is not an abstract concept. Professional mathematicians/grad students/high level undergrads don't think of the determinant via some weird geometric intuition, as that won't really provide enough information or rigor to do anything useful.

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