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

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.

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?

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...

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).

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.

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

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

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

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.

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

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 .

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.