Coincidently, I read today a paper [1], which has a nice video demo here [2] describing a very similar system but with only one type of cells and a simple rule. Surprisingly enough, particles form cell type structure and seem to "reproduce".
really nice!
in your fitness function - wouldn't you prefer to normalize somehow for the the line's length? i.e. - longer lines (going between two far away pins) will cover more dark pixels, and are more likely to be picked, even though they might also cover many bright pixels. I wonder if the average darkness of pixels along the line will work better?
This is fun!
I took the boys average weight data given in [1]
months from conception, average weight (kilos)
(9, 3.25)
(15, 7.5)
(21, 9.97)
(33, 12.88)
(45, 14.97)
(69, 18.97)
and saw that it really looks like a logarithmic function. So, fitted y to log(x), got y~7.4685*log(x)-13, with R^2=0.9965 (thank you R!). Extrapolated back, and found that at conception, the fetus weighs -inf kilos. Not surprising result, I must say. However, I was surprised to find that only after five months of gestation, the embryo reaches the critical mass of 0 kilos.
Science!
could be useful for companies (like the one I work at) for employees to tweet via the company official account which is handled by marketing. (If you don't already), you may want a review panel for outgoing tweets, for which (I think) you can charge.
I don't believe Facebook set up this contest in order to find solutions to a problem they have. The biggest clue is that the graph given to contenders was a directed graph, unlike Facebook's social graph.
It seems very likely to me they used that problem in order to find good candidates for their data-group, which is exactly what this contest was all about. Either that, or they are trying to expand the social graph of Instagram :)
It's a very interesting field. Can insurance companies detect the early stages of Parkinson's when you call their call-centers and change your rates accordingly? Here's a related dissertation on detecting mental health condition by using voice recordings http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-55... (haven't read it yet, but was meaning to for quite a while)
Being a machine learning (and Kaggle competitions) novice, can you explain what it means that your feature selection didn't properly implement cross-validation?
different use cases. Leap motion is for (extreme) short distance manipulation, which probably gives it the higher resolution, while Kinect is for mid-distance manipulation, which works well for games.
I wonder though why would one opt for Leap motion and not buy a real touch screen if he needs to be that close to the screen anyway.
I see that you are using url2png's api, which means that even rendering a fraction of the monthly links will be costly. I'm creating a similar tool and opting (for now) to create the thumbnails offline and on my own (using webkit2png). Can you comment on your decision?
I'd say it's introductory, up to the chapter it discusses SVMs. Than it becomes incomprehensible, unless you have prior knowledge of SVMs and kernel methods.
I removed some parameters from the request out of privacy concerns. You can see those scores if you use a tool like firebug while loading the homepage.
I wholeheartedly agree with you on this. Maybe it's due to the fact that I'm not a native English speaker, but I never meant to insinuate otherwise (after all, Google+ is very very young and the score should come from somewhere). I just wanted to share the fact that Google+ makes this score available.
implemented (as a little button above the graph, not to scare the casual users). Now you can more easily see how the "zombie apocalypse" is going to play out
Coincidently, I read today a paper [1], which has a nice video demo here [2] describing a very similar system but with only one type of cells and a simple rule. Surprisingly enough, particles form cell type structure and seem to "reproduce".
[1] http://www.nature.com/articles/srep37969
[2] https://www.youtube.com/watch?v=makaJpLvbow