HN user
Data Scientist at Itaú, São Paulo, Brazil
There was a challenge at stack overflow to encode an image in a tweet (140 characters at the time):
https://stackoverflow.com/questions/891643/twitter-image-enc...
Some of the solutions took a similar approach that used geometric primitives.
Most journals require that the authors sign an agreement in which the publisher retains the copyright of the paper (e.g. [1]).
Although a few publishers allow the authors to provide a pdf of the paper in their personal home-page, they do allow it to be uploaded to an open access platform.
[1] https://www.elsevier.com/about/company-information/policies/...
I also think that Anaconda is great. However, I hope that in the future we could install numpy, matplotlib, jupyter, etc. just using pip.
Flickr was -- and maybe still is -- very useful for the computer vision research community.
"Mining of Massive Datasets" by Leskovec, Rajaraman and Ullman is very good.
Although the post gives a link to the Amazon page of the book, PDFs of the chapters are free to download at the official book web site[1].
My first impression was the same as Mithaldu, but then I tried one of the other games and it was more responsive.
I think that the NES emulator is bad advertising for your (very nice) product.
A challenge would be that coffee trees have very deep roots [1].
I have the impression that Pepsi is much more popular in US than in Brazil.
"However, yet another recent theory proposes that grasses competing for water and nutrients - limited resources in the Namib desert - create the circles, explaining why they never overlap."
Maybe this model could be used to analyze this hypothesis.
Nice, but I think it is not real, just marketing for the new Planet of the Apes movies.
Very interesting approach.
I have worked with image feature extraction in the past. Although using DCT coefficients has been used as a way to analyze texture features, the idea idea of generating the hash (step 5) seems to be new.
I am curious however on why you are discarding color information. Usually for reverse image search this kind of information can be quite useful.
Log-binning can be useful. However it has some disadvantages.
I think that in your case your data (server response time?) looks good because you probably have a log-logistic or log-normal distribution.
Suppose you were working with values that are exponentially distributed which is also a reasonable hypothesis for your data. In that case the log-binned histogram would like a plateau with the exception of the beginning and ending bins. In this scenario a linear-binning approach would probably be better.
Unfortunately, I think that there is no approach for bucketing that is good for all situations. Usually the best approach will depend on your data and also on what you are trying to analyze.
Maybe this link can be useful for you:
Very nice game.
One problem for me is that by default the comments from other users are shown, sometimes with the solution for the level.
Another great book missing in the article is "Mining Massive Datasets" [1] by Jure Leskovec, Anand Rajaraman and Jeff Ullman.
I like the idea, the problem would be the let my address publicly available to everyone.
Is there any style or guidelines for citing code (e.g. as we have APA for citing papers)?
Usually when I want to give credit to a software or code that I have used I cite the paper that describes the that software or code.
Another point: if you attribute a DOI to your code, that could result in it "stealing" the citations from a paper that describe that code.
Also, in some computer science areas conference papers can be another useful resource.
Mendeley does not give you access to paywalled papers. You have to download them yourself.
That is a very relevant question, since Mendeley is free.
Reading the Web site the only advantage that I found is that it allows you to use your Dropbox account to sync the files, while for Mendeley, if you want more space than a free account offers, you have to pay.
I really like the idea of the Web site as a whole: explaining concepts from computer vision in a simple way.
When I was starting my masters course I was interested in learning what the concept of bag of words in computer vision was all about. Although it is straightforward technique, there are few examples on the Web explaining how to implement it (clustering the feature vectors and etc.)
Another important aspect that I hope they adopt from TF2 is that even if you use default items (e.g. weapons) you will not be in disadvantage when playing with users that spent money on items.
The distribution of links pointing to a Web site follows a power-law distribution (few sites get most of the links) [1].
It is very likely that the number of visits follow a similar power-law distribution.
At 60m a considerable portion of the Brazilian amazon would be invaded by the sea.
Since cs.cmu.edu seems to have trouble handling the 'HN effect', this other link on the story (from cmu.edu) may be useful:
http://www.cmu.edu/homepage/society/2014/spring/google-vp-na...
Did they reduce the price or is glass still $1,500? I could not find this information on the web site.
I am very curious about what will be the final price for glass and more specifically, if it will be more expensive or cheaper than an iPhone or equivalent Android phone.
I also thought about that too. It would be nice to hear from people working at Dropbox if this was the source of inspiration for the app name and page design.
There was also the invitation text which might contain private messages.
Maybe it is really ray-traced. Notice that the simulation allows you to control camera focus and aperture.