Tag clouds, despite of being useful, can look a bit dull. My office mate, Bernie Acs, pointed me to a more aesthetic appealing one written in Flash for Wordpress. Roy Tank has released the WP-Cumulus plugin that makes the new 3D tag cloud available as a widget. I couldn’t help myself getting it installed. If you scroll down you will see it in action. Roy, kudos for you!
LaTeX reference management is superb. However, some publisher want to have the citations arranged in some predefined manner. Yes, they are usually all different. However, if your are using LaTeX and BibTex no much need to be done to format your references right. The natbib package provides you with the tools to satisfy almost any publisher requirements.
A while ago, I wrote about “Publishing yourself online”. Among other solutions, Lulu, seemed to be a more traditional publishing method, with the twist you do it your self, and you can decide what kind of book you want (e-book, paperback, hardcover, etc.). The other day I was spending some cycles trying to find out if they provide LaTeX templates, and no, they are a Microsoft shop (with the option of dealing with Open Office). They, however allow you to upload PDFs, so you can also prepare manuscripts in LaTeX. I searched a bit, and run into and very useful post by Veta Project describing how to take your LaTeX document and make it Lulu friendly. Worth taking a look at it… ...
Pier Luca Lanzi sent an email the other about help SigEvolution newsletter by taking an on-line survey. The survey was host at SurveyMonkey.com. I had never run into this guys before, but after digging a bit, the idea is pretty sweet. Need to run a survey? Just register to their site, create the survey, graph the link to it, and spread it around. Also, they allow you to upload surveys to their servers. As I said, pretty interesting option if you want to run a survey and do not want to stand up your own version of it. ...
The other day I was playing to see how much I could squeeze out of a genetic algorithm written in Python. The code below shows the example I used. The first part implements a simple two loop version of a traditional allele random mutation. The second part is coded using numpy 2D arrays. The code also measures the time spent on both implementations using cProfile. from numpy import * pop_size = 2000 l = 200 z = zeros((pop_size,l)) def mutate () : for i in xrange(pop_size): for j in xrange(l) : if random.random()<0.5 : z[i,j] = random.random() import cProfile cProfile.run('mutate()') def mutate_matrix () : r = random.random(size=(pop_size,l))<0.5 v = random.random(size=(pop_size,l)) k = r*v + logical_not(r)*z cProfile.run('mutate_matrix()') If you run the code listed above you may get something similar to ...