I could not respond rapidily to some comments this week, so I decided to make it an entry. Being on the road, I cannot produce this morning's Saturday morning cartoon. However, it looks like the first Saturday morning cartoon was featured in Reddit which gave a boost to its stats (now at 669 views):Creepy ? I am not sure, it definitely led one commenter to ask:
Can anyone point to a good introductory tutorial for compressed sensing?
If a video can get people to be interested in the matter, this is a good thing. Tutorials can be found in the Big Picture Introduction. The video was also featured in the Anti Memoirs blog. I don't read Farsi but I think the commenters liked it.
A comment on yesterday's entry written by Anonymous:
wow l1 constraints for portfolio optimization, brilliant! we actually managing money have never thought of that (and seen that it doesn't mean shit in practice). our feeble little minds just haven't been able to make the visionary leap required to go from the l2 to the l1!
l-1 regularization solvers are rooted in empirical results found in Geophysics in the 1970's. The fact that there was no theoretical justification for this technique did not deter researchers in the field to use it extensively. The portfolio paper as presented by Ingrid Daubechies in the video provides a firmer justification for using this technique. As she shows, it seems that most of the emphasis in the portofolio field has been on identifying the l_1 minimization as a way of obtaining positive solutions but little understanding seemed to have been gained from the fact that the solver usually also solved for the sparsest solutions. This is a key insight, it opens the door to techniques of compressed sensing for the acquisition of "sparse" positions. In effect, one wonders how the work on identifying good measurement matrices might provide an original investement strategy.
I realize that traders have a difficult job, especially the ones working for formerly non-FDIC regulated entities who may now be working under a different set of rules. However, this is a working paper for an international regulator (the ECB). As an average citizen, I sure would want the ECB or any other Feds to have an understanding on how to wisely spend worldwide tax-payer's money in different bail-outs. For one, we are beginning to see that commercial actors requiring bailouts is becoming non-sparse, and any insight on how to make this a sparser set would be a welcoming sight.
In another entry, Matthieu asked the following:
I'd like to know a little bit more about "It may be a good scheme for the Arduino,wink, wink :-)".
What do you mean? For what purpose do you use it?
Have you ever been considering new OMAP3 based hardware like Beagleboard and Gumstix Overo?
I was hinting to a discussion I had with another reader. This idea is that we are beginning to see many different low-cost and easy to use platforms destined to acquire signals from the physical world. How can one use these platforms to implement low cost and easily implementable CS hardware ? The random lens imager is a perfect example of this thinking (even though it still requires some work on the calibration side). The Rice single pixel camera and its illumination based sister are a little expensive but I don't think that the Hyperspectral imager at Duke is that expensive. I'll definitely come back to this idea later.
Finally, in another entry, Lloyd Belleza
hi. u have here interesting cs topics. i am an IT student and trying to look for a thesis topic. if ever you could help me come up with a topic, i would surely appreciate it. thank u
If anybody has an idea, please contact Lloyd.
Credit Photo: me. View of Greenland from 30,000 feet.