JSMB/SMB talk

I just gave an invited plenary talk at the joint annual meeting of the Japanese Society of Mathematical Biology and Society of Mathematical Biology in Osaka Japan.  I talked about my work on steroid-regulated gene transcription.  The slides are here.  Previous posts on the topic, including background summaries, can be found here.

Tim’s Vermeer

Jan Vermeer has been one of my favourite painters ever since I saw his famous “The Girl with the Pearl Earring” painting that was on display in Toronto in the 1980’s. I’ve been on a quest to see all of his paintings although its been on hiatus for the past ten years. Here is the list of what I’ve seen so far (I have five left). You only need to stand in front of a Vermeer for a few seconds to be mesmerized. I stood in front of “The Music Lesson” in Buckingham Palace for at least an hour. The guard started joking with me because I was so transfixed. This is why I’ve been intrigued by recent suggestions by artist David Hockney and others that some great old masters like Vermeer and van Eyck may have used optical aids like camera obscura. Well, inventor Tim Jenison has taken this theory to another level by attempting to completely recreate Vermeer’s Music Lesson using a set up of mirrors and lenses that he (re)invented. The endeavor is documented in the film Tim’s Vermeer directed by Teller of Penn and Teller fame. Whether you believe the theory or not (I actually do and it doesn’t detract at all for my love of Vermeer), what this film does do so well is to show what dedication, thought, patience, and careful execution can accomplish. I got tired just watching him paint the threads in a Persian rug using his optical tool.

Selection of the week

What is called classical music mostly refers to the Western symphony orchestra tradition that starts in the seventeenth century with Vivaldi and peaks in the early twentieth with Mahler. While classical music remains popular, my unscientific sampling of concert hall audiences indicates that the demographic skews to retirement age and above. I don’t know if this means that a generation of music lovers is about to depart or that people only have the patience to sit through a long concert when they are older. In an attempt to introduce a new generation to classical music, I thought I would present a selection each week. And what’s a better way to kick it off then with the pseudo-Baroque precursor to heavy metal, the Praeludium and Allegro by Fritz Kreisler. Kreisler performed in the first half of the twentieth century. He was one of the greatest violin virtuosos of all time and also wrote some great violin ditties. Here is a performance by the then 13 year old Canadian/American violinist Leila Josefowicz in 1991.

Talk at Jackfest

I’m currently in Banff, Alberta for a Festschrift for Jack Cowan (webpage here). Jack is one of the founders of theoretical neuroscience and has infused many important ideas into the field. The Wilson-Cowan equations that he and Hugh Wilson developed in the early seventies form a foundation for both modeling neural systems and machine learning. My talk will summarize my work on deriving “generalized Wilson-Cowan equations” that include both neural activity and correlations. The slides can be found here. References and a summary of the work can be found here. All videos of the talks can be found here.

 

Addendum: 17:44. Some typos in the talk were fixed.

Addendum: 18:25. I just realized I said something silly in my talk.  The Legendre transform is an involution because the transform of the transform is the inverse. I said something completely inane instead.

Zero Matlab 2015

So I have bitten the bullet and am committed to phasing out Matlab completely by 2015.  I have Julia installed and can sort of run code in it although I have no idea how to change directories.  cd() doesn’t seem to work for me.  I have also tried to install matplotlib on my MacBook Pro running OS 10.8 using a SciPy superpack but it does not seem to work yet.  When I try to plot something, nothing happens but I have some bouncing rocket ships in my dock.  Feel free to let me know what I’m doing wrong.

 

Addendum: I installed iPython using Pip and I can plot out of iPython

The need for speed

Thanks for all the comments about the attributes of Python and Julia. It seems to me that the most prudent choice is to learn Python and Julia. However, what I would really like to know is just how fast these languages really are and here is the test. What I want to do is to fit networks of coupled ODEs (and  PDEs) to data using MCMC (see here). This means I need a language that loops fast. An example in pseudo-Matlab code would be

for n = 1:N

for i = 1:T

y(i+1) = M\y(i)

end

Compare to data and set new parameters

end

where h is a parameter and M is some matrix (say 1000 dimensional), which is sometimes a Toeplitz matrix but not always. Hence, in each time step I need to invert a matrix, which can depend on time so I can’t always precompute, and do a matrix multiplication. Then in each parameter setting step I need to sum an objective function like the mean square error over all the data points. The code to do this in C or Fortran can be pretty complicated because you have to keep track of all the indices and call linear algebra libraries. I thus want something that has the simple syntax of Matlab but is as fast as C. Python seems to be too slow for our needs but maybe we haven’t optimized the code. Julia seems like the perfect fit but let me know if I am just deluded.