Ganesh Swami

I came across a very cool project to rank academic journals: EigenFactor.org. Quoting the about page:

Eigenfactor.org is a non-commercial academic research project sponsored by the Bergstrom lab in the Department of Biology at the University of Washington. We aim to develop novel methods for evaluating the influence of scholarly periodicals and for mapping the structure of academic research. We are committed to sharing our findings with interested members of the public, including librarians, journal editors, publishers, and authors of scholarly articles.

The ranking is done using an algorithm similar to Google’s PageRank. Read about the other features on their “Why EigenRank?” page.

Here for example is a listing of the highest ranking (by eigenfactor) journals in Mathematical Physics:

1. PHYSICAL REVIEW E
2. JOURNAL OF PHYSICS A-MATHEMATICAL AND GE
3. COMMUNICATIONS IN MATHEMATICAL PHYSICS
4. JOURNAL OF COMPUTATIONAL PHYSICS
5. JOURNAL OF MATHEMATICAL PHYSICS
6. PHYSICA D-NONLINEAR PHENOMENA
7. JOURNAL OF STATISTICAL PHYSICS
8. COMPUTER PHYSICS COMMUNICATIONS
9. MODERN PHYSICS LETTERS A
10. CHAOS

I’m yet to figure out a way to make use of this service effectively. My day job deals with Medical Imaging, which is a form of Computer Vision, which in turn falls under Artificial Intelligence. Computational Anatomy, one of the emerging subfields of medical imaging is fairly mathematical, using ideas from group theory and functional analysis. To add more to the mix, one of the most used algorithms for image/volume segmentation is the Level Set Method, first published in the Journal of Computational Physics. I can’t judge a paper just by looking at where it’s published, can I?

I remember how expensive flat screen televisions were a couple of years back. These days it’s hard to find a CRT if you want to buy one.

(via digitimes)

John Backus died on Saturday. He was the father of FORTRAN, Turing Award winner, and one of the names behind the Backus-Naur form (Peter Naur, 2005 Turing Award winner, was the other).

…

His Turing Award lecture is also a remarkable read. Titled, “Can programming be liberated from the Von Neumann style,” it laid the groundwork for functional programming…

(via Geomblog)

Another interesting paper came out last week on the preprint archive, “A matrix generalization of Euler identity.” The Euler identity reads,

and is traditionally derived from the Taylor expansion of sine and cosine. In this paper, the author generalizes the Euler identity to matrices. He does this by introducing a complex matrix, known as the imaginary unit matrix

Just like , .

He then proves the Euler identity for these matrices as:

for all real x.

Through bbgm, I was introduced to the idea to Tumblelogs. I really like the format that is used to display content. I think limiting one post per page really helps focus the reading, and it doesn’t necessarily have to be short posts. I’m going to adopt this format for my blog over the next few months. Unfortunately, I’m currently swamped with work, so it’ll take atleast a month to see any changes on this site.