I know regular expressions

About 12 years ago, I learned regular expressions (also learned regular language in undergrad automata class). I'm just so proud of myself everytime I use complicated replacements. I think everybody should learn it. At least, I won't say everybody should learn vim.

5ms vs 2ms with Silverman's rule

Averaged squared Hellinger divergence computed for the same dataset, just different kernel sizes.

Uncoupled oscillators

Can uncoupled oscillators with common input and output process information? For example, audio filter banks are constructed in similar manner. Each frequency components are separately processed and then combined at the end. The key is the phase distribution in case of oscillators. That's all they have. But individual oscillators can only see their own one. What type of interaction between the input and individual phase would make this a useful computational device or memory device?

Inter trial stationarity

When conducting multiple trials of an experiment, we hope to be able to assume that the trials are independent of each other and the experimental conditions are stationary. But how can we show this? Some sort of statistical test would be nice...but that would still require independence, wouldn't it? So many things to assume to release a little bit of assumption. What an irony.

SugarSync!

SugarSync is a very nice and fast file system sync tool.

It also has a web-based MUSIC PLAYER, so that I don't need to install the client and sync all the files first. When I am working on somebody else's computer this is superb. Also it syncs with my blackberry. Nice!

Point process space and spike train space

In ITL (information theoretic learning; @CNEL), instead of using a kernel between two data samples, an expectation of product of probability estimate is used as a kernel. It's called the cross information potential (CIP). Therefore, the kernel is really between statistics of two datasets, not just two sample points. Of course, when a single data point is used to estiamte the PDF, it becomes just like the normal kernel method. The inner product defined by CIP uses the statistics of the data (which can be equivalently represented by the mean of samples in the RKHS). [See Xu, Paiva, Park, Principe 2009]
Same thing is growing in my research on spike train lately. Instead of defining a kernel between two spike trains, I would first estimate point processes from a set of spike train and then create an inner product based on CIP or other cpd kernels.
The question is where this would be useful. The single trial approximation is not so useful because the structure over the trials is lost. It would be same as the mCI RKHS we proposed. [See Paiva, Park, Principe 2009] The biggest problem is how to get a nonparametric estimator for point processes. They are very high dimension!!

Donation to TeX

I donated $20 each to TUG and MikTeX. I love TeX/LaTeX!
@ $q(t \cdot \pi)$