"Il Park"

Pheww...

This is why I don't use my real name. This one time I was so tired so I walked like a ghost to get some hot chocolate (coffee is too strong for me), I forgot to give one of my fast food names; Michael, Peter, Andy, Andrew, and what not.

This is what happens....I become ill.

Current Problems

1. Kernel size problem

How can I obtain the kernel size from the data? I fix a kernel size for 1D and scale it appropriately to all dimensions. So for each dimension, I considering the scale of analysis to be equal -- this is against the PDF estimation principle. For each dimension if I want the best kernel size, I should estimate the kernel size every time. Instead, I am using the kernel size like the scale analysis parameter for spike train distance methods. But again, this contradicts with the scaling of kernel size for both number of samples and dimension.
I have too little data spread in many different spaces to obtain a nearest neighbor or variance based kernel size.

2. Cutting and Stitching long spike trains

For a smaller number of spike trains, it is advisable to control the spike count distribution to be concentrated on small dimensions. But then, if the discriminability is not within that chosen interval, it would not work.
If we try to perform multiple hypothesis tests and combine the results, we would suffer from the multiple comparison problem. Adjustments to the significance size such as Bonferroni correction is required.

How to get to UF Shands CDC for hospotal scrubs

Go to the east end of the hospital where the gift shop is.
Take the elevator for the basement.
The door with the buzzer is the place you are looking for.

better estimator...but why?

Instead of Monte Carlo integrating the Hellinger divergence directly, if I use the Phi-divergence form (modified to be symmetric), then both the convergence speed is faster and the power of the hypothesis test is faster...at least in the Poisson vs PTST case.

Old:


New:

Total variation (Phi-divergence)

Synchrony detectors

My paper on a new point process model got rejected because of the estimation method I introduced was far from complete. I was wondering if I should try to decompose signals into precisely timed components and others. That would be more practical, and I can use the existing synchrony detection methods on multiple trials to detect those events. Hmmm

Incognito mode of Chrome

I love the incognito mode key combination of google chrome browser, "Ctrl-Shift-N".
It instantly forks a fresh browser process that does not share any cookies with my other browser windows. I use it whenever I see a suspicious link that someone sent or is 'tinyurl'ed. These can be dangerous because they can sometimes use the already logged in brower/tab's cookies to do stuff. For example, the viral twitter applications use this to send messages to people you follow, without you noticing (you just click a wrong button). I can't wait to see how Chrome will be advancing with plugins.

Q: Integrative management of personal information

My data are not integrated nor very well organized. I use a somewhat distributed and categorized storage system, but I really wish I had something that can do the following. Does anybody know such a software system?

Requirements
1. backed up: nothing is more frustrating than loosing all your information at once
2. secure: I want to be able to dump all the sensitive information in my brain to somewhere
3. control I want to be the person who controls my information, not bound by some EULA that I won't read.
4. widely used data format: in case I want to migrate the data to some other format in the future
5. keyboard only navigation: I don't like moving my hand to the mouse too often
6. simple hierarchical organization + tags
7. fast text search
8. versioning