Science careers has a website called myIDP (individual development plan). You can assess your skills, interests, and values as a scientist. What should I plan according to my self-reported scores? Here's my result (click for full size image):
Saturday, January 05, 2013
Sunday, August 14, 2011
One too many blogs
I think I have one too many blogs. Every time there's a new trendy social network or blog service, I join the trend, at least to see what's going on. But I think I have one too many to properly manage them. I started writing lab notes and short thoughts on facebook and twitter, and this blog has been abandoned for a while now...maybe it's time to transform it to a food war blog?
Monday, November 15, 2010
I'm suspecting that Time Warner Cable Internet at Austin Texas is throttling my traffic
The biggest problem I have in Austin is the internet at home. At first I had such a hard time trying to get AT&T DSL to work, and eventually gave up. Then time warner cable internet seems to be working fine for a while, but then suddenly one day I end up with minimal throughput.
Especially streaming video content and non-image binary downloads! I complained a couple of times, and it returned its speed for a while, but then again, I have no traffic what so ever for specific types of media as well as over all speed.
Currently I have around 18% packet loss, 60 kbps throughput and this is unbearable. If you go to http://www.youtube.com/my_speed#, you can see your recent traffic history. According to the net, they have max download cap. WTH!
I hate to complain online, but I think I will just have to live without internet until I find a better solution. (Well I'll tether my phone which will give me at least 1Mbps in emergency.)
P.S. I am not alone:
- http://www.sagelewis.com/2010/11/13/i-think-time-warner-cable-is-throttling-and-shaping-my-bandwidth/
- http://www.satelliteguys.us/time-warner-cable-forum/201609-twc-throttling-youtube.html
Wednesday, July 21, 2010
UT Austin postdoc related info
A very inactive postdoc mailing list:
https://utlists.utexas.edu/sympa/info/postdocs
There is not university apartment for postdocs. (confirmed via email)
New employee info:
http://www.utexas.edu/hr/current/new/index.html
Calendar for UT Austin students - Fall semester starts August 20, 2010
http://registrar.utexas.edu/calendars/
Postdoc associations:
http://www.utexas.edu/ogs/postdocs/utorgs.html
Austin Newspaper
http://www.dailytexanonline.com/
https://utlists.utexas.edu/sympa/info/postdocs
There is not university apartment for postdocs. (confirmed via email)
New employee info:
http://www.utexas.edu/hr/current/new/index.html
Calendar for UT Austin students - Fall semester starts August 20, 2010
http://registrar.utexas.edu/calendars/
Postdoc associations:
http://www.utexas.edu/ogs/postdocs/utorgs.html
Austin Newspaper
http://www.dailytexanonline.com/
Sunday, May 09, 2010
Friday, February 05, 2010
"Il Park"
Sunday, January 10, 2010
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 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.
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