hs
Joined 18 years ago
Joined 18 years ago
for breast cancer: the highest offender = obesity is 8.7%, only a multiple of two to the mean <4%
to me it only means that the cause of throat and colon cancer is relatively known (smoke and meat) while it's gray area for breast cancer
smoke -> throat kinda makes sense meat -> bowel? (turns out meat contains no fiber, and lack of fiber accounts for 12.2% so lumping meat and lack of fiber equal a whopping 33.3%) ... makes sense?
FTA (scroll down): Colorectal / Bowel : meat 21.1%, obesity 13%, lack-of-fibre 12.2%
it's not surprising meat and lack-of-fibre are correlated (meat contains no fibre)
this requires a mean to identify where the unique good is (if it exists at all). would rfid fix this? hmmm shrug
then we can script to extract the xml file get the ted videos, like:
wget -c -O "1984/Nicholas Negroponte in 1984 makes 5 predictions.mp4" http://www.ted.com/talks/download/video/4837/talk/230 wget -c -O "1990/Frank Gehry as a young rebel.mp4" http://www.ted.com/talks/download/video/4047/talk/231 wget -c -O "1998/Aimee Mullins on running.mp4" http://www.ted.com/talks/download/video/5996/talk/443
...
wget -c -O "2011/Wadah Khanfar - A historic moment in the Arab world.mp4" http://www.ted.com/talks/download/video/11071/talk/1084 wget -c -O "2011/Wael Ghonim - Inside the Egyptian revolution.mp4" http://www.ted.com/talks/download/video/11086/talk/1086
but there are countries that have less #visit but more expensive (it suggests patients go to doctor not only for consultation) ... so #visit is an unreliable predictor so is cost
to me it seems the most predictive is location (yes, the country names are data points too) ... if i want to live long, i'll settle in japan and do what the average japanese for their healthy lifestyle
age/cost (the slope)
also, how do you identify your datapoints in scatter-plot neatly?
to me this graph gives surprisingly rich information packed
the china emperor: that's maybe because there's no photograph of the emperor circulating. if there is, one can make comparison to eyes, age, coin, ground-legal-measure etc and clean out the outlier data. averaging the rest can give good enough estimate but not super accurate.
the scrapper was written in newlisp (save search result pages with curl, use regex to match and collect the ids). it's probably easier to write in other languages, but that's what i knew.
i used wget and curl to loop over the ids but it's too slow because they download the whole page. later i found out about 'curl -i' (header only) and a million ids was done in about hour or two (i moved my operation from my home's 64kbps to my colo datacenter mbps internet).
my account is no longer exist (probably banned); however, i do still have a screenshot of me having 3000 female-only-friends and 70000 non-hidden-females 'look back at my profile'
initially, i talked to any interesting woman; however, later i made a strict rule to only respond to women who wrote at me. there were just too many fake female-accounts.
i got a couple of dates from this feat; however, i met my wife in a traditional catholic youth retreat. when i let her know about this friendster thingy, she just laughed. now i'm happily married with a 15-months-old boy.