Saturday, 28 September 2013
In part 2 of the Spiderman series, Dr Boob looks at the amazing properties of spider silk and how Peter Parker might harness various technologies to appropriately use it.
It's the final show from Dr Boob for a while and we will miss him greatly! But he's not disappearing completely - show him you care over on twitter - @doctor_boob
Tune in to this episode here.
Cover by Nippoten
Songs in this episode:
Sunday, 15 September 2013
This is our last Science of superheroes for a while so we thought we'd look at one of the big guys. Over two episodes, Dr Boob examines Spiderman and in episode one, he specifically looks at how to manipulate Peter Parker's DNA using a virus to transport engineered DNA into his cells. It is by changing his genetic structure that we can allow him to have his superhero abilities, which for Spiderman are largely exaggerated spider traits as well as something called a "Spidey sense".
Tune in to this episode here.
Cover image from NanAmy-BoT
Songs in the podcast by:
The popularity of Triple J's annual Hottest 100 has made my wonder what my favourite songs of all time are and whether I could come up with a list based on some actual data. The information I have to use is my iTunes data since 2005. Being only 8 years of my life, this data set is limited. But with any luck (that is, if the assumptions hold true) the following algorithms will stay appropriate into the future and require only minor tweaking. What we're trying to do is come up with a method that will tell me, from my listening habits in iTunes, what my favourite songs are. Whether you actually listen to your favourite songs more than others is a debate for another time.
iTunes doesn't tell you when songs were played, just how many times, so the useful parameters we can export for each song are "Play Count" (p) and "Date Added". If we add up all the individual play counts, we get the "Total Play Count" for the entire collection (P). Date Added can be turned into the number of days the song has been in the collection - time (t). We also know the number of songs in the collection now (N) and at various times in the past when I've exported the data.
An easy first-cut model is to simply divide each song's play count by its time in the collection and order the songs by this rate of play. As a first attempt this may seem logical, however the problem is that it is heavily biased towards newer songs. You're likely to listen to a song a few times after you add it before it slips back into your various playlists. It also doesn't take into account that there are more songs in the collection now than at the start.
What we need to do is come up with an equation that tells us how many times a song is expected to have been played depending on when it was added. We can then compare this number to how many times it was actually played and order the songs by this ratio.
This second version suffers from the same biasing problem as the first, but does take into account that the number of songs in the collection is changing over time. This is important as if you assume that you listen to music for about the same amount of time each day, then the more songs you have in your collection, the less likely you are to randomly hear the same song twice. Hence, songs that are played regularly when the collection is small should not be treated in the same way as songs played with the same frequency when the collection is large. N0 is the number of songs in the collection at t0. This model assumes that the number of songs in the collection grows linearly over time (A and B are constants) - that is, the same number of songs are added each month. This is about right for my collection. The integration is left as an exercise for the reader (hint, you get a log function).
This final version takes into account that when you add new songs to your collection that you like, you are likely to listen to them quite a lot, independently of the number of songs that are already there - that is, they get added to a "new songs" playlist. The novelty of a new song eventually wears off, so the way we've modelled this is to use an exponential factor. You can tweek the coefficients (C and D) by thinking about the "half life" of a new song. The integration is left as an exercise for the reader (hint, you get a log function and an exponential).
The equation now contains two components - the first modelling the number of plays expected through random play and the second the impact of adding new songs to the collection. The model suggests that I play the same number of songs each year (apart from a barely perceptible increase due to the exponential factor) and it seems to work pretty well. This model won't work if and when I swap over to streaming music, as opposed to owning it, as my major form of music consumption, but for now it's holding up. Having played around with the coefficients, the list as it stands is below. It pretty much represents upbeat songs I go running with and songs my 2-year old likes - for whatever reason, he likes Korean pop music! I have to think that the novelty of Psy will wear off over time, but Hall and Oates, they'll never die.