Last week came in some pretty great results! I've been training an LRCN deep neural network to discriminate planetary light curves in partnership with the CFA.
The new desks are coming along! I only need to add lights, and then I'll call it done!
I was recently challenged to fit the entirety of Bohemian Rhapsody into 8Kb of data. I thought it might be impossible, as the original file is 5.7Mb. This would be a compression of 712.5x. However, the challenge said nothing about audio quality, so I still thought it might be possible. To begin, I cut the sample rate to 2k, the lowest any audio format I found supported, changed to opus format, and set compression to 10.
I reduced it to 120kb. That is 475x. The audio was terrible, practically turning Bohemian Rhapsody into death metal. I then decided to try using midi. Using a compressed midi, I reduced Bohemian Rhapsody to exactly 8Kb: 712.5x compression!
My roommate and I have been working to make our dorm room as amazing as possible. We currently have an Alexa device up and working (had to finagle that one with the school's network) and some very nice carpeting. I'm also creating a custom desk and two tables (pictures below). I'll update with a tour of the room once it is finished.
In partnership with Harvard's Center for Astrophysics (CfA), I have begun creating a planet classifier. The plan is to use a deep LRCN network to classify based on light curves. In the past week, I have created a PCA/SVC model to establish a baseline. In theory, the LRCN should do better, but the SVC is already significantly over chance. There were three categories: planet, eclipsing binary (often confused for planets), and junk. The ROC curves and PCA Dimensions vs Accuracy are below. Considering it's a three class problem, a 70% accuracy on validation data is pretty darn good. : )