Phil 6.21.17

6:45 – 7:45

  • Got the travel info from HCIC. Looks like they got tickets just for the conference, and I don’t have the energy to try to get that fixed. I’ll just bring my running shoes.
  • Multiple movement modes by large herbivores at multiple spatiotemporal scales
    • Interestingly, the rate of movement by individuals depended strongly on the amount of time they spent in groups, with highly gregarious individuals being much more sedentary than more solitary individuals (19). Far-roaming, solitary individuals had a higher risk of mortality than did more sedentary, gregarious individuals (19). Hence, sociality triggered changes in movement modes that had important demographic consequences.
  • Socially informed random walks: incorporating group dynamics into models of population spread and growth (Ref 19 from above)
  • Behavioral Change Point Analysis
    • in Python (and a bridge to [R])
    • Change-Point Analysis: A Powerful New Tool For Detecting Changes
      • Change-point analysis is a powerful new tool for determining whether a change has taken place. It is capable of detecting subtle changes missed by control charts. Further, it better characterizes the changes detected by providing confidence levels and confidence intervals. When collecting online data, a change-point analysis is not a replacement for control charting. But, because a change-point analysis can provide further information, the two methods can be used in a complementary fashion. When analyzing historical data, especially when dealing with large data sets, change-point analysis is preferable to control charting. A change-point analysis is more powerful, better characterizes the changes, controls the overall error rate, is robust to outliers, is more flexible and is simpler to use. This article describes how to perform a change-point analysis and demonstrates its capabilities through a number of examples.
  • The Similarity Metric
    • We propose a new “normalized information distance”, based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the similarity metric. This theory forms the foundation for a new practical tool.

9:00 – 5:00 BRI

  • Got FoxyProxy working and downloaded the data. The clustering seems non-optimal. Rerunning locally to see what’s going on
  • Need to write an adaptor to build a common excel files from subsequent clustering runs
  • Need to get the previous run. Got the input and output files for both. They are the same length, which is odd. Pinging Heath. These are both older data, clustered differently. What I’ve pulled is the newest data.
  • Meeting on remote dev environments. Lots of security discussions. My main concerns are on usability WRT bandwidth and network quality.
  • Dug up the CSE billing info and updated the credit cards
  • Adding method in membership_history_builder.py that will organize multiple cluster files into a single dataframe.
    • Built the set containing the common elements
    • Need to build  Dataframe of the correct dimensions and populate it
    • Need to run the membership code over that and plot the results