Phil 5.29.18

Insane, catastrophic rain this weekend. That’s the top of a guardrail in the middle of the scene below:

7:00 – 4:30 ASRC MKT

  • The Neural Representation of Social Networks
    • The computational demands associated with navigating large, complexly bonded social groups are thought to have significantly shaped human brain evolution. Yet, research on social network representation and cognitive neuroscience have progressed largely independently. Thus, little is known about how the human brain encodes the structure of the social networks in which it is embedded. This review highlights recent work seeking to bridge this gap in understanding. While the majority of research linking social network analysis and neuroimaging has focused on relating neuroanatomy to social network size, researchers have begun to define the neural architecture that encodes social network structure, cognitive and behavioral consequences of encoding this information, and individual differences in how people represent the structure of their social world.
  • This website is amazing, linear algebra with interactive examples. Vectors, matrix, dot product, etc, cool resource for learning
  • Web Literacy for Student Fact-Checkers: …and other people who care about facts.
    • Author: Mike Caulfield
    • We Should Put Fact-Checking Tools In the Core Browser
      • Years ago when the web was young, Netscape (Google it, noobs!) decided on its metaphor for the browser: it was a “navigator”. <—— this!!!!
        • Navigator: a person who directs the route or course of a ship, aircraft, or other form of transportation, especially by using instruments and maps.
        • Browser: a person who looks casually through books or magazines or at things for sale.
  • Deep Learning Hunts for Signals Among the Noise
    • Interesting article that indicates that deep learning generalizes through some form of compression. If that’s true, then the teurons and layers are learning how to coordinate (who recognizes what), which means dimension reduction and localized alignment (what are the features that make a person vs. a ship). Hmmm.
  • More Bit by Bit
  • Really enjoying Casualties of Cool, btw. Lovely sound layering. Reminds me of Dark Side of the Moon / Wish you were here Pink Floyd
  • Why you need to improve your training data, and how to do it
    • sleep_lost1
  • No scrum today
  • Travel briefing – charge to conference code
  • Complexity Explorables
    • Ride my Kuramotocycle!
      • This explorable illustrates the Kuramoto model for phase coupled oscillators. This model is used to describe synchronization phenomena in natural systems, e.g. the flash synchronization of fire flies or wall-mounted clocks. The model is defined as a system of NN oscillators. Each oscillator has a phase variable θn(t)θn(t) (illustrated by the angular position on a circle below), and an angular frequency ωnωn that captures how fast the oscillator moves around the circle.
    • Into the Dark
      • This explorable illustrates how a school of fish can collectively find an optimal location, e.g. a dark, unexposed region in their environment simply by light-dependent speed control. The explorable is based on the model discussed in Flock’n Roll, which you may want to explore first. This is how it works: The swarm here consists of 100 individuals. Each individual moves around at a constant speed and changes direction according to three rules
  • More cool software: is a powerful open source geospatial analysis tool for large-scale data sets.
  • White paper. Good progress! I like the conclusions