7:00 – 5:30 ASRC GOES

• Social network proximity predicts similar trajectories of psychological states: Evidence from multi-voxel spatiotemporal dynamics put this in the lit review!

  • Temporal trajectories of multivoxel patterns capture meaningful individual differences.

  • Inter-subject similarity in pattern trajectories predicts social network proximity.

  • Friends may be exceptionally similar in how attentional states evolve over time.

  • There are distinct behavioral effects of neural response pattern and magnitude trajectories.

• Irrational Politics, Unreasonable Culture: Justin Smith and Jessica Riskin held on January 29, 2020
  • Shouting and shaming, lying and trolling: How did we ever learn to speak to one another the way we do now? In matters political and cultural, public and private, on social media and in major newsrooms, it seems as though over the past few years a bizarre and frightening irrationality has taken hold of our discourse. But what is irrationality, and what is that thing—reason—with which we oppose it? 
  • Jessica Riskin is involved in some cross-cultural project at Stanford that is trying to bring the arts and STEM into closer orbits (techies and fuzzies?)? Send an email
  • Justin Smith is interested on the effects of algorithms on thinking. Can’t find any writing, but he gave a talk: “The Algorithmic Production of Social Kinds,” a lecture-performance at DAU, Paris, February 12, 2019.
• Dissertation
  • Slides
• Mission drive meeting
  • Send status report to Erik
  • 3/31/20 milestones
    • Evaluate GOES 16 and 17 high-fidelity simulators as training sources for multivariate anomaly detection
      • Evaluate transfer learning from GOES 16 <-> GOES 17
    • Evaluate reaction wheel scenarios on lofi model as training source for multivariate anomaly detection for GOES 17 and GOES 17
      • Evaluate transfer learning between models trained using hifi simulator data

7:00 – 4:00 PhD

• Dissertation. Here’s how to do a timeline (support.office.com/en-us/article/create-a-timeline). Nope – this is horrible
• Let’s try Python. Woohoo! It’s overkill, but looks great:
• Here’s the code:

  import matplotlib.pyplot as plt
  import numpy as np
  from datetime import datetime
  
  
  names = ['Le Bon', 'Arendt', 'Martindale', 'Moscovici & Doise', 'Grunbaum',
           'Kauffman', 'Card & Pirolli', 'Bacharach', 'Olfati-Saber', 'Munson & Resnick', 'Stephens',
           'Galotti', 'Bastos']
  
  dates = ['1895', '1951', '1991', '1994', '1998', '1993', '1999', '2006', '2007', '2010', '2011', '2017', '2019']
  
  namedates = []
  for i in range(len(names)):
      namedates.append("{} ({})".format(names[i], dates[i]))
  
  
  # Convert date strings (e.g. 2014-10-18) to datetime
  dates = [datetime.strptime(d, "%Y") for d in dates]
  
  # Choose some nice levels
  levels = np.tile([-5, 5, -4, 4, -3, 3, -2, 2, -1, 1],
                   int(np.ceil(len(dates)/6)))[:len(dates)]
  
  # Create figure and plot a stem plot with the date
  fig, ax = plt.subplots(figsize=(8.8, 4), constrained_layout=True)
  ax.set(title="Literature")
  
  markerline, stemline, baseline = ax.stem(dates, levels,
                                           linefmt="C3-", basefmt="k-",
                                           use_line_collection=True)
  
  plt.setp(markerline, mec="k", mfc="w", zorder=3)
  
  # Shift the markers to the baseline by replacing the y-data by zeros.
  markerline.set_ydata(np.zeros(len(dates)))
  
  # annotate lines
  vert = np.array(['top', 'bottom'])[(levels > 0).astype(int)]
  for d, l, r, va in zip(dates, levels, namedates, vert):
      ax.annotate(r, xy=(d, l), xytext=(-3, np.sign(l)*3),
                  textcoords="offset points", va=va, ha="right")
  
  # remove y axis and spines
  ax.get_yaxis().set_visible(False)
  for spine in ["left", "top", "right"]:
      ax.spines[spine].set_visible(False)
  
  ax.margins(y=0.1)
  plt.subplots_adjust(left=0.1, right= 0.9)
  plt.show()

• PhD Day
  • UMBC PROF-IT