6:00 – 2:30 ASRC PhD, NASA

• Antonio didn’t make much in the way of modifications, so I think the paper is now done. Ask tomorrow if it’s alright to put this version on ArXive.
• ‘Nothing on this page is real’: How lies become truth in online America
  • A new message popped onto Blair’s screen from a friend who helped with his website. “What viral insanity should we spread this morning?” the friend asked. “The more extreme we become, the more people believe it,” Blair replied.
  • “No matter how racist, how bigoted, how offensive, how obviously fake we get, people keep coming back,” Blair once wrote, on his own personal Facebook page. “Where is the edge? Is there ever a point where people realize they’re being fed garbage and decide to return to reality?”
• Blind appears to be the LinkedIn version of Secret/Whisper
  • Blind is an anonymous social networking platform for professionals. Work email-verified professionals can connect with coworkers and other company/industry professionals by holding meaningful conversations on a variety of different topics.
• Started reading Third Person. It really does look like the literature is thin:
  • A crucial consideration when editing our previous volume, Second Person, was to give close attention to the underexamined area of tabletop role-playing games. Generally speaking, what scholarly consideration these games have received has cast them as of historical interest, as forerunners of today’s digital games. In his chapter here, Ken Rolston-the designer of major computer role-playing games such as the Elder Scrolls titles Morrowind and Oblivion-says that his strongest genre influences are tabletop RPGs and live-action role-playing (IARP) games. He considers nonwired RPGs to be a continuing vital force, and so do we. (Page 7)
• Quick meeting with Wayne
  • CHI Play 2019
    • CHI PLAY is the international and interdisciplinary conference (by ACM SIGCHI) for researchers and professionals across all areas of play, games and human-computer interaction (HCI). We call this area “player-computer interaction.”  22–25 October 2019
  • Conversation Map: An Interface for Very-Large-Scale Conversations
    • Very large-scale conversation (VLSC) involves the exchange of thousands of electronic mail (e-mail) messages among hundreds or thousands of people. Usenet newsgroups are good examples (but not the only examples) of online sites where VLSCs take place. To facilitate understanding of the social and semantic structure of VLSCs, two tools from the social sciences—social networks and semantic networks—have been extended for the purposes of interface design. As interface devices, social and semantic networks need to be flexible, layered representations that are useful as a means for summarizing, exploring, and cross-indexing the large volumes of messages that constitute the archives of VLSCs. This paper discusses the design criteria necessary for transforming these social scientific representations into interface devices. The discussion is illustrated with the description of the Conversation Map system, an implemented system for browsing and navigating VLSCs.
  • Terra Nova blog
  • Nic Ducheneaut
    • My research pioneered the use of large-scale, server-side data for modeling behavior in video games. At Xerox PARC I founded the PlayOn project, which conducted the longest and largest quantitative study of user behavior in World of Warcraft (500,000+ players observed over 5 years). At Ubisoft, I translated my findings into practical recommendations for both video game designers and business leaders. Today, as the co-founder and technical lead of Quantic Foundry, I help game companies bridge analytics and game design to maximize player engagement and retention.
  • Nick Yee
    • I’m the co-founder and analytics lead of Quantic Foundry, a consulting practice around game analytics. I combine social science, data science, and an understanding of the psychology of gamers to generate actionable insights in gameplay and game design.
  • Celia pierce
    • Celia Pearce is a game designer, artist, author, curator, teacher, and researcher specializing in multiplayer gaming and virtual worlds, independent, art, and alternative game genres, as well as games and gender. 
  • T. L. Taylor
    • T.L. Taylor is is a qualitative sociologist who has focused on internet and game studies for over two decades. Her research explores the interrelations between culture and technology in online leisure environments. 
  • MIT10: A Reprise – Democracy and Digital Media
    • Paper proposals might address the following topics/issues:
      • politics of truth/lies, alternative facts
      • media, authoritarianism, and polarization
      • diversity in gaming / livestreaming / esports
      • making or breaking publics with algorithmic cultures/machine learning/AI
      • environmental media (from medium theory to climate change) and activism
      • media infrastructures as public utilities or utility publics?
      • social media, creating consensus, and bursting filter bubbles
      • designing media technologies for inclusion
      • the #metoo movement and its impact
      • social media platforms (FaceBook, Twitter, Instagram, etc), politics, and civic responsibility
      • Twitter, viral videos, and the new realities of political advertising
    • Please submit individual paper proposals, which should include a title, author(s) name, affiliation, 250-word abstract, and 75-word biographical statement to this email address: media-in-transition@mit.edu — by February 1, 2019. Early submissions are encouraged and we will review them on a rolling basis. Full panel proposals of 3 to 4 speakers can also be submitted, and should include a panel title and the details listed above for each paper, as well as a panel moderator. We notify you of the status of your proposals by February 15, 2019 at the latest.
• Continuing Characterizing Online Public Discussions through Patterns of Participant Interactions. Sheesh, that’s a long article. 21 pages!
• More Grokking: Here’s a very simple full NN:

  # based on https://github.com/iamtrask/Grokking-Deep-Learning/blob/master/Chapter6%20-%20Intro%20to%20Backpropagation%20-%20Building%20Your%20First%20DEEP%20Neural%20Network.ipynb
  import numpy as np
  import matplotlib.pyplot as plt
  
  # variables ------------------------------------------
  
  # one weight for each column (or light - the things we're sampling)
  weight_array = np.random.rand(3)
  alpha = 0.1
  
  # the samples. Columns are the things we're sampling
  streetlights_array = np.array([[1, 0, 1],
                                 [ 0, 1, 1 ],
                                 [ 0, 0, 1 ],
                                 [ 1, 1, 1 ],
                                 [ 0, 1, 1 ],
                                 [ 1, 0, 1 ]])
  
  # The data set we want to map to. Each entry in the array matches the corresponding streetlights_array roe
  walk_vs_stop_array = np.array([0, 1, 0, 1, 1, 0])
  
  error_plot_mat = [] # for drawing plots
  weight_plot_mat = [] # for drawing plots
  iter = 0
  max_iter = 1000
  epsilon = 0.001
  total_error = 2 * epsilon
  
  while total_error > epsilon:
      total_error = 0
      for row_index in range(len(walk_vs_stop_array)):
          input_array = streetlights_array[row_index]
          goal_prediction = walk_vs_stop_array[row_index]
  
          prediction = input_array.dot(weight_array)
          error = (goal_prediction - prediction) ** 2
          total_error += error
  
          delta = prediction - goal_prediction
          weight_array = weight_array - (alpha * (input_array * delta))
  
          print("[{}] Error: {}, Weights: {}".format(iter, total_error, weight_array))
          error_plot_mat.append([total_error, error])
          weight_plot_mat.append(weight_array.copy())
  
          iter += 1
          if iter > max_iter:
              break
  
  
  f1 = plt.figure(1)
  plt.plot(error_plot_mat)
  plt.title("error")
  plt.legend(["total_error", "error"])
  #f1.show()
  
  f2 = plt.figure(2)
  plt.plot(weight_plot_mat)
  names = []
  for i in range(len(weight_array)):
      names.append("weight[{}]".format(i))
  plt.legend(names)
  plt.title("weights")
  
  #f2.show()
  plt.show()

• And here is it learning
  

  

7:00 – ASRC PhD/BD/NASA

• Started to write up the study design for Belief Spaces/Places in a Google doc
• More Grokking ML – ok progress
  • GitHub repo
• Riot – a glossy Matrix collaboration client for the web. http://riot.im
• Let‘s Chat is a persistent messaging application that runs on Node.js and MongoDB. It’s designed to be easily deployable and fits well with small, intimate teams. (GitHub)
• Mattermost is an open source, self-hosted Slack-alternative. As an alternative to proprietary SaaS messaging, Mattermost brings all your team communication into one place, making it searchable and accessible anywhere. It’s written in Golang and React and runs as a production-ready Linux binary under an MIT license with either MySQL or Postgres. (GitHub)
• PHPbb is hosted on Dreamhost
• Sprint planning
• Analysis of visitors’ mobility patterns through random walk in the Louvre museum
  • This paper proposes a random walk model to analyze visitors’ mobility patterns in a large museum. Visitors’ available time makes their visiting styles different, resulting in dissimilarity in the order and number of visited places and in path sequence length. We analyze all this by comparing a simulation model and observed data, which provide us the strength of the visitors’ mobility patterns. The obtained results indicate that shorter stay-type visitors exhibit stronger patterns than those with the longer stay-type, confirming that the former are more selective than the latter in terms of their visitation type.
• Same Story, Different Story The Neural Representation of Interpretive Frameworks
  • Differences in people’s beliefs can substantially impact their interpretation of a series of events. In this functional MRI study, we manipulated subjects’ beliefs, leading two groups of subjects to interpret the same narrative in different ways. We found that responses in higher-order brain areas—including the default-mode network, language areas, and subsets of the mirror neuron system—tended to be similar among people who shared the same interpretation, but different from those of people with an opposing interpretation. Furthermore, the difference in neural responses between the two groups at each moment was correlated with the magnitude of the difference in the interpretation of the narrative. This study demonstrates that brain responses to the same event tend to cluster together among people who share the same views.
• Similar neural responses predict friendship
  • Computational Social Neuroscience Lab 
    • The Computational Social Neuroscience Lab is located in the Department of Psychology at UCLA.We study how our brains allow us to represent and navigate the social world. We take a multidisciplinary approach to research that integrates theory and methods from cognitive neuroscience, machine learning, social network analysis, and social psychology.
  • Authors
    • Carolyn Parkinson (Scholar)
    •  Adam M. Kleinbaum (Scholar)
    • Thalia Wheatley (Scholar)
  • Research has borne out this intuition: social ties are forged at a higher-than expected rate between individuals of the same age, gender, ethnicity, and other demographic categories. This assortativity in friendship networks is referred to as homophily and has been demonstrated across diverse contexts and geographic locations, including online social networks [2, 3, 4, 5] (Page 2)
  • When humans do forge ties with individuals who are dissimilar from themselves, these relationships tend to be instrumental, task-oriented (e.g., professional collaborations involving people with complementary skill sets [7]), and short-lived, often dissolving after the individuals involved have achieved their shared goal. Thus, human social networks tend to be overwhelmingly homophilous [8]. (Page 2)
    • This means that groups can be more efficient, but prone to belief stampede
  • Remarkably, social network proximity is as important as genetic relatedness and more important than geographic proximity in predicting the similarity of two individuals’ cooperative behavioral tendencies [4] (Page 2)
  • how individuals interpret and respond to their environment increases the predictability of one another’s thoughts and actions during social interactions [14], since knowledge about oneself is a more valid source of information about similar others than about dissimilar others. (Page 2)
    • There is a second layer on top of this which may be more important. How individuals respond to social cues (which can have significant survival value in a social animal) may be more important than day-to-day reactions to the physical environment.
  • Here we tested the proposition that neural responses to naturalistic audiovisual stimuli are more similar among friends than among individuals who are farther removed from one another in a real-world social network. Measuring neural activity while people view naturalistic stimuli, such as movie clips, offers an unobtrusive window into individuals’ unconstrained thought processes as they unfold [16] (page 2)
  • Social network proximity appears to be significantly associated with neural response similarity in brain regions involved in attentional allocation, narrative interpretation, and affective responding (Page 2)
  • We first characterized the social network of an entire cohort of students in a graduate program. All students (N = 279) in the graduate program completed an online survey in which they indicated the individuals in the program with whom they were friends (see Methods for further details). Given that a mutually reported tie is a stronger indicator of the presence of a friendship than an unreciprocated tie, a graph consisting only of reciprocal (i.e., mutually reported) social ties was used to estimate social distances between individuals. (Page 2)
    • I wonder if this changes as people age. Are there gender differences?
  • The videos presented in the fMRI study covered a range of topics and genres (e.g., comedy clips, documentaries, and debates) that were selected so that they would likely be unfamiliar to subjects, effectively constrain subjects’ thoughts and attention to the experiment (to minimize mind wandering), and evoke meaningful variability in responses across subjects (because different subjects attend to different aspects of them, have different emotional reactions to them, or interpret the content differently, for example). (Page 3)
    • I think this might make the influence more environmental than social. It would be interesting to see how a strongly aligned group would deal with a polarizing topic, even something like sports.
  • Mean response time series spanning the course of the entire experiment were extracted from 80 anatomical regions of interest (ROIs) for each of the 42 fMRI study subjects (page 3)
    • 80 possible dimensions. It would be interesting to see this in latent space. That being said, there is no dialog here, so no consensus building, which implies no dimension reduction.
  • To test for a relationship between fMRI response similarity and social distance, a dyad-level regression model was used. Models were specified either as ordered logistic regressions with categorical social distance as the dependent variable or as logistic regression with a binary indicator of reciprocated friendship as the dependent variable. We account for the dependence structure of the dyadic data (i.e., the fact that each fMRI subject is involved in multiple dyads), which would otherwise underestimate the standard errors and increase the risk of type 1 error [20], by clustering simultaneously on both members of each dyad [21, 22].
  • For the purpose of testing the general hypothesis that social network proximity is associated with more similar neural responses to naturalistic stimuli, our main predictor variable of interest, neural response similarity within each student dyad, was summarized as a single variable. Specifically, for each dyad, a weighted average of normalized neural response similarities was computed, with the contribution of each brain region weighted by its average volume in our sample of fMRI subjects. (Page 3)
  • To account for demographic differences that might impact social network structure, our model also included binary predictor variables indicating whether subjects in each dyad were of the same or different nationalities, ethnicities, and genders, as well as a variable indicating the age difference between members of each dyad. In addition, a binary variable was included indicating whether subjects were the same or different in terms of handedness, given that this may be related to differences in brain functional organization [23]. (page 3)
  • Logistic regressions that combined all non-friends into a single category, regardless of social distance, yielded similar results, such that neural similarity was associated with a dramatically increased likelihood of friendship, even after accounting for similarities in observed demographic variables. More specifically, a one SD increase in overall neural similarity was associated with a 47% increase in the likelihood of friendship (logistic regression: ß = 0.388; SE = 0.109; p = 0.0004; N = 861 dyads). Again, neural similarity improved the model’s predictive power above and beyond observed demographic similarities, χ2(1) = 7.36, p = 0.006. (Page 4)
  • To gain insight into what brain regions may be driving the relationship between social distance and overall neural similarity, we performed ordered logistic regression analyses analogous to those described above independently for each of the 80 ROIs, again using cluster-robust standard errors to account for dyadic dependencies in the data. This approach is analogous to common fMRI analysis approaches in which regressions are carried out independently at each voxel in the brain, followed by correction for multiple comparisons across voxels. We employed false discovery rate (FDR) correction to correct for multiple comparisons across brain regions. This analysis indicated that neural similarity was associated with social network proximity in regions of the ventral and dorsal striatum … Regression coefficients for each ROI are shown in Fig. 6, and further details for ROIs that met the significance threshold of p < 0.05, FDR-corrected (two tailed) are provided in Table 2. (Page 4)
    • So the latent space that matters involves something on the order of 7 – 9 regions? I wonder if the actions across regions are similar enough to reduce further. I need to look up what each region does.
  • 
  • Results indicated that average overall (weighted average) neural similarities were significantly higher among distance 1 dyads than dyads belonging to other social distance categories … distance 4 dyads were not significantly different in overall neural response similarity from dyads in the other social distance categories. All reported p-values are two-tailed. (Page 4)
  • Within the training data set for each data fold, a grid search procedure [24] was used to select the C parameter of a linear support vector machine (SVM) learning algorithm that would best separate dyads according to social distance. (Page 5)
  • As shown in Fig. 8, the classifier tended to predict the correct social distances for dyads in all distance categories at rates above the accuracy level that would be expected based on chance alone (i.e., 25% correct), with an overall classification accuracy of 41.25%. Classification accuracies for distance 1, 2, 3, and 4 dyads were 48%, 39%, 31%, and 47% correct, respectively. (Page 6)
  • where the classifier assigned the incorrect social distance label to a dyad, it tended to be only one level of social distance away from the correct answer: when friends were misclassified, they were misclassified most often as distance 2 dyads; when distance 2 dyads were misclassified, they were misclassified most often as distance 1 or 3 dyads, and so on. (Page 6)
  • The results reported here are consistent with neural homophily: people tend to be friends with individuals who see the world in a similar way. (Page 7)
  • Brain areas where response similarity was associated with social network proximity included subcortical areas implicated in motivation, learning, affective processing, and integrating information into memory, such as the nucleus accumbens, amygdala, putamen, and caudate nucleus [27, 28, 29]. Social network proximity was also associated with neural response similarity within areas involved in attentional allocation, such as the right superior parietal cortex [30,31], and regions in the inferior parietal lobe, such as the bilateral supramarginal gyri and left inferior parietal cortex (which includes the angular gyrus in the parcellation scheme used [32]), that have been implicated in bottom-up attentional control, discerning others’ mental states, processing language and the narrative content of stories, and sense-making more generally [33, 34, 35]. (Page 7)
  • However, the current results suggest that social network proximity may be associated with similarities in how individuals attend to, interpret, and emotionally react to the world around them. (Page 7)
    • Both the environmental and social world
  • A second, not mutually exclusive, possibility pertains to the “three degrees of influence rule” that governs the spread of a wide range of phenomena in human social networks [43]. Data from large-scale observational studies as well as lab-based experiments suggest that wide-ranging phenomena (e.g., obesity, cooperation, smoking, and depression) spread only up to three degrees of geodesic distance in social networks, perhaps due to social influence effects decaying with social distance to the extent that the they are undetectable at social distances exceeding three, or to the relative instability of long chains of social ties [43]. Although we make no claims regarding the causal mechanisms behind our findings, our results show a similar pattern. (Page 8)
    • Does this change with the level of similarity in the group?
  • pre-existing similarities in how individuals tend to perceive, interpret, and respond to their environment can enhance social interactions and increase the probability of developing a friendship via positive affective processes and by increasing the ease and clarity of communication [14, 15]. (Page 8)

Finished Meltdown. Need to write up some notes.

Think about using a CMAC or Deep CMAC for function learning, because NIST. Also, can it be used for multi-dimensional learning?

• Cerebellar model articulation controller
• Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller
• RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology
  • A hybrid control system, integrating principal and compensation controllers, is developed for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. This hybrid control system is based on sliding-mode technique and uses a recurrent cerebellar model articulation controller (RCMAC) as an uncertainty observer. The principal controller containing an RCMAC uncertainty observer is the main controller, and the compensation controller is a compensator for the approximation error of the system uncertainty. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. The Taylor linearization technique is employed to increase the learning ability of RCMAC and the adaptive laws of the control system are derived based on Lyapunov stability theorem and Barbalat’s lemma so that the asymptotical stability of the system can be guaranteed. Finally, the proposed design method is applied to control a biped robot. Simulation results demonstrate the effectiveness of the proposed control scheme for the MIMO uncertain nonlinear system
• Github CMAC TF projects