Phil 6.22.18

7:00 – 5:30 ASRC MKT

  • Twitter experiment on a fake Gary Indiana secession. IFTTT retweeting leads to interesting behavior.
  • Fixed FlockingShape casting by adding a customDrawStep(GraphicsContext gc) to the SmartShape base class that’s called from draw().
  • Add records to each agent that store a list of source and agent influences at each time sample. It should include the name of the item and the amount of influence. Probably save as an XML file, since it has too many dimensions. The file could then be used to create terms or spreadsheets.
    • Started on CAInfluence class which will be added to CA classes in an arrayList in BaseCA;
  • More file conversion with Bob
  • Project MERCATOR proposal
  • Meeting with Sy

Phil 6.21.18

7:00 – 4:00 ASRC MKT

  • Add an attractor scalar for agents that’s normally zero. A vector to each agent within the SIH is calculated and scaled by the attractor scalar. That vector is then added to the direction vector to the agent – done
  • Remove the heading influence based on site – done
  • Add a white circle to the center of the agent that is the size of the attraction scalar. Done
  • Add attraction radius slider that is independent of the SIH. -done
  • Add a ‘site trajectory’ to the spreadsheet that will have the site lists (and their percentage?)
  • There is now an opportunity for a poster and a demo at SASO
  • Add stories, lists and maps to implication slides – done
  • Got all my connections set up
  • Successfully converted and deployed cosmos-2
  • Voted!

Phil 6.20.18

7:00 – 9:00 2:00 – 5:00 ASRC MKT

  • Redo doodle for all of August – done
  • Schooling Fish May Offer Insights Into Networked Neurons
    • Iain Couzin is deciphering the rules that govern group behavior. The results might provide a fresh perspective on how networks of neurons work together.
  • City arts and lectures: The New Science Of Psychedelics With Michael Pollan
    • Psychedelics reduce the section of the brain that have to do with the sense of self. Pollan thinks that this also happens with certain types of rhythmic music and in crowd situations. This could be related to stampedes and flocking.
    • LSD May Chip Away at the Brain’s “Sense of Self” Network
      • Brain imaging suggests LSD’s consciousness-altering traits may work by hindering some brain networks and boosting overall connectivity
  • Add an attractor scalar for agents that’s normally zero. A vector to each agent within the SIH is calculated and scaled by the attractor scalar. That vector is then added to the direction vector to the agent – done?
  • Remove the heading influence based on site – done
  • Add a white circle to the center of the agent that is the size of the attraction scalar. Done
  • Add a ‘site trajectory’ to the spreadsheet that will have the site lists (and their percentage?)
  • Worked on A2P white paper with Aaron.
  • Worked on a response to Dr. Li’s response

ASRC IRAD 9:00 – 2:00

  • Mind meld with Bob
    • Revisit Yarn
    • Excel stuff?
    • Connect to AWS using bastion. Look in FoxyProxy how to. I need certs
    • Drop on rabbit to deploy to CI and QA and NESDIS  ONE (production)
    • Don’t want sensitive information in Git. We use sharepoint instead
    • Notes and screenshots in document.

Phil 6.19.18

7:00 – 9:00, 4:00 – 5:00 ASRC MKT

  • Here’s a list of organizations that are mobilizing to help immigrant children separated from their families
  • SASO trip
  • Rebuilt all the binaries, now I need to put them on the thumb drive – done
  • Added knobs to the implications slide. They sit next to the dimension and SIH lines. I realize that my slide deck is becoming a physical version of a memory palace.
  • Continuing Irrational Exuberance, though feeling like I should be reading Axelrod. Bring Evolution of Cooperation on the flight?
  • Naive Diversification Strategies in Defined Contribution Saving Plans
    • There is a worldwide trend toward defined contribution saving plans and growing interest in privatized social security plans. In both environments, individuals are given some responsibility to make their own asset allocation decisions, raising concerns about how well they do at this task. This paper investigates one aspect of the task, namely diversification. We show that many investors have very naive notions about diversification. For example, some investors follow what we call the 1/n strategy: they divide their contributions evenly across the funds offered in the plan. When this strategy (or others only slightly more sophisticated) is used, the assets chosen depend greatly on the make-up of the funds offered in the plan. We find evidence of naive diversification strategies both in experiments using employees at the University of California and the actual behavior of participants in a wide range of savings plans. In particular, we find the proportion of the assets the participants invest in stocks depends strongly on the proportion of stock funds in the plan. The results raise very serious questions about how privatized social security systems should be designed, questions that would be ignored in most economic analyses.
    • This is very much a dimension reduction exercise.
  • A2P maintenance proposal

9:00 – 4:00 ASRC A2P

  • Coming up to speed on the Angular interface
    • Logging into CI and QA
    • Dashboard configurations

Phil 6.18.18

ASRC MKT 7:00 – 8:00

  • Nice ride on Saturday on Skyline drive
  • Using Social Network Information in Bayesian Truth Discovery
    • We investigate the problem of truth discovery based on opinions from multiple agents who may be unreliable or biased. We consider the case where agents’ reliabilities or biases are correlated if they belong to the same community, which defines a group of agents with similar opinions regarding a particular event. An agent can belong to different communities for different events, and these communities are unknown a priori. We incorporate knowledge of the agents’ social network in our truth discovery framework and develop Laplace variational inference methods to estimate agents’ reliabilities, communities, and the event states. We also develop a stochastic variational inference method to scale our model to large social networks. Simulations and experiments on real data suggest that when observations are sparse, our proposed methods perform better than several other inference methods, including majority voting, the popular Bayesian Classifier Combination (BCC) method, and the Community BCC method.
  • Scale-free correlations in starling flocks
    • From bird flocks to fish schools, animal groups often seem to react to environmental perturbations as if of one mind. Most studies in collective animal behavior have aimed to understand how a globally ordered state may emerge from simple behavioral rules. Less effort has been devoted to understanding the origin of collective response, namely the way the group as a whole reacts to its environment. Yet, in the presence of strong predatory pressure on the group, collective response may yield a significant adaptive advantage. Here we suggest that collective response in animal groups may be achieved through scale-free behavioral correlations. By reconstructing the 3D position and velocity of individual birds in large flocks of starlings, we measured to what extent the velocity fluctuations of different birds are correlated to each other. We found that the range of such spatial correlation does not have a constant value, but it scales with the linear size of the flock. This result indicates that behavioral correlations are scale free: The change in the behavioral state of one animal affects and is affected by that of all other animals in the group, no matter how large the group is. Scale-free correlations provide each animal with an effective perception range much larger than the direct inter-individual interaction range, thus enhancing global response to perturbations. Our results suggest that flocks behave as critical systems, poised to respond maximally to environmental perturbations.
  • Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study
    • By reconstructing the three-dimensional positions of individual birds in airborne flocks of a few thousand members, we show that the interaction does not depend on the metric distance, as most current models and theories assume, but rather on the topological distance. In fact, we discovered that each bird interacts on average with a fixed number of neighbors (six to seven), rather than with all neighbors within a fixed metric distance. We argue that a topological interaction is indispensable to maintain a flock’s cohesion against the large density changes caused by external perturbations, typically predation. …
  • Thread on the failure to replicate the Stanford Prison Experiment by Alex Haslam (scholar) (home page). Paper coming soon
    • The Stanford Prison Experience—as it is presented in textbooks—presents human nature as naturally conforming to oppressive systems. This is a lesson that extends well beyond prison systems and the field criminology—but it’s wrong. Alex and his colleagues (especially Steve Reicher) have been arguing for years that conformity often emerges when leaders cultivate a sense of shared identity. This is an active, engaged process—very different from automatic and mindless conformity.
  • Started Irrational Exuberance, by Robert Shiller
  • Send note to Don, Aaron and Shimei
  • Read Ego-motion in Self-Aware Deep Learning on Medium. It’s about reflective learning of navigation in physical spaces, though I wonder if there is an equivalent process in belief spaces. Looked through scholar and
  • Slide prep and Fika walkthrough
    • Went well. Ravi suggested adding another slide that discusses the methods in detail, while Sy pretty much demanded that I get rid of “Questions” and put the title of the paper in its place
    • When adding the detail for Ravi, I discovered that the simulator and map reconstruction did not handle single, high dimensional agents well, so I spent a few hours fixing bugs to get the screen captures to build the slides.

Phil 6.15.18

7:00 – 6:00 ASRC MKT

  • Montaigne and the Art of Conversation held on June 11, 2018
    • Michel de Montaigne, the inventor of the essay and the greatest philosopher of the Renaissance, who is often imagined to be a solitary figure, lost in his library, writing to himself. However, his understanding of the practice of philosophy and the cultivation of the self were deeply social and tied to the give and take of debate and disputation among friends. Hampton’s talk—his “conversation”—will focus on one of Montaigne’s greatest essays, “On the Art of Conversation.” It will place the essay in Montaigne’s thought, and in the tradition of “philosophical conversation” that underpins the humanist tradition in the European West.
  • Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks (Thread overview)
    • Moreover, convolutional networks have precisely the same order-to-chaos transition as fully-connected networks, with vanishing gradients in the ordered phase and exploding gradients in the chaotic phase.
  • Susan Li (ML articles on Medium)
  • Working on slides. Walk through with Wayne today at 4:00
  • Re-read the paper. I’ve forgotten what’s in it!
  • Forward the Yao article, since it’s an example of what I’m modelling. It belongs up with the Strava maps
  • Strava maps are about discerning environment from behavior. Physical and social structures are visible (shorelines, mountains, and borders), from the perspective of road cyclists, who have simple rules:
    • Up is fun
    • Stations of the cross
    • Different populations on Strava (Commuter, mtn, road, etc)
    • Maps to Hofstede’s cultural dimensions
  • Meeting with Wayne to go over slides. Lots of rework. There is a difference in proposal and DC slides, which are showing a research direction, and a paper, which is showing a result.

Phil 6.13.18

7:00 – 4:00 ASRC MKT

  • International driver’s license – done
  • Add visually-impaired labels to paper – done
  • Start slides
  • Interesting article on dimension reduction: The faces of God in America: Revealing religious diversity across people and politics What strikes me about this study is actually how similar the depictions are. In belief space, this would be a closely woven neighborhood. It would be interesting to see an equivalent study on a less anthropomorphic deity like Vishnu… journal.pone.0198745.g002
    • Literature and art have long depicted God as a stern and elderly white man, but do people actually see Him this way? We use reverse correlation to understand how a representative sample of American Christians visualize the face of God, which we argue is indicative of how believers think about God’s mind. In contrast to historical depictions, Americans generally see God as young, Caucasian, and loving, but perceptions vary by believers’ political ideology and physical appearance. Liberals see God as relatively more feminine, more African American, and more loving than conservatives, who see God as older, more intelligent, and more powerful. All participants see God as similar to themselves on attractiveness, age, and, to a lesser extent, race. These differences are consistent with past research showing that people’s views of God are shaped by their group-based motivations and cognitive biases. Our results also speak to the broad scope of religious differences: even people of the same nationality and the same faith appear to think differently about God’s appearance.
  • Finished paper
  • Working on talk

personal

  • Shopping – done
  • taxes
  • laundry – done
  • generator/un-grounded short extension cord – done. Works!

Phil 6.12.18

7:00 – 4:30 ASRC MKT

  • Listening to Clint Watts on his new book
    • “When you don’t know what to believe, you will fall back on your biases”
    • 3 levels of Russian recruitment
      • Useful Idiot
      • Fellow Traveler
      • Agent
    • “They don’t have to make up fake news, There is plenty of fake news for them to employ”
    • Huh. He’s responsible for Hamilton 68, and is interested to extending to beyond Russian Misinfo.
  • Polarization and Fake News: Early Warning of Potential Misinformation Targets
    • Walter Quattrociocchi (scholar)
    • Users polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this paper, we introduce a general framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users behavior on online social media, making a first, important step towards the smoothing of polarization and the mitigation of misinformation phenomena.
  • Trend of Narratives in the Age of Misinformation
    • Walter Quattrociocchi (scholar)
    • Social media enabled a direct path from producer to consumer of contents changing the way users get informed, debate, and shape their worldviews. Such a {\em disintermediation} weakened consensus on social relevant issues in favor of rumors, mistrust, and fomented conspiracy thinking — e.g., chem-trails inducing global warming, the link between vaccines and autism, or the New World Order conspiracy. 
      In this work, we study through a thorough quantitative analysis how different conspiracy topics are consumed in the Italian Facebook. By means of a semi-automatic topic extraction strategy, we show that the most discussed contents semantically refer to four specific categories: environment, diet, health, and {\em geopolitics}. We find similar patterns by comparing users activity (likes and comments) on posts belonging to different semantic categories. However, if we focus on the lifetime — i.e., the distance in time between the first and the last comment for each user — we notice a remarkable difference within narratives — e.g., users polarized on geopolitics are more persistent in commenting, whereas the less persistent are those focused on diet related topics. Finally, we model users mobility across various topics finding that the more a user is active, the more he is likely to join all topics. Once inside a conspiracy narrative users tend to embrace the overall corpus.
  • More SASO paper
    • Finished explanation of the one simple trick
    • Need to add accessibility descriptions for pix

Phil 6.11.18

7:00 – 6:00 ASRC MKT

  • More Bit by Bit. Reading the section on ethics. It strikes me that simulation could be a way to cut the PII Gordion Knot in some conditions. If a simulation can be developed that generates statistically similar data to the desired population, then the simulated data and the simulation code can be released to the research community. The dataset becomes infinite and adjustable, while the PII data can be held back. Machine learning systems trained on the simulated data can then be evaluated on the confidential data. The differences in the classification by the ML systems between real data and simulated data can also provide insight into the gaps in fidelity of the simulated data, which would provide an ongoing improvement to the simulation, which could in turn be released to the community.
  • Continuing with the cleanup of the SASO paper. Mostly done but some trimming of redundent bits and the “Ose Simple Trick” paragraph.
  • SASO travel link
    • Monday prices: SASO
  • Fika
    • Come up with 3-5 options for a finished state for the dissertation. It probably ranges from “pure theory” through “instance based on theory” to “a map generated by the system that matches the theory”
    • Once the SASO paper is in, set up a “wine and cheese” get together for the committee to go over the current work and discuss changes to the next phase
    • Start on a new IRB. Emphasize how everyone will have the same system to interact with, though their interactions will be different. Emphasize that the system has to allow open interaction to provide the best chance to realize theoretical results.
    • Will and I are on the hook for a Fika about LaTex

Phil 6.8.18

7:00 – 3:30 ASRC MKT

  • We should attend this:  IEEE International Symposium on Technology and Society
    • Nov. 13 & 14th, Washington DC
    • ISTAS is a multi-disciplinary and interdisciplinary forum for engineers, policy makers, entrepreneurs, philosophers, researchers, social scientists, technologists, and polymaths to collaborate, exchange experiences, and discuss the social implications of technology.
  • More Bit by Bit
    • This looks really good. It’s on how social networks and behavior co-evolve: Social selection and peer influence in an online social network
      • Disentangling the effects of selection and influence is one of social science’s greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends—except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes.
  • Cleaning up the SASO paper. Lots of good suggestions.
  • Got Aaron up to 16.5 on the 16 mile loop today!

Phil 6.7.18

7:00 – 4:30 ASRC MKT

  • Che Dorval
  • Done with the whitepaper! Submitted! Yay! Add to ADP
  • The SLT meeting went well, apparently. Need to determine next steps
  • Back to Bit by Bit. Reading about mass collaboration. eBird looks very interesting. All kinds of social systems involved here.
    • Research
      • Deep Multi-Species Embedding
        • Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Applied to bird observational data from the citizen science project \textit{eBird}, we demonstrate how the DMSE model discovers inter-species relationships to outperform single-species distribution models (random forests and SVMs) as well as competing multi-label models. Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling. An important domain contribution of the DMSE model is the ability to discover and describe species interactions while simultaneously learning the shared habitat preferences among species. As an additional contribution, we provide a graphical embedding of hundreds of bird species in the Northeast US.
  • Start fixing This one Simple Trick
    • Highlighted all the specified changes. There are a lot of them!
    • Started working on figure 2, and realized (after about an hour of Illustrator work) that the figure is correct. I need to verify each comment before fixing it!
  • Researched NN anomaly detection. That work seems to have had its heyday in the ’90s, with more conventional (but computationally intensive) methods being preferred these days.
  • I also thought that Dr. Li’s model had a time-orthogonal component for prediction, but I don’t think that’s true. THe NN is finding the frequency and bounds on its own.
  • Wrote up a paragraph expressing my concerns and sent to Aaron.

Phil 6.6.18

7:00 – 4:30 ASRC MKT

  • Finished the white paper
  • Peer review of Dr. Li’s AIMS work
  • Computational Propaganda in the United States of America: Manufacturing Consensus Online
    • Do bots have the capacity to influence the flow of political information over social media? This working paper answers this question through two methodological avenues: A) a qualitative analysis of how political bots were used to support United States presidential candidates and campaigns during the 2016 election, and B) a network analysis of bot influence on Twitter during the same event. Political bots are automated software programs that operate on social media, written to mimic real people in order to manipulate public opinion. The qualitative findings are based upon nine months of fieldwork on the campaign trail, including interviews with bot makers, digital campaign strategists, security consultants, campaign staff, and party officials. During the 2016 campaign, a bipartisan range of domestic and international political actors made use of political bots. The Republican Party, including both self-proclaimed members of the “alt-right” and mainstream members, made particular use of these digital political tools throughout the election. Meanwhile, public conversation from campaigners and government representatives is inconsistent about the political influence of bots. This working paper provides ethnographic evidence that bots affect information flows in two key ways: 1) by “manufacturing consensus,” or giving the illusion of significant online popularity in order to build real political support, and 2) by democratizing propaganda through enabling nearly anyone to amplify online interactions for partisan ends. We supplement these findings with a quantitative network analysis of the influence bots achieved within retweet networks of over 17 million tweets, collected during the 2016 US election. The results of this analysis confirm that bots reached positions of measurable influence during the 2016 US election. Ultimately, therefore, we find that bots did affect the flow of information during this particular event. This mixed methods approach shows that bots are not only emerging as a widely-accepted tool of computational propaganda used by campaigners and citizens, but also that bots can influence political processes of global significance.

Phil 6.5.18

7:00 – 6:00 ASRC

  • Read the SASO comments. Most are pretty good. My reviewer #2 was #3 this time. There is some rework that’s needed. Most of the comments are good, even the angry ones from #3, which are mostly “where is particle swarm optimization???”
  • Got an example quad chart from Helena that I’m going to base mine on
  • Neat thing from Brian F: grayson-map-2
  • Lots. Of. White. Paper.