Category Archives: Dissertation

Phil 8.20.19

Chores calls!

Trump, Qanon and an impending judgment day: Behind the Facebook-fueled rise of The Epoch Times

  • By the numbers, there is no bigger advocate of President Donald Trump on Facebook than The Epoch Times. The small New York-based nonprofit news outlet has spent more than $1.5 million on about 11,000 pro-Trump advertisements in the last six months, according to data from Facebook’s advertising archive — more than any organization outside of the Trump campaign itself, and more than most Democratic presidential candidates have spent on their own campaigns. Those video ads — in which unidentified spokespeople thumb through a newspaper to praise Trump, peddle conspiracy theories about the “Deep State,” and criticize “fake news” media — strike a familiar tone in the online conservative news ecosystem. The Epoch Times looks like many of the conservative outlets that have gained followings in recent years.

7:00 – 4:00 ASRC GOES

  • Dissertation
    • I just found out that the UMBC, UMD, and TSU are Jan 2 – 22. I’ll be doing my defense in there somewhere
    • Working on the TACJ section
  • Add controllers for the reaction wheels so that a “go to AZ=30, EL=20, R= -10” can work
    • Getting close:
    • Control
  • Next task is to subclass the ReactionWheelController to pitch, roll, and yaw controllers, so that they can manipulate their data separately
    • Voltage should control the velocity and direction of the wheel
    • Simulator can be told to add drag to a wheel
  • I think Communications of the ACM is the next place to try the AI Weapons paper. It’s 5,000-ish words, so it looks like it should fit:
    • CACM
  • Finished TAAS article and letter. Notified Antonio

Phil 8.19.19

ASRC GOES 7:00 – 4:00

  • Adding the journalism framing example to the introduction
  • Continue to work all data transfers into the Data Dictionary so that effective, tagged training in the sim can happen
    • done-ish? Need to do another layer and figure out how to set up the command and response buffers – done


Data Dictionary as a spreadsheet

Phil 8.16.19

7:00 – 4:00 ASRC GOES

  • Dissertation
    • More introduction. Finished the first pass. Maybe add something about journalism, and how the system could create something new?
  • Put the commands and responses in the DB so we get tagging
  • Add controllers for the reaction wheels so that a “go to AZ=30, EL=20, R= -10” can work
  • Have the dictionary save to a database?

Phil 8.15.19

7:00 – 5:00 ASRC GOES

  • Antonio has the TAAS paper
  • Dissertation
    • Starting at the beginning. Paragraphs have been written!
  • Demo at NOAA today
    • Change the timeseries filename to include date and time information – done
    • Demo went well. Bruce helped a lot, which means he’s on board with the concepts
  • Thinking about maintaining anonymity on JuryRoom
    • Anonymity is the default
    • In the menu, there is something that says “You are posting as XX”, where XX is a 2-character label that is generated for every Jury.
    • In the same spot is a “Post as Yyyyy”, where Yyyyy is the login name. Clicking that will reverse the statements so that the bar reads “You are posting as Yyyyy”
    • If posting, all players get a color, rather than their Icon
    • In the database, the presence of a label is a flag for anonymous posting. If the user is presenting as themselves, then that string is NULL, so it’s an easy test.
  • Social learning strategies regulate the wisdom and madness of interactive crowds
    • Why groups of individuals sometimes exhibit collective ‘wisdom’ and other times maladaptive ‘herding’ is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model fitting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity among individuals, with rates of copying increasing with group size, leading to high probabilities of herding among large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated ‘wisdom of the crowd’ effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a long-standing puzzle in the literature.
  • Locally noisy autonomous agents improve global human coordination in network experiments
    • Coordination in groups faces a sub-optimization problem1,2,3,4,5,6 and theory suggests that some randomness may help to achieve global optima7,8,9. Here we performed experiments involving a networked colour coordination game10 in which groups of humans interacted with autonomous software agents (known as bots). Subjects (n = 4,000) were embedded in networks (n = 230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locations. We show that bots acting with small levels of random noise and placed in central locations meaningfully improve the collective performance of human groups, accelerating the median solution time by 55.6%. This is especially the case when the coordination problem is hard. Behavioural randomness worked not only by making the task of humans to whom the bots were connected easier, but also by affecting the gameplay of the humans among themselves and hence creating further cascades of benefit in global coordination in these heterogeneous systems.

Phil 8.14.19

7:00 – 8:00 ASRC GOES

  • pyforest – lazy-import of all popular Python Data Science libraries. Stop writing the same imports over and over again.
    • pyforest lazy-imports all popular Python Data Science libraries so that they are always there when you need them. If you don’t use a library, it won’t be imported. When you are done with your script, you can export the Python code for the import statements.
  • Ping Antonio about TAAS. Important points are round-tripping ABS, and enabling navigation as a way of prediction
  • Transition text from TAAS to Dissertation
  • Mission Drive – nope, couldn’t get in
    • Show Bruce model and control
    • Meeting
    • pip3 install openpyxl, for some reason
    • Pretty pictures!


  • Meeting with Will this evening
    • after a moderate amount of flailing, got his Slack message files into a database

Phil 8.13.19

7:00 – 5:00ASRC GOES

  • TAAS/DIssertation
    • Rolling in changes. At Belief Space Cartography
    • Done! Need to ping Antonio
  • Nice Jersey! Sizing chart
  • More control system work
    • Add data dictionary – done
    • Add main controller – done
    • Put sim under controller – done
    • Add “Attitude Controller” for reaction wheels and sensors
    • Add “Reaction Wheel” controller?
  • Leaving early
  • Wakaito meeting


7:00 – 5:00 ASRC GEOS

  • Another thought about groups thinking like neurons. Alcohol is like dopamine. It encourages connections between individuals, but the communication can become “noisier”
  • Call SSA
  • Dissertation/TAAS
    • Discuss survey
  • GEOS model
    • Finish control/simulation framework
    • Get access to the rotation matrix and calculate transformations for (1, 0, 0), (0, 1, 0), and (0, 0, 1). Use those for pitch roll and yaw measurements
    • Write out these values (excel) and see if we can do a proof-of-concept that shows prediction of the reaction wheels from the PRY measurements
  • Helped Heera with her code
  • Read through and edited TAAS

Phil 8.9.19

7:00 – 5:00 ASRC GEOS

  • Something else for image repair: Deep Image Prior
    • Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs.
  • Dissertation/TAAS
  • GEOS Sim
    • Building RCS controllers!
    • Record data
    • Spark lines
    • Excel outputs
    • Start physics

Phil 8.8.19

7:00 – 4:00 ASRC GEOS

  • TAAS/Dissertation research_design
  • Paperwork for dad’s cert
  • More GEOS-R sim.
    • Fixed the camera rotation order
    • Create some classes to handle all this
      • GEOS-TopController
      • GEOS_Simulator
      • GEOS_R_Model
    • Record data
    • Spark lines
    • Excel outputs
    • Start physics


  • Meeting with Aaron to discuss cyber RFI

Phil 8.7.19

7:00 – 4:00 ASRC GEOS

  • TAAS/Dissertation research_design
  • Swing by Charlestown on the way to Mission Drive today? Done, but no luck. Did get a card though. Done!
  • Order cards – done
  • Read BAA, triage white paper – started
  • Got my STL travel report submitted
  • Got my education assistance reimbursement and pre-approval in
  • Uninstalled Panda3D and then pip installed it again. Now all python versions are aligned, so I have panda3d and numpy
  • More GEOS-R sim. Nope – didn’t check in all my changes last night.
    • Create some classes to handle all this
    • Record data
    • Spark lines
    • Excel outputs
    • Start physics
  • Super interesting discussion with a waitress about JuryRoom that started with Sriracha catsup

Phil 8.6.19

7:00 – 4:30 ASRC GEOS

  • Dissertation and TAAS – working out the research plan section
  • More satellite sim.
    • Got the graphics part done! Now for sparklines and the physics sim


  • Waikato meeting
    • Suggested added quote or snippet to notification
    • Arron can recruit antibubblers
    • Advertise on FB and Reddit? Twitter really isn’t a debate forum

Phil 8.2.19

7:00 – 3:30 ASRC GEOS

  • Send George a note about Dad
  • Scan hotel receipt (done) and fill out expense report – started, but couldn’t make it work. I need to get a new charge number. Spent hours on this trying to submit a travel expense report that didn’t have exceptions. SAP Concur is as bad an application as I have ever used.
  • Write down thoughts on inhibition and excitation in groups. Basically, when a group is engaged in discussion, some links are excitatory – a small group will engage in discussion, while others participate less or not at all – they are inhibited. These kind of discussions are almost always mediated by an explicit or implicit leader. The consensus that develops is greatly influenced by who is excited and who is inhibited. Also discuss typicality, or the clustering of belief around central items (examples of furniture have chairs and tables as high typicality examples)
  • Dissertation
    • Work on flowchart(s)
  • Generalize cube, size in 3 dimensions and normals from cross products
  • Change cylinder so that normals are from cross products – done, after considerable flailing.
  • Start on sphere and cone?
    • Cone! Cone

Phil 8.1.19

7:00 – 3:30 ASRC GEOS

  • Cancel service at Bob’s – done
  • Scan hotel receipt and fill out expense report
  • Write up some USPTO thoughts – done
  • School reimbursement and approval for 899 – forms filled out, waiting for signatures
  • Write down thoughts on inhibition and excitation in groups. Basically, when a group is engaged in discussion, some links are excitatory – a small group will engage in discussion, while others participate less or not at all – they are inhibited. These kind of discussions are almost always mediated by an explicit or implicit leader. The consensus that develops is greatly influenced by who is excited and who is inhibited.
  • July progress email – done
  • Dissertation
    • Work on flowchart(s)
  • Distributed Memory and the Representation of General and Specific Information
    • We describe a distributed model of information processing and memory and apply it to the representation of general and specific information. The model consists of a large number of simple processing elements which send excitatory and inhibitory signals to each other via modifiable connections. Information processing is thought of as the process whereby patterns of activation are formed over the units in the model through their excitatory and inhibitory interactions. The memory trace of a processing event is the change or increment to the strengths of the interconnections that results from the processing event. The traces of separate events are superimposed on each other in the values of the connection strengths that result from the entire set of traces stored in the memory. The model is applied to a number of findings related to the question of whether we store abstract representations or an enumeration of specific experiences in memory. The model simulates the results of a number of important experiments which have been taken as evidence for the enumeration of specific experiences. At the same time, it shows how the functional equivalent of abstract representations- prototypes, logogens, and even rules-can emerge from the superposition of traces of specific experiences, when the conditions are right for this to happen. In essence, the model captures the structure present in a set of input patterns; thus, it behaves as though it had learned prototypes or rules, to the extent that the structure of the environment it has learned about can be captured by describing it in terms of these abstractions.
  • Leveraging Meta Information in Short Text Aggregation
    • Analysing topics in short texts (e.g., tweets and new headings) is a challenging task because short texts often contain insufficient word co-occurrence information, which is important to learn good topics in conventional topic topics. To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. Our model can generate more interpretable topics as well as document clusters. We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model. Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.

Phil 7.31.19

7:00 – 4:30 ASRC IRAD?

  • Flying home from St Louis
  • Dissertation
    • Finished the “my hut” section
    • Started on the research design. Going to have to fire up illustrator and create a new flow chart
    • Enrolled in 899
    • Need to set up a meeting with Shimei after
  • Antonio came back with suggestions about what to do to the TAAS paper, including a flow chart, which is what I’ll do for the dissertation as well
  • Example online dissertation that’s pretty cool, and a good reference for format, content and style. Linked Research on a Decentralized Web