Category Archives: Phlog Post

Phil 1.10.19

7:00 – 4:00 ASRC

  • The fragility of decentralised trustless socio-technical systems
    • The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms within the broader ecosystem that includes social and communication networks. As an example, we analyse the flash-crash observed on 21st June 2017 in the Ethereum platform and show that a major phenomenon of social coordination led to a catastrophic cascade of events across several interconnected systems. We propose the concept of “emergent centralisation” to describe situations where a single system becomes critically important for the functioning of the whole ecosystem, and argue that such situations are likely to become more and more frequent in interconnected socio-technical systems. We anticipate that the systemic approach we propose will have implications for future assessments of trustless systems and call for the attention of policy-makers on the fragility of our interconnected and rapidly changing world.
  • Realized this morning that the weight matrix is a connectivity matrix between the neurons. That means that there are some very interesting things that we could do with partially connected layers. Sending signals just to adjacent downstream nodes in 2D – nD
  • More DNN post. Need to incorporate neuron graphs with the weight graphs, and update the sections of code about graphing. Done! And yet, somehow I’m still tweaking…
  • Working on the NoI. It’s a grind… Done? Sent off to John D.
  • Back to Docker
    • docker build -t friendlyhello . # Create image using this directory’s Dockerfile
    • docker run -p 4000:80 friendlyhello # Run “friendlyname” mapping port 4000 to 80
    • docker run -d -p 4000:80 friendlyhello # Same thing, but in detached mode
    • docker container ls # List all running containers docker container ls -a # List all containers, even those not running
    • docker container stop <hash> # Gracefully stop the specified container
    • docker container kill <hash> # Force shutdown of the specified container
    • docker container rm <hash> # Remove specified container from this machine
    • docker container rm $(docker container ls -a -q) # Remove all containers
    • docker image ls -a # List all images on this machine
    • docker image rm <image id> # Remove specified image from this machine
    • docker image rm $(docker image ls -a -q) # Remove all images from this machine
    • docker login # Log in this CLI session using your Docker credentials
    • docker tag <image> username/repository:tag # Tag <image> for upload to registry
    • docker push username/repository:tag # Upload tagged image to registry
    • docker run username/repository:tag # Run image from a registry
  • Ok, let’s see how to integrate with IntelliJ – Nope, reworking the data structures for better queries (and best practices as well). Sigh.

Phil 1.9.18

ASRC NASA(?) 7:00 – 6:30

  • Selective Exposure to Misinformation: Evidence from the consumption of fake news during the 2016 U.S. presidential campaign
    • Though some warnings about online “echo chambers” have been hyperbolic, tendencies toward selective exposure to politically congenial content are likely to extend to misinformation and to be exacerbated by social media platforms. We test this prediction using data on the factually dubious articles known as “fake news.” Using unique data combining survey responses with individual-level web trac histories, we estimate that approximately 1 in 4 Americans visited a fake news website from October 7-November 14, 2016. Trump supporters visited the most fake news websites, which were overwhelmingly pro-Trump. However, fake news consumption was heavily concentrated among a small group — almost 6 in 10 visits to fake news websites came from the 10% of people with the most conservative online information diets. We also find that Facebook was a key vector of exposure to fake news and that fact-checks of fake news almost never reached its consumers.
  • Need to write justifications for Don – Done
  • More DNN from scratch
    • Added plotting of neurons converging to values. Now I need to change the writeup
  • Aaron’s sick. Not sure what the task for today should be. Antibubbles?
  • Downloading and installing Docker
    • Has to run as admin
    • Got Hello world running after getting this error:
      • C:\Windows\System32>docker run hello-world
      • docker: error during connect: Post http://%2F%2F.%2Fpipe%2Fdocker_engine/v1.39/containers/create: open //./pipe/docker_engine: The system cannot find the file specified. In the default daemon configuration on Windows, the docker client must be run elevated to connect. This error may also indicate that the docker daemon is not running.
      • See ‘docker run –help’.
    • You have to run the “Docker” app
    • Created a “Hello World” in python and containerized it. It runs!
    • Had to set up virtualization on the laptop
  • Connected to the ASRC gitlab and set up the IDE to use it
  • Write up a 250 word Notice of Intent
    • Notice of Intent (NOI) to Propose Material in a NOI is confidential and will be used for NASA planning purposes only, unless otherwise stated in the FA. An NOI is submitted by logging into NSPIRES at Space is provided for the applicant to provide, at a minimum, the following information, although additional special requests may also be indicated:
      • A Short Title of the anticipated proposal (50 characters or less); 7
      • A Full Title of the anticipated proposal (which should not exceed 254 characters and is of a nature that is understandable by a scientifically trained person);
      • A brief description of the primary research area(s) and objective(s) of the anticipated work (the information in this item does not constrain in any way the proposal summary that must be submitted with the final proposal); and
      • The names of any Co-Is and/or Collaborators as known at the time the NOI is submitted. In order to enter these names those team members must have previously accessed and registered in NSPIRES themselves; a Principle Investigator (PI) cannot do this for them. 
  • Meeting with Shimei. Long! Discussed the NN code, RPGs and D&D, mapmaking
    • Send list of map quality markers from dissertation
    • Send some links about D&D and Play-by-post

Phil 1.8.18

7:00 – ASRC NASA

  • Software meeting at 9:00 in Beltsville
    • Products group
    • Attach an adder to the overhead?
    • 12.5% per bill on contract, so 10 contracts support one person
    • Currently covered for 3 months
    • AIMS 2019, TACLAMBDA have been approved (for the next 3 months?)
    • $300k from corporate across all groups.
    • Tasking for the next three months
    • Taking 2 modules out of A2P and making them compatible with AIMS.
    • Erik Velte runs TACLAMBDA
    • Evaluate the modules within A2P and migrate to TACLAMBDA (90% phil)
    • Some kind of machine learning for visa applications (RFP)?
    • Machine learning BAA?
    • We’re all ASTS, with TS signed by Eric/T
    • JPSS/NPP – changing from instrument data to telemetry
  • Sprint planning Meeting
  • Working on nn blog post

Phil 1.7.19

7:00 – 5:00 ASRC

  • Call Tim – The week looks dry
  • Schedule Physical – try tomorrow?
  • Continue with A guided tour through a dirt-simple “deep” neural network. Finished learning, started graphing
  • Downloaded the latest antibubbles and ran processing
  • More financial forecasting?
  • Sprint review?
    • Prepping by adding in all the things that I wound up doing
  • Worked on getting Aaron’s code working, which required installing MSVC 2017 which required me redistributing apps to clear up space on the SSD drive.

Phil 1.5.19

It seems to me that this might also be important for validating machine learning models. Getting a critical level for false classification might really help

  • The quest for an optimal alpha
    • Researchers who analyze data within the framework of null hypothesis significance testing must choose a critical “alpha” level, α, to use as a cutoff for deciding whether a given set of data demonstrates the presence of a particular effect. In most fields, α = 0.05 has traditionally been used as the standard cutoff. Many researchers have recently argued for a change to a more stringent evidence cutoff such as α = 0.01, 0.005, or 0.001, noting that this change would tend to reduce the rate of false positives, which are of growing concern in many research areas. Other researchers oppose this proposed change, however, because it would correspondingly tend to increase the rate of false negatives. We show how a simple statistical model can be used to explore the quantitative tradeoff between reducing false positives and increasing false negatives. In particular, the model shows how the optimal α level depends on numerous characteristics of the research area, and it reveals that although α = 0.05 would indeed be approximately the optimal value in some realistic situations, the optimal α could actually be substantially larger or smaller in other situations. The importance of the model lies in making it clear what characteristics of the research area have to be specified to make a principled argument for using one α level rather than another, and the model thereby provides a blueprint for researchers seeking to justify a particular α level.

Working more on A guided tour through a dirt-simple “deep” neural network