7:00 – 5:00 ASRC
Dissertation – more work on the research design section. Adding unexpected results
- Adding the storing of step and model to the genome
- Added step
- While working on adding data, I realized that I was re-calculating fitness for genomes that had already been tested. Added skip function if there was already a population’s worth of data
- AI/ML meeting -showed the current work with the evolver and the motivation for ensembles
- AIMS/A2P meeting
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Commission launches call to create the European Digital Media Observatory The European Commission has published a call for tenders to create the first core service of a digital platform to help fighting disinformation in Europe. The European Digital Media Observatory will serve as a hub for fact-checkers, academics and researchers to collaborate with each other and actively link with media organisations and media literacy experts, and provide support to policy makers. The call for tenders opened on 1 October and will run until 16 December 2019.
ASRC GOES 7:00 – 7:00
- Expense Report!
- Call Erikson!
- Change safe to low risk
- Tweaking the Research Design chapter
- See if the run broke or completed this weekend – IT restarted the machine. Restarted and let it cook. I seem to have fixed the GPU bug, since it’s been running all day. It’s 10,000 models!
- Look into splitting up and running on AWS
- Rather than explicitly gathering ten runs each time for each genome, I could hash the runs by the genome parameters. More successful genomes will be run more often.
- Implies a BaseEvolver, LazyEvolver, and RigerousEvolver class
- Neural Network Based Optimal Control: Resilience to Missed Thrust Events for Long Duration Transfers
- (pdf) A growing number of spacecraft are adopting new and more efficient forms of in-space propulsion. One shared characteristic of these high efficiency propulsion techniques is their limited thrust capabilities. This requires the spacecraft to thrust continuously for long periods of time, making them susceptible to potential missed thrust events. This work demonstrates how neural networks can autonomously correct for missed thrust events during a long duration low-thrust transfer trajectory. The research applies and tests the developed method to autonomously correct a Mars return trajectory. Additionally, methods for improving the response of neural networks to missed thrust events are presented and further investigated.
- Ping Will for Thursday rather than Wednesday done – it seems to be a case where the first entry is being duplicated
- Arpita’s presentation:
- Information Extraction from unstructured text
- logfile analysis
- Why is the F1 score so low on open coding with human tagging?
- Annotation generation slide is not clear
Crap – It’s October already!
7:00 – 4:00 ASRC GOES
- Unsupervised Thinking – a podcast about neuroscience, artificial intelligence and science more broadly
- Cleanup of most of the sections up to and through the terms part of the Theory section.
- Fix problem with the fitness values? Also, save the best chromosome always. Fixed I think. Testing.
- So there’s a big problem, which I kind of knew about. The random initialization of weights makes a HUGE difference in the performance of the model. I discovered this while looking at the results of the evolver, which saves the best of each generation and saves them out to a spreadsheet:
- If you look at row 8, you see a lovely fitness of 0.9, or 90%. Which was the best value from the evolver runs. However, after sorting on the parameters so that they were grouped, it became obvious that there is a HUGE variance in the results. The lowest fitness is 30%, and the average fitness for those values is actually 50%. I tried running the parameters on multiple trained models and got results that agree. These values are all over the place (the following images are 20%, 30%, 60%, and 80% accuracy, and all using the same parameters):
- To adress this, I need to be able to run a population and get the distribution stats (number of runs, mean, min, max, variance) and add that to the spreadsheet. Started by adding some randomness to the demo data generating function, which should do the trick. I’ll start on the rest tomorrow.
- Yikes! I’m going to try installing the release version of TF. it should be just pip install tensorflow-gpu. Done! Didn’t break anything 🙂
7:00 – 7:00 ASRC GOES
- Evolutionary hyperparameter tuning. It’s working (60% is better than my efforts), but there’s a problem with the fitness values? Also, I want to save the best chromosome always
- Reread weapons paper
- Meeting with Aaron M – going to try to rework the paper a bit for ICSE 2020. Deadline is Oct 29.
- Some interesting discussion on how review systems should work
- Also some thoughts about how military AI in a hyperkinetic environment would have to negotiate cease-fires, sue for peace, etc.
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Done with the ride. Here are my stats:
Getting ready for a fun trip:
12th International Conference on Agents and Artificial Intelligence – Dammit, the papers are due October 4th. This would be a perfect venue for the GPT2 agents
Novelist Cormac McCarthy’s tips on how to write a great science paper
Unveiling the relation between herding and liquidity with trader lead-lag networks
- We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade. The method relies on the Kinetic Ising model to describe how information propagates among traders, assigning a positive or negative “opinion” to all agents about whether the traded asset price will go up or down. This opinion is reflected by their trading behavior, but whenever the trader is not active in a given time window, a missing value will arise. Using a recently developed inference algorithm, we are able to reconstruct a lead-lag network and to estimate the unobserved opinions, giving a clearer picture about the state of supply and demand in the market at all times.
We apply our method to a dataset of clients of a major dealer in the Foreign Exchange market at the 5 minutes time scale. We identify leading players in the market and define a herding measure based on the observed and inferred opinions. We show the causal link between herding and liquidity in the inter-dealer market used by dealers to rebalance their inventories.