Monthly Archives: November 2019

Phil 11.12.19

7:00 – 4:00 ASRC GOES

  • Dissertation – Human study discussion
    • “Degrees of Freedom” are different from “dimensions”. Dimensions, as used in machine learning, mean a single parameter that can be varied, discretely or continuously. Degrees of freedom define a continuous space that can contain things that are not contained in the dimensions. Latitude and Longitude do not define the globe. They serve as a way to show relationships between regions on the globe.
  • How news media are setting the 2020 election agenda: Chasing daily controversies, often burying policy
    • Our topic analysis of ~10,000 news articles on the 2020 Democratic candidates, published between March and October in an ideological diverse range of 28 news outlets, reveals that political coverage, at least this cycle, tracks with the ebbs and flows of scandals, viral moments and news items, from accusations of Joe Biden’s inappropriate behavior towards women to President Trump’s phone call with Ukraine. (A big thanks to Media Cloud.)
  • Neat visualization – a heatmap plus a mean. I’d like to try adding things like variance to this. From Large scale and information effects on cooperation in public good games. Looks like the Seaborn library might be able to do this.


  • Evolver – more GPU allocation and threading
    • Training – load and unload GPUs using thread pools
      • Updating EvolutionaryOptimizer
        • got threads working
        • Added enums, which meant that I had to handle enum key values in my ExcelUtils class
        • Updated the TimeSeriesML2 whl
        • Started folding gpu management into PyBullet. Making sure that everything still works first… It does!
      • Ok, back to TimeSeriesML2 to make nested genomes
        • Added a parent/child relationship to EvolveAxis so that it’s possible to a top-level parent (self.parent == None) to step down the tree of all the children to get the new appropriate values. These will need to be assembled into an argument string. Figure that part out tomorrow.
    • Predicting – load and use models in real time

Phil 11.11.19

7:00 –  8:00 PhD

Phil 11.8.19

7:00 – 3:00 ASRC GOES

  • Dissertation
    • Usability study! Done!
    • Discussion. This is going to take some framing. I want to tie it back to earlier navigation, particularly the transition from stories and mappaemundi to isotropic maps of Ptolemy and Mercator.
  • Sent Don and Danilo sql file
  • Start satellite component list
  • Evolver
    • Adding threads to handle the GPU. This looks like what I want (from here):
      import logging
      import concurrent.futures
      import threading
      import time
      def thread_function(name):
"Task %s: starting on thread %s", name, threading.current_thread().name)
"Task %s: finishing on thread %s", name, threading.current_thread().name)
      if __name__ == "__main__":
          num_tasks = 5
          num_gpus = 1
          format = "%(asctime)s: %(message)s"
          logging.basicConfig(format=format, level=logging.INFO,
          with concurrent.futures.ThreadPoolExecutor(max_workers=num_gpus) as executor:
              result =, range(num_tasks))
"Main    : all done")

      As you can see, it’s possible to have a thread for each gpu, while having them iterate over a larger set of tasks. Now I need to extract the gpu name from the thread info. In other words,  ThreadPoolExecutor-0_0 needs to map to gpu:1.

    • Ok, this seems to do everything I need, with less cruft:
      import concurrent.futures
      import threading
      import time
      from typing import List
      import re
      last_num_in_str_re = '(\d+)(?!.*\d)'
      prog = re.compile(last_num_in_str_re)
      def thread_function(args:List):
          num = # get the last number in a string
          gpu_str = "gpu:{}".format(int(
          print("{}: starting on  {}".format(args["name"], gpu_str))
          print("{}: finishing on  {}".format(args["name"], gpu_str))
      if __name__ == "__main__":
          num_tasks = 5
          num_gpus = 5
          task_list = []
          for i in range(num_tasks):
              task = {"name":"task_{}".format(i), "value":2+(i/10)}
          with concurrent.futures.ThreadPoolExecutor(max_workers=num_gpus) as executor:
              result =, task_list)
          print("Finished Main")

      And that gives me:

      task_0: starting on  gpu:1
      task_1: starting on  gpu:2
      task_0: finishing on  gpu:1, after sleeping 2.0 seconds
      task_2: starting on  gpu:1
      task_1: finishing on  gpu:2, after sleeping 2.1 seconds
      task_3: starting on  gpu:2
      task_2: finishing on  gpu:1, after sleeping 2.2 seconds
      task_4: starting on  gpu:1
      task_3: finishing on  gpu:2, after sleeping 2.3 seconds
      task_4: finishing on  gpu:1, after sleeping 2.4 seconds
      Finished Main

      So the only think left is to integrate this into TimeSeriesMl2

Phil 11.7.19

7:00 – 5:00 ASRC GOES

  • Dissertation
  • ML+Sim
    • Save actual and inferred efficiency to excel and plot
    • Create an illustration that shows how the network is trained, validated against the sim, then integrated into the operating system. (maybe show a physical testbed for evaluation?)
    • Demo at the NSOF
      • Went ok. Next steps are a sufficiently realistic model that can interpret an actual malfunction
      • Put together a Google Doc/Sheet that has the common core elements that we can model most satellites (LEO, MEO, GEO, and HEO?). What are the common components between cubesats and the James Webb?
      • Detection of station-keeping failure is a possibility
      • Also, high-dynamic phases, like orbit injection might be low-ish fruit
    • Tomorrow, continue on the GPU assignment in the evolver

Phil 11.6.19

7:00 – 3:00 ASRC GOES

  • Simulation for training ML at UMD: Improved simulation system developed for self-driving cars 
    • University of Maryland computer scientist Dinesh Manocha, in collaboration with a team of colleagues from Baidu Research and the University of Hong Kong, has developed a photo-realistic simulation system for training and validating self-driving vehicles. The new system provides a richer, more authentic simulation than current systems that use game engines or high-fidelity computer graphics and mathematically rendered traffic patterns.
  • Dissertation
    • Send out email setting the date/time to Feb 21, from 11:00 – 1:00. Ask if folks could move the time earlier or later for Wayne – done
    • More human study – I think I finally have a good explanation of the text convergence.
  • Maybe work of the evolver?
    • Add nested variables
    • Look at keras-tuner code to see how GPU assignment is done
      • So it looks like they are using gRPC as a way to communicate between processes? grpc
      • I mean, like separate processes, communicating via ports grpc2
      • Oh. This is why. From the tf.distribute documentation tf.distribute
      • No – wait. This is from the TF distributed training overview pagetf.distribute2
      • And that seems to straight up work (assuming that multiple GPUs can be called. Here’s an example of training:
        strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
        with strategy.scope():
            model = tf.keras.Sequential()
            # Adds a densely-connected layer with 64 units to the model:
            model.add(layers.Dense(sequence_length, activation='relu', input_shape=(sequence_length,)))
            # Add another:
            model.add(layers.Dense(200, activation='relu'))
            model.add(layers.Dense(200, activation='relu'))
            # Add a layer with 10 output units:
            loss_func = tf.keras.losses.MeanSquaredError()
            opt_func = tf.keras.optimizers.Adam(0.01)
            model.compile(optimizer= opt_func,
            noise = 0.0
            full_mat, train_mat, test_mat = generate_train_test(num_funcs, rows_per_func, noise)
  , test_mat, epochs=70, batch_size=13)
            model.evaluate(train_mat, test_mat)

        And here’s an example of predicting

        strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
        with strategy.scope():
            model = tf.keras.models.load_model(model_name)
            full_mat, train_mat, test_mat = generate_train_test(num_funcs, rows_per_func, noise)
            predict_mat = model.predict(train_mat)
            # Let's try some immediate inference
            for i in range(10):
                pitch = random.random()/2.0 + 0.5
                roll = random.random()/2.0 + 0.5
                yaw = random.random()/2.0 + 0.5
                inp_vec = np.array([[pitch, roll, yaw]])
                eff_mat = model.predict(inp_vec)
                print("input: pitch={:.2f}, roll={:.2f}, yaw={:.2f}  efficiencies: pitch={:.2f}%, roll={:.2f}%, yaw={:.2f}%".
                      format(inp_vec[0][0], inp_vec[0][1], inp_vec[0][2], eff_mat[0][0]*100, eff_mat[0][1]*100, eff_mat[0][2]*100))
    • Look at TF code to see if it makes sense to add to the project. Doesn’t look like it, but I think I can make a nice hyperparameter/architecture search API using this, once validated
  • Mission Drive meeting and demo – went ok. Will Demo at NSOF tomorrow

Phil 10.5.19

“Everything that we see is a shadow cast by that which we do not see.” – Dr. King



ASRC GOES 7:00 – 4:30

  • Dissertation – more human study. Pretty smooth progress right now!
  • Cleaning up the sim code for tomorrow – done. All the prediction and manipulation to change the position data for the RWs and the vehicle are done in the inference section, while the updates to the drawing nodes are separated.
  • I think this is the code to generate GPT-2 Agents?:

Phil 11.4.19

7:00 – 9:00 ASRC GOES

  • Cool thing: Our World in Data
    • The goal of our work is to make the knowledge on the big problems accessible and understandable. As we say on our homepage, Our World in Data is about Research and data to make progress against the world’s largest problems.
  • Dissertation – more human study
  • This is super-cool: The Future News Pilot Fund: Call for ideas
    • Between February and June 2020 we will fund and support a community of changemakers to test their promising ideas, technologies and models for public interest news, so communities in England have access to reliable and accurate news about the issues that matter most to them.
  • October status report
  • Sim + ML next steps:
    • I can’t do ensemble realtime inference because I’d need a gpu for each model. This means that I need to get the best “best” model and use that
    • Run the evolver to see if something better can be found
    • Add “flywheel mass” and “vehicle mass” to dictionary and get rid of the 0.05 value – done
    • Set up a second model that uses the inferred efficiency to move in accordance with the actual commands. Have them sit on either side of the origin
      • Graphics are done
      • Need to make second control system and ‘sim’ that uses inferred efficiency. Didn’t have to do all that. What I’m really doing is calculating rw angles based on the voltage and inferred efficiency. I can take the commands from the control system for the ‘actual’ satellite.


  • ML seminar
    • Showed the sim, which runs on the laptop. Then everyone’s status reports
  • Meeting with Aaron
    • Really good discussion. I think I have a handle on the paper/chapter. Added it to the ethical considerations section

Phil 11.3.19

Listening to the On Being interview with angel Kyodo williams

We are in this amazing moment of evolving, where the values of some of us are evolving at rates that are faster than can be taken in and integrated for peoples that are oriented by place and the work that they’ve inherited as a result of where they are.

This really makes me think of the Wundt curve (FMRI analysis here?), and how misalignment between a bourgeoisie class (think elites) and a proletariat class. Without day-to-day existence constraints, it’s possible for elites to move individually and in small groups through less traveled belief spaces. Proletarian concerns are have more “red queen” elements, so you need larger workers movements to make progress.

Phil 11.1.19

7:00 – 3:00 ASRC GOES


  • Hugging Face: State-of-the-Art Natural Language Processing in ten lines of TensorFlow 2.0
    • Hugging Face is an NLP-focused startup with a large open-source community, in particular around the Transformers library. 🤗/Transformers is a python-based library that exposes an API to use many well-known transformer architectures, such as BERTRoBERTaGPT-2 or DistilBERT, that obtain state-of-the-art results on a variety of NLP tasks like text classification, information extraction, question answering, and text generation. Those architectures come pre-trained with several sets of weights. 
  • Dissertation
    • Starting on Human Study section!
    • For once there was something there that I could work with pretty directly. Fleshing out the opening
  • OODA paper:
    • Maximin (Cass Sunstein)
      • For regulation, some people argue in favor of the maximin rule, by which public officials seek to eliminate the worst worst-cases. The maximin rule has not played a formal role in regulatory policy in the Unites States, but in the context of climate change or new and emerging technologies, regulators who are unable to conduct standard cost-benefit analysis might be drawn to it. In general, the maximin rule is a terrible idea for regulatory policy, because it is likely to reduce rather than to increase well-being. But under four imaginable conditions, that rule is attractive.
        1. The worst-cases are very bad, and not improbable, so that it may make sense to eliminate them under conventional cost-benefit analysis.
        2. The worst-case outcomes are highly improbable, but they are so bad that even in terms of expected value, it may make sense to eliminate them under conventional cost-benefit analysis.
        3. The probability distributions may include “fat tails,” in which very bad outcomes are more probable than merely bad outcomes; it may make sense to eliminate those outcomes for that reason.
        4. In circumstances of Knightian uncertainty, where observers (including regulators) cannot assign probabilities to imaginable outcomes, the maximin rule may make sense. (It may be possible to combine (3) and (4).) With respect to (3) and (4), the challenges arise when eliminating dangers also threatens to impose very high costs or to eliminate very large gains. There are also reasons to be cautious about imposing regulation when technology offers the promise of “moonshots,” or “miracles,” offering a low probability or an uncertain probability of extraordinarily high payoffs. Miracles may present a mirror-image of worst-case scenarios.
  • Reaction wheel efficiency inference
    • Since I have this spiffy accurate model, I think I’m going to try using it before spending a lot of time evolving an ensemble
    • Realized that I only trained it with a voltage of +1, so I’ll need to abs(delta)
    • It’s working!


  • Next steps:
    • I can’t do ensemble realtime inference because I’d need a gpu for each model. This means that I need to get the best “best” model and use that
    • Run the evolver to see if something better can be found
    • Add “flywheel mass” and “vehicle mass” to dictionary and get rid of the 0.05 value
    • Set up a second model that uses the inferred efficiency to move in accordance with the actual commands. Have them sit on either side of the origin
  • Committed everything. I think I’m done for the day