Phil 5.8.17

7:00 – 8:00 Research

  • INTEL-SA-00075 vulnerability! Download and run Intel-SA-00075-GUI!
  • A good weekend off. Big, cathartic 88 mile ride on Sunday, and the Kinetic Sculpture race on Saturday
  • Working on the cluster visualization. Updating Intellij at home first
    • installed networkx
    • networkx_tutorial (Code from this post)is working
    • installed xlrd
    • membership_history_builder is working
    • Working on printing out the memberships, then I’ll start diagramming
  • Thinking about how to start Thursday. I think I’ll try reading in blogs to LMN and show differences between students. then bring up flocking, then go into the material

8:30 – 4:00 BRC

  • Analyzing data
  • Showed Aaron the results on the generated and actual data. He’s pretty happy
    • Column mismatches between January and current data
    • Present in Jan data, but not in May:
      • First Excel crash of the day
      • Got the column difference working. It’s pretty sweet, actually: 
        df1_cols = set(df1.columns.values)
df2_cols = set(df2.columns.values)

diff_cols = df2_cols ^ df1_cols

        That’s it.

      • Generated a report on different columns. Tomorrow I need to build a reduced DataFrame that has only the common columns, sort both on column names and then iterate to find the level of similarity.
    • Something’s wrong with? 
      calc_naive_fitness_landscape()

Phil 5.5.17

Research 7:00 – 8:00

  • Some interesting books:
    • Facing the Planetary: Entangled Humanism and the Politics of Swarming: Connolly focuses on the gap between those regions creating the most climate change and those suffering most from it. He addresses the creative potential of a “politics of swarming” by which people in different regions and social positions coalesce to reshape dominant priorities.
    • Medialogies: Reading Reality in the Age of Inflationary Media:  The book invites us to reconsider the way reality is constructed, and how truth, sovereignty, agency, and authority are understood from the everyday, philosophical, and political points of view.
    • At the Crossroads: Lessons and Challenges in Computational Social Science With tools borrowed from Statistical Physics and Complexity, this new area of study have already made important contributions, which in turn have fostered the development of novel theoretical foundations in Social Science and Economics, via mathematical approaches, agent-based modelling and numerical simulations. [free download!]
  • Finished Online clustering, fear and uncertainty in Egypt’s transition. Notes are here
  • The compass within Head direction cells have been hypothesized to form representations of an animal’s spatial orientation through internal network interactions. New data from mice show the predicted signatures of these internal dynamics. 
    • I wonder if these neurons are fired when information orientation changes?

8:30 – 3:00 BRC

  • Giving up on graph-tool since I can’t get it installed. Trying plotly next. Nope. Expensive and too html-y. Networkx for the win? Starting the tutorial
    • Well this is really cool: You might notice that nodes and edges are not specified as NetworkX objects. This leaves you free to use meaningful items as nodes and edges. The most common choices are numbers or strings, but a node can be any hashable object (except None), and an edge can be associated with any object x using G.add_edge(n1,n2,object=x).
    • Very nice. And with this, I am *done* for the week: 
      import networkx as nx
import matplotlib.pyplot as plt

#  Create the graph
G=nx.Graph(name="test", creator="Phil")

#  Create the nodes. Can be anything but None
G.add_node("foo")
G.add_node("bar")
G.add_node("baz")

#  Link edges to nodes
G.add_edge("foo", "bar")
G.add_edge("foo", "baz")
G.add_edge("bar", "baz")

#  Draw
#  Set the positions using a layout
pos=nx.circular_layout(G) # positions for all nodes

#  Draw the nodes, setting size transparancy and color explicitly
nx.draw_networkx_nodes(G, pos,
                nodelist=["foo", "bar"],
                node_color='g',
                node_size=300,
                alpha=0.5)
nx.draw_networkx_nodes(G, pos,
                nodelist=["baz"],
                node_color='b',
                node_size=600,
                alpha=0.5)

#  Draw edges and labels using defaults
nx.draw_networkx_edges(G,pos)
nx.draw_networkx_labels(G,pos)

#  Render to pyplot
plt.show()

print("G.graph = {0}".format(G.graph))
print("G.number_of_nodes() = {0}".format(G.number_of_nodes()))
print("G.number_of_edges() = {0}".format(G.number_of_edges()))
print("G.adjacency_list() = {0}".format(G.adjacency_list()))
    • firstGraphDrawing
  • Short term goals
    • Show that it works in reasonable ways on our well characterized test data
    • See how much clustering changes from run to run
    • Compare differences between manifold learning techniques
    • Examine how it maps to the individual user data

Phil 5.4.17

Star wars day

7:00 – 8:00, 4:00 – 6:00 Research

  • Continuing Online clustering, fear and uncertainty in Egypt’s transition. Notes are here
  • Meeting with Wayne
    • Current trajectory is good
      • HCIC poster with clusters
      • What to do July+? Build ResearchBrowser. Anything else?
      • Also, try to put together a summary in the blog before each meeting
    • Add Wayne as coauthor if we get through the next gate
    • Got to talk about the future of work. My perspective is that machines will be able to meet all needs essentially for free, so we need to build an economy on a human value-add, like the Bugatti Veyron. The goal is to support the creation of items/experiences with a human value-add and an economy built around that.

8:30 – 3:30

  • Fixing all of the broken code on CI
  • Migrated all the machine-learning python code so everything matches
  • changed the algorithm from subdivision to naive
  • Working on CI 
    t-SNE: 15 sec
New best cluster: EPS = 0.1, Cluster size = 3.0
clusters = 17
Total  = 1179
clustered = 1042
unclustered = 137

Algorithm naive took 17.68 seconds to execute and found 17.0 clusters
  • Fixed the cluster output so that it won’t save clusters that have impossible names

Phil 5.3.17

7:00 – 8:00 Research

8:30 – 6:30 BRC

  • Workshop on deep learning
  • I think I’ll have the time to work with network graphs based on the temporal coherence work using the libraries mentioned in this post
    • Looking through graph-tool‘s documentation
    • First, add all the vertices, which are all the clusters and all the agents: 
      v1 = g.add_vertex()

      Then, connect each agent to its clusters:

      e = g.add_edge(v1, v2)

      Then draw:

      graph_draw(g, vertex_text=g.vertex_index,output_size=(200, 200), output="two-nodes.png")

      After that, there seem to be all kinds of analytics

    • Aaron didn’t got to the conference, so we worked on rolling in all the chnges. The reducers work fantastically well, though there is a pile of testing that needs to be done.
    • And I learned that to get a row out of n numpy matrix, you do mat[row], rather than mat[row:]
    • Pretty pictures for the isomap run

Phil 5.2.17

7:00 – 8:00 Research

8:30 – 2:30 BRC

  • Got the reducers in, now I need to colorize the original df for display. Done. The results aren’t great though. Below are results for isomap:

    The images show

    • The XY positions of the reduced data. I’ve added a bit of jitter so it’s possible to see all the points. They should be pretty evenly distributed, but as you can see, the lower right has a much greater population.
    • This is backed up by the color mapped images of the original clusters, were the majority of the rows are black, and the other values are all in the bottom-right square
    • The 3D fitness landscape made via subsurface shown in 3D
    • and in 2D
  • A roughly similar run (and yes, they vary a lot!) is shown with a brute-force (naive)surfacer. Actually, it may makes sense to use the naive surfacer on the reduced data since it’s so much faster:

Phil 5.1.17

7:00 – 8:00, 3:00 – 4:00 Research

  • Rita Allen Foundation – June 30 deadline for proposals
  • On the power of maps: Electoral map spurred Trump’s NAFTA change of heart
  • The Neural Basis of Map Comprehension and Spatial Abilities
  • Neurobiological bases of reading comprehension: Insights from neuroimaging studies of word level and text level processing in skilled and impaired readers
  • Reading Online clustering, fear and uncertainty in Egypt’s transition
    • Marc Lynch (webpage),
    • Deen Freelon (webpage) associate professor in the School of Communication at American University in Washington, DC. My primary research interests lie in the changing relationships between technology and politics, and encompass the study of weblogs, online forums, social media, and other forms of interactive media with political applications. Collecting and analyzing large amounts of such data (i.e. millions of tweets, Facebook wall posts, etc.) require methods drawn from the fields of computer science and information science, which I am helping to adapt to the long-standing interests of political communication research.
    • Sean Aday (From GWU) focuses on the intersection of the press, politics, and public opinion, especially in relation to war and foreign policy. He has published widely on subjects ranging from the effects of watching local television news to coverage of Elizabeth Dole’s presidential run to media coverage of the wars in Iraq and Afghanistan.Before entering academia, Dr. Aday served as a general assignment reporter for the Kansas City Star in Kansas City, MO; the Milwaukee Journal in Milwaukee, WI; and the Greenville News in Greenville, SC. He graduated from the Medill School of Journalism at Northwestern University in 1990.
    • …research has demonstrated the role played by social media in overcoming the transaction costs associated with organizing collective action against authoritarian regimes, in temporarily empowering activists against state violence, in transmitting images and ideas to the international media, and in intensifying the dynamics of social mobilization.
      • There is some kind of relationship between frictionlessness and credibility. Disbelief is a form of friction that needs to be overcome.
    • We argue that social media tends to exacerbate and intensify those factors which make failure more likely than in comparable cases which did not feature high levels of social media usage. Social media promotes the clustering of individuals into communities of the likeminded, and that these clusters have distinctly damaging implications during uncertain transitions.
      • I would add “as designed”, but uncertainty sets up an entirely different dynamic, which I doubt the designers took into account.
    • Users within these clusters tend to be exposed primarily to identity-confirming and enemy-denying information and rhetoric, which encourages the consolidation of in-group solidarity and out-group demonization. The speed, intensity, and intimacy of social media tends to exacerbate polarization during moments of crisis, and then to entrench very different narratives about those events in the aftermath.

8:30 – 2:30 BRC

  • Aaron’s and Bob’s grandmother’s passed away on Saturday. Aside from the important stuff which I can’t do anything about, there is the urgent issue about how to deal with the sprint impacts
  • HIPAA training!
  • Which machine learning algorithm should I use? machine-learning-cheet-sheet
  • Social media data collection tools
  • I got blindsided by reference rather than value. I built a dictionary that contained all the information about an attempt, but it was saving the references, which meant all the entries were the same, so no performance data! So, to ‘update’ an array in a way that maintains a reference to the old data, you need to do it like this: 
    min_max_c = [min_max_c[MIN], mid_c]
  • And we get some nice pictures. The fit is better too:

    Results!

    256x256
Algorithm subdivision took 3.12 seconds to execute and found 9.0 clusters
Algorithm naive took 7.00seconds to execute and found 8.0 clusters

512x512
Algorithm subdivision took 12.17seconds to execute and found 16.0 clusters
Algorithm naive took 30.15 seconds to execute and found 13.0 clusters
  • Starting to fold in Aaron’s code 
    if args.reducer:
    lm = ManifoldLearning()
    if args.reducer == 'lle':
        mat = lm.lle(df.as_matrix())
    elif args.reducer == 'isomap':
        mat = lm.isomap(df.as_matrix())
    elif args.reducer == 'mds':
        mat = lm.mds(df.as_matrix())
    elif args.reducer == 'spectral':
        mat = lm.spectral_embedding(df.as_matrix())
    elif args.reducer == 'tsne':
        mat = lm.tsne(df.as_matrix())
    df = pd.DataFrame(mat, index=df.index.values, columns=['X', 'Y']) #  Assume 2D???
  • Fika Ali’s presentation
    • What is O&M training?
    • Move the mechanism up front? I was wondering what the device was
    • Paraphrasing scenarios is ok
    • Example of finding? A specific error with a response and how it was coded?
    • ‘Perpetuating stigma’ text too far indented
    • Designers Should Also?
    • slide 25 are critical should be is critical
    • Same error should be some error
    • Overall
      • It’s a lot of words. More pictures?
      • The icon works, but maybe is a little confusing
      • Helena – don’t we already know this? THe contextual issues is de-emphasized
      • William, what does the literature say on adoption? Add a brief overview of previous work. Particularly in public places? The contribution is context.
      • Stacy lean heavily on facial recognition literature. THis can show why accuracy may b overweighted.
      • Amy – focus on the bigger points. Do the hook first. Scenarios that would make things obvious. Walking into the wrong bathroom.
      • Phil – figuring out context is hard! How do you do that?
      • Amy – too heavy on process, and not enough on motivations. Lean on the quotes, they tell a better story. Fewer than 5 slides are motivations. Add an outline so they know what’s coming up. So people can know howm much time to devote to emails
      • Helena ‘You can read the details in the paper’
      • Stacy – I want to hear be excited about your talk
      • Amy, Stacy – make a recording to listen to. Pay attention to pacing, pauses, etc.

Phil 4.28.17

7:00 – 8:00 Research

8:30 – 4:30 BRC

  • Working on finding an exit condition for the subdivision surface
  • I’m currently calculating the corners of a constricting rectangle that contracts towards the best point. Each iteration is saved, and I’m working on visualizing that surface, but my brain has shut down, and I can do simple math anymore.
  • Had a thought for Aaron about how to visualize his dimension reduction. Turns out to do well.

Aaron 4.27.17

  • Cycling
    • Got a late start in the office today, so as soon as I get in I got my gear on for a brain cleaning ride. Pushed really hard today, and combined with some nice weather and low traffic hit my first 16+ average MPH door-to-door. Landed a 16.4 mph average, and felt really proud of it.
  • Focus today was on learning some more about Manifold learning and its applications for reduction of high dimensional data for unsupervised learning.
    • SciKit includes some great documentation and resources including a working sample comparing various Manifold learning techniques against test data sets.
    • My goal now is to take the sorted data_generator.py code from yesterday and compare the manifold learning examples against the clustered output of the unreduced data. Once I have a benchmark set up I can do the same for the sample live data.
    • The output of the SciKit examples in MatPlotLib is really attractive as well.manifold_learning_sample

Phil 4.27.17

7:00 – 9:00 Research

  • Some more echo chamber flocking: Iran Deal Is More Popular Than Ever, Poll Shows 170426_iran-1Republicans registered the biggest uptick in support for the deal, which has been heavily criticized by GOP lawmakers since its inception in July 2015: 53 percent of Republican voters said they supported it, compared with 37 percent who backed it last summer and just 10 percent who supported it shortly after it was announced. Democratic support for the deal has been largely unchanged since August, and a larger share of independents are getting on board, from 41 percent in August to 48 percent now.
  • Finishing corrections to paper
  • This really is my phase I research question: If ‘laws of motion’ can indeed be ascribed to behavior, we should be able to model the effects of those laws. The question them becomes what form do these models take? Also, how do we detect these behaviors with domain independence and at scale?
  • Submitted!
  • The Relevance of Hannah Arendt’s Reflections on Evil:Globalization and Rightlessness

BRC 9:30 5:00

  • Continuing Subdivision surfacing
  • didn’t like the documentation on sortedcollections. going to try panda Series
  • Allowable options in an arg: 
    parser.add_argument("--algorithm", type=str, choices=['naive', 'subdivision', 'genetic'], default='naive', help="hill climbing algorithm")

    Note that range(), which returns a list should also work

  • And here’s how you get the key/values from a pandas Series: 
    print("calc_subdivision_fitness_landsape(): key = {0}, val = {1}".format(fitness.index[0], fitness.values[0]))
  • Looks like it’s working. I think I should be using the average of the 4 fitnesses to decide if I’m done 
    calc_subdivision_fitness_landsape(): fitness = 
1    10.0
0     7.0
3     6.0
2     6.0
dtype: float64
calc_subdivision_fitness_landsape(): fitness = 
1    10.0
0     7.0
3     6.0
2     6.0
dtype: float64
calc_subdivision_fitness_landsape(): fitness = 
1    10.0
0     7.0
3     6.0
2     6.0
dtype: float64
done calc_subdivision_fitness_landsape

Phil 4.26.17

7:00 – 8:30 Research

  • Proofreading and tweaking the CSCW paper.
  • Finished the paper edit. Started to roll in the changes
  • Made a 10D chart of the explorer probability distribution. I think it tells the story better:
  •  ExplorerPDF
  • Had to install a dictionary in TexStudio. This helped a lot.
  • Started rolling in the changes to the tex file

BRC 9:00 – 4:30

  • Looks like the sort changes to the data_generator.py code haven’t been pushed yet
  • Starting on subdivision surfacing 
    def calc_subdivision_fitness_landsape(self, eps_step: float, min_cluster: int) -> pandas.DataFrame:
    # create the four extreme corners. These will work their way in
    # calculate halfway points
    # keep the square with the greatest (single? average?) value
    # repeat until an epsilon, max value, or max iterations are reached
    # construct a sparse matrix with spacing equal to the smallest spacing
    # fill in the values that have been calculated
    # build a dataframe and return it for visualization
  • I need to sort a dict, so I’m trying SortedContainers.
  • Then things went off the hails a bit, and I wrote a haiku program as a haiku that prints itself: 
    def haiku(sequence):
    this_is_not_needed = ""
    return "".join(sequence)

if __name__ == "__main__":
    f = open('haiku.py')
    print(haiku(f.readlines()[:3]))

Aaron 4.25.17

  • Wasted a ton of time today tracking down progress of integration of additional teams into our program.
  • Spent a couple of hours tackling a poster presentation to be delivered at a technical leadership summit next week. I’ll be presenting the “Advanced Analytics” presentation and discussing all of our tools, and capabilities. Phil helped a lot, and I ended up quite pleased with the results. One of the nice things is we were able to include screenshots of actual tools and graphs of the data we’re using. I think this will be a nice difference from the rest of the presenters.
  • Did some good pair programming with Phil on the Pandas DataFrame.sort issue, moved to the non-deprecated version of DataFrame.sort_values and got it working correctly at all matrix sizes.

Phil 4.25.17

7:00 – 8:30 Research

  • Wikipedia founder Jimmy Wales launches Wikitribune, a large-scale attempt to combat fake news
  • Listening to the BBC Business Daily on Machine Learning. They had an interview with Joanna J Bryson (Scholar). She has an approach for explaining the behavior of AI that seems to involve simulation? Here are some papers that look interesting:
    • Behavior Oriented Design (MIT Dissertation: Intelligence by Design: Principles of Modularity and Coordination for Engineering Complex Adaptive Agents)
    • Learning from Play: Facilitating character design through genetic programming and human mimicry
      • Mimicry and play are fundamental learning processes by which individuals can acquire behaviours, skills and norms. In this paper we utilise these two processes to create new game characters by mimicking and learning from actual human players. We present our approach towards aiding the design process of game characters through the use of genetic programming. The current state of the art in game character design relies heavily on human designers to manually create and edit scripts and rules for game characters. Computational creativity approaches this issue with fully autonomous character generators, replacing most of the design process using black box solutions such as neural networks. Our GP approach to this problem not only mimics actual human play but creates character controllers which can be further authored and developed by a designer. This keeps the designer in the loop while reducing repetitive labour. Our system also provides insights into how players express themselves in games and into deriving appropriate models for representing those insights. We present our framework and preliminary results supporting our claim.
    • Replicators, Lineages and Interactors: One page note on cultural evolution
      • If we adopt the other option and refer to culture itself is the lineage, then the culture itself can evolve since the replicators are the ideas and practices that exist within that culture. However, if it is the culture that is the lineage, we cannot say that it evolves when it takes more territory, in the same way that a species does not evolve with more individuals. Adaptation is presently understood to be about changes in the frequency of replicators, not about absolute numbers of interactors. In sum, cultural evolution (changes of practices within a group) is necessarily a separate process from cultural group selection (changes of the frequency of group-types at a specific location).
    • The behavior-oriented design of modular agent intelligence
    • Should probably cite some of these and a reference to Behavior-Oriented Design in the conclusions section of the paper
  • Continuing Examining the Alternative Media Ecosystem through the Production of Alternative Narratives of Mass Shooting Events on Twitter
    • We collected data using the Twitter Streaming API, tracking on the following terms (shooter, shooting, gunman, gunmen, gunshot, gunshots, shooters, gun shot, gun shots, shootings) for a ten-month period between January 1 and October 5, 2016. This collection resulted in 58M total tweets. We then scoped that data to include only tweets related to alternative narratives of the event—false flag, falseflag, crisis actor, crisisactor, staged, hoax and “1488”.
      • These keywords specify a ‘primary information space’. Bag-of-words of text correlated with each term could make this a linear axis
    • Of 15,150 users who sent at least one tweet with a link, only 1372 sent (over the course to the collection period) tweets citing more than one domain.
      • This is the difference between implicit behaviors (clicking, reading, navigating) and explicit actions. Twitter monitors what people are willing to write
    • Interestingly, the two most influential Domains in Alternative Narrative Tweets Interesting, the two most highly tweeted domains were both associated with significant automated account or “bot” activity. The Real Strategy, an alternative news site with a conspiracy theory orientation, is the most tweeted domain in our dataset (by far). The temporal signature of tweets citing this domain reveals a consistent pattern of coordinated bursts of activity at regular intervals generated by 200 accounts that appear to be connected to each other (via following relationships) and coordinated through an external tool.
      • There is clearly a desire to have a greater effect through the use of bots. Two questions: 1) How does this work? 2) How did this emerge?
    • The InfoWars domain, an alternative news website that focuses on Alt-Right and conspiracy theory themes, was the second-most tweeted domain, but as (Figure 1) shows it was only tenuously connected to one other node.
      • Why? Is InforWars more polarized? Is it using something other than Twitter?
      • Infowars Inbound links
        Domain score Domain trust score Domain Backlinks IP Address Country First seen Last seen
        0 0 breakingnewsfeed.com 1857029 174.129.22.101 us 2015-09-28 2017-03-26
        4 4 e-graviton.com 1335835 67.210.126.35 us 2014-01-19 2017-03-21
        33 39 prisonplanet.com 648958 69.16.175.42 us 2013-06-07 2017-03-25
        1 0 nwostop.com 346153 104.28.28.16 us 2014-01-19 2017-03-21
        13 31 nwoo.org 182060 81.0.208.215 cz 2013-06-07 2017-03-26
        12 30 conservative-headlines.com 151778 104.18.50.72 us 2016-06-27 2017-03-22
        1 0 america2fear.com 92766 69.64.46.138 us 2014-11-14 2017-03-23
        4 29 subbmitt.com 49288 64.251.23.173 us 2015-02-04 2017-03-26
        14 30 anotherdotcom.com 47195 174.129.236.72 us 2014-10-02 2017-03-20
        1 0 exzacktamountas.com 43748 208.100.60.13 us 2016-06-08 2017-03-24

9:00 – 5:30 BRC

  • John is having trouble getting Linux running on the laptop
    • No luck. Re-submitting for an Alienware deskside
  • Back to getting the temporal coherence. last try to finish up, then switching to fitness landscape optimization, which I dreamed about last night
  • Finished coherence! Had to include a state check for a timeline to see if a DIRTY state had been touched with an update. If not, then the timeline is set to CLOSED. If a new cluster appears that would have had some overlap, a new timeline is created anyway. This could be an optional behavior.
    • Still need to test rigorously across multiple data sets
  • Long scrum, then ML meeting.
    • Hard tasks
      • TF server set up to work in our environment
      • Pre-calculated models to speed up training from research browser
      • T-SNE or other mapping of returned CSE text to support exploration
      • Fast, on-the-fly classification and entity extraction within the research browser framework. Plus interactive training
      • NMF (or other) topic extraction tied to human labeling and curation, plus cross-user validation of topics
  • Poster with Aaron later? Yep. Couple of hours. Done?
  • Oh, just why? Spent an hour on this before going brute force: 
    def get_last_cluster(self) -> ClusterSample:
    # return self._cluster_dict[self._cluster_dict.keys()[-1] TODO: This should work
    toReturn = None
    for key in self._cluster_dict:
        toReturn = self._cluster_dict[key]
    return toReturn
  • Walked through some gradient descent regression code with Bob. More tomorrow?
  • Got the new sort working with Aaron. Much faster progress as a pair

Phil 4.24.17

7:00 – 8:00, 3:00 – 4:00  Research

  • Continuing to tweak paper
  • Starting Examining the Alternative Media Ecosystem through the Production of Alternative Narratives of Mass Shooting Events on Twitter
    • From the introduction. Do I need something like this? Our contributions include an increased understanding of the underlying nature of this subsection of alternative media — which hosts conspiratorial content and conducts various anti-globalist political agendas. Noting thematic convergence across domains, we theorize about how alternative media may contribute to conspiratorial thinking by creating a false perception of information diversity.
  • Conspiracy Theories
  • A cool thing on explorers and veloviewer: maxsquare Here’s an overview of the project
  • Brownbag
    • Teaching abstract concepts to children and tweens (STEM)
    • Cohesive understanding of science over time
    • Wearable technology as the gateway for elementary-school-aged kids? Research shows that they find them valuable
    • How are these attributes measured? <——-!!!!!!!
    • Live visualization sensing and visualization
    • Zephyr bioharness
    • Gender/age differences? Augmented reality? Through-phone?
    • leylanorooz.com/research/

8:30 – 2:30 BRC

  • Expense report! Done? Had to get a charge number, and re enter. Took forever.
  • Found out that I’m getting a laptop rather than what I asked for
    • Having John install Ubuntu and verify that multiple monitors work in Linux
  • Helped Bob set up Git repo
  • Still working on temporal coherence. Think I’ve figured out the logic. Now I need to set clusters in ClusterTimelines
  • Learned how to do Enums in Python

Phil 4.21.1

6:00 – 7:00 Research

8:30 – 5:00 BRC

  • Need to think about handling time, so we can see if people are getting better
  • All hands meeting
    • Transforming healthcare WRT identifying risks and anomalies for the purpose of reducing variance in care. From what to what?
  • 4 things:
    • Technology: get to and stay at the leading edge of what we’re marketing. Investment commitment (CCRi These guys? Alias, Commonwealth university)
    • Sales development
    • Partnering
    • Capital (direct raise from investors) Alignment at a capital level?
  • V2 & V3 timelines and capabilities
  • Sales and capital story
  • Discussion (2 hours)

Phil 4.20.17

7:00 – 8:00 Research

8:30 – 6:00, 7:00 – 10:00 BRC

  • Drove up to NJ
  • Still working on temporal coherence of clusters. Talked through it with Aaron, and we both believe it’s close
  • Another good discussion with Bob
  • BRC dinner meet-n-greet