7:00 – 8:00 Research

• Never made it to Fika yesterday. Worked a solid 13.5 hours. Artificial deadlines are pretty dumb.
• Continuing with registration paperwork and other loose ends
  • HCIC travel (May 19), poster (June 2) – Attempting to register. Account created
    • Registered!
    • Travel email sent. Sat June 24 – Sat Jul 1
  • PhD Review (May 26) – started. Sure hope It incrementally saves…
    • Nope – starting over, offline. Making progress…

9:00 – 5:00 BRC

• Sprint review. Went well, I think
• Working on turning the PPT into a document

Well, the weekend ended on a sad, down note. Having problems getting motivated.

7:00 – 8:00 Research

• Filled out CI 2017 form
• Started HCIC registration
• Started PhD review

8:30 – 8:00PM BRC

• Run clustering on t-SNE again with Bob’s settings. It’s…. OK. We think that MDS and LLE are better for now, but there are almost certainly hyper parameter tweaking that we can do.
• Here’s n example of actual data with lots of error between runs: but by adjusting the hyperparameter ‘neighbors’ from 5 (above) to 10 (below), we get a completely different result: Here, you can see that no cluster shared its nodes with any other cluster. That’s what we want. Stable, but with good granularity.
• We can play some games on the clustering by seeing what happens when we remove some columns from our data. Here’s the above data with gender included and excluded: It’s possible to see that several items that were in cluster (0) distribute out when gender don’t override associated clusters.
• Had a weird issue where LLE clustering on our test data that worked with neighbors = 10, now needs neighbors > 12 to work. Not sure why that’s happening.
• Need to write up a report generator that does the following:  For each cluster in the set that we are comparing:
  

  size
  stable/total
  list of stable

Tasks:

• Collective Intelligence details (May 18) – Done
• HCIC travel (May 19), poster (June 2) – Attempting to register. Account created
• PhD Review (May 26) – started. Sure hope It incrementally saves…
• CHIIR 2018
  • 1 October 2017 *- Full papers and Perspectives papers due
  • 22 October 2017* – Short papers, Demos, Workshops and Tutorials proposals due
  • 1 November 2017* – Doctoral Consortium applications due
  • 15 December 2017* – Notification of acceptance

7:00 – 8:00 Research

• I searched for better Electome images (unannotated) and found a few. They are on the previous post. I’ll seriously start to work on the poster this weekend.
• Cleaned up some code, and made the writing of an image file optional. Still figuring out the best way to do helper classes in Python
• Starting to learn the analytic capabilities of Networkx, which is its core as I understand. Going to start to characterize the networks and compare against stored sets (like the Karate Club) that is in the library
• List of algorithms

8:30 – 4:30 BRC

• Working on cluster member sequence visualization.
  • Needed to make unclustered user configurable
  • Needed to make timeline sequence, where OPEN and CLOSED states are enabled, user configurable
• Results! LLE looks to be the best by far:

  

  

  

  

  

  
  

7:00 – 8:00, 4:00-7:00 Research

• Guest lecture for CSCW class! Notes!
  • And it went well. Fun, actually.
• Working on making the line width based on the time spent in the cluster, the cluster size a function of its lifespan, and the agent size, a function of time spent in a cluster
  • While working on the last part, I realized that I was including ‘unclustered’ (-1) as a cluster. This made all the agents the same size, and also messed up the cluster collating, since unclustered can be the majority in some circumstances. Fixing this made everything much better: so now I need to rerun all the variations. Done! Rough boaster poster:
• Found better Electome images. More project info here and here: 

9:00 – 3:00 BRC

• Finished temporal coherence. Now we can compare across multiple cluster attempts. Tomorrow I’ll set up the cluster_optomizer to make multiple runs and produce an Excel file containing columns of cluster attempts

7:00 – 8:00

• Systematic exploration of unsupervised methods for mapping behavior
• Thinking about the stories I can tell with the GP sim.
  • Start together with same settings.
  • Disconnect
  • Slide exploit to max
• Need to download blog entries
• Working on graphing. Success!!!!! Now I need to discriminate agents from clusters, and exploit from explore. But this shows polarized vs. diverse clustering. I’m pretty sure I can get all kinds of statistics out of this too!
• Better version. Ran all the permutations:
• explore_1.6_exploit_3.2_ran 04_14_17-08_38_48. Green are clusters, Red are Exploit, Blue are Explore
• Need to make the line width based on the time spent in the cluster, and the cluster size a function of its lifespan

9:00 – 5:00 BRC

• Working on showing where the data broke. Looks like Talend
• For future referrence, How to turn a dict of rows into a DataFrame and then how to access all the parts:

  import pandas as pd
  
  d1 = {'one':1.1, 'two':2.1, 'three':3.1}
  d2 = {'one':1.2, 'three':3.2}
  d3 = {'one':1.3, 'two':2.3, 'three':3.3}
  rows = {'row1':d1, 'row2':d2}
  rows['row3'] = d3
  df = pd.DataFrame(rows)
  df = df.transpose()
  print(df)
  
  for index, row in df.iterrows():
      print(index)
      for key, val in row.iteritems():
          print("{0}:{1}".format(key, val))

• Helped Aaron with the writeups
• And it turns out that all the work I did “could be done in an hour”. So back to clustering and AI work. If there is a problem with the data, I know that it works with the test data. Others can figure out where the problem is, since they can handle it so quickly.

7:00 – 8:00 Research

• More clustering. Here’s the list of agents by clusters. An OPEN state means that the simulation finished with agents in the cluster. Num_entries is: the lifetime of the cluster. For these runs, the max is 200. Id is the ‘name’ of the cluster. Tomorrow, I’ll try to get this drawn using networkx.

  timeline[0]:
  Id = cluster_0
  State = ClusterState.OPEN
  Num entries = 200
  {'ExploitSh_52', 'ExploreSh_43', 'ExploitSh_56', 'ExploreSh_2', 'ExploreSh_5', 'ExploitSh_73', 'ExploitSh_95', 'ExploreSh_19', 'ExploreSh_4', 'ExploitSh_87', 'ExploitSh_76', 'ExploreSh_3', 'ExploitSh_93', 'ExploreSh_32', 'ExploreSh_41', 'ExploreSh_17', 'ExploitSh_88', 'ExploitSh_77', 'ExploreSh_39', 'ExploitSh_85', 'ExploreSh_40', 'ExploitSh_64', 'ExploreSh_34', 'ExploreSh_22', 'ExploitSh_99', 'ExploreSh_1', 'ExploitSh_97', 'ExploitSh_69', 'ExploreSh_29', 'ExploitSh_58', 'ExploitSh_62', 'ExploreSh_23', 'ExploreSh_36', 'ExploreSh_11', 'ExploitSh_80', 'ExploitSh_82', 'ExploreSh_21', 'ExploitSh_75', 'ExploitSh_72', 'ExploitSh_89', 'ExploitSh_86', 'ExploreSh_37', 'ExploitSh_84', 'ExploitSh_81', 'ExploreSh_15', 'ExploitSh_51', 'ExploreSh_44', 'ExploitSh_83', 'ExploitSh_94', 'ExploreSh_16', 'ExploitSh_53', 'ExploitSh_67', 'ExploitSh_74', 'ExploreSh_45', 'ExploreSh_26', 'ExploreSh_12', 'ExploreSh_13', 'ExploitSh_92', 'ExploreSh_9', 'ExploreSh_28', 'ExploitSh_50', 'ExploreSh_8', 'ExploreSh_30', 'ExploreSh_49', 'ExploitSh_59', 'ExploitSh_57', 'ExploreSh_42', 'ExploitSh_65', 'ExploitSh_54', 'ExploitSh_61', 'ExploitSh_66', 'ExploitSh_55', 'ExploitSh_78', 'ExploitSh_68', 'ExploitSh_79', 'ExploitSh_91', 'ExploitSh_71', 'ExploreSh_7', 'ExploitSh_98', 'ExploitSh_60', 'ExploitSh_70', 'ExploreSh_10', 'ExploitSh_90', 'ExploreSh_46', 'ExploitSh_96', 'ExploreSh_47', 'ExploitSh_63'}
  
  timeline[1]:
  Id = cluster_1
  State = ClusterState.OPEN
  Num entries = 200
  {'ExploreSh_25', 'ExploreSh_6', 'ExploreSh_38', 'ExploreSh_43', 'ExploreSh_49', 'ExploreSh_1', 'ExploreSh_2', 'ExploreSh_20', 'ExploreSh_33', 'ExploreSh_48', 'ExploreSh_5', 'ExploreSh_29', 'ExploreSh_15', 'ExploreSh_42', 'ExploreSh_24', 'ExploreSh_19', 'ExploreSh_4', 'ExploreSh_44', 'ExploreSh_16', 'ExploreSh_23', 'ExploreSh_36', 'ExploreSh_11', 'ExploreSh_3', 'ExploreSh_27', 'ExploreSh_35', 'ExploreSh_32', 'ExploreSh_17', 'ExploreSh_26', 'ExploreSh_21', 'ExploreSh_12', 'ExploreSh_18', 'ExploreSh_45', 'ExploreSh_41', 'ExploitSh_79', 'ExploreSh_13', 'ExploreSh_0', 'ExploreSh_39', 'ExploreSh_7', 'ExploreSh_9', 'ExploreSh_28', 'ExploreSh_40', 'ExploreSh_31', 'ExploreSh_10', 'ExploreSh_46', 'ExploreSh_37', 'ExploreSh_14', 'ExploreSh_47', 'ExploreSh_8', 'ExploreSh_30', 'ExploreSh_34', 'ExploreSh_22'}
  
  timeline[2]:
  Id = cluster_2
  State = ClusterState.CLOSED
  Num entries = 56
  {'ExploreSh_25', 'ExploreSh_1', 'ExploreSh_33', 'ExploreSh_29', 'ExploreSh_5', 'ExploreSh_48', 'ExploreSh_15', 'ExploreSh_19', 'ExploreSh_36', 'ExploreSh_3', 'ExploreSh_11', 'ExploreSh_35', 'ExploreSh_45', 'ExploreSh_17', 'ExploreSh_26', 'ExploreSh_41', 'ExploitSh_79', 'ExploreSh_13', 'ExploreSh_9', 'ExploreSh_40', 'ExploreSh_31', 'ExploreSh_37', 'ExploreSh_47', 'ExploreSh_30', 'ExploreSh_22'}
  
  timeline[3]:
  Id = cluster_3
  State = ClusterState.CLOSED
  Num entries = 16
  {'ExploreSh_25', 'ExploreSh_6', 'ExploreSh_43', 'ExploreSh_2', 'ExploreSh_48', 'ExploreSh_5', 'ExploreSh_15', 'ExploreSh_42', 'ExploreSh_24', 'ExploreSh_4', 'ExploreSh_44', 'ExploreSh_3', 'ExploreSh_26', 'ExploreSh_17', 'ExploreSh_41', 'ExploreSh_21', 'ExploreSh_32', 'ExploreSh_13', 'ExploreSh_9', 'ExploreSh_7', 'ExploreSh_28', 'ExploreSh_37', 'ExploreSh_8', 'ExploreSh_30', 'ExploreSh_49', 'ExploreSh_22'}
  
  timeline[4]:
  Id = cluster_4
  State = ClusterState.CLOSED
  Num entries = 30
  {'ExploreSh_6', 'ExploreSh_1', 'ExploreSh_2', 'ExploreSh_20', 'ExploreSh_33', 'ExploreSh_48', 'ExploreSh_15', 'ExploreSh_24', 'ExploreSh_4', 'ExploreSh_16', 'ExploreSh_23', 'ExploreSh_3', 'ExploreSh_11', 'ExploreSh_26', 'ExploreSh_41', 'ExploreSh_17', 'ExploreSh_32', 'ExploreSh_18', 'ExploreSh_13', 'ExploreSh_9', 'ExploreSh_46', 'ExploreSh_37', 'ExploreSh_8', 'ExploreSh_30', 'ExploreSh_49', 'ExploreSh_22'}
  
  timeline[5]:
  Id = cluster_5
  State = ClusterState.CLOSED
  Num entries = 28
  {'ExploreSh_25', 'ExploreSh_43', 'ExploreSh_2', 'ExploreSh_48', 'ExploreSh_29', 'ExploreSh_42', 'ExploreSh_24', 'ExploreSh_4', 'ExploreSh_44', 'ExploreSh_36', 'ExploreSh_35', 'ExploreSh_45', 'ExploreSh_17', 'ExploreSh_26', 'ExploreSh_12', 'ExploreSh_0', 'ExploreSh_28', 'ExploreSh_40', 'ExploreSh_31', 'ExploreSh_46', 'ExploreSh_37', 'ExploreSh_14', 'ExploreSh_47', 'ExploreSh_8', 'ExploreSh_30', 'ExploreSh_22'}
  
  timeline[6]:
  Id = cluster_6
  State = ClusterState.CLOSED
  Num entries = 10
  {'ExploreSh_40', 'ExploreSh_25', 'ExploreSh_18', 'ExploreSh_27', 'ExploreSh_10', 'ExploreSh_13', 'ExploreSh_20', 'ExploreSh_0', 'ExploreSh_37', 'ExploreSh_14', 'ExploreSh_36', 'ExploreSh_11', 'ExploreSh_39', 'ExploreSh_42', 'ExploreSh_22'}
  
  timeline[7]:
  Id = cluster_7
  State = ClusterState.CLOSED
  Num entries = 9
  {'ExploreSh_38', 'ExploreSh_2', 'ExploreSh_4', 'ExploreSh_46', 'ExploreSh_16', 'ExploreSh_33', 'ExploreSh_47', 'ExploreSh_14', 'ExploreSh_11', 'ExploreSh_27', 'ExploreSh_35', 'ExploreSh_45'}
  
  timeline[8]:
  Id = cluster_8
  State = ClusterState.CLOSED
  Num entries = 25
  {'ExploreSh_21', 'ExploreSh_38', 'ExploreSh_19', 'ExploreSh_2', 'ExploreSh_13', 'ExploreSh_44', 'ExploreSh_1', 'ExploreSh_10', 'ExploreSh_16', 'ExploreSh_47', 'ExploreSh_5', 'ExploreSh_48', 'ExploreSh_42', 'ExploreSh_35', 'ExploreSh_22', 'ExploreSh_32'}
  
  timeline[9]:
  Id = cluster_9
  State = ClusterState.OPEN
  Num entries = 16
  {'ExploreSh_17', 'ExploreSh_6', 'ExploreSh_24', 'ExploreSh_19', 'ExploreSh_10', 'ExploreSh_20', 'ExploreSh_46', 'ExploreSh_33', 'ExploreSh_14', 'ExploreSh_3', 'ExploreSh_39', 'ExploreSh_7', 'ExploreSh_45'}

• Network Dynamics and Simulation Science Laboratory – need to go through publications and venues for these folks
• Dynamic Spirals Put to Test: An Agent-Based Model of Reinforcing Spirals Between Selective Exposure, Interpersonal Networks, and Attitude Polarization
  • Within the context of partisan selective exposure and attitude polarization, this study investigates a mutually reinforcing spiral model, aiming to clarify mechanisms and boundary conditions that affect spiral processes—interpersonal agreement and disagreement, and the ebb and flow of message receptions. Utilizing agent-based modeling (ABM) simulations, the study formally models endogenous dynamics of cumulative processes and its reciprocal effect of media choice behavior over extended periods of time. Our results suggest that interpersonal discussion networks, in conjunction with election contexts, condition the reciprocal effect of selective media exposure and its attitudinal consequences. Methodologically, results also highlight the analytical utility of computational social science approaches in overcoming the limitations of typical experimental and observations studies.

8:30 – 5:30 BRC

• Logical Graphs: Control Flow in TensorFlow – Sam Abrahams (slides)
• Went digging through the input data. It is *not* the same. Generated lots of data
• When checking, we can tell that the data in the database is the same as it was in January
• Now looking to see if the data on CI is good

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()

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()))

  • 
• 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

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

7:00 – 8:00 Research

• Still working on building a population map. I downloaded the population density from here, and a python library for looking at GeoTiff files (georasters). And the Wilbur terrain generator
• 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
• Continuing Online clustering, fear and uncertainty in Egypt’s transition. Notes are here
• Homophilic Clustering by Locally Asymmetric Geometry
• Social Identity Theory – provides grounding for antibelief and other flocking characteristics
• Interactive Narrative: An Intelligent Systems Approach 
  • Citations from Google Scholar

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

    

    

    

    
    

7:00 – 8:00 Research

• Continuing Online clustering, fear and uncertainty in Egypt’s transition. Notes are here
• Transitions from Authoritarian Rule
  • Political science scholars consider the four-volume work Transitions from Authoritarian Rule to be a foundational text for studying the process of democratization, specifically in those cases where an authoritarian regime is giving way to some form of democratic government. The most important of the four books is without a doubt the fourth volume, Tentative Conclusions about Uncertain Democracies, also known as “the little green book.”Transitions from Authoritarian Rule was the first book in any language to systematically compare the process of transition from authoritarianism across a broad range of countries. Political democracy is not the only possible outcome. Guillermo O’Donnell, Philippe C. Schmitter, and Laurence Whitehead emphasize that it’s not the revolution but the transition that is critical to the growth of a democratic state. This ground-breaking insight remains highly relevant as the ramifications of the Arab Spring continue to play out.
• The Politics of Uncertainty: Sustaining and Subverting Electoral Authoritarianism (Oxford Studies in Democratization)
• HR-diagrams are a great example of a behavior map. I think I would even contend that a map can be defined as a diagram that supports the plotting of trajectories
• A self-organizing semantic map for information retrieval
• Computer method and system for topographical representation of textual data (patent – could be a problem for the ResearchBrowser?)
• When Deceitful Chats Look Truthful (Self organizing maps!)
• Fear and Loathing across Party Lines: New Evidence on Group Polarization
• Prior Exposure Increases Perceived Accuracy of Fake News

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:

  

  

  

  
  

  

  
  

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.
    • Social media data collection tools
  • 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? 
• 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.

7:00 – 8:00 Research

• Submitted the paper. Need to start on the HCIC poster next
• Continuing Examining the Alternative Media Ecosystem through the Production of Alternative Narratives of Mass Shooting Events on Twitter. Notes are here. Finished!
• The menace of unreality: How the Kremlin weaponizes information, culture and money

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.

• 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.