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?
- 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:
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
- 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.