- Sep 2020
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iriss.stanford.edu iriss.stanford.edu
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2020 Conference on Computational Sociology | IRiSS. (n.d.). Retrieved 30 September 2020, from https://iriss.stanford.edu/css/conferences/2020-conference-computational-sociology
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Romanini, Daniele, Sune Lehmann, and Mikko Kivelä. ‘Privacy and Uniqueness of Neighborhoods in Social Networks’. ArXiv:2009.09973 [Physics], 21 September 2020. http://arxiv.org/abs/2009.09973.
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www.visualcapitalist.com www.visualcapitalist.com
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Ali, A. (2020, August 28). Visualizing the Social Media Universe in 2020. Visual Capitalist. https://www.visualcapitalist.com/visualizing-the-social-media-universe-in-2020/
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- Aug 2020
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Holtz, D., Zhao, M., Benzell, S. G., Cao, C. Y., Rahimian, M. A., Yang, J., Allen, J., Collis, A., Moehring, A., Sowrirajan, T., Ghosh, D., Zhang, Y., Dhillon, P. S., Nicolaides, C., Eckles, D., & Aral, S. (2020). Interdependence and the cost of uncoordinated responses to COVID-19. Proceedings of the National Academy of Sciences, 117(33), 19837–19843. https://doi.org/10.1073/pnas.2009522117
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Young, J.-G., Cantwell, G. T., & Newman, M. E. J. (2020). Robust Bayesian inference of network structure from unreliable data. ArXiv:2008.03334 [Physics, Stat]. http://arxiv.org/abs/2008.03334
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journals.plos.org journals.plos.org
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Aleta, A., Arruda, G. F. de, & Moreno, Y. (2020). Data-driven contact structures: From homogeneous mixing to multilayer networks. PLOS Computational Biology, 16(7), e1008035. https://doi.org/10.1371/journal.pcbi.1008035
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- Jul 2020
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www.youtube.com www.youtube.comYouTube1
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Supporting Open Science Data Curation, Preservation, and Access by Libraries. (2020, June 25). https://www.youtube.com/watch?v=SbmGWHpzAHs&feature=youtu.be
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www.sciencedirect.com www.sciencedirect.com
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Fontana, M., Iori, M., Montobbio, F., & Sinatra, R. (2020). New and atypical combinations: An assessment of novelty and interdisciplinarity. Research Policy, 49(7), 104063. https://doi.org/10.1016/j.respol.2020.104063
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arxiv.org arxiv.org
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Gupta, H., & Porter, M. A. (2020). Mixed Logit Models and Network Formation. ArXiv:2006.16516 [Physics, Stat]. http://arxiv.org/abs/2006.16516
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arxiv.org arxiv.org
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Lovato, J., Allard, A., Harp, R., & Hébert-Dufresne, L. (2020). Distributed consent and its impact on privacy and observability in social networks. ArXiv:2006.16140 [Physics]. http://arxiv.org/abs/2006.16140
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- Jun 2020
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zoom.us zoom.us
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Welcome! You are invited to join a webinar: Supporting Open Science Data Curation, Preservation, and Access by Libraries. After registering, you will receive a confirmation email about joining the webinar. (n.d.). Zoom Video. Retrieved June 28, 2020, from https://zoom.us/webinar/register/2615905946283/WN_W6dYUXQFTqGQjGAZPRB74w
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psyarxiv.com psyarxiv.com
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Borsboom, D., Blanken, T., Dablander, F., Tanis, C., van Harreveld, F., & van Mieghem, P. (2020). BECON methodology [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/53ey9
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UKRN position on covid 19 research. (2020 May 01). School of Psychological Science | University of Bristol. http://www.bristol.ac.uk/psychology/research/ukrn/news/2020/ukrn-position-on-covid-19-research.html
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Murphy, C., Laurence, E., & Allard, A. (2020). Deep learning of stochastic contagion dynamics on complex networks. ArXiv:2006.05410 [Cond-Mat, Physics:Physics, Stat]. http://arxiv.org/abs/2006.05410
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Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020). Echo Chambers on Social Media: A comparative analysis. ArXiv:2004.09603 [Physics]. http://arxiv.org/abs/2004.09603
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Our Work on COVID-19. (n.d.). Facebook Data for Good. Retrieved April 20, 2020, from https://dataforgood.fb.com/docs/covid19/
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Eroglu, D. (2020). Revealing Dynamics, Communities, and Criticality from Data. Physical Review X, 10(2). https://doi.org/10.1103/PhysRevX.10.021047
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Cantwell, G. T., Liu, Y., Maier, B. F., Schwarze, A. C., Serván, C. A., Snyder, J., & St-Onge, G. (2020). Thresholding normally distributed data creates complex networks. Physical Review E, 101(6), 062302. https://doi.org/10.1103/PhysRevE.101.062302
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- May 2020
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Rosenblatt, S. F., Smith, J. A., Gauthier, G. R., & Hébert-Dufresne, L. (2020). Immunization Strategies in Networks with Missing Data. ArXiv:2005.07632 [Physics, q-Bio]. http://arxiv.org/abs/2005.07632
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www.nature.com www.nature.com
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Zdeborová, L. (2020). Understanding deep learning is also a job for physicists. Nature Physics, 1–3. https://doi.org/10.1038/s41567-020-0929-2
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www.thelancet.com www.thelancet.com
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Rybniker, J., & Fätkenheuer, G. (2020). Importance of precise data on SARS-CoV-2 transmission dynamics control. The Lancet Infectious Diseases, S1473309920303595. https://doi.org/10.1016/S1473-3099(20)30359-5
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Dahl Fitjar, R. (2020, May 9). The density and connectedness of cities now appear as weaknesses. LSE Business Review. https://blogs.lse.ac.uk/businessreview/2020/05/09/the-density-and-connectedness-of-cities-now-appear-as-weaknesses/
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www.nature.com www.nature.com
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Vespignani, A., Tian, H., Dye, C. et al. Modelling COVID-19. Nat Rev Phys (2020). https://doi.org/10.1038/s42254-020-0178-4
Tags
- policy
- transmission dynamics
- is:article
- computational modeling
- forecast
- challenge
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- epidemiology
- superspreading
- China
- war time
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- mathematics
- open data
- contact tracing
- infection
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- prediction
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Annotators
URL
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arxiv.org arxiv.org
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Qian, Y., Expert, P., Panzarasa, P., & Barahona, M. (2020). Geometric graphs from data to aid classification tasks with graph convolutional networks. ArXiv:2005.04081 [Physics, Stat]. http://arxiv.org/abs/2005.04081
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www.economist.com www.economist.com
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Countries are using apps and data networks to keep tabs on the pandemic. (2020 March 26). The Economist. https://www.economist.com/briefing/2020/03/26/countries-are-using-apps-and-data-networks-to-keep-tabs-on-the-pandemic?fsrc=newsletter&utm_campaign=the-economist-today&utm_medium=newsletter&utm_source=salesforce-marketing-cloud&utm_term=2020-05-07&utm_content=article-link-1
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www.tandfonline.com www.tandfonline.com
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Fenton, N. E., Neil, M., Osman, M., & McLachlan, S. (2020). COVID-19 infection and death rates: The need to incorporate causal explanations for the data and avoid bias in testing. Journal of Risk Research, 0(0), 1–4. https://doi.org/10.1080/13669877.2020.1756381
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link.aps.org link.aps.org
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Krönke, J., Wunderling, N., Winkelmann, R., Staal, A., Stumpf, B., Tuinenburg, O. A., & Donges, J. F. (2020). Dynamics of tipping cascades on complex networks. Physical Review E, 101(4), 042311. https://doi.org/10.1103/PhysRevE.101.042311
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- Apr 2020
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security.googleblog.com security.googleblog.com
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Our approach strikes a balance between privacy, computation overhead, and network latency. While single-party private information retrieval (PIR) and 1-out-of-N oblivious transfer solve some of our requirements, the communication overhead involved for a database of over 4 billion records is presently intractable. Alternatively, k-party PIR and hardware enclaves present efficient alternatives, but they require user trust in schemes that are not widely deployed yet in practice. For k-party PIR, there is a risk of collusion; for enclaves, there is a risk of hardware vulnerabilities and side-channels.
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Jiang, C., Gao, J., & Magdon-Ismail, M. (2020). Inferring Degrees from Incomplete Networks and Nonlinear Dynamics. ArXiv:2004.10546 [Physics]. http://arxiv.org/abs/2004.10546
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trello.com trello.com
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Collective Intelligence and COVID-19 | Trello. (n.d.). Retrieved April 20, 2020, from https://trello.com/b/STdgEhvX/collective-intelligence-and-covid-19
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haveibeenpwned.com haveibeenpwned.com
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github.com github.com
- Feb 2020
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www.geospatialworld.net www.geospatialworld.net
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GIS and Spatial Analytics Market: Global Size
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- Sep 2017
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digital.hbs.edu digital.hbs.edu
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Marc Rysman - BU
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- Apr 2016
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googleguacamole.wordpress.com googleguacamole.wordpress.com
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followed a TAGS Explorer of a conference hashtag
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- Dec 2015
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mfeldstein.com mfeldstein.com
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increased investment in professional development and teaching-friendly tenure and promotion practices
Even those who adopt a taylorist model to education may understand that “it takes money to save money”.
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