- Last 7 days
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www.nber.org www.nber.org
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Céspedes, L. F., Chang, R., & Velasco, A. (2020). The Macroeconomics of a Pandemic: A Minimalist Model (Working Paper No. 27228; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27228
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www.nber.org www.nber.org
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Atkeson, A. (2020). What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios (Working Paper No. 26867; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26867
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www.nature.com www.nature.com
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Aleta, A., Martín-Corral, D., Pastore y Piontti, A., Ajelli, M., Litvinova, M., Chinazzi, M., Dean, N. E., Halloran, M. E., Longini Jr, I. M., Merler, S., Pentland, A., Vespignani, A., Moro, E., & Moreno, Y. (2020). Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour, 1–8. https://doi.org/10.1038/s41562-020-0931-9
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www.nber.org www.nber.org
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Allcott, H., Boxell, L., Conway, J. C., Gentzkow, M., Thaler, M., & Yang, D. Y. (2020). Polarization and Public Health: Partisan Differences in Social Distancing during the Coronavirus Pandemic (Working Paper No. 26946; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26946
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www.nber.org www.nber.org
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Malani, A., Soman, S., Asher, S., Novosad, P., Imbert, C., Tandel, V., Agarwal, A., Alomar, A., Sarker, A., Shah, D., Shen, D., Gruber, J., Sachdeva, S., Kaiser, D., & Bettencourt, L. M. A. (2020). Adaptive Control of COVID-19 Outbreaks in India: Local, Gradual, and Trigger-based Exit Paths from Lockdown (Working Paper No. 27532; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27532
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Bethune, Z. A., & Korinek, A. (2020). Covid-19 Infection Externalities: Trading Off Lives vs. Livelihoods (Working Paper No. 27009; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27009
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www.nber.org www.nber.org
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Chaudhuri, S., Lo, A. W., Xiao, D., & Xu, Q. (2020). Bayesian Adaptive Clinical Trials for Anti‐Infective Therapeutics during Epidemic Outbreaks (Working Paper No. 27175; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27175
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covid-19.iza.org covid-19.iza.org
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Donsimoni. J. R., Glawion. R., Plachter. B., Walde. K., (2020). Projecting the Spread of COVID-19 for Germany. Institute of labor economics. Retrieved from: https://covid-19.iza.org/publications/dp13094/
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covid-19.iza.org covid-19.iza.org
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Abel. M., Brown. W., (2020) Prosocial Behavior in the Time of COVID-19: The Effect of Private and Public Role Models. Institute of labor and economics. Retrieved from:https://covid-19.iza.org/publications/dp13207/
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- Aug 2020
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psyarxiv.com psyarxiv.com
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Galbadage, T., Peterson, B. M., Wang, D. C., Wang, J. S., & Gunasekera, R. S. (2020). Biopsychosocial and Spiritual Implications of Patients with COVID-19 Dying in Isolation [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/7um3x
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www.sciencedirect.com www.sciencedirect.com
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Quinn, A. E., Trachtenberg, A. J., McBrien, K. A., Ogundeji, Y., Souri, S., Manns, L., Rennert-May, E., Ronksley, P., Au, F., Arora, N., Hemmelgarn, B., Tonelli, M., & Manns, B. J. (2020). Impact of payment model on the behaviour of specialist physicians: A systematic review. Health Policy, 124(4), 345–358. https://doi.org/10.1016/j.healthpol.2020.02.007
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Acemoglu, D., Chernozhukov, V., Werning, I., & Whinston, M. D. (2020). Optimal Targeted Lockdowns in a Multi-Group SIR Model (Working Paper No. 27102; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27102
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Bianchi, F., Faccini, R., & Melosi, L. (2020). Monetary and Fiscal Policies in Times of Large Debt: Unity is Strength (Working Paper No. 27112; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27112
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www.nber.org www.nber.org
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Kominers, S. D., Pathak, P. A., Sönmez, T., & Ünver, M. U. (2020). Paying It Backward and Forward: Expanding Access to Convalescent Plasma Therapy Through Market Design (Working Paper No. 27143; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27143
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www.nber.org www.nber.org
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Baqaee, D., Farhi, E., Mina, M. J., & Stock, J. H. (2020). Reopening Scenarios (Working Paper No. 27244; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27244
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- Jul 2020
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Holme, P. (2020). Fast and principled simulations of the SIR model on temporal networks. ArXiv:2007.14386 [Physics, q-Bio]. http://arxiv.org/abs/2007.14386
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www.nber.org www.nber.org
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Jinjarak, Y., Ahmed, R., Nair-Desai, S., Xin, W., & Aizenman, J. (2020). Pandemic Shocks and Fiscal-Monetary Policies in the Eurozone: COVID-19 Dominance During January - June 2020 (Working Paper No. 27451; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27451
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www.nber.org www.nber.org
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Aksoy, C. G., Eichengreen, B., & Saka, O. (2020). The Political Scar of Epidemics (Working Paper No. 27401; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27401
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www.nber.org www.nber.org
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Caballero, R. J., & Simsek, A. (2020). A Model of Asset Price Spirals and Aggregate Demand Amplification of a (Working Paper No. 27044; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27044
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www.nber.org www.nber.org
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Baqaee, D., & Farhi, E. (2020). Nonlinear Production Networks with an Application to the Covid-19 Crisis (Working Paper No. 27281; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27281
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Gupta, S., Montenovo, L., Nguyen, T. D., Rojas, F. L., Schmutte, I. M., Simon, K. I., Weinberg, B. A., & Wing, C. (2020). Effects of Social Distancing Policy on Labor Market Outcomes (Working Paper No. 27280; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27280
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Another Ruby gem, Spira, allows graph data to be used as model objects
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www.cambridge.org www.cambridge.org
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Al-Ubaydli, O., Lee, M. S., List, J. A., Mackevicius, C. L., & Suskind, D. (undefined/ed). How can experiments play a greater role in public policy? Twelve proposals from an economic model of scaling. Behavioural Public Policy, 1–48. https://doi.org/10.1017/bpp.2020.17
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arxiv.org arxiv.org
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Baker, C. M., Campbell, P. T., Chades, I., Dean, A. J., Hester, S. M., Holden, M. H., McCaw, J. M., McVernon, J., Moss, R., Shearer, F. M., & Possingham, H. P. (2020). From climate change to pandemics: Decision science can help scientists have impact. ArXiv:2007.13261 [Physics]. http://arxiv.org/abs/2007.13261
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arxiv.org arxiv.org
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Di Lauro, F., Berthouze, L., Dorey, M. D., Miller, J. C., & Kiss, I. Z. (2020). The impact of network properties and mixing on control measures and disease-induced herd immunity in epidemic models: A mean-field model perspective. ArXiv:2007.06975 [Physics, q-Bio]. http://arxiv.org/abs/2007.06975
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www.nber.org www.nber.org
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Atkeson, A. (2020). How Deadly Is COVID-19? Understanding The Difficulties With Estimation Of Its Fatality Rate (Working Paper No. 26965; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26965
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www.nber.org www.nber.org
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Fernández-Villaverde, J., & Jones, C. I. (2020). Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States, and Cities (Working Paper No. 27128; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27128
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www.nber.org www.nber.org
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Fajgelbaum, P., Khandelwal, A., Kim, W., Mantovani, C., & Schaal, E. (2020). Optimal Lockdown in a Commuting Network (Working Paper No. 27441; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27441
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science.sciencemag.org science.sciencemag.org
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Candido, D. S., Claro, I. M., Jesus, J. G. de, Souza, W. M., Moreira, F. R. R., Dellicour, S., Mellan, T. A., Plessis, L. du, Pereira, R. H. M., Sales, F. C. S., Manuli, E. R., Thézé, J., Almeida, L., Menezes, M. T., Voloch, C. M., Fumagalli, M. J., Coletti, T. M., Silva, C. A. M. da, Ramundo, M. S., … Faria, N. R. (2020). Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science. https://doi.org/10.1126/science.abd2161
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science.sciencemag.org science.sciencemag.org
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Metcalf, C. J. E., Morris, D. H., & Park, S. W. (2020). Mathematical models to guide pandemic response. Science, 369(6502), 368–369. https://doi.org/10.1126/science.abd1668
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www.youtube.com www.youtube.com
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Shorenstein APARC. (2020, June 10). Rebooting Business After COVID-19: A View From China. https://www.youtube.com/watch?v=5DDqlzp_gKc
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www.edsurge.com www.edsurge.com
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At the substitution level, you are substituting a cup of coffee that we could make at home or school with a cup of coffee from Starbucks. It’s still coffee: there’s no real change.
Love this example with one of my favorite things: coffee! Having these examples are very helpful to me, this article not only provides examples, though, it explains why they are examples of each
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The SAMR model allows you the opportunity to evaluate why you are using a specific technology, design tasks that enable higher-order thinking skills, and engage students in rich learning experiences.
Clearly stated purpose of the SAMR model!
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www.hippasus.com www.hippasus.com
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The SAMR Ladder:Questions and Transitions
Helpful resource here
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psyarxiv.com psyarxiv.com
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Webster, G. D., Mahar, E., & Wongsomboon, V. (2020). American Psychology Is Becoming More International, But Too Slowly: Comment on Thalmayer et al. (2020). https://doi.org/10.31234/osf.io/wqmer Ame
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Brooks, H. Z., Kanjanasaratool, U., Kureh, Y. H., & Porter, M. A. (2020). Disease Detectives: Using Mathematics to Forecast the Spread of Infectious Diseases [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/mvn9z
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www.nature.com www.nature.com
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Block, P., Hoffman, M., Raabe, I. J., Dowd, J. B., Rahal, C., Kashyap, R., & Mills, M. C. (2020). Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nature Human Behaviour, 4(6), 588–596. https://doi.org/10.1038/s41562-020-0898-6
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www.apurple.co www.apurple.co
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complete analysis of Grubhub’s business plan. Know what is its key activities, resources, value propositions, partners, and revenue streams.
know the details of the Grubhub Business model you ought to know how does Grubhub work?
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www.ox.ac.uk www.ox.ac.uk
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Oxford leads development of risk prediction model for more tailored COVID-19 shielding advice | University of Oxford. (n.d.). Retrieved 23 June 2020, from http://www.ox.ac.uk/news/2020-06-22-oxford-leads-development-risk-prediction-model-more-tailored-covid-19-shielding
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blogs.bmj.com blogs.bmj.com
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Covid-19 has decimated independent U.S. primary care practices—How should policymakers and payers respond? (2020, July 2). The BMJ. https://blogs.bmj.com/bmj/2020/07/02/covid-19-has-decimated-independent-u-s-primary-care-practices-how-should-policymakers-and-payers-respond/
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gh.bmj.com gh.bmj.com
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Buitendijk, S., Ward, H., Shimshon, G., Sam, A. H., Sharma, D., & Harris, M. (2020). COVID-19: An opportunity to rethink global cooperation in higher education and research. BMJ Global Health, 5(7), e002790. https://doi.org/10.1136/bmjgh-2020-002790
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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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psyarxiv.com psyarxiv.com
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Rahman, M., Ali, G. G. M. N., Li, X. J., Paul, K. C., & Chong, P. H. J. (2020). Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fz4ry
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- Jun 2020
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www.medrxiv.org www.medrxiv.org
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Larremore, D. B., Wilder, B., Lester, E., Shehata, S., Burke, J. M., Hay, J. A., Tambe, M., Mina, M. J., & Parker, R. (2020). Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance. MedRxiv, 2020.06.22.20136309. https://doi.org/10.1101/2020.06.22.20136309
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arxiv.org arxiv.org
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Altmann, E. G. (2020). Spatial interactions in urban scaling laws. ArXiv:2006.14140 [Physics]. http://arxiv.org/abs/2006.14140
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Zhang, L., & Peixoto, T. P. (2020). Statistical inference of assortative community structures. ArXiv:2006.14493 [Cond-Mat, Physics:Physics, Stat]. http://arxiv.org/abs/2006.14493
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Ben-David, S. (2018). Clustering—What Both Theoreticians and Practitioners are Doing Wrong. ArXiv:1805.08838 [Cs, Stat]. http://arxiv.org/abs/1805.08838
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Cheng, C., Barceló, J., Hartnett, A. S., Kubinec, R., & Messerschmidt, L. (2020). COVID-19 Government Response Event Dataset (CoronaNet v.1.0). Nature Human Behaviour, 1–13. https://doi.org/10.1038/s41562-020-0909-7
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stm.sciencemag.org stm.sciencemag.org
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Silverman, J. D., Hupert, N., & Washburne, A. D. (2020). Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States. Science Translational Medicine. https://doi.org/10.1126/scitranslmed.abc1126
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www.excellentwebworld.com www.excellentwebworld.com
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A Complete Guide on Food Delivery Business Model – Its Types and ChallengesYou are here:HomeApps & SoftwareA Complete Guide on Food…
Building an online food delivery business will help you in the present as well as the future. As we all know about the present COVID 19 crisis and to let businesses survive in this pandemic, going digital is important.
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www.researchgate.net www.researchgate.net
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Sharma, N., Uttrani, S., & Dutt, V. (2020, June 19). Modeling the Absence of Framing Effect in an Experience-based Covid-19 Disease Problem. 18th Annual Meeting of the International Conference on Cognitive Modelling. https://www.researchgate.net/publication/342313460_Modeling_the_Absence_of_Framing_Effect_in_an_Experience-based_Covid-19_Disease_Problem
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psyarxiv.com psyarxiv.com
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Barbaro, N., Richardson, G. B., Nedelec, J. L., & Liu, H. (2020). Assessing Effects of Life History Antecedents on Age at Menarche and Sexual Debut Using a Genetically Informative Design [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/xqfg8
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covid19-projections.com covid19-projections.com
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COVID-19 Infections Tracker. (n.d.). COVID-19 Projections Using Machine Learning. Retrieved June 20, 2020, from https://covid19-projections.com/infections-tracker/
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psyarxiv.com psyarxiv.com
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Gibson Miller, J., Hartman, T. K., Levita, L., Martinez, A. P., Mason, L., McBride, O., … Bentall, R. (2020, April 20). Capability, opportunity and motivation to enact hygienic practices in the early stages of the COVID-19 outbreak in the UK. https://doi.org/10.31234/osf.io/typqv
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www.nytimes.com www.nytimes.com
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Kliff, Sarah. ‘How’s the Economy Doing? Watch the Dentists’. The New York Times, 10 June 2020, sec. The Upshot. https://www.nytimes.com/2020/06/10/upshot/dentists-coronavirus-economic-indicator.html.
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Horn, S. R., Weston, S. J., & Fisher, P. (2020). Identifying causal role of COVID-19 in immunopsychiatry models [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/w4d5u
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psyarxiv.com psyarxiv.com
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Del Giudice, M. (2020). All About AIC [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/7hmgz
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arxiv.org arxiv.org
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de Arruda, G. F., Méndez-Bermúdez, J. A., Rodrigues, F. A., & Moreno, Y. (2020). Universality of eigenvector delocalization and the nature of the SIS phase transition in multiplex networks. ArXiv:2005.08074 [Cond-Mat, Physics:Physics]. http://arxiv.org/abs/2005.08074
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www.researchgate.net www.researchgate.net
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Mclachlan, S., Lucas, P., Kudakwashe Dube, Hitman, G. A., Osman, M., Kyrimi, E., Neil, M., & Fenton, N. E. (2020). The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach. https://doi.org/10.13140/RG.2.2.27042.66243
Tags
- likelihood
- contact tracing
- app
- lang:en
- prediction
- containment
- Bayesian
- COVID-19
- is:preprint
- network model
- limitation
- digital solution
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arxiv.org arxiv.org
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Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D., & Chen, H. (2020). Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs. ArXiv:2005.07427 [Cs, Stat]. http://arxiv.org/abs/2005.07427
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lifehacker.com lifehacker.com
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Facebook already harvests some data from WhatsApp. Without Koum at the helm, it’s possible that could increase—a move that wouldn’t be out of character for the social network, considering that the company’s entire business model hinges on targeted advertising around personal data.
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www.metascience2019.org www.metascience2019.org
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Yang Yang: The Replicability of Scientific Findings Using Human and Machine Intelligence (Video). Metascience 2019 Symposium. https://www.metascience2019.org/presentations/yang-yang/
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www.pnas.org www.pnas.org
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Yang, Y., Youyou, W., & Uzzi, B. (2020). Estimating the deep replicability of scientific findings using human and artificial intelligence. Proceedings of the National Academy of Sciences, 117(20), 10762–10768. https://doi.org/10.1073/pnas.1909046117
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Frederick, J. K., Raabe, G. R., Rogers, V., & Pizzica, J. (2020, May 30). A Model of Distance Special Education Support Services Amidst COVID-19. Retrieved from psyarxiv.com/q362v
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machinelearningmastery.com machinelearningmastery.com
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epsilon. Is a very small number to prevent any division by zero in the implementation (e.g. 10E-8). Further, learning rate decay can also be used with Adam. The paper uses a decay rate alpha = alpha/sqrt(t) updted each epoch (t) for the logistic regression demonstration. The Adam paper suggests: Good default settings for the tested machine learning problems are alpha=0.001, beta1=0.9, beta2=0.999 and epsilon=10−8 The TensorFlow documentation suggests some tuning of epsilon: The default value of 1e-8 for epsilon might not be a good default in general. For example, when training an Inception network on ImageNet a current good choice is 1.0 or 0.1. We can see that the popular deep learning libraries generally use the default parameters recommended by the paper. TensorFlow: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08. Keras: lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0. Blocks: learning_rate=0.002, beta1=0.9, beta2=0.999, epsilon=1e-08, decay_factor=1. Lasagne: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08 Caffe: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08 MxNet: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8 Torch: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8
Should we expose EPS as one of the experiment parameters? I think that we shouldn't since it is a rather technical parameter.
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- May 2020
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psyarxiv.com psyarxiv.com
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Freeston, M. H., Tiplady, A., Mawn, L., Bottesi, G., & Thwaites, S. (2020, April 14). Towards a model of uncertainty distress in the context of Coronavirus (Covid-19). https://doi.org/10.31234/osf.io/v8q6m
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www.nber.org www.nber.org
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Eichenbaum, M., Rebelo, S., & Trabandt, M. (2020). The Macroeconomics of Epidemics (No. w26882; p. w26882). National Bureau of Economic Research. https://doi.org/10.3386/w26882
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psyarxiv.com psyarxiv.com
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Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (2020, April 14). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. https://doi.org/10.31234/osf.io/tfs7c
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github.com github.com
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Deepset-ai/haystack. (2020). [Python]. deepset. https://github.com/deepset-ai/haystack (Original work published 2019)
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www.repository.cam.ac.uk www.repository.cam.ac.uk
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Toxvaerd, F. M. O. (2020). Equilibrium Social Distancing [Working Paper]. Faculty of Economics, University of Cambridge. https://doi.org/10.17863/CAM.52489
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wellcomeopenresearch.org wellcomeopenresearch.org
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Endo, A., Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Abbott, S., Kucharski, A. J., & Funk, S. (2020). Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China. Wellcome Open Research, 5, 67. https://doi.org/10.12688/wellcomeopenres.15842.1
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github.com github.com
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The diagram was generated with rails-erd
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github.com github.com
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Domain Model
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www.nyteknik.se www.nyteknik.se
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Forskare: ”Se upp med komplexa coronamodeller – de kan överträffa verkligheten”. (2020 April 24). Ny Teknik. https://www.nyteknik.se/opinion/forskare-se-upp-med-komplexa-coronamodeller-de-kan-overtraffa-verkligheten-6994339
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arxiv.org arxiv.org
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Given the disjoint vocabularies (Section2) andthe magnitude of improvement over BERT-Base(Section4), we suspect that while an in-domainvocabulary is helpful, SCIBERTbenefits mostfrom the scientific corpus pretraining.
The specific vocabulary only slightly increases the model accuracy. Most of the benefit comes from domain specific corpus pre-training.
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We construct SCIVOCAB, a new WordPiece vo-cabulary on our scientific corpus using the Sen-tencePiece1library. We produce both cased anduncased vocabularies and set the vocabulary sizeto 30K to match the size of BASEVOCAB. The re-sulting token overlap between BASEVOCABandSCIVOCABis 42%, illustrating a substantial dif-ference in frequently used words between scien-tific and general domain texts
For SciBERT they created a new vocabulary of the same size as for BERT. The overlap was at the level of 42%. We could check what is the overlap in our case?
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academic.oup.com academic.oup.com
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Although we could have constructed new WordPiece vocabulary based on biomedical corpora, we used the original vocabulary of BERTBASE for the following reasons: (i) compatibility of BioBERT with BERT, which allows BERT pre-trained on general domain corpora to be re-used, and makes it easier to interchangeably use existing models based on BERT and BioBERT and (ii) any new words may still be represented and fine-tuned for the biomedical domain using the original WordPiece vocabulary of BERT.
BioBERT does not change the BERT vocabulary.
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def _tokenize(self, text): split_tokens = [] if self.do_basic_tokenize: for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) else: split_tokens = self.wordpiece_tokenizer.tokenize(text) return split_tokens
How BERT tokenization works
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github.com github.com
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My initial experiments indicated that adding custom words to the vocab-file had some effects. However, at least on my corpus that can be described as "medical tweets", this effect just disappears after running the domain specific pretraining for a while. After spending quite some time on this, I have ended up dropping the custom vocab-files totally. Bert seems to be able to learn these specialised words by tokenizing them.
sbs experience from extending the vocabulary for medical data
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Since Bert does an excellent job in tokenising and learning this combinations, do not expect dramatic improvements by adding words to the vocab. In my experience adding very specific terms, like common long medical latin words, have some effect. Adding words like "footballs" will likely just have negative effects since the current vector is already pretty good.
Expected improvement of extending the BERT vocabulary
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Annotators
URL
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jalammar.github.io jalammar.github.io
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As is the case in NLP applications in general, we begin by turning each input word into a vector using an embedding algorithm.
What is the embedding algorithm for BERT?
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blog.usejournal.com blog.usejournal.com
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:It is fairly expensive (four days on 4 to 16 Cloud TPUs), but is a one-time procedure for each language
Estimates on the model pre-training from scratch
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en.wikipedia.org en.wikipedia.org
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www.nature.com www.nature.com
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West, R., Michie, S., Rubin, G. J., & Amlôt, R. (2020). Applying principles of behaviour change to reduce SARS-CoV-2 transmission. Nature Human Behaviour, 1–9. https://doi.org/10.1038/s41562-020-0887-9
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www.bmj.com www.bmj.com
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Response to “Modelling the pandemic”: Reconsidering the quality of evidence from epidemiological models. (2020). https://www.bmj.com/content/369/bmj.m1567/rr-0
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Fan, R., Xu, K., & Zhao, J. (2020). Weak ties strengthen anger contagion in social media. ArXiv:2005.01924 [Cs]. http://arxiv.org/abs/2005.01924
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Pham, T. M., Kondor, I., Hanel, R., & Thurner, S. (2020). The effect of social balance on social fragmentation. ArXiv:2005.01815 [Nlin, Physics:Physics]. http://arxiv.org/abs/2005.01815
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psyarxiv.com psyarxiv.com
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Britwum, K., Catrone, R., Smith, G. D., & Koch, D. S. (2020, May 5). A University Based Social Services Parent Training Model: A Telehealth Adaptation During the COVID-19 Pandemic. https://doi.org/10.31234/osf.io/gw3cd
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I do not understand what is the threat model of not allowing the root user to configure Firefox, since malware could just replace the entire Firefox binary.
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psyarxiv.com psyarxiv.com
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Rotella, A. M., & Mishra, S. (2020, April 24). Personal relative deprivation negatively predicts engagement in group decision-making. https://doi.org/10.31234/osf.io/6d35w
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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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psyarxiv.com psyarxiv.com
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Edelsbrunner, P. A., & Thurn, C. (2020, April 22). Improving the Utility of Non-Significant Results for Educational Research. https://doi.org/10.31234/osf.io/j93a2
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Moyers, S. A., & Hagger, M. S. (2020, April 20). Physical activity and sense of coherence: A meta-analysis. https://doi.org/10.31234/osf.io/d9e3k
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psyarxiv.com psyarxiv.com
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Dai, B., Fu, D., Meng, G., Qi, L., & Liu, X. (2020, April 25). The effects of governmental and individual predictors on COVID-19 protective behaviors in China: a path analysis model. https://doi.org/10.31234/osf.io/hgzj9
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Etilé, F., Johnston, D., Frijters, P., & Shields, M. (2020, April 16). Psychological Resilience to Major Socioeconomic Life Events. https://doi.org/10.31234/osf.io/vp48c
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psyarxiv.com psyarxiv.com
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Moya, M., Willis, G. B., Paez, D., Pérez, J. A., Gómez, Á., Sabucedo, J. M., … Salanova, M. (2020, April 23). La Psicología Social ante el COVID19: Monográfico del International Journal of Social Psychology (Revista de Psicología Social). https://doi.org/10.31234/osf.io/fdn32
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Lades, L., Laffan, K., Daly, M., & Delaney, L. (2020, April 22). Daily emotional well-being during the COVID-19 pandemic. https://doi.org/10.31234/osf.io/pg6bw
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