- Mar 2021
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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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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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science.sciencemag.org science.sciencemag.org
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Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V. (2020). Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science. https://doi.org/10.1126/science.abb9789
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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.biorxiv.org www.biorxiv.org
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Bertana, A., Chetverikov, A., Bergen, R. S. van, Ling, S., & Jehee, J. F. M. (2020). Dual strategies in human confidence judgments. BioRxiv, 2020.09.17.299743. https://doi.org/10.1101/2020.09.17.299743
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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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www.sciencedirect.com www.sciencedirect.com
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Rahnev, D. (2020). Confidence in the Real World. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2020.05.005
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arxiv.org arxiv.org
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Kozlowski, Diego, Jennifer Dusdal, Jun Pang, and Andreas Zilian. ‘Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation’. ArXiv:2011.02887 [Physics], 5 November 2020. http://arxiv.org/abs/2011.02887.
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journals.plos.org journals.plos.org
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Holme, P. (2021). Fast and principled simulations of the SIR model on temporal networks. PLOS ONE, 16(2), e0246961. https://doi.org/10.1371/journal.pone.0246961
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Cantwell, G. T., Kirkley, A., & Newman, M. E. J. (2020). The friendship paradox in real and model networks. ArXiv:2012.03991 [Physics]. http://arxiv.org/abs/2012.03991
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www.medrxiv.org www.medrxiv.org
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Gupta, R. K., Marks, M., Samuels, T. H. A., Luintel, A., Rampling, T., Chowdhury, H., Quartagno, M., Nair, A., Lipman, M., Abubakar, I., Smeden, M. van, Wong, W. K., Williams, B., & Noursadeghi, M. (2020). Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study. MedRxiv, 2020.07.24.20149815. https://doi.org/10.1101/2020.07.24.20149815
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trailblazer.to trailblazer.to
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we head straight into an additional terminus, or end event as it’s called in BPMN
no
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www.nature.com www.nature.com
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Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V., Pinior, B., Thurner, S., & Klimek, P. (2020). Ranking the effectiveness of worldwide COVID-19 government interventions. Nature Human Behaviour, 4(12), 1303–1312. https://doi.org/10.1038/s41562-020-01009-0
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- Feb 2021
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www.scientificamerican.com www.scientificamerican.com
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McKenna, S. (n.d.). COVID Models Show How to Avoid Future Lockdowns. Scientific American. Retrieved 26 February 2021, from https://www.scientificamerican.com/article/covid-models-show-how-to-avoid-future-lockdowns/
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arxiv.org arxiv.org
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Aletti, G., Crimaldi, I., & Saracco, F. (2020). A model for the Twitter sentiment curve. ArXiv:2011.05933 [Physics]. http://arxiv.org/abs/2011.05933
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osf.io osf.io
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Smaldino, Paul E., and Cailin O’Connor. ‘Interdisciplinarity Can Aid the Spread of Better Methods Between Scientific Communities’. MetaArXiv, 5 November 2020. https://doi.org/10.31222/osf.io/cm5v3.
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link.aps.org link.aps.org
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Wang, X., Sirianni, A. D., Tang, S., Zheng, Z., & Fu, F. (2020). Public Discourse and Social Network Echo Chambers Driven by Socio-Cognitive Biases. Physical Review X, 10(4), 041042. https://doi.org/10.1103/PhysRevX.10.041042
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link.aps.org link.aps.org
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Ye, Y., Zhang, Q., Ruan, Z., Cao, Z., Xuan, Q., & Zeng, D. D. (2020). Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission. Physical Review E, 102(4), 042314. https://doi.org/10.1103/PhysRevE.102.042314
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journals.plos.org journals.plos.org
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Anderson, S. C., Edwards, A. M., Yerlanov, M., Mulberry, N., Stockdale, J. E., Iyaniwura, S. A., Falcao, R. C., Otterstatter, M. C., Irvine, M. A., Janjua, N. Z., Coombs, D., & Colijn, C. (2020). Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing. PLOS Computational Biology, 16(12), e1008274. https://doi.org/10.1371/journal.pcbi.1008274
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advances.sciencemag.org advances.sciencemag.org
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Stewart, A. J., McCarty, N., & Bryson, J. J. (2020). Polarization under rising inequality and economic decline. Science Advances, 6(50), eabd4201. https://doi.org/10.1126/sciadv.abd4201
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trailblazer.to trailblazer.to
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Intuitively, you understand the flow just by looking at the BPMN diagram. And, heck, we haven’t even discussed BPMN or any terminology, yet!
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trailblazer.to trailblazer.to
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Around 2 years ago I decided to end the experiment of “TRB PRO” as I felt I didn’t provide enough value to paying users. In the end, we had around 150 companies and individuals signed up, which was epic and a great funding source for more development.
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We’re now relaunching PRO, but instead of a paid chat and (never existing) paid documentation, your team gets access to paid gems, our visual editor for workflows, and a commercial license.
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2019.trailblazer.to 2019.trailblazer.to
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We use a subset of BPMN for the visual language in the editor, but added our own set of restrictions and semantics to it.
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en.wikipedia.org en.wikipedia.org
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Business Process Model and Notation (BPMN) is a standard for business process modeling that provides a graphical notation for specifying business processes in a Business Process Diagram (BPD),[3] based on a flowcharting technique very similar to activity diagrams from Unified Modeling Language (UML).
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Hickok, A., Kureh, Y., Brooks, H. Z., Feng, M., & Porter, M. A. (2021). A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs. ArXiv:2102.06825 [Nlin, Physics:Physics]. http://arxiv.org/abs/2102.06825
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github.com github.com
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Compared to existing Ruby desktop frameworks, such as Shoes, Bowline's strengths are its adherence to MVC and use of HTML/JavaScript.
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en.wikipedia.org en.wikipedia.org
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As with other software patterns, MVC expresses the "core of the solution" to a problem while allowing it to be adapted for each system.
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Aminpour, P., Gray, S. A., Singer, A., Scyphers, S. B., Jetter, A. J., Jordan, R., Murphy, R., & Grabowski, J. H. (2021). The diversity bonus in pooling local knowledge about complex problems. Proceedings of the National Academy of Sciences, 118(5). https://doi.org/10.1073/pnas.2016887118
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Tepper, S., & Neil Lewis, J. (2021). When the Going Gets Tough, How Do We Perceive the Future? PsyArXiv. https://doi.org/10.31234/osf.io/pkaxn
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Study shows vaccine nationalism could cost rich countries US$4.5 trillion. (2021, January 25). ICC - International Chamber of Commerce. https://iccwbo.org/media-wall/news-speeches/study-shows-vaccine-nationalism-could-cost-rich-countries-us4-5-trillion/
Tags
- treatment
- economic
- global
- government
- development
- nationalism
- vaccination
- lang:en
- is:news
- economy
- funding
- model
- vaccine
- COVID-19
- ICC
Annotators
URL
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www.bmj.com www.bmj.com
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Group, B. M. J. P. (2021). Update to living systematic review on prediction models for diagnosis and prognosis of covid-19. BMJ, 372, n236. https://doi.org/10.1136/bmj.n236
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www.reddit.com www.reddit.com
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Not to mention 80% of our sales are laptops and desktops running, you guessed it, a Linux desktop. So, unlike Red Hat and Canonical, we live or die based on how good that experience is.
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- Jan 2021
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developer.mozilla.org developer.mozilla.org
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The background-origin CSS property sets the background's origin: from the border start, inside the border, or inside the padding.
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developer.mozilla.org developer.mozilla.org
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journals.plos.org journals.plos.org
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Parag. K. V., Donnelly. C. A., (2020) Using information theory to optimise epidemic models for real-time prediction and estimation. PLOS. Retrieved from https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007990
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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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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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- Dec 2020
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pubs.usgs.gov pubs.usgs.gov
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INTERACTION OF GROUND WATER AND STREAMS
gambar model yang sederhana. akan bagus kalau kita dapat menggambarkan sendiri (walaupun hanya dengan tangan) interaksi yang sama di daerah kita.
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Eyal describes the theory called The Fogg Behavior Model which states that for a behavior (B) to occur, three things must be present at the same time: motivation (M), ability (A), and a trigger (T). More succinctly, B = MAT.
Fogg Behavior Model says that for a Behavior (B) to occur 3 things have to be present at the same time:
- Motivation (M)
- Ability (A)
- Trigger (T)
B = MAT
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hacks.mozilla.org hacks.mozilla.org
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Better community building: At the moment, MDN content edits are published instantly, and then reverted if they are not suitable. This is really bad for community relations. With a PR model, we can review edits and provide feedback, actually having conversations with contributors, building relationships with them, and helping them learn.
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Better contribution workflow: We will be using GitHub’s contribution tools and features, essentially moving MDN from a Wiki model to a pull request (PR) model. This is so much better for contribution, allowing for intelligent linting, mass edits, and inclusion of MDN docs in whatever workflows you want to add it to (you can edit MDN source files directly in your favorite code editor).
Tags
- helping others
- relationship (people)
- community building
- helping others to learn
- online community
- open source community
- opportunity
- opportunity to improve/fix something
- software preferences are personal
- reverting a previous decision/change/commit
- efficiency (human efficiency)
- reverting: creates negative experience
- receiving feedback
- advantages/merits/pros
- pull request workflow
- encouraging feedback
- community (for a project or product)
- contribution workflow
- community relations
- flexibility to use the tool that you prefer
- wiki model
Annotators
URL
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psyarxiv.com psyarxiv.com
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Rocca, R., & Yarkoni, T. (2020). Putting psychology to the test: Rethinking model evaluation through benchmarking and prediction. PsyArXiv. https://doi.org/10.31234/osf.io/e437b
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- Nov 2020
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news.ycombinator.com news.ycombinator.com
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There's a huge area of seemingly obvious user-centric products that don't exist simply because there isn't a working business model to support it.
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developer.mozilla.org developer.mozilla.org
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CSS Object Model (CSSOM)
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Soderberg, C. K., Errington, T., Schiavone, S. R., Bottesini, J. G., Thorn, F. S., Vazire, S., Esterling, K. M., & Nosek, B. A. (2020). Research Quality of Registered Reports Compared to the Traditional Publishing Model. MetaArXiv. https://doi.org/10.31222/osf.io/7x9vy
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stats.idre.ucla.edu stats.idre.ucla.edu
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Linear mixed models are an extension of simple linear models to allow both fixed and random effects, and are particularly used when there is non independence in the data, such as arises from a hierarchical structure
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dagster.io dagster.ioDagster1
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We love dbt because of the values it embodies. Individual transformations are SQL SELECT statements, without side effects. Transformations are explicitly connected into a graph. And support for testing is first-class. dbt is hugely enabling for an important class of users, adapting software engineering principles to a slightly different domain with great ergonomics. For users who already speak SQL, dbt’s tooling is unparalleled.
when using [[dbt]] the [[transformations]] are [[SQL statements]] - already something that our team knows
Tags
Annotators
URL
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blog.getdbt.com blog.getdbt.com
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We then estimate the relative weight each touch played in leading to a conversion. This estimation is done by allocating “points” to touches: each conversion is worth exactly one point, and that point is divvied up between the customer’s touches. There are four main ways to divvy up this point:First touch: Attribute the entire conversion to the first touchLast touch: Attribute the entire conversion to the last touchForty-twenty-forty: Attribute 40% (0.4 points) of the attribution to the first touch, 40% to the last touch, and divide the remaining 20% between all touches in betweenLinear: Divide the point equally among all touches
[[positional attribution]] works by identifying the touch points in the lifecycle, and dividing up the points across those touches.
There are four main ways to divvy up this pointing
[[question]] What are the four main ways to divvy up positional attribution]]
- [[first touch]]
- [[last touch]]
- [[fourty-twenty-fourty]]
- [[linear]]
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Once you have pageviews in your warehouse, you’ll need to do two thingsSessionization: Aggregate these pageviews into sessions (or “sessionization”) writing logic to identify gaps of 30 minutes or more.User stitching: If a user first visits your site without any identifying information (typically a `customer_id` or `email`), and then converts at a later date, their previous (anonymous) sessions should be updated to include their information. Your web tracking system should have a way to link these sessions together.This modeling is pretty complex, especially for companies with thousands of pageviews a day (thank goodness for incremental models 🙌). Fortunately, some very smart coworkers have written packages to do the heavy lifting for you, whether your page views are tracked with Snowplow, Segment or Heap. Leverage their work by installing the right package to transform the data for you.
[[1. Gather your required data sources]] - once we have data, we need to do two things [[sessionization]] - the aggregation of pageviews / etc into a session
and [[user stitching]] - when we have a user without any identifying information, and then converts - kind of like the anonymous users / signups - and trying to tie them back to a source
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1. Gather your required data sourcesSessions:Required dbt techniques: packagesWe want to use a table that represents every time a customer interacts with our brand. For ecommerce companies, the closest thing we can get to for this is sessions. (If you’re instead working for a B2B organization, you should consider using a table of interactions between your sales team and a potential customer from your CRM).Sessions are discrete periods of activity by a user on a website. The industry standard is to define a session as a series of activities followed by a 30-minute window without any activity.
[[1. Gather your required data sources]]
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How to build an attribution model
[[How to build an attribution model]]
- [[1. Gather your required data sources]]
- [[2. Find all sessions before conversion]]
- [[3. Calculate the total sessions and the session index]]
- [[3. Allocate points]]
- [[4. Bonus Join in revenue value]]
- [[5. Bonus Join with ad spend data]]
- [[6. Ship it!]]
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The attribution data modelIn reality, it’s impossible to know exactly why someone converted to being a customer. The best thing that we can do as analysts, is provide a pretty good guess. In order to do that, we’re going to use an approach called positional attribution. This means, essentially, that we’re going to weight the importance of various touches (customer interactions with a brand) based on their position (the order they occur in within the customer’s lifetime).To do this, we’re going to build a table that represents every “touch” that someone had before becoming a customer, and the channel that led to that touch.
One of the goals of an [[attribution data model]] is to understand why someone [[converted]] to being a customer. This is impossible to do accurately, but this is where analysis comes in.
There are some [[approaches to attribution]], one of those is [[positional attribution]]
[[positional attribution]] is that we are weighting the importance of touch points - or customer interactions, based on their position within the customer lifetime.
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transparent attribution model. You’re not relying on vendor logic. If your sales team feels like your attribution is off, show them dbt docs, walk them through the logic of your model, and make modifications with a single line of SQL
[[transparent attribution model]]
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The most flexible attribution model. You own the business logic and you can extend it however you want, and change it easily when you business changes
[[flexible attribution model]]
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hat’s it. Really! By writing SQL on top of raw data you get: The cheapest attribution model. This playbook assumes you’re operating within a modern data stack , so you already have the infrastructure that you need in place: You’re collecting events data with a tool like Snowplow or Segment (though Segment might get a little pricey) You’re extracting data from ad platforms using Stitch or Fivetran You’re loading data into a modern, cloud data warehouse like Snowflake, BigQuery, or Redshift And you’re using dbt so your analysts can model data in SQL
[[cheapest attribution model]]
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So what do you actually need to build an attribution model?Raw data in your warehouse that represents customer interactions with your brand. For ecommerce companies, this is website visits. For B2B customers, it might be conversations with sales teams.SQL
to build an [[attribution model]] we need the raw data - this raw data should capture the [[customer interactions]], and in our case - also partner interactions, or people working with the partner?
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github.com github.com
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This is addressing a security issue; and the associated threat model is "as an attacker, I know that you are going to do FROM ubuntu and then RUN apt-get update in your build, so I'm going to trick you into pulling an image that _pretents_ to be the result of ubuntu + apt-get update so that next time you build, you will end up using my fake image as a cache, instead of the legit one." With that in mind, we can start thinking about an alternate solution that doesn't compromise security.
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arxiv.org arxiv.org
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Wunderling, N., Krönke, J., Wohlfarth, V., Kohler, J., Heitzig, J., Staal, A., Willner, S., Winkelmann, R., & Donges, J. F. (2020). Modelling nonlinear dynamics of interacting tipping elements on complex networks: The PyCascades package. ArXiv:2011.02031 [Nlin, Physics:Physics]. http://arxiv.org/abs/2011.02031
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- Oct 2020
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www.youtube.com www.youtube.com
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ORWG Virtual Meeting 08/09/2020 https://www.youtube.com/playlist?list=PLOA0aRJ90NxvXtMt5Si5ukmR9LYfvDueB (n.d.)
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medium.com medium.com
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Modules from the following layer can require anything from all the previous layers, but not vice versa.
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www-sciencedirect-com.libproxy.nau.edu www-sciencedirect-com.libproxy.nau.edu
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In order to inform the development and implementation of effective online learning environments, this study was designed to explore both instructors' and students' online learning experiences while enrolled in various online courses. The study investigated what appeared to both support and hinder participants' online teaching and learning experiences.
The authors discuss the issue of community and engagement in online graduate programs. They carried out a small case study and used a Cognitive Apprenticeship Model to examine a successful program in Higher Education. They found that students feel too many online classes are just reading and writing, regurgitating rather than applying, and lack sufficient connection with the instructor and with other students, They recommend some strategies to fix that, but admit that more work is needed. 9/10
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learn-us-east-1-prod-fleet01-xythos.s3.amazonaws.com learn-us-east-1-prod-fleet01-xythos.s3.amazonaws.com
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The educator’s role in self-directed learning
Fostering self-directed learning through strategy is discussed by Bailey et al. (2019) in chapter 1 of “Self-Directed Learning for the 21st Century: Implications for Higher Education.” The authors review the changing role of the educator and the learner based on respective self-directed teaching strategies (problem-based learning, cooperative learning, process-oriented learning) and the learner’s propensity for self-directed learning. In addition to providing principles to promote self-directed learning, the Grow and Borich models for implementing said learning were briefly reviewed. 8/10
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humanmooc.pressbooks.com humanmooc.pressbooks.com
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Cognitive Presence “is the extent to which learners are able to construct and confirm meaning through sustained reflection and discourse” (Community of Inquiry, n.d, para. 5). Video is often used as a unidirectional medium with information flowing from the expert or instructor to the learner. To move from transmission of content to construction of knowledge, tools such as Voice Thread (VoiceThread, 2016) support asynchronous conversation in a multimedia format.
The author, Kendra Grant, is the Director of Professional Development and Learning for Quillsoft in Toronto Canada. Grant helps business succeed in education design and support. In this article Grant discusses how quickly the learning environment has changed through technological development. Grant explores the RAT Model, which guides instructors in the "use of technology to help transform instructional practice." Grant then examines the Community of Inquiry model, which seeks to create meaningful instruction through social, cognitive and teaching presence. Grant concludes by providing general principles for creating a positive video presence.
Rating: 8/10
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github.com github.com
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virtual-dom exposes a set of objects designed for representing DOM nodes. A "Document Object Model Model" might seem like a strange term, but it is exactly that. It's a native JavaScript tree structure that represents a native DOM node tree.
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www.nature.com www.nature.com
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Grimm, V., Johnston, A. S. A., Thulke, H.-H., Forbes, V. E., & Thorbek, P. (2020). Three questions to ask before using model outputs for decision support. Nature Communications, 11(1), 4959. https://doi.org/10.1038/s41467-020-17785-2
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Sia, S. F., Yan, L.-M., Chin, A. W. H., Fung, K., Choy, K.-T., Wong, A. Y. L., Kaewpreedee, P., Perera, R. A. P. M., Poon, L. L. M., Nicholls, J. M., Peiris, M., & Yen, H.-L. (2020). Pathogenesis and transmission of SARS-CoV-2 in golden hamsters. Nature, 583(7818), 834–838. https://doi.org/10.1038/s41586-020-2342-5
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www.pnas.org www.pnas.org
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Karatayev, Vadim A., Madhur Anand, and Chris T. Bauch. ‘Local Lockdowns Outperform Global Lockdown on the Far Side of the COVID-19 Epidemic Curve’. Proceedings of the National Academy of Sciences 117, no. 39 (29 September 2020): 24575–80. https://doi.org/10.1073/pnas.2014385117.
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www.medrxiv.org www.medrxiv.org
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Kaplan, Edward H, Dennis Wang, Mike Wang, Amyn A Malik, Alessandro Zulli, and Jordan H Peccia. ‘Aligning SARS-CoV-2 Indicators via an Epidemic Model: Application to Hospital Admissions and RNA Detection in Sewage Sludge’. Preprint. Infectious Diseases (except HIV/AIDS), 29 June 2020. https://doi.org/10.1101/2020.06.27.20141739.
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- Sep 2020
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BPMN Viewer and Editor Use bpmn-js to display BPMN 2.0 diagrams on your website. Embed it as a BPMN 2.0 web modeler into your applications and customize it to suit your needs.
Tags
Annotators
URL
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en.wikipedia.org en.wikipedia.org
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Business Process Model and Notation (BPMN) is a graphical representation for specifying business processes in a business process model.
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ideas.repec.org ideas.repec.org
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Kubinec, Robert & Carvalho, Luiz & Barceló, Joan & Cheng, Cindy & Hartnett, Allison & Messerschmidt, Luca & Duba, Derek & Cottrell, Matthew Sean, 2020. "Partisanship and the Spread of COVID-19 in the United States," SocArXiv jp4wk, Center for Open Science. Retrieved from: https://ideas.repec.org/p/osf/socarx/jp4wk.html
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scattered-thoughts.net scattered-thoughts.net
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www.medrxiv.org www.medrxiv.org
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Team, I. C.-19 F., & Hay, S. I. (2020). COVID-19 scenarios for the United States. MedRxiv, 2020.07.12.20151191. https://doi.org/10.1101/2020.07.12.20151191
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psyarxiv.com psyarxiv.com
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Yang, Scott Cheng-Hsin, Chirag Rank, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto. ‘Unifying Recommendation and Active Learning for Information Filtering and Recommender Systems’. Preprint. PsyArXiv, 25 August 2020. https://doi.org/10.31234/osf.io/jqa83.
Tags
- exploration-exploitation tradeoff
- Internet
- predictive accuracy
- algorithms
- machine learning
- information filtering
- computer science
- experimental approach
- active learning
- recommender system
- is:preprint
- lang:en
- AI
- parameterized model
- recommendation accuracy
- cognitive science
- artificial intelligence
Annotators
URL
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github.com github.com
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mongoose.model
mongoose.model()
When you call mongoose.model() on a schema, Mongoose compiles a model for you. The first argument is the singular name of the collection your model is for. Mongoose automatically looks for the plural, lowercased version of your model name. https://mongoosejs.com/docs/models.html#compiling
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- Aug 2020
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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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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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www.nber.org www.nber.org
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Eichenbaum, M. S., Rebelo, S., & Trabandt, M. (2020). The Macroeconomics of Epidemics (Working Paper No. 26882; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26882
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www.nature.com www.nature.com
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Bertuzzo, E., Mari, L., Pasetto, D., Miccoli, S., Casagrandi, R., Gatto, M., & Rinaldo, A. (2020). The geography of COVID-19 spread in Italy and implications for the relaxation of confinement measures. Nature Communications, 11(1), 4264. https://doi.org/10.1038/s41467-020-18050-2
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threadreaderapp.com threadreaderapp.com
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The RAT model sees software development as an off-line program-construction activity composed of these parts: defining, decomposing, estimating, implementing, assembling, and finishing
This is what can lead to the 'there is only version 1.0' problem - and improvements / iterations fall to the sidelines.
This can have a number of consequences
- over designed / engineered
- doing unnecessary work
- lack of user feedback and ability to accommodate it
- rigid / fragile architecture
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Kreye, J., Reincke, S. M., Kornau, H.-C., Sánchez-Sendin, E., Corman, V. M., Liu, H., Yuan, M., Wu, N. C., Zhu, X., Lee, C.-C. D., Trimpert, J., Höltje, M., Dietert, K., Stöffler, L., Wardenburg, N. von, Hoof, S. van, Homeyer, M. A., Hoffmann, J., Abdelgawad, A., … Prüss, H. (2020). A SARS-CoV-2 neutralizing antibody protects from lung pathology in a COVID-19 hamster model. BioRxiv, 2020.08.15.252320. https://doi.org/10.1101/2020.08.15.252320
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www.nature.com www.nature.com
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Shi, W., Wang, L., & Qin, J. (2020). Extracting user influence from ratings and trust for rating prediction in recommendations. Scientific Reports, 10(1), 13592. https://doi.org/10.1038/s41598-020-70350-1
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arxiv.org arxiv.org
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Engelhardt, R., Hendricks, V. F., & Stærk-Østergaard, J. (2020). The Wisdom and Persuadability of Threads. ArXiv:2008.05203 [Physics]. http://arxiv.org/abs/2008.05203
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www.nber.org www.nber.org
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Coibion, O., Gorodnichenko, Y., & Weber, M. (2020). Does Policy Communication During Covid Work? (Working Paper No. 27384; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27384
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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.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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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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www.nber.org www.nber.org
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Avery, C., Bossert, W., Clark, A., Ellison, G., & Ellison, S. F. (2020). Policy Implications of Models of the Spread of Coronavirus: Perspectives and Opportunities for Economists (Working Paper No. 27007; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27007
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www.nber.org www.nber.org
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Alvarez, F. E., Argente, D., & Lippi, F. (2020). A Simple Planning Problem for COVID-19 Lockdown (Working Paper No. 26981; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26981
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www.nber.org www.nber.org
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Barnett, M., Buchak, G., & Yannelis, C. (2020). Epidemic Responses Under Uncertainty (Working Paper No. 27289; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27289
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www.nber.org www.nber.org
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Razin, A., Sadka, E., & Schwemmer, A. H. (2020). DEglobalizaion and Social Safety Nets in Post-Covid-19 Era: Textbook Macroeconomic Analysis (Working Paper No. 27239; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27239
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Alfaro, L., Faia, E., Lamersdorf, N., & Saidi, F. (2020). Social Interactions in Pandemics: Fear, Altruism, and Reciprocity (Working Paper No. 27134; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27134
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Eichenbaum, M. S., Rebelo, S., & Trabandt, M. (2020). Epidemics in the Neoclassical and New Keynesian Models (Working Paper No. 27430; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27430
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www.nber.org www.nber.org
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Jordà, Ò., Singh, S. R., & Taylor, A. M. (2020). Longer-run Economic Consequences of Pandemics (Working Paper No. 26934; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26934
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www.nber.org www.nber.org
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Berger, D. W., Herkenhoff, K. F., & Mongey, S. (2020). An SEIR Infectious Disease Model with Testing and Conditional Quarantine (Working Paper No. 26901; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26901
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Gormsen, N. J., & Koijen, R. S. J. (2020). Coronavirus: Impact on Stock Prices and Growth Expectations (Working Paper No. 27387; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27387
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www.nber.org www.nber.org
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Fujita, Shigeru, Giuseppe Moscarini, and Fabien Postel-Vinay. ‘Measuring Employer-to-Employer Reallocation’. Working Paper. Working Paper Series. National Bureau of Economic Research, July 2020. https://doi.org/10.3386/w27525.
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www.nber.org www.nber.org
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Chari, Varadarajan V, Rishabh Kirpalani, and Christopher Phelan. ‘The Hammer and the Scalpel: On the Economics of Indiscriminate versus Targeted Isolation Policies during Pandemics’. Working Paper. Working Paper Series. National Bureau of Economic Research, May 2020. https://doi.org/10.3386/w27232.
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Stock, James H. ‘Data Gaps and the Policy Response to the Novel Coronavirus’. Working Paper. Working Paper Series. National Bureau of Economic Research, March 2020. https://doi.org/10.3386/w26902.
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www.nber.org www.nber.org
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Burke, Marshall, Anne Driscoll, Jenny Xue, Sam Heft-Neal, Jennifer Burney, and Michael Wara. ‘The Changing Risk and Burden of Wildfire in the US’. Working Paper. Working Paper Series. National Bureau of Economic Research, June 2020. https://doi.org/10.3386/w27423.
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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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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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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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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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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.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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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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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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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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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
Tags
Annotators
URL
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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
- Bayesian
- digital solution
- network model
- app
- containment
- prediction
- contact tracing
- is:preprint
- lang:en
- likelihood
- limitation
- COVID-19
Annotators
URL
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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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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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stackoverflow.com stackoverflow.com
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def save(self, *args, **kwargs): return
def delete(self, *args, **kwargs): return
from django.db import models
class MyReadOnlyModel(models.Model): class Meta: managed = False
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psyarxiv.com psyarxiv.com
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Colombo, R., Wallace, M., & Taylor, R. S. (2020, April 11). An Essential Service Decision Model for Applied Behavior Analytic Providers During Crisis. https://doi.org/10.31234/osf.io/te8ha
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psyarxiv.com psyarxiv.com
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Rafiei, F., & Rahnev, D. (2020, April 9). Does the diffusion model account for the effects of speed-accuracy tradeoff on response times?. https://doi.org/10.31234/osf.io/bhj85
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journal.sjdm.org journal.sjdm.org
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Dhami, Mandeep & Olsson, Henrik. (2008). Evolution of the interpersonal conflict paradigm. Judgment and Decision Making. 3. 547-569. http://journal.sjdm.org/8510/jdm8510.pdf
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www.w3.org www.w3.org
- Mar 2020
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matomo.org matomo.org
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Did you know accurate data reporting is often capped? Meaning once your website traffic reaches a certain limit, the data then becomes a guess rather than factual.This is where tools like Google Analytics becomes extremely limited and cashes in with their GA360 Premium suite. At Matomo, we believe all data should be reported 100% accurately, or else what’s the point?
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www.kqed.org www.kqed.org
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But she's also addicted to her phone."
What we sow is what we reap!
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techcrunch.com techcrunch.com
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So it’s not surprising that Facebook is so coy about explaining why a certain user on its platform is seeing a specific advert. Because if the huge surveillance operation underpinning the algorithmic decision to serve a particular ad was made clear, the person seeing it might feel manipulated. And then they would probably be less inclined to look favorably upon the brand they were being urged to buy. Or the political opinion they were being pushed to form. And Facebook’s ad tech business stands to suffer.
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news.humanpresence.io news.humanpresence.io
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Rojas-Lozano claimed that the second part of Google’s two-part CAPTCHA feature, which requires users to transcribe and type into a box a distorted image of words, letters or numbers before entering its site, is also used to transcribe words that a computer cannot read to assist with Google’s book digitization service. By not disclosing that, she argued, Google was getting free labor from its users.
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www.digital-democracy.org www.digital-democracy.org
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threat model
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- Feb 2020
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github.com github.com
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www.umich.edu www.umich.edu
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* Information-Processing Analysis : about the mental operations used by a person who has learned a complex skills
this sounds a lot more involved unless you are working off a basic set of assumptions for mental operations and complex skills. Further understanding of psychological research and learning theories would be needed.
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Dick and Carey Model
what the heck is this website, lmao.
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etec.ctlt.ubc.ca etec.ctlt.ubc.ca
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The Kemp Model of Instructional Design
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hypothes.is hypothes.is
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www.aerocityincall.com www.aerocityincall.com
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Annotators
URL
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- Jan 2020
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www.w3.org www.w3.org
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The Web Annotation Data Model specification describes a structured model and format to enable annotations to be shared and reused across different hardware and software platforms.
The publication of this web standard changed everything. I look forward to true testing of interoperable open annotation. The publication of the standard nearly three years ago was a game changer, but the game is still in progress. The future potential is unlimited!
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