- Feb 2021
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www.nature.com www.nature.com
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Pullano, G., Di Domenico, L., Sabbatini, C. E., Valdano, E., Turbelin, C., Debin, M., Guerrisi, C., Kengne-Kuetche, C., Souty, C., Hanslik, T., Blanchon, T., Boëlle, P.-Y., Figoni, J., Vaux, S., Campèse, C., Bernard-Stoecklin, S., & Colizza, V. (2021). Underdetection of cases of COVID-19 in France threatens epidemic control. Nature, 590(7844), 134–139. https://doi.org/10.1038/s41586-020-03095-6
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www.tandfonline.com www.tandfonline.com
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Xu, Z., & Guo, H. (2018). Using Text Mining to Compare Online Pro- and Anti-Vaccine Headlines: Word Usage, Sentiments, and Online Popularity. Communication Studies, 69(1), 103–122. https://doi.org/10.1080/10510974.2017.1414068
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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McMurtry, C. M. (2020). Managing immunization stress-related response: A contributor to sustaining trust in vaccines. Canada Communicable Disease Report, 46(6), 210–218. https://doi.org/10.4745/ccdr.v46i06a10
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github.com github.com
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image-lidar fusion algorithm
This is quite popular recent two years - hybrid method, multi-modality
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KITTI
dataset
Tags
Annotators
URL
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medium.com medium.com
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But I’m afraid it’s perfectly possible to ship one version of your code to GitHub and a different version to npm.
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The point is, just because you don’t see it, doesn’t mean it’s not happening. It’s been more than two years and as far as I know, no one has ever noticed one of my requests. Maybe it’s been in your site this whole time
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Also the URL looks a lot like the 300 other requests to ad networks your site makes.
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I’d notice the network requests going out!Where would you notice them? My code won’t send anything when the DevTools are open (yes even if un-docked).I call this the Heisenberg Manoeuvre: by trying to observe the behaviour of my code, you change the behaviour of my code.
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github.com github.com
- Jan 2021
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stackoverflow.com stackoverflow.com
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If you manage to make Svelte aware of what needs to be tracked, chances are that the resulting code will be more performant than if you roll your own with events or whatever. In part because it will use Svelte's runtime code that is already present in your app, in part because Svelte produces seriously optimized change tracking code, that would be hard to hand code all while keeping it human friendly. And in part because your change tracking targets will be more narrow.
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- Dec 2020
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developer.mozilla.org developer.mozilla.org
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This creates an options object with a getter function for the passive property; the getter sets a flag, passiveSupported, to true if it gets called. That means that if the browser checks the value of the passive property on the options object, passiveSupported will be set to true; otherwise, it will remain false. We then call addEventListener() to set up a fake event handler, specifying those options, so that the options will be checked if the browser recognizes an object as the third parameter.
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- Nov 2020
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imfeld.dev imfeld.dev
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Svelte's advantage here is that it indicates the need for an update at the place where the associated data is updated, instead of at each place the data is used. Then each template expression of reactive statement is able to check very quickly if it needs to rerender or not.
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But you can still run into strange race conditions where the browser displays stale data depending on if some other unrelated code has caused a digest update to run after the buggy code or not.
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medium.com medium.com
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The advantage of ngOnChanges() is that we get all the changes at once if the component has several @Input()s. However, if we have a single @Input() a setter is probably the better approach.
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www.bio-rad.com www.bio-rad.com
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To detect foreign DNA in 5 ml of lake water, 15 ml of lake water must be screened.
Why is this multiplied by 3? Related to statistical error of subsampling - rule of three)
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- Oct 2020
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psyarxiv.com psyarxiv.com
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Jaeger, B., Oud, B., Williams, T., Krumhuber, E., Fehr, E., & Engelmann, J. B. (2020, October 20). Trustworthiness detection from faces: Does reliance on facial impressions pay off?. https://doi.org/10.31234/osf.io/ayqeh
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Identify your user agents When deploying software that makes requests to other sites, you should set a custom User-Agent header to identify the software and provide a means to contact its maintainers. Many of the automated requests we receive have generic user-agent headers such as Java/1.6.0 or Python-urllib/2.1 which provide no information on the actual software responsible for making the requests.
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github.com github.com
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Perhaps we should detect URLSearchParams objects differently (using duck typing detection instead of instanceof window.URLSearchParams, for example) but the solution isn't adding a specific polyfill to Axios (as it'd increase the bundle size and still won't work with other polyfills).
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ponyfoo.com ponyfoo.comPony Foo1
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Sometimes we can’t implement a solution that’s fully spec-compliant, and in those cases using a polyfill might be the wrong answer. A polyfill would translate into telling the rest of the codebase that it’s okay to use the feature, that it’ll work just like in modern browsers, but it might not in edge cases.
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www.coe.int www.coe.int
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AI and control of Covid-19 coronavirus. (n.d.). Artificial Intelligence. Retrieved October 15, 2020, from https://www.coe.int/en/web/artificial-intelligence/ai-and-control-of-covid-19-coronavirus
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academic.oup.com academic.oup.com
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Abbott, K. R., & Sherratt, T. N. (2013). Optimal sampling and signal detection: Unifying models of attention and speed–accuracy trade-offs. Behavioral Ecology, 24(3), 605–616. https://doi.org/10.1093/beheco/art001
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www.reddit.com www.reddit.com
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r/BehSciResearch—Review on combatting the COVID misinformation flood. (n.d.). Reddit. Retrieved October 12, 2020, from https://www.reddit.com/r/BehSciResearch/comments/j9mrlp/review_on_combatting_the_covid_misinformation/
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www.scientificamerican.com www.scientificamerican.com
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Nouri, A. B., Ali. (n.d.). COVID Misinformation Is Killing People. Scientific American. Retrieved October 12, 2020, from https://www.scientificamerican.com/article/covid-misinformation-is-killing-people1/
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www.ecdc.europa.eu www.ecdc.europa.eu
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New tool for the early detection of public health threats from Twitter data: Epitweetr. (2020, October 1). European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/news-events/new-tool-early-detection-public-health-threats-twitter-data-epitweetr
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www.theguardian.com www.theguardian.com
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Scientists study whether immune response wards off or worsens Covid. (2020, October 4). The Guardian. http://www.theguardian.com/science/2020/oct/04/scientists-study-whether-immune-response-wards-off-or-worsens-covid
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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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www.scientificamerican.com www.scientificamerican.com
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Daley, J. (n.d.). Millions of Rapid COVID-19 Antigen Tests May Help Fill the Testing Gap. Scientific American. Retrieved September 30, 2020, from https://www.scientificamerican.com/article/millions-of-rapid-covid-19-antigen-tests-may-help-fill-the-testing-gap/
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stackoverflow.com stackoverflow.com
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the promise specification explicitly does not make a distinction
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www.nature.com www.nature.com
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LOD was defined as <x>bi + ksbi, where <x>bi equals the mean of the no-template controls, sbi is s.d. of no-template controls and k = 2.479 (99% confidence interval)
ddPCR
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Annotators
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www.medrxiv.org www.medrxiv.org
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López, J. A. M., Arregui-Garcĺa, B., Bentkowski, P., Bioglio, L., Pinotti, F., Boëlle, P.-Y., Barrat, A., Colizza, V., & Poletto, C. (2020). Anatomy of digital contact tracing: Role of age, transmission setting, adoption and case detection. MedRxiv, 2020.07.22.20158352. https://doi.org/10.1101/2020.07.22.20158352
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www.thelancet.com www.thelancet.com
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Chin, A. W. H., Chu, J. T. S., Perera, M. R. A., Hui, K. P. Y., Yen, H.-L., Chan, M. C. W., Peiris, M., & Poon, L. L. M. (2020). Stability of SARS-CoV-2 in different environmental conditions. The Lancet Microbe, 1(1), e10. https://doi.org/10.1016/S2666-5247(20)30003-3
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- Aug 2020
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psyarxiv.com psyarxiv.com
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Tybur, J. M., Lieberman, D., Fan, L., Kupfer, T., & de Vries, R. E. (2020). Behavioral immune tradeoffs: Interpersonal value relaxes social pathogen avoidance [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/ec8uw
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openreview.net openreview.net
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Santosh, R., Guntuku, S. C., Schwartz, H., Eichstaedt, J., & Ungar, L. (2020). Detecting Symptoms using Context-based Twitter Embeddings during COVID-19. https://openreview.net/forum?id=DFJhXXPZrM7
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www.medrxiv.org www.medrxiv.org
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Herper, M. (2020, July 1). Covid-19 vaccine from Pfizer and BioNTech shows positive results. CNBC. https://www.cnbc.com/2020/07/01/coronavirus-vaccine-from-pfizer-and-biontech-shows-positive-results-report-says.html
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pubs.acs.org pubs.acs.org
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Shan, B., Broza, Y. Y., Li, W., Wang, Y., Wu, S., Liu, Z., Wang, J., Gui, S., Wang, L., Zhang, Z., Liu, W., Zhou, S., Jin, W., Zhang, Q., Hu, D., Lin, L., Zhang, Q., Li, W., Wang, J., … Haick, H. (2020). Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath. ACS Nano. https://doi.org/10.1021/acsnano.0c05657
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www.thelancet.com www.thelancet.com
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Peeters, A., Mullins, G., Becker, D., Orellana, L., & Livingston, P. (2020). COVID-19’s impact on Australia’s health research workforce. The Lancet, 396(10249), 461. https://doi.org/10.1016/S0140-6736(20)31533-6
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www.thelancet.com www.thelancet.com
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Mohammadi, A., Esmaeilzadeh, E., Li, Y., Bosch, R. J., & Li, J. Z. (2020). SARS-CoV-2 detection in different respiratory sites: A systematic review and meta-analysis. EBioMedicine, 0(0). https://doi.org/10.1016/j.ebiom.2020.102903
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www.medrxiv.org www.medrxiv.org
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Cevik, M., Tate, M., Lloyd, O., Maraolo, A. E., Schafers, J., & Ho, A. (2020). SARS-CoV-2, SARS-CoV-1 and MERS-CoV viral load dynamics, duration of viral shedding and infectiousness: A living systematic review and meta-analysis. MedRxiv, 2020.07.25.20162107. https://doi.org/10.1101/2020.07.25.20162107
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www.youtube.com www.youtube.com
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Identifying social media manipulation with OSoMe tools. (2020, August 11). https://www.youtube.com/watch?v=1BMv0PrdVGs&feature=youtu.be
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www.theguardian.com www.theguardian.com
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Carl Bergstrom: “People are using data to bullshit.” (2020, August 1). The Guardian. http://www.theguardian.com/science/2020/aug/01/carl-bergstrom-people-are-using-data-to-bullshit
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- Jul 2020
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osf.io osf.io
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Tasnim, S., Hossain, M. M., & Mazumder, H. (2020). Impact of rumors or misinformation on coronavirus disease (COVID-19) in social media [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/uf3zn
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www.medrxiv.org www.medrxiv.org
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Golding, N., Russell, T. W., Abbott, S., Hellewell, J., Pearson, C. A. B., Zandvoort, K. van, Jarvis, C. I., Gibbs, H., Liu, Y., Eggo, R. M., Edmunds, J. W., & Kucharski, A. J. (2020). Reconstructing the global dynamics of under-ascertained COVID-19 cases and infections. MedRxiv, 2020.07.07.20148460. https://doi.org/10.1101/2020.07.07.20148460
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www-ncbi-nlm-nih-gov.ezproxy.rice.edu www-ncbi-nlm-nih-gov.ezproxy.rice.edu
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LoD = LoB + 1.645(SD low concentration sample)
LoD is the lowest analyte concentration likely to be reliably distinguished from the LoB and at which detection is feasible. LoD is determined by utilising both the measured LoB and test replicates of a sample known to contain a low concentration of analyte.
LoB is the highest apparent analyte concentration expected to be found when replicates of a blank sample containing no analyte are tested.
LoB = meanblank + 1.645(SDblank)
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www.medrxiv.org www.medrxiv.org
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Zhong, H., Wang, Y., Shi, Z., Zhang, L., Ren, H., He, W., Zhang, Z., Zhu, A., Zhao, J., Xiao, F., Yang, F., Liang, T., Ye, F., Zhong, B., Ruan, S., Gan, M., Zhu, J., Li, F., Li, F., … Zhao, J. (2020). Characterization of Microbial Co-infections in the Respiratory Tract of hospitalized COVID-19 patients. MedRxiv, 2020.07.02.20143032. https://doi.org/10.1101/2020.07.02.20143032
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twitter.com twitter.com
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New Scientist on Twitter: “Thread on #covid19 trends in the US: Coronavirus infections have surged since the start of June from around 20,000 new cases a day to over 60,000. (1/4) https://t.co/wVFwHWczYR” / Twitter. (n.d.). Twitter. Retrieved July 19, 2020, from https://twitter.com/newscientist/status/1283387188391149571
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Lang, T. (2020). Plug COVID-19 research gaps in detection, prevention and care. Nature, 583(7816), 333–333. https://doi.org/10.1038/d41586-020-02004-1
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Mallapaty, S. (2020). The mathematical strategy that could transform coronavirus testing. Nature. https://doi.org/10.1038/d41586-020-02053-6
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Song, S. (2020). China Experience in Controlling COVID-19. https://doi.org/10.31235/osf.io/gfnep
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osf.io osf.io
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Hossain, M. M., McKyer, E. L. J., & Ma, P. (2020). Applications of artificial intelligence technologies on mental health research during COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/w6c9b
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www.medrxiv.org www.medrxiv.org
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Davis, J. T., Chinazzi, M., Perra, N., Mu, K., Piontti, A. P. y, Ajelli, M., Dean, N. E., Gioannini, C., Litvinova, M., Merler, S., Rossi, L., Sun, K., Xiong, X., Halloran, M. E., Longini, I. M., Viboud, C., & Vespignani, A. (2020). Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA. MedRxiv, 2020.07.06.20140285. https://doi.org/10.1101/2020.07.06.20140285
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- Jun 2020
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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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Fleming, N. (2020). Coronavirus misinformation, and how scientists can help to fight it. Nature. https://doi.org/10.1038/d41586-020-01834-3
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www.thelancet.com www.thelancet.com
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The Lancet. (2020). Sustaining containment of COVID-19 in China. The Lancet, 395(10232), 1230. https://doi.org/10.1016/S0140-6736(20)30864-3
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twitter.com twitter.com
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Hironori Funabiki on Twitter
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onlinelibrary.wiley.com onlinelibrary.wiley.com
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Dewa, L. H., Lawrence‐Jones, A., Crandell, C., Jaques, J., Pickles, K., Lavelle, M., Pappa, S., & Aylin, P. (n.d.). Reflections, impact and recommendations of a co-produced qualitative study with young people who have experience of mental health difficulties. Health Expectations, n/a(n/a). https://doi.org/10.1111/hex.13088
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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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Ortiz, E., García-Pérez, G., & Serrano, M. Á. (2020). Geometric detection of hierarchical backbones in real networks. ArXiv:2006.03207 [Physics]. http://arxiv.org/abs/2006.03207
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www.theatlantic.com www.theatlantic.com
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Meyer, A. C. M., Robinson. (2020, May 21). ‘How Could the CDC Make That Mistake?’ The Atlantic. https://www.theatlantic.com/health/archive/2020/05/cdc-and-states-are-misreporting-covid-19-test-data-pennsylvania-georgia-texas/611935/
Tags
- CDC
- exposure
- bad science
- COVID-19
- reopening
- infection rate
- conflation
- outbreak detection
- antibody
- confusion
- concern
- viral
- is:news
- USA
- lang:en
- testing
Annotators
URL
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theconversation.com theconversation.com
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Yasseri, T. (n.d.). Dominic Cummings: How the internet knows when you’ve updated your blog. The Conversation. Retrieved June 1, 2020, from http://theconversation.com/dominic-cummings-how-the-internet-knows-when-youve-updated-your-blog-139517
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- May 2020
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arxiv.org arxiv.org
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Aslak, U., & Alessandretti, L. (2020). Infostop: Scalable stop-location detection in multi-user mobility data. ArXiv:2003.14370 [Physics]. http://arxiv.org/abs/2003.14370
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thebulletin.org thebulletin.org
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Kim, H. (2020, March 20). South Korea learned its successful Covid-19 strategy from a previous coronavirus outbreak: MERS. Bulletin of the Atomic Scientists. https://thebulletin.org/2020/03/south-korea-learned-its-successful-covid-19-strategy-from-a-previous-coronavirus-outbreak-mers/
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psyarxiv.com psyarxiv.com
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Michalak, N. M., Sng, O., Wang, I., & Ackerman, J. (2020, May 14). Sounds of sickness: Can people identify infectious disease using sounds of coughs and sneezes?. https://doi.org/10.1098/rspb.2020.0944
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github.com github.com
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URL
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Riolo, M. A., & Newman, M. E. J. (2020). Consistency of community structure in complex networks. Physical Review E, 101(5), 052306. https://doi.org/10.1103/PhysRevE.101.052306
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Kaplan, E. H., & Forman, H. P. (2020). Logistics of Aggressive Community Screening for Coronavirus 2019. JAMA Health Forum, 1(5), e200565–e200565. https://doi.org/10.1001/jamahealthforum.2020.0565
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www.nejm.org www.nejm.org
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Chu, H. Y., Englund, J. A., Starita, L. M., Famulare, M., Brandstetter, E., Nickerson, D. A., Rieder, M. J., Adler, A., Lacombe, K., Kim, A. E., Graham, C., Logue, J., Wolf, C. R., Heimonen, J., McCulloch, D. J., Han, P. D., Sibley, T. R., Lee, J., Ilcisin, M., … Bedford, T. (2020). Early Detection of Covid-19 through a Citywide Pandemic Surveillance Platform. New England Journal of Medicine, NEJMc2008646. https://doi.org/10.1056/NEJMc2008646
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www.medrxiv.org www.medrxiv.org
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Shental, N., Levy, S., Skorniakov, S., Wuvshet, V., Shemer-Avni, Y., Porgador, A., & Hertz, T. (2020). Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers. MedRxiv, 2020.04.14.20064618. https://doi.org/10.1101/2020.04.14.20064618
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www.medrxiv.org www.medrxiv.org
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Wurtzer, S., Marechal, V., Mouchel, J.-M., & Moulin, L. (2020). Time course quantitative detection of SARS-CoV-2 in Parisian wastewaters correlates with COVID-19 confirmed cases. MedRxiv, 2020.04.12.20062679. https://doi.org/10.1101/2020.04.12.20062679
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www.thelancet.com www.thelancet.com
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Lohse, S., Pfuhl, T., Berkó-Göttel, B., Rissland, J., Geißler, T., Gärtner, B., Becker, S. L., Schneitler, S., & Smola, S. (2020). Pooling of samples for testing for SARS-CoV-2 in asymptomatic people. The Lancet Infectious Diseases, S1473309920303625. https://doi.org/10.1016/S1473-3099(20)30362-5
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- Apr 2020
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www.medrxiv.org www.medrxiv.org
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Adams, E. R., Anand, R., Andersson, M. I., Auckland, K., Baillie, J. K., Barnes, E., Bell, J., Berry, T., Bibi, S., Carroll, M., Chinnakannan, S., Clutterbuck, E., Cornall, R. J., Crook, D. W., Silva, T. D., Dejnirattisai, W., Dingle, K. E., Dold, C., Eyre, D. W., … Sanchez, V. (2020). Evaluation of antibody testing for SARS-Cov-2 using ELISA and lateral flow immunoassays. MedRxiv, 2020.04.15.20066407. https://doi.org/10.1101/2020.04.15.20066407
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psyarxiv.com psyarxiv.com
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Wolf, M. G. (2020, April 26). Survey Uses May Influence Survey Responses. https://doi.org/10.31234/osf.io/c4hd6
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covid19mm.github.io covid19mm.github.io
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Third Report. (2020, April 17). COVID-19 Mobility Monitoring Project. https://covid19mm.github.io//in-progress/2020/04/17/third-report.html
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arxiv.org arxiv.org
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Nanni, M., Andrienko, G., Boldrini, C., Bonchi, F., Cattuto, C., Chiaromonte, F., Comandé, G., Conti, M., Coté, M., Dignum, F., Dignum, V., Domingo-Ferrer, J., Giannotti, F., Guidotti, R., Helbing, D., Kertesz, J., Lehmann, S., Lepri, B., Lukowicz, P., … Vespignani, A. (2020). Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. ArXiv:2004.05222 [Cs]. http://arxiv.org/abs/2004.05222
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www.dfg.de www.dfg.de
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DFG, German Research Foundation—Call for Multidisciplinary Research into Epidemics and Pandemics in Response to the Outbreak of SARS-CoV-2. (n.d.). Retrieved April 15, 2020, from https://www.dfg.de/en/research_funding/announcements_proposals/2020/info_wissenschaft_20_20/
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- Feb 2020
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github.com github.com
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www.ruby-toolbox.com www.ruby-toolbox.com
- Sep 2019
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stackblitz.com stackblitz.com
- Mar 2018
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www.brookings.edu www.brookings.edu
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J.M. Berger Former Brookings Expert
Paying attention to the qualifications of the author(s)/composer(s) is another crucial role in crap detection at it will help discern whether or not to take the piece seriously or to use it for further research.
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Markaz
In the Rheinghold text , he explains the importance of pay attention the website layout as well as content. However, in doing so, you must tune your crap detection and remember that not everything with a fancy layout is reliable, and vice versa.
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I took a detailed look at how ISIS functions online, breaking it down into a five-part template, which can be implemented in different ways depending on the target’s disposition:
Rather than simply stating information, the author (Berger) explains his source and the way in which he broke his research down into smaller categories. This citation is also apart of crap detection with a reliable source.
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detected through social media analysis,
The implementing of this specific link gives important attribution and increases source reliability. The text makes a statement and is able to back it up with an external, secure source.
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there are practical and ethical limits to how much we can interdict discovery.
Though Rheinghold stresses the importance of crap detection and researching your sources, he accepts the fact that there a limits that we reach in terms of discernment of validity. This is shown as the ISIS busters reach ethical and practical limits of search. It is important in the way that one mustn't get overwhelmed with finding the true source origin because you can only go so far.
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stripping away the mystique and focusing on the mechanics.
Rheinghold stresses the importance of looking at the base of things, rather than simply the makeup and what you see initially, it is important to dig deeper and look at sources from a questionable yet structured angle.
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Monday, November 9, 2015
The article ends in 'edu' which, as Rheinghold states, increases estimation of its credibility.
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This post originally appeared on VOX-Pol.
Considering that the origin of this post comes from a non-secure site, that appears a tad amateur - also brings forth speculation. It is a blog site, and considering this - I somehow take what is posted 'with a grain of salt'.
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How does ISIS acquire new recruits online and convince them to take action? J.M. Berger explains, arguing that efforts to counter terrorists’ online activity can be more effective if the mechanics are clearly understood.
I begin critiquing this article based on Rheinghold's initial conversation with his daughter. In the text Rheinghold suggests using a free internet service - Whois , in order to search for validity in research. After plugging this domain name into the site, I find that the name of the registered owner is 'Educase'. Educase is a nonprofit core data service for research and analysis.
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How terrorists recruit online (and how to stop it)
I will be connecting this text through Howard Rheinghold's "Crap Detection 101" from chapter 2 of his book Net Smart - How to Thrive Online. This allows for further critic of this article in terms of this theme.
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www.cjig.cn www.cjig.cn
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视频烟雾检测研究进展
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- Feb 2017
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angular.io angular.io
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how it uses zones
Does anyone have an authoritative link for this concept of zones and how they work? It'd be much appreciated.
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- Jan 2017
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www.bitbybitbook.com www.bitbybitbook.com
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Early event detection problems can go here. Two example cases just came to my mind are: 1- in emergency response: detecting a disaster quickly is important. 2- in computational journalism: many locals suddenly start talking about an event means something newsworthy is going on.
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- Nov 2016
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journals.plos.org journals.plos.org
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Finally, by assuming the non-detection of a species to indicate absence from a given grid cell, we introduced an extra level of error into our models. This error depends on the probability of false absence given imperfect detection (i.e., the probability that a species was present but remained undetected in a given grid cell [73]): the higher this probability, the higher the risk of incorrectly quantifying species-climate relationships [73].
This will be an ongoing challenge for species distribution modeling, because most of the data appropriate for these purposes is not collected in such a way as to allow the straightforward application of standard detection probability/occupancy models. This could potentially be addressed by developing models for detection probability based on species and habitat type. These models could be built on smaller/different datasets that include the required data for estimating detectability.
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- Nov 2015
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www.cs.umd.edu www.cs.umd.edu
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Presentation summarizing an approach to duplicate web page detection that was developed by a researcher whilst at Google in the early 2000s
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- Sep 2015
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Given an LSH familyH, the LSH scheme amplifiesthe gap between the high probabilityP1and the lowprobabilityP2by concatenating several functions
Useful recap of LSH
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Recent survey paper for hashing-based approaches to similarity search
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URL
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www.csd.uoc.gr www.csd.uoc.gr
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This paper has a very useful overview of previous work that is worth reading under section 9.
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We used the following publicly available real datasets in the experiment
Datasets used are DBPL, ENRON, UNIREF-4GRAM. All small (<1M records) in web terms and I would guess, all with small document sizes.
Given a lengthy paper, could potentially divide into smaller documents (1 doc === 1 page) and do signature calculation on a per-page basis. This could have the benefit of bounding the search time by limiting the number of pages that need to be rendered to text in order to start the lookup process.
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