3,491 Matching Annotations
  1. Aug 2020
    1. Lozano, R., Fullman, N., Mumford, J. E., Knight, M., Barthelemy, C. M., Abbafati, C., Abbastabar, H., Abd-Allah, F., Abdollahi, M., Abedi, A., Abolhassani, H., Abosetugn, A. E., Abreu, L. G., Abrigo, M. R. M., Haimed, A. K. A., Abushouk, A. I., Adabi, M., Adebayo, O. M., Adekanmbi, V., … Murray, C. J. L. (2020). Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 0(0). https://doi.org/10.1016/S0140-6736(20)30750-9

    1. Ray, E. L., Wattanachit, N., Niemi, J., Kanji, A. H., House, K., Cramer, E. Y., Bracher, J., Zheng, A., Yamana, T. K., Xiong, X., Woody, S., Wang, Y., Wang, L., Walraven, R. L., Tomar, V., Sherratt, K., Sheldon, D., Reiner, R. C., Prakash, B. A., … Consortium, C.-19 F. H. (2020). Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S. MedRxiv, 2020.08.19.20177493. https://doi.org/10.1101/2020.08.19.20177493

    1. Nguyen, L. H., Drew, D. A., Graham, M. S., Joshi, A. D., Guo, C.-G., Ma, W., Mehta, R. S., Warner, E. T., Sikavi, D. R., Lo, C.-H., Kwon, S., Song, M., Mucci, L. A., Stampfer, M. J., Willett, W. C., Eliassen, A. H., Hart, J. E., Chavarro, J. E., Rich-Edwards, J. W., … Zhang, F. (2020). Risk of COVID-19 among front-line health-care workers and the general community: A prospective cohort study. The Lancet Public Health, 0(0). https://doi.org/10.1016/S2468-2667(20)30164-X

    1. Menni, C., Valdes, A. M., Freidin, M. B., Sudre, C. H., Nguyen, L. H., Drew, D. A., ... & Visconti, A. (2020). Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature Medicine, 1-4.

    1. Cluster 0 words: family, home, mother, war, house, dies, Cluster 0 titles: Schindler's List, One Flew Over the Cuckoo's Nest, Gone with the Wind, The Wizard of Oz, Titanic, Forrest Gump, E.T. the Extra-Terrestrial, The Silence of the Lambs, Gandhi, A Streetcar Named Desire, The Best Years of Our Lives, My Fair Lady, Ben-Hur, Doctor Zhivago, The Pianist, The Exorcist, Out of Africa, Good Will Hunting, Terms of Endearment, Giant, The Grapes of Wrath, Close Encounters of the Third Kind, The Graduate, Stagecoach, Wuthering Heights, Cluster 1 words: police, car, killed, murders, driving, house, Cluster 1 titles: Casablanca, Psycho, Sunset Blvd., Vertigo, Chinatown, Amadeus, High Noon, The French Connection, Fargo, Pulp Fiction, The Maltese Falcon, A Clockwork Orange, Double Indemnity, Rebel Without a Cause, The Third Man, North by Northwest, Cluster 2 words: father, new, york, new, brothers, apartments, Cluster 2 titles: The Godfather, Raging Bull, Citizen Kane, The Godfather: Part II, On the Waterfront, 12 Angry Men, Rocky, To Kill a Mockingbird, Braveheart, The Good, the Bad and the Ugly, The Apartment, Goodfellas, City Lights, It Happened One Night, Midnight Cowboy, Mr. Smith Goes to Washington, Rain Man, Annie Hall, Network, Taxi Driver, Rear Window, Cluster 3 words: george, dance, singing, john, love, perform, Cluster 3 titles: West Side Story, Singin' in the Rain, It's a Wonderful Life, Some Like It Hot, The Philadelphia Story, An American in Paris, The King's Speech, A Place in the Sun, Tootsie, Nashville, American Graffiti, Yankee Doodle Dandy, Cluster 4 words: killed, soldiers, captain, men, army, command, Cluster 4 titles: The Shawshank Redemption, Lawrence of Arabia, The Sound of Music, Star Wars, 2001: A Space Odyssey, The Bridge on the River Kwai, Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb, Apocalypse Now, The Lord of the Rings: The Return of the King, Gladiator, From Here to Eternity, Saving Private Ryan, Unforgiven, Raiders of the Lost Ark, Patton, Jaws, Butch Cassidy and the Sundance Kid, The Treasure of the Sierra Madre, Platoon, Dances with Wolves, The Deer Hunter, All Quiet on the Western Front, Shane, The Green Mile, The African Queen, Mutiny on the Bounty,

      The top IMDB films fit into 2 basic clusters, and 4 main clusters (this project used K-means with a target of 5 but actually clusters 1 and 2 both fit the crime category, and all except Cluster 3 are centred around violence).

      1. War whilst with Family and at home (violence external whilst passively defending the safety of the self/family)
      2. Crime (violence on a smaller scale)
      3. New York crime family / mafia (violence in the family)
      4. Musicals (non-violence)
      5. War as soldiers (violence on a large scale on the front lines)

      If this list is representative of the human psyche we have only 2 basic modes of being: Violence / Musical.

    1. his dream of it being as easy to “insert facts, data, and models in political discussion as it is to insert emoji” 😉 speaks to a sort of consumerist, on-demand thirst for snippets, rather than a deep understanding of complexity. It’s app-informed, drag-and-drop data for instant government.
    1. Bartik, A. W., Cullen, Z. B., Glaeser, E. L., Luca, M., Stanton, C. T., & Sunderam, A. (2020). The Targeting and Impact of Paycheck Protection Program Loans to Small Businesses (Working Paper No. 27623; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27623

    1. Cajner, T., Crane, L. D., Decker, R. A., Grigsby, J., Hamins-Puertolas, A., Hurst, E., Kurz, C., & Yildirmaz, A. (2020). The U.S. Labor Market during the Beginning of the Pandemic Recession (Working Paper No. 27159; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27159

    1. Brynjolfsson, E., Horton, J. J., Ozimek, A., Rock, D., Sharma, G., & TuYe, H.-Y. (2020). COVID-19 and Remote Work: An Early Look at US Data (Working Paper No. 27344; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27344

    1. Valhalla aims to revise the memory model for Java to allow for immutable types, which are more complex than primitives, but less flexible than objects. Sometimes you have more complex data that doesn’t change over the course of that object’s lifespan; burdening it with the overhead of a class is unnecessary. The initial proposal put it more succinctly: “Codes like a class, works like an int.” “For things like big data for machine learning or for natural language, Valhalla promises to represent data in a way that allows the JVM to fully take advantage of modern hardware architectures that have changed dramatically since Java was created,” said Saab.
  2. Jul 2020
    1. Ruby has some really nice libraries for working with linked data. These libraries allow you to work with the data in both a graph and resource-oriented fashion, allowing a developer to use the techniques that best suit his or her use cases and skills.
    2. Another Ruby gem, Spira, allows graph data to be used as model objects
    1. As a result, web browsers can provide only minimal assistance to humans in parsing and processing web pages: browsers only see presentation information.
    1. To verify that your structured data is correct, many platforms provide validation tools. In this tutorial, we'll validate our structured data with the Google Structured Data Validation Tool.
    2. Valid AMP pages do not require schema.org structured data, but some platforms like Google Search require it for certain experiences like the Top stories carousel. It's generally a good idea to include structured data. Structured data helps search engines to better understand your web page, and to better display your content in Search Engine Result Pages (e.g., in rich snippets).
    1. As mentioned earlier in these guidelines, it is very important that controllers assess the purposes forwhich data is actually processed and the lawful grounds on which it is based prior to collecting thedata. Often companies need personal data for several purposes, and the processing is based on morethan one lawful basis, e.g. customer data may be based on contract and consent. Hence, a withdrawalof consent does not mean a controller must erase data that are processed for a purpose that is basedon the performance of the contract with the data subject. Controllers should therefore be clear fromthe outset about which purpose applies to each element of data and which lawful basis is being reliedupon.
    2. If there is no other lawful basisjustifying the processing (e.g. further storage) of the data, they should be deleted by the controller.
    3. In cases where the data subject withdraws his/her consent and the controller wishes to continue toprocess the personal data on another lawful basis, they cannot silently migrate from consent (which iswithdrawn) to this other lawful basis. Any change in the lawful basis for processing must be notified toa data subject in accordance with the information requirements in Articles 13 and 14 and under thegeneral principle of transparency.
    4. Data minimization, anonymisation and datasecurity are mentioned as possible safeguards.73Anonymisation is the preferred solution as soon asthe purpose of the research can be achieved without the processing of personal data.
    1. Some vendors may relay on legitimate interest instead of consent for the processing of personal data. The User Interface specifies if a specific vendor is relating on legitimate interest as legal basis, meaning that that vendor will process user’s data for the declared purposes without asking for their consent. The presence of vendors relying on legitimate interest is the reason why within the user interface, even if a user has switched on one specific purpose, not all vendors processing data for that purpose will be displayed as switched on. In fact, those vendors processing data for that specific purpose, relying only on legitimate interest will be displayed as switched off.
    2. Under GDPR there are six possible legal bases for the processing of personal data.
    1. drawing evidence-based conclusions

      One thing that is not obvious about Hypothesis, is that you can also use it to annotate data sheets — that's easiest if they are CSV files published on the web.

    1. Do jeszcze bardziej przytłaczających wniosków doszła Julianne Holt-Lunstad, która, posiłkując się wynikami 70 badań naukowych, ogłosiła, że samotność zwiększa śmiertelność w takim samym stopniu co otyłość czy wypalanie 15 papierosów dziennie. Z kolei Nicole Valtorty z Uniwersytetu Newcastle ustaliła, że prawdopodobieństwo ataku serca u osób osamotnionych rośnie o 29 proc., a zagrożenie udarem – o 32 proc. „To niezależny czynnik przyczyniający się do śmierci. Może cię po prostu zabić. Znajduje się na tej samej liście co choroby serca i rak – twierdzi dr Josh Klapow, psycholog kliniczny z Uniwersytetu Alabamy.

      Data on health consequences of being alone

    2. Z danych GUS-u i tych zebranych przez portale randkowe wynika, że w Polsce w ciągu ostatnich 10 lat liczba osób żyjących samotnie wzrosła o 34 proc.
    3. Wśród krajów europejskich w niechlubnym rankingu zwycięża jednak Szwecja, w stolicy której samotnie mieszka aż 58 proc.(!) populacji. Z kolei w Stanach Zjednoczonych odsetek ten wynosi 27 proc. (w Nowym Jorku prawie 50 proc.) i cały czas rośnie – dla porównania w roku 1920 jednoosobowe gospodarstwo domowe prowadziło tam 5 proc. obywateli.

      Percentage of people living alone

    1. the market size: the global note-taking management software market is estimated to reach $1.35 billion by 2026, growing at a CAGR of 5.32% from 2019 to 2026greater scope for innovation: eg., be it creating a task list, a roadmap, or a design repository, Notion can handle it alllack of satisfaction: it’s noted that people always use a combination of note-taking apps and hardly stick to one for a long time

      Three reasons why we constantly see more note-taking apps, which in return increase our paradox of choice

    1. Jeffrey, B., Walters, C. E., Ainslie, K. E. C., Eales, O., Ciavarella, C., Bhatia, S., Hayes, S., Baguelin, M., Boonyasiri, A., Brazeau, N. F., Cuomo-Dannenburg, G., FitzJohn, R. G., Gaythorpe, K., Green, W., Imai, N., Mellan, T. A., Mishra, S., Nouvellet, P., Unwin, H. J. T., … Riley, S. (2020). Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Research, 5, 170. https://doi.org/10.12688/wellcomeopenres.15997.1

    1. One of these semiotizing processes is the extraction, interpretation and reintegration of web data from and into human subjectivities.

      Machine automation becomes another “subjectivity” or “agentivity”—an influential one, because it is the one filtering and pushing content to humans.

      The means of this automated subjectivity is feeding data capitalism: more content, more interaction, more behavioral data produced by the users—data which is then captured (“dispossessed”), extracted, and transformed into prediction services, which render human behavior predictable, and therefore monetizable (Shoshana Zuboff, The Age of Surviellance Capitalism, 2019).