https://plato.stanford.edu/entries/russell-paradox/
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John von Neumann’s (1925) untyped method for dealing with paradoxes, and with Russell’s paradox in particular, is simple and ingenious. Von Neumann introduces a distinction between membership and non-membership and, on this basis, draws a distinction between sets and classes. An object is a member (simpliciter) if it is a member of some class; and it is a non-member if it is not a member of any class.
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https://en.wikipedia.org/wiki/Russell%27s_paradox
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Informal presentation Most sets commonly encountered are not members of themselves. For example, consider the set of all squares in the plane. This set is not itself a square in the plane, thus it is not a member of itself. Let us call a set “normal” if it is not a member of itself, and “abnormal” if it is a member of itself. Clearly every set must be either normal or abnormal. The set of squares in the plane is normal. In contrast, the complementary set that contains everything which is not a square in the plane is itself not a square in the plane, and so it is one of its own members and is therefore abnormal. Now we consider the set of all normal sets, R, and try to determine whether R is normal or abnormal. If R were normal, it would be contained in the set of all normal sets (itself), and therefore be abnormal; on the other hand if R were abnormal, it would not be contained in the set of all normal sets (itself), and therefore be normal. This leads to the conclusion that R is neither normal nor abnormal: Russell’s paradox.
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https://en.wikipedia.org/wiki/History_of_numerical_weather_prediction
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In 1922, Lewis Fry Richardson published the first attempt at forecasting the weather numerically. Using a hydrostatic variation of Bjerknes’s primitive equations,[2] Richardson produced by hand a 6-hour forecast for the state of the atmosphere over two points in central Europe, taking at least six weeks to do so.[3] His forecast calculated that the change in surface pressure would be 145 millibars (4.3 inHg), an unrealistic value incorrect by two orders of magnitude. The large error was caused by an imbalance in the pressure and wind velocity fields used as the initial conditions in his analysis.[2] The first successful numerical prediction was performed using the ENIAC digital computer in 1950 by a team led by American meteorologist Jule Charney. The team include Philip Thompson, Larry Gates, and Norwegian meteorologist Ragnar Fjørtoft, applied mathematician John von Neumann, and computer programmer Klara Dan von Neumann, M. H. Frankel, Jerome Namias, John C. Freeman Jr., Francis Reichelderfer, George Platzman, and Joseph Smagorinsky.[5][6][7] They used a simplified form of atmospheric dynamics based on solving the barotropic vorticity equation over a single layer of the atmosphere, by computing the geopotential height of the atmosphere’s 500 millibars (15 inHg) pressure surface.[8] This simplification greatly reduced demands on computer time and memory, so the computations could be performed on the relatively primitive computers of the day.[9] When news of the first weather forecast by ENIAC was received by Richardson in 1950, he remarked that the results were an “enormous scientific advance.”[2] The first calculations for a 24‑hour forecast took ENIAC nearly 24 hours to produce,[2] but Charney’s group noted that most of that time was spent in “manual operations”, and expressed hope that forecasts of the weather before it occurs would soon be realized.[8]
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Expanding Free At-Home Testing for Americans: Today, the President will announce new steps to ensure that Americans has access to free at-home testing. First, the more than 150 million Americans with private insurance – who now are able to get tests covered in physician offices, pharmacies, and clinics with no cost sharing – will also be able to get at-home tests reimbursed by their insurance.
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The most widely available antigen test seems to be BinaxNOW, from Abbott. You should also feel comfortable using QuickVue, Ellume and Flowflex, among others.
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https://www.nytimes.com/2021/11/24/briefing/thanksgiving-covid-rapid-test.html
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Molnupiravir acts by introducing mutations into the viral genome during viral replication. A metabolite of the drug is picked up by a viral enzyme called RNA-dependent RNA polymerase and incorporated into the viral genome, eventually causing so many errors that the virus can no longer survive.
…
Paxlovid acts by inhibiting an enzyme that’s needed to process some viral proteins into their final, functional form. But the drug is a combination of an antiviral and another drug, called ritonavir, which helps to prevent enzymes in the liver from breaking down the antiviral before it has a chance to disable the coronavirus. Ritonavir, a component of some HIV treatment cocktails, can affect how some other medications are metabolized by the body.
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Molnupiravir & Paxlovid
https://www.nature.com/articles/d41586-021-03074-5
https://www.pixpa.com/blog/photography-pricing-guide
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Rates Based on Level of Experience
Experience is a significant factor in deciding how much does a photographer makes. Photography prices are typically based on the level of knowledge and experience, as indicated below.
Amateurs and Hobbyists – Amateurs and hobbyists commonly charge $25-$75 an hour for images normally used on blogs, small websites, or for local advertising. The fee is generally under $100. However, they may not follow standard photography practices and have other lines of work.
Student Photographers – Students typically have more experience than amateurs and hobbyists in that they’ve received some form of training. Their typical hourly rate is $50 – $100 per hour. Prices of student photographers can vary based several factors – the school that they are studying in, the stage of their education, whether they have worked with and assisted other professional photographers, and, last but not least, their photography portfolio. Students with brilliant student photography portfolios can command the same rates as professional photographers.
Entry-Level Photographers – The entry-level or semi-professional photographers charge $50-$150 per hour or $25-$125 per image. They’re not as committed and often have other lines of work that they rely on.
Professional Photographers with experience – Professional, experienced photographers usually charge $75 – $250 per hour or per image. They rely solely on their photography work to make a living and typically have invested a lot in their equipment, and have more experience in both the pre-production and post-production stages of photography.
Top Professional Photographers – Top professional photographers, known as high-end photographers, charge $250 – $500 per hour or $200 -$1200 per image. This elite group of photographers command top dollar for their work and can even be paid up to $10,000 per day depending on their area of specialty. They cover areas such as sports, fashion, entertainment, film, documentaries for TV, movies, billboards or magazines.
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One of the most successful applications of machine learning to scientific modeling is in MD. Researchers study the properties of materials and molecules by using classical Newtonian dynamics to track the nuclei in a system. One critical issue in MD is how to model the PES that describes the interaction between the nuclei. Traditionally, modelers have dealt with the problem in several ways. One approach, ab initio MD, was developed in 1985 by Roberto Car and Michele
Parrinello7 and computes the interatomic forces on the fly using models based on first principles, such as density functional theory.8 Although the approach accurately describes the system under
consideration, it‘s computationally expensive: The maximum system size that one can handle is limited to thousands of atoms. Another approach uses empirical formulas to model a PES. The method is efficient, but guessing the right formula that can model the PES accurately enough is a difficult task, particularly for complicated systems, such as multicomponent alloys. In 2007 Jörg Behler and Parrinello introduced the idea of using neural networks to model the PES.9 In that new paradigm, a quantum mechanics model generates data that are used to train a neural-network-based PES model.
7. R. Car, M. Parrinello, Phys. Rev. Lett. 55, 2471 (1985).
https://doi.org/10.1103/PhysRevLett.55.2471
9. J. Behler, M. Parrinello, Phys. Rev. Lett. 98, 146401 (2007). https://doi.org/10.1103/PhysRevLett.98.146401
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https://physicstoday.scitation.org/doi/10.1063/PT.3.4793
mentions Parrinelllo classic paper :
#8 has QM & NN (NN models for PES)
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Not a shortcut but check out Opener. It is well worth a couple bucks for the functionality it adds to your phone.
https://apps.apple.com/us/app/opener-open-links-in-apps/id989565871 “}}
https://www.reddit.com/r/shortcuts/comments/kovwoi/send_links_from_chrome_to_safari_in_iphone/
linkstream2 microblog 