The strongest predictor of whether someone believed in COVID-19-related misinformation and risks related to the vaccine was whether they viewed COVID-19 prevention efforts in terms of symbolic strength and weakness. In other words, this group focused on whether an action would make them appear to fend off or “give in” to untoward influence.

[…]

Our findings highlight the limits of countering misinformation directly, because for some people, literal truth is not the point.

In 1939, upon arriving late to his statistics course at the University of California, Berkeley, George Dantzig — a first-year graduate student — copied two problems off the blackboard, thinking they were a homework assignment. He found the homework “harder to do than usual,” he would later recount, and apologized to the professor for taking some extra days to complete it. A few weeks later, his professor told him that he had solved two famous open problems in statistics. Dantzig’s work would provide the basis for his doctoral dissertation and, decades later, inspiration for the film Good Will Hunting.

Dantzig received his doctorate in 1946, just after World War II, and he soon became a mathematical adviser to the newly formed U.S. Air Force. As with all modern wars, World War II’s outcome depended on the prudent allocation of limited resources. But unlike previous wars, this conflict was truly global in scale, and it was won in large part through sheer industrial might. The U.S. could simply produce more tanks, aircraft carriers and bombers than its enemies. Knowing this, the military was intensely interested in optimization problems — that is, how to strategically allocate limited resources in situations that could involve hundreds or thousands of variables.

Jupiter's core is a fuzzy core, not a solid core. It has a bizarre magnetic field and the NASA probe that was there or near there in 2017 confirmed that it has a core (as opposed to the theory that it has no core at all). But it's not solid (as was another possible scientific hypothesis). The discovery by the Juno spacecraft was ultimately an unexpected one and the spacecraft was able to help scientists dismiss or debunk several theories regarding the planet. Large regions of Jupiter are non-convective. And in addition, Saturn may have a fuzzy core too.

Just summarizing what I got from the video.

Your thoughts?

cross-posted from: https://lemmygrad.ml/post/9401581

Jupiter's core is a fuzzy core, not a solid core. It has a bizarre magnetic field and the NASA probe that was there or near there in 2017 confirmed that it has a core (as opposed to the theory that it has no core at all). But it's not solid (as was another possible scientific hypothesis). The discovery by the Juno spacecraft was ultimately an unexpected one and the spacecraft was able to help scientists dismiss or debunk several theories regarding the planet. Large regions of Jupiter are non-convective. And in addition, Saturn may have a fuzzy core too.

Just summarizing what I got from the video.

Your thoughts?

“So several times a year we’re taking these potshots at people on the Earth and fortunately so far missing. So far we’ve been very lucky, but it won’t last.”

Deorbiting Starlink satellites may not pose a risk to people, but Dr McDowell said they may still prove problematic.

Scientists are still trying to understand what impact this rate of deorbits might have on the Earth’s atmosphere.

The Australian National University (ANU) operates a quantum random number generator (QRNG) that produces true random numbers by measuring quantum fluctuations of vacuum[^1]. The system generates random bits at 5.7 Gbits/s and makes them freely available through both a web interface and API[^5].

Unlike traditional pseudorandom number generators that rely on mathematical algorithms and seeds, ANU's QRNG creates genuine randomness by detecting quantum phenomena - specifically the electromagnetic field fluctuations that occur in a vacuum due to zero-point energy[^1].

The service offers multiple ways to access the random numbers:

• Direct web interface for visualization and downloads
• JSON API for programmatic access
• Pre-generated random number files up to 5GB in size
• Integration libraries for various programming languages including Python, R, Java, and .NET[^5]

The QRNG has practical applications in:

• Generating cryptographic keys
• Randomized clinical trials
• Computer game simulations
• Password generation
• Weather prediction modeling[^5]

The technical implementation is documented in peer-reviewed physics journals, with the quantum random number generation process detailed in Applied Physics Letters and Physical Review Applied[^1].

[^1]: ANU QRNG – Quantum random numbers [^5]: Frequently asked questions – ANU QRNG

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