This is a great write up. It's also weirdly similar to a video I happened upon yesterday playing around with raw Hubble imagery: https://www.youtube.com/watch?v=1gBXSQCWdSI
He take a few minutes to get to the punch line. Feel free to skip ahead to around 5:30.
I know what you mean by your comment, but you might want to give catch 22 another try sometime. I'd say that the start with the Texan is far from the best the book gets. Reading it, I think you'll find Yossarian more and more understandable and the situations more and more bleakly comic as you go.
Or don't. There's no end of great novels out there.
Several of the candidate variable objects are characterized in the results section of the paper. The model is also tested for effectiveness against synthetic data. It appears to be a useful method and the paper describes a plausible path for it to aid future discovery.
Everywhere? This is a crazy thing to claim. I was also recently in Japan and I never took a car anywhere. I'm sure there are particular routes that are badly served but come on.
> In fact up until a recent funding method change from the Trump Administration, most grant money was subject to "overhead"--a nebulous nonsensical accounting trick that allowed the university administration to get upwards of 60% of the dollars that are earmarked for grants.
We're better than this here. Don't spread misinformation. First of all overhead is listed as a percentage, such as 55% or 60% or whatever but the university doesn't get that fraction of the total grant. You work up the so called direct costs, ie the line item salaries of the researchers, the reagents, etc. and then the overhead is 60% of that figure. So it would work out to be 38% of the total dollars granted.
It's also not a trick. It's a negotiated amount that is supposed to avoid each grant requesting some amortized fraction of the cost of office space and other necessary but shared expenses.
I and most people agree that's it's possibly too high, but it's ignorant to treat it like a scam.
In short, the authors and NASA strongly disagree with the decision to retract and argue that this is clearly outside of the typical norms for what retraction is supposed to represent. A paper being wrong isn't and shouldn't be the standard for retraction, particularly in this case when the original paper was published with multiple technical responses and rejoinders.
From the article this seems much less bad than the headline might imply. This idea predates trump and it does not eliminate scientific review of proposals. That said, whether the cost savings itself is worth what might be a diminishment in review quality is hard to say. I can only comment from the NSF side of things to say that peer review of proposals is a mixed bag and will unavoidably run into human error and individual predispositions regarding scientific importance so maybe this isn't a bad approach to try.
There have been several of these deep dives into manipulated speed runs and they seldom disappoint. Since we are on HN, I'll mention another mathy one that was summarized by Matt Parker: https://www.youtube.com/watch?v=8Ko3TdPy0TU
That's good advice in that context and speaks to the difference in standards of evidence between normal expository or persuasive writing in school and scientific writing. In a scientific paper if you assert something it's important to flag to the reader what kind of claim this is, eg something demonstrated by other research, something that directly follows from your evidence, or something that could possibly be true given prior knowledge and your evidence.
This is good stuff but I'll say that it isn't as comprehensive as all that. These studies and findings are almost entirely focused on simple recall knowledge (isolated facts, vocabulary). That's an important part of learning certainly and it informs research on learning higher order concepts but it's not the full story.
Just to name one example, folks might look into research on conceptual change theory (eg Chi or Posner). This theory helps explain why a concept like electricity is so challenging to learn. The reason, in brief, being that naive conceptions make a category error and think of electricity as a thing rather than a process. And this theory then informs instructional practice. Specifically, teachers should be aware of difficult concepts and should design activities that force students to confront the contradictions between their naive models and more accurate/complete ones.
Mickie Chi also has fascinations research on active learning (ICAP) and related work on the effectiveness of peer learning.
This is gonna be me if or when my quite functional dumb LG from 2012 ever gives up the ghost. I just don't see the appeal of smart TVs when that functionality can be outsourced to a cheaper modular device.
I think the author probably exaggerates her point a bit (fair enough; she's an opinion writer), but I do find it appealing to imagine that OpenStreetMap would have broad buy-in and would lead to more innovation and diversity in the digital maps marketplace.
Yeah. I think if I read Dream Machine first I would have enjoyed Wizards less. But some of the history was new to me which meant that I enjoyed Wizards for those aspects and then Dream Machine for the detailed look into why it happened.