Imagine if Richard Feynman used his IQ as a metric for deciding whether he should become a physicist. Physics would not be the same.
I am certain that there are mathematicians below, near, and above an IQ of 145 that all have great research productivity. IQ tests do not approximate the creativity, effort, and collaboration required in a mathematician. Not to mention the dubious nature of the 145 claim.
Of course, there are some people that will have a greater aptitude for mathematics than others. But you do not need to be a genius, and this is echoed by Terence Tao [0].
Just to complement your post, Richard Feynman's quote on the topic:
“I was an ordinary person who studied hard. There are no miracle people. It happens they get interested in this thing and they learn all this stuff, but they’re just people.”
I dunno man but I always believed Feynman was expressing a very “aw shucks” everyman type of sensibility to motivate his students but really he’s a genius who just never saw himself on par with the other genius demigod scientists of his time but still far removed from common people like me for example. Or he knew he was exceptional but he just liked to distinguish himself from the more square academic types by appealing to the regular people.
Either way, I never bought his claim that he was not exceptional.
Also possible that Feynman had superb verbal-mathematical ability and bad visual-spatial ability and took a visual-spatial test. It's unusual but not incredibly so. I am the same way.
It uses Apple HealthKit, so anything that writes heart rate data to Apple Health. In most cases this will be Apple Watch, but other devices with heart rate sensors should work too.
Which data did you use? Since the leagues were from the Quarterfinals and onwards, was it data from the group stages, euro qualifiers? Averaging the last 5 games would introduce a lot of variance, especially in tournament football.
I was using data from the group stages. And I agree that just using the last 5 games had a lot of variance, but unfortunately, I didn't have data from before the Euro.
This approach would be interesting to use in league football (La Liga, Premier League, etc) as there is a lot more data available across different seasons.
I think I remember reading somewhere that electric planes would have a much lower energy density than liquid fuel, making its utility minimal. How does hydrogen-powered flight compare regarding energy density?
Any physicists in here that could layout a path of going from rudimentary first year level physics knowledge to being able to understand on a deeper level topics such as gravitational waves?
These articles are interesting but are very abstract when you do not have knowledge from first principles.
I’m a scientist who works on LIGO and next generation detectors and I don’t have a deep knowledge of the physics behind gravitational waves either! But realistically you should pick up some lecture notes on general relativity: https://www.damtp.cam.ac.uk/user/tong/gr.html
This is a basic question, but how is the accuracy of the predicted biomolecular interactions measured? Are the predicted interactions compared to known interactions? How would the accuracy of predicting unknown interactions be assessed?
Accuracy can be assessed two main ways: computationally and experimentally. Computationally, they would compare the predicted structures and interactions with known data from databases like PDB (Protein Database). Experimentally, they can use tools like x-ray crystallography and NMR (nuclear magnetic resonance) to obtain the actual molecule structure and compare it to the predicted result. The outcomes of each approach would be fed back into the model for refining future predictions.
AlphaFold very explicitly (unless something has changed) removes NMR structures as references because they are not accurate enough. I have a PhD in NMR biomolecular structure and I wouldn't trust. the structures for anything.
Hmm, I would say its always worth to share knowledge. Could you paste some links or maybe type a few key-words for anyone willing to reasearch the topic further on his own.
Looking at the supplementary material (section 2.5.4) for the AlphaFold 3 paper it reads to me like they still use NMR structures for training, but not for evaluating performance of the model.
I think it's implicit in their description of filtering the training set, where they say they only include structures with resolution of 9A or less. NMR structures don't really have a resolution, that's more specific to crystallography. However, I can't actually verify that no NMR structures were included without directly inspecting their list of selected structures.
I think it is very plausible that they don't use NMR structures here, but I was looking for a specific statement on it in the paper. I think your guess is plausible, but I don't think the paper is clear enough here to be sure about this interpretation.
Yes, thanks for calling that out. In verifying my statement I actually was confused because you can see they filter NMR out of the eval set (saying so explicitly) but don't say that in the test set section (IMHO they should be required to publish the actual selection script so we can inspect the results).
> Input mmCIFs are restricted to have resolution less than 9 Å. This is not a very restrictive filter and only removes around 0.2% of structures
NMR structures are more than 0.2% so that doesn't fit to the assumption that they implicitly remove NMR structures here. But if I filter by resolution on the PDB homepage it does remove essentially all NMR structures. I'm really not sure what to think here, the description seems too soft to know what they did exactly.
interesting observation and experience. must have made thesis development complex, assuming the realization dawned on you during the phd.
what do you trust more than NMR?
AF's dependence on MSAs also seems sub-optimal; curious to hear your thoughts?
that said, it's understandable why they used MSAs, even if it seems to hint at winning CASP more than developing a generalizable model.
arguably, MSA-dependence is the wise choice for early prediction models as demonstrated by widespread accolades and adoption, i.e., it's an MVP with known limitations as they build toward sophisticated approaches.
My realizations happened after my PhD. When I was writing my PhD I still believed we would solve the protein folding and structure prediction problems using classical empirical force fields.
It wasn't until I started my postdocs, where I started learning about protein evolutionary relationships (and competing in CASP), that I changed my mind.
I wouldn't say it so much as "multiple sequence alignments"; those are just tools to express protein relationships in a structured way.
If Alphafold now, or in the future, requires no evolutionary relationships based on sequence (uniprot) and can work entirely by training on just the proteins in PDB (many of which are evoutionarily related) and still be able to predict novel folds, it will be very interesting times. The one thing I have learned is that evolutionary knowledge makes many hard problems really easy, because you're taking advantage of billions of years of nature and an easy readout.
From the Methods section of the paper (under "study design"): "We also included meta-analyses that pooled data from primary prospective/retrospective cohort or case-control studies. These studies were the focus because of their ability to assess causality for observational research".
While "causality" is a strong word in the sentence above, the data is much stronger than simple correlation. Of course, outright causality has not been established, but the evidence to determine a predictive association is strong.
I don't have much advice on that front since that is one thing I've never actually had an issue with (unlike literally everything else about it, from craft to marketing).
Ideas tend to come for me all the time, _but_ I noticed they sometimes flow even more aggressively when I read other fiction books as well as when I go for a walk out in nature. I think consuming stories just generates more stories.
For my last novella, I actually brainstormed it with ChatGPT. I already had a general idea and theme, but needed to brainstorm some plot points, character motivations, etc. I found using ChatGPT as a more advanced rubber ducky helped spark the imagination and flesh out the plot. At one point I even did it through ChatGPT voice on my phone while on a bus, and actually _talking_ through it in some ways seemed even more effective.
Very true about consuming more stories. As Stephen King famously said, "If you want to be a writer, you must do two things above all others: read a lot and write a lot."
Yeah, I've heard other authors using ChatGPT in a similar way. Could definitely be helpful.
It depends on what you mean specifically by "worth your time"? What "value" (in the sense that you're using) are you trying to get from the book? If you are reading to solve a specific problem, then a book being worth your time would depend on its ability to help explain the problem or approximate ways towards a solution. However, if you are reading non-fiction for other reasons, such as leisure, then the value-approach becomes more complicated.
I am certain that there are mathematicians below, near, and above an IQ of 145 that all have great research productivity. IQ tests do not approximate the creativity, effort, and collaboration required in a mathematician. Not to mention the dubious nature of the 145 claim.
Of course, there are some people that will have a greater aptitude for mathematics than others. But you do not need to be a genius, and this is echoed by Terence Tao [0].
[0] https://terrytao.wordpress.com/career-advice/does-one-have-t...