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The problem is: Since we don't regularly full-body scan healthy people, we don't know how healthy, or say, still ok, looks like. This will create a lot of false positives and potential harm from unneccessary follow-up procedures and treatments.
I think it's difficult to objectively quantify "effectively solved". A good source with loads of information about many diseases and their clinical status is OpenTargets, e.g. https://platform.opentargets.org/disease/EFO_0003860
If I tell you I have function f with f(a) = 10 and on it's path from a to b, the graph first increaes by 5 units then by another 10, and then later on drops by 25 units, you can immediately deduce that f(b) = f(a) + (+5 +10 -25) = 0. The fundamental theorem of calculus uses the same concept:
To see why \int_a^b f(x) dx = F(b) - F(a) with F'(x) = f(x),
we replace f with f' (and hence F with f) and get
\int_a^b f'(x) dx = f(b) - f(a).
Re-arranging terms, we get
f(b) = f(a) + \int_a^b f'(x) dx.
The last line just says: The value of function f at point b is is the value at point a plus the sum of all the infinitely many changes the function goes through on its path from a to b.
As someone who has written his PhD thesis in LaTeX, I wish Markdown, or more specifically Quarto, would have existed at that time. Spent too mich time setting up basic stuff that is now just 2 pre-defines styles away.
I work in a corporate setting and the money and time we wasted because of Zscaler and its SSL inspection [1] is beyond your wildest imagination. Whenever I see a "SSL certificate problem: self-signed certificate in certificate chain" error, I know I'm in trouble.
[1] https://www.zscaler.com/resources/security-terms-glossary/wh...
I really don't understand why people think it's a good idea to use csv. In english settings, the comma can be used as 1000-delimiter in large numbers, e.g. 1,000,000 for on million, in German, the comma is used as decimal place, e.g. 1,50€ for 1 euro and 50 cents. And of course, commas can be used free text fields. Given all that, it is just logical to use tsv instead!
There is a saying about Gauss: when another mathematician came to show him a new result, Gauss would remark that he had already worked on it, open a drawer in his desk, and pull out a pile of papers on the same topic.
Oh wow, I wish I could give more than one upvote for this reference!
Or a good one, forcing governments to have robust infrastructure that this info isn't useful. Similar reasoning as with security and open source software.
I'm a data scientist and a lot of my R code are dplyr-chains a la data |> select(features) |> filter(low_quality) |> mutate(feature=...). It just saves time to comment on what those chains do instead of having go through them every time I want to change something.
I'll basically do
$ man tool | <how do I do this with the tool>Could you please elaborate on this? Do I get this right that you can set up your your command line so that you can pipe something to a command that sends this something together with a question to an LLM? Or did you just mean that metaphorically? Sorry if this is a stupid question.
Having gone through the explainations of the Transformer Explainer [1], I now have a good intuition for GPT-2. Is there a resource that gives intuition on what changes since then improve things like more conceptually approaching a problem, being better at coding, suggesting next steps if wanted etc? I have a feeling this is a result of more than just increasing transformer blocks, heads, and embedding dimension.
I once bought an Office 2016 license and when I installed it this year on a new laptop, it turned itself into a trimmed down O365. After the first Office update, I got a non-closable ad next to my Excel spreadsheet to upgrade to a full O365. Even more, I was only able to save files to OneDrive and not locally. That was not what I originally paid for!
Our liveable breathable atmosphere is razor thin compared to the size of earth.
If earth were a grapefruit, our atmosphere would be ~1mm thick!
The problem for me is not getting my DNA sequenced but not having to trust a third party with my genetic information. As wirtten in the article, they only achieved a 13% coverage (even less if because you have to assume that not all base calls are correct), which is not useful for any sort of genetic analysis. So the title is really misleading.
Terence Tao uses a trick, I think he calls "structured procrastination": When there is a thing he doesn't want to do, he recalls another thing he doesn't want to do more. This way he's procrastinating on the other thing by doing the not favoured one.
The consensus here seems to be that Python is missing a pipe operator. That was one of the things I quickly learned to appreciate when transitioning from Mathematica to R. It makes writing data science code, where the data are transformed by a series of different steps, so much more readable and intuitive.
I know that Python is used for many more things than just data science, so I'd love to hear if in these other contexts, a pipe would also make sense. Just trying to understand why the pipe hasn't made it into Python already.
This sort of thing is exactly like preventative whole body MRI scans. It's very noisy, very overwhelming data that is only statistically useful in cases we're not even sure about yet. To use it in a treatment program is witchcraft at this moment, probably doing more harm than good.
The child of a friend of mine has PTEN-Hamartom-Tumor-Syndrom, a tendency to develop tumors throughout life due to a mutation in the PTEN gene. The poor child gets whole body MRIs and other check-ups every half year. As someone in biological data science, I always tell the parents how difficult it will be to prevent false positives, because we don't have a lot of data on routine full body check-ups on healty people. We just know the huge spectrum on how healthy/ok tissue looks like.
I would plot the destination matrix as a jeatap where each row is a departure and each column an arrival and color is the number of trips. Additionally, you could cluster the rows and columns of this heatmap.
On a related note: Transporting a human in a car is (in relation to weight and size) like using a standard shopping cart to transport two 1L bottles of water. So the next time you walk through a pedestrian area, imagine everyone carrying a bag would use a shopping cart instead. That would be a huge traffic jam -- exactly like what you see on the road!
Could someone maybe give a high-level explanation into why commercial ILP solvers (e.g. Gurobi) are that much better than free/open-source ones? Is it because ILP is inherently that difficult to solve (I know it's NP-hard), that the best solvers are just a large ensemble of heuristics for very specific sub-problems and thus no general "good" strategy has made it's way into the public domain?
I had similar issues in the past. Setting up renv, everything seems good, but after working in a project for a few weeks and installing packages, renv constantly complains about the library being out-of-sync and resolving these complaints took way longer than new ones came around. I think renv has good intentions, but there are just too many edge cases (Bioconductor, installing an experimental package from Github, weird package dependencies etc.), that it always failed me in a real-world scenario.
Until there is a radically new version of {popular programming language} with breaking changes and no new and correct answers to train on.
Don't get me wrong, I love LaTeX, having written my PhD thesis in it. But with the current tools, I would use Quarto instead. It's much easier, you can still "inject" LaTeX and it's quicker for less technical collaborators to adapt.
most software, as we know it, will be replaced by AI systems that are trained rather than programmed
The problem with this are all the edge cases. There are more ways unforseen circumstances can arise as you can train for. That's why you should do a lot of input checks in production.
I had a look at your trial description (https://clinicaltrials.gov/study/NCT06424912)
Are you planning to publish the longitudinal data, esp. of endpoints 2 and 3 (prostate size, urinary flow). It would greatly add to the public understanding of this procedure. Why didn't you go for PSA? It's easy to obtain altough one probably wouldn't expect significant changes in this short time frame.
Why not mix R and Python in interactive analysis workflows: 1) Download positron: https://github.com/posit-dev/positron 2) Set up a quarto (.qmd) notebook 3) Set up R and Python code chunks in tour quarto document 4a) Use reticulate to spawn a Python session inside R and exchange objects beween both languages (https://github.com/posit-dev/positron/pull/4603) 4b) Write a few helper functions that pass objects between R and Python by reading/writing a temporary file.
I boosted my geography knowledge by playing Sporcle, e.g. https://www.sporcle.com/games/g/world
But in my explanation, there is no x axis.
Interesting insight why applying a log transform often makes data normally distributed: Pretty much all laws of nature are multiplications (F=ma, PV=nRT, etc). If you start with i.i.d random variables and multiply them, you get log-normal data by virtue of the central limit theorem (because multiplications are additions on a log scale; and the CLT is also somewhat robust to non iid-ness). Thinking of data as the result of a lot of multiplications of influential factors, we thus get a log-normal distribution.