davidktr

full paper here: // deleted. Looks like my choice of an anonymous file hoster was inappropriate.

Funny, just two hours ago I saw my first Starlink satellite train running across the sky. It felt spooky and futuristic at the same time.

No half-baked products? Have you ever tried using the translation feature in the book app? That is the most infuriating, dumb piece of software I have seen in a long time.

I went jogging in Brooklyn (Williamsburg) and stepped on a huge rat. Poor dude was just having breakfast in one of the garbage heaps on the sidewalk. Never experienced that in Europe before.

Have you tried it in the forest behind Apple?

So could we get members of Congress to write positive RT reviews?

Interesting perspective, thanks for sharing.

WallStreetMillenial on Youtube just had a nice explanation why comparing risk for self driving cars and human drivers is not that simple. At least for Tesla, the answer is "hell no".

https://youtu.be/wCJ7fNoEUsY?t=287

To add another source, the Atlantic had a piece on birthrates in South Korea last March: https://archive.li/Fm93M

Quoting from the article:

There are a lot of reasons people decide not to have a baby. Young Koreans cite as obstacles the high cost of housing in greater Seoul (home to roughly half the country’s 52 million citizens), the expense of raising a child in a hypercompetitive academic culture, and grueling workplace norms that are inhospitable to family life, especially for women, who are still expected to do the bulk of housework and child care. But these explanations miss a more basic dynamic: the deterioration in relations between women and men—what the Korean media call a “gender war.”

Unfortunately, there seems to be little commonality in how men and women see the issue. Hard to imagine any solution at all.

ctrl+f "real estate"

"Another economic factor in the falling birthrate is the rising price of real estate and a resulting increase in unmarried or late-marrying people. The tradition in Korea is for men to prepare a family home before marrying, and the rising cost of housing in recent years has thereby triggered a decline in marriage rates."

anyone can learn to fix them at home with very few tools

Everytime I read comments like this I feel dumb. I'm fixing my own bikes, but it takes a lot of time and quite a few tools. Just learning how to properly adjust the derailleur took me quite a few hours. Youtube university forgot to mention that Shimano GRX 400 front derailleur has intermediate gears.

Many bike parts are not really standardized. It seems almost impossible to know in advance whether some part fits on my bike, I mostly have to try. Also, manufacturers keep changing how certain things work, and then Youtube university may be misleading because they show a previous generation.

Parking where you want can be a problem. You won't have issues if you use a parking garage or park in the outskirts and then use public transportation.

If I understand your hypothesis correctly, the obvious counterexample is German. Germany had a very weak central authority for most of its history, a steady influx of non-native speakers, and still maintained its complexity.

In Finland, ethnicity is fairly homogenous, and so is culture and language. The only exceptions are the Sami people (10,000 in Finland) and Finns of historically Swedish ethnicity. So in that sense, the white people of Finland are very homogenous.

I strongly believe that US-based categories of ethnicity do not work well in European contexts. We also try to avoide the term "race", that has a bit of historical baggage over here.

But do "we" really ignore the tacit knowledge? I think you are wrong that "scientific literature" pretends those things.

The issue of "context of discovery" vs "context of justification" is a hundred years old. And modern accounts of measurement and modeling make it quite clear that there is much more going on than simply writing down numbers. Theories/hypotheses can come from anywhere, I don't think that is even disputed.

However, when doing science, maths, engineering, the question is often what people are even saying, and what counts as evidence that something works. You cannnot do this without abstractions, and at some point we'll have to agree what those abstractions are. Should we leave it to individual experience if LK-99 is a superconductor?

You don't need formal maths training to raise a barn or build a kayak because there is brutal feedback if you are wrong. You'll need maths training to learn how to be right about things that will not give you any feedback.

The Firefox addon Sponsorblock blocks almost 100% of those ads. Together with uBlock Origin, I have an almost ad-free Youtube experience.

I understood that reference.

To add some content: Dirk Müller is a German doom prophet who some day, eventually, will have predicted it all along.

If you have an hour to spare, I found this video by an experienced pilot extremely helpful: https://www.youtube.com/watch?v=e5AGHEUxLME

Deep down the brain is 100% discrete: Neurons are either firing or not. To me, the brain's biggest mistery is how it goes from this to doing all the analogue stuff, and ends up with our capacity to deal with symbols.

Imagine two scientists, Bob and Alice. Bob has spent the last 5 years examining a theory thoroughly. Now he can explain down to the last detail why the theory does not hold water, and why generations of researchers have been wrong about the issue. Unfortunately, he cannot offer an alternative, and nobody else can follow his long winded arguments anyway.

Meanwhile, Alice has spent the last 5 years making the best possible use of the flawed theory, and published a lot of original research. Sure, many of her publications are rubbish, but a few contain interesting results. Contrary to Bob, Alice can show actual results and has publications.

Who do you believe will remain in academia? And, according to public perception, will seem more like an actual scientist?

You just described the majority of scientific papers. A "working set of instructions" is not really feasible in most cases. You can't include every piece of hard- and software required to replicate your own setup.

As others said, markwhen is awesome, but I'm not sure I'd use it as a standalone tool. I would definitely pay for a VSCode and Obsidian plugin to easily integrate it in existing workflows.

Is there any way to accept pledges for such plugins to gauge how many people would really pay for it, and how much?

Thanks for the detailed answer, I really appreciate the insight. I work in research myself, so I'm familiar with the general constraints.

I was certainly unaware of the size of the data coming from the detectors. If speed is the argument that beats all others, I rest my case. From what I read on the root.cern website, root is a data analysis and simulation environment, so I was not aware of the aspect of prototyping for online use.

Because I spent a lot of time thinking how software development can work in an analysis heavy research environment, I still would like to comment on some of your points. To distribute binaries and source code, packages work very well for us. Especially if you want to reuse software components in unseen contexts, packages and a package registry makes the most sense.

The use case "re-run online algorithms in offline analysis" is a very familiar one. In my line of work, we do that daily: Switching between online and offline to test + deploy algorithms. Vastly smaller scale, of course. But to us, packages are the first part of the solution. All you do is change the data source. For offline, it's local data or a remote DB, for online, it's an interface such as a websocket.

The second part of the solution are unit- and integration tests. Other users will immediately see what you did (or didn't) test. Again, packages are the distribution system of choice. This has nothing to do with Matlab/Python/R/Julia. Rust has crates.io, JS has npmjs, even Java has something like Maven Central.

Regarding the funded-by-taxpayers argument: The issue I see here is that the cool ML, simulation, data analysis stuff which the CERN people do remains in the root ecosystem. If they used something like PyPI, I could use their stuff too. I have a lot of clustering problems, especially on time series. With a more or less proprietary system like root, I can't use any of CERN's implementations.

Regarding "researchers don't have time to learn new languages": If you look on github.com/root-project/root/issues and root-forum.cern.ch, there are suspiciously many questions regarding "how can I make use of root and Python libraries", and "X doesn't work in root, what do". Newbs have to learn root as well, and they seem to like using Python at least as an enhancement.

But do you really need these features, already available in Matlab/Python/R/Julia/Lisp/? Or did the the C++ folks simply refuse to learn other languages?

From what I have seen in R and Python, the main reason for speed issues are incompetent programmers. Certainly, bad C++ code is much faster than bad Python code, but there is also the effort to build/maintain/document/teach Root to noobs.

Hot take: It's really about preferences, not features.

Is that so? The most dignified, people with the most money, are also causing the most desctruction.

I find it odd to say people are fighting climate change if they actively contribute to climate's destruction. Doing it for a short time with a clearly stated end date would be one thing. However, almost nobody emits less than the crucial threshold of 2 t CO2/year.

At some point you have to admit that if you aren't doing it, you aren't doing it.

No, complete plays is simply their measure of user satisfaction. If I like a song, I usually listen to it until the end. If I do not like a song, I usually skip it after a few seconds.

How would you measure user satisfaction?

(not OP) Verifiability/falsifiability are big words, mostly it is not clear what that means in a specific case. Crucially, that is not what journals/editors/reviewers do. They check if they find the contribution convincing, novel, and in line with the discipline's community standards, nothing more.

And for this you cannot use Python's multiprocessing because ... ? Sure, moving data between processes is slow because of pickling [0]. However, I'm using parallel processing for the things you suggested, and for these it works great.

If I really had the use case and needed threads, I'd much rather use C++ bindings in a Python package than rebuilding the whole thing. Guess it depends on the scale we are talking about.

[0] https://pythonspeed.com/articles/faster-multiprocessing-pick...

Of course there are big problems with how we do science. Much of it is garbage. Imagine most software was written by junior engineers, without any code review or input from seniors. That is today's science.

Most scientific legwork is done by absolute beginners, i.e., graduate students. They often lack a support structure to focus on what they have learned so far. Most of the world is not Oxbridge, MIT, Stanford.

Where are the beginners' supervisors, you might ask? Chasing the latest trend to secure funding. Pondering how their line of research can be formulated as buzzword-du-jour markov chain. Ass kissing the dean to get department funding.

Having worked in research for 15 years, I am certain about two things: (1) The scientific method yields better results than doing things freehand. (2) Randomly axing 50% of academia would improve the situation.

Good luck defining what "full documentation" means.