Sigh. This is a guy who is clearly passionate about his hobby and was excited to share it with us. Leave it to hn to shit all over it.
Some people fly to Africa and shoot elephants for fun - as far as hobbies go, this is fairly benign. If you care about the environment, I mean actually care, then you should recognize that policing how many computers someone have in their closet is counterproductive. I don't buy the "every little bit helps" argument because there is always an opportunity cost. Attention spans are limited and focusing on this non-issue does nothing but draw attention away from things that actually matter like meat consumption and fossil fuels.
Just to clarify: ArXiv is a pre-print server. By definition anything uploaded there has not (yet) undergone peer review. Posting papers there before they're published in a journal (which can take many months) is standard practice, not "bypassing publishing practices" or a "leak" per se. In this case there seems to be something fishy going on, but the fact that the papers were made public in this way is not strange in itself.
I also hear that it's not uncommon for superconductivity groups to intentionally include "errors" in their early drafts to avoid competitors copying their work before they're ready to publish.
Let me paint you a picture: You have data coming off the detectors at a rate of a couple of hundreds of GB/s (after pre-filters implemented in FPGAs etc) that needs to be processed and filtered in real time with output written to disk and tape at about one GB/s. We're talking really CPU intensive processing here: Kalman filters, clustering algortihms, evaluating machine learning models. The facility is one of a kind and operating cost is in the billions per year so downtime is unthinkable, this stuff needs to work. Offline, you're running very, very, detailed (and CPU heavy) simulations. All in all, you have some hundreds of petabytes of data that are constantly being processed and reprocessed for hundreds of different purposes. These systems have many millions of lines of code between them, a lot of which needs to be shared between them. Offline analysis needs to re-run online algorithms and so on - you need a single stack for all systems. You have some hundreds of thousands of CPU cores to run all of this. Due to how academia works, beyond a couple of large core datacenters, resources are mostly spread out in hundreds of locations globally so that each participating university can have maintain a cluster on their premises for teaching/research/funding reasons. You need an efficient way to get the data that a program needs to where it is running, or preferably move the program to where the data is. This is not a tech company, there's no revenue so throwing money at the problem is not an option - it's all funded by tax payers so efficiency is paramount. What language do you reach for? Matlab? Lol. The closest analogy I can think of are some big trading systems and large scale ML inference and content serving at FAANG and the like. That's all usually java or C++.
Oh, one more thing: There's very few professional developers dedicated to this. A lot of it is built and maintained by grad students and researchers in-between writing papers. They're smart people, and they can code, but they have neither time nor interest in learning a new language or framework every other year. They move around. A lot. It wouldn't work to have different tech stacks for different projects - you need to pick one solution, not just for one area but for the entire field. So people can spend less time learning and more time doing. There's no one available to migrate legacy code because some new cool language appeared or yesterday's cool library isn't maintained anymore. These projects run for decades. Whatever tech you pick you must be certain that it will still be around and supported 10, 20, 30 years later. That the code still runs and the data that you paid billions for can still be read.
Root is absolutely, mind-blowingly, amazing. It gets a bad rap because it forces you to use primitives that were designed back in the early nineties. If you're "just" trying to analyze some data, your experience will indeed be "horrible" compared to what's offered by Python, R, Matlab, or Julia. But beyond that... Root adds fully working reflection to C++. Root gives you dynamic library loading and reloading - you can fix a bug or add a new feature, recompile parts of your program and keep working without restarting it. Root has a feature complete C++ interpreter, with scripting and a REPL loop. You can work with it completely interactively. After prototyping you can save your code as a script. After identifying performance critical parts of your code, you can compile them and get the full power of bare metal C++, without changing anything about the code. Yes this is technically possible with e.g. python + numba as well, but not as straight-forward. Root is fully interoperable with Python and R - you can mix scripts and REPLs between the languages and pass objects between them. Root can serialize any object, without requiring any custom code whatsoever (some serious dark magic needed for this). In fact you can pause your entire program and save it to disk or send it over the network to keep running somewhere else. Root has its own file format for efficiently storing massive amounts of data in arbitrarily complex structures. It can stream it over the network too, with probabilistic read ahead and caching for maximum efficiency. Root comes with libraries for physics/math/stats that rival those of the largest commercial and open source offerings. Each one of these is a massive technical achievement and Root has had most of them for decades now. Oh, and it has largely maintained backwards compatibility through all this time as well.
Of course, very few people outside of CERN need all of this. Even within CERN, many projects don't. But for those who do, there are very few - if any - alternatives.
It doesn't really "generate" a field. The EM field is always there, permeating the universe. An acceleting electron will _disturb_ the EM field (depositing some energy and momentum into it), and this disturbance will propagate through the field at the speed of light (naturally, since at the right energy level such a disturbance is what we call light). At high energies the disturbance will be finely localized in space and behave like a particle, which we call a photon. It's fine to refer to it as such also at lower energies, but slightly misleading because at the very low energies that we talk about here ("radio") it is very spread out in space and behaves more like a wave (with a wavelength of ~meters). In the case of AC, electrons are moving "back and forth" over a short distance (somewhat simplified but useful picture) with the same effect. Think about moving your hand up and down through water - you will create a wave.
If you are looking for quality I suggest you look at the IPCC reports. Each word is carefully chosen, every claim backed by mountains of evidence. They're written to be read and understood by non-experts. They exist to inform decision making that will literally determine the fate of our species. As such, they may be failing at their goal, but not for a lack of effort by the authors.
Yeah this is spaced repetition. In this case the purpose is to memorize facts. But the asterisk method asks if you understand a concept. The purpose of revisiting asterisks is not to aid memorization via spaced repetition but to check if the understanding has improved after reading additional chapters. This seems like an important distinction.
The NZZ, for what it is worth, is probably the most highly regarded newspaper in Switzerland when it comes to journalistic integrity. It has a clear political bias to the right, but very high credibility.
It's true though: "common modern Yoga poses", as popularly practiced in the west and in India, are derived as much from Western gymnastics as they are from classical Hatha yoga, as the GP said. I don't think that that in any way denies the diversity of yoga, or its ancient roots. It's simply a fact that traditionally, asanas were only of marginal importance and they were almost exclusively seated poses intended for meditation. There is no downward dog in the medieval texts, no Surya Namasakar, no vinyasas...
Postural yoga was popularized in India in the 1930s. Hatha yoga had been out of fashion for centuries at that point. Famous teachers like Swami Kuvalayananda, Yogendra and Krishnamacharya revived it and incorporated the standing poses from contemporary gymnastics and, for the first time, movements.
For some, maintaining a connection to the Hatha roots were more important than others. Pattabhi Jois claimed that his Ashtanga yoga (the origin of modern Mysore style in India and Power yoga in the West) was based a 5000 year old sanskrit text that Krishnamacharya had discovered, but unfortunately it was "eaten by ants".
Cultures change over time and the Silk Road is a two-way street. We all learn from each other and that is, for the most part, a good thing.
Perhaps not as different as you think! Most of the "known matter" is indeed H and He drifting in otherwise "empty space". Stars, while very dense, are few and far between on cosmic scales. In terms of mass, the ratio is about 4:1. So you'd get quite similar answers from both calculations.
The LHC is in Switzerland (not the UK) and in fact I'm still here. I was a data scientist for while. These days I'm back in research, but in a different field.
There is a huge demand for CS skills of all kinds. CERN has one of the largest SCADA installations in the world, runs its own internet exchange point, operates a network of computing centers with a million cpu and hundreds of PB of data, and maintains many many millions of lines of code of specialized software, just to give you an idea.
It is a fantastic place to work, the level of expertise and dedication among the people there (scientists and engineers alike) is very very high.
3 sigma is termed "evidence" in particle physcis (while "discovery" is reserved for 5 sigma) and is often enough to get people interested. Keep in mind that these are just numbers and must be interpreted in context. There have been tensions in various heavy flavour measurements for a looong time and across multiple experiments. It is very plausible that this is where we'll finally see new physics at the LHC. So it is worth serious consideration.
The super-luminous neutrinos had 6 sigma significance and still no one took it seriously because it was simply such a ridiculous claim. Obviously the number of zeros in your p-value don't mean a damn if you haven't plugged in your equipment correctly!
I'm a physicist who spent seven years working at the LHC and I've never heard of a "purposeful" move away from the term beauty quark. It just fell out of favor but some, including myself, still like to use that name on occasion, personally I like it much better.
The particle does not have a name in the official (PDG) listing, only a symbol "b". The "bottom" quantum number is universally called as such so one talks of "bottom hadrons" and so on. On the other hand the general area of research is called "b-physics" or "beauty physics", LHCb is the LHC-beauty experiment and so on. It makes perfect sense that this article uses "beauty" throughout for consistency.
At the end of the day its just a name and this particle happens to have more than one (as does the J/psi and others) and neither is more whimsical than the other. Physicists tend to be quite whimsical anyway, particle physicists perhaps especially so.
My vision is good and I also don't know if the word you quoted was supposed to be in English, French of Irish? The point being, a screen reader has no more or less information than a human reader.
It might be better then to invest effort in improving language inference heuristics, which would help in all applications (pdf documents, text files) than to try to build in support in each underlying protocol.
> the fundemental approach to statistical analysis is flawed
This is a very strong assertion. What changes do you think are needed?
> With enough data, good enough filters, and a wide selection of adjustments for background processes, any model can be made to work.
Sure. Which is why no scientist will care that a particular signal model "can be made to work". You try everything you can to explain your data with only background processes and only if this fails do you consider alternatives. The more adjustments you allow, the harder it becomes to favor signal over background.
> Putting the blocks together is fundementally an excercise in bias, and truly limiting this bias requires significant discipline that is highly disincentivized and therefore uncommon
Another wild accusation without evidence. Why do you believe this is true? And if it is, where are all the false discoveries? In a small team with less oversight, I'm sure cutting corners happens. In a large experiment like those discussed here? No way. The embarrassment of having to retract a false discovery is a pretty strong incentive to ensure the integrity of results, and they have enough internal controls to enforce it.
The whole thing is just a big straw man argument. Well, actually the piece is pretty incoherent and I found it hard to find a red thread through the musings on such diverse topics as WIMPs, pentaquarks and the functioning of drift chambers. But the core of it seems to be at she thinks that scientists are now sitting on massive piles of data, mine through it looking for any oddities and then jump on the chance to publish a discovery of a new physical phenomenon as soon as one is found.
I can't judge whether she is just ignorant or deliberately misleading (she claims to have worked in physics), but this is just not how experiments are done. You almost always start with a theoretical prediction by a new theory, and there is no lack of theories out there so it has to be a pretty good one that has plenty of justification to merit attention enough to invest the 10+ person-years of effort it typically takes to conduct a rigorous analysis of a chunk of data from a modern large scale scientific instrument. You carefully demonstrate in various side bands your ability to correctly model every background process and instrumental effect that could influence the measurement. After that, if the data very clearly favors the new prediction over the null hypothesis, you may publish the discovery in order to invite other scientists to confirm or refute it. This does not mean that you or your peers accept that the new theory is correct.
And this is all supposedly in support of the thesis that large experimental facilities are a waste of money, which is patently nonsensical. They have been estimated to pay for themselves in direct returns alone (training of students and researchers, spin-off applications to silicon sensors, cryo technology, vacuum technology, lasers, accelerators, computing, etc). And the long term importance of investments in fundamental research are incalculable. For perspective, remember that the electron was considered a "useless" discovery back in 1897.
> the sloppy way with which science attribute importance to whatever is statistically significant.
But we don't. Just because an effect is statistically significant doesn't mean it could not also be explained by some systematic effect and physicists are very aware of that.
The famous five sigma rule prevents a claim of discovery without statistically significant evidence. It does NOT mean that we automatically accept every five sigma deviation from the background model as new physics.
Some people fly to Africa and shoot elephants for fun - as far as hobbies go, this is fairly benign. If you care about the environment, I mean actually care, then you should recognize that policing how many computers someone have in their closet is counterproductive. I don't buy the "every little bit helps" argument because there is always an opportunity cost. Attention spans are limited and focusing on this non-issue does nothing but draw attention away from things that actually matter like meat consumption and fossil fuels.