Actually, LLNL (the site of El Capitan) has a process for requesting Dedicated Application Time (a DAT) where you use up to a whole machine, usually over a weekend. They occur fairly regularly. Mostly it's lots of individual users and jobs, like you said though.
Steels can undergo a transition to becoming brittle when they get cold (called a “ductile-to-brittle” transition). It’s important to know what the properties would be like in this regime and -70C is enough to get there (even 0C can be enough, depending on the alloy).
The reason this person may have thought the -70C test eqs stupid is because a sub will never be working in conditions much colder than the freezing temperature of water (which is not strongly pressure dependent, btw), since the water would want to freeze - not good for the boat.
It's not "initial conditions" as in time=0. Thermodynamic state variables are path independent, so "initial conditions" in this context means the conditions of the reference point in the process from the point you start measuring.
At least for the AR-15 platform (the weapons pictured in the article), only the part that bears the serial number is regulated. Everything else can be bought freely and legally. For the AR-15, that part is the lower receiver.
A project I work on has a `RELEASING.rst` in the repo, which I include in the docs [1]. It contains a checklist of steps with the code. The actual deployment is automated with a GitHub Action to deploy to PyPI.
The checklist makes sure I hit all the things I need to do. There's still places to screw up the manual steps, replacing the right versions, etc. It's not perfect and there's a bunch of things I want to improve still. I try to write it clearly enough so that someone who's never released a project ever just needs to follow the steps.
> Due to the inhibition of van der Waals adhesion at the liquid interface, the electrode was remarkably resistant to deactivation via coking caused by solid carbonaceous species.
MOXIE is solid state technology and having solid C reaction products would very likely deactivate the cell by clogging up the triple phase boundaries where the reactions occur.
I use Goodreads, but only like a letterboxd for books. Anyone have a recommendation of something with a better experience than goodreads where I can track my read, reading and wanting to read?
Recommendations are not that important to me and “social” features even less so.
Think of it from the scientist’s perspective. You can’t ignore the work in journals who have business models and policies you do not agree with.
Pretending research doesn’t exist slows progress and it is anti-science to put your head in the sand because you don’t agree with the location that knowledge was published in.
If you did read the papers in journals you don’t agree with, built on their ideas and didn’t cite them, then you just committed plagiarism and obscured the scientific record.
The “link to the PDF of the author’s homepage” trick doesn’t work because eventually that page will go away, as we have seen over and over in the age of the web. Part of the value journals add is promising to archive the work _forever_. They don’t promise it will be free forever (or at all) - which is what needs to change.
The answer isn’t to “not cite”, but to not publish there in the first place. That takes systemic change. Change of both the incentives: “how do I get promomoted?” or, fundamentally: “how do I make an impact and measure it?”. Citations are the de facto standard right now. It will change when we can measure impact (and get more promotions, grants, etc.) in a way that doesn’t favor the richest journals getting richer.
> Will we reach a point where an M1-like full-desktop SOC includes enough processing, storage and graphics power that it satisfies any user's desktop/laptop needs?
I doubt it. It used to be that things requiring a cluster of computers 20 years ago now runs on desktops while clusters and HPC systems run even bigger calculations.
With more power, memory, and storage, things will trickle down and new approaches will take their place.
In my research, it would be great if I could use an algorithm scaling at N^5 which gives high accuracy, but it’s too expensive for most N, so pretty much everyone uses an N^3 scaling approach which is less accurate.
The first paragraph in the "Isolation of single emitters in silicon" section of the scientific paper[1] says that they "implanted" carbon ions into silicon. The rest of the paper discusses how they were able to detect these point defects (carbon atoms).
The outlook, as other commenters mentioned, is that this means we could theoretically detect other single atom point defects in silicon, such as impurities or quantum dots.
This is definitely compelling. I’m still going to try holding out until the Apple Silicon 16”, but my computer is more and more reminding me how long ago 2015 was.
Even though SymPy is written in Python, there are several options to go much faster. The simplest is when actually using the symbolic expression to do some calculation. For that SymPy has a `lambdify`, which can make a callable using NumPy as the backend. They also have a variety of code printers to write your expression in C/Fortran/Julia/<many others> that you can compile to be very fast.
There’s also the SymEngine project, which is a rewrite of SymPy in C++ with bindings to other languages (like Python where it is a drop-in API replacement for SymPy). It’s not finished yet, but the CAS is a lot faster and it’s lambdify can give you an internal representation or an LLVM-compiled version, both of which are much faster than SymPy in compile-time and run-time for our expressions (which are often quite large, but simple in terms of the mathematical functions used).