Why not add a pressure relief valve on the quench path with a very loud whistle? That should be enough to take care of such rare and compounded failures.
What does recall means in this context? De-energizing the superconductor and shipping it back? Seems like a waste and a planning nightmare.
A bursting disc is commonly used -- the diameter of the quench pipe is typically around 20 - 30 cm. The gas flow rates are insane; a PRV would fail and likely still not reduce the pressure inside quickly enough.
Remember, cryostats are like Russian dolls suspended on torsion wire. You want the mass of the metal inside to be as low as possible because it forms cold bridges to the outside world and increases the boil-off rate. Quenches should not happen once the magnet leaves the factory, but until that point it's not uncommon for a machine to have several "training" quenches as the (typically NbSn or NbTi) superconducting wire effectively anneals in place. A fixable giant hole in the top (with a graphite, insulating series of bursting discs) is the approach usually taken.
I do not work with MRIs, but I work next to the guys who run the NMRs (which is the ~same technology). It is my understanding that all of these super cooled magnets are designed for the eventuality of an emergency quench. Which means the machine has a direct path to evacuate the gas, and it should be piped into the building's HVAC so that if a quench does happen, the people in the area do not suffocate because of lack of oxygen.
A surprising amount of maintenance can occur while the magnets are cold and energized. My armchair-uniformed-guess is that they can replace the not-always-working relief path without venting.
One approach is to perform the bitstream parsing and Huffman decoding concurrently, while carrying out the LZ77 decoding sequentially. This method does not rely on a specialized encoder, such as pigz, that isolates LZ references into chunks.
In neuroscience, predictive coding [1] is a theory that proposes the brain makes predictions about incoming sensory information and adjusts them based on any discrepancies between the predicted and actual sensory input. It involves simultaneous learning and inference, and there is some research [2] that suggests it is related to back-propagation.
Given that large language models perform some kind of implicit gradient descent during in-context learning, it raises the question of whether they are also doing some form of predictive coding. If so, could this provide insights on how to better leverage stochasticity in language models?
I'm not particularly knowledgeable in the area of probabilistic (variational) inference, I realize that attempting to draw connections to this topic might be a bit of a stretch.
This make me wonder if flat-earth and other limiting cosmological views are mental shields against the realization that nothing matters except our peculiar existence.
generally limited world views regardless of the facts are a mental shield of people afraid of something. the problem is, the fear is so deep beneath the crust of hatred and other negative emotions that getting to it and working with it is often practically impossible
What does recall means in this context? De-energizing the superconductor and shipping it back? Seems like a waste and a planning nightmare.