They trained the model (U-Net) with frames from numerical solutions of the hydrodynamical equations.
Today, we are bringing other exciting results involving black holes and AI. We released a new paper:
"Black Hole Weather Forecasting with Deep Learning: A Pilot Study"
Work by Roberta Duarte (@import_robs), Rodrigo Nemmen (@nemmen) and João Paulo Navarro (from @NVIDIABrasil).
They trained the model (U-Net) with frames from numerical solutions of the hydrodynamical equations.
We want to investigate if deep learning can be a new method to simulate accurately in less time!
1- The model simulating only one system after learning only from this system
2- The model simulating an unseen system after training with several systems with different initial conditions
The result is that the model can simulate up to 8e4 gravitational time accurately with a speed-up of 30000x faster!
However, they hid one system to understand the generalization power of the model!
It simulated the unseen system for 4e4 gravitational time, showing that the model can generalize the black hole physics presented in the dataset!
More from Science
Hard agree. And if this is useful, let me share something that often gets omitted (not by @kakape).
Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.
You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.
The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.
In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.
The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.
Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.
Feels like the next thing we're going to need is a ranking system for how concerning "variants of concern\u201d actually are.
— Kai Kupferschmidt (@kakape) January 15, 2021
A lot of constellations of mutations are concerning, but people are lumping together variants with vastly different levels of evidence that we need to worry.
You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.
The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.
In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.
The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.
JUST ONE PERSON—UK 🇬🇧 scientists think one immunocompromised person who cleared virus slowly & only partially wiped out an infection, leaving behind genetically-hardier viruses that rebound & learn how to survive better. That’s likely how #B117 started. 🧵 https://t.co/bMMjM8Hiuz
2) The leading hypothesis is that the new variant evolved within just one person, chronically infected with the virus for so long it was able to evolve into a new, more infectious form.
same thing happened in Boston in another immunocompromised person that was sick for 155 days.
3) What happened in Boston with one 45 year old man who was highly infectious for 155 days straight before he died... is exactly what scientists think happened in Kent, England that gave rise to #B117.
4) Doctors were shocked to find virus has evolved many different forms inside of this one immunocompromised man. 20 new mutations in one virus, akin to the #B117. This is possibly how #B1351 in South Africa 🇿🇦 and #P1 in Brazil 🇧🇷 also evolved.
5) “On its own, the appearance of a new variant in genomic databases doesn’t tell us much. “That’s just one genome amongst thousands every week. It wouldn’t necessarily stick out,” says Oliver Pybus, a professor of evolution and infectious disease at Oxford.
2) The leading hypothesis is that the new variant evolved within just one person, chronically infected with the virus for so long it was able to evolve into a new, more infectious form.
same thing happened in Boston in another immunocompromised person that was sick for 155 days.
3) What happened in Boston with one 45 year old man who was highly infectious for 155 days straight before he died... is exactly what scientists think happened in Kent, England that gave rise to #B117.
Immunocompromised 45 year old suffered from #COVID19 for 155 days before he died. The virus was changing very quickly inside the man's body\u2014it acquired a big cluster of >20 mutations\u2014resembled the same ones seen in #B117 & #B1351. (NPR audio Part 1 of 2)\U0001f9f5https://t.co/7kWiBZ1xGk pic.twitter.com/ZJ7AExB78Y
— Eric Feigl-Ding (@DrEricDing) February 8, 2021
4) Doctors were shocked to find virus has evolved many different forms inside of this one immunocompromised man. 20 new mutations in one virus, akin to the #B117. This is possibly how #B1351 in South Africa 🇿🇦 and #P1 in Brazil 🇧🇷 also evolved.
2) NPR report audio part 2 of 2:
— Eric Feigl-Ding (@DrEricDing) February 8, 2021
Dr. Li couldn't believe what they found. "I was shocked," he says. "When I saw the virus sequences, I knew that we were dealing with something completely different and potentially very important." pic.twitter.com/HT3Yt6djFd
5) “On its own, the appearance of a new variant in genomic databases doesn’t tell us much. “That’s just one genome amongst thousands every week. It wouldn’t necessarily stick out,” says Oliver Pybus, a professor of evolution and infectious disease at Oxford.
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One of those strategies which I like is Iron Fly✈️
Few important points on Iron fly stategy
This is fixed loss🔴 defined stategy ,so you are aware of your losses . You know your risk ⚠️and breakeven points to exit the positions.
Risk is defined , so at psychological🧠 level you are at peace🙋♀️
How to implement
1. Should be done on Tuesday or Wednesday for next week expiry after 1-2 pm
2. Take view of the market ,looking at daily chart
3. Then do weekly iron fly.
4. No need to hold this till expiry day .
5.Exit it one day before expiry or when you see more than 2% within the week.
5. High vix is preferred for iron fly
6. Can be executed with less capital of 3-5 lakhs .
https://t.co/MYDgWkjYo8 have R:2R so over all it should be good.
8. If you are able to get 6% return monthly ,it means close to 100% return on your capital per annum.
