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).

The authors used deep learning to simulate the dynamics of gas accreting onto a black hole, i.e., black hole weather forecasting.

They trained the model (U-Net) with frames from numerical solutions of the hydrodynamical equations.
Numerical simulations are time-consuming. A simple simulation can take as long as 7 days to finish. If we go with more complex simulations, this time may increase.

We want to investigate if deep learning can be a new method to simulate accurately in less time!
In the paper, they discussed two examples:

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
In the first example, they trained the model with a single system and analyzed how the model simulates by iterative predictions.

The result is that the model can simulate up to 8e4 gravitational time accurately with a speed-up of 30000x faster!
In the 2nd example, they fed the model seven different simulations with the same physics but other initial conditions. They informed the model how the initial conditions differ from one to another.

However, they hid one system to understand the generalization power of the model!
They analyzed how the model can simulate an unseen system only by looking at previous systems!

It simulated the unseen system for 4e4 gravitational time, showing that the model can generalize the black hole physics presented in the dataset!
In the second example, the model can also simulate the systems it learned from:
For more details, please check it out on arXiv: https://t.co/VBh3RQnhov

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.
@mugecevik is an excellent scientist and a responsible professional. She likely read the paper more carefully than most. She grasped some of its strengths and weaknesses that are not apparent from a cursory glance. Below, I will mention a few points some may have missed.
1/


The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
2/

The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/


The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/


The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/

You May Also Like

IMPORTANCE, ADVANTAGES AND CHARACTERISTICS OF BHAGWAT PURAN

It was Ved Vyas who edited the eighteen thousand shlokas of Bhagwat. This book destroys all your sins. It has twelve parts which are like kalpvraksh.

In the first skandh, the importance of Vedvyas


and characters of Pandavas are described by the dialogues between Suutji and Shaunakji. Then there is the story of Parikshit.
Next there is a Brahm Narad dialogue describing the avtaar of Bhagwan. Then the characteristics of Puraan are mentioned.

It also discusses the evolution of universe.(
https://t.co/2aK1AZSC79 )

Next is the portrayal of Vidur and his dialogue with Maitreyji. Then there is a mention of Creation of universe by Brahma and the preachings of Sankhya by Kapil Muni.


In the next section we find the portrayal of Sati, Dhruv, Pruthu, and the story of ancient King, Bahirshi.
In the next section we find the character of King Priyavrat and his sons, different types of loks in this universe, and description of Narak. ( https://t.co/gmDTkLktKS )


In the sixth part we find the portrayal of Ajaamil ( https://t.co/LdVSSNspa2 ), Daksh and the birth of Marudgans( https://t.co/tecNidVckj )

In the seventh section we find the story of Prahlad and the description of Varnashram dharma. This section is based on karma vaasna.
Master Thread of all my threads!

Hello!! 👋

• I have curated some of the best tweets from the best traders we know of.

• Making one master thread and will keep posting all my threads under this.

• Go through this for super learning/value totally free of cost! 😃

1. 7 FREE OPTION TRADING COURSES FOR


2. THE ABSOLUTE BEST 15 SCANNERS EXPERTS ARE USING

Got these scanners from the following accounts:

1. @Pathik_Trader
2. @sanjufunda
3. @sanstocktrader
4. @SouravSenguptaI
5. @Rishikesh_ADX


3. 12 TRADING SETUPS which experts are using.

These setups I found from the following 4 accounts:

1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4.


4. Curated tweets on HOW TO SELL STRADDLES.

Everything covered in this thread.
1. Management
2. How to initiate
3. When to exit straddles
4. Examples
5. Videos on