"SIAM Conference on Applied Linear Algebra (LA21)": https://t.co/tphdkkMjO5

#SIAMLA21

"SIAM Conference on Applications of Dynamical Systems (DS21)": https://t.co/pgDv1M1nHM

💡 Now, researchers at DeepMind, a Google-owned artificial intelligence company, have used AI to study what’s happening to the molecules in glass as it hardens. DeepMind’s artificial neural network was able to predict how the molecules move over extremely long timescales, using only a “snapshot” of their physical arrangement at one moment in time. According to DeepMind’s Victor Bapst, even though the microscopic structure of a glass appears featureless, “the structure is maybe more predictive of the dynamics than people thought.”

https://www.quantamagazine.org/why-is-glass-rigid-signs-of-its-secret-structure-emerge-20200707/

Data science and the art of modelling

Hykel Hosni, Angelo Vulpiani

https://arxiv.org/abs/2007.04095

Datacentric enthusiasm is growing strong across a variety of domains. Whilst data science asks unquestionably exciting scientific questions, we argue that its contributions should not be extrapolated from the scientific context in which they originate. In particular we suggest that the simple-minded idea to the effect that data can be seen as a replacement for scientific modelling is not tenable. By recalling some well-known examples from dynamical systems we conclude that data science performs at its best when coupled with the subtle art of modelling

Fourth edition of "Machine Learning in Network Science"! Satellite @netsci2020. 19 Sept, 13-18 CET. Deadline for abstracts 31/07. All details here: https://t.co/gvpjL98I9g

I'll be teaching our intro to proofs class in the fall. This is where our students first learn LaTeX. I spent the last few days making this video for them, "A Quick Introduction to #LaTeX."

Here's a link to the video: https://t.co/4MgbqxHaU9.
The topics I cover are shown.

💡 https://www.quantamagazine.org/the-math-of-social-distancing-is-a-lesson-in-geometry-20200713/

🦄 نوشته‌هایی برای ورود به گرایش فیزیک سیستم‌های پیچیده:


🔸 پیچیدگی چیست؟

1️⃣ پروژه «پیچیدگی برای همه»
2️⃣ شرح پیچیدگی؛ دفترچه‌ای برای توضیح مفهوم پیچیدگی بر اساس آرا صاحب‌نظران این حوزه
3️⃣ سیستم‌های پیچیده: «ماهیت و ویژگی‌»
4️⃣ داستان پیچیدگی : «چرا بیشتر، متفاوت است؟»


🔹 یادگیری سیستم‌های پیچیده به طور حرفه‌ای

5️⃣ یادگیری سیستم‌های پیچیده رو از کجا و چه‌طور شروع کنیم؟!
6️⃣ پیشنهادهایی برای دانشجویان تحصیلات تکمیلی سیستم‌های پیچیده​
7️⃣ لیست اساتید دانشگاه ایران که روی موضوع سیستم‌های پیچیده کار می‌کنند

=======
@sitpor

Abstract: Thermodynamics provides a robust conceptual framework and set of laws that govern the exchange of energy and matter. Although these laws were originally articulated for macroscopic objects, nanoscale systems also exhibit “thermodynamic¬-like” behavior – for instance, biomolecular motors convert chemical fuel into mechanical work, and single molecules exhibit hysteresis when manipulated using optical tweezers. To what extent can the laws of thermodynamics be scaled down to apply to individual microscopic systems, and what new features emerge at the nanoscale? I will describe some of the challenges and recent progress – both theoretical and experimental – associated with addressing these questions. Along the way, my talk will touch on non-equilibrium fluctuations, “violations” of the second law, the thermodynamic arrow of time, nanoscale feedback control, strong system-environment coupling, and quantum thermodynamics.

The event is free and open to all, held on Zoom (pre-register)

Interesting new paper! Related questions about agency in/and/of networks has been gnawing at my side for years.
https://arxiv.org/abs/2007.05300

🎞 https://www.youtube.com/watch?v=2MOkFf7Luzw

🧑🏻‍🏫 The mobility network of scientists: analyzing temporal correlations in scientific careers

https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00279-x

The mobility of scientists between different universities and countries is important to foster knowledge exchange. At the same time, the potential mobility is restricted by geographic and institutional constraints, which leads to temporal correlations in the career trajectories of scientists. To quantify this effect, we extract 3.5 million career trajectories of scientists from two large scale bibliographic data sets and analyze them applying a novel method of higher-order networks. We study the effect of temporal correlations at three different levels of aggregation, universities, cities and countries. We find strong evidence for such correlations for the top 100 universities, i.e. scientists move likely between specific institutions. These correlations also exist at the level of countries, but cannot be found for cities. Our results allow to draw conclusions about the institutional path dependence of scientific careers and the efficiency of mobility programs.

All the slides and videos are now displayed on the ICTP activity page - Programme section.

These videos can also be accessed from the ICTP-QLS YouTube channel.

indico.ictp.it/event/9409/

#سخنرانی‌های_خوب
Prof. Chris Jarzynski on "Scaling Down the Laws of Thermodynamics"

این سخنرانی چندان فنی نبود که آدم تازه‌کار اذیت بشه. هر کسی که ترمودینامیک و مکانیک آماری کلاسیک رو خوب بلد باشه می‌تونه دنبال کنه. ایده اینه که ترمودینامیک اساسا برای سیستم‌های بزرگ‌مقیاس تشکیل شده از تعداد زیادی ذره نوشته میشه. اما آیا میشه برای سیستمی که در ابعاد نانومتری هم زندگی می‌کنه ترمودینامیک نوشت؟ بله، میشه! فقط تا حدودی ترمودینامیک آشنایی که می‌شناسیم باید تغییر کنه. آیا ملاحظات کوانتومی هم باید در نظر گرفته بشه؟ نه لزوما!

Abstract: Thermodynamics provides a robust conceptual framework and set of laws that govern the exchange of energy and matter. Although these laws were originally articulated for macroscopic objects, nanoscale systems also exhibit “thermodynamic¬-like” behavior – for instance, biomolecular motors convert chemical fuel into mechanical work, and single molecules exhibit hysteresis when manipulated using optical tweezers. To what extent can the laws of thermodynamics be scaled down to apply to individual microscopic systems, and what new features emerge at the nanoscale? I will describe some of the challenges and recent progress – both theoretical and experimental – associated with addressing these questions. Along the way, my talk will touch on non-equilibrium fluctuations, “violations” of the second law, the thermodynamic arrow of time, nanoscale feedback control, strong system-environment coupling, and quantum thermodynamics.

از آقای Jarzynski، درس‌گفتارها ویدیوهای کلاس فیزیک آماری غیرتعادلی در نشانی زیر وجود دارد:

Introduction to Nonequilibrium Statistical Physics
C. Jarzynski, Spring 2020

Analysis and microscopic modeling of systems away from thermal equilibrium. Linear response theory, ergodicity, Brownian motion, Monte Carlo modeling, thermal ratchets, far-from-equilibrium fluctuation relations. Introduction to the theoretical tools of nonequilibrium phenomena and their application to problems in physics, chemistry and biology.

tqdm: A fast, extensible progress bar

Instantly make your loops show a smart progress meter - just wrap any iterable with tqdm(iterable), and you're done!

https://tqdm.github.io/

Behavior change in economic and epidemic models

https://www.marcopangallo.it/blog/2020/07/13/behavior-change-in-economic-and-epidemic-models/

This post is for epidemiologists to understand what economists mean when they say that epidemic models should be “forward-looking”. And it is for economists to try and persuade them that incorporating behavior change in an “ad-hoc” fashion is just fine. I argue that all differences boil down to the type of mathematics that the two disciplines typically use – economists are used to “fixed-point mathematics”, epidemiologists to “recursive mathematics”. All in all, behavior change is incorporated by default in economic models, although in a highly unrealistic way; on the contrary, epidemiologists need to remind themselves to explicitly introduce behavior change, but when they do so they have the flexibility to make it much more realistic.

💉 15 July — Positive trial results raise hopes for a top vaccine candidate

https://www.nature.com/articles/d41586-020-00502-w?utm_source=twt_nnc&utm_medium=social&utm_campaign=naturenews&sf236003714=1

A leading COVID-19 vaccine candidate generates an immune response against the virus and causes few side effects, according to preliminary data from a phase I safety study with 45 participants.

The vaccine is being co-developed by Moderna in Cambridge, Massachusetts, and the US National Institute of Allergy and Infectious Diseases. It consists of RNA instructions that prompt human cells to make the virus’s spike protein, generating an immune response

Most side effects were mild, although three participants who got the highest dose experienced worse complications, such as a high fever.

After the injections, all participants produced immune proteins called antibodies capable of recognizing the SARS-CoV-2 virus, as well as ‘neutralizing antibodies’ that can block infection. A 30,000-participant phase III trial to test whether the vaccine can prevent COVID-19 is set to begin in late July.

In this session of Andreas Lauschke's #DataScience with #Mathematica series, he provides an introduction to Dynamic Programming for the data scientist. Watch the video, consisting of theory and illustrative examples, here:

https://youtu.be/MZnOL17CHdY

How does individual cognition influence collective behavior?

https://t.co/0ref9WD08Q https://t.co/OAesHffzcJ

https://www.pnas.org/content/early/2020/07/14/1920554117