Complex Systems Studies
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What's up in Complexity Science?!
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#complexity #complex_systems #networks #network_science

📨 Contact us: @carimi
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aleta2018.pdf
5.1 MB
Aleta, A., & Moreno, Y. (2018). Multilayer Networks in a Nutshell. Annual Review of Condensed Matter Physics, 10(1). doi:10.1146/annurev-conmatphys-031218-013259
Sci-Hub
aleta2018.pdf
Complex systems are characterized by many interacting units that give rise to emergent behavior. A particularly advantageous way to study these systems is through the analysis of the networks that encode the interactions among the system constituents. During the past two decades, network science has provided many insights in natural, social, biological, and technological systems. However, real systems are often interconnected, with many interdependencies that are not properly captured by single-layer networks. To account for this source of complexity, a more general framework, in which different networks evolve or interact with each other, is needed. These are known as multilayer networks. Here, we provide an overview of the basic methodology used to describe multilayer systems as well as of some representative dynamical processes that take place on top of them. We round off the review with a summary of several applications in diverse fields of science.
Don’t miss this next #mtllectureseries on Wednesday: “Chemically Active Matter” presented by Ramin Golestanian from MPI for Dynamics and Self-Organization.

Registration under👇https://t.co/CMdSKLTieC
'Animal social networks - an introduction for complex systems scientists'.

https://t.co/JuoFgCjrjQ
👉Why do people respond so differently?
👉What do we know about immunity and how long it might last?
👉Has the virus developed any worrying mutations?
👉How well will a vaccine work?
👉What's the origin of the virus?


https://t.co/n5d5DqUBat
https://mhpc.it/how-apply

Standard applications for the academic year 2020/2021 are now open!

     Send your application: https://pica.cineca.it/sissa/sissa-ilas-mhpc-2020/

     DEADLINE: July 10, 2020 - 11:59 AM
💡 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.
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
🧑🏻‍🏫 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.