Complex Systems Studies

Doing Scientific Machine Learning (SciML) With Julia 07/26/2020, 5:00 PM — 8:30 PM GMT+3 Chris Rackauckas Scientific machine learning combines differentiable programming, scientific simulation (differential equations, nonlinear solvers, etc.), and machine learning (deep learning) in order impose physical constraints on machine learning and automatically learn biological models. Given the composibility of Julia, many have noted that it is positioned as the best language for this set of numerical techniques, but how to do actually "do" SciML? This workshop gets your hands dirty.

🛠 JuliaCon 2020 https://t.co/ueQZfLoI8L JuliaCon tutorials start today. "Julia has become the fastest-growing language for computational science, and many of the top numerical researchers recently adopted it and joined the worldwide collaboration."

Controlling a complex system near its critical point via temporal correlations https://www.nature.com/articles/s41598-020-69154-0 Here we test whether the time correlation properties allow systems exhibiting a phase transition to self-tune to their critical point. We describe results in three models: the 2D Ising ferromagnetic model, the 3D Vicsek flocking model and a small-world neuronal network model. We demonstrate that feedback from the autocorrelation function of the order parameter fluctuations shifts the system towards its critical point.

🎞 Football is becoming boring? A Network Science Approach by Taha Yasseri at IC2S2 2020. https://youtu.be/oQRxOGdQ2Fw Pre-print: Maimone, V. M., & Yasseri, T. (2019). Football is becoming boring; Network analysis of 88 thousands matches in 11 major leagues. arXiv preprint arXiv:1908.08991.

We have several frontend engineer positions open. We'd prefer to get at least one senior, but if you feel you are more a mid-level you're also welcome :) https://careers.deliveryhero.com/global/en/job/JR0000628/Senior-Frontend-Engineer-Marketing-Tech-f-m-d #deliveryhero #frontend #javascript #berlin

The Unreasonable Effectiveness of Mathematics in the Natural Sciences is an essay about mathematical concepts and their applicability written by Nobel Prize winning physicist Eugene Wigner. It's a great read: https://t.co/7Vivp1iQaH A great paper for your summer reading list 📚 https://t.co/4AYy4AZI9k

چهارشنبه ساعت۱۸:۳۰ به وقت تهران What don’t we know about #machinelearning? Dive in with this Joint ICTP-SISSA Online Colloquium tomorrow, 22 July, at 16:00 CET! Here’s all the info & how to join in 🎥 https://t.co/6VxNZLoy69

💉 https://twitter.com/UniofOxford/status/1285210154984710145?s=19

Simplicial complexes and dynamics - G. Bianconi - SIFS Colloquium https://t.co/S6ZWDGgy58

💡 https://www.quantamagazine.org/what-is-an-individual-biology-seeks-clues-in-information-theory-20200716/

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

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

💉 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.

Behavior change in economic and epidemic models http://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.

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/

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.

#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.

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/

🧑🏻‍🏫 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.

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