💰 #PhD #postdoc

We are hiring doctoral and postdoctoral researchers at the Social Networks Lab of ETH Zürich.🇨🇭

Details and application: https://t.co/RAtrd5HL0T

Renormalization, writes David Tong, a theorist at the University of Cambridge, is “arguably the single most important advance in theoretical physics in the past 50 years.”

I agree! Ken Wilson "changed physics forever."

https://t.co/R4FbcVY6ie

💰 I am looking to hire a #postdoc (start date flexible) @SFUPhysics in Vancouver, to do #nonequilibrium #thermodynamics (theory, computation, and collaboration with experiments) applied to #molecularmachines. Details:
https://t.co/MX9ZqsKt5T

دو نوشته مهم برای کسانی که به تازگی در #کنکور #کارشناسی شرکت کرده‌اند و قصد #تحصیل در رشته #فیزیک را دارند:

🎯 چهارسال فیزیک!

🎯 کنکوری‌ها حواستان باشد جوگیر نشوید؛ در علم جایی برای جوگیرها نیست!

@sitpor

کوانتا مگزین نوشته جالبی منتشر کرده در مورد بازبهنجارش و اثرش روی فیزیک. شاید برای هر کسی که سر و کارش با فیزیکه، اوایل موضوعاتی مثل نسبیت و مکانیک کوانتومی ذهنشو درگیر کنه، اما به نظر من برای کسی که تجربه‌ بیشتری در فیزیک داشته باشه قطعا یکی از هیجان‌انگیزترین ایده‌ها، ایده بازبهنجارشه.

💡یک دوره کوتاه و مقدماتی در مورد بازبهنجارش در سیتپور وجود داره:
🔗 sitpor.org/complex-systems/renormalization/

برای یک مقدمه فنی‌تر می‌تونید به درس‌گفتارهای دیوید تانگ یا دکتر کریمی‌پور نگاه کنید.
—————
@sitpor

💡 nature.com/articles/s41567-020-1018-2

💡 nature.com/articles/s41567-020-0981-y

🎲 alexdanco.com/2020/04/09/life-is-made-of-unfair-coin-flips

Breaking news at #netsci2020: this year Euler Award has just been announced and it goes to Prof. ​Alessandro Vespignani!

https://t.co/j6njDNY7ye

#articles Generalized entropies, density of states, and non-extensivity

Sámuel G. Balogh, Gergely Palla, Péter Pollner & Dániel Czégel

Scientific Reports volume 10, Article number: 15516 (2020)

Abstract
The concept of entropy connects the number of possible configurations with the number of variables in large stochastic systems. Independent or weakly interacting variables render the number of configurations scale exponentially with the number of variables, making the Boltzmann–Gibbs–Shannon entropy extensive. In systems with strongly interacting variables, or with variables driven by history-dependent dynamics, this is no longer true. Here we show that contrary to the generally held belief, not only strong correlations or history-dependence, but skewed-enough distribution of visiting probabilities, that is, first-order statistics, also play a role in determining the relation between configuration space size and system size, or, equivalently, the extensive form of generalized entropy. We present a macroscopic formalism describing this interplay between first-order statistics, higher-order statistics, and configuration space growth. We demonstrate that knowing any two strongly restricts the possibilities of the third. We believe that this unified macroscopic picture of emergent degrees of freedom constraining mechanisms provides a step towards finding order in the zoo of strongly interacting complex systems.

#articles Pseudo-Darwinian evolution of physical flows in complex networks

Geoffroy Berthelot, Liubov Tupikina, Min-Yeong Kang, Bernard Sapoval & Denis S. Grebenkov

Scientific Reports volume 10, Article number: 15477 (2020)

Abstract
The evolution of complex transport networks is investigated under three strategies of link removal: random, intentional attack and “Pseudo-Darwinian” strategy. At each evolution step and regarding the selected strategy, one removes either a randomly chosen link, or the link carrying the strongest flux, or the link with the weakest flux, respectively. We study how the network structure and the total flux between randomly chosen source and drain nodes evolve. We discover a universal power-law decrease of the total flux, followed by an abrupt transport collapse. The time of collapse is shown to be determined by the average number of links per node in the initial network, highlighting the importance of this network property for ensuring safe and robust transport against random failures, intentional attacks and maintenance cost optimizations.

The 2020 ER Prize of the #netscisociety goes to Dr. Sonia Kefi

https://t.co/92HKGBfr0f

Three fantastic #postdoc positions at CSH Vienna in economics, health, and foundations of complex systems. Deadline October 31st. https://t.co/e75eVxApEK

On episode 60 of #WittyPodcast I talk to MIT Lecturer & Author about making computer science approachable to kids and adults, and teaching with virtual reality tools!

https://www.wittypod.com/episodes/anabell-mit-lecturer

📺 Italy 🇮🇹 was the first Western country to be heavily affected by #COVID19. The government & community, across all levels, reacted strongly & turned around the trajectory of the epidemic with a series of science-based measures. This video tells the story of 🇮🇹’s experience. https://t.co/ZjGNAuZnyl

#phd #postdoc

To young scientists interested in network epidemiology, I am hiring! happy to receive candidatures from any levels - undergrad to postdoc. Topics: multi-pathogen, covid19, flu, outbreak analysis. Write me if you are interested

https://chiara-poletto.github.io/

📺 Anthony Fauci delivers a 48-minute lecture for an MIT #covid19 class, taking stock about what we now understand about the disease. https://t.co/6Bi4ILhCN4

📺 از سخنرانی‌های ارائه شده در کنفرانس #netsci2020:

Efficient (limited time) reachability estimation in temporal networks

Arash Badie Modiri, Aalto University

How to measure extent of an spreading process from every possible starting point over a temporal network easily and efficiently? In this presentation we discuss an O(E Log E) method to do exactly that, using static event graphs and HyperLogLog cardinality estimator data structure.

📺 Video 🔗 Publication 🔗 Slides

Cite as: Badie-Modiri, A., Karsai, M., & Kivelä, M. (2020). Efficient limited-time reachability estimation in temporal networks. Physical Review E, 101(5), 052303.

💡 Errors in Probability: Continuous and Discrete Distributions 1. Classifying as discrete, continuous or mixed
These https://t.co/eaLziPAbSg