Up to 9 #PhD contracts will be offered soon IFISC mallorca, including one contract to work with me and Miguel Cornelles at the interface of complex systems and machine learning. Please spread the word !! Details here 👉 lnkd.in/dEPy6fNq

اینجا کدهای کتاب
An Introduction to Modeling Neuronal Dynamics, Christoph Börgers 2017
را به صورت یک پکیج پایتون پیاده سازی کردم که میشه کدها رو به صورت آنلاین و بدون نصب پکیجی و مستقل از سیستم عاملی که استفاده می کنید اجرا کرد.
کتاب خوبی برای یادگیری هست. پکیج میتونه هنگام تدریس استفاده شود.

ده فصل از کتاب آماده شده. باقی فصل ها به زودی اضافه می شود.

https://github.com/Ziaeemehr/mndynamics/tree/main/mndynamics/examples

Defining physicists’ relationship with AI

As physicists are increasingly reliant on artificial intelligence (AI) methods in their research, we ponder the role of human beings in future scientific discoveries. Will we be guides to AI, or be guided by it?

https://www.nature.com/articles/s42254-022-00544-1

If you (or your students) are interested in #PhD or #postdoc positions at Aalto University on topics related to the Web & its impact on individuals/society, contact me!

http://www.juhikulshrestha.com/

How did complex systems emerge from chaos? Physicist Sean Carroll explains.

When you cast a visible light shadow you also cast a thermal shadow. But while the former disappear when you walk away, an infrared thermography shows you the latter staying on the wall.

[source: https://buff.ly/3RsB6r4]

"A Compact Introduction to Fractional Calculus" (by Alexander I. Zhmakin): https://arxiv.org/abs/2301.00037

"A Compact Introduction to Fractional Calculus is presented including basic definitions, fractional differential equations and special functions."

"Information content of note transitions in the music of J. S. Bach"
Suman Kulkarni, Sophia U. David, Christopher W. Lynn, Dani S. Bassett https://arxiv.org/abs/2301.00783

Music has a complex structure that expresses emotion and conveys information. Humans process that information through imperfect cognitive instruments that produce a gestalt, smeared version of reality. What is the information that humans see? And how does their perception relate to (and differ from) reality? To address these questions quantitatively, we analyze J. S. Bach's music through the lens of network science and information theory. Regarded as one of the greatest composers in the Western music tradition, Bach's work is highly mathematically structured and spans a wide range of compositional forms, such as fugues and choral pieces. Conceptualizing each composition as a network of note transitions, we quantify the information contained in each piece and find that different kinds of compositions can be grouped together according to their information content. Moreover, we find that Bach's music is structured for efficient communication; that is, it communicates large amounts of information while maintaining small deviations of the inferred network from reality. We probe the network structures that enable this rapid and efficient communication of information -- namely, high heterogeneity and strong clustering. Taken together, our findings shed new light on the information and network properties of Bach's compositions. More generally, we gain insight into features that make networks of information effective for communication.

Excellent video on Chaotic Dynamical Systems
https://www.youtube.com/watch?v=PDeN3iCtyNY

Playlist
Course Website: http://faculty.washington.edu/sbrunton/me564/

A life in statistical mechanics
An oral history interview on the lifelong involvement of Joel Lebowitz in the development of statistical mechanics.

Part 1: From Chedar in Taceva to Yeshiva University in New York
https://arxiv.org/abs/1702.04810

#Postdoc position on Network Epidemiology

https://iddjobs.org/jobs/postdoctoral-research-position-on-network-epidemiology?s=09

سلام
مثل سال‌های گذشته از هفته دیگه ما کلاس فشرده «روش‌های ریاضی در علم شبکه» رو در آلتو خواهیم داشت. این درس برای دانشجویان کارشناسی ارشد و دکتری رشته‌های محاسباتی طراحی شده. دوره شامل ۶ قسمت و هر قسمت متشکل از یک جلسه درس و دو جلسه حل تمرینه، یکی برای رفع اشکال و دیگری برای حل و فصل مسائل به صورت کامل. این درس امتحان نداره. در عوض یک پروژه برای تحویل دادن داره. اسلایدها و ویدیو ضبط شده هر جلسه کلاس درس (از سال گذشته) به همراه تمرین‌ها برای همگان به رایگان در دسترسه. نیومن، منبع اصلی این درسه و جزئیات بیشتر در نشانی زیر موجوده:
https://mycourses.aalto.fi/course/view.php?id=36677

من معلم حل تمرین این درس هستم. اگر کسی پیش‌نیازهای لازم رو بلده و علاقه‌مند به گذروندن این دوره‌س می‌تونه کلاس رو دنبال کنه و پاسخ تمرین‌ها و پروژه درس رو به ایمیل شخصی من ارسال کنه. من تلاشم رو می‌کنم تا در اولین فرصت اون‌ها رو تصحیح کنم و نتایجشون رو اطلاع بدم. اگر کسی مطابق با استاندارد این درس، دوره رو با موفقیت گذروند می‌تونم بهش دست‌خطی بدم که اگر جایی ارزش داشت، ازش استفاده کنه. بچه‌های رشته سیستم‌های پیچیده احتمالا این درس رو جذاب خواهند یافت :)

Mathematical Methods for Network Science
Department of Computer Science - Aalto University

Topics:
Basic models and the typical approaches in network science
Probability generating functions, Galton-Watson process, percolation threshold
Component size distributions (using PGF's)
Network evolution models and processes on networks
Exponential random graphs, block models
Message passing methods on complex networks

اگر کسی در دانشگاه‌های مختلف دوست داره به من کمک کنه، لطفا بهم پیام بده. @carimi

عباس ریزی abbas.sitpor.org

Notre Dame lecture about the dawn of the random walk

How can you stop a pandemic from sweeping the world? Can ancient Greek proportions predict the stock market? And why is learning to play chess so much easier for computers than learning to read a sentence?
https://youtu.be/r21X597Fays

"Introduction to Julia" presented by Jose Storopoli at JuliaCon 2022
Recording https://youtube.com/watch?v=uiQpwMQZBTA
This workshop is geared towards anyone who wants to start using Julia. It will be an extremely accessible overview of Julia.

Open #PhD position on machine learning and AI for public health. Come work with us! Details and how to apply here: https://soundai.sorbonne-universite.fr/dl/subjects/s/ff30ae/r/UUjfFIYDQSO6MIJTUPWMyA

deadline Jan 31

Available #PhD positions at NTNU math department:
PhD 1: [PDEs, SPDEs, mean field games, etc] + [neuro, stoch. control theory, etc]
PhD 2: [Comp/spatial/Bayesian stats, SPDEs, etc] + [fluid mechanics]

Deadline: 01/31/2023
https://www.jobbnorge.no/en/available-jobs/job/237922/2-phd-positions-in-the-project-imod-an-interdisciplinary-approach-to-data-based-modelling

Laplacian renormalization group for heterogeneous networks
Pablo Villegas, Tommaso Gili, Guido Caldarelli & Andrea Gabrielli


The renormalization group is the cornerstone of the modern theory of universality and phase transitions and it is a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its application to complex networks has proven particularly challenging, owing to correlations between intertwined scales. To date, existing approaches have been based on hidden geometries hypotheses, which rely on the embedding of complex networks into underlying hidden metric spaces. Here we propose a Laplacian renormalization group diffusion-based picture for complex networks, which is able to identify proper spatiotemporal scales in heterogeneous networks. In analogy with real-space renormalization group procedures, we first introduce the concept of Kadanoff supernodes as block nodes across multiple scales, which helps to overcome detrimental small-world effects that are responsible for cross-scale correlations. We then rigorously define the momentum space procedure to progressively integrate out fast diffusion modes and generate coarse-grained graphs. We validate the method through application to several real-world networks, demonstrating its ability to perform network reduction keeping crucial properties of the systems intact.

https://www.nature.com/articles/s41567-022-01866-8

A zoom lens for networks

Renormalization is a technique based on a repeated coarse-graining procedure used to study scale invariance and criticality in statistical physics. Now, an expansion of the renormalization toolbox allows to explore scale invariance in real-world networks.

https://www.nature.com/articles/s41567-022-01842-24

Check out our latest paper about our general purpose network library Reticula:
https://www.sciencedirect.com/science/article/pii/S2352711022002199

It natively supports (directed & undirected) (dyadic & hypergraph) (static & temporal) networks. C++ with Python bindings.

Install with pip on Python 3.8+:
$ python -m pip install -U reticula

Currently only supports Linux (glibc >= 2.17). Future windows + MacOS support is planned.

If this sounds interesting, do check out the documentation at reticula.network.

Modern computational studies of the glass transition

The physics of the glass transition and amorphous materials continues to attract the attention of a wide research community after decades of effort. Supercooled liquids and glasses have been studied numerically since the advent of molecular dynamics and Monte Carlo simulations, and computer studies have greatly enhanced both experimental discoveries and theoretical developments. In this #Review, we provide a modern perspective on this area. We describe the need to go beyond canonical methods when studying the glass transition — a problem that is notoriously difficult in terms of timescales, length scales and physical observables. We summarize recent algorithmic developments to achieve enhanced sampling and faster equilibration by using replica-exchange methods, cluster and swap Monte Carlo algorithms, and other techniques. We then review some major advances afforded by these tools regarding the statistical mechanical description of the liquid-to-glass transition, and the mechanical, vibrational and thermal properties of the glassy solid.

https://www.nature.com/articles/s42254-022-00548-x