💻 https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-096-introduction-to-c-january-iap-2011/

💥 One reason #fractional differential equations can be useful is that fractional derivatives contain non-local information.

A wonderful interpretation of the fractional laplacian is through long jump random walks: https://t.co/aWmJUCFKef

#opp

〽️ 4 postdoc positions in Complex Systems (IFISC, Mallorca)

IFISC offers four junior postdoc positions (1+1 years) to work in any of the research lines of its María de Maeztu program on Information processing in and by Complex Systems
L1: Information processing in biological systems
L2: Brain-inspired analog computing in photonic and electronic systems
L3: Quantum information: decoherence, dissipation, and transmission
L4: Information processing in socio-technical systems
Additional details and how to apply can be found at
🌐 https://ifisc.uib-csic.es/en/about-ifisc/join-us/open-positions-post-docs-2019/

⏱Deadline for application: January 15, 2019

🎲 @ComplexSys

Trending PhysRevE:

Information loss under coarse graining: A geometric approach

https://t.co/2v0DnakLFI

🌔 https://github.com/oxfordinternetinstitute/sds-python

🔔 #زنگ_پژوهش با موضوع "تحلیل بحران اقتصادی کشور از منظر فیزیک اقتصاد"
🗣 علی حسینی
🗓 یکشنبه، ۱۱ آذرماه
🕜 ساعت ۱۳:۳۰ تا ۱۴:۳۰
🏛 دانشکده‌ فیزیک دانشگاه شریف

© @anjoman_elmi_phys_sut
🎲 @ComplexSys

#سمینار_عمومی این هفته
ترسیم نقشه روشنفکری مطالعات خاورمیانه با استفاده از 《تحلیل هم‌استنادی نویسندگان》

- سه‌شنبه ۶ آذر؛ ساعت ۱۶ الی ۱۷
- تالار ابن هیثم، دانشکده‌ فیزیک

کانال انجمن علمی فیزیک بهشتی
@sbu_physics

The Master program "Neural Information Processing - Computational 
Neuroscience" covers theoretical and computational aspects of neuroscience.

Faculty include:
Peter Dayan, Matthias Bethge, Zhaoping Li, Martin Giese, Alexander 
Ecker, Philipp Berens, Fabian Sinz, Anna Levina and many more!

Students obtain extensive training in computational modeling of neural 
systems, machine learning data analysis and neuroscience. While the 
first year is dedicated to course work at the graduate level, the second 
year provides hands-on research experience in leading labs in lab 
rotations and during thesis work. After finishing the Master program, 
students can smoothly transition to our PhD program and continue their 
research.

The program provides research-oriented training in a wide spectrum of 
basic computational neuroscience topics with different options:

     machine learning for neuroscience and neural data analysis
     models of neural dynamics and coding
     motor control, rehabilitation robotics and brain-computer interfaces
     systems neuroscience and neurophysiology
     data science, bioinformatics and programming
     behaviour and cognition

The deadline for applications is January, 15th (written documents must 
be in Tübingen).

For more information please visit:
https://www.bccn-tuebingen.de/education/master-of-science-neural-information-processing.html

Please forward to interested students at the BSc level

Evoplex: A platform for agent-based modeling on networks
“extensible platform for developing agent-based models and multi-agent systems on networks”
https://t.co/wiFM5ADs2a

☄️ سنگ بنای مکانیک آماری شبکه‌های پیچیده در حقیقت این ایده بوده که «پیوندها» ذرات #موثر سیستم هستند و نه «رئوس»!


The Grand Canonical ensemble of weighted networks

Andrea Gabrielli, Rossana Mastrandrea, Guido Caldarelli, Giulio Cimini

https://arxiv.org/pdf/1811.11805

The cornerstone of statistical mechanics of complex networks is the idea that the links, and not the nodes, are the effective particles of the system. Here we formulate a mapping between weighted networks and lattice gasses, making the conceptual step forward of interpreting weighted links as particles with a generalised coordinate. This leads to the definition of the grand canonical ensemble of weighted complex networks. We derive exact expressions for the partition function and thermodynamic quantities, both in the cases of global and local (i.e., node-specific) constraints on density and mean energy of particles. We further show that, when modelling real cases of networks, the binary and weighted statistics of the ensemble can be disentangled, leading to a simplified framework for a range of practical applications.

#سمینارهای_هفتگی مرکز شبکه‌های پیچیده و مردم‌شناسی دانشگاه شهید بهشتی

⏰ یکشنبه، ۱۱ آذر، ساعت ۱۶:۴۵
🏛 محل برگزاری: سالن ابن هیثم
@mhakim

Introduction to Renormalization
Lead instructor: Simon DeDeo

https://www.complexityexplorer.org/courses/67-introduction-to-renormalization

Syllabus

Introduction to Renormalization
Markov Chains
Cellular Automata
Ising Model
Krohn-Rhodes Theorem
A Classical Analogy for Renormalization in Quantum Electrodynamics
Conclusion: The Future of Renormalization & Rate Distortion Theory
Homework

🎞 https://youtu.be/gYq-3l-fgc0

I'm looking forward to introducing Bayesian past network inference to an interdisciplinary audience of network scientists and *archeologists* this Thursday. Connected Past is such a cool meeting idea! https://t.co/yx51QyS2ET

The 2019 Summer Institute in Computational Social Science will have a partner location in Istanbul https://t.co/bcMchnVVsj

🎞 https://www.aparat.com/v/SFQqk

🧩 Speaking of many-model thinking, where would science be without philosophy?

Here's our 2011 Miller Scholar, brilliant author Rebecca Goldstein, on Hidden Forces Podcast to discuss the enduring importance of unquantifiable big questions like, "What makes life worth living?"

https://www.hiddenforces.io/podcast/show/rebecca-goldstein-mattering-philosophy

🍭 https://www.coursera.org/specializations/mathematics-machine-learning?utm_medium=email&utm_source=marketing&utm_campaign=9CeGEPnDEeiJ46nv251fRw