🏅 THE RENORMALIZATION GROUP AND CRITICAL PHENOMENA
Nobel lecture, 8 December 1982
by KENNETH G. WILSON

https://www.nobelprize.org/nobel_prizes/physics/laureates/1982/wilson-lecture.pdf

💎 David Tong:
Lectures on Statistical Physics

This is an introductory course on Statistical Mechanics and Thermodynamics given to final year undergraduates. They were last updated in May 2012. Full lecture notes come in around 190 pages. Individual chapters and problem sets can also be found below:
🔗 http://www.damtp.cam.ac.uk/user/tong/statphys.html

—------------------------------------------------
A second course on statistical mechanics, covering non-equilibrium phenomenon, can be found here:
🔗 http://www.damtp.cam.ac.uk/user/tong/kinetic.html

This is a graduate course on topics in non-equilibrium statistical mechanics, covering kinetic theory, stochastic processes and linear response. It is aimed at masters students and PhD students. The full set of lecture notes are around 100 pages.

💵 https://seekingalpha.com/amp/article/4117011-can-complexity-science-save-economic-day

🔖 From Ecology to Finance (and Back?): Recent Advancements in the Analysis of Bipartite Networks

🔗 https://arxiv.org/pdf/1710.10143

Mika J. Straka, Guido Caldarelli, Tiziano Squartini, Fabio Saracco

📌 ABSTRACT
Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analysis of their topological structures has revealed the ubiquitous presence of properties which seem to characterize many - apparently different - systems. Nestedness, for example, has been observed in plants-pollinator as well as in country-product trade networks. This has raised questions about the significance of these patterns, which are often believed to constitute a genuine signature of self-organization. Here, we review several methods that have been developed for the analysis of such evidence. Due to the interdisciplinary character of complex networks, tools developed in one field, for example ecology, can greatly enrich other areas of research, such as economy and finance, and vice versa. With this in mind, we briefly review several entropy-based bipartite null models that have been recently proposed and discuss their application to several real-world systems. The focus on these models is motivated by the fact that they show three very desirable features: analytical character, general applicability and versatility. In this respect, entropy-based methods have been proven to perform satisfactorily both in providing benchmarks for testing evidence-based null hypotheses and in reconstructing unknown network configurations from partial information. On top of that, entropy-based models have been successfully employed to analyze ecological as well as economic systems, thus representing an ideal, interdisciplinary tool to approach the study of bipartite complex systems.

✔️ https://www.quantamagazine.org/the-mathematical-shape-of-big-science-data-20131004/?utm_content=buffer95c19&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

🔖Phase Coexistence in Insect Swarms
Michael Sinhuber and Nicholas T. Ouellette

🔗 https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.178003

📌 ABSTRACT
Animal aggregations are visually striking, and as such are popular examples of collective behavior in the natural world. Quantitatively demonstrating the collective nature of such groups, however, remains surprisingly difficult. Inspired by thermodynamics, we applied topological data analysis to laboratory insect swarms and found evidence for emergent, material-like states. We show that the swarms consist of a core “condensed” phase surrounded by a dilute “vapor” phase. These two phases coexist in equilibrium, and maintain their distinct macroscopic properties even though individual insects pass freely between them. We further define a pressure and chemical potential to describe these phases, extending theories of active matter to aggregations of macroscopic animals and laying the groundwork for a thermodynamic denoscription of collective animal groups.

🎼 Emergence
http://radio.seti.org/episodes/Emergence

Your brain is made up of cells. Each one does its own, cell thing. But remarkable behavior emerges when lots of them join up in the grey matter club. You are a conscious being – a single neuron isn’t.

Find out about the counter-intuitive process known as emergence – when simple stuff develops complex forms and complex behavior – and all without a blueprint.

🔗 http://traffic.libsyn.com/arewealone/BiPiSci13-10-14.mp3

Guests:

👨🏻‍💼Randy Schekman - Professor of molecular and cell biology, University of California, Berkeley, 2013 Nobel Prize-winner
👨🏻‍💼Steve Potter - Neurobiologist, biomedical engineer, Georgia Institute of Technology
👨🏻‍💼 Terence Deacon - Biological anthropologist, University of California, Berkeley
👨🏻‍💼 Simon DeDeo - Research fellow at the Santa Fe Institute
👨🏻‍💼 Leslie Valiant - Computer scientist, Harvard University, author of Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World

BiPiSci13-10-14

💡 https://www.quantamagazine.org/the-atomic-theory-of-origami-20171031/

#Geometry #Mathematics #Origami #Phase_Transitions #Statistical_Physics #Topology

PhD program

http://sc.cs.cmu.edu/

🎞 Second-Order Phase Transitions: Beyond Landau-Ginzburg Theory
Zohar Komargodski (Weizmann)

http://media.physics.harvard.edu/video/html5/?id=COLLOQ_KOMARGODSKI_111416

🎞 Learning and Inference When There Is Little Data
Yasser Roudi (NTNU)

http://media.physics.harvard.edu/video/html5/?id=COLLOQ_ROUDI_050216

اولین #کارگاه #علم_داده:
"Big Data"

🗓 25آبان، 2 و 9 آذر 96
🕰 ساعت 9 الی 12:30
📍دانشكده فيزيك دانشگاه شهيد بهشتی

ثبت نام و اطلاعات تکمیلی:
http://sbuphysics.ir
http://rusherg.com

@sbu_physics

Percolation and cancer: The physics of network phase transitions can help illuminate the process of tumorigenesis

💡 http://www.nature.com/news/the-new-thermodynamics-how-quantum-physics-is-bending-the-rules-1.22937

1️⃣ But what *is* a Neural Network? | Deep learning, Part 1
🔗 https://www.aparat.com/v/XkQFy

2️⃣ Gradient descent, how neural networks learn | Deep learning, part 2
🔗 https://www.aparat.com/v/uvUxW

3️⃣ What is backpropagation and what is it actually doing? | Deep learning
🔗 https://www.aparat.com/v/EZ9RV

3️⃣*️⃣ Backpropagation calculus | Appendix to deep learning
🔗 https://www.aparat.com/v/0tSKg

#سمینارهای_هفتگی گروه سیستم‌های پیچیده و علم شبکه دانشگاه شهید بهشتی

🔹دوشنبه، ۱۵ آبان ماه، ساعت ۴:۰۰ - کلاس۱ دانشکده فیزیک دانشگاه شهید بهشتی.

@carimi