Summer School on Complex Socio-Technical Systems, 4-8/09
#IFISC10years #SocioComplex17

https://sociocomplex2017.ifisc.uib-csic.es/

🎞 http://www.aparat.com/v/LphAK

Dr. Paul Hooper challenges Dr. Eleanor Power to discuss how religion and suffering bring people together while eating mouth scalding New Mexico green chile!

🔅 http://www.cidid.org/publications-1/2017/7/14/monte-carlo-profile-confidence-intervals-for-dynamic-systems

🔗 https://static1.squarespace.com/static/53d8f599e4b0ba978ec978d1/t/5969291bc534a5cc075a1e3f/1500064029155/20170126.full.pdf

Thomas House explains the maths behind memes

Whoosh! Whoosh!
#CollectiveBehavior

🗞 Structure-based control of complex networks with nonlinear dynamics

Jorge Gomez Tejeda Zañudo, Gang Yang, Réka Albert

🔗 http://pnas.org/content/114/28/7234.abstract.html?etoc

🚩 Significance
Many biological, technological, and social systems can be encoded as networks over which nonlinear dynamical processes such as cell signaling, information transmission, or opinion spreading take place. Despite many advances in network science, we do not know to what extent the network architecture shapes our ability to control these nonlinear systems. Here we extend a recently developed control framework that addresses this question and apply it to real networks of diverse types. Our results highlight the crucial role of a network’s feedback structure in determining robust control strategies, provide a dynamic-detail-independent benchmark for other control methods, and open up a promising research direction in the control of complex networks with nonlinear dynamics.

📌 ABSTRACT
What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework’s applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

🎞 http://www.aparat.com/v/tJos2

(slides: 👇)

When is a lead safe (or not)?
Insights from complex systems

Rayleigh Taylor instability in @sidefx #Houdini...

🔸 http://www.nature.com/news/lights-camera-crispr-biologists-use-gene-editing-to-store-movies-in-dna-1.22288?sf97829834=1&WT.mc_id=TWT_NatureNews

🔸 Using the physics of surface tension to model linguistic evolution and language boundary.

https://physics.aps.org/articles/v10/80

🗞 Maximum Entropy Flow Networks
Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham

🔗 https://arxiv.org/pdf/1701.03504

📌 ABSTRACT
Maximum entropy modeling is a flexible and popular framework for formulating statistical models given partial knowledge. In this paper, rather than the traditional method of optimizing over the continuous density directly, we learn a smooth and invertible transformation that maps a simple distribution to the desired maximum entropy distribution. Doing so is nontrivial in that the objective being maximized (entropy) is a function of the density itself. By exploiting recent developments in normalizing flow networks, we cast the maximum entropy problem into a finite-dimensional constrained optimization, and solve the problem by combining stochastic optimization with the augmented Lagrangian method. Simulation results demonstrate the effectiveness of our method, and applications to finance and computer vision show the flexibility and accuracy of using maximum entropy flow networks.

🔸 http://science.sciencemag.org/content/357/6347/138

Motion on the Rössler Attractor

🌀 Stochastic Dynamics out of Equilibrium
CEB Trimester, Institut Henri Poincaré, 2017

🎞 24 videos:
https://www.youtube.com/playlist?list=PL9kd4mpdvWcDWCAqqvRe1pOxYtNUqEjZW

🎯 It is common practice in statistical mechanics to use models of large interacting assemblies governed by #stochastic dynamics. In this context “equilibrium” is understood as stochastically (time) reversible dynamics with respect to a prescribed #Gibbs_measure. The IHP trimester “#Stochastic_Dynamics #Out_of_Equilibrium” will focus on various aspects of #nonequilibrium dynamics. #Non-reversible dynamics have features which cannot occur at #equilibrium and for which novel methods have to be developed. In the recent years there have been important advances in the three domains relevant to this trimester

- Transport in non-equilibrium statistical mechanics;
- Towards more efficient simulation methods;
- Life sciences.

and this has led to challenging open questions. This trimester aims at bringing together an audience coming from all the involved domains, to explore these new directions: physicists, mathematicians from various domains, computer scientists, as well as researchers working at the interface between biology, physics and mathematics.

http://www.ihp.fr/ceb/t2-2017
https://indico.math.cnrs.fr/e/stoneq17

🎞 F. Guerra “Equilibrium and off equilibrium properties of ferromagnetic and disordered statistical mechanics systems”

http://www.aparat.com/v/9tp6c