🗞 Maximum entropy sampling in complex networks

Filippo Radicchi, Claudio Castellano

🔗 https://arxiv.org/pdf/1703.03858

📌 ABSTRACT
Many real-world systems are characterized by stochastic dynamical rules where a complex network of dependencies among individual elements probabilistically determines their state. Even with full knowledge of the network structure and of the stochastic rules of the dynamical process, the ability to predict system configurations is generally characterized by large uncertainty. Sampling a fraction of the nodes and deterministically observing their state may help to reduce the uncertainty about the unobserved nodes. However, choosing these points of observation with the goal of maximizing predictive power is a highly nontrivial task, depending on the nature of the stochastic process and on the structure of the underlying network. Here, we introduce a computationally efficient algorithm to determine quasi-optimal solutions for arbitrary stochastic processes defined on generic sparse topologies. We show that the method is effective for various processes on different substrates. We further show how the method can be fruitfully used to identify the best nodes to label in semi-supervised probabilistic classification algorithms.

🔹Beyond Big Data: Identifying Important Information for Real World Challenges
http://necsi.edu/projects/yaneer/information/?platform=hootsuite

📄 http://www.nature.com/articles/srep43194

Two researcher positions in e-science http://www.uu.se/en/about-uu/join-us/details/?positionId=134648

Interdisciplinary PhD in Cognitive and Network science at Aix-Marseille University

🗞 http://pnas.org/content/114/11/2887.short?rss=1

👀 http://micheletizzoni.github.io/news/2017/03/13/phd-position.html

🗞 http://www.nature.com/articles/s41598-017-00160-5

⭕️ PhD position open, "Temporal networks: from network theory to brain science"

http://doc2amu.univ-amu.fr/en/temporal-networks-from-network-theory-to-brain-science

http://www.biophysics.org/2017mexico/Home/tabid/6979/Default.aspx
Emerging Concepts in Ion Channel Biophysics
October 10 - 13, 2017
Mexico City, Mexico

🔹 https://mystudentvoices.com/why-complexity-is-different-ecd498e0eccb#.o9q3xkcso

🔹 Teaching epidemiologists to code.
http://www.episkills.com/

⚔️ 💵 ♦️Game Theory I - Static Games
Lead instructor: Justin Grana

🔗 https://www.complexityexplorer.org/tutorials/69-game-theory-i-static-games


⚡️ About the Tutorial:
Game theory is the standard quantitative tool for analyzing the interactions of multiple decision makers. Its applications extend to economics, biology, engineering and even cyber security. Furthermore, many complex systems involve multiple decision makers and thus a full analysis of such systems necessitates the tools of game theory. This course is designed to provide a high-level introduction to static, non-cooperative game theory. The main goal of this course is to introduce students to the idea of a Nash Equilibrium and how the Nash Equilibrium solution concept can be applied to a number of scenarios. Students are assumed to be familiar with the concept of expected value and the basics of probability. While calculus is not required for the majority of the course, lesson 7 focuses on an example that employs calculus. However, lesson 7 can be skipped without any harm in understanding lessons 8 − 10.

🎞 http://www.aparat.com/v/OIlG5

💡 https://www.instagram.com/p/BRvxqUqDxkl/

🔹 Pulsing to their own beat: What's cuing salmon migration patterns? | Santa Fe Institute

https://www.santafe.edu/news-center/news/pulsing-their-own-beat-whats-cuing-salmon-migration-patterns

🔹 Fractals used to estimate depth of all the world's lakes.

http://www.sciencemag.org/news/2017/03/world-s-lakes-are-much-shallower-thought-mathematical-analysis-suggests?utm_source=newsfromscience&utm_medium=twitter&utm_campaign=deeplake-11848