💡A brief history of deep learning, including the rise, fall, and renaissance of neural networks:
http://news.mit.edu/2017/explained-neural-networks-deep-learning-0414

Clear, informative article on #neural_networks and #deep_learning, with lots of good historical background and insights.

🗞 Modeling interactions between political parties and electors

F. Bagarello, F. Gargano

🔗 https://arxiv.org/abs/1704.05453

📌 ABSTRACT
In this paper we extend some recent results on an operatorial approach to the description of alliances between political parties interacting among themselves and with a basin of electors. In particular, we propose and compare three different models, deducing the dynamics of their related {\em decision functions}, i.e. the attitude of each party to form or not an alliance. In the first model the interactions between each party and their electors are considered. We show that these interactions drive the decision functions towards certain asymptotic values depending on the electors only: this is the {\em perfect party}, which behaves following the electors' suggestions. The second model is an extension of the first one in which we include a rule which modifies the status of the electors, and of the decision functions as a consequence, at some specific time step. In the third model we neglect the interactions with the electors while we consider cubic and quartic interactions between the parties and we show that we get (slightly oscillating) asymptotic values for the decision functions, close to their initial values. This is the {\em real party}, which does not listen to the electors. Several explicit situations are considered in details and numerical results are also shown.

Evolutionary dynamics on any population structure

http://www.nature.com/nature/journal/v544/n7649/full/nature21723.html

🎞 http://www.aparat.com/v/ocdvZ

🎞 http://www.aparat.com/v/B1VS4

#سمینارهای_هفتگی گروه سیستم‌های پیچیده و علم شبکه دانشگاه شهید بهشتی

🔹دوشنبه، ۰۴ اردیبهشت ‌ماه، ساعت ۴/۵ - کلاس ۴ دانشکده فیزیک دانشگاه شهید بهشتی

@carimi

#Clean your #Data online and easy:
http://vis.stanford.edu/wrangler/

💭 Albert-László Barabási on the diversity of networks
http://physicstoday.scitation.org/do/10.1063/PT.5.3050/full/

ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks. | bioRxiv
http://biorxiv.org/content/early/2017/03/23/119099

🎞 http://www.aparat.com/v/hMq0H

🗞 The Emergence of Consensus

Andrea Baronchelli

🔗 https://arxiv.org/pdf/1704.07767

📌 ABSTRACT
The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. This paper overviews the main dimensions over which the debate has unfolded and discusses some representative results, with a focus on those situations in which consensus emerges `spontaneously' in absence of centralised institutions. Covered topics include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks, and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.

💡 http://www.csmonitor.com/Science/Complexity/2017/0424/Why-predicting-the-future-is-more-than-just-horseplay

🌀 The Problem of Action at a Distance in Networks and the Emergence of Preferential Attachment from Triadic Closure

Jérôme Kunegis, Fariba Karimi, Jun Sun

🔗 https://arxiv.org/pdf/1408.0119

(Submitted on 1 Aug 2014 (v1), last revised 24 Apr 2017 (this version, v2))

📌 ABSTRACT
In this paper, we characterise the notion of preferential attachment in networks as action at a distance, and argue that it can only be an emergent phenomenon -- the actual mechanism by which networks grow always being the closing of triangles. After a review of the concepts of triangle closing and preferential attachment, we present our argument, as well as a simplified model in which preferential attachment can be derived mathematically from triangle closing. Additionally, we perform experiments on synthetic graphs to demonstrate the emergence of preferential attachment in graph growth models based only on triangle closing.

🌀 Deadline coming up!
Calling all young quantitative biologists/biophysicists:
there are post-doctoral positions open in ICTP's Quantitative Life Science section.

🔥 Apply by 30 April!

http://ictp.it/wqbq4

💻 http://theconversation.com/the-internet-is-enabling-scientists-to-understand-how-collective-memory-works-75883

System Dynamics Summer School

🌀 The case for safe zones in Syria
http://www.necsi.edu/research/ethnicviolence/safezones