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Fall is here but no colors in Toronto. Either green, or dropped.

   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman    

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Date: Mon, 23 Oct 2017 11:03:10 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
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To: "[utf-8] Barry" <[log in to unmask]>
Subject: [utf-8] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at 

Introduction to Focus Issue: Complex Cardiac Dynamics 

Even after over a century of active research, the heart continues to 
reveal new complexities in behavior and remains difficult to understand 
fully. Multi-scale dynamics ranging from cellular and subcellular behavior 
to chambers of the heart and the full organ make analysis complicated. In 
addition, different types of heart functions, including electrical wave 
signaling, mechanical contraction, and blood flow, present separate 
challenges. Theory, numerical modeling, and experiments provide different 
contributions to our understanding of cardiac processes and behavior. This 
Focus Issue includes papers from across all these spectra and addresses a 
number of interesting open questions regarding the complex dynamics of the 

Identifying Self-Organization and Adaptability in Complex Adaptive Systems

    Self-organization and adaptability are critical properties of complex adaptive systems (CAS), and their analysis provides insight into the design of these systems, consequently leading to real-world advancements. However, these properties are difficult to analyze in real-world scenarios due to performance constraints, metric design, and limitations in existing modeling tools. Several metrics have been proposed for their identification, but metric effectiveness under the same experimental settings has not been studied before. In this paper we present an observation tool, part of a complex adaptive systems modeling framework, that allows for the analysis of these metrics for large-scale complex models. We compare and contrast a wide range of metrics implemented in our observation tool. Our experimental analysis uses the classic model of Game of Life to provide a baseline for analysis, and a more complex Emergency Department model to further explore the suitability of these
metrics and the modeling and analysis challenges faced when using them.

Identifying Self-Organization and Adaptability in Complex Adaptive Systems

Lachlan Birdsey ; Claudia Szabo ; Katrina Falkner

Published in: Self-Adaptive and Self-Organizing Systems (SASO), 2017 IEEE 11th International Conference on

Source: ( )

The misleading narrative of the canonical faculty productivity trajectory

    Scholarly productivity impacts nearly every aspect of a researcher˙˙s 
career, from their initial placement as faculty to funding and tenure 
decisions. Historically, expectations for individuals rely on 60 years of 
research on aggregate trends, which suggest that productivity rises 
rapidly to an early-career peak and then gradually declines. Here we show, 
using comprehensive data on the publication and employment histories of an 
entire field of research, that the canonical narrative of ˙˙rapid rise, 
gradual decline˙˙ describes only about one-fifth of individual faculty, 
and the remaining four-fifths exhibit a rich diversity of productivity 
patterns. This suggests existing models and expectations for faculty 
productivity require revision, as they capture only one of many ways to 
have a successful career in science.

The misleading narrative of the canonical faculty productivity trajectory
Samuel F. Way, Allison C. Morgan, Aaron Clauset, and Daniel B. Larremore

Source: ( )

Network control principles predict neuron function in the Caenorhabditis elegans connectome 

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure˙˙function relationship in biological, social, and technological networks1, 2, 3. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans4, 5, 6, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation7, 8, 9, 10, 11, 12, 13, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow
us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Network control principles predict neuron function in the Caenorhabditis elegans connectomeNetwork control principles predict neuron function in the Caenorhabditis elegans connectome
Gang Yan, Petra E. Vértes, Emma K. Towlson, Yee Lian Chew, Denise S. Walker, William R. Schafer & Albert-László Barabási

Nature (2017) doi:10.1038/nature24056

Source: ( )

Mastering the game of Go without human knowledge 

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo˙˙s own move selections and also the winner of AlphaGo˙˙s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100˙˙0 against the
previously published, champion-defeating AlphaGo.

Mastering the game of Go without human knowledgeMastering the game of Go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel & Demis Hassabis

Nature 550, 354˙˙359 (19 October 2017) doi:10.1038/nature24270

Source: ( )

It might be argued that since Alpha Go learned from human knowledge and Alpha Go Zero learned from Alpha Go, then Alpha Go Zero does require (indirect) human knowledge. Still, the results are impressive and relevant.

Interview about the Conference on Complex Systems 2017

    In this episode, Haley interviews Dr. Carlos Gershenson who is a research professor, Editor-in-Chief of Complexity Digest, and a Co-Chair member of the Conference on Complex Systems. Dr. Gershenson discusses this year's conference and how it is relevant to events happening in the world today. He also shares details about next year's Conference on Complex Systems.

Source: ( )

Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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