Complexes

Two generalizations of the traveling salesman problem in which sites change their position in time are presented. The way the rank of different trajectory lengths changes in time is studied using the rank diversity. We analyze the statistical properties of rank distributions and rank dynamics and give evidence that the shortest and longest trajectories are more predictable and robust to change, that is, more stable. Trajectory stability in the traveling salesman problem Sergio Sánchez, Germinal Cocho, Jorge Flores, Carlos Gershenson, Gerardo Iñiguez, Carlos Pineda https://arxiv.org/abs/1708.06945

Traffic signals are ubiquitous devices that first appeared in 1868. Recent advances in information and communications technology (ICT) have led to unprecedented improvements in such areas as mobile handheld devices (i.e., smartphones), the electric power industry (i.e., smart grids), transportation infrastructure, and vehicle area networks. Given the trend towards interconnectivity, it is only a matter of time before vehicles communicate with one another and with infrastructure. In fact, several pilots of such vehicle-to-vehicle and vehicle-to-infrastructure (e.g. traffic lights and parking spaces) communication systems are already operational. This survey of autonomous and self-organized traffic signaling control has been undertaken with these potential developments in mind. Our research results indicate that, while many sophisticated techniques have attempted to improve the scheduling of traffic signal control, either real-time sensing of traffic patterns or a priori knowledge of tra

Self-organizing traffic lights have shown considerable improvements compared to traditional methods in computer simulations. Self-organizing methods, however, use sophisticated sensors, increasing their cost and limiting their deployment. We propose a novel approach using simple sensors to achieve self-organizing traffic light coordination. The proposed approach involves placing a computer and a presence sensor at the beginning of each block; each such sensor detects a single vehicle. Each computer builds a virtual environment simulating vehicle movement to predict arrivals and departures at the downstream intersection. At each intersection, a computer receives information across a data network from the computers of the neighboring blocks and runs a self-organizing method to control traffic lights. Our simulations showed a superior performance for our approach compared with a traditional method (a green wave) and a similar performance (close to optimal) compared with a self-organizing

We present a set of Matlab/Octave functions to compute measures of emergence, self-organization, and complexity applied to discrete and continuous data. These measures are based on Shannon’s information and differential entropy. Examples from different datasets and probability distributions are provided to show how to use our proposed code. Santamaría-Bonfil, G., Gershenson, C. & Fernández, N. (2017). A package for measuring emergence, self-organization, and complexity based on Shannon entropy. Frontiers in Robotics and AI , 4 :10. http://journal.frontiersin.org/article/10.3389/frobt.2017.00010/full

Highlights • The useful of quantitative indicators of ecological complexity is evaluated. • Chaos should not be confused with complexity. • Light and temperature cause different ranges of complexity in the gradient. • Homoeostasis variation is related to the seasonal changes and transitions. • Autopoiesis reveals groups with higher and lower degree of autonomy. Abstract Measuring complexity is fast becoming a key instrument to compare different ecosystems at various scales in ecology. To date there has been little agreement on how to properly describe complexity in terms of ecology. In this regard, this manuscript assesses the significance of using a set of proposed measures based on information theory. These measures are as follows: emergence, self-organization, complexity, homeostasis and autopoiesis. A combination of quantitative and qualitative approaches was used in the data analysis with the aim to apply these proposed measures. This study system

//Please forward to whom might be interested CCS'17: The Conference on Complex Systems 2017  Cancun, Mexico. September 17-22.  http://ccs17.unam.mx The flagship conference of the  Complex Systems Society  will go to Latin America for the first time in 2017. The Mexican complex systems community is enthusiast to welcome colleagues to one of our richest destinations: Cancun. The conference will include presentations by the recipient of the Nobel Prize in Chemistry Mario Molina (environment), Raissa D'Souza (network science), Ranulfo Romo (neuroscience), Jaime Urrutia-Fucugauchi (geophysics), Antonio Lazcano (origins of life), Marta González (human mobility), Dirk Brockmann (epidemiology), Kristina Lerman (information sciences), Stefano Battiston (economics), John Quackenbush (computational biology), Giovanna Miritello (data science), and more TBA. We invite abstract contributions (500 words maximum) for oral presentations or posters in the following tracks:

//Please forward to whom may be interested.

 The National Autonomous University of Mexico (UNAM) has an open call for postdoctoral fellowships to start in  September, 2017 . 

Candidates should have obtained a PhD degree within the last five years to the date of the beginning of the fellowship.
 There will be another call to begin March, 2018 closing around June 2017.
 The area of interests of candidates should fall within complex systems, networks, artificial life, urbanism, information, evolution, cognition, robotics, and/or philosophy.

 Interested candidates should send CV and a tentative project/research interests (1 paragraph) to cgg-at-unam.mx by  Tuesday, January 31st  (paperwork has to be made before February 3rd).  Postdoctoral fellowships are between one and two years (after renewal).
 Spanish is not a requisite.
 Accepted candidates would be working at the Computer Science Department ( http://turing.iimas.unam.mx  ) of the IIMAS ( http://www.iimas.unam.mx