Author: cxdig

Complex Networks: Theory, Methods, and Applications | Lake Como School of Advanced Studies

Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline—including sociology, transportation, economics and finance, biology, and myriad others—and the study of “network science” has thus become a crucial component of modern scientific education.

The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.

 

Spring School
COMPLEX NETWORKS: THEORY, METHODS, AND APPLICATIONS
(4th edition)
Lake Como School of Advanced Studies
Villa del Grumello, Como, Italy, 14-18 May 2018

*** DEADLINE FOR APPLICATION: February 18, 2018 ***

Source: ntmd.lakecomoschool.org

What intelligent machines can learn from a school of fish

Science fiction visions of the future show us AI built to replicate our way of thinking — but what if we modeled it instead on the other kinds of intelligence found in nature? Robotics engineer Radhika Nagpal studies the collective intelligence displayed by insects and fish schools, seeking to understand their rules of engagement. In a visionary talk, she presents her work creating artificial collective power and previews a future where swarms of robots work together to build flood barriers, pollinate crops, monitor coral reefs and form constellations of satellites.

Source: www.ted.com

Estimating savings in parking demand whem using shared vehicles for home-work commuting

The increasing availability and adoption of shared vehicles as an alternative to personally-owned cars presents ample opportunities for achieving more efficient transportation in cities. With private cars spending on the average over 95\% of the time parked, one of the possible benefits of shared mobility is the reduced need for parking space. While widely discussed, a systematic quantification of these benefits as a function of mobility demand and sharing models is still mostly lacking in the literature. As a first step in this direction, this paper focuses on a type of private mobility which, although specific, is a major contributor to traffic congestion and parking needs, namely, home-work commuting. We develop a data-driven methodology for estimating commuter parking needs in different shared mobility models, including a model where self-driving vehicles are used to partially compensate flow imbalance typical of commuting, and further reduce parking infrastructure at the expense of increased traveled kilometers. We consider the city of Singapore as a case study, and produce very encouraging results showing that the gradual transition to shared mobility models will bring tangible reductions in parking infrastructure. In the future-looking, self-driving vehicle scenario, our analysis suggests that up to 50% reduction in parking needs can be achieved at the expense of increasing total traveled kilometers of less than 2%.

 

Estimating savings in parking demand whem using shared vehicles for home-work commuting
Dániel Kondor, Hongmou Zhang, Remi Tachet, Paolo Santi, Carlo Ratti

Source: arxiv.org

Call for Postdoctoral position in Data Science at Central European University

The Intellectual Theme Initiative “Just data” at Central European University is looking for an exceptional candidate to fill one open postdoctoral position in data science, broadly defined.

The research will be conducted with the research group directed by Prof. Roberta Sinatra and Prof. Michael Szell, with unique opportunities for collaborations with other excellent faculty at Central European University. The group currently focuses on quantitative projects at the boundary of computational social science and network science, including science of science, social dynamics, urban sustainability, data visualization, and fundamental questions in complex systems.

We welcome applicants with strong background in fields including statistical physics, applied mathematics, machine learning, artificial intelligence, social network analysis, complex systems, or other closely related fields. Our priority is to attract technically strong researchers who are interested in asking bold, new questions with data. A stated passion in data analysis is a must; excellent organizational and interpersonal skills are essential. The applicant will conduct research using large data sets, and be involved in the development of interactive data visualization tools.

Source: www.robertasinatra.com