OUR RESEARCHAlgorithms embedded in physical systems
The Alephsys Lab is part of the department of Computer Engineering and Mathematics at the "Universitat Rovira i Virgili" of Tarragona. The core of our research activity is aimed at investigating the laws governing the structure and dynamics of complex networked systems. We are actively researching on subjects such as:
Self Organized Systems
Winning European research money does not depend only on a well-funded research base. We find that it is also contingent on national governments' ability to retain their own scientists ('stickiness') and to attract others from abroad ('attractiveness').
We analysed statistical indicators of EU scientists' mobility for 2007–14 to determine the stickiness and attractiveness of different countries. We quantified attractiveness and stickiness as the relative difference between the numbers of incoming or remaining researchers, respectively, and of outgoing ones.
For both measures, we found that the higher the value, the better were that country's chances of securing European research funding. The United Kingdom and Sweden are examples of high scorers in both; Italy is among the lowest.
We conclude that there is a 'rich-get-richer' effect for countries that have high attractiveness and stickiness scores. Those nations also boast a high gross domestic product per capita and tend to invest more in research and development. This means that they can lure and retain the best researchers by providing competitive salaries and a guaranteed future in research.
To represent election results, as well as many other types of human data, it is crucial to construct maps where the size of geographic areas, e.g. administrative units as municipalities, is proportional to their population. This is not the case for standard maps, and special algorithms are needed to obtain "cartograms", i.e. maps deformed according to some desired demographic property.
The city space is represented in grids. Each layer represent high or low levels of some particular aspect. These data can come from data sources of different nature (e.g. institutional, crowdsourced, ...) Each person can decide how much each level matters to him/her. A normalized weighted sum of all layers, tailored upon the weights decided by the users, gives a personalized layer that tells him which areas to prefer and which to avoid, solely based on his preferences and the city data sources. We call this the combined individual layer.