Lecturers: Marco Dentz and Juan Hidalgo (IDAEA-CSIC)
Postgraduate course addressed to physicists, chemists and engineers, organized in five 2-hour sessions:
- April 29: Introduction to transport in heterogeneous media.
- May 13: Langevin and Fokker-Planck equations
- May 20: Dispersion
- June 3: Continuous time random walks
- July 1: Trapping models
All sessions will take place on Monday, from 11 to 13 h, at room 3.20 (3rd floor of Physics UB, new building, campus Sud de Pedralbes).
Emilio Hernandez-Garcia, IFISC (CSIC-UIB, Palma de Mallorca)
Factors such as competition for water or nutrients or interactions with herbivores drive spatial instabilities in landscapes of terrestrial plants, resulting in pattern formation phenomena that have been a subject of intense research in the last years. Observations from air and side-scan sonar data have recently revealed analogous pattern forming phenomena in submerged vegetation in the Mediterranean Sea , mainly in meadows of seagrasses such as Posidonia oceanica and Cymodocea nodosa. Starting from growth rules of these clonal plants, we have derived a macroscopic model for the plant density that is able to provide an explanation to the observed submarine patterns of isolated ‘fairy circles’, and landscapes of spots and stripes. The essential ingredient is a competitive interaction at a distance of 20-30m. Beyond a qualitative description of the observed patterns, and their prevalence under different meadow conditions, the model fits well measurements of the population density of Posidonia, which show great variability close to the coast, where patterns typically appear.
Work done in collaboration with D. Ruiz, D. Gomila, T. Sintes, N. Marbà and C. Duarte
 D. Ruiz-Reynés, D. Gomila, T. Sintes, E. Hernández-García, N. Marbà and C.M. Duarte, Fairy circle landscapes under the sea, Science Advances 3, e1603262 (2017).
Prof. Manlio de Domenico (Fundazione Bruno Kessler - ICT)
Per què les notícies falses s'estenen més i més ràpid que les notícies reals? Quins són els bots socials i quin és el seu paper en la divulgació de la informació errònia? En aquesta xerrada respondrem aquest tipus de preguntes i mostrarem com és possible manipular l'opinió pública amb la nova ciència de les xarxes i la intel·ligència artificial. Com a cas pràctic, comentarem els fets ocorreguts en les xarxes socials durant el referèndum català de l'1 d'octubre de 2017. A la part final de la nostra xerrada, proporcionarem algunes pautes i contramesures per defensar-nos de la desinformació i la manipulació en línia.
Joaquín Goñi (Purdue University, USA)
In the 17th century, physician Marcello Malpighi observed the existence of patterns of ridges and sweat glands on fingertips. This was a major breakthrough and originated a long and continuing quest for ways to uniquely identify individuals based on fingerprints. In the modern era, the concept of fingerprinting has expanded to other sources of data, such as voice recognition and retinal scans. It is only in the last few years that technologies and methodologies have achieved high-quality data for individual human brain imaging, and the subsequent estimation of structural and functional connectivity. In this context, the next challenge for human identifiability is posed on brain data, particularly on brain networks, both structural and functional.
Here I present how the individual fingerprint of a human structural or functional connectome (as represented by a network) can be maximized from a reconstruction procedure based on group-wise decomposition in a finite number of orthogonal brain connectivity modes. By using data from the Human Connectome Project and from a local cohort, I also introduce different extensions of this work, including an extended version of the framework for inter-scanner identifiability, and an extended version of the framework for disentangling heritability and environmental brain network traits.
Jordi Piñero (UPF)
In this talk we will discuss the concept of ``liquid brains'' as the widespread class of cognitive living neural networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained. This stands in contrast with standard neural systems. How such a class of systems are capable of displaying cognitive abilities, from learning to decision-making? Collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics.
Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (``solid brains''), ant colonies and the immune system. We show that, in spite of their idiosyncratic differences, these systems do share key statistical properties with standard neural systems in terms of formal descriptions, while strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. Moreover, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting and adapting to external signals. Finally, we will also outline the computational and evolutionary aspects for the immune system as a liquid brain and its implications on the network structure and dynamics.