Heatmaps in soccer: event vs tracking datasets

We investigate how similar heatmaps of soccer players are when constructed from (i) event datasets and (ii) tracking datasets. When using event datasets, we show that the scale at which the events are grouped strongly influences the correlation with the tracking heatmaps. Furthermore, there is an optimal scale at which the correlation between event and tracking heatmaps is the highest. However, even at the optimal scale, correlations between both approaches are moderate. Furthermore, there is high heterogeneity in the players' correlation, ranging from negative values to correlations close to the unity. We show that the number of events performed by a player does not crucially determine the level of correlation between both heatmaps. Finally, we analyzed the influence of the player position, showing that defenders are the players with the highest correlations while forwards have the lowest.Comment: 6 pages, 5 figure

Insulator materials for interface passivation of Cu(In,Ga)Se2 thin films

In this work, Metal-Insulator-Semiconductor (MIS) structures were fabricated in order to study different types of insulators, namely, aluminum oxide (Al2O3), silicon nitride (Si3Nx) and silicon oxide (SiOx) to be used as passivation layers in Cu(In,Ga)Se2 (CIGS) thin film solar cells. The investigated stacks consisted of SLG/Mo/CIGS/insulator/Al. Raman scattering and Photoluminescence measurements were done to verify the insulator deposition influence on the CIGS surface. In order to study the electrical properties of the CIGS-insulator interface, capacitance vs. conductance and voltage (C-G-V) measurements were done to estimate the number and polarity of fixed insulator charges (Qf). The density of interface defects (Dit) was estimated from capacitance vs. conductance and frequency (C-G-f) measurements. This study evidences that the deposition of the insulators at high temperatures (300 ºC) and the use of sputtering technique cause surface modification on the CIGS surface. We found that, by varying the SiOx deposition parameters, it is possible to have opposite charges inside the insulator, which would allow its use in different device architectures. The material with lower Dit values was Al2O3 when deposited by sputtering.publishe

Can multilayer brain networks be a real step forward?: Comment on “Network science of biological systems at different scales: A review” by M. Gosak et al

Various aspects of functional brain activity seem to capture genuine aspects of the functional organization of brain networks, making a MN representation more than a convenient representation tool. However, a series of fundamental problems arise with this new approach, which make the interpretation of multilayer brain networks a terra incognita that will need to be explored in the near future

Brain synchronizability, a false friend

Synchronization plays a fundamental role in healthy cognitive and motor function. However, how synchronization depends on the interplay between local dynamics, coupling and topology and how prone to synchronization a network is, given its topological organization, are still poorly understood issues. To investigate the synchronizability of both anatomical and functional brain networks various studies resorted to the Master Stability Function (MSF) formalism, an elegant tool which allows analysing the stability of synchronous states in a dynamical system consisting of many coupled oscillators. Here, we argue that brain dynamics does not fulfil the formal criteria under which synchronizability is usually quantified and, perhaps more importantly, this measure refers to a global dynamical condition that never holds in the brain (not even in the most pathological conditions), and therefore no neurophysiological conclusions should be drawn based on it. We discuss the meaning of synchronizability and its applicability to neuroscience and propose alternative ways to quantify brain networks synchronization

Successful strategies for competing networks

Competitive interactions represent one of the driving forces behind evolution and natural selection in biological and sociological systems. For example, animals in an ecosystem may vie for food or mates; in a market economy, firms may compete over the same group of customers; sensory stimuli may compete for limited neural resources to enter the focus of attention. Here, we derive rules based on the spectral properties of the network governing the competitive interactions between groups of agents organized in networks. In the scenario studied here the winner of the competition, and the time needed to prevail, essentially depend on the way a given network connects to its competitors and on its internal structure. Our results allow assessment of the extent to which real networks optimize the outcome of their interaction, but also provide strategies through which competing networks can improve on their situation. The proposed approach is applicable to a wide range of systems that can be modelled as networks. Copyright © 2013 Macmillan Publishers Limited. All rights reserved

Reconstructing functional brain networks: Have we got the basics right?

Both at rest and during the executions of cognitive tasks, the brain continuously creates and reshapes complex patterns of correlated dynamics. Thus, brain functional activity is naturally described in terms of networks, i.e., sets of nodes, representing distinct subsystems, and links connecting node pairs, representing relationships between them. Recently, brain function has started being investigated using a statistical physics understanding of graph theory, an old branch of pure mathematics (Newman, 2010). Within this framework, network properties are independent of the identity of their nodes, as they emerge in a non-trivial way from their interactions. Observed topologies are instances of a network ensemble, falling into one of few universality classes and are therefore inherently statistical in nature. Functional network reconstruction comprises various steps: first, nodes are identified; then, links are established according to a certain metric. This gives rise to a clique with an all-to-all connectivity. Deciding which links are significant is done by choosing which values of these metrics should be taken into account. Finally, network properties are computed and used to characterize the network. Each of these steps contains an element of arbitrariness, as graph theory allows characterizing systems once a network is reconstructed, but is neutral as to what should be treated as a system and to how to isolate its constituent parts. Here we discuss some aspects related to the way nodes, links and networks in general are defined in system-level studies using noninvasive techniques, which may be critical when interpreting the results of functional brain network analyses

Editorial: On the relation of dynamics and structure in brain networks

Despite more than a century-long effort, the functioning of the few-pound lump of white and grey matter that forms the brain remains at least partially a mystery. Physicists have made some significant contributions to the understanding of brain physiology, none perhaps more notable than Hodgkin and Huxley’s, who discovered the ionic basis of nerve cell conduction. But could they also help shedding light on how large numbers of neurons interact to give rise to sophisti- cated behaviour? Although complex, a neural system is in fact essentially a physical device meant to perform specific functions. As such, brain design must obey general engineering principles, which shape it at all scales from neuronal sub-components to the whole system scales. 1 Observable anatomy and physiology of the brain can be thought of as resulting from selective evolutionary pressures that managed trade-offs between energy consumption and adaptiveness, favouring energyefficient wiring and coding patterns 2,3 and ultimately resulting in a non-random spatial and temporal structure of brain anatomy and dynamics. Making sense of this structure is therefore key to our understanding of the emergence of brain function