16,907 research outputs found

    Time series irreversibility: a visibility graph approach

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    We propose a method to measure real-valued time series irreversibility which combines two differ- ent tools: the horizontal visibility algorithm and the Kullback-Leibler divergence. This method maps a time series to a directed network according to a geometric criterion. The degree of irreversibility of the series is then estimated by the Kullback-Leibler divergence (i.e. the distinguishability) between the in and out degree distributions of the associated graph. The method is computationally effi- cient, does not require any ad hoc symbolization process, and naturally takes into account multiple scales. We find that the method correctly distinguishes between reversible and irreversible station- ary time series, including analytical and numerical studies of its performance for: (i) reversible stochastic processes (uncorrelated and Gaussian linearly correlated), (ii) irreversible stochastic pro- cesses (a discrete flashing ratchet in an asymmetric potential), (iii) reversible (conservative) and irreversible (dissipative) chaotic maps, and (iv) dissipative chaotic maps in the presence of noise. Two alternative graph functionals, the degree and the degree-degree distributions, can be used as the Kullback-Leibler divergence argument. The former is simpler and more intuitive and can be used as a benchmark, but in the case of an irreversible process with null net current, the degree-degree distribution has to be considered to identifiy the irreversible nature of the series.Comment: submitted for publicatio

    The role of bot squads in the political propaganda on Twitter

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    Social Media are nowadays the privileged channel for information spreading and news checking. Unexpectedly for most of the users, automated accounts, also known as social bots, contribute more and more to this process of news spreading. Using Twitter as a benchmark, we consider the traffic exchanged, over one month of observation, on a specific topic, namely the migration flux from Northern Africa to Italy. We measure the significant traffic of tweets only, by implementing an entropy-based null model that discounts the activity of users and the virality of tweets. Results show that social bots play a central role in the exchange of significant content. Indeed, not only the strongest hubs have a number of bots among their followers higher than expected, but furthermore a group of them, that can be assigned to the same political tendency, share a common set of bots as followers. The retwitting activity of such automated accounts amplifies the presence on the platform of the hubs' messages.Comment: Under Submissio

    Self-Evaluation Applied Mathematics 2003-2008 University of Twente

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    This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

    Investigation of Causality Pattern Structure for the Exploration of Dynamic Time-Varying Behaviour

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    The analysis of time-varying interactions within multivariate systems has seen a great deal of interest within the last decade, with the international oil market being an archetypal and important system that demonstrates this behaviour. However, unlike work on static systems, research on time-varying systems rarely leverages specific information on the inter-system interactions for understanding the systems temporal dynamics. This thesis utilises this information to present methodologies for new descriptions of these systems, focussing on the international oil market. This is achieved via three experiments. The first experiment expands upon the state-of-the-art methodologies for investigating these systems; complex networks. Presenting a novel complex network approach that encodes the transitional behaviour of the dynamic interactions. The work introduces: two transition metrics, a complex network, and various metrics and properties of this network. Using this approach it is shown that for the international oil market the evolution favours staying in similar causality patterns before switching to a new group of similar patterns. The second experiment puts forth two novel paradigms for the evolution of a dynamic multivariate system, and from these paradigms the principle features that drive the systems dynamics. It is also shown demonstrated that a p-value representation of causality can improve the description of the dynamics. Through dimensional reduction based on these paradigms and prediction of the systems future states on the reduced system, that the international oil market dynamics are well captured by the total change in causality of the system. The third experiment further explores and validates a hypothesis of the international oil markets dynamics based on the findings of the first two experiments. Proposing a approach for the formal definition of such system dynamics, and applying this to the proposed hypothesis. This hypothesis is then validated via a novel clustering approaches to determine that the international oil markets state is primarily contained within clusters that slightly vary around central causality patterns, and that the system does not follow a repeated gradual change when transitioning between these clusters. This work allows for a more detailed and alternative description of a system's dynamic behaviour than those given by other current methodologies
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