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Zanin M.,INNAXIS Foundation and Research Institute | Pisarchik A.N.,Technical University of Madrid
Information Sciences | Year: 2014

We present a novel permutation algorithm for fast encryption of a large amount of data, such as 3D images and real-time videos. The proposed P-Box algorithm takes advantage of Gray code properties and allows fast encryption with high information diffusion. The algorithm is optimized for integer q-bit operations (q=8,16,32,...), allowing a direct implementation in almost any hardware platform, while avoiding rounding errors of floating-point operations. By combining the P-Box with chaotic S-Box based on the logistic map, we design a complete, highly secure and fast cryptosystem. © 2014 Elsevier Inc. All rights reserved.

Boccaletti S.,CNR Institute for Complex Systems | Bianconi G.,Queen Mary, University of London | Criado R.,Rey Juan Carlos University | Criado R.,Technical University of Madrid | And 9 more authors.
Physics Reports | Year: 2014

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics. © 2014 Elsevier B.V.

Fleurquin P.,Institute Fisica Interdisciplinar y Sistemas Complejos IFISC CSIC UIB | Fleurquin P.,Innaxis Foundation and Research Institute | Ramasco J.J.,Institute Fisica Interdisciplinar y Sistemas Complejos IFISC CSIC UIB | Eguiluz V.M.,Institute Fisica Interdisciplinar y Sistemas Complejos IFISC CSIC UIB
Proceedings of the 10th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2013 | Year: 2013

The upsetting consequences of weather conditions are well known to any person involved in air transportation. Still the quantification of how these disturbances affect delay propagation and the effectiveness of managers and pilots interventions to prevent possible large-scale system failures needs further attention. In this work, we employ an agent-based data-driven model developed using real flight performance registers for the entire US airport network and focus on the events occurring on October 27 2010 in the United States. A major storm complex that was later called the 2010 Superstorm took place that day. Our model correctly reproduces the evolution of the delayspreading dynamics. By considering different intervention measures, we can even improve the model predictions getting closer to the real delay data. Our model can thus be of help to managers as a tool to assess different intervention measures in order to diminish the impact of disruptive conditions in the air transport system.

Zanin M.,Innaxis Foundation and Research Institute | Zanin M.,New University of Lisbon | Papo D.,Technical University of Madrid | Sousa P.A.,New University of Lisbon | And 4 more authors.
Physics Reports | Year: 2016

The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed. © 2016 Elsevier B.V.

Papo D.,Technical University of Madrid | Zanin M.,New University of Lisbon | Zanin M.,Innaxis Foundation and Research Institute | Pineda-Pardo J.A.,Technical University of Madrid | And 3 more authors.
Philosophical Transactions of the Royal Society B: Biological Sciences | Year: 2014

Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks has generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different fromthe brain.We discuss some important caveats in thewholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Zanin M.,Innaxis Foundation and Research Institute | Zanin M.,Polytechnic University of Mozambique | Zanin M.,New University of Lisbon | Lillo F.,Normal School of Pisa | Lillo F.,Santa Fe Institute
European Physical Journal: Special Topics | Year: 2013

Air transport is a key infrastructure of modern societies. In this paper we review some recent approaches to air transport, which make extensive use of theory of complex networks. We discuss possible networks that can be defined for the air transport and we focus our attention to networks of airports connected by flights. We review several papers investigating the topology of these networks and their dynamics for time scales ranging from years to intraday intervals, and consider also the resilience properties of air networks to extreme events. Finally we discuss the results of some recent papers investigating the dynamics on air transport network, with emphasis on passengers traveling in the network and epidemic spreading. © 2013 EDP Sciences and Springer.

Zanin M.,New University of Lisbon | Zanin M.,Innaxis Foundation and Research Institute | Sousa P.A.,New University of Lisbon | Menasalvas E.,Technical University of Madrid
EPL | Year: 2014

We propose a novel measure to assess the presence of meso-scale structures in complex networks. This measure is based on the identification of regular patterns in the adjacency matrix of the network, and on the calculation of the quantity of information lost when pairs of nodes are iteratively merged. We show how this measure is able to quantify several meso-scale structures, like the presence of modularity, bipartite and core-periphery configurations, or motifs. Results corresponding to a large set of real networks are used to validate its ability to detect non-trivial topological patterns. © CopyrightEPLA, 2014.

Zanin M.,INNAXIS Foundation and Research Institute
Proceedings of the 10th USA/Europe Air Traffic Management Research and Development Seminar, ATM 2013 | Year: 2013

In the continuous effort for ensuring increasing levels of safety, it is of utmost importance to understand the reasons behind the occurrence of operational errors. In this contribution, we propose the use of the Trajectory Synchronization Likelihood metric for the analysis of two types of events: situations resulting in a reduced separation between aircrafts, and situations that might have resulted in similar conditions but were solved on time. Results indicate that unsolved events are associated with highly synchronized pairs of aircraft, which have been deviated from the usual expected trajectories. This opens new way for the development of more effective automated safety systems, capable of detecting in real time events that are known to have a high probability of resulting in a conflict.

Zanin M.,Innaxis Foundation and Research Institute | Zanin M.,New University of Lisbon
Physica A: Statistical Mechanics and its Applications | Year: 2015

Functional networks, i.e. networks representing dynamic relationships between the components of a complex system, have been instrumental for our understanding of, among others, the human brain. Due to limited data availability, the multi-layer nature of numerous functional networks has hitherto been neglected, and nodes are endowed with a single type of links even when multiple relationships coexist at different physical levels. A relevant problem is the assessment of the benefits yielded by studying a multi-layer functional network, against the simplicity guaranteed by the reconstruction and use of the corresponding single layer projection. Here, I tackle this issue by using as a test case, the functional network representing the dynamics of delay propagation through European airports. Neglecting the multi-layer structure of a functional network has dramatic consequences on our understanding of the underlying system, a fact to be taken into account when a projection is the only available information. © 2015 Elsevier B.V. All rights reserved.

Zanin M.,Innaxis Foundation and Research Institute | Zanin M.,New University of Lisbon
Physica A: Statistical Mechanics and its Applications | Year: 2014

Complex networks have been extensively used to study the topological and dynamical characteristics of transportation systems, although far less attention has been devoted to the analysis of specific problems arising in everyday operations. In this work, the use of a network representation is proposed for studying the appearance of Loss of Separation events, a kind of safety occurrence in which two aircraft violate the minimal separation while airborne. The topological analysis of networks representing the structure of traffic flows allows identifying situations in which the probability of appearance of such events is increased. Beyond these specific results, this work demonstrates the usefulness of the complex network approach in the analysis of operational patterns and occurrences. © 2014 Elsevier B.V. All rights reserved.

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