# Cognition  (C) Session 2

## Chair: Simon Dedeo

 9 Committed activists and the reshaping of status-quo social consensus [abstract] Abstract: The role of committed minorities in shaping public opinion has been recently addressed with the help of multi-agent models. However, previous studies focused on homogeneous populations where zealots stand out only for their stubbornness. Here, we consider the more general case in which individuals are characterized by different propensities to communicate. In particular, we correlate commitment with a higher tendency to push an opinion, acknowledging the fact that individuals with unwavering dedication to a cause are also more active in their attempts to promote their message. We show that these activists are not only more efficient in spreading their message but that their efforts require an order of magnitude fewer individuals than a randomly selected committed minority to bring the population over to a new consensus. Finally, we address the role of communities, showing that partisan divisions in the society can make it harder for committed individuals to flip the status-quo social consensus. Close Dina Mistry, Qian Zhang, Nicola Perra and Andrea Baronchelli 195 Stochastic heterogeneous mean field approximation of the utterance selection model [abstract] Abstract: The utterance selection model (USM) for language change (Baxter et al. 2006) is a stochastic agent-based model developed to simulation language change. In this model, agents are vertices of a graph and interact along its edges by stochastically producing utterances and learning from them. The dynamics of such an agent-based model is defined at the agent level and it is usually difficult to deduce the average dynamics of the complete population. In the original paper, the authors derived a continuous time limit in the form of a Fokker-Planck equation. This limit is only valid for a restricted set of parameters. In this talk, I will derive a new continuous time limit of the USM, which has no constraints on parameters and use it to derive a coarse-grained population level approximation of the dynamics. This approximation is a stochastic version of the heterogeneous mean field approximation. Using this approximation, I will characterize the dynamics of the USM at the population level. In particular, I will show that the population dynamics of the system can mainly be captured by three parameters. The analysis also reveals a finite-size effect in the dynamics. Close Jérôme Michaud 583 Detection and Analysis of Political Leaders’ Facial Emotions:The Impact on Voters' Behavior [abstract] Abstract: Facial emotions are believed to be very expressive social stimuli; hence significant amount of efforts have been made within the past two decades to detect, study and analyze these emotions in a way to reveal specific human behaviors and characteristics, in this paper we aim to decode some of the detected facial emotions on political leaders in a way to find potential correlations between their facial expressions and impacts on voter preference decisions in the United States, we study facial emotions detected on images of selected Republican and Democratic presidential candidates during their most controversial campaign speeches and debates for the 2016 United States presidential election. Our facial detection application is based on the Face API recently developed by Microsoft Cognitive Services, which is designed to recognize eight universal groups of facial emotions including sadness, neutral, contempt, disgust, anger, surprise, fear and happiness. 180 images were collected and analyzed for Donald Trump, John Kasich and Ted Cruz; Republican party nominees, also Hillary Clinton and Bernie Sanders for the Democratic party nominees, the results of the detected groups of facial emotions are scored forming an eight-dimensional vector and then re-scaled to two-dimensional vectors using Principal Component Analysis technique, the findings are discussed in the context of direct correlation between some facial expressions and voters decisions for the primary presidential election taking place since February first 2016. Close Alaa Alazzam and Hiroki Sayama 514 Method of assessment of textual emotiveness with use of psycholinguistic markers on base of morphological features for analysis of social processes in networks and blogs [abstract] Abstract: A combined approach to identify emotionally colored texts, which reflect the excited state of its authors and also make the sentiment analysis of these texts is proposed. This approach is based on one side on use of psycholinguistic markers that are calculated on the basis of the morphological characteristics of the text and on other side on use of object oriented sentiment analyser based on SVM classification. A complex indicator reflecting emotiveness of texts on the basis of the core group of markers was presented. On an example of two thematic collections it was shown that on the basis of that complex indicator the most emotional topic could be automatically detected. In this article an integrated approach is presented which combines the assessment of the text using the sentiment analysis method and context-independent psycholinguistic markers based on morphological features. The proposed approach can be a useful extension of Social Mining methods in different languages and it can be applicable in developing methods in the fields of affective and personality computing. Close Alexandr Sboev, Dmitry Gudovskikh, Ivan Moloshnikov and Roman Rybka 528 Industrialisation by Invitation: A Community Detection Approach to Mapping FDI-related Knowledge Diffusion in Ireland. [abstract] Abstract: As Ireland emerges from recession, riding a wave of growth largely driven by the presence of large profitable foreign multi-nationals, recurring questions surrounding the long-term durability of the so-called 'industrialisation by invitation' approach to modernisation persist. If Ireland is to benefit from this strategy, there needs to be significant transfer of knowledge between foreign and domestic firms, thus enabling the latter to emerge as global competitors in their own right. In order to investigate patterns of knowledge diffusion within the Irish economy, here we build a network of labour transitions (job switches) between foreign-owned multi-nationals and domestic firms using a new dataset constructed from the Irish economic census of 2014. Using network techniques for community detection, we identify a highly modular network structure, as workers tend to switch to a narrow set of similar industries that share their own skill set. We find that while some sectors such as pharmaceuticals, with a high share of foreign firms, are largely disconnected from the wider economy, other sectors such as financial services, IT and food processing are more integrated with domestic economic activities. This analysis suggests that policies focusing on increasing labour mobility between certain sectors, and hence enabling workers to move more freely throughout the economy, could result in improved knowledge and expertise transfer from foreign to domestic firms (and vice versa). Close Eoin Flaherty, Matte Hartog and Neave O'Clery

# Physics  (P) Session 1

## Chair: Sumit Sourabh

 333 Combination of physical and chemical factors as a source of spatiotemporal dissipative patterns in nonlinear chemical systems with the inhomogeneous temperature field [abstract] Abstract: Spatiotemporal and spatial non-equilibrium patterns in chemical systems can emerge due to the coupling of nonlinear chemical kinetics with the diffusion of reacting particles, with eventual contribution from excitable characteristics. All these phenomena are explainable in terms of the assumption of the isothermal conditions. In our searches for new mechanisms underlying the spatiotemporal instabilities in aqueous media we studied several chemical systems in which hydrogen peroxide was an oxidant for various sulfur-containing species. In one of these systems, containing hydrogen peroxide and thiocyanates as the main reactants [1], which produces sustained oscillations, we discovered the emergence of luminescent patterns. Based on experimental studies and numerical modeling of the reaction kinetics, we found that the observed patterns were essentially the phase waves, caused by the spatially inhomogenous distribution of the solution temperature which affected the local frequency of oscillations. This finding allowed us to take control over the evolution of these phase waves by externally imposed temperature gradient applied to quasi 1-dimensional thin-layer reactor [2]. The second chemical system, containing hydrogen peroxide and thiosulfates, produces only a single oscillatory peak, but in the presence of externally imposed temperature gradient became a source of color front progressing along the reactor. The mechanistic reason for the observed instability is again the dependence of the local chemical reaction kinetics on temperature, i.e. the thermokinetic coupling. Also in this case the experimental findings were successfully reproduced by numerical modeling [3]. We consider these phenomena the novel and rather unique examples of instabilities caused by thermokinetic coupling in liquid media, instead of typical isothermal reaction-diffusion coupling. References: [1] M. Orbán, J. Am. Chem. Soc., 108 (1986) 6893 [2] A. Wiśniewski, M. T. Gorzkowski, K. Pekala, M. Orlik, J. Phys. Chem. A, 117 (2013) 11155 [3] M. Jędrusiak, M. Orlik, J. Phys. Chem. B, 120 (2016) 3169 Close Marek Orlik 44 Complexity of slip avalanches in flowing granular matter [abstract] Abstract: The search for scale-bridging relations in the deformation of amorphous materials presents a current challenge with tremendous applications in material science, engineering and geology. While generic features in the flow and microscopic dynamics support the idea of a universal scaling theory of deformation, direct microscopic evidence remains poor. We study the evolution of slowly sheared granular systems deforming via discrete strain bursts (slips). The granular sample consisting of 105 hard spheres is subjected to applied shear and studied with the combination of two techniques – precise stress-strain measurements and 3D laser sheet imaging. Fluctuations in the stress-strain profile allow us to calculate the magnitude of small internal slip avalanches occurring in the sample due to the shear. 3D laser sheet imaging allows us to visualize each individual slip event, estimate its spatial distribution and connect it to fluctuation in the stress-strain curve. By combining macroscopic force fluctuation measurements with internal strain imaging, we demonstrate the existence of robust scaling relations from particle-scale to macroscopic flow [1]. The presence of the power-law distributions characterizing the spatial and temporal properties of the avalanches suggests the presence of the externally induced critical state in the system. Moreover by building the 3D-map of the critical stress distribution through the system we observe a strongly connected complex network spanning through the whole sample. At a certain critical point the external stress distributed through such network can lead to a creation of a system-wide avalanche resulting in a complete system failure/reconfiguration. These experimental results pave the way to a new universal theory of deformation allowing prediction and possibly prevention of the large avalanches and their negative effects. [1] D.V. Denisov, K.A. Lorincz, J. T. Uhl, K. A. Dahmen & P. Schall, “Universality of slip avalanches in flowing granular matter”, Nature Communications 7, 10641 (2016). Close Dmitry Denisov, Kinga Lorincz, Karin Dahmen and Peter Schall 126 Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes [abstract] Abstract: Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks’ structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve global and local statistics associated with the nodes’ embedding in a metric space. Comparing the original network’s and the resulting surrogates’ global characteristics allows to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links. We apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes’ spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into different classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling underlying complex system structure from spatial embedding of nodes in many fields, ranging from social systems over infrastructure and neurophysiology to climatology. Close Marc Wiedermann, Jonathan F. Donges, Jürgen Kurths and Reik Donner 394 Randomization techniques for the analysis of dynamical processes on temporal networks [abstract] Abstract: Randomization techniques deal with the controlled destruction of given temporal or topological structures in complex networks. This is done by resampling certain motifs of the original (empirical) temporal network, such as the edges between nodes or the temporal order of interactions. By comparing how a given dynamical process evolves on the randomized network with how it evolves on the original network, we may identify how the different characteristics affect the dynamical process. Randomization techniques provide a powerful tool for the study of dynamical processes on temporal networks. They may be applied in very general settings as they are purely numerical and non-parametric. They may notably be applied to systems for which no realistic model exists, which is the case for most real systems. A multitude of different randomization techniques exists [Holme, EPJB (2015)], each destroying certain characteristics while preserving others. However, no general procedure exists for their application. Researchers are thus confronted with the non-trivial problem of how to choose/develop techniques and in which order to apply them to be able to identify the important characteristics for each given dynamical phenomenon and dataset under study. As a first step towards a general methodology for randomization-based inference, we propose a taxonomy of existing randomization techniques, based on their methodological nature, their effect on dynamical and topological characteristics of temporal networks, and their known effects on dynamical processes taking place on the networks. This collection should help researches wanting to apply randomization techniques to the study of a given phenomena, providing guidelines for which techniques to apply to most effectively divide the space of possibilities. It is our hope that it may serve as a starting point for the development of a principled randomization-based approach for the characterization of general dynamical networked systems. Close Laetitia Gauvin, Mathieu Génois, Márton Karsai, Taro Takaguchi, Eugenio Valdano and Christian Lyngby Vestergaard 377 The hidden universality of movement in cities [abstract] Abstract: The dynamics of how people collectively visit different places in cities determines the population’s mixing rate and ultimately drives the socio-economic development of urban areas. Despite the crucial role of the temporal dimension of movement, the laws of attraction to locations that give rise to 'pulsating' population flows with varying frequencies of visitation have remained elusive. In this paper we show the existence of a surprisingly simple scaling function that directly connects i) the number of people attracted to a location, ii) their travel distance from home and iii) their visiting frequency. By combining first principles calculations with dimensional arguments, we find that the collective influx of individuals decreases with the product of travel distance and visiting frequency in form of a power law (slope ≈ -2). This hitherto hidden regularity allows for the prediction of the frequency-distance distribution by just counting the total number of visitors to a given location. The trajectories derived from anonymized mobile phone records of millions of individuals in various countries worldwide confirm that empirical population flows obey the derived scaling function in virtually all tested areas. This suggests that the collective visitation dynamics follow the same underlying principles, regardless of the detailed cultural, socio-economic and infrastructural conditions. Finally, we show how deviations from the inverse square law allow for the identification of locations that trigger significantly more (or less) traffic than should be expected from the total visitor counts. The derived scaling function thus provides an appropriate baseline for the identification of unusual hotspots of activity or under-performing regions in need of stimulation. The revealed dynamics places an important constraint on any theory of human spatial organization, and provides a microscopic basis for traffic forecasting, urban planning and epidemiology. Close Markus Schlapfer, Michael Szell, Carlo Ratti and Geoffrey West

# Foundations & ICT & Physics  (FIP) Session 1

## Chair: Louis Dijkstra

 11 Random matrix theory and decoherence in quantum systems [abstract] Abstract: Random matrix theory (RMT) is a tool to study complex quantum systems. In this talk I will present the basic idea of RMT, and how it can be used to study open quantum systems and present some of our results concerning decoherence. In particular, we shall show how purity decay can be predicted with this tool, show that generic quantum open systems display non-markovian behaviour and how this behaviour is smeared out when the coupling between the system of interest and the environment is weak. The transition from non-Markovian to Markovian dynamics for generic environments , Phys. Rev. A 93, 012113 (2016) Random density matrices versus random evolution of open systems, J. Phys. A: Math. Theor. 48 425005 (2015) A random matrix theory of decoherence, New J. Phys. 10, 115016 (2008) Close Carlos Pineda 52 Relaxation of disordered memristive networks [abstract] Abstract: We discuss the average relaxation properties of the internal memory in models of pure memristive networks. We consider the simplest linear model of memristor unit and introduce a dynamical equation for the evolution of internal memory in terms of projection operators. We find that for the case of passive components the dynamics is described by an orthogonal projection operator and for the case of active elements by a non-orthogonal projector. We analyze the average properties of internal memory parameters for random projection operators, and find that this is well described by a slow relaxation evolution if no active components are present. We provide a simple explanation for the emerging slow relaxation as a superposition of exponential relaxations with broad time scales range. Close Francesco Caravelli, Fabio Lorenzo Traversa and Massimiliano Di Ventra 292 Activity Dynamics in Collaboration Networks [abstract] Abstract: Many online collaboration networks struggle to gain user activity and become self-sustaining due to the ramp-up problem or dwindling activity within the system. Prominent examples include online encyclopedias such as (Semantic) MediaWikis, Question and Answering portals such as StackOverflow, online ontology editors and repositories, such as WebProtégé or BioPortal, and many others. Only a small fraction of these systems manage to reach self-sustaining activity, a level of activity that prevents the system from reverting to a non-active state. In this paper, we model and analyze activity dynamics in synthetic and empirical collaboration networks. Our approach is based on two opposing and well-studied principles: (i) without incentives, users tend to lose interest to contribute and thus, systems become inactive, and (ii) people are susceptible to actions taken by their peers (social or peer influence). With the activity dynamics model that we introduce in this paper we can represent typical situations of such collaboration networks. For example, activity in a collaborative network, without external impulses or investments, will vanish over time, eventually rendering the system inactive. However, by appropriately manipulating the activity dynamics and/or the underlying collaboration networks, we can jump-start a previously inactive system and advance it towards an active state. To be able to do so, we first describe our model and its underlying mechanisms. We then provide illustrative examples of empirical datasets and characterize the barrier that has to be breached by a system before it can become self-sustaining in terms of critical mass and activity dynamics. Additionally, we expand on this empirical illustration and introduce a new metric p—the Activity Momentum—to assess the activity robustness of collaboration networks. Full paper: http://dl.acm.org/citation.cfm?id=2873060 Close Simon Walk, Denis Helic, Florian Geigl and Markus Strohmaier 526 Stochastic dynamics and predictability of big hits in online videos [abstract] Abstract: The competition for the attention of users is a central element of the Internet. Crucial issues are the origin and predictability of big hits, the few items that capture a big portion of the total attention. We address these issues analyzing 10 million time series of videos’ views from YouTube. We find that the average gain of views is linearly proportional to the number of views a video already has, in agreement with usual rich-get-richer mechanisms and Gibrat’s law, but this fails to explain the prevalence of big hits. The reason is that the fluctuations around the average views are themselves heavy tailed. Based on these empirical observations, we propose a stochastic differential equation with Lévy noise as a model of the dynamics of videos. We show how this model is substantially better in estimating the probability of an ordinary item becoming a big hit, which is considerably underestimated in the traditional proportional-growth models. Close José M. Miotto, Holger Kantz and Eduardo Altmann 444 Temporal density of complex networks and ego-community dynamics. [abstract] Abstract: At first, we say that a ego-community structure is a probability measure defined on the set of network nodes. Any subset of nodes may engender its own ego-community structure around. Many community detection algorithms can be modified to yield a result of this type, for instance, the personalized pagerank. Next, we present a continuous version of Viard-Latapy-Magnien link streams, that we call "temporal density". Classical kernel density estimation is used to move from discrete link streams to their continuous counterparts. Using matrix perturbation theory we can prove that ego-community structure changes smoothly when the network evolves smoothly. This is very important, for example, for visualization purposes. Combining the temporal density and personalized pagerank methods, we are able to visualize and study the evolution of the ego-community structures of complex networks with a large number of temporal links. We illustrate and validate our approach using "Primary school temporal network data" provided by sociopatterns.org, and we show how the temporal density can be applied to the study of very large datasets, such as a collection of tweets written by European Parliament candidates during European Parliament election in 2014. Main Topic: Foundations of Complex Systems Sub Topic: Social networks Close Sergey Kirgizov and Eric Leclercq 568 Finitely Supported Mathematics [abstract] Abstract: Many (experimental) sciences don't work or assume actual infinity. Finitely Supported Mathematics (FSM) is introduced as a mathematics dealing with a more relaxed notion of (in)finiteness. FSM has strong connections with the Fraenkel-Mostowski (FM) permutative model of Zermelo-Fraenkel set theory with atoms. However, FSM can characterize infinite algebraic structures using their finite supports. More exactly, FSM is ZF mathematics rephrased in terms of finitely supported structures by using an infinite set of atoms. In FSM, 'sets' are replaced either by invariant sets' (sets endowed with some group actions satisfying a finite support requirement) or by finitely supported sets' (finitely supported elements in the powerset of an invariant set), and developed a theory of invariant algebraic structures'. We describe FSM by using principles (rather than axioms), and the principles of constructing FSM have historical roots both in the definition of Tarski logical notions' and in the Erlangen Program of F.Klein for the classification of geometries according to invariants under suitable groups of transformations. There exist other connections between FSM, admissible sets and Gandy machines. The main principle of constructing FSM is that all the structures have to be invariant or finitely supported. As a consequence, we cannot obtain a property in FSM only by involving a ZF result without an appropriate proof reformulated according to the finite support requirement. Moreover, not every ZF result can be directly reformulated in terms of finitely supported objects because, given an invariant set, some of its subsets might be non-finitely supported (an example is given by a simultaneously infinite and coinfinite subset of the invariant set of all atoms). We have specific techniques of reformulating ZF properties of algebraic structures in FSM. More details are presented in the papers published by the authors in the last 2 years. Close Andrei Alexandru and Gabriel Ciobanu