Namur Institute For Complex Systems

Making the most of your presentation by Principiae
2019-03-22 14:00 - 16:30 (Europe/Paris) | E13, 8 Rempart de la Vierge, Namur, Belgium
Abstract: Strong presentation skills are a key to success for researchers, yet many speakers are at a loss to tackle the task. Systematic as they otherwise can be in their work, they go at it intuitively or haphazardly, with much good will but seldom good results. In this talk, Dr Doumont proposes a systematic way to prepare and deliver an oral presentation: he covers structure, slides, and delivery, as well as stage fright.

Speaker: An engineer (UCLouvain) and PhD in applied physics (Stanford), Jean-luc Doumont is acclaimed worldwide for his no-nonsense approach, his highly applicable, often life-changing recommendations on a wide range of topics, and Trees, maps, and theorems, his book about “effective communication for rational minds.”
For additional information, visit www.principiae.be.
A drink will follow the conference.

Registration
Registration is free but mandatory through the following web form.
PhD students will receive a certificate with ECTS mention for their doctoral training.

Anna Kiriliouk (UNamur)
2019-03-12 13:00 - 14:00 | E25
Title: Climate event attribution using multivariate peaks-over-thresholds modelling
Abstract: Quantifying the human influence on climate change and identifying potential causes is a highly relevant research area which is often referred to as detection and attribution. A common approach is to compare the probability of an extreme event in the factual world to the probability of an extreme event in a counterfactual world, i.e., a world that might have been if no humans would have existed. The event probabilities can be calculated using large scale climate model runs that simulate the evolution of the climate with and without anthropogenic forcings. The Fraction of Attributable Risk (FAR) is defined as the relative ratio of event probabilities in the factual and in the counterfactual world. Estimating the FAR will allow us to quantify the extent to which human activities have increased the risk of occurrence of an extreme event. We propose a model for the FAR based on the multivariate generalized Pareto distribution, i.e., the asymptotic distribution of suitably normalized exceedances over a high threshold. The model is used to quantify the increased risk of an extreme rainfall event in central Europe.

Ines Wilms (Maastricht University, Netherlands)
2019-03-07 13:00 - 14:00 | E25
Title: Multi-class Vector AutoRegressive Models for Multi-Market Commodity Data
Abstract: Vector AutoRegressive (VAR) models form a special case of multivariate regression models in that the response variables are observed over time and modeled as a function of their own past values. We use VAR models to study dynamics among agricultural, metal and energy commodity returns. As the increasing integration of the world economy suggests commodity dynamics to be comparable for different markets, we aim to jointly analyze these dynamics across markets. To this end, we introduce a sparse estimator of the Multi-Class Vector AutoRegressive model. We jointly estimate several VAR models, one for each market (“class”), to borrow strength across markets. Our methodology encourages effects to be similar across markets, while still allowing for small differences between them. Moreover, we focus on multi-class estimation of high-dimensional VAR models, i.e. models with a large number of time series relative to the time series length. Therefore, our estimate is sparse: unimportant effects are estimated as exactly zero, which facilitates the interpretation of the results.
This is joint work with Luca Barbaglia and Christophe Croux

naXys board meeting
2019-02-15 12:00 - 14:00

Martin Gueuning (UNamur)
2019-02-14 13:00 - 14:00 | E25
Title: Information Diffusion on Twitter: exploiting the timings of Retweets
Abstract: Online social networks such as Twitter have emerged as influential platforms for real-time information diffusion. Due to various factors, a tweet may become viral and initiate a cascade of retweets. In this work, we first show how the timings of the retweets reveal information about how the tweet propagated on the underlying network. In particular, we show under which circumstances the transition of the tweet from one group to another on Twitter leaves fingerprints on its retweets timings [1]. Then, we leverage these findings to address the question of multi-targets selection for maximal information diffusion. We present SmartInf, an algorithm providing a list of influential users of the network only based on an historic of popular cascades, or refining the list when the ego networks of the users may be extracted. We illustrate the performance of SmartInf against the baselines on both real-world and simulated data [2] .
[1] Temporal patterns of (re)tweets reveal cascade migration. AK Bhowmick, M Gueuning, JC Delvenne, R Lambiotte, B Mitra. Proceedings of the 2017 IEEE/ACM International Conference on Advances in in Social Networks Analysis and Mining.
[2] Temporal patterns of (re)tweets help to detect influential nodes for information diffusion in social networks. AK Bhowmick, M Gueuning, JC Delvenne, R Lambiotte, B Mitra. Submitted.

Justin Delloye (Luxembourg Institute of Socio-Economic Research LISER)
2018-12-20 13:00 - 14:00 | E25
Title: A New Approach to Complexity in Economic Geography
Abstract: The design of efficient regional policies calls for a reliable understanding of complex regional dynamics. Dedicated scientific efforts have historically been split up in two distinct research strands that are Location Theory and Complex Systems. In this presentation, I propose a conciliating modelling approach and I use it to address multiple equilibria issues in a model of New Economic Geography. In this field, the question of equilibrium selection traditionally reduces to a dependence on initial conditions. In more recent core-periphery models, the introduction of heterogeneous preferences generates exogenous shocks and therefore complex path-dependent dynamics. However, these recent models use averaging procedures which prevent them from discussing path-dependent dynamics. Starting from the footloose entrepreneur model, this method proposes a novel approach to adjustment dynamics, based on stochastic migration models, by which the dynamics of the population distribution is a continuous-time Markov chain. Using a diffusion approximation, it computes the evolution of the transition probability function in the state space of the regional system, which enables to discuss in particular the appearance of multiple peaks, denoting substantial divergences of sample paths. It enables further to identify, in explicit time units and at both short- and long-term horizons, periods when heterogeneous preferences may substantially affect the regional dynamic. Thus, this method raises new opportunities to improve the operational potential of New Economic Geography models.

Fabian Bastin (Université de Montréal)
2018-12-13 13:00 - 14:00 | E25
Title: Staffing optimization in call centers
Abstract: Call centers are prominent in modern life, and labor intensive. We aim to determine the minimum number of agents in a multi-skill call center, while ensuring the desired level of quality of service to customers. We consider the case where the arrival rates cannot be forecast perfectly, leading to a two-stage stochastic optimization problem. The arrival rates are modeled as random variables with large variance (uncertainty) in the first stage of the staffing decision process. The number of agents can however be adjusted in a second stage, under some penalty costs, when the information on the arrival rates is more accurate. The quality of service is measured by the service level, defined as the proportion of calls answered within a given time, called the acceptable wait threshold. Since the number of calls coming into the call center is unknown, we only require that the service level constraints are satisfied with given (high) probabilities. These constraints cannot be evaluated analytically and are approximated using sample averages computed using simulation. We use a decomposition approach, based on the L-shaped method, to efficiently address the resulting optimization problem. We provide numerical studies to illustrate the performance of the decomposition algorithm, along with a validation study of the approach.
Joint work with Anh Thuy Ta, Wyean Chan, and Pierre L’Ecuyer.

Thomas Parmentier (UGhent)
2018-12-06 13:00 - 14:00 | E25
Title: Ants and their associates as a model system to test host-symbiont network dynamics
Abstract: An extremely large number of organisms engage in symbiotic relationships with ants. Interestingly, these relationships are highly variable in terms of host-specificity and span the entire mutualism-antagonism continuum. In this talk, we present different types of network analyses focusing on ant-symbiont interactions. We argue that ant-symbiont communities are highly suitable to test a wide range of network hypotheses in terrestrial ecosystems.

Germain Van Bever (UNamur)
2018-11-29 13:00 - 14:00 | E25
Title: Symmetric Component Analysis and Functional Independent Component Analysis
Abstract: With the increase in measurement precision, functional data is becoming common practice. Relatively few techniques for analysing such data have been developed, however, and a first step often consists in reducing the dimension via Functional PCA, which amounts to diagonalising the covariance operator. Joint diagonalisation of a pair of scatter functionals has proved useful in many different setups, such has Independent Component Analysis (ICA), Invariant Coordinate Selection (ICS), etc.
After an introduction to classical ICA techniques (and a look into several extensions of ICS), the main part of this talk will consist in extending the Fourth Order Blind Identification procedure to the case of data on a separable Hilbert space (with classical FDA setting being the go-to example). In the finite-dimensional setup, this procedure provides a matrix Γ such that ΓX has independent components, if one assumes that the random vector X satisfies X = ΨZ, where Z has independent marginals and Ψ is an invertible mixing matrix. When dealing with distributions on Hilbert spaces, two major problems arise: (i) the notion of marginals is not naturally defined and (ii) the covariance operator is, in general, non invertible. These limitations are tackled by reformulating the problem in a coordinate-free manner and by imposing natural restrictions on the mixing model.
The proposed procedure is shown to be Fisher consistent and affine invariant. A sample estimator is provided and its convergence rates are derived. The procedure is amply illustrated on simulated and real datasets.
References
[1] J.-F. Cardoso, Source Separation Using Higher Moments Proceedings of IEEE international conference on acoustics, speech and signal processing 2109-2112.
[2] D. Tyler, F. Critchley, L. Dumbgen and H. Oja, Invariant Co-ordinate Selection J. R. Statist. Soc. B., 2009, 71, 549–592.
[3] J. Ramsay and B.W. Silverman Functional Data Analysis 2nd edn. Springer, New York, 2006.

Alexis Coyette (UNamur)
2018-11-22 13:00 - 14:00 | E25
Title: Modeling the rotation of Titan
Abstract: Observation of the rotation of synchronously rotating satellites can help to probe their interior. We study here the variations in rotation rate and the polar motion of Titan due to (1) the gravitational force exerted by Saturn at orbital period and (2) exchanges of angular momentum between the seasonally varying atmosphere and the solid surface. The deviation of the mass distribution from hydrostaticity can significantly increase the diurnal libration and decrease the amplitude of the seasonal libration. By including a large spectrum of atmospheric perturbations, the smaller than synchronous rotation rate measured by Cassini in the 2004–2009 period (Meriggiola et al., 2016) could be explained by the atmospheric forcing. The effect of the non-hydrostatic mass distribution is less important for polar motion, which is more sensitive to flow in the subsurface ocean. For some interior models of Titan, the amplitude of its polar motion can be resonantly amplified. Future observations of the polar motion of Titan could help constraining some properties of its interior structure as the ice shell thickness and ocean density.

Elio Tuci (UNamur)
2018-11-08 13:00 - 14:00 | E25
Title: Three recent research works in swarm robotics
Abstract: In this talk, I briefly illustrate objectives and achievements of three experiments that I have recently conducted with the support of my PhD students. All three works fall into the research area of swarm robotics/swarm intelligence.
In the first study, we have developed a control system to allow a swarm of robots to cooperatively transport objects that due to their mass, cannot be moved by single robots. The controller is a dynamic neural network synthesised using evolutionary computation techniques. The study shows that the robots do not need to feel forces to coordinate their action in order to find a common direction of transport.
In the second study, we have looked at the process of aggregation in swarm of robots with “informed robots”. These are robots that have been instructed on which site to aggregate, among those available in the environment. Our study looks at the proportion of informed robots required to gather all the swarm on a selected aggregation site.
In the third study, we have tested a probabilistic adaptive defence mechanism to allow a swarm of agents to limit the disruptive effects of a communication manipulation attack implemented as modification of data in transit events. The results of our study show that the proposed mechanism can be largely effective in multiple context differing for the proportion of malicious agents carrying out the attack.
I am happy to provide further details/readings for each of these works and to discuss about potential collaborations with anyone interested in pursuing interdisciplinary research work in the area of bio-inspired robotics, swarm robotics, swarm intelligence.

Alexandre Mayer (UNamur)
2018-10-25 13:00 - 14:00 | E25
Title: An evolutionary computing approach to optical engineering
Abstract: We present a genetic algorithm that we developed in order to address computationally expensive optimization problems in optical engineering. The idea consists in working with a population of individuals that represent possible solutions to the problem. The best individuals are selected. They generate new individuals for the next generation. Random mutations in the coding of parameters are introduced. This strategy is repeated from generation to generation until the algorithm converges to the global optimum of the problem considered. For computationally expensive problems, it makes sense to analyze the data collected by the algorithm in order to infer more rapidly the final solution. The objective is ideally to determine the global optimum by a single run of the genetic algorithm and with a reduced number of fitness evaluations. The use of a mutation operator that acts on randomly-shifted Gray codes contributes to this objective by helping the genetic algorithm to escape local optima and by enabling a wider diversity of displacements. These techniques reduce the computational cost of optical engineering problems, where the design parameters have a finite resolution and are limited to a realistic range.

naXys kick-off meeting
2018-09-20 14:00 - 21:30 | Le Grenier de l’Arsenal, 11 rue Bruno, Namur, Belgium
Founded in 2010 as a research center, naXys has recently evolved into an Institute. Join us to celebrate our kick-off meeting.
Program
14h: Welcome & Introduction by Prof. Carine Michiels, UNamur Vice-Rector for Research and Prof. Anne-Sophie Libert, Director of naXys
14h30: Prof. Vito Latora (Queen Mary University of London, UK), Complex networks with many layers
15h15: Prof. Damien Ernst (ULiège, Belgium), Uber-like models for the electrical industry
16h: Coffee break
16h30: Prof. Carlos Melian (EAWAG, ETH Domain, Switzerland), Tangling the webs of life
17h15: Prof. Glenn Magerman (ULB, Belgium), Production networks in economics
18h-21h30 : Poster session & Walking dinner
Registration
Registration is free but mandatory, before Wednesday, September 13th, through the following web form.
We strongly encourage poster submissions in all naXys research directions. PhD students will receive certificate with ECTS mention for their doctoral training.
Abstracts
Prof. Vito Latora (Queen Mary University of London, UK), Complex networks with many layers

Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems and metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. As a result, complex networks have become an essential ingredient in the background of any scientist [1]. In this seminar I will present an overview of the new theory and methods of network science, of the main results found, and of some of the still open challenges.
I will concentrate, in particular, on the structure and dynamics of multi-layer networks [2][3], namely multiplex networks and temporal networks, discussing cases where the presence of many layers gives rise to the emergence of novel behaviours, otherwise unobserved in single-layer networks [4-6].
[1] V. Latora, V. Nicosia and G. Russo, Complex Networks: Principles, Methods and Applications, Cambridge University Press (2017)
[2] Structural measures for multiplex networks Battiston, Nicosia, Latora, Phys. Rev. E89, 032804 (2014)
[3] Growing multiplex networks V. Nicosia, G. Bianconi, V. Latora, M. Barthelemy, Phys. Rev. Lett. 111, 058701 (2013)
[4] Structural reducibility of multilayer networks, M. De Domenico, V. Nicosia, A. Arenas, V. Latora, Nature Comm 6, 6864 (2015)
[5] Collective phenomena emerging from the interactions between dynamical processes in multiplex networks, Nicosia, Skardal, Arenas, Latora, Phys. Rev. Lett. 118, 138302 (2017)
[6] Mobility and congestion in dynamical multilayer networks with finite storage capacity Manfredi, Di Tucci, Latora, Phys. Rev. Lett. 120, 068301 (2018)

Prof. Damien Ernst (ULiège, Belgium), Uber-like models for the electrical industry

During this presentation Professor Ernst will present new organizational models for the electrical industry. These are Uber-type models that are extremely destabilizing for traditional players in the electricity sector. He will explain the different economic forces in place that lead to the emergence of such models. He will also detail the great opportunities they offer for accelerating the energy transition and for researchers who are interested in analyzing and controlling very complex systems.

Prof. Carlos Melian (EAWAG, ETH Domain, Switzerland), Tangling the webs of life

Most complex systems consist of elements that interact within and across levels in a broad range of spatiotemporal scales. In biology, gene interactions and expression dynamics together with plasticity determine the architecture of traits of individuals, competitive and cooperative interactions among individuals influence population dynamics, and many types of interactions among species affect the dynamics of communities and ecosystems. Such systems, usually represented as single networks, have been partitioned in domains to explain the intricate phenomena that occur at each of the levels and scales, but can have complex dynamics when interdependencies among networks belonging to different levels or scales occur. In this talk, I will introduce interdependencies among networks to gain insights about biological systems. I will ask the many open challenges ahead when merging real-world data across levels and scales with process-based modeling to infer the dynamics of interdependent networks. In the end, I will discuss whether disentangling a single network is enough to understand the origin, evolution and coexistence of biodiversity on Earth and that we might need data-driven and process-based theory to tangle networks with one another to fully understand the webs of life.

Prof. Glenn Magerman (ULB, Belgium), Production networks in economics

Firms are not isolated islands of production, but they interact through supplier-buyer relationships and personal network contacts. While intuitive, this observation is mostly at odds with standard economic reasoning of anonymous markets. However, the absence of micro data on firm-to-firm transactions has made the development of production network models in economics elusive until very recently, as it is ex ante hard to understand which mechanisms generate outcomes that are at odds with existing models. In this talk, I will first discuss some stylized facts on the Belgian production network, the data which shows unprecedented detail along many dimensions. Second, I will highlight some elements of the models we develop, and how they expand typical firm-level models in micro-economics. Next, I will discuss how these network models can generate new implications for propagation and aggregation (i.e. “micro-to-macro”). Finally, we will touch upon dynamics.

Eleonora Andreotti (Università di Torino, Italy)
2018-06-21 13:00 - 14:00 | E25
Title: Statistical properties of non linear random walks on networks
Abstract: Random walks simulate various interacting entities (the nodes) which exchange ’particles’ according to the topological structure defined by the links and they are considered to introduce a dynamics on networks. These stochastic dynamical systems can be applied to model complex systems like transportation networks, ecological systems, neural networks, economic systems etc.. In the linear case the statistical properties are defined by single particles dynamics and the equilibrium distribution depends on the network structure. We introduce non linear effects by assuming a finite transportation capacity of the links or a finite capacity in the nodes. As a consequence the transition probabilities depend on the dynamical state of the network and one cannot derive statistical properties of the system from single particle dynamics. We show that non-linear effects can be described by introducing an entropic force among the node states which allows to derive a master equation for the evolution of the probability distribution of the node population. This entropic force has a relevant effect on stationary distribution and the relaxation time scale depends on the numerosity of the population so that the thermodynamics limit is non trivial and the lifetime of the transient state is very long. Using numerical simulation we derive an analytical form of the entropic force and we study the dependence of the stationary distribution on the network topology.

Takaaki Aoki (Kagawa University, Japan)
2018-06-19 13:00 - 14:00 | E25
Title: Cities and roads as pattern formation of their co-evolving dynamics on real-world landscape

Abstract: Cities and their inter-connected transport networks form part of the fundamental infrastructure developed by human societies. Their organisation reflects a complex interplay between many natural and social factors, including inter alia natural resources, landscape, and climate on the one hand, combined with business, commerce, politics, diplomacy and culture on the other. Nevertheless, despite this complexity, there has been some success in capturing key aspects of city growth and network formation in relatively simple models that include non-linear positive feedback loops. However, these models are typically embedded in an idealised, homogeneous space, leading to regularly-spaced, lattice-like distributions arising from Turing-type pattern formation. Here we argue that the geographical landscape plays a much more dominant, but neglected role in pattern formation. To examine this hypothesis, we evaluate the weighted distance between locations based on a least cost path across the natural terrain, determined from high-resolution digital topographic databases for the Hokkaido region of Japan. These weights are included in a co-evolving, dynamical model of both population aggregation in cities, and movement via an evolving transport network. We compare the results from the stationary state of the system with current population distributions from census data, and show a reasonable fit, both qualitatively and quantitatively, compared with models in homogeneous space. Thus we infer that that addition of weighted topography from the natural landscape to these models is both necessary and almost sufficient to reproduce the majority of the real-world spatial pattern of city sizes and locations in this example.

Ludovic Renson (University of Bristol, UK)
2018-06-07 13:00 - 14:00 | E25
Title: Exploring the dynamics of nonlinear experiments using control-based continuation
Abstract: With the constant drive for better performance and efficiency, technological boundaries are being pushed to their limits. In mechanics, this often means that the dynamic behaviour of structures becomes increasingly nonlinear. Nonlinearity can arise, for instance, from the large displacements and rotations of flexible components (such as blades in wind turbines).
While a significant effort has been, and is, devoted to the mathematical modelling and numerical analysis of such systems, relatively little research addresses the issue of rigourous experimental testing. In fact, until now, there has been no general, systematic method that can directly measure and characterise nonlinear dynamic behaviour during laboratory tests. Nonlinear systems are still tested as linear ones. Time series are collected for a whole range of excitation parameters and one relies on post-processing tools to understand the behaviour of the system. So far, this sort of approach has not allowed quantitative comparisons between experiments and mathematical models; hence it is extremely challenging to incorporate nonlinear features into model development and validation processes.
In this talk, I will present a method, control-based continuation (CBC), which uses sensors and actuators to intelligently probe a physical system. Combining feedback control with numerical continuation algorithms, CBC modifies, on-line, the excitation applied to the system in order to isolate the nonlinear behaviour of interest. In this way, CBC offers the best conditions to analyse these dynamic features in detail, to follow them as inputs and controllable parameters are changed, and to detect and track boundaries between qualitatively different types of behaviour.

naXys Board meeting
2018-06-05 14:00 - 16:00 | E25

Pietro Coletti (University of Hasselt)
2018-05-23 13:00 - 14:00 | E25
Title: Regular school closure & influenza epidemics: a data-driven spatial transmission model for Belgium

Abstract: School closure is often considered as an option to mitigate influenza epidemics because of its potential to reduce transmission in children and then in the community. The policy is still however highly debated because of controversial evidence. Moreover, the specific mechanisms leading to mitigation are not clearly identified. We introduced a stochastic spatial age-specific metapopulation model to assess the role of holiday-associated behavioral changes and how they affect seasonal influenza dynamics. The model is applied to Belgium, parameterized with country-specific data on social mixing and travel, and calibrated to the 2008/2009 influenza season. It includes behavioral changes occurring during weekend vs. week, and holiday vs. school-term. Several experimental scenarios are explored to identify the relevant social and behavioral mechanisms.
Stochastic numerical simulations show that holidays considerably delay the peak of the season and mitigate its impact. Changes in mixing patterns are responsible for the observed effects, whereas changes in travel behavior do not alter the epidemic. Weekends are important in slowing down the season by periodically dampening transmission. Christmas holidays have the largest impact on the epidemic, however later school breaks may help in reducing the epidemic size, stressing the importance of considering the full calendar.
Changes in the way individuals establish contacts during holidays are the key ingredient explaining the mitigating effect of regular school closure. Our findings highlight the need to quantify these changes in different demographic and epidemic contexts in order to provide accurate and reliable evaluations of closure effectiveness. They also suggest strategic policies in the distribution of holiday periods to minimize the epidemic impact.

Nikos Kouvaris (UNamur)
2018-05-15 13:00 - 14:00 | E25
Title: Feedback induced stationary and oscillatory patterns in complex bistable networks
Abstract: In this talk we will present theoretical and experimental findings on pattern formation in bistable chemical networks. Effects of feedbacks on self-organization phenomena will also be discussed. For regular trees, an approximate analytical theory for localized stationary patterns under application of global feedbacks is constructed. Using it, properties of such patterns in different parts of the parameter space are discussed. We will also show that localized oscillatory patterns can be formed in these networks if the global feedback is applied with certain time delay. Linear stability analysis has revealed a critical time delay for which the system undergoes a Hopf bifurcation and exhibits limit cycles of small amplitudes. Furthermore, we will present results from numerical investigations for large random Erdös-Rényi and scale-free networks. In both kinds of systems localized active patterns consist of a subnetwork, whose size decreases as the feedback intensity increases. In the later random networks local feedbacks affecting only the hubs or the peripheral nodes are also considered.

10th CECI Scientific Day @UNamur
2018-05-04 13:30 - 17:30

APRES-MIDI de la RECHERCHE à l'UNamur
2018-05-03 08:00 - 17:00

Aivar Sootla (University of Oxford)
2018-04-19 13:00 - 14:00 | E25
Title: Applications of Monotone Systems Theory to Synthetic Biology
Abstract: In this talk, we will discuss some mathematical problems arising in the fast developing field of synthetic biology. Synthetic biology is concerned with creating genetically modified bacteria for the production of useful chemical compounds (e.g., drugs, bio-degradable plastic, fuels etc), for detecting viruses, toxins or prohibited substances (e.g. in a biological sample, water supply), and even for new methods of drug delivery. Due to complexity and variability of bacterial systems modelling presents a serious challenge. Furthermore, quantitative data analysis is impeded by technological and physical constraints, therefore scientists often rely on qualitative analysis of their designs. From the modelling perspective, qualitative analysis is not always straightforward unless models have specific properties. We will argue that a class of models called monotone can offer effective tools for qualitative analysis of biological processes. Monotone systems often appear in biological modelling and possess a powerful property: a partial order in their trajectories. Partial order enables a number of stability results, which are crucial for simplified analysis and control of nonlinear systems. Equipped with the Koopman operator framework, we present methods for analysis and control of monotone systems, which are computationally efficient and can provide answers for a range of parameters.

Julien Petit (Ecole Royale Militaire - UNamur)
2018-04-10 13:00 - 14:00 | E22
Title : Peculiarities of reaction and diffusion on temporal networks
Abstract: A common assumption in the ever-increasing literature on network-defined dynamical systems is to view the underlying graph as a static entity. In many cases however, the temporal properties of the network – its fluctuating links and appearing and vanishing nodes – prove critical.
This presentation is firstly meant as an overview of linear (diffusion) processes on temporal networks. After a survey of recent methods and results, we subsequently focus on a random walk model with lasting node-to-node interactions. Relying on analytics and numerics, we discuss how the Markov property of the process is lost even when using memoryless probability distributions for the model-driven edges dynamics, and the independent nodes processes.
If time permits and in an attempt to highlight other results about systems evolving on top of temporal networks, and which stem from the intrinsic network dynamics, we finally consider a generic nonlinear model. In particular, this setting allows us to discuss pattern formation and oscillations death in reaction-diffusion systems through the Turing mechanism, under the assumption of a fast-varying periodic topology.

Jean Teyssandier (Cornell University, USA)
2018-03-29 13:00 - 14:00 | E25
Title: Orbital evolution during planet-⁠disc interactions
Abstract: The origin and wide distribution of eccentricities and inclinations in planetary systems remains to be explained, in particular in the context of planet-disc interactions. In this talk I will present a study of the evolution of eccentricity in discs and planets that combines methods from celestial mechanics and hydrodynamics, and takes the form of a linear secular theory. I will compare the predictions of this linear theory against direct hydrodynamical simulations and show how eccentricity can grow. I will discuss what these results imply for the orbital structure of protoplanetary discs and young planetary systems. I will also discuss how moderate inclinations can be generated via similar mechanisms.

Alexey Medvedev (UNamur)
2018-03-08 13:00 - 14:00 | E25
Title: Modelling structure and predicting dynamics of discussion threads in online boards using Hawkes processesAbstract: Online social platforms provide a fruitful source of information about social interaction. Depending on the platform, various tree-like cascading patterns emerge as a consequence of such interaction. For example, on Twitter or on Facebook people interact via resharing messages, which turns into cascade trees of reshares, in email networks people forward messages to their peers resulting in trees of email forwards, in online boards like Digg or Reddit people interact via discussing particular posts, which leaves a trace of discussion trees. The two main questions arise: what is the shape of these cascades and how to predict the dynamics of their evolution? The question of evolution of discussion threads is now gradually being understood. By now researchers studied only the structural evolution of discussion trees and the dynamical properties are left out of consideration. Nevertheless, there was proposed a sort of a mean-field model for dynamics and structure, however by the design the model has limited utility in practice. We consider cascades given by discussion trees of posts in online board Reddit. The dataset of Reddit discussion threads consists of all posts and comments submitted to Reddit from Jan, 2008 till Jan, 2015. The dataset in total contains more than 150 million posts and around 1.4 billion comments. We propose a model of discussion trees generation based on the self-exciting Hawkes processes, which represents both the tree structure and temporal information. We use the dataset of Reddit discussion threads to show that structurally trees resemble Galton-Watson trees with a special root offspring distribution, and distinct the cases when the dynamics of comments attraction can be well predicted using Hawkes processes.

Alexandre Bovet (UNamur - UCL)
2018-03-01 13:00 - 14:00 | E25
Title: Opinion dynamics and fake versus traditional news influence in Twitter
Abstract: We investigate the opinion of Twitter users during the 2016 US elections using a large scale dataset of more than 170 millions tweets. We develop a method to infer the opinion of Twitter users regarding the candidates by using a combination of natural language processing of the tweet contents, machine learning classification and analysis of the hashtags co-occurrence network. We study the temporal social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter opinion trend follows the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy.
Going beyond the daily opinion analysis and analyzing the level of activity, the repartition of the supporters between the strongly connected giant component and the rest of the network and the daily fluctuations in the number of users reveal a clear dichotomy between the behavior of supporters of each candidate. Although Clinton supporters are the majority in Twitter, Trump supporters are generally more active and more constant in their support, while Clinton supporters are less active and show their support only occasionally.
To understand the role of information diffusion on Twitter opinion dynamics, we consider tweets containing URLs directing to news outlet websites. In particular, we compare websites known to diffuse fake news compared to traditional, fact-based, news outlets. We find that 29% of the tweets linking to news outlets points to websites containing fake or extremely biased news. Analyzing the information diffusion networks, we find that user diffusing fake news form more connected and less heterogeneous networks than users in traditional news diffusion networks. While influencers of traditional news outlets are journalists and public figures with verified Twitter accounts, most influencers of fake news and extremely biased websites are unknown users or users with deleted Twitter accounts. Finally, an analysis of the activity dynamics of influencers reveals that influencers of tradition news are driving the most part of Twitter while fake news influencers are, in fact, mostly driven by the activity of Trump supporters.

naXys extended Board meeting
2018-02-22 13:00 - 14:00 | E25

François Lamoline (UNamur)
2018-02-20 13:00 - 14:00 | E25
Title: Well-Posedness of Boundary Controlled and Observed Stochastic Port-Hamiltonian systems
Abstract: On finite-dimensional spaces, the well-posedness is not a concern and is usually not even mentioned. However, on infinite-dimensional spaces, establishing the well-posedness is of paramount importance and paves the way for dealing with control/estimation, transfer function, etc. In this talk, stochastic port-Hamiltonian systems on infinite-dimensional spaces governed by Ito stochastic differential equations and with boundary control and observation operators are introduced and some properties of this new class of systems are studied. They are an extension of stochastic port-Hamiltonian systems defined on a finite-dimensional state space. The concept of well-posedness in the sense of Weiss and Salamon is generalized to the stochastic context. Under this extended definition, stochastic port-Hamiltonian systems are shown to be well-posed. The theory is illustrated on an example of a vibrating string subject to a Hilbert-space valued Gaussian white noise process.

Eve-Aline Dubois (UNamur)
2018-02-15 13:00 - 14:00 | E25
Titre: The early times of stationary cosmologies
Abstract: The Steady-state model of Bondi and Gold, on the one hand, and Hoyle’s model on the other hand are the most famous examples of stationary cosmologies. However, they are actually quite different in terms of motivations and content and were not the only stationary models. In this talk, we will look back at the very beginning of relativistic cosmology and the first stationary cosmological models. They were lead by various motivations and suggest a surprising phenomenon: creation of matter. From Einstein to Hoyle, we will follow the different stationary cosmologies, their development and their interplay.

Justin Buhendwa Nyenyezi (UNamur)
2018-01-25 13:00 - 14:00
Title: Polynomial preconditioners versus matrix-splitting preconditioners for linear systems arising in 3D medical image registration
Abstract: Many methods exist to solve large sparse linear systems. However, a given method may perform well for a particular problem while it may not work as well for another [2]. For designing ecient system solvers, one may analyze structures of general operators or matrices used by the algorithms. In non-rigid medical image registration, matrices are derived from physical principles, modelled as Partial Dierential Equations (PDEs). Although these systems are often sparse and structured, they are very large and ill-conditioned. Thus, their solvers are time consuming and their complexity in operation counts is polynomial. As a consequence, fast and superfast direct methods reveal numerical instabilities and thus lead to breakdowns or inaccurate solutions. The most used alternative are iterative system solvers that enable a reduction of the number of expensive operations such as matrix-vector products and thus a speed up of the registration process. Although iterative solvers provide only an approximation of the solution, they are well suited for very large systems when cheap and well suited preconditioners are available. Preconditioners may be stationary (Jacobi, Gauss-Seidel or SOR) and non-stationary (e.g, polynonmial or low-rank inverse approximation).
In this study, we are especially interested in benchmarking [3, 4] iterative system solvers on a large set of 3D medical images. These system solvers are based on the Conjugate Gradient method but dierent from the preconditioning techniques [6, 5] . The ongoing results conrm [4] that there is no single system solver that is the best for all the problems. However, the results indicate which solver has the highest probability of being the best within a factor f ∈ [1, ∞[ and considering a limited computional budget in terms of time and storage require-
ments.
References
[1] Oseledets, Ivan and Tyrtyshnikov, Eugene and Zamarashkin, Nickolai. Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines. Image Processing, IEEE Transactions on, 16:12,28792890, 2007.
[2] Barrett, Richard and & all. Templates for the solution of linear systems: building blocks for iterative methods. SIAM , 1994.
[3] Moré, Jorge J and Wild, Stefan M. Benchmarking derivative-free optimization algorithms. SIAM Journal on Optimization , 20:1,172191, 2009.
[4] Gould, Nicholas and Scott, Jennifer. The State-of-the-Art of Preconditioners for Sparse Linear Least-Squares Problems. ACM Transactions on Mathematical Software (TOMS) , 43:4,36, 2017.
[5] Nocedal, Jorge and Wright, Stephen J. Numerical optimization. Springer New York , 2, 2006.
[6] SAAD,Yousef Iterative methods for sparse linear systems. Society for Industrial and Applied Mathematics , 2003.
