Binding Social and Cultural Networks: A Model

Until now, most studies carried onto social or semantic networks have considered each of these networks independently. Our goal here is to bring a formal frame for studying both networks empirically as well as to point out stylized facts that would explain their reciprocal influence and the emergence of clusters of agents, which may also be regarded as ''cultural cliques''. We show how to apply the Galois lattice theory to the modeling of the coevolution of social and conceptual networks, and the characterization of cultural communities. Basing our approach on Barabasi-Albert's models, we however extend the usual preferential attachment probability in order to take into account the reciprocal influence of both networks, therefore introducing the notion of dual distance. In addition to providing a theoretic frame we draw here a program of empirical tests which should give root to a more analytical model and the consequent simulation and validation. In a broader view, adopting and actually implementing the paradigm of cultural epidemiology, we could proceed further with the study of knowledge diffusion and explain how the social network structure affects concept propagation and in return how concept propagation affects the social network.Comment: 8 pages, 3 figures (v2: typos, minor corrections in section 3.2) (v3: examples, figures added

Complex systems science: expert consultation report

Executive SummaryA new programme of research in Complex Systems Science must be initiated by FETThe science of complex systems (CS) is essential to establish rigorous scientific principles on which to develop the future ICT systems that are critical to the well-being, safety and prosperity of Europe and its citizens. As the “ICT incubator and pathfinder for new ideas and themes for long-term research in the area of information and communication technologies” FET must initiate a significant new programme of research in complex systems science to underpin research and development in ICT. Complex Systems Science is a “blue sky” research laboratory for R&D in ICT and their applications. In July 2009, ASSYST was given a set of probing questions concerning FET funding for ICT-related complex systems research. This document is based on the CS community’s response.Complex systems research has made considerable progress and is delivering new scienceSince FET began supporting CS research, considerable progress has been made. Building on previous understanding of concepts such as emergence from interactions, far-from-equilibrium systems, border of chaos and self-organised criticality, recent CS research is now delivering rigorous theory through methods of statistical physics, network theory, and computer simulation. CS research increasingly demands high-throughput data streams and new ICT-based methods of observing and reconstructing, i.e. modelling, the dynamics from those data in areas as diverse as embryogenesis, neuroscience, transport, epidemics, linguistics, meteorology, and robotics. CS research is also beginning to address the problem of engineering robust systems of systems of systems that can adapt to changing environments, including the perplexing problem that ICT systems are too often fragile and non-adaptive.Recommendation: A Programme of Research in Complex Systems Science to Support ICTFundamental theory in Complex Systems Science is needed, but this can only be achieved through real-world applications involving large, heterogeneous, and messy data sets, including people and organisations. A long-term vision is needed. Realistic targets can be set. Fundamental research can be ensured by requiring that teams include mathematicians, computer scientists, physicists and computational social scientists.One research priority is to develop a formalism for multilevel systems of systems of systems, applicable to all areas including biology, economics, security, transportation, robotics, health, agriculture, ecology, and climate change. Another related research priority is a scientific perspective on the integration of the new science with policy and its implementation, including ethical problems related to privacy and equality.A further priority is the need for education in complex systems science. Conventional education continues to be domain-dominated, producing scientists who are for the most part still lacking fundamental knowledge in core areas of mathematics, computation, statistical physics, and social systems. Therefore:1. We recommend that FET fund a new programme of work in complex systems science as essential research for progress in the development of new kinds of ICT systems.2. We have identified the dynamics of multilevel systems as the area in complex systems science requiring a major paradigm shift, beyond which significant scientific progress cannot be made.3. We propose a call requiring: fundamental research in complex systems science; new mathematical and computational formalisms to be developed; involving a large ‘guinea pig’ organisation; research into policy and its meta-level information dynamics; and that all research staff have interdisciplinary knowledge through an education programme.Tangible outcomes, potential users of the new science, its impact and measures of successUsers include (i) the private and public sectors using ICT to manage complex systems and (ii) researchers in ICT, CSS, and all complex domains. The tangible output of a call will be new knowledge on the nature of complex systems in general, new knowledge of the particular complex system(s) studied, and new knowledge of the fundamental role played by ICT in the research and implementation to create real systems addressing real-world problems. The impact of the call will be seen through new high added-value opportunities in the public and private sectors, new high added-value ICT technologies, and new high added-value science to support innovation in ICT research and development. The measure of success will be through the delivery of these high added-value outcomes, and new science to better understand failures

Lattices for Dynamic, Hierarchic & Overlapping Categorization: the Case of Epistemic Communities: Lattice-based dynamic and overlapping taxonomies: the case of epistemic communities

14 pages, 8 figures; this is a preprint of the published version, whose final title is actually "Lattice-based dynamic and overlapping taxonomies: the case of epistemic communities".We present a method for hierarchic categorization and taxonomy evolution description. We focus on the structure of epistemic communities (ECs), or groups of agents sharing common knowledge concerns. Introducing a formal framework based on Galois lattices, we categorize ECs in an automated and hierarchically structured way and propose criteria for selecting the most relevant epistemic communities - for instance, ECs gathering a certain proportion of agents and thus prototypical of major fields. This process produces a manageable, insightful taxonomy of the community. Then, the longitudinal study of these static pictures makes possible an historical description. In particular, we capture stylized facts such as field progress, decline, specialization, interaction (merging or splitting), and paradigm emergence. The detection of such patterns in social networks could fruitfully be applied to other contexts

NOBEL, LE JEU DE LA DECOUVERTE SCIENTIFIQUE

Popper a rompu avec une tradition épistémologique ancienne en introduisant une dissymétrie entre vérifiabilité et réfutation. Cette conception a d'importantes répercussions sur la manière d'envisager la croissance des connaissances scientifiques et l'activité du chercheur. La vérité, qui avait pu être considérée comme un but pour la recherche scientifique, est placée hors d'atteinte. Sans indicateur évident pour marquer le terme de ses recherches, le chercheur doit alors faire, en fonction de ses motivations, un compromis entre l'exploration des théories possibles et des manières de les tester, et l'exploitation de théories qui auront été suffisamment corroborées. Si les thèses épistémologiques de Popper sont pertinentes, ce compromis exploration/exploitation au niveau du chercheur a des conséquences notables sur le développement des connaissances scientifiques et notamment, sur la fiabilité des théories acceptées. Ce sont ces conséquences que nous nous proposons d'étudier par une approche analytique, expérimentale et computationnelle, dont nous présentons ici les grandes lignes et les premiers résultats. Au delà de préoccupations purement épistémologiques, cette étude cherche à proposer un schéma générique pour l'approche d'un vaste ensemble de phénomènes d'élaboration collective et distribuée de connaissances ou d'artefacts.découverte collective, développement de la connaissance, compromis exploration/exploitation, épistemologie popperienne, knowledge managment distribué

Towards a digital model of zebrafish embryogenesis. Integration of Cell Tracking and Gene Expression Quantification

We elaborate on a general framework composed of a set of computational tools to accurately quantificate cellular position and gene expression levels throughout early zebrafish embryogenesis captured over a time-lapse series of in vivo 3D images. Our modeling strategy involves nuclei detection, cell geometries extraction, automatic gene levels quantification and cell tracking to reconstruct cell trajectories and lineage tree which describe the animal development. Each cell in the embryo is then precisely described at each given time t by a vector composed of the cell 3D spatial coordinates (x; y; z) along with its gene expression level g. This comprehensive description of the embryo development is used to assess the general connection between genetic expression and cell movement. We also investigate genetic expression propagation between a cell and its progeny in the lineage tree. More to the point, this paper focuses on the evolution of the expression pattern of transcriptional factor goosecoid (gsc) through the gastrulation process between 6 and 9 hours post fertilization (hpf

Optimal viable path search for a cheese ripening process using a multi-objective EA.

International audienceViability theory is a very attractive theoretical approach for the modeling of complex dynamical systems. However, its scope of application is limited due to the high computational power it necessitates. Evolutionary computation is a convenient way to address some issues related to this theory. In this paper, we present a multi-objective evolutionary approach to address the optimisation problem related to the computation of optimal command profiles of a complex process. The application we address here is a real size problem from dairy industry, the modeling of a Camembert cheese ripening process. We have developed a parallel implementation of a multiobjective EA that has produced a Pareto front of optimal control profiles (or trajectories), with respect to four objectives. The Pareto front was then analysed by an expert who selected a interesting compromise, yielding a new control profile that seems promising for industrial applications

Spatio-temporal filtering with morphological operators for robust cell migration estimation in "in-vivo" images

The understanding of the embryogenesis in living systems requires reliable quantitative analysis of the cell migration throughout all the stages of development. This is a major challenge of the "in-toto" reconstruction based on different modalities of "in-vivo" imaging techniques -spatio-temporal resolution and image artifacts and noise. Several methods for cell tracking are available, but expensive manual interaction -time and human resources- is always required to enforce coherence. Because of this limitation it is necessary to restrict the experiments or assume an uncontrolled error rate. Is it possible to obtain automated reliable measurements of migration? can we provide a seed for biologists to complete cell lineages efficiently? We propose a filtering technique that considers trajectories as spatio-temporal connected structures that prunes out those that might introduce noise and false positives by using multi-dimensional morphological operators

Image Processing Challenges in the Creation of Spatiotemporal Gene Expression Atlases of Developing Embryos

To properly understand and model animal embryogenesis it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains and cell dynamics. Such challenge has been confronted in recent years by a surge of atlases which integrate a statistically relevant number of different individuals to get robust, complete information about their spatiotemporal locations of gene patterns. This paper will discuss the fundamental image analysis strategies required to build such models and the most common problems found along the way. We also discuss the main challenges and future goals in the field

An Automatic Quantification and Registration Strategy to Create a Gene Expression Atlas of Zebrafish Embryogenesis

In order to properly understand and model the gene regulatory networks in animals development, it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains. In this paper, we propose a complete computational framework to fulfill this task and create a 3D Atlas of the early zebrafish embryogenesis annotated with both the cellular localizations and the level of expression of different genes at different developmental stages. The strategy to construct such an Atlas is described here with the expression pattern of 5 different genes at 6 hours of development post fertilization