Median evidential c-means algorithm and its application to community detection

Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed. The median variant relaxes the restriction of a metric space embedding for the objects but constrains the prototypes to be in the original data set. Due to these properties, MECM could be applied to graph clustering problems. A community detection scheme for social networks based on MECM is investigated and the obtained credal partitions of graphs, which are more refined than crisp and fuzzy ones, enable us to have a better understanding of the graph structures. An initial prototype-selection scheme based on evidential semi-centrality is presented to avoid local premature convergence and an evidential modularity function is defined to choose the optimal number of communities. Finally, experiments in synthetic and real data sets illustrate the performance of MECM and show its difference to other methods

Toward a relational concept of uncertainty: about knowing too little, knowing too differently, and accepting not to know

Uncertainty of late has become an increasingly important and controversial topic in water resource management, and natural resources management in general. Diverse managing goals, changing environmental conditions, conflicting interests, and lack of predictability are some of the characteristics that decision makers have to face. This has resulted in the application and development of strategies such as adaptive management, which proposes flexibility and capability to adapt to unknown conditions as a way of dealing with uncertainties. However, this shift in ideas about managing has not always been accompanied by a general shift in the way uncertainties are understood and handled. To improve this situation, we believe it is necessary to recontextualize uncertainty in a broader way¿relative to its role, meaning, and relationship with participants in decision making¿because it is from this understanding that problems and solutions emerge. Under this view, solutions do not exclusively consist of eliminating or reducing uncertainty, but of reframing the problems as such so that they convey a different meaning. To this end, we propose a relational approach to uncertainty analysis. Here, we elaborate on this new conceptualization of uncertainty, and indicate some implications of this view for strategies for dealing with uncertainty in water management. We present an example as an illustration of these concepts. Key words: adaptive management; ambiguity; frames; framing; knowledge relationship; multiple knowledge frames; natural resource management; negotiation; participation; social learning; uncertainty; water managemen

Taking Mermin's Relational Interpretation of QM Beyond Cabello's and Seevinck's No-Go Theorems

In this paper we address a deeply interesting debate that took place at the end of the last millennia between David Mermin, Adan Cabello and Michiel Seevinck, regarding the meaning of relationalism within quantum theory. In a series of papers, Mermin proposed an interpretation in which quantum correlations were considered as elements of physical reality. Unfortunately, the very young relational proposal by Mermin was too soon tackled by specially suited no-go theorems designed by Cabello and Seevinck. In this work we attempt to reconsider Mermin's program from the viewpoint of the Logos Categorical Approach to QM. Following Mermin's original proposal, we will provide a redefinition of quantum relation which not only can be understood as a preexistent element of physical reality but is also capable to escape Cabello's and Seevinck's no-go-theorems. In order to show explicitly that our notion of ontological quantum relation is safe from no-go theorems we will derive a non-contextuality theorem. We end the paper with a discussion regarding the physical meaning of quantum relationalism.Comment: 19 pages, 1 phot

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

Orthogonal vector computations

Quantum computation is the suitable orthogonal encoding of possibly holistic functional properties into state vectors, followed by a projective measurement.Comment: 8 pages, 2 figures, some revisions and addition

Relational Capability: An Indicator of Collective Empowerment

We define a new index for the collective empowerment of populations based on the capability of actors to have relationships and to enter into networks. This index, called “relational capability” (RC), is dynamic in the sense that the weights of its various components vary across time according to how close the population is to some poverty threshold. It relies on a shift of anthropological viewpoint, putting human relationships at the forefront. RC, which can be formalized in gametheoretic terms of networks, paves the way towards the solution of a number of unsolved issues: Reconciling autonomy and interdependence; unifying the aggregation of individual characteristics with the collective level; questioning unjust institutions and political structures within Sen’s and Nussbaum’s framework of capabilities.Empowerment; Escaping Poverty Index; Index; Relational Capability

Relational Capability : An Indicator of Collective Empowerment

Nous définissons un nouvel indicateur de l'empowerment collectif des populations, basé sur la capacité des acteurs à entrer en relation et à participer à des réseaux. Cet indicateur de " capacité relationnelle " (RC) est dynamique au sens où les pondérations attribuées à ses composantes varient dans le temps selon la façon dont une population se rapproche d'un certain seuil de pauvreté. Elle se fonde sur un changement de perspective anthropologique, qui place les relations humaines au premier plan. La capacité relationnelle, qui peut être formalisée selon le modèle de la théorie des jeux appliquée aux réseaux, ouvre des perspectives de résolution de plusieurs problèmes : réconcilier l'autonomie et l'interdépendance ; unifier l'agrégation des caractéristiques individuelles avec le niveau collectif ; mettre en question les institutions et les structures politiques injustes, dans le cadre de l'approche des capacités définie par Sen et Nussbaum.Capacité relationnelle ; Empowerment ; Indicateur ; Indicateur de sortie de pauvreté