Transmission or 'creative fidelity'? The institutional communicator's role in the Church today

The role of the institutional communicator in the Church today has a special relevance derived from the credibility crisis suffered by this institution. A greatly increased awareness to this theme leads to the discovery of profound and essential dimensions of this role, which are discussed in this article. The focus is on the credibility of the communicator, who, on one hand is called to take full responsibility for what he says, but on the other hand speaks in the name of and through a collective subject—the Church or one of its specific constituent parts—that doesn't always receive much appreciation from its numerous audiences; it is actually often perceived to be "biased" as an institution, it is widely opposed and criticized. An in-depth analysis regarding the role of credibility from a sociological point of view is illustrated by the application of the three roles identified by Erving Goffman—animator, author and principal—to the figure of the institutional communicator, underlining his responsibilities as communication co-leader. A comparison with the concept of translator as a mediator illuminates other characteristics of the communicator, and functions as a basis to comment on some of the virtues (both personal and professional), which he must possess, enhancing both his credibility and efficiency. The application of the concept of creative fidelity (fidélité créatrice) from the French philosopher Gabriel Marcel, together with the interrelation between comprehension and exposition in a comprehensible manner (hermeneutics and creativity), highlights the necessity of reconsidering the importance of communication and of the communicator within the decision-making process

Characterizing spatial point processes by percolation transitions

A set of discrete individual points located in an embedding continuum space can be seen as percolating or non-percolating, depending on the radius of the discs/spheres associated with each of them. This problem is relevant in theoretical ecology to analyze, e.g., the spatial percolation of a tree species in a tropical forest or a savanna. Here, we revisit the problem of aggregating random points in continuum systems (from $2$ to $6-$dimensional Euclidean spaces) to analyze the nature of the corresponding percolation transition in spatial point processes. This problem finds a natural description in terms of the canonical ensemble but not in the usual grand-canonical one, customarily employed to describe percolation transitions. This leads us to analyze the question of ensemble equivalence and study whether the resulting canonical continuum percolation transition shares its universal properties with standard percolation transitions, analyzing diverse homogeneous and heterogeneous spatial point processes. We, therefore, provide a powerful tool to characterize and classify a vast class of natural point patterns, revealing their fundamental properties based on percolation phase transitions.Comment: 22 pages, 13 figure

Laplacian renormalization group for heterogeneous networks

The renormalization group is the cornerstone of the modern theory of universality and phase transitions and it is a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its application to complex networks has proven particularly challenging, owing to correlations between intertwined scales. To date, existing approaches have been based on hidden geometries hypotheses, which rely on the embedding of complex networks into underlying hidden metric spaces. Here we propose a Laplacian renormalization group diffusion-based picture for complex networks, which is able to identify proper spatiotemporal scales in heterogeneous networks. In analogy with real-space renormalization group procedures, we first introduce the concept of Kadanoff supernodes as block nodes across multiple scales, which helps to overcome detrimental small-world effects that are responsible for cross-scale correlations. We then rigorously define the momentum space procedure to progressively integrate out fast diffusion modes and generate coarse-grained graphs. We validate the method through application to several real-world networks, demonstrating its ability to perform network reduction keeping crucial properties of the systems intact

Is a Sociology of Hope Possible? An Attempt to Recompose a Theoretical Framework and a Research Programme

The societal changes of the last century, especially in the aftermath of World War II, have led thinkers to imagine philosophical anthropology centred on the concept of hope. From very different perspectives, authors such as Ernst Bloch, Erich Fromm, and Hannah Arendt understood that hope is deeply connected with the condition and destiny of humanity. Various sociologists have developed concepts closely linked with hope: action, social change, utopia, revolution, emancipation, innovation, and trust. However, a coherent and systematic analysis is yet to emerge. Taking up the threads of this rich but fragmented reflection, this paper intends to outline the traits of a “sociology of hope” as a tool for critically interpreting today’s society and the processes of change crisscrossing it, starting from some crucial questions: Who are the actors and historical bearers of hope? What are the main socio-historical forms of hope? What social, political, and cultural conditions favour the emergence and strengthening of this disposition? What are the effects and consequences on personal and social life

Laplacian paths in complex networks: Information core emerges from entropic transitions

Complex networks usually exhibit a rich architecture organized over multiple intertwined scales. Information pathways are expected to pervade these scales reflecting structural insights that are not manifest from analyses of the network topology. Moreover, small-world effects correlate with the different network hierarchies complicating the identification of coexisting mesoscopic structures and functional cores.We present a communicability analysis of effective information pathways throughout complex networks based on information diffusion to shed further light on these issues. We employ a variety of brand-new theoretical techniques allowing for: (i) bring the theoretical framework to quantify the probability of information diffusion among nodes, (ii) identify critical scales and structures of complex networks regardless of their intrinsic properties, and (iii) demonstrate their dynamical relevance in synchronization phenomena. By combining these ideas, we evidence how the information flow on complex networks unravels different resolution scales. Using computational techniques, we focus on entropic transitions, uncovering a generic mesoscale object, the information core, and controlling information processing in complex networks. Altogether, this study sheds much light on allowing new theoretical techniques paving the way to introduce future renormalization group approaches based on diffusion distances

Organization and hierarchy of the human functional brain network lead to a chain-like core

The brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming ahighly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions

The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

Abstract Network neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions

The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors' mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases