Hierarchical information clustering by means of topologically embedded graphs

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies

Hierarchical information clustering by means of topologically embedded graphs

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.Comment: 33 Pages, 18 Figures, 5 Table

Causal inference in multisensory perception and the brain

To build coherent and veridical multisensory representations of the environment, human observers consider the causal structure of multisensory signals: If they infer a common source of the signals, observers integrate them weighted by their reliability. Otherwise, they segregate the signals. Generally, observers infer a common source if the signals correspond structurally and spatiotemporally. In six projects, the current PhD thesis investigated this causal inference model with the help of audiovisual spatial signals presented to human observers in a ventriloquist paradigm. A first psychophysical study showed that sensory reliability determines causal inference via two mechanisms: Sensory reliability modulates how observers infer the causal structure from spatial signal disparity. Further, sensory reliability determines the weight of audiovisual signals if observers integrate the signals under assumption of a common source. Using multivariate decoding of fMRI signals, three PhD projects revealed that auditory and visual cortical hierarchies jointly implement causal inference. Specific regions of the hierarchies represented constituent spatial estimates of the causal inference model. In line with this model, anterior regions of intraparietal sulcus (IPS) represent audiovisual signals dependent on visual reliability, task-relevance, and spatial disparity of the signals. However, even in case of small signal discrepancies suggesting a common source, reliability-weighting in IPS was suboptimal as compared to a Maximum Estimation Likelihood model. By temporally manipulating visual reliability, the fifth PhD project demonstrated that human observers learn sensory reliability from current and past signals in order to weight audiovisual signals, consistent with a Bayesian learner. Finally, the sixth project showed that if visual flashes were rendered unaware by continuous flash suppression, the visual bias of the perceived auditory location was strongly reduced but still significant. The reduced ventriloquist effect was presumably mediated by the drop of visual reliability accompanying perceptual unawareness. In conclusion, the PhD thesis suggests that human observers integrate multisensory signals according to their causal structure and temporal regularity: They integrate the signals if a common source is likely by weighting them proportional to the reliability which they learnt from the signals’ history. Crucially, specific regions of cortical hierarchies jointly implement these multisensory processes

Vector like gauge theories with almost massless fermions on the lattice

A truncation of the overlap (domain wall fermions) is studied and a criterion for reliability of the approximation is obtained by comparison to the exact overlap formula describing massless quarks. We also present a truncated version of regularized, pure gauge, supersymmetric models. The mechanism for generating almost masslessness is shown to be a generalized see-saw which can also be viewed as a version of Froggatt-Nielsen's method for obtaining natural large mass hierarchies. Viewed in this way the mechanism preserving the mass hierarchy naturally avoids preserving even approximately axial U(1). The new insights into the source of the mass hierarchy suggest ways to increase the efficiency of numerical simulations of QCD employing the truncated overlap.Comment: 35 pages, TeX, 4 figures using eps

Hierarchically nested factor model from multivariate data

We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records.Comment: 7 pages, 5 figures; accepted for publication in Europhys. Lett. ; the Appendix corresponds to the additional material of the accepted letter

Structures, inner values, hierarchies and stages: essentials for developmental robot architectures

In this paper we try to locate the essential components needed for a developmental robot architecture. We take the vocabulary and the main concepts from Piaget’s genetic epistemology and Vygotsky’s activity theory. After proposing an outline for a general developmental architecture, we describe the architectures that we have been developing in the recent years - Petitagé and Vygovorotsky. According to this outline, various contemporary works in autonomous agents can be classified, in an attempt to get a glimpse into the big picture and make the advances and open problems visible

Short communication: a hierarchy of items within Eysenck’s EPI

Based on the recent finding of a hierarchical scale for Neuroticism in the NEO-Five Factor Inventory, two further personality inventories: the Eysenck Personality Inventory and Goldberg’s International Personality Item Pool were analysed using the Mokken Scaling Procedure for hierarchical scales. Items from two dimensions of the Eysenck Personality Inventory: Neuroticism and Extraversion produced hierarchical scales of 12 and five items, respectively. The Neuroticism items ran from items expressing mild to more extreme worry and the Extraversion items ran from mild sociability to more extreme ‘showing off’. The utility of hierarchical scales in personality measurement is discussed in terms of furthering theoretical understanding of personality and also practical application. In addition, the reasons why only one of these scales should produce hierarchical sets of items is discussed

Relational discrepancies and emotion : The moderating roles of relationship type and relational discrepancy valence

Peer reviewedPostprin

Networks in the shadow of markets and hierarchies : calling the shots in the visual effects industry

The nature and organisation of creative industries and creative work has increasingly been at the centre of academic and policy debates in recent years. The differentiation of this field, economically and spatially, has been tied to more general arguments about the trend towards new trust-based, network forms of organization and economic coordination. In the first part of this paper, we set out, unpack and then critique the conceptual and empirical foundations of such claims. In the main section of the paper, we draw on research into a particular creative sector of the economy - the visual effects component of the film industry - a relatively new though increasingly important global production network. By focusing both on firms and their workers, and drawing on concepts derived from global value chain, labour process and institutional analysis, we aim to offer a more realistic and grounded analysis of creative work within creative industries. The analysis begins with an attempt to explain the power dynamics and patterns of competition and collaboration in inter-firm relations within the Hollywood studio-dominated value chain, before moving to a detailed examination of how the organisation of work and reemployment relations are central to the capturing of value. On the basis of that evidence, we conclude that trust-based networks and collaborative communities play some part in accessing and acquiring leverage in the value chain, but do not explain the core mechanisms of resource allocation, coordination and work organisation