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Search Results: 1 - 9 of 9 matches for " Viroli "
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El sentido olvidado del patriotismo republicano
Viroli, Maurizio
Isegoría , 2001,
Abstract: In this article Maurizio Viroli shows that although republican patriotism has a cultural dimension it is primarily a political passion based upon the experiencia of citizenship. Patriotism is the love for a free republic and for its way of life: il vivere libero. In this sense republican patriotism is almost the contrary of nationalism. For nationalists patriotism is the love for a modest liberty: the freedom to enjoy in the shadow of the throne one′s dwelling and vineyard in peace. For republican patriots love of country is an artificial passion, for nationalists it is a natural one. As a conclusion, Viroli suggest that republican patriotism is able to answer the dylemas of modern democracy beyond the choice between the myth of civic nationalism and the horror of ethnic nationalism. En este artículo Maurizio Viroli muestra que aunque el patriotismo republicano tiene una dimensión cultural es, sobre todo, una pasión política basada en la experiencia de la ciudadanía. Patriotismo es el amor por una república libre y por su forma de vida: il vivere libero. En este sentido, el patriotismo es casi lo opuesto al nacionalismo. Para los nacionalistas el patriotismo es el amor por una libertad más modesta: la libertad de disfrutar a la sombra del trono, y en paz, de la casita y el majuelo. Para los patriotas republicanos el amor por la patria es una pasión artificial, para los nacionalistas es natural. Como conclusión, Viroli sugiere que el patriotismo republicano es capaz de responder a los dilemas de la democracia moderna al ir más allá de la elección entre el mito del nacionalismo cívico y el horror del nacionalismo étnico.
Stochastic model selection for Mixtures of Matrix-Normals
Cinzia Viroli
Statistics , 2010,
Abstract: Finite mixtures of matrix normal distributions are a powerful tool for classifying three-way data in unsupervised problems. The distribution of each component is assumed to be a matrix variate normal density. The mixture model can be estimated through the EM algorithm under the assumption that the number of components is known and fixed. In this work we introduce, develop and explore a Bayesian analysis of the model in order to provide a tool for simultaneous model estimation and model selection. The effectiveness of the proposed method is illustrated on a simulation study and on a real example.
Type-based Self-stabilisation for Computational Fields
Ferruccio Damiani,Mirko Viroli
Computer Science , 2015,
Abstract: Emerging network scenarios require the development of solid large-scale situated systems. Unfortunately, the diffusion/aggregation computational processes therein often introduce a source of complexity that hampers predictability of the overall system behaviour. Computational fields have been introduced to help engineering such systems: they are spatially distributed data structures designed to adapt their shape to the topology of the underlying (mobile) network and to the events occurring in it, with notable applications to pervasive computing, sensor networks, and mobile robots. To assure behavioural correctness, namely, correspondence of micro-level specification (single device behaviour) with macro-level behaviour (resulting global spatial pattern), we investigate the issue of self-stabilisation for computational fields. We present a tiny, expressive, and type-sound calculus of computational fields, and define sufficient conditions for self-stabilisation, defined as the ability to react to changes in the environment finding a new stable state in finite time. A type-based approach is used to provide a correct checking procedure for self-stabilisation.
Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data
Laura Anderlucci,Cinzia Viroli
Statistics , 2014, DOI: 10.1214/15-AOAS816
Abstract: We propose a novel approach for modeling multivariate longitudinal data in the presence of unobserved heterogeneity for the analysis of the Health and Retirement Study (HRS) data. Our proposal can be cast within the framework of linear mixed models with discrete individual random intercepts; however, differently from the standard formulation, the proposed Covariance Pattern Mixture Model (CPMM) does not require the usual local independence assumption. The model is thus able to simultaneously model the heterogeneity, the association among the responses and the temporal dependence structure. We focus on the investigation of temporal patterns related to the cognitive functioning in retired American respondents. In particular, we aim to understand whether it can be affected by some individual socio-economical characteristics and whether it is possible to identify some homogenous groups of respondents that share a similar cognitive profile. An accurate description of the detected groups allows government policy interventions to be opportunely addressed. Results identify three homogenous clusters of individuals with specific cognitive functioning, consistent with the class conditional distribution of the covariates. The flexibility of CPMM allows for a different contribution of each regressor on the responses according to group membership. In so doing, the identified groups receive a global and accurate phenomenological characterization.
Quantile-based classifiers
Christian Hennig,Cinzia Viroli
Statistics , 2013,
Abstract: Quantile classifiers for potentially high-dimensional data are defined by classifying an observation according to a sum of appropriately weighted component-wise distances of the components of the observation to the within-class quantiles. An optimal percentage for the quantiles can be chosen by minimizing the misclassification error in the training sample. It is shown that this is consistent, for $n \to \infty$, for the classification rule with asymptotically optimal quantile, and that, under some assumptions, for $p\to\infty$ the probability of correct classification converges to one. The role of skewness of the involved variables is discussed, which leads to an improved classifier. The optimal quantile classifier performs very well in a comprehensive simulation study and a real data set from chemistry (classification of bioaerosols) compared to nine other classifiers, including the support vector machine and the recently proposed median-based classifier (Hall et al., 2009), which inspired the quantile classifier.
A factor mixture analysis model for multivariate binary data
Silvia Cagnone,Cinzia Viroli
Statistics , 2010,
Abstract: The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous population. The heterogeneity is taken into account by replacing the traditional assumption of Gaussian distributed factors by a finite mixture of multivariate Gaussians. The aim of the proposed model is twofold: it allows to achieve dimension reduction when the data are dichotomous and, simultaneously, it performs model based clustering in the latent space. Model estimation is obtained by means of a maximum likelihood method via a generalized version of the EM algorithm. In order to evaluate the performance of the model a simulation study and two real applications are illustrated.
Standard Type Soundness for Agents and Artifacts
F. Damiani,P. Giannini,A. Ricci,M. Viroli
Scientific Annals of Computer Science , 2012,
Abstract: Formal models, core calculi, and type systems, are important tools for rigorously stating the more subtle details of a language, to characterise and study its features and the correctness properties of its programs. In this paper we present {FsimpAL} (FsimpaALlong), a formal calculus modelling the agent and artifact program abstractions provided by the simpA{} agent framework. The formalisation is largely inspired by extsc{Featherweight Java}. It is based on reduction rules applied at certain evaluation contexts, properly adapted to the concurrency nature of simpA{}. On top of this calculus we introduce a standard type system and prove its soundness, so as to guarantee that the execution of a well-typed program does not get stuck. Namely, all primitive mechanisms of agents (activity execution), artifacts (field/property access and step execution), and their interaction (observation and invocation) are guaranteed to be used in a way that is structurally compliant with the corresponding definitions: hence, there will not be run-time errors due to {FsimpAL} distinctive primitives.
Organizing the Aggregate: Languages for Spatial Computing
Jacob Beal,Stefan Dulman,Kyle Usbeck,Mirko Viroli,Nikolaus Correll
Computer Science , 2012,
Abstract: As the number of computing devices embedded into engineered systems continues to rise, there is a widening gap between the needs of the user to control aggregates of devices and the complex technology of individual devices. Spatial computing attempts to bridge this gap for systems with local communication by exploiting the connection between physical locality and device connectivity. A large number of spatial computing domain specific languages (DSLs) have emerged across diverse domains, from biology and reconfigurable computing, to sensor networks and agent-based systems. In this chapter, we develop a framework for analyzing and comparing spatial computing DSLs, survey the current state of the art, and provide a roadmap for future spatial computing DSL investigation.
Modelling overdispersion heterogeneity in differential expression analysis using mixtures
Elisabetta Bonafede,Franck Picard,Stéphane Robin,Cinzia Viroli
Statistics , 2014,
Abstract: Next-generation sequencing technologies now constitute a method of choice to measure gene expression. Data to analyze are read counts, commonly modeled using Negative Binomial distributions. A relevant issue associated with this probabilistic framework is the reliable estimation of the overdispersion parameter, reinforced by the limited number of replicates generally observable for each gene. Many strategies have been proposed to estimate this parameter, but when differential analysis is the purpose, they often result in procedures based on plug-in estimates, and we show here that this discrepancy between the estimation framework and the testing framework can lead to uncontrolled type-I errors. Instead we propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.
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