Prudens lector

The Didascalicon or art of reading was composed by Hugh of Saint-Victor before the summer 1121, when the library of his abbey was beginning to be constituted. To what extent does this program of studies at Saint-Victor clarify the history of this library? If it cannot reveal the library’s composition, arrangement and the way it was used, Hugh’s treatise does inform the historian about the meaning of “reading” (lectio) at Saint-Victor, that was a process of learning that is first collective, then personal, and led, by means of arborescent divisions, to a structuring of the world and of all that can be known. The goal of all reading was to pave the way for the assimilation of Holy Scripture, but also to restore the divine image and likeness in man by replacing ignorance, the effect of original sin, by wisdom. Indeed, the requirement to know oneself, if it is well understood, results in paying attention to everything knowable. By thus reconciling interior progress and encyclopaedic curiosity, the Master of Saint-Victor established the intellectual and spiritual bases of a humanist library that would be open to all knowledge.Le Didascalicon ou art de lire a été composé avant l’été 1121 par Hugues de Saint-Victor, à un moment où la bibliothèque de son abbaye commençait à se constituer. Dans quelle mesure la lecture de ce programme des études victorines éclaire-t-elle l’histoire de cette bibliothèque? À défaut de nous renseigner sur sa composition, son rangement et son fonctionnement, le traité hugonien instruit l’historien sur le sens de la «lecture» (lectio) à Saint-Victor, processus collectif d’abord, puis personnel, d’apprentissage, qui aboutit à structurer le monde et le connaissable au moyen de divisions arborescentes. La fin de toute lecture est de préparer à l’assimilation des Écritures saintes, mais aussi de restaurer l’image et la ressemblance divines en l’homme en remplaçant l’ignorance, effet du péché originel, par la sagesse, car l’exigence bien comprise de se connaître soi conduit à s’intéresser aussi à tout ce qui est connaissable. En conciliant ainsi progrès intérieur et curiosité encyclopédique, le maître de Saint-Victor a posé les bases intellectuelles et spirituelles d’une bibliothèque humaniste, ouverte à tous les savoirs

Philologie et histoire de la pensée médiévale : autour d’Hugues de Saint-Victor

Dans le siècle qui a précédé la création de l’université de Paris, l’école de Saint-Victor de Paris, fondée vers 1108 sur les pentes de la Montagne Sainte-Geneviève, a joué un rôle de laboratoire d’idées et de méthodes intellectuelles, récemment rappelé lors d’un colloque tenu à Paris en 2008 sur cette école et son rayonnement. Si elle eut Guillaume de Champeaux pour fondateur institutionnel, Hugues de Saint-Victor doit plutôt en être tenu pour le fondateur intellectuel. Durant la vingtaine d..

Philologie et histoire de la pensée médiévale : autour d’Hugues de Saint-Victor

Dans le siècle qui a précédé la création de l’université de Paris, l’école de Saint-Victor de Paris, fondée vers 1108 sur les pentes de la Montagne Sainte-Geneviève, a joué un rôle de laboratoire d’idées et de méthodes intellectuelles, récemment rappelé lors d’un colloque tenu à Paris en 2008 sur cette école et son rayonnement. Si elle eut Guillaume de Champeaux pour fondateur institutionnel, Hugues de Saint-Victor doit plutôt en être tenu pour le fondateur intellectuel. Durant la vingtaine d..

Dynamics Of A Free Pitching Flexible Cantilever Naca0012 Airfoil At Transitional Reynolds Numbers

Paper presented at 2018 Canadian Society of Mechanical Engineers International Congress, 27-30 May 2018.The dynamics of a free pitching flexible cantilever NACA 0012 airfoil were investigated at transitional Reynolds numbers. This work builds on previous investigations based on a quasi-2D rigid wing, moving elastically in pitch and heave. Wind tunnel tests were performed at various speeds, and three limit cycle oscillation (LCO) branches were observed. Further work is required to supplement this preliminary analysis, such as modeling, FEA simulation, and evaluation of the strain and acceleration information of the wing deformatio

Sparse Bayesian neural networks for regression: Tackling overfitting and computational challenges in uncertainty quantification

Neural networks (NNs) are primarily developed within the frequentist statistical framework. Nevertheless, frequentist NNs lack the capability to provide uncertainties in the predictions, and hence their robustness can not be adequately assessed. Conversely, the Bayesian neural networks (BNNs) naturally offer predictive uncertainty by applying Bayes' theorem. However, their computational requirements pose significant challenges. Moreover, both frequentist NNs and BNNs suffer from overfitting issues when dealing with noisy and sparse data, which render their predictions unwieldy away from the available data space. To address both these problems simultaneously, we leverage insights from a hierarchical setting in which the parameter priors are conditional on hyperparameters to construct a BNN by applying a semi-analytical framework known as nonlinear sparse Bayesian learning (NSBL). We call our network sparse Bayesian neural network (SBNN) which aims to address the practical and computational issues associated with BNNs. Simultaneously, imposing a sparsity-inducing prior encourages the automatic pruning of redundant parameters based on the automatic relevance determination (ARD) concept. This process involves removing redundant parameters by optimally selecting the precision of the parameters prior probability density functions (pdfs), resulting in a tractable treatment for overfitting. To demonstrate the benefits of the SBNN algorithm, the study presents an illustrative regression problem and compares the results of a BNN using standard Bayesian inference, hierarchical Bayesian inference, and a BNN equipped with the proposed algorithm. Subsequently, we demonstrate the importance of considering the full parameter posterior by comparing the results with those obtained using the Laplace approximation with and without NSBL

Estimation de la valeur génétique des chevaux de sport d'après les sommes gagnées dans les compétitions équestres françaises

International audienc

Exploring hierarchical framework of nonlinear sparse Bayesian learning algorithm through numerical investigations

Sparse Bayesian learning (SBL) has been extensively utilized in data-driven modeling to combat the issue of overfitting. While SBL excels in linear-in-parameter models, its direct applicability is limited in models where observations possess nonlinear relationships with unknown parameters. Recently, a semi-analytical Bayesian framework known as nonlinear sparse Bayesian learning (NSBL) was introduced by the authors to induce sparsity among model parameters during the Bayesian inversion of nonlinear-in-parameter models. NSBL relies on optimally selecting the hyperparameters of sparsity-inducing Gaussian priors. It is inherently an approximate method since the uncertainty in the hyperparameter posterior is disregarded as we instead seek the maximum a posteriori (MAP) estimate of the hyperparameters (type-II MAP estimate). This paper aims to investigate the hierarchical structure that forms the basis of NSBL and validate its accuracy through a comparison with a one-level hierarchical Bayesian inference as a benchmark in the context of three numerical experiments: (i) a benchmark linear regression example with Gaussian prior and Gaussian likelihood, (ii) the same regression problem with a highly non-Gaussian prior, and (iii) an example of a dynamical system with a non-Gaussian prior and a highly non-Gaussian likelihood function, to explore the performance of the algorithm in these new settings. Through these numerical examples, it can be shown that NSBL is well-suited for physics-based models as it can be readily applied to models with non-Gaussian prior distributions and non-Gaussian likelihood functions. Moreover, we illustrate the accuracy of the NSBL algorithm as an approximation to the one-level hierarchical Bayesian inference and its ability to reduce the computational cost while adequately exploring the parameter posteriors

Analyse statistique et génétique des gains des pur sang anglais de trois ans dans les courses plates françaises

International audienc

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19 : a study protocol

Introduction: The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. Methods and analysis: We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model’s predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states. Ethics and dissemination: Approved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication