Cinéma, machine à mondes : essai sur les films à univers multiples

Dans cet ouvrage, l'auteur propose une conceptualisation théorique de la coprésence en un même film de mondes multiples en abordant différents paramètres (hétérogénéité de la facture de l'image, pratiques du montage alterné, typologie des enchâssements, expansion sérielle, etc.) sur la base d'un corpus de films de fiction récents qui appartiennent pour la plupart au genre de la science-fiction (Matrix, Dark City, Avalon, Resident Evil, Avatar,...). Issue de la filmologie, la notion de « diégèse » y est développée à la fois dans le potentiel d'autonomisation dont témoigne la conception mondaine qui semble dominer aujourd'hui à l'ère des jeux vidéo, dans ses liens avec le récit et dans une perspective intermédiale. Les films discutés ont la particularité de mettre en scène des machines permettant aux personnages de passer d'un monde à l'autre : les modes de figuration de ces technologies sont investigués en lien avec les imaginaires du dispositif cinématographique et les potentialité du montage. La comparaison entre les films (Tron et son récent sequel, Totall Recall et son remake) et entre des oeuvres filmiques et littéraires (en particulier les nouvelles de Philip K. Dick et Simlacron 3 de Galouye) constitue un outil d'analyse permettant de saisir la contemporanéité de cette problématique, envisagée sur le plan esthétique dans le contexte de l'imagerie numérique.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Love machine

This poster provides advice on the use of condoms as a method of protection from unplanned pregnancy and sexually transmitted infections (STIs). It also provides contact details for the�Genito Urinary Medicine (GUM) clinics in Northern Ireland.

Infectious disease among enslaved African Americans at Eaton's Estate, Warren County, North Carolina, ca. 1830-1850

The skeletal remains of 17 people buried in the Eaton Ferry Cemetery in northern North Carolina provide a means of examining health and infectious disease experience in the XIX century South. The cemetery appears to contain the remains of African Americans enslaved on the Eaton family estate from approximately 1830-1850, and thus offers a window into the biological impacts of North American slavery in the years preceding the Civil War. The sample includes the remains of six infants, one child, and one young and nine mature adults (five men, four women, and one unknown). Skeletal indices used to characterize health and disease in the Eaton Ferry sample include dental caries, antemortem tooth loss, enamel hypoplasia, porotic hyperostosis, periosteal lesions, lytic lesions, and stature. These indicators reveal a cumulative picture of compromised health, including high rates of dental disease, childhood growth disruption, and infectious disease. Specific diseases identified in the sample include tuberculosis and congenital syphilis. Findings support previous research on the health impacts of slavery, which has shown that infants and children were the most negatively impacted segment of the enslaved African American population.

An empirical study of machine learning techniques applied to image watermarking

A study of how the machine learning technique, known as gentleboost, could improve different digital watermarking methods such as LSB, DWT, DCT2 and Histogram shifting.

Warren County, June 30, 2002

County Audit Report

Warren County, June 30, 2003

County Audit Report

Warren County, Independent Auditor's Reports, Basic Financial Statements and Supplementary Information, Schedule of Findings and Questioned Costs, June 30, 2004

County Audit Report