886 resultados para Machine Typed Document
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El Grup presenta un document d'opinió amb la voluntat d'intervenir en el debat sobre la interrupció voluntària de l'embaràs i proposarmesures normatives que posin fi a l'actual situació d¿inseguretat jurídica derivadade la distorsionada aplicació que s'efectua de la desfasada legislació vigent.L'avortament és un veritable problema de salut pública i tant l'ètica com el drethan de plantejar pautes de conducta assumibles per la majoria dels ciutadans irespectuoses amb les minories, tenint sempre com a marc de referència i principiindeclinable el respecte als Drets Humans.
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Aquest document parteix del reconeixement del principi d'autonomia de les personesi de la llibertat de consciència, ideològica i religiosa. I precisament per això exposael risc que, en la pràctica sanitària, l'exercici de l'objecció de consciència pugui fercol·lisió amb els drets dels usuaris. Per aquesta raó, l'Administració i les diversesinstitucions involucrades, han de prendre les mesures necessàries per facilitar la compatibilitat de l'exercici dels drets i les decisions de totes les persones implicades.
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Numérisation partielle de reliure
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El document pretén aportar pautes útilsals ciutadans, als centres assistencials i a l'Administració, i que subministra criterisètics i jurídics que ajuden a aclarir el debat sobre si les decisions sanitàries adoptadespels cristians anomenats Testimonis de Jehovà, sobre la base de les seves creencesreligioses, han de ser respectades encara que la seva conseqüència fi nal sigui lapèrdua de la vida.
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A recent study suggests that sex-specific dispersal rates can be quantitatively estimated on the basis of sex- and state-specific (pre- vs. postdispersal) F-statistics. In the present paper, we extend this approach to account for the hierarchical structure of natural populations, and we validate it through individual-based simulations. The model is applied to an empirical data set consisting of 536 individuals (males, females, and predispersal juveniles) of greater white-toothed shrews (Crocidura russula), sampled according to a hierarchical design and typed for seven autosomal microsatellite loci. From this dataset, dispersal is significantly female biased at the local scale (breeding-group level), but not at the larger scale (among local populations). We argue that selective pressures on dispersal are likely to depend on the spatial scale considered, and that short-distance dispersal should mainly respond to kin interactions (inbreeding or kin competition avoidance), which exert differential pressure on males and females.
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[Table des matières] 1.1. Bref historique de la stratégie nationale de lutte contre le cancer. 1.2. Mandat d'évaluation. 1. 3. Approche d'évaluation choisie. 1.4. Phase 1 : programme d'évaluation 1999. 2. Conclusions et recommandations générales. 2.1. Stratégie et concept directeur. 2.2. Structure soutenant le programme national de lutte contre le cancer. 2.3. Rôle et fonctionnement des différents organes du programme national. 2.4. Collaborations. 2.5. Monitoring des programmes, évaluation de projets spécifiques et indicateurs à disposition pour l'évaluation globale. 3. Propositions pour la suite de l'évaluation. 4. Résumé de l'étude 1 : évaluation de la conception et de la mise en oeuvre de la stratégie au niveau national. 5. Studie 2 : Inventar der vorhandene Datenquellen und Indikatoren. 5.1. Zusammenfassung. 5.2. Allgemeine Schlussfolgerungen und Empfehlungen. 6. Studie 3 : Konzeptualisierung und Stand der Umsetzung der vier Krebsbekämpfungsprogramme. 6.1. Einleitung. 6.2. Zusammenfassung der programmübergreifende Ergebnisse : zum Konzeptualisierungsprozess, zum Steuerungsprozess, zur Vernetzung innerhalb der Programme und im relevanten Umfeld. 6.3. Zusammenfassung der programmspezifischen Ergebnisse : Brustkrebs, Hautkrebs, Lungenkrebs, Darmkrebs. 6.4. Empfehlungen : Programmübergreifende Empfehlungen, ergänzende programmspezifische Empfehlungen.
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Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.
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The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.
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Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.
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This document summarizes the discussion and findings of a workshop on intelligent technologies for earthwork construction held in West Des Moines, Iowa, on April 14–16, 2009. This meeting follows a similar workshop conducted in 2008. The objective of the meeting was to provide a focused discussion on identifying research and implementation needs/strategies to advance intelligent compaction and automated machine guidance technologies. Technical presentations, interactive working breakout sessions, and a panel discussion comprised the workshop. About 100 attendees representing state departments of transportation, Federal Highway Administration, contractors, equipment manufacturers, and researchers participated in the workshop.
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La présente étude est à la fois une évaluation du processus de la mise en oeuvre et des impacts de la police de proximité dans les cinq plus grandes zones urbaines de Suisse - Bâle, Berne, Genève, Lausanne et Zurich. La police de proximité (community policing) est à la fois une philosophie et une stratégie organisationnelle qui favorise un partenariat renouvelé entre la police et les communautés locales dans le but de résoudre les problèmes relatifs à la sécurité et à l'ordre public. L'évaluation de processus a analysé des données relatives aux réformes internes de la police qui ont été obtenues par l'intermédiaire d'entretiens semi-structurés avec des administrateurs clés des cinq départements de police, ainsi que dans des documents écrits de la police et d'autres sources publiques. L'évaluation des impacts, quant à elle, s'est basée sur des variables contextuelles telles que des statistiques policières et des données de recensement, ainsi que sur des indicateurs d'impacts construit à partir des données du Swiss Crime Survey (SCS) relatives au sentiment d'insécurité, à la perception du désordre public et à la satisfaction de la population à l'égard de la police. Le SCS est un sondage régulier qui a permis d'interroger des habitants des cinq grandes zones urbaines à plusieurs reprises depuis le milieu des années 1980. L'évaluation de processus a abouti à un « Calendrier des activités » visant à créer des données de panel permettant de mesurer les progrès réalisés dans la mise en oeuvre de la police de proximité à l'aide d'une grille d'évaluation à six dimensions à des intervalles de cinq ans entre 1990 et 2010. L'évaluation des impacts, effectuée ex post facto, a utilisé un concept de recherche non-expérimental (observational design) dans le but d'analyser les impacts de différents modèles de police de proximité dans des zones comparables à travers les cinq villes étudiées. Les quartiers urbains, délimités par zone de code postal, ont ainsi été regroupés par l'intermédiaire d'une typologie réalisée à l'aide d'algorithmes d'apprentissage automatique (machine learning). Des algorithmes supervisés et non supervisés ont été utilisés sur les données à haute dimensionnalité relatives à la criminalité, à la structure socio-économique et démographique et au cadre bâti dans le but de regrouper les quartiers urbains les plus similaires dans des clusters. D'abord, les cartes auto-organisatrices (self-organizing maps) ont été utilisées dans le but de réduire la variance intra-cluster des variables contextuelles et de maximiser simultanément la variance inter-cluster des réponses au sondage. Ensuite, l'algorithme des forêts d'arbres décisionnels (random forests) a permis à la fois d'évaluer la pertinence de la typologie de quartier élaborée et de sélectionner les variables contextuelles clés afin de construire un modèle parcimonieux faisant un minimum d'erreurs de classification. Enfin, pour l'analyse des impacts, la méthode des appariements des coefficients de propension (propensity score matching) a été utilisée pour équilibrer les échantillons prétest-posttest en termes d'âge, de sexe et de niveau d'éducation des répondants au sein de chaque type de quartier ainsi identifié dans chacune des villes, avant d'effectuer un test statistique de la différence observée dans les indicateurs d'impacts. De plus, tous les résultats statistiquement significatifs ont été soumis à une analyse de sensibilité (sensitivity analysis) afin d'évaluer leur robustesse face à un biais potentiel dû à des covariables non observées. L'étude relève qu'au cours des quinze dernières années, les cinq services de police ont entamé des réformes majeures de leur organisation ainsi que de leurs stratégies opérationnelles et qu'ils ont noué des partenariats stratégiques afin de mettre en oeuvre la police de proximité. La typologie de quartier développée a abouti à une réduction de la variance intra-cluster des variables contextuelles et permet d'expliquer une partie significative de la variance inter-cluster des indicateurs d'impacts avant la mise en oeuvre du traitement. Ceci semble suggérer que les méthodes de géocomputation aident à équilibrer les covariables observées et donc à réduire les menaces relatives à la validité interne d'un concept de recherche non-expérimental. Enfin, l'analyse des impacts a révélé que le sentiment d'insécurité a diminué de manière significative pendant la période 2000-2005 dans les quartiers se trouvant à l'intérieur et autour des centres-villes de Berne et de Zurich. Ces améliorations sont assez robustes face à des biais dus à des covariables inobservées et covarient dans le temps et l'espace avec la mise en oeuvre de la police de proximité. L'hypothèse alternative envisageant que les diminutions observées dans le sentiment d'insécurité soient, partiellement, un résultat des interventions policières de proximité semble donc être aussi plausible que l'hypothèse nulle considérant l'absence absolue d'effet. Ceci, même si le concept de recherche non-expérimental mis en oeuvre ne peut pas complètement exclure la sélection et la régression à la moyenne comme explications alternatives. The current research project is both a process and impact evaluation of community policing in Switzerland's five major urban areas - Basel, Bern, Geneva, Lausanne, and Zurich. Community policing is both a philosophy and an organizational strategy that promotes a renewed partnership between the police and the community to solve problems of crime and disorder. The process evaluation data on police internal reforms were obtained through semi-structured interviews with key administrators from the five police departments as well as from police internal documents and additional public sources. The impact evaluation uses official crime records and census statistics as contextual variables as well as Swiss Crime Survey (SCS) data on fear of crime, perceptions of disorder, and public attitudes towards the police as outcome measures. The SCS is a standing survey instrument that has polled residents of the five urban areas repeatedly since the mid-1980s. The process evaluation produced a "Calendar of Action" to create panel data to measure community policing implementation progress over six evaluative dimensions in intervals of five years between 1990 and 2010. The impact evaluation, carried out ex post facto, uses an observational design that analyzes the impact of the different community policing models between matched comparison areas across the five cities. Using ZIP code districts as proxies for urban neighborhoods, geospatial data mining algorithms serve to develop a neighborhood typology in order to match the comparison areas. To this end, both unsupervised and supervised algorithms are used to analyze high-dimensional data on crime, the socio-economic and demographic structure, and the built environment in order to classify urban neighborhoods into clusters of similar type. In a first step, self-organizing maps serve as tools to develop a clustering algorithm that reduces the within-cluster variance in the contextual variables and simultaneously maximizes the between-cluster variance in survey responses. The random forests algorithm then serves to assess the appropriateness of the resulting neighborhood typology and to select the key contextual variables in order to build a parsimonious model that makes a minimum of classification errors. Finally, for the impact analysis, propensity score matching methods are used to match the survey respondents of the pretest and posttest samples on age, gender, and their level of education for each neighborhood type identified within each city, before conducting a statistical test of the observed difference in the outcome measures. Moreover, all significant results were subjected to a sensitivity analysis to assess the robustness of these findings in the face of potential bias due to some unobserved covariates. The study finds that over the last fifteen years, all five police departments have undertaken major reforms of their internal organization and operating strategies and forged strategic partnerships in order to implement community policing. The resulting neighborhood typology reduced the within-cluster variance of the contextual variables and accounted for a significant share of the between-cluster variance in the outcome measures prior to treatment, suggesting that geocomputational methods help to balance the observed covariates and hence to reduce threats to the internal validity of an observational design. Finally, the impact analysis revealed that fear of crime dropped significantly over the 2000-2005 period in the neighborhoods in and around the urban centers of Bern and Zurich. These improvements are fairly robust in the face of bias due to some unobserved covariate and covary temporally and spatially with the implementation of community policing. The alternative hypothesis that the observed reductions in fear of crime were at least in part a result of community policing interventions thus appears at least as plausible as the null hypothesis of absolutely no effect, even if the observational design cannot completely rule out selection and regression to the mean as alternative explanations.
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The determination of the age of a document is a very frequent query, however it is also one of the most challenging and controversial areas of questioned document examination. Several approaches were defined to address this problem. The first is based on the introduction date of raw constituents (like paper or ink) on the market. The second approach is based on the aging of documents, which is unfortunately not only influenced by passing time, but also by storage conditions and document composition. The third approach considers the relative age of documents and aims at reconstructing their chronology. The three approaches are equally complex to develop and encounter quantity of problems which are not negligible. This article aims to expose the potential applications and limitations of current ink dating methods. Method development and validation and the interpretation of evidence prove to be essential criteria for the dating of documents.