1,720,977 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Apprentissage de dynamiques dans les séries temporelles d’images satellites multispectrales
No full textThanks to the Earth observation missions such as the Copernicus programm of the European Union, vast amounts of satellite images from the surface of the Earth are now available. These data enable, among other applications, to monitor the state of environments such as forests, glaciers and oceans or to facilitate the coordination of rescues in case of natural disasters such as floods and forest fires. Thus, the methods for processing satellite images, often based on artificial neural networks, are of great interest for these applications. However, training these models with a supervised learning approach requires annotating large quantities of data for each of the applications considered, and processing time series of images (rather than isolated images) is still a difficulty. In this thesis, we propose to adopt a self-supervised approach, aiming at learning a dynamical model of evolution from unlabelled data, which are publicly and massively available. We introduce several methods for using such a dynamical model to solve various practical tasks, notably by integrating it in a variational data assimilation cost. Although we focus on time series of forest images taken by the Sentinel-2 satellites, we ambition to develop methods that are applicable to various types of time series, including outside the field of satellite imagery.Via les missions d’observation relevant, par exemple, du programme Copernicus de l’Union européenne, de très grandes quantités d’images satellites multispectrales de la surface de la Terre sont aujourd’hui disponibles. Ces données permettent, entre autres, de surveiller l’état d’environnements tels que les forêts, glaciers et océans ou de faciliter l’organisation des secours lors de catastrophes naturelles telles que les inondations et incendies. Ainsi, les méthodes de traitement des données issues de satellites, souvent basées sur des réseaux de neurones artificiels, sont d’un grand intérêt pour ces applications. Cependant, entraîner ces modèles avec un paradigme d’apprentissage supervisé requiert l’annotation de grandes quantités de données pour chacune des applications considérées, et la prise en compte de séries temporelles d’images (plutôt que d’images seules) reste difficile. Dans cette thèse, nous proposons d’adopter une approche auto-supervisée, dans le but d’apprendre un modèle dynamique d’évolution à partir de données non annotées, disponibles publiquement et massivement. Nous proposons différentes méthodes pour utiliser un tel modèle dynamique pour résoudre différentes tâches pratiques, notamment en l’intégrant dans un coût variationnel d’assimilation de données. Bien que nous nous concentrions sur des séries temporelles d’images de forêts prises par les satellites Sentinel-2, nous avons pour ambition de développer des méthodes applicables à divers types de séries temporelles, y compris en dehors du champ de l’imagerie satellitaire
Apprentissage de dynamiques dans les séries temporelles d’images satellites multispectrales
No full textThanks to the Earth observation missions such as the Copernicus programm of the European Union, vast amounts of satellite images from the surface of the Earth are now available. These data enable, among other applications, to monitor the state of environments such as forests, glaciers and oceans or to facilitate the coordination of rescues in case of natural disasters such as floods and forest fires. Thus, the methods for processing satellite images, often based on artificial neural networks, are of great interest for these applications. However, training these models with a supervised learning approach requires annotating large quantities of data for each of the applications considered, and processing time series of images (rather than isolated images) is still a difficulty. In this thesis, we propose to adopt a self-supervised approach, aiming at learning a dynamical model of evolution from unlabelled data, which are publicly and massively available. We introduce several methods for using such a dynamical model to solve various practical tasks, notably by integrating it in a variational data assimilation cost. Although we focus on time series of forest images taken by the Sentinel-2 satellites, we ambition to develop methods that are applicable to various types of time series, including outside the field of satellite imagery.Via les missions d’observation relevant, par exemple, du programme Copernicus de l’Union européenne, de très grandes quantités d’images satellites multispectrales de la surface de la Terre sont aujourd’hui disponibles. Ces données permettent, entre autres, de surveiller l’état d’environnements tels que les forêts, glaciers et océans ou de faciliter l’organisation des secours lors de catastrophes naturelles telles que les inondations et incendies. Ainsi, les méthodes de traitement des données issues de satellites, souvent basées sur des réseaux de neurones artificiels, sont d’un grand intérêt pour ces applications. Cependant, entraîner ces modèles avec un paradigme d’apprentissage supervisé requiert l’annotation de grandes quantités de données pour chacune des applications considérées, et la prise en compte de séries temporelles d’images (plutôt que d’images seules) reste difficile. Dans cette thèse, nous proposons d’adopter une approche auto-supervisée, dans le but d’apprendre un modèle dynamique d’évolution à partir de données non annotées, disponibles publiquement et massivement. Nous proposons différentes méthodes pour utiliser un tel modèle dynamique pour résoudre différentes tâches pratiques, notamment en l’intégrant dans un coût variationnel d’assimilation de données. Bien que nous nous concentrions sur des séries temporelles d’images de forêts prises par les satellites Sentinel-2, nous avons pour ambition de développer des méthodes applicables à divers types de séries temporelles, y compris en dehors du champ de l’imagerie satellitaire
Apprentissage de dynamiques dans les séries temporelles d’images satellites multispectrales
No full textThanks to the Earth observation missions such as the Copernicus programm of the European Union, vast amounts of satellite images from the surface of the Earth are now available. These data enable, among other applications, to monitor the state of environments such as forests, glaciers and oceans or to facilitate the coordination of rescues in case of natural disasters such as floods and forest fires. Thus, the methods for processing satellite images, often based on artificial neural networks, are of great interest for these applications. However, training these models with a supervised learning approach requires annotating large quantities of data for each of the applications considered, and processing time series of images (rather than isolated images) is still a difficulty. In this thesis, we propose to adopt a self-supervised approach, aiming at learning a dynamical model of evolution from unlabelled data, which are publicly and massively available. We introduce several methods for using such a dynamical model to solve various practical tasks, notably by integrating it in a variational data assimilation cost. Although we focus on time series of forest images taken by the Sentinel-2 satellites, we ambition to develop methods that are applicable to various types of time series, including outside the field of satellite imagery.Via les missions d’observation relevant, par exemple, du programme Copernicus de l’Union européenne, de très grandes quantités d’images satellites multispectrales de la surface de la Terre sont aujourd’hui disponibles. Ces données permettent, entre autres, de surveiller l’état d’environnements tels que les forêts, glaciers et océans ou de faciliter l’organisation des secours lors de catastrophes naturelles telles que les inondations et incendies. Ainsi, les méthodes de traitement des données issues de satellites, souvent basées sur des réseaux de neurones artificiels, sont d’un grand intérêt pour ces applications. Cependant, entraîner ces modèles avec un paradigme d’apprentissage supervisé requiert l’annotation de grandes quantités de données pour chacune des applications considérées, et la prise en compte de séries temporelles d’images (plutôt que d’images seules) reste difficile. Dans cette thèse, nous proposons d’adopter une approche auto-supervisée, dans le but d’apprendre un modèle dynamique d’évolution à partir de données non annotées, disponibles publiquement et massivement. Nous proposons différentes méthodes pour utiliser un tel modèle dynamique pour résoudre différentes tâches pratiques, notamment en l’intégrant dans un coût variationnel d’assimilation de données. Bien que nous nous concentrions sur des séries temporelles d’images de forêts prises par les satellites Sentinel-2, nous avons pour ambition de développer des méthodes applicables à divers types de séries temporelles, y compris en dehors du champ de l’imagerie satellitaire
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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