1,720,975 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
Population-based personalization of a 2D diffusion-based model of myocardial infarct
No full textInternational audienceSimple and effective infarct models are needed to enhance cardiac biophysical models or generate more accurate ground truth data for machine learning applications. This study presents a diffusion-based approach for modeling infarct shapes, applied to generate synthetic 2D infarct images. The diffusion is non-uniform, with different diffusion coefficients assigned to various sectors of the myocardium. We integrate a population-based approach to learn the model parameters and personalize them to a real population. This personalization method is relevant for models with randomness, such as our model, and is achieved through a gradient-free algorithm (CMA-ES), optimizing a distribution similarity metric between real and synthetic populations. We assess this approach on 2D infarct segmentations from LGE MR images and aligned to a reference geometry, from 117 patients with acute myocardial infarct. We generate 500 synthetic images and evaluate their characteristics using an attribute-based variational auto-encoder (AR-VAE), which captures a latent space with key infarct characteristics specifically disentangled on specific dimensions (transmurality, size, and orientation). Our model offers a simple, physically-based method to generate infarcts with diverse shapes. The evaluation shows that the synthetic population accurately reflects the distributions of the key infarct characteristics, with more sophisticated infarct shapes compared to previous simpler models also personalized with the population-based approach
Post-processing and spatial alignment on cardiac MRI data (delayed enhancement, segmentations from CVI42 software)
No full textPopulation-based personalization of a 2D diffusion-based model of myocardial infarct
No full textInternational audienceSimple and effective infarct models are needed to enhance cardiac biophysical models or generate more accurate ground truth data for machine learning applications. This study presents a diffusion-based approach for modeling infarct shapes, applied to generate synthetic 2D infarct images. The diffusion is non-uniform, with different diffusion coefficients assigned to various sectors of the myocardium. We integrate a population-based approach to learn the model parameters and personalize them to a real population. This personalization method is relevant for models with randomness, such as our model, and is achieved through a gradient-free algorithm (CMA-ES), optimizing a distribution similarity metric between real and synthetic populations. We assess this approach on 2D infarct segmentations from LGE MR images and aligned to a reference geometry, from 117 patients with acute myocardial infarct. We generate 500 synthetic images and evaluate their characteristics using an attribute-based variational auto-encoder (AR-VAE), which captures a latent space with key infarct characteristics specifically disentangled on specific dimensions (transmurality, size, and orientation). Our model offers a simple, physically-based method to generate infarcts with diverse shapes. The evaluation shows that the synthetic population accurately reflects the distributions of the key infarct characteristics, with more sophisticated infarct shapes compared to previous simpler models also personalized with the population-based approach
Post-processing and spatial alignment on cardiac MRI data (delayed enhancement, segmentations from CVI42 software)
No full textPopulation-based personalization of a 2D diffusion-based model of myocardial infarct
No full textInternational audienceSimple and effective infarct models are needed to enhance cardiac biophysical models or generate more accurate ground truth data for machine learning applications. This study presents a diffusion-based approach for modeling infarct shapes, applied to generate synthetic 2D infarct images. The diffusion is non-uniform, with different diffusion coefficients assigned to various sectors of the myocardium. We integrate a population-based approach to learn the model parameters and personalize them to a real population. This personalization method is relevant for models with randomness, such as our model, and is achieved through a gradient-free algorithm (CMA-ES), optimizing a distribution similarity metric between real and synthetic populations. We assess this approach on 2D infarct segmentations from LGE MR images and aligned to a reference geometry, from 117 patients with acute myocardial infarct. We generate 500 synthetic images and evaluate their characteristics using an attribute-based variational auto-encoder (AR-VAE), which captures a latent space with key infarct characteristics specifically disentangled on specific dimensions (transmurality, size, and orientation). Our model offers a simple, physically-based method to generate infarcts with diverse shapes. The evaluation shows that the synthetic population accurately reflects the distributions of the key infarct characteristics, with more sophisticated infarct shapes compared to previous simpler models also personalized with the population-based approach
Population-based personalization of a 2D diffusion-based model of myocardial infarct
No full textInternational audienceSimple and effective infarct models are needed to enhance cardiac biophysical models or generate more accurate ground truth data for machine learning applications. This study presents a diffusion-based approach for modeling infarct shapes, applied to generate synthetic 2D infarct images. The diffusion is non-uniform, with different diffusion coefficients assigned to various sectors of the myocardium. We integrate a population-based approach to learn the model parameters and personalize them to a real population. This personalization method is relevant for models with randomness, such as our model, and is achieved through a gradient-free algorithm (CMA-ES), optimizing a distribution similarity metric between real and synthetic populations. We assess this approach on 2D infarct segmentations from LGE MR images and aligned to a reference geometry, from 117 patients with acute myocardial infarct. We generate 500 synthetic images and evaluate their characteristics using an attribute-based variational auto-encoder (AR-VAE), which captures a latent space with key infarct characteristics specifically disentangled on specific dimensions (transmurality, size, and orientation). Our model offers a simple, physically-based method to generate infarcts with diverse shapes. The evaluation shows that the synthetic population accurately reflects the distributions of the key infarct characteristics, with more sophisticated infarct shapes compared to previous simpler models also personalized with the population-based approach
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