1,721,016 research outputs found
The Approximation of the Dissimilarity Projection
No full textDiffusion magnetic resonance imaging (dMRI) data allow to reconstruct the 3D pathways of axons within the white matter of the brain as a tractography. The analysis of tractographies has drawn attention from the machine learning and pattern recognition communities providing novel challenges such as finding an appropriate representation space for the data. Many of the current learning algorithms require the input to be from a vectorial space. This requirement contrasts with the intrinsic nature of the tractography because its basic elements, called streamlines or tracks, have different lengths and different number of points and for this reason they cannot be directly represented in a common vectorial space. In this work we propose the adoption of the dissimilarity representation which is an Euclidean embedding technique defined by selecting a set of streamlines called prototypes and then mapping any new streamline to the vector of distances from prototypes. We investigate the degree of approximation of this projection under different prototype selection policies and prototype set sizes in order to characterise its use on tractography data. Additionally we propose the use of a scalable approximation of the most effective prototype selection policy that provides fast and accurate dissimilarity approximations of complete tractographies
Diagram showing QA calculation from a spin distribution function.
No full text<p>The red outer contour represents the spin distribution function calculated by generalized q-sampling imaging, whereas the sphere at the center is the isotropic component estimated by its minimum value. A QA value is defined for each peak orientation, and it serves as an index to differentiate less salient peaks and to selectively remove them. (Eleftherios Garyfallidis, "Towards an accurate brain tractography", PhD thesis, University of Cambridge, 2012, use with permission).</p
Advanced Atlas of 80 Bundles in MNI space
No full textThis is a dataset to be used by the RecoBundles algorithm to automatically extract anatomically relevant bundles from tractograms. RecoBundles is available in DIPY (http://dipy.org). The algorithm is explained in the paper:Garyfallidis, Eleftherios, et al. "Recognition of white matter bundles using local and global streamline-based registration and clustering." NeuroImage 170 (2017): 283-297.https://www.ncbi.nlm.nih.gov/pubmed/28712994This dataset is curated from the dataset explained in the following paper:Yeh, Fang-Cheng, et al. "Population-averaged atlas of the macroscale human structural connectome and its network topology." NeuroImage 178 (2018): 57-68.https://www.sciencedirect.com/science/article/pii/S1053811918304324All the bundles and trk (Trackvis) files are in MNI space ICBM 2009a.Do contact the DIPY developers at https://gitter.im/nipy/dipy if you need the bundles at ICBM 2009c space.For the Fornix (F) both left and right sides are included in one file. It is possible to easily separate them if you need to. Send an e-mail to [email protected] to learn how.If you use this dataset please cite Garyfallidis et al. 2017 and Yeh et al. 2018 (see above).Happy RecoBundling!</div
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Simple model bundle atlas for RecoBundles
No full textThis is a dataset to be used by the RecoBundles algorithm available in DIPY (http://dipy.org). The algorithm is explained in the paper:Garyfallidis, Eleftherios, et al. "Recognition of white matter bundles using local and global streamline-based registration and clustering." NeuroImage 170 (2017): 283-297.https://www.ncbi.nlm.nih.gov/pubmed/28712994This dataset is curated from the dataset explained in the following paper Yeh, Fang-Cheng, et al. "Population-averaged atlas of the macroscale human structural connectome and its network topology." NeuroImage 178 (2018): 57-68.https://www.sciencedirect.com/science/article/pii/S1053811918304324All the bundles and trk (Trackvis) files are in MNI space ICBM 2009a.</div
Atlas of 30 Human Brain Bundles in MNI space
No full textThis is a dataset to be used by the RecoBundles algorithm to automatically extract anatomically relevant bundles from tractograms. RecoBundles is available in DIPY (http://dipy.org). The algorithm is explained in the paper:Garyfallidis, Eleftherios, et al. "Recognition of white matter bundles using local and global streamline-based registration and clustering." NeuroImage 170 (2017): 283-297.https://www.ncbi.nlm.nih.gov/pubmed/28712994This dataset is curated from the dataset explained in the following paper:Yeh, Fang-Cheng, et al. "Population-averaged atlas of the macroscale human structural connectome and its network topology." NeuroImage 178 (2018): 57-68.https://www.sciencedirect.com/science/article/pii/S1053811918304324All the bundles and trk (Trackvis) files are in MNI space ICBM 2009a.Do contact the DIPY developers at https://gitter.im/nipy/dipy if you need the bundles at ICBM 2009c space.For the Fornix (F) both left and right sides are included in one file. It is possible to easily separate them if you need to. Send an e-mail to [email protected] to learn how.If you use this dataset please cite Garyfallidis et al. 2017 and Yeh et al. 2018 (see above).Research reported in this publication was supported primarily by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R01EB027585.Happy RecoBundling!</div
Variations on the Author
Full text link“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
Full text linkWe 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
Full text linkWe 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
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