1,721,000 research outputs found
Robustness in Weighted Networks
No full textIn the last two decades, Network science has become a strategic field of research thanks to both
the increased availability of large datasets, and the strong development of high-performance computing technologies and methodologies. Different types of data will produce different types of complex networks in terms of structure, connectivity, and complexity. Examples range from biology to business and from technology to sociology. A network is said to have a community structure if the nodes are densely connected within groups but sparsely connected between them (1). A number of methods
for community detection have been proposed. However, their implementation leaves unaddressed the
question of the statistical validation of the results. A first method to statistically test the robustness
of undirected and unweighted networks was proposed in (2; 3). The method uses a configuration
model as a null random model to test the hypothesis that the detected communities are due only to
the random position of the edges in the graph.
In this work we propose a Machine Learning approach to perform robustness analysis for weighted
Networks
The physical retrieval methodology for IASI: the delta-IASI code
No full textThis paper describes a physical-based methodology for the retrieval of geophysical parameters (temperature, water vapor and ozone) from highly resolved infrared radiance, and presents the algorithm which implements the procedure. The algorithm we have implemented is mostly intended for the Infrared Atmospheric Sounding Interferometer which is planned to be flown on the first European Meteorological Operational Satellite (Metop/1) in 2006. Nevertheless, with minor modifications, the code is well suited for any nadir viewing satellite and airborne infrared sensor with a sampling rate in the range of 0.1–2 cm−1. Basically, the implementation of the inverse scheme follows Rodgers' Statistical Regularization method. However, an additional regularization parameter is introduced in the inverse scheme which gains to the algorithm the capability of improving the retrieval accuracy and to constraint the step size of Newton updates in such a way to lead iterates toward the feasible region of the inverse solution. Although, the paper mostly focuses on documenting and discussing the mathematical details of the inverse method, retrieval exercises have been provided, which exemplify the use and potential performance of the method. These retrieval exercises have been performed for the Infrared Atmospheric Sounding Interferometer. In addition, examples of application to real observations have been discussed based on the Interferometric Monitoring Greenhouse (IMG) gases Fourier Transform Spectrometer which has flown on the Japanese Advanced Earth Observation Satellite
robin2: accelerating single-cell data clustering evaluation
No full textMotivation: The rapid expansion of single-cell RNA sequencing (scRNA-seq) technologies has increased the need for robust and scalable clustering evaluation methods. To address these challenges, we developed robin2, an optimized version of our R package robin. It introduces enhanced computational efficiency, support for high-dimensional datasets, and harmonious integration with R's base functionalities for robust network analysis. Results: robin2 offers improved functionality for clustering stability validation and enables systematic evaluation of community detection algorithms across various resolutions and pipelines. The application to Tabula Muris and PBMC scRNA-seq datasets confirmed its ability to identify biologically meaningful cell subpopulations with high statistical significance. The new version reduces computational time by 9-fold on large-scale datasets using parallel processing. Availability and implementation: The robin2 package is freely available on CRAN at https://CRAN.R-project.org/package=robin. Comprehensive documentation and a detailed analysis vignette are available on GitHub at https://drighelli.github.io/scrobinv2/index.html
ROBustness In Network (robin): an R Package for Comparison and Validation of Communities
Full text linkIn network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset
Cloud Detection, Temperature and Water Vapor Retrieval from Hyperspectral Sounder Observations
No full textNew generation meteorological satellites carry infrared sensors able to sense the earth
emission spectrum at very high spectral resolution. The related problems of cloud detection and
inversion for geophysical parameters are addressed in this paper
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
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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