In astronomy, sky surveys contain a large number of light-emitting sources, often with intensities close to the noise level. Automatic extraction of astronomical objects is therefore needed. SExtractor is a widely used program for automated source extraction and cataloguing, but it is not optimal with faint extended sources. Using SExtractor as a reference, the paper describes an improvement of a previous method proposed by the authors. It is a Max-Tree-based method for extraction of faint extended sources without using a stronger image smoothing. The Max-Tree structure is a hierarchical representation of an image, in which attributes can be computed in every node. Object detection is performed on the nodes of the tree and it relies on the distribution of a statistic calculated using the power attribute, compared to the expected distribution in case of noise. Statistical tests are presented, a comparison with the object extraction of SExtractor is shown and results are discussed.
Max-trees, or component trees, are graph structures that represent the connected components of an image in a hierarchical way. Nowadays, many application fields rely on images with high-dynamic range or floating point values. Efficient sequential algorithms exist to build trees and compute attributes for images of any bit depth. However, we show that the current parallel algorithms perform poorly already with integers at bit depths higher than 16 bits per pixel. We propose a parallel method combining the two worlds of flooding and merging max-tree algorithms. First, a pilot max-tree of a quantized version of the image is built in parallel using a flooding method. Later, this structure is used in a parallel leaf-to-root approach to compute efficiently the final max-tree and to drive the merging of the sub-trees computed by the threads. We present an analysis of the performance both on simulated and actual 2D images and 3D volumes. Execution times are about better than the fastest sequential algorithm and speed-up goes up to on 64 threads.
In astronomy, images are produced by sky surveys containing a large number of objects. SExtractor is a widely used program for automated source extraction and cataloguing but struggles with faint extended sources. Using SExtractor as a reference, the paper describes an improvement of a previous method proposed by the authors. It is a Max-Tree-based method for extraction of faint extended sources without stronger image smoothing. Node filtering depends on the noise distribution of a statistic calculated from attributes. Run times are in the same order.
The state of open science needs to be monitored to track changes over time and identify areas to create interventions to drive improvements. In order to monitor open science practices, they first need to be well defined and operationalized. To reach consensus on what open science practices to monitor at biomedical research institutions, we conducted a modified 3-round Delphi study. Participants were research administrators, researchers, specialists in dedicated open science roles, and librarians. In rounds 1 and 2, participants completed an online survey evaluating a set of potential open science practices, and for round 3, we hosted two half-day virtual meetings to discuss and vote on items that had not reached consensus. Ultimately, participants reached consensus on 19 open science practices. This core set of open science practices will form the foundation for institutional dashboards and may also be of value for the development of policy, education, and interventions.
In this paper, we compare the distribution of Elsevier Scopus subject areas of authors documents, their bibliographical references and their citing documents. We compute the complement of the Herfindahl-Hirschman (CHH) index as a measure of multidisciplinarity. We analyse a sample of 120 researchers belonging to two groups, one from the Italian Institute of Technology (IIT, whose work is expected to be highly multidisciplinary) and one from the National Institute for Nuclear Physics (INFN, whose work is expected to be much less multidisciplinary). We show that the two groups are distinguishable through the measured index values. By using the subject areas of authors bibliographical references we obtain a better identification of the two groups than relying on the subject areas of the authors documents. We then extend the analysis to 3,317 researchers belonging to seven Italian Scientific-Disciplinary sectors (SSD) providing insights about the degree of multidisciplinarity within each SSD. The results seem interesting for assessing the interdisciplinarity of younger researchers with scarce scientific output and few citations.
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