1,721,127 research outputs found
Feature augmentation for the inversion of the Fourier transform with limited data
Get PDFWe investigate an interpolation/extrapolation method that, given scattered observations of the Fourier transform, approximates its inverse. The interpolation algorithm takes advantage of modeling the available data via a shape-driven interpolation based on variably scaled Kernels (VSKs), whose implementation is here tailored for inverse problems. The so-constructed interpolants are used as inputs for a standard iterative inversion scheme. After providing theoretical results concerning the spectrum of the VSK collocation matrix, we test the method on astrophysical imaging benchmarks
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
Compressed sensing and Sequential Monte Carlo for solar hard X-ray imaging
Get PDFWe describe two inversion methods for the reconstruction of hard Xray solar images. The methods are tested against experimental visibilities recorded by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) and synthetic visibilities based on the design of the Spectrometer/Telescope for Imaging X-rays (STIX)
Reliability of the Italian version of the International Spinal Cord Injury Pain Basic Data Set
No full textStudy design: Multicentric prospective psychometric study. Objective: To provide a translation of the International Spinal Cord Injury Pain Basic Data Set (ISCIPBDS) for Italian persons and to evaluate the interrater reliability of the translated version. Setting: Ten Italian rehabilitation centres specialized in spinal injury care. Methods: The initial translation was performed by two medical doctors who had an in-depth knowledge of spinal cord injury (SCI), and then a back translation (from Italian to English) was given to an accredited agency. Sixty-six participants with SCI (53 men, 13 women; mean ± SD age: 53.4 ± 16.0 years) were evaluated by means of the Italian version of the ISCIPBDS by two different examiners. Intraclass correlation coefficient (ICC) or Cohen’s Kappa (ĸ) was calculated to test the interrater agreement for the test−retest cases. Results: All 66 participants had at least one pain problem and 34% of them had only one type of pain. A good interrater agreement was obtained in terms of number of pain (ICC = 0.781), type of pain (ĸ = 0.683), pain intensity (ICC = 0.798), correspondence of pain localization (ĸ = 0.750), and the value of the pain interference in day-to-day activities, overall mood and night’s sleep (ICC = 0.827, ICC = 0.861 and ICC = 0.724, respectively). Eventually a prominent prevalence of neuropathic pain was recorded (64% from the first examiner and 62% from the second one). Conclusions: The authors propose the Italian version of ISCIPBDS that can be used for research and clinical evaluation of pain in SCI persons; it shows a significant interrater reliability
Interactive graphical system for segmentation of multimodal medical volumes using fuzzy clustering
No full textAn interpolation/extrapolation approach to X-ray imaging of solar flares
No full textWe describe an interpolation/extrapolation procedure that recon- structs X-ray maps of solar flares using as input data sparse samples of the Fourier transform of the radiation flux, named visibilities. The algorithm is based on two steps: in the first step the performance of an interpolation rou- tine is optimized by representing the visibilities according to favorable coor- dinates in the frequency plane. In the second step two extrapolation schemes are introduced, respectively based on the projection and the thresholding of the Landweber iterative method. The procedure is validated against realistic synthetic visibilities and applied to experimental measurements provided by the NASA satellite Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI)
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
Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers
No full textSolar active regions can significantly disrupt the Sun-Earth space environment, leading to severe space weather events such as solar flares or coronal mass ejections. Consequently, the automatic classification of active region groups is a crucial starting point for accurately and promptly predicting solar activity. This study presents our application of deep learning techniques to classify active region cutouts based on the Mount Wilson classification scheme. We explore the latest advantages in image classification architectures, ranging from convolutional neural networks to vision transformers, alongside modern training procedures, including on-the-fly data augmentations and transfer learning. We aim at evaluating the respective strengths and limitations of different neural network architectures in classifying solar active region cutouts. We observed that combining magnetogram and continuum image types enhances model performance by leveraging complementary features from diverse inputs. When considering only magnetograms, data-efficient image transformers achieve the best performance, suggesting that these models can better capture the spatial complexity of magnetograms. Models trained exclusively on continuum images exhibit overall lower performance, suggesting that continuum images, due to their more homogeneous nature, offer less spatial variability
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