1,721,133 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
Multi-realization seismic data processing with deep variational preconditioners
No full textGeophysical data processing is traditionally treated as a deterministic-centric discipline. Seismic data are usually processed through a chain of algorithms, where a given output becomes the input of a subsequent processing step. Geophysicists have recently started to recognize the importance of quantifying uncertainties within each processing step, such that they could be used as input to subsequent imaging and inversion processes. We present a two-steps approach where a Variational AutoEncoder (VAE) is first trained to generate realistic samples of the input seismic data at hand; parametric Variational Inference is then utilized to optimize the parameters of the VAE latent distribution for a given observed data and modelling operator of interest, and sample multiple realizations from the optimized parametric posterior distributions. Seismic deblending as well as joint interpolation and wavefield separation are used to showcase the proposed methodology.The author thanks KAUST and the DeepWave Consortium spon-sors for supporting this research
Probabilistic seismic interpolation with the implicit prior of a deep denoiser
No full textGeophysicists have long recognized the importance of quantifying the uncertainty associated with geophysical inverse problems, whether they are used for processing, imaging, or parameter estimation purposes. The inability to create representative prior and proposal distributions has, however, hindered the widespread adoption of acceptance-rejection algorithms, such as those from the family of Monte-Carlo Markov Chain methods. We present a flexible approach to probabilistic sampling that leverages the ability of denoising neural networks to provide direct access to the gradient of the log-probability of interest. The proposed algorithm can produce high-quality, diverse samples from both unconditional and conditional probability distributions, the latter being of particular interest when solving probabilistic inverse problems. A successful application is presented in the context of seismic interpolation on both synthetic and field data.The author thanks KAUST and the DeepWave Consortium spon-sors for supporting this research, as well as Equinor and theVolve license partners for releasing the field dataset
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
Seismic deblending with a hard data constraint
No full textDeblending is the process of separating seismic data that have been acquired by firing one (or more) sources at reduced time intervals. State-of-the-art deblending algorithms iteratively project the unexplained part of the blended data into the solution space and add it to the current solution; the remaining blending noise is then filtered out from the signal by thresholding in a suitable domain. This process is equivalent to applying the proximal gradient algorithm to an objective function composed of the squared Euclidean norm of data residual alongside a convex regularizer (e.g., L1 norm of the solution transformed in a sparse domain). As a consequence of the choice of such an objective function, it is common practice at the end of the iterations to ‘add back’ to the solution any coherent energy that may have remained in the residual. In this work, I propose a more natural formulation to honour the blended data at each iteration of the deblending process; this is achieved by utilizing a hard data constraint that replaces the squared Euclidean norm of the data residual with an affine set indicator function. The resulting objective function can be solved using both the Half-Quadratic Splitting and the Alternating Direction Method of Multipliers algorithms; numerical examples reveal the superiority of both approaches compared to the conventionally used proximal gradient solver, with the latter performing better in both 2D and 3D examples.The author thanks KAUST for supporting this research. Allnumerical examples have been produced using the PyLops frame-work (Ravasi and Vasconcelos, 2020)
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
Geophysical inverse problems with measurement-guided diffusion models
No full textSolving inverse problems with the reverse process of a diffusion model represents an appealing avenue to produce highly realistic, yet diverse solutions from incomplete and possibly noisy measurements, ultimately enabling uncertainty quantification at scale. However, because of the intractable nature of the score function of the likelihood term (i.e., ), various samplers have been proposed in the literature that use different (more or less accurate) approximations of such a gradient to guide the diffusion process towards solutions that match the observations. In this work, I consider two sampling algorithms recently proposed under the name of Diffusion Posterior Sampling (DPS) and Pseudo-inverse Guided Diffusion Model (PGDM), respectively. In DSP, the guidance term used at each step of the reverse diffusion process is obtained by applying the adjoint of the modeling operator to the residual obtained from a one-step denoising estimate of the solution. On the other hand, PGDM utilizes a pseudo-inverse operator that originates from the fact that the one-step denoised solution is not assumed to be deterministic, rather modeled as a Gaussian distribution. Through an extensive set of numerical examples on two geophysical inverse problems (namely, seismic interpolation and seismic inversion), I show that two key aspects for the success of any measurement-guided diffusion process are: i) our ability to re-parametrize the inverse problem such that the sought after model is bounded between -1 and 1 (a pre-requisite for any diffusion model); ii) the choice of the training dataset used to learn the implicit prior that guides the reverse diffusion process. Numerical examples on synthetic and field datasets reveal that PGDM outperforms DPS in both scenarios at limited additional cost.This publication is based on work supported by the King Abdullah University of Science and Technology (KAUST). The author thanks the DeepWave sponsors for their support
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