1,720,974 research outputs found
Neural prediction of Lagrangian drift trajectories on the sea surface
No full textInternational audienceThis study proposes a new Deep Learning approach for the simulation of Lagrangian drift at the sea surface with the objective to overcome current limitations of existing model-based and learning-based methods. The proposed framework, called DriftNet, is inspired by the Eulerian Fokker-Planck representation of Lagrangian drift. DriftNet is able to simulate the Lagrangian trajectory of a fluid parcel given the corresponding Eulerian Sea Surface Currents and the spatially-explicit encoding of the parcel's initial position. The efficacy of DriftNet is demonstrated through three benchmarks: two benchmarks involving fully simulated ocean data and one combining operational ocean reanalysis model along with in-situ drifters data. The study focuses on two regions with different dynamical regimes: North East Pacific and Gulf Stream. The findings indicate that DriftNet outperforms current state-of-the-art model-based and learning-based methods. Additionally, this study explores how Sea Surface Height -derived from both modeled and satellite-derived -affects Lagrangian drift simulations when used as input to DriftNet. SIGNIFICANCE STATEMENT: We propose a novel Deep Learning model, DriftNet, for conditional generation of Lagrangian trajectories on the sea surface. Our model is based on EulerianFokker-Planck formalism and can be conditioned by multiple geophysical fields. We highlight the overall over-performance of DriftNet compared to the baseline model-based and learning-based approaches. We put in evidence the capacity of the proposed method to extract pertinent information from various geophysical fields, both from modeled and observed data. We highlight the significant impact of the observed Sea Surface Height when combined to Sea Surface Currents in the quality of the generation of Lagrangian trajectories. sea surface dynamics, especially regarding mesoscale ocean dynamics [Prants et al. (2017)]. Due to their sequential nature, these schemes also face scalability challenges when simulating large ensembles of drift trajectories. By contrast, probabilistic data-driven schemes usually leverage first-order Markovian models and naturally account for uncertainties in the drift process. However, they are primarily suited for relatively coarse space-time resolutions. Extending these schemes to capture fine-scale patterns poses a challenge [Fine et al. (1998)]. For these two first categories, the simulation of the Lagrangian drift relies on location-wise velocities at each time step, which may only be applicable for smooth ocean currents velocity fields. Learning-based approaches, and more specifically, Deep Learning has recently emerged as a novel class of numerical tools for the learning-based simulation of movement patterns. Among learning-based approaches to predict Lagrangian drift in the context of fluid dynamics and oceanography, we may cite machine learning approaches [Zhang et al. (2023); Kim et al. (2024)] and the following neural networks models: feed-forward neural networks [Grossi et al. (2020)], convolutional neural networks [Zheng et al.</div
Neural prediction of Lagrangian drift trajectories on the sea surface
No full textInternational audienceThis study proposes a new Deep Learning approach for the simulation of Lagrangian drift at the sea surface with the objective to overcome current limitations of existing model-based and learning-based methods. The proposed framework, called DriftNet, is inspired by the Eulerian Fokker-Planck representation of Lagrangian drift. DriftNet is able to simulate the Lagrangian trajectory of a fluid parcel given the corresponding Eulerian Sea Surface Currents and the spatially-explicit encoding of the parcel's initial position. The efficacy of DriftNet is demonstrated through three benchmarks: two benchmarks involving fully simulated ocean data and one combining operational ocean reanalysis model along with in-situ drifters data. The study focuses on two regions with different dynamical regimes: North East Pacific and Gulf Stream. The findings indicate that DriftNet outperforms current state-of-the-art model-based and learning-based methods. Additionally, this study explores how Sea Surface Height -derived from both modeled and satellite-derived -affects Lagrangian drift simulations when used as input to DriftNet. SIGNIFICANCE STATEMENT: We propose a novel Deep Learning model, DriftNet, for conditional generation of Lagrangian trajectories on the sea surface. Our model is based on EulerianFokker-Planck formalism and can be conditioned by multiple geophysical fields. We highlight the overall over-performance of DriftNet compared to the baseline model-based and learning-based approaches. We put in evidence the capacity of the proposed method to extract pertinent information from various geophysical fields, both from modeled and observed data. We highlight the significant impact of the observed Sea Surface Height when combined to Sea Surface Currents in the quality of the generation of Lagrangian trajectories. sea surface dynamics, especially regarding mesoscale ocean dynamics [Prants et al. (2017)]. Due to their sequential nature, these schemes also face scalability challenges when simulating large ensembles of drift trajectories. By contrast, probabilistic data-driven schemes usually leverage first-order Markovian models and naturally account for uncertainties in the drift process. However, they are primarily suited for relatively coarse space-time resolutions. Extending these schemes to capture fine-scale patterns poses a challenge [Fine et al. (1998)]. For these two first categories, the simulation of the Lagrangian drift relies on location-wise velocities at each time step, which may only be applicable for smooth ocean currents velocity fields. Learning-based approaches, and more specifically, Deep Learning has recently emerged as a novel class of numerical tools for the learning-based simulation of movement patterns. Among learning-based approaches to predict Lagrangian drift in the context of fluid dynamics and oceanography, we may cite machine learning approaches [Zhang et al. (2023); Kim et al. (2024)] and the following neural networks models: feed-forward neural networks [Grossi et al. (2020)], convolutional neural networks [Zheng et al.</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
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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