1,721,030 research outputs found
Apprentissage profond pour reconstruire la hauteur de la surface océanique à partir d’observations satellites multivariées
No full textCette thèse de doctorat porte sur la reconstruction d'images satellites de la surface de l'océan à partir de mesures éparses et bruitées. Son objectif est l'estimation de la hauteur de la mer (SSH), une variable importante pour approximer les courants de surface. Elle est actuellement mesurée par des altimètres pointant au nadir, laissant de nombreuses zones non observées. Les cartes complètes de SSH sont produites en utilisant des interpolations optimales linéaires présentant une faible résolution effective.D'autre part, la température de surface de la mer (SST) est observée sur des zones plus étendues et est physiquement liée aux courants géostrophiques à travers l'advection.Cette thèse explore les algorithmes d'apprentissage profond pour estimer les champs de SSH. En s'appuyant sur des années de données de simulation et d'observations, les réseaux neuronaux profonds sont capables d'apprendre des relations complexes entre les variables SSH et SST. Nous utilisons ces algorithmes ainsi que les observations de température, pour reconstruire la SSH d'abord dans une perspective de réduction d'échelle sur une simulation physique. Ensuite, nous considèrerons le problème de son interpolation sur des données de simulation et d'observation, en nous concentrant particulièrement sur la manière de transférer l'apprentissage dans des contextes opérationnels. Enfin, nous adaptons notre méthode pour produire des estimations en temps réel et des prévisions.This Ph.D. thesis focuses on reconstructing satellite images of the ocean surface from sparse and noisy measurements. Our objective is the Sea Surface Height (SSH), an important variable to estimate surface currents. It is retrieved through nadir-pointing altimeters, leaving important observation gaps due to their remote sensing technology. Complete SSH maps are produced using linear Optimal Interpolations with low effective resolution.On the other hand, Sea Surface Temperature (SST) products have much higher data coverage, and SST is physically linked to geostrophic currents through advection.This thesis explores deep learning algorithms to estimate SSH fields. Relying on years of data from simulation and observations, deep neural networks are able to learn complex relationships between SSH and SST variables. Using these algorithms and SST observations, we first enhance SSH mapping from a downscaling perspective on a physical simulation. Then, we tackle the SSH interpolation problem on simulation and observation data, with a particular focus on how to transfer the learning in operational settings. Finally, we adapt our method to produce near real-time and forecast estimations
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
Can a neural network learn a numerical model ?
No full textInternational audienceNumerical models are used to simulate the evolution of atmosphere or ocean dynamics. They are implemented through a computer code, that contains predefined rules specifying how to compute the evolution of some outputs (e.g sea surface height) from inputs (e.g. previous states of the model, satellite or in situ observations of other parameters). A machine learning approach, in contrast, infers its internal set of rules from a large amount of data. In many fields (image recognition, automatic translation, speech recognition, ...), the more traditional methods, which rely on predefined rules, have been outperformed by machine learning algorithms. This performance was made possible by advances in Convolutional and Recurrent Neural Networks. This work addresses the question of the application and the usefulness of machine learning for numerical modeling in Geophysics. Results are presented using a demonstration model : A shallow-water model including a forcing by the wind, a diffusive term and a dissipation term. We evaluate the ability of a neural network to reproduce the numerical model "rules" given only the output fields of the model. We also investigate the ability of this neural network to simulate only some specific parts of the numerical model (e.g. diffusion or dissipation) and discuss the potential combination of approaches
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
Leveraging Deterministic Weather Forecasts for In Situ Probabilistic Temperature Predictions via Deep Learning
No full textInternational audienceWe propose a neural network approach to produce probabilistic weather forecasts from a deterministic numerical weather prediction. Our approach is applied to operational surface temperature outputs from the Global Deterministic Prediction System up to 10-day lead times, targeting METAR observations in Canada and the United States. We show how postprocessing performance is improved by training a single model for multiple lead times. Multiple strategies to condition the network for the lead time are studied, including a supplementary predictor and an embedding. The proposed model is evaluated for accuracy, spread, distribution calibration, and its behavior under extremes. The neural network approach decreases the continuous ranked probability score (CRPS) by 15% and has improved distribution calibration compared to a naive probabilistic model based on past forecast errors. Our approach increases the value of a deterministic forecast by adding information about the uncertainty, without incurring the cost of simulating multiple trajectories. It applies to any gridded forecast including the recent machine learning-based weather prediction models. It requires no information regarding forecast spread and can be trained to generate probabilistic predictions from any deterministic forecast
Infering the Chlorophyll-A Vertical Distribution in the Ocean From Satellite Data by Using Hidden Markov Models and Self Organizing Maps
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