Production de carbone organique dissous par le complexe macrophytes-épiphytes : effets de facteurs physico-chimiques, implications sur la productivité des communautés de macrophytes et incidence à l'échelle de l'écosystème

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Modélisation cohérente de la diffusion électromagnétique par des surfaces de mer tridimensionnelles en incidence rasante.: Application aux radars HF à ondes de surface.

This thesis deals with the analysis of the electromagnetic interactions between surface waves at HF frequencies, and a time-evolving sea surface. A simulator comprising three specific elements has been developed.The first one enables the user to model a time-varying sea surface in three dimensions, by the application of the linear wave theory. The second one simulates the interaction between the electromagnetic wave at grazing incidence and the sea surface using an exact model based on the method of moments. Lastly, a post-processing tool allows the plotting and the analysis of the resulting Doppler spectra. Validation tests are presented. Various simulations on static and dynamic surfaces (sinusoids and sea surfaces) reveal the Bragg resonance phenomenon and the Doppler effect respectively. The influence of the sea surface and the radar configuration is investigated. The simulation results show a good fit with published data and ONERA measurements. Finally, a film of pollutant on the surface is introduced in the model by the addition of a surface pressure (corresponding here to an attenuation of the heights of the waves). The effect of the presence of the film on the Doppler spectra is analyzed.Cette thèse repose sur l'analyse des interactions électromagnétiques (EM) entre les ondes de surface, aux fréquences HF, et une surface de mer évoluant dans le temps. Un simulateur comprenant trois modules spécifiques est développé. Le premier élément permet de modéliser une surface de mer en 3D variant dans le temps, par application de la théorie linéaire des vagues. Le second fait interagir une onde EM en incidence rasante avec une surface de mer grâce à un modèle exact qui s'appuie sur la méthode des moments. Enfin, un outil de post-traitement offre la possibilité de tracer et d'analyser des spectres Doppler (SD) résultants. Une étude portant sur des surfaces statiques puis dynamiques est menée. Elle fait apparaître respectivement le phénomène de résonance de Bragg et l'effet Doppler. Des données déjà publiées ou mesurées indiquent une bonne adéquation avec les SD simulés. Finalement, l'effet sur les SD, de la présence d'un film de polluant sur la surface de mer, est analysé

De la détection d'évènements sonores violents par SVM dans les films

National audienceThis article studies the behaviour of a state-of-the-art support vector machine audio event detection approach, applied to violent event detection in movies. The events we are trying to detect are screams, gunshots, explosions. Contrary to others studies, we show that the state-of-theart approach does not lead to good results on this task. A study on the repartition of samples into subsets in a cross validation protocol helps explain those results and highlights a generalisation problem due to a polymorphism of considered classes. This polymorphism is demonstrated by the computation the divergence between the samples of the test database and the training database.Cet article étudie le comportement d'une approche classique, à l'état de l'art, pour la détection d'événements sonores par machines à vecteurs supports, appliquée à la détection d'événements violents dans les films. Les événements sonores considérés, liés à la présence de violence, sont les Cris, les Coups de feu et les Explosions. Nous montrons que, contrairement aux résultats d'autres études, l'approche état de l'art ne donne pas de bons résultats sur cette tâche. Une étude sur la répartition des échantillons en sous-ensembles dans un protocole de validation croisée permet d'expliquer ces résultats et met en évidence un problème de généralisation, dû au polymorphisme des classes considérées. Ce polymorphisme est démontré par un calcul de divergence entre les échantillons de la base de test et ceux de la base d'apprentissage

Audio Event Detection in Movies using Multiple Audio Words and Contextual Bayesian Networks

International audienceThis article investigates a novel use of the well known audio words representations to detect specific audio events, namely gunshots and explosions, in order to get more robustness towards soundtrack variability in Hollywood movies. An audio stream is processed as a sequence of stationary segments. Each segment is described by one or several audio words obtained by applying product quantization to standard features. Such a representation using multiple audio words constructed via product quantisation is one of the novelties described in this work. Based on this representation, Bayesian networks are used to exploit the contextual information in order to detect audio events. Experiments are performed on a comprehensive set of 15 movies, made publicly available. Results are comparable to the state of the art results obtained on the same dataset but show increased robustness to decision thresholds, however limiting the range of possible operating points in some conditions. Late fusion provides a solution to this issue

Annotating, Understanding, and Predicting Long-term Video Memorability

International audienceMemorability can be regarded as a useful metric of video importance to help make a choice between competing videos. Research on computational understanding of video memorability is however in its early stages. There is no available dataset for modelling purposes, and the few previous attempts provided protocols to collect video memorability data that would be difficult to generalize. Furthermore, the computational features needed to build a robust memorability predictor remain largely undiscovered. In this article, we propose a new protocol to collect long-term video memorability annotations. We measure the memory performances of 104 participants from weeks to years after memorization to build a dataset of 660 videos for video memorability prediction. This dataset is made available for the research community. We then analyze the collected data in order to better understand video memorability, in particular the effects of response time, duration of memory retention and repetition of visualization on video memorability. We finally investigate the use of various types of audio and visual features and build a computational model for video memorability prediction. We conclude that high level visual semantics help better predict the memorability of videos

Collecting, Analyzing and Predicting Socially-Driven Image Interestingness

International audienceInterestingness has recently become an emerging concept for visual content assessment. However, understanding and predicting image interestingness remains challenging as its judgment is highly subjective and usually context-dependent. In addition, existing datasets are quite small for in-depth analysis. To push forward research in this topic, a large-scale interestingness dataset (images and their associated metadata) is described in this paper and released for public use. We then propose computational models based on deep learning to predict image interestingness. We show that exploiting relevant contextual information derived from social metadata could greatly improve the prediction results. Finally we discuss some key findings and potential research directions for this emerging topic

MediaEval 2017 Predicting Media Interestingness Task

Non peer reviewe

Mercury Export From Freshwater to Estuary: Carbocentric Science Elucidates the Fate of a Toxic Compound in Aquatic Boreal Environments

The chemistry of mercury in freshwater systems, particularly man-made reservoirs, has received a great deal of attention owing to the high toxicity of the most common organic form, methylmercury. Although methylmercury bioaccumulation in reservoirs and natural lakes has been extensively studied at all latitudes, the fate of the different forms of mercury (total vs. dissolved; organic vs. inorganic) along the entire river-estuary continuum is less well documented. In fact, the difficulty of integrating the numerous parameters involved in mercury speciation in such large study areas, combined with the technical difficulties in sampling and analyzing mercury, have undoubtedly hindered advances in the field. At the same time, carbocentric science has grown exponentially in the last 25 years, and the common fate of carbon and mercury in freshwater has become increasingly clear with time. This literature review, by presenting the knowledge acquired in these two fields, aims to better understand the extent of mercury export from boreal inland waters to estuaries and to investigate the possible downstream ecotoxicological impact of reservoir creation on mercury bioavailability to estuarine food webs and local communities

MediaEval 2018: Predicting Media Memorability Task

In this paper, we present the Predicting Media Memorability task, which is proposed as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation. Participants are expected to design systems that automatically predict memorability scores for videos, which reflect the probability of a video being remembered. In contrast to previous work in image memorability prediction, where memorability was measured a few minutes after memorization, the proposed dataset comes with short-term and long-term memorability annotations. All task characteristics are described, namely: the task's challenges and breakthrough, the released data set and ground truth, the required participant runs and the evaluation metrics