Kymatio: Scattering Transforms in Python

The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks. All transforms may be executed on a GPU (in addition to CPU), offering a considerable speed up over CPU implementations. The package also has a small memory footprint, resulting inefficient memory usage. The source code, documentation, and examples are available undera BSD license at https://www.kymat.io

Modélisation fovéale, autorégressive et neuronale de séries temporelles

This dissertation studies unsupervised time-series modelling. We first focus on the problem of linearly predicting future values of a time-series under the assumption of long-range dependencies, which requires to take into account a large past. We introduce a family of causal and foveal wavelets which project past values on a subspace which is adapted to the problem, thereby reducing the variance of the associated estimators. We then investigate under which conditions non-linear predictors exhibit better performances than linear ones. Time-series which admit a sparse time-frequency representation, such as audio ones, satisfy those requirements, and we propose a prediction algorithm using such a representation. The last problem we tackle is audio time-series synthesis. We propose a new generation method relying on a deep convolutional neural network, with an encoder-decoder architecture, which allows to synthesize new realistic signals. Contrary to state-of-the-art methods, we explicitly use time-frequency properties of sounds to define an encoder with the scattering transform, while the decoder is trained to solve an inverse problem in an adapted metric.Cette thèse s'intéresse à la modélisation non-supervisée de séries temporelles univariées. Nous abordons tout d'abord le problème de prédiction linéaire des valeurs futures séries temporelles gaussiennes sous hypothèse de longues dépendances, qui nécessitent de tenir compte d'un large passé. Nous introduisons une famille d'ondelettes fovéales et causales qui projettent les valeurs passées sur un sous-espace adapté au problème, réduisant ainsi la variance des estimateurs associés. Dans un deuxième temps, nous cherchons sous quelles conditions les prédicteurs non-linéaires sont plus performants que les méthodes linéaires. Les séries temporelles admettant une représentation parcimonieuse en temps-fréquence, comme celles issues de l'audio, réunissent ces conditions, et nous proposons un algorithme de prédiction utilisant une telle représentation. Le dernier problème que nous étudions est la synthèse de signaux audios. Nous proposons une nouvelle méthode de génération reposant sur un réseau de neurones convolutionnel profond, avec une architecture encodeur-décodeur, qui permet de synthétiser de nouveaux signaux réalistes. Contrairement à l'état de l'art, nous exploitons explicitement les propriétés temps-fréquence des sons pour définir un encodeur avec la transformée en scattering, tandis que le décodeur est entraîné pour résoudre un problème inverse dans une métrique adaptée

Modélisation fovéale, autorégressive et neuronale de séries temporelles

This dissertation studies unsupervised time-series modelling. We first focus on the problem of linearly predicting future values of a time-series under the assumption of long-range dependencies, which requires to take into account a large past. We introduce a family of causal and foveal wavelets which project past values on a subspace which is adapted to the problem, thereby reducing the variance of the associated estimators. We then investigate under which conditions non-linear predictors exhibit better performances than linear ones. Time-series which admit a sparse time-frequency representation, such as audio ones, satisfy those requirements, and we propose a prediction algorithm using such a representation. The last problem we tackle is audio time-series synthesis. We propose a new generation method relying on a deep convolutional neural network, with an encoder-decoder architecture, which allows to synthesize new realistic signals. Contrary to state-of-the-art methods, we explicitly use time-frequency properties of sounds to define an encoder with the scattering transform, while the decoder is trained to solve an inverse problem in an adapted metric.Cette thèse s'intéresse à la modélisation non-supervisée de séries temporelles univariées. Nous abordons tout d'abord le problème de prédiction linéaire des valeurs futures séries temporelles gaussiennes sous hypothèse de longues dépendances, qui nécessitent de tenir compte d'un large passé. Nous introduisons une famille d'ondelettes fovéales et causales qui projettent les valeurs passées sur un sous-espace adapté au problème, réduisant ainsi la variance des estimateurs associés. Dans un deuxième temps, nous cherchons sous quelles conditions les prédicteurs non-linéaires sont plus performants que les méthodes linéaires. Les séries temporelles admettant une représentation parcimonieuse en temps-fréquence, comme celles issues de l'audio, réunissent ces conditions, et nous proposons un algorithme de prédiction utilisant une telle représentation. Le dernier problème que nous étudions est la synthèse de signaux audios. Nous proposons une nouvelle méthode de génération reposant sur un réseau de neurones convolutionnel profond, avec une architecture encodeur-décodeur, qui permet de synthétiser de nouveaux signaux réalistes. Contrairement à l'état de l'art, nous exploitons explicitement les propriétés temps-fréquence des sons pour définir un encodeur avec la transformée en scattering, tandis que le décodeur est entraîné pour résoudre un problème inverse dans une métrique adaptée

Influence of organic amendments on diuron leaching through an acidic and a calcareous vineyard soil using undisturbed lysimeters.

9 pagesInternational audienceThe influence of different organic amendments on diuron leaching was studied through undisturbed vineyard soil columns. Two composts (A and D), the second at two stages of maturity, and two soils (VR and Bj) were sampled. After 1 year, the amount of residues (diuron þ metabolites) in the leachates of the VR soil (0.19e0.71%) was lower than in the Bj soil (4.27e8.23%), which could be explained by stronger diuron adsorption on VR. An increase in the amount of diuron leached through the amended soil columns, compared to the blank, was observed for the Bj soil only. This result may be explained by the formation of mobile complexes between diuron and water-extractable organic matter (WEOM) through the Bj soil, or by competition between diuron and WEOM for the adsorption sites in the soil. For both soils, the nature of the composts and their degree of maturity did not significantly influence diuron leaching

Evaluating equilibrium and non-equilibrium transport of bromide and isoproturon in disturbed and undisturbed soil columns.

19 pagesInternational audienceIn this study, displacement experiments of isoproturon were conducted in disturbed and undisturbed columns of a silty clay loam soil under similar rainfall intensities. Solute transport occurred under saturated conditions in the undisturbed soil and under unsaturated conditions in the sieved soil because of a greater bulk density of the compacted undisturbed soil compared to the sieved soil. The objective of this work was to determine transport characteristics of isoproturon relative to bromide tracer. Triplicate column experiments were performed with sieved (structure partially destroyed to simulate conventional tillage) and undisturbed (structure preserved) soils. Bromide experimental breakthrough curves were analyzed using convective-dispersive and dual-permeability (DP) models (HYDRUS-1D). Isoproturon breakthrough curves (BTCs) were analyzed using the DP model that considered either chemical equilibrium or non-equilibrium transport. The DP model described the bromide elution curves of the sieved soil columns well, whereas it overestimated the tailing of the bromide BTCs of the undisturbed soil columns. A higher degree of physical non-equilibrium was found in the undisturbed soil, where 56% of total water was contained in the slow-flow matrix, compared to 26% in the sieved soil. Isoproturon BTCs were best described in both sieved and undisturbed soil columns using the DP model combined with the chemical non-equilibrium. Higher degradation rates were obtained in the transport experiments than in batch studies, for both soils. This was likely caused by hysteresis in sorption of isoproturon. However, it cannot be ruled out that higher degradation rates were due, at least in part, to the adopted first-order model. Results showed that for similar rainfall intensity, physical and chemical non-equilibrium were greater in the saturated undisturbed soil than in the unsaturated sieved soil. Results also suggested faster transport of isoproturon in the undisturbed soil due to higher preferential flow and lower fraction of equilibrium sorption sites

Quantifying the contribution of nitrification and denitrification to the nitrous oxide flux using 15N tracers.

8 pagesInternational audienceMicrobial transformations of nitrification and denitrification are the main sources of nitrous oxide (N2O) from soils. Relative contributions of both processes to N2O emissions were estimated on an agricultural soil using 15N isotope tracers (15NH4+ or 15NO3-), for a 10-day batch experiment. Under unsaturated and saturated conditions, both processes were significantly involved in N2O production. Under unsaturated conditions, 60% of N-N2O came from nitrification, while denitrification contributed around 85-90% under saturated conditions. Estimated nitrification rates were not significantly different whatever the soil moisture content, whereas the proportion of nitrified N emitted as N2O changed from 0.13 to 2.32%. In coherence with previous studies, we interpreted this high value as resulting from the decrease in O2 availability through the increase in soil moisture content. It thus appears that, under limiting aeration conditions, some values for N2O emissions through nitrification could be underestimated

Influence de l'apport d'amendements organiques sur les émissions de N2O et de N2 par les sols, au cours de la dénitrification, révélée par le traçage isotopique 15

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