Causal Discovery and Prediction: Methods and Algorithms

We are not only observers but also actors of reality. Our capability to intervene and alter the course of some events in the space and time surrounding us is an essential component of how we build our model of the world. In this doctoral thesis we introduce a generic a-priori assessment of each possible intervention, in order to select the most cost-effective interventions only, and avoid unnecessary systematic experimentation on the real world. Based on this a-priori assessment, we propose an active learning algorithm that identifies the causal relations in any given causal model, using a least cost sequence of interventions. There are several novel aspects introduced by our algorithm. It is, in most case scenarios, able to discard many causal model candidates using relatively inexpensive interventions that only test one value of the intervened variables. Also, the number of interventions performed by the algorithm can be bounded by the number of causal model candidates. Hence, fewer initial candidates (or equivalently, more prior knowledge) lead to fewer interventions for causal discovery. Causality is intimately related to time, as causes appear to precede their effects. Cyclical causal processes are a very interesting case of causality in relation to time. In this doctoral thesis we introduce a formal analysis of time cyclical causal settings by defining a causal analog to the purely observational Dynamic Bayesian Networks, and provide a sound and complete algorithm for the identification of causal effects in the cyclic setting. We introduce the existence of two types of hidden confounder variables in this framework, which affect in substantially different ways the identification procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs.Comment: PhD Thesis, 101 pages. arXiv admin note: text overlap with arXiv:1610.0555

Identifiability and transportability in dynamic causal networks

In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for identification of Dynamic Causal Networks, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder variables that affect in substantially different ways the identification procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs. We further propose a procedure for the transportability of causal effects in Dynamic Causal Network settings, where the result of causal experiments in a source domain may be used for the identification of causal effects in a target domain.Preprin

Comparison of methods employed to extract information contained in seafloor backscatter

International audienceSeabed maps are based on quantities extracted from measurements of the seafloor‘s acoustic response by sonar systems such as single-beam echo-sounders (SBES), multibeam echo-sounders (MBES) or sidescan sonars (SSS). In this paper, a comparison of various strategies to estimate the backscattering strength (BS) from recorded time-series, i.e. seabed echoes extracted from pings, is presented. The work hypotheses are based on processed data from a SBES designed to be tilted mechanically. Ideal survey conditions are taken into account and the seafloor is supposed to be rough so that BS is assumed to be equivalent to the Rayleigh probability density function parameter. Classical methods such as averaging corrected (sonar equation) backscattered single values over a set of pings to estimate BS are compared to other methods exploiting several time-samples being part of pings. Simulated data is considered to estimate BS in different situations (several estimators, natural/squared values, number of samples and pings). The best estimator to reach a 0.1dB uncertainty is proposed, and a formula governing the number of time-samples and pings needed to reach an accurate BS estimation according to the measurement conditions is derived

Marine observations with a harmonic single-beam echo-sounder

International audienceTo characterise the seabed or water-column targets with acoustics, it is common to use multiple frequencies and therefore several sonar transducers or echo-sounders. The single beam echo-sounder we present here is able, thanks to non-linearity of the sea water, to generate more than three harmonics above its fundamental transmitted frequency, in effect producing four distinct frequencies with a single echo-sounder. In addition, all transmitted signals are perfectly in phase because they are carried by the same pulse, which has obvious benefits for further processing of the echoes. In this presentation, after a short review of the entire system, its application to seabed characterisation using the reflectivity level (acoustic backscattering strength from the seafloor) will be exposed. Further developments of plans to use this echo-sounder for fishery acoustics will then be highlighted, based on datasets acquired in the Bay of Brest (France). (Project funded by ANR and DGA / ANR-14-ASTR-0022-00)

API design for machine learning software: experiences from the scikit-learn project

Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library

Improving a BEM Yaw Model Based on NewMexico Experimental Data and Vortex/CFD Simulations

Les travaux présentés ici ont pour but d'évaluer la capacité de différents modèles à reproduire le sillage ainsi que les efforts sur les pales d'une éolienne testée en soufflerie dans le cadre du projet MexNext (IEA Wind), et améliorer un modèle BEM (Blade Element Momentum) par l'analyse des résultats. Deux solveurs développés en interne à IFPEN (AeroDeeP, CASTOR), basés sur les méthodes BEM (Blade Element Momentum) et vortex ainsi qu'un modèle CFD-AL (Computational Fluid Dynamics - Actuator Line) open-source, SOWFA, basé sur l'outil OpenFOAM ont été mis en oeuvre. Les principaux résultats sont donnés ci-après : la BEM permet d'obtenir des résultats proches des méthodes ?avancées"", même pour le cas en dérapage. Pour ce faire, un nouveau modèle a été introduit. Bien que les essais ne soient pas parfaitement reproduits, ce modèle simple améliore nettement les résultats. Dans le sillage, la méthode vortex permet d'obtenir des résultats très proches des mesures du fait de la faible diffusion du modèle

Evaluation du devenir des produits phytopharmaceutiques en plein champ en fonction des pratiques culturales pour le développement d’une agriculture éco responsable (Sol-Phy-Ly). Rapport d’activités final 2018-2021.

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Evaluation du devenir des produits phytopharmaceutiques en plein champ en fonction des pratiques culturales : Sol-Phy-Ly

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