Bootstrap for neural model selection

Bootstrap techniques (also called resampling computation techniques) have introduced new advances in modeling and model evaluation. Using resampling methods to construct a series of new samples which are based on the original data set, allows to estimate the stability of the parameters. Properties such as convergence and asymptotic normality can be checked for any particular observed data set. In most cases, the statistics computed on the generated data sets give a good idea of the confidence regions of the estimates. In this paper, we debate on the contribution of such methods for model selection, in the case of feedforward neural networks. The method is described and compared with the leave-one-out resampling method. The effectiveness of the bootstrap method, versus the leave-one-out methode, is checked through a number of examples.Comment: A la suite de la conf\'{e}rence ESANN 200

Approximation techniques for neuromimetic calculus

Approximation Theory plays a central part in modern statistical methods, in particular in Neural Network modeling. These models are able to approximate a large amount of metric data structures in their entire range of definition or at least piecewise. We survey most of the known results for networks of neurone-like units. The connections to classical statistical ideas such as ordinary Least Squares are emphasized

Identication of fuzzy function via interval analysis

22 pages, accepté Reliable ComputingA number of techniques have been introduced to construct fuzzy models from measured data. One of the most common is the use of mathematical parametric models. In this paper, a new approach based on interval analysis is proposed to compute guaranteed estimates of suitable characteristics of the set of all values of the fuzzy parameter vector such that the error between experimental data and the model outputs belongs to some predefined feasible set. Subpavings consisting of boxes with nonzero width are used to encompass all the acceptable values of the parameter vector. The problem of estimating the parameters of the model is viewed as one of set inversion, which is solved in an approximate but guaranteed way with the tools of interval analysis. The estimation task is formulated here as a constrained optimization problem. Our approach emphasizes the use of interval mathematics for fuzzy representation, which is especially suited to nonlinear models, a situation where most available methods fail to provide any guarantee on the results. An algorithm is proposed, which makes it possible to obtain all fuzzy parameter vectors that are consistent with the data. Properties of this algorithm are established and illustrated on a simple example

Un algorithme pour la planification de trajectoire basé sur le calcul ensembliste

National audienceNous proposons dans cet article une solution originale et garantie à la "planification de trajectoire" en utilisant une approche ensembliste. Le système est capable de traiter des tâches compliquées dans un environnement encombré d'obstacles, avec pour seule entrée la géométrie de l'e nvironnement, un point de départ et un point d'arrivée. La tâche de planification est résolue en combinant le calcul par intervalle et la théorie des graphes. La méthode est illustrée sur un cas idéalisé dans lequel le robot est choisi circulaire et les obstacles composés de figures géométriques simples

Optimal overlayer inspired by Photuris firefly improves light-extraction efficiency of existing light-emitting diodes

In this paper the design, fabrication and characterization of a bioinspired overlayer deposited on a GaN LED is described. The purpose of this overlayer is to improve light extraction into air from the diode's high refractive-index active material. The layer design is inspired by the microstructure found in the firefly Photuris sp. The actual dimensions and material composition have been optimized to take into account the high refractive index of the GaN diode stack. This two-dimensional pattern contrasts other designs by its unusual profile, its larger dimensions and the fact that it can be tailored to an existing diode design rather than requiring a complete redesign of the diode geometry. The gain of light extraction reaches values up to 55% with respect to the reference unprocessed LED.Comment: 9 pages, 9 Figures, published in Optics Expres

Genomic evidence of functional diversity in DPANN archaea, from oxic species to anoxic vampiristic consortia

DPANN archaea account for half of the archaeal diversity of the biosphere, but with few cultivated representatives, their metabolic potential and environmental functions are poorly understood. The extreme geochemical and environmental conditions in meromictic ice-capped Lake A, in the Canadian High Arctic, provided an isolated, stratified model ecosystem to resolve the distribution and metabolism of uncultured aquatic DPANN archaea living across extreme redox and salinity gradients, from freshwater oxygenated conditions, to saline, anoxic, sulfidic waters. We recovered 28 metagenome-assembled genomes (MAGs) of DPANN archaea that provided genetic insights into their ecological function. Thiosulfate oxidation potential was detected in aerobic Woesearchaeota, whereas diverse metabolic functions were identified in anaerobic DPANN archaea, including degradation and fermentation of cellular compounds, and sulfide and polysulfide reduction. We also found evidence for “vampiristic” metabolism in several MAGs, with genes coding for pore-forming toxins, peptidoglycan degradation, and RNA scavenging. The vampiristic MAGs co-occurred with other DPANNs having complementary metabolic capacities, leading to the possibility that DPANN form interspecific consortia that recycle microbial carbon, nutrients and complex molecules through a DPANN archaeal shunt, adding hidden novel complexity to anaerobic microbial food webs

Statistical modelling by neural networks in gamma-spectrometry

International audienceLayered Neural Networks are a class of models based on neural computation and have been applied to the measurement of uranium enrichment. The usual methods consider a limited number of $X$- and $\gamma$-ray peaks, and require calibrated instrumentation for each sample. Since the source-detector ensemble geometry conditions critically differ between such measurements, the spectral region of interest is normally reduced to improve the accuracy of such conventional methods by focusing on the $K_{\alpha}X$ region where the three elementary components are present. Such measurements lead to the desired ratio. Experimental data have been used to study the performance of neural networks involving a Maximum-Likelihood Method. The encoding of the data by a Neural Network approach is a promising method for the measurement of uranium ${}^{235}U$ and ${}^{238}U$ in infinitely thick samples

Optimized adaptive MPC for lateral control of autonomous vehicles

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksAutonomous vehicles are the upcoming solution to most transportation problems such as safety, comfort and efficiency. The steering control is one of the main important tasks in achieving autonomous driving. Model predictive control (MPC) is among the fittest controllers for this task due to its optimal performance and ability to handle constraints. This paper proposes an adaptive MPC controller (AMPC) for the path tracking task, and an improved PSO algorithm for optimising the AMPC parameters. Parameter adaption is realised online using a lookup table approach. The propose AMPC performance is assessed and compared with the classic MPC and the Pure Pursuit controller through simulationsPeer ReviewedPostprint (author's final draft

Alzheimer's disease early detection from sparse data using brain importance maps

Statistical methods are increasingly used in the analysis of FDG-PET images for the early diagnosis of Alzheimer's disease. We will present a method to extract information about the location of metabolic changes induced by Alzheimer's disease based on a machine learning approach that directly links features and brain areas to search for regions of interest (ROIs). This approach has the advantage over voxel-wise statistics to also consider the interactions between the features/voxels. We produce "maps" to visualize the most informative regions of the brain and compare the maps created by our approach with voxel-wise statistics. In classification experiments, using the extracted map, we achieved classification rates of up to 95.5%

Genomic evidence for sulfur intermediates as new biogeochemical hubs in a model aquatic microbial ecosystem

Background: The sulfur cycle encompasses a series of complex aerobic and anaerobic transformations of S-containing molecules and plays a fundamental role in cellular and ecosystem-level processes, influencing biological carbon transfers and other biogeochemical cycles. Despite their importance, the microbial communities and metabolic pathways involved in these transformations remain poorly understood, especially for inorganic sulfur compounds of intermediate oxidation states (thiosulfate, tetrathionate, sulfite, polysulfides). Isolated and highly stratified, the extreme geochemical and environmental features of meromictic ice-capped Lake A, in the Canadian High Arctic, provided an ideal model ecosystem to resolve the distribution and metabolism of aquatic sulfur cycling microorganisms along redox and salinity gradients. Results: Applying complementary molecular approaches, we identified sharply contrasting microbial communities and metabolic potentials among the markedly distinct water layers of Lake A, with similarities to diverse fresh, brackish and saline water microbiomes. Sulfur cycling genes were abundant at all depths and covaried with bacterial abundance. Genes for oxidative processes occurred in samples from the oxic freshwater layers, reductive reactions in the anoxic and sulfidic bottom waters and genes for both transformations at the chemocline. Up to 154 different genomic bins with potential for sulfur transformation were recovered, revealing a panoply of taxonomically diverse microorganisms with complex metabolic pathways for biogeochemical sulfur reactions. Genes for the utilization of sulfur cycle intermediates were widespread throughout the water column, co-occurring with sulfate reduction or sulfide oxidation pathways. The genomic bin composition suggested that in addition to chemical oxidation, these intermediate sulfur compounds were likely produced by the predominant sulfur chemo- and photo-oxidisers at the chemocline and by diverse microbial degraders of organic sulfur molecules. Conclusions: The Lake A microbial ecosystem provided an ideal opportunity to identify new features of the biogeochemical sulfur cycle. Our detailed metagenomic analyses across the broad physico-chemical gradients of this permanently stratified lake extend the known diversity of microorganisms involved in sulfur transformations over a wide range of environmental conditions. The results indicate that sulfur cycle intermediates and organic sulfur molecules are major sources of electron donors and acceptors for aquatic and sedimentary microbial communities in association with the classical sulfur cycl