Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid-infrared spectroscopy

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The p-Laplacian problem -del & sdot; ((mu + |del u|(p-2))del u) = f is considered, where mu is a given positive number. An anisotropic a posteriori residual-based error estimator is presented. The error estimator is shown to be equivalent, up to higher ord ...

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The goal of this work is to use anisotropic adaptive finite elements for the numerical simulation of aluminium electrolysis. The anisotropic adaptive criteria are based on a posteriori error estimates derived for simplified problems. First, we consider an ...

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Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or the ...

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Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid-infrared spectroscopy

Chattez avec Graph Search

Data Heterogeneity in Linear Distributed Estimation and Learning

Unsupervised Multi-view UAV Image Geo-localization via Iterative Rendering

Spectral Estimators for High-Dimensional Matrix Inference

Partial discharge localization in power transformer tanks using machine learning methods

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Intermediate complexity atmospheric modeling in complex terrain: is it right?

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Anisotropic Adaptive Finite Elements for a p-Laplacian Problem

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Error estimates for SUPG-stabilised Dynamical Low Rank Approximations

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Extensions of Peer Prediction Incentive Mechanisms

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Augmenting locomotor perception by remapping tactile foot sensation to the back

Uncertainty quantification by direct propagation of shallow ensembles

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

Graph generative deep learning models with an application to circuit topologies

Timing and spatial selection bias in rapid extreme event attribution