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Institution

University of Grenoble

EducationSaint-Martin-d'Hères, France
About: University of Grenoble is a education organization based out in Saint-Martin-d'Hères, France. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 25658 authors who have published 45143 publications receiving 909760 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors review the mathematical foundations of model averaging along with the diversity of approaches available and stress the importance of non-parametric methods such as cross-validation for a reliable uncertainty quantification of model-averaged predictions.
Abstract: In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions.

187 citations

Journal ArticleDOI
B. Abi1, R. Acciarri2, M. A. Acero3, George Adamov4  +966 moreInstitutions (155)
TL;DR: The Deep Underground Neutrino Experiment (DUNE) as discussed by the authors is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model.
Abstract: The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE's physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology.

187 citations

Journal ArticleDOI
TL;DR: It is shown that a simple self-consistent scheme at the GW level, with an update of the quasiparticle energies, not only leads to a much better agreement with reference values, but also significantly reduces the impact of the starting DFT functional.
Abstract: We perform benchmark calculations of the Bethe–Salpeter vertical excitation energies for the set of 28 molecules constituting the well-known Thiel’s set, complemented by a series of small molecules representative of the dye chemistry field. We show that Bethe–Salpeter calculations based on a molecular orbital energy spectrum obtained with non-self-consistent G0W0 calculations starting from semilocal DFT functionals dramatically underestimate the transition energies. Starting from the popular PBE0 hybrid functional significantly improves the results even though this leads to an average −0.59 eV redshift compared to reference calculations for Thiel’s set. It is shown, however, that a simple self-consistent scheme at the GW level, with an update of the quasiparticle energies, not only leads to a much better agreement with reference values, but also significantly reduces the impact of the starting DFT functional. On average, the Bethe–Salpeter scheme based on self-consistent GW calculations comes close to the...

187 citations

Journal ArticleDOI
TL;DR: In this paper, the state-of-the-art literature on post-mortem analysis of Li-ion cells, including disassembly methodology as well as physico-chemical characterization methods for battery materials.
Abstract: Improvement of life-time is an important issue in the development of Li-ion batteries. Aging mechanisms limiting the life-time can efficiently be characterized by physico-chemical analysis of aged cells with a variety of complementary methods. This study reviews the state-of-the-art literature on Post-Mortem analysis of Li-ion cells, including disassembly methodology as well as physico-chemical characterization methods for battery materials. A detailed scheme for Post-Mortem analysis is deduced from literature, including pre-inspection, conditions and safe environment for disassembly of cells, as well as separation and post-processing of components. Special attention is paid to the characterization of aged materials including anodes, cathodes, separators, and electrolyte. More specifically, microscopy, chemical methods sensitive to electrode surfaces or to electrode bulk, and electrolyte analysis are reviewed in detail. The techniques are complemented by electrochemical measurements using reconstruction methods for electrodes built into half and full cells with reference electrode. The changes happening to the materials during aging as well as abilities of the reviewed analysis methods to observe them are critically discussed.

187 citations

Journal ArticleDOI
TL;DR: It is hypothesize that each conversion from one plastid type into another is either accompanied or even preceded by significant changes in plastsid transcription suggesting that these changes represent important determinants of plastID morphology and protein composition and, hence, the plastids type.
Abstract: Plastids display a high morphological and functional diversity. Starting from an undifferentiated small proplastid, these plant cell organelles can develop into four major forms: etioplasts in the dark, chloroplasts in green tissues, chromoplasts in colored flowers and fruits and amyloplasts in roots. The various forms are interconvertible into each other depending on tissue context and respective environmental condition. Research of the last two decades uncovered that each plastid type contains its own specific proteome that can be highly different from that of the other types. Composition of these proteomes largely defines the enzymatic functionality of the respective plastid. The vast majority of plastid proteins is encoded in the nucleus and must be imported from the cytosol. However, a subset of proteins of the photosynthetic and gene expression machineries are encoded on the plastid genome and are transcribed by a complex transcriptional apparatus consisting of phage-type nuclear-encoded RNA polymerases and a bacterial-type plastid-encoded RNA polymerase. Both types recognize specific sets of promoters and transcribe partly over-lapping as well as specific sets of genes. Here we summarize the current knowledge about the sequential activity of these plastid RNA polymerases and their relative activities in different types of plastids. Based on published plastid gene expression profiles we hypothesize that each conversion from one plastid type into another is either accompanied or even preceded by significant changes in plastid transcription suggesting that these changes represent important determinants of plastid morphology and protein composition and, hence, the plastid type.

187 citations


Authors

Showing all 25961 results

NameH-indexPapersCitations
Dieter Lutz13967167414
Marcella Bona137139192162
Nicolas Berger137158196529
Cordelia Schmid135464103925
J. F. Macías-Pérez13448694715
Marina Cobal132107885437
Lydia Roos132128489435
Tetiana Hryn'ova131105984260
Johann Collot131101882865
Remi Lafaye131101283281
Jan Stark131118687025
Sabine Crépé-Renaudin129114282741
Isabelle Wingerter-Seez12993079689
James Alexander12988675096
Jessica Levêque129100670208
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023166
2022698
20215,126
20205,328
20195,192
20184,999