Reliable uncertainties: Error correlation, rotated error bars, and linear regressions in three-isotope plots and beyond

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The two-step electron transfer during bacterial reduction of UVI to UIV is typically accompanied by mass-independent fractionation of the 238U and 235U isotopes, whereby the heavy isotope accumulates in the reduced product. However, the role of the UV inte ...

Sorption of mercury (Hg) in soils is suggested to be predominantly associated with organic matter (OM). However, there is a growing collection of research that suggests that clay minerals and oxides are also important solid phases for the sorption of solu ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

The oxygen isotope compositions of fossil biocalcites, such as foraminifera, bivalves, brachiopods, and belemnites have allowed for reconstructions of sea surface and deep ocean temperatures throughout the Phanerozoic and constitute the most important reco ...

The young water fraction represents the portion of water molecules in a stream that have entered the catchment relatively recently, typically within 2-3 months. It can be reliably estimated in spatially heterogeneous and nonstationary catchments from the a ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

The High Intensity Proton Accelerator facility (HIPA) delivers a 590 MeV cw (50.6 MHz) proton beam with up to 1.4 MW beam power (2.4 mA) to spallation and meson production targets serving particle physics experiments and material research. The main acceler ...

Central aortic diastolic pressure decay time constant ( ) is according to the two-element Windkessel model equal to the product of total peripheral resistance (R) times total arterial compliance (C ). As such, it is related to arterial stiffness, which has ...

We describe a novel method to compute the components of dynamo tensors from direct magnetohydrodynamic (MHD) simulations. Our method relies upon an extension and generalization of the standard H & ouml;gbom CLEAN algorithm widely used in radio astronomy to ...

Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

For the dimensioning and assessment of structures, it is common practice to compare action effects with sectional resistances. Extensive studies have been performed to quantify the model uncertainty on the resistance side. However, for statically indetermi ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

As a follow-up to the CEA-EPFL PETALE experimental program on stainless steel nuclear data, the EPFL initiated an open-loop pile-oscillation experimental program in the CROCUS reactor: BLOOM. A reproduction of the critical experiments of PETALE, the progra ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

We perform an error analysis of a fully discretised Streamline Upwind Petrov Galerkin Dynamical Low Rank (SUPG-DLR) method for random time-dependent advection-dominated problems. The time integration scheme has a splitting-like nature, allowing for potenti ...

To date, the vast majority of studies seeking to link discharge to solute concentrations have been based on representations of fluid age distributions in watersheds that are time-invariant. As increasingly detailed spatial and temporal datasets become avai ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

Reliable uncertainties: Error correlation, rotated error bars, and linear regressions in three-isotope plots and beyond

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The isotopic signature of U(V) during bacterial reduction

Organic matters, but inorganic matters too: column examination of elevated mercury sorption on low organic matter aquifer material using concentrations and stable isotope ratios

Random matrix methods for high-dimensional machine learning models

Diagenetic isotope exchange in biocalcites for paleoclimate reconstruction

Technical note: Two-component electrical-conductivity-based hydrograph separation employing an exponential mixing model (EXPECT) provides reliable high-temporal-resolution young water fraction estimates in three small Swiss catchments

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

On distributional autoregression and iterated transportation

High power electrostatic beam splitter for a proton beamline

Novel theory and potential applications of central diastolic pressure decay time constant

Iterative removal of sources to model the turbulent electromotive force

ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

Toward Reliable Human Pose Forecasting With Uncertainty

Design of an Open-Loop Pile-Oscillation Program in the CROCUS Reactor

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Error estimates for SUPG-stabilised Dynamical Low Rank Approximations

Isotope Ratio - Discharge Relationships of Solutes Derived From Weathering Reactions

Distributional Regression and Autoregression via Optimal Transport