High-dimensional peaks-over-threshold inference

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The J–V bump and hysteresis in perovskite solar cells are studied combining measurement and simulation, linking them to spatial collection efficiency changes from ionic charge accumulation, as visualised in external quantum efficiency (EQE) spectra. ...

To constrain cosmological models beyond ACDM, the development of the Euclid analysis pipeline requires simulations that capture the non-linear phenomenology of such models. We present an overview of numerical methods and N-body simulation codes developed t ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model conside ...

The manuscript describes the development of a spatial Agent-based Simulation to model the effect of public policies on private houseowner’s decisions concerning their heating system. The methodology utilized comprises of an empirical survey to determine th ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

We are interested in testing properties of distributions with systematically mislabeled samples. Our goal is to make decisions about unknown probability distributions, using a sample that has been collected by a confused collector, such as a machine-learni ...

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 ...

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (envir ...

Atomistic simulations performed with a family of model potential with tunable hardness have proven to be a great tool for advancing the understanding of wear processes at the asperity level. They have been instrumental in finding a critical length scale, w ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Antarctic precipitation remains poorly characterized and understood, especially within the boundary layer. This is due in part to a still-limited amount of surface-based remote sensing observations. A suite of cloud and precipitation remote-sensing instrum ...

In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright–Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and is based on exact simulation techniques that are of s ...

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 ...

Two related methods for inverting line-integrated measurements are presented in this research paper in the context of the recent deuterium-tritium experiments in the JET tokamak. Unlike traditional methods of tomography, these methods rely on making use of ...

An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

High-dimensional peaks-over-threshold inference

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Visualising ionic screening in perovskite solar cells: a bumpy ride along the J–V curve

Euclid preparation: LXII. Simulations and non-linearities beyond Lambda cold dark matter. 1. Numerical methods and validation

Spectral Estimators for High-Dimensional Matrix Inference

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Spatial Agent-Based Modelling and Simulation to Evaluate on Public Policies for Energy Transition

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

Extensions of Peer Prediction Incentive Mechanisms

Distribution Testing with a Confused Collector

Random matrix methods for high-dimensional machine learning models

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Graph generative deep learning models with an application to circuit topologies

Boosting likelihood learning with event reweighting

Multi-Scale and Multimodal Species Distribution Modeling

A story of two transitions: From adhesive to abrasive wear and from ductile to brittle regime

Partial discharge localization in power transformer tanks using machine learning methods

Radar-Derived Snowfall Microphysical Properties at Davis, Antarctica

Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

Timing and spatial selection bias in rapid extreme event attribution

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

An equilibrated flux a posteriori error estimator for defeaturing problems