Modeling of distortion caused by Markov-model burst packet losses in video transmission

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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 ...

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 ...

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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Advanced antenna system (AAS) is a viable option for 5G millimeter-wave (mmWave) applications. AAS single element is favored to be dual-polarized, wideband, high gain, and compact in order to be utilized for 5G antenna arrays. In this paper, a low complexi ...

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 ...

Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, ...

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 ...

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We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The proposed model can be viewed as a zero-sum game between a statistician choosing a ...

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

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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 ...

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

The growing demand for online gait phase (GP) estimation, driven by advancements in exoskeletons and prostheses, has prompted numerous approaches in the literature. Some approaches explicitly use time, while others rely on state variables to estimate the G ...

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 ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

Human thermo-physiology models (HTPM) are useful tools to assess dynamic and non-uniform human thermal states. However, they are developed based on the physiological data of an average person. In this paper, we present a detailed evaluation of two sophisti ...

Modeling of distortion caused by Markov-model burst packet losses in video transmission

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Error estimates for SUPG-stabilised Dynamical Low Rank Approximations

Spectral Estimators for High-Dimensional Matrix Inference

Anisotropic Adaptive Finite Elements for a p-Laplacian Problem

Partial discharge localization in power transformer tanks using machine learning methods

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

A mmWave Low Complexity and Low-Cost Super Wideband Dual-Polarized Aperture Coupled Antenna for 5G Applications

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

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

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

Graph generative deep learning models with an application to circuit topologies

Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

Quantization for Decentralized Learning Under Subspace Constraints

Timing and spatial selection bias in rapid extreme event attribution

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

An equilibrated flux a posteriori error estimator for defeaturing problems

Distributionally Robust Linear Quadratic Control

Modeling local thermal responses of individuals: Validation of advanced human thermo-physiology models