Multiple phase derivative estimation using autoregressive modeling in holographic interferometry

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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 ...

Motivated by global analysis of aircraft-based measurements of air pollutants and climate variables, and specifically the COVID-19 pandemic’s possible impact on ozone concentrations, a functional autoregressive model is proposed to capture global spatio-te ...

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Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

A new statistical wake meandering (SWM) model is proposed that improves on existing models in the literature. Compared to the existing SWM models, the proposed model has a closed description that does not require simulations to create look-up tables while ...

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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 ...

This study compares three imputation methods applied to the field observations of hydraulic head in subsurface hydrology. Hydrogeological studies that analyze the timeseries of groundwater elevations often face issues with missing data that may mislead bot ...

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 ...

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The electron density close to the extraction grids and the co-extracted electrons represent a crucial issue when operating negative ion sources for fusion reactors. An excessive electron density in the plasma expansion region can indeed inhibit the negativ ...

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

We introduce the elliptical Ornstein-Uhlenbeck (OU) process, which is a generalisation of the well-known univariate OU process to bivariate time series. This process maps out elliptical stochastic oscillations over time in the complex plane, which are obse ...

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Cerebrovascular reactivity (CVR), defined as the cerebral blood flow response to a vasoactive stimulus, is an imaging biomarker with demonstrated utility in a range of diseases and in typical development and aging processes. A robust and widely implemented ...

We consider the problem of estimating the autocorrelation operator of an autoregressive Hilbertian process. By means of a Tikhonov approach, we establish a general result that yields the convergence rate of the estimated autocorrelation operator as a funct ...

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

LoRaWAN is nowadays one of the most popular protocols for low-power Internet of Things communications. Although its physical layer, namely LoRa, has been thoroughly studied in the literature, aspects related to the synchronization of LoRa receivers have re ...

We present a novel technique of neutron noise detection and experimental data interpretation developed during the EU H2020 project CORTEX aiming to improve the capabilities for identification and localization of neutron noise sources. The experimental data ...

Multiple phase derivative estimation using autoregressive modeling in holographic interferometry

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On distributional autoregression and iterated transportation

Functional autoregressive processes on a spherical domain for global aircraft-based atmospheric measurements

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Sparse autoregressive neural networks for classical spin systems

An improved statistical wake meandering model

Random matrix methods for high-dimensional machine learning models

Spectral Estimators for High-Dimensional Matrix Inference

Comparison of Three Imputation Methods for Groundwater Level Timeseries

Distributional Regression and Autoregression via Optimal Transport

Directional sensitivity of cortical neurons towards TMS induced electric fields

Numerical and experimental investigations of a microwave interferometer for the negative ion source SPIDER

Modeling Structured Data in Attention-based Models

Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

The elliptical Ornstein-Uhlenbeck process

Networks with correlated edge processes

Comparing end-tidal CO2, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI

On the rate of convergence for the autocorrelation operator in functional autoregression

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

A Low-Complexity LoRa Synchronization Algorithm Robust to Sampling Time Offsets

Characterization of High Harmonic Frequencies in Reactor Noise Experiments Within the CORTEX Project