Publications associées à Data transformation (statistics)

Roles of Clinical Features and Chest CT in Predicting the Outcomes of Hospitalized Patients with COVID-19 Developing AKI

This research aimed to evaluate the clinical features and computed tomography (CT) scans associated with poor outcomes in COVID-19 patients with acute kidney injury (AKI). A total of 351 COVID-19 patients (100 AKI, 251 non-AKI) hospitalized at Imam Hossein ...

IRANIAN SOC NEPHROLGY2023

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Nicolas Henri Bernard Flammarion, Aditya Vardhan Varre, Loucas Pillaud-Vivien

Motivated by the recent successes of neural networks that have the ability to fit the data perfectly \emph{and} generalize well, we study the noiseless model in the fundamental least-squares setup. We assume that an optimum predictor fits perfectly inputs ...

Advances in Neural Information Processing Systems 20212021

Real-Time Multi-Ion-Monitoring Front-End With Interference Compensation by Multi-Output Support Vector Regressor

Giovanni De Micheli, Sandro Carrara, Mandresy Ivan Ny Hanitra, Francesca Criscuolo

Ion-sensors play a major role in physiology and healthcare monitoring since they are capable of continuously collecting biological data from body fluids. Nevertheless, ion interference from background electrolytes present in the sample is a paramount chall ...

2021

Optimal errors and phase transitions in high-dimensional generalized linear models

Nicolas Macris, Jean François Emmanuel Barbier, Florent Gérard Krzakala, Lenka Zdeborová

Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this paper we analyze GLMs when the data matrix is random, as relevant in problems such as compressed sensing, error-corre ...

National Academy of Sciences2019

Statistical inference in ensemble modeling of cellular metabolism

Vassily Hatzimanikatis, Marc-Olivier Boldi, Tuure Eelis Hameri

Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic pa ...

2019

Interferon lambda 3/4 polymorphisms are associated with AIDS-related Kaposi's sarcoma

Jacques Fellay, Patrick Taffe

Background: Kaposi's sarcoma, the most common AIDS-related cancer, represents a major public concern in resource-limited countries. Single nucleotide polymorphisms within the Interferon lambda 3/4 region (IFNL3/4) determine the expression, function of IFNL ...

LIPPINCOTT WILLIAMS & WILKINS2018

A posteriori error estimation for the steady Navier-Stokes equations in random domains

Fabio Nobile, Marco Picasso, Diane Sylvie Guignard

We consider finite element error approximations of the steady incompressible Navier-Stokes equations defined on a randomly perturbed domain, the perturbation being small. Introducing a random mapping, these equations are transformed into PDEs on a fixed re ...

Elsevier2017

Handheld reflectance acquisition of paintings

Martin Vetterli, Sabine Süsstrunk, Loïc Arnaud Baboulaz, Damien Firmenich, Niranjan Thanikachalam

Relightable photographs are alternatives to traditional photographs as they provide a richer viewing experience. However, the complex acquisition systems of existing techniques have restricted its usage to specialized setups. We introduce an easy-to-use an ...

Ieee-Inst Electrical Electronics Engineers Inc2017

Data-Interpretation methodologies for Non-Linear Earthquake Response Predictions of Damaged Structures

Ian Smith, Pierino Lestuzzi, Yves Sylvain Gilles Reuland

Seismic exposure of buildings presents difficult engineering challenges. The principles of seismic design involve structures that sustain damage and still protect inhabitants. Precise and accurate knowledge of the residual capacity of damaged structures is ...

2017

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies

Stephan Morgenthaler

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomi ...

Wiley-Blackwell2016