Publications associées à Spurious relationship

Networks with correlated edge processes

This article proposes methods to model non-stationary temporal graph processes motivated by a hospital interaction data set. This corresponds to modelling the observation of edge variables indicating interactions between pairs of nodes exhibiting dependenc ...

OXFORD UNIV PRESS2023

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

Ali Falsafi, Shekoofeh Yaghmaei

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

Causal modelling of heavy-tailed variables and confounders with application to river flow

Anthony Christopher Davison, Valérie Chavez

Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows a ...

SPRINGER2022

Application of optimal spline subspaces for the removal of spurious outliers in isogeometric discretizations

Espen Sande

We show that isogeometric Galerkin discretizations of eigenvalue problems related to the Laplace operator subject to any standard type of homogeneous boundary conditions have no outliers in certain optimal spline subspaces. Roughly speaking, these optimal ...

2022

A framework for occupancy detection and tracking using floor-vibration signals

Ian Smith, Slah Drira

In sensed buildings, information related to occupant movement helps optimize important func-tionalities such as caregiving, energy management, and security enhancement. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. Th ...

2022

Tests of mutual independence among several random vectors using univariate and multivariate ranks of nearest neighbours

Soham Sarkar

Testing mutual independence among several random vectors of arbitrary dimensions is a challenging problem in Statistics, and it has gained considerable interest in recent years. In this article, we propose some nonparametric tests based on different notion ...

TAYLOR & FRANCIS LTD2021

Using footstep-induced vibrations for occupant detection and recognition in buildings

Ian Smith, Yves Sylvain Gilles Reuland, Sai Ganesh Sarvotham Pai, Slah Drira

Occupant detection and recognition support functional goals such as security, healthcare, and energy management in buildings. Typical sensing approaches, such as smartphones and cameras, undermine the privacy of building occupants and inherently affect the ...

2021

Instrumented 5-Time Sit-To-Stand Test: Parameters Predicting Serious Falls beyond the Duration of the Test

Kamiar Aminian, Anisoara Ionescu, Arash Atrsaei, Yves Henchoz

Background: Falls are a major cause of injuries in older adults. To evaluate the risk of falls in older adults, clinical assessments such as the 5-time sit-to-stand (5xSTS) test can be performed. The development of inertial measurement units (IMUs) has pro ...

2021

Measuring causality between collaborative and individual gaze metrics for collaborative problem-solving with intelligent tutoring systems

Kshitij Sharma, Jennifer Kaitlyn Olsen

When students are working collaboratively and communicating verbally in a technology-enhanced environment, the system cannot track what collaboration is happening outside of the technology, making it difficult to fully assess the collaboration of the stude ...

2021

Bayesian Monte Carlo assimilation for the PETALE experimental programme using inter-dosimeter correlation

Andreas Pautz, Vincent Pierre Lamirand, Dimitri Rochman, Axel Guy Marie Laureau

This article presents the methodology developed to generate and use dosimeter covariances and to estimate nuisance parameters for the PETALE experimental programme. In anticipation of the final experimental results, this work investigates the consideration ...

2020

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables

Negar Kiyavash, Saber Salehkaleybar, Kun Zhang

We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, the inferred causal relationships among the observed ...

Microtome Publishing2020

Occupancy detection and tracking in buildings using floor-vibration signals

Slah Drira

In sensed buildings, information related to occupant movement helps optimize important functionalities such as security enhancement, energy management, and caregiving. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. The ...

EPFL2020

Occupant- detection strategy using footstep-induced floor vibrations

Ian Smith, Yves Sylvain Gilles Reuland, Sai Ganesh Sarvotham Pai, Slah Drira

Identification of occupant presence and location inside buildings is essential to functional goals such as security, healthcare, and energy management. Floor-vibration measurements, induced by footstep impacts, provide a non-intrusive sensing method for oc ...

ACM2019

Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART

Charlotte Marie Bürki

The emission parameterization is a crucial part of numerical pollen dispersion models. This paper shows that Artificial Neural Networks (ANNs) can substantially improve the performance of the Ambrosia pollen emission in numerical pollen dispersion models s ...

Elsevier2019

Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study

Anne Daniëlle Koelewijn

This paper compares predictions of metabolic energy expenditure in gait using seven metabolic energy expenditure models to assess their correlation with experimental data. Ground reaction forces, marker data, and pulmonary gas exchange data were recorded f ...

2019

Joint Fusion Learning of Multiple Time Series Prediction

Orçun Gümüs

Accurate traffic density estimations is essential for numerous purposes like the developing successful transit policies or to forecast future traffic conditions for navigation. Current developments in the machine learning and computer systems bring the tra ...

2019

A user's guide for α-synuclein biomarker studies in biological fluids: Perianalytical considerations

Hilal Lashuel

Parkinson's disease biomarkers are needed to increase diagnostic accuracy, to objectively monitor disease progression and to assess therapeutic efficacy as well as target engagement when evaluating novel drug and therapeutic strategies. This article summar ...

Wiley-Blackwell2017

Biomass Modelling: estimating thermodynamic properties from the elemental composition

François Maréchal, Emanuela Peduzzi, Guillaume Boissonnet

In the context of modelling biomass conversion processes, the accurate representation of biomass, which is a complex and highly variable material, is of crucial importance. This study provides a rather simple and flexible way to represent biomass, especial ...

2016

Fast Distributed Correlation Discovery Over Streaming Time-Series Data

Karl Aberer, Saket Sathe, Tian Guo

The dramatic rise of time-series data in a variety of contexts, such as social networks, mobile sensing, data centre monitoring, etc., has fuelled interest in obtaining real-time insights from such data using distributed stream processing systems. One such ...

2015

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

Alcherio Martinoli, Ali Marjovi, Adrian Arfire

We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and ...

IEEE2015