Publications associées à Données manquantes

Comparison of Three Imputation Methods for Groundwater Level Timeseries

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 ...

MDPI2023

Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform

Francesco Mondada, Jessica Elke Dehler Zufferey, Barbara Bruno, Laila Abdelsalam El-Hamamsy

The associated peer reviewed article that will appear in the International Journal of STEM education : El-Hamamsy, L., Bruno, B., Audrin, C., Chevalier, M., Avry S., Dehler Zufferey, J., and Mondada, F. (2023). How are Primary School Computer Science Curri ...

Zenodo2023

Dataset for the evaluation of the scalability of a primary school Digital Education curricular reform

Francesco Mondada, Jessica Elke Dehler Zufferey, Barbara Bruno, Laila Abdelsalam El-Hamamsy

Corresponding article : El-Hamamsy, L.*, Monnier, E.-C. *, Chessel-Lazzarotto F., Liégeois G., Bruno, B., Dehler Zufferey, J., and Mondada, F. (2023). An Adapted Cascade Model to Scale Primary School Digital Education Curricular Reforms and Teacher Profess ...

Zenodo2023

We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"

Matias Janvin, Mats Julius Stensrud

We summarize what we consider to be the two main limitations of the "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event" (Schmidli et al. 2022). First, the authors did not give detailed guidance on how to choose an appropriate esti ...

TAYLOR & FRANCIS INC2023

Localizing Unsynchronized Sensors With Unknown Sources

Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the source ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Estimation of Self-Exciting Point Processes from Time-Censored Data

Thomas Alois Weber, Philipp Schneider

Self-exciting point processes, widely used to model arrival phenomena in nature and society, are often difficult to identify. The estimation becomes even more challenging when arrivals are recorded only as bin counts on a finite partition of the observatio ...

2023

The Debiased Spatial Whittle likelihood

Sofia Charlotta Olhede

We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle l ...

WILEY2022

Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer

Marc Conrad Russwurm

Data imputation of incomplete image sequences is an essential prerequisite for analyzing and monitoring all development stages of plants in precision agriculture. For this purpose, we propose a conditional Wasserstein generative adversarial network TransGr ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

High-dimensional Data Cubes

Christoph Koch, Sachin Basil John

This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can ...

ASSOC COMPUTING MACHINERY2022

High-dimensional Data Cubes

Christoph Koch, Sachin Basil John

This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can ...

2022

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Amit Sharma

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

Mehdi Gholam

Background Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-lear ...

CAMBRIDGE UNIV PRESS2021

Imputation of missing information in worldwide patent data

Gaétan Jean A de Rassenfosse

We present a general method for imputing missing information in the Worldwide Patent Statistical Database (PATSTAT) and make the resulting datasets publicly available. The PATSTAT database is the de facto standard for academic research using patent data. C ...

2021

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

Robert West, Alberto Garcia Duran

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state ...

IEEE2021

Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts

Claudiu-Cristian Musat, Diego Matteo Antognini, Martin Milenkoski

In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about user preferences. We expand the variational autoencoder collaborative filtering from a single-domain to a multi-domain setting. T ...

2021

ROS-Neuro: A common middleware for BMI and robotics. The acquisition and recorder packages

José del Rocio Millán Ruiz, Luca Tonin, Stefano Tortora

Recent advances in the Brain-Machine Interface (BMI) and in the robotic fields allowed researchers to design a new generation of brain-actuated neuroprostheses to control a variety of assistive devices-ranging from powered wheelchairs to telepresence robot ...

IEEE2019

Electrification of urban mobility: The case of catenary-free buses

Michel Bierlaire, Riccardo Scarinci

Electric mobility is now a reality, and services based on electric vehicles, such as electric car-sharing and electric buses, are becoming part of urban mobility. These new modes bring new challenges for the design and operations of the systems that requir ...

ELSEVIER SCI LTD2019

Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

Convolutional Neural Networks (CNN) are the leading models for human body landmark detection from RGB vision data. However, as such models require high computational load, an alternative is to rely on depth images which, due to their more simple nature, ca ...

SPRINGER INTERNATIONAL PUBLISHING AG2018

The geographic evolution of political cleavages in Switzerland: A network approach to assessing levels and dynamics of polarization between local populations

Shin Alexandre Koseki

Scholarly studies and common accounts of national politics enjoy pointing out the resilience of ideological divides among populations. Building on the image of political cleavages and geographic polarization, the regionalization of politics has become a tr ...

2018

Extreme Image Completion

Sabine Süsstrunk, Radhakrishna Achanta, Nikolaos Arvanitopoulos Darginis

It is challenging to complete an image whose 99 percent pixels are randomly missing. We present a solution to this extreme image completion problem. As opposed to existing techniques, our solution has a computational complexity that is linear in the number ...

Ieee2017