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Related publications (22)

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

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

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

Comparison of Three Imputation Methods for Groundwater Level Timeseries

Andrea Rinaldo

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

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

Philipp Schneider, Thomas Alois Weber

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

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

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

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

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

Amit Sharma, Naman Goel

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

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

Space-time modelling of extreme events

Anthony Christopher Davison, Raphaël Huser

Max-stable processes are the natural analogues of the generalized extreme-value distribution when modelling extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replicati ...

Wiley-Blackwell2014

Structural identification methods for full-scale bridges

Ian Smith, James-Alexandre Goulet, Irwanda Laory

Results of two doctoral theses, one using a model-based approach and the other using model-free methods, are summarized. Both included case studies of full-scale bridges. While model-based approaches are appropriate for individual infrastructure-management ...

2013

Sparse Iterative Closest Point

Sofien Bouaziz, Mark Pauly, Andrea Tagliasacchi

Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. Thes ...

2013

Spectral modeling of time series with missing data

Singular spectrum analysis is a natural generalization of principal component methods for time series data. In this paper we propose an imputation method to be used with singular spectrum-based techniques which is based on a weighted combination of the for ...

Elsevier2013

Sparsity and the Bayesian perspective

Anaïs Marie Rassat, Jean-Luc Starck

Sparsity has recently been introduced in cosmology for weak-lensing and cosmic microwave background (CMB) data analysis for different applications such as denoising, component separation, or inpainting (i.e., filling the missing data or the mask). Although ...

Edp Sciences S A2013

A Probabilistic Approach to Handle Missing Data for Multi-Sensory Activity Recognition

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Hesam Sagha

Context and activity recognition in complex scenarios is prone to data loss due to disconnections, sensor failure, transmission problems, etc. This generally implies significant changes in the recognition performance. In the case of classifier fusion fault ...

2010

Methodologies for model-free data interpretation of civil engineering structures

Ian Smith, Daniele Posenato

Structural health monitoring (SHM) has the potential to provide quantitative and reliable data on the real condition of structures, observe the evolution of their behaviour and detect degradation This paper presents two methodologies for model-free data in ...

Elsevier2010

System identification via nuclear norm regularization for simulated moving bed processes from incomplete data sets

Colin Neil Jones

The application of nuclear norm regularization to system identification was recently shown to be a useful method for identifying low order linear models. In this paper, we consider nuclear norm regularization for identification of simulated moving bed proc ...

IEEE2009

System identification with missing data via nuclear norm regularization

Colin Neil Jones

2009

Model-free data interpretation for continuous monitoring of complex structures

Daniele Posenato

Structural Health Monitoring (SMH) has the potential to provide reliable, quantitative data on the real condition of structures. Early detection of damage or deterioration can enable preventive repair and reduce maintenance costs. Although obtaining data f ...

EPFL2009

Iterative rigid body transformation estimation for visual 3-D object tracking

Micha Hersch, Aude Billard

We present a novel yet simple 3D stereo vision tracking algorithm which computes the position and orientation of an object from the location of markers attached to the object. The novelty of this algorithm is that it does not assume that the markers are tr ...

2008

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