Data scrubbing

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

Nicolas Jean Philippe Guex, Cristian Carmeli, Murielle Bochud

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While ...

Nature Research2021

Impact of Memory Voltage Scaling on Accuracy and Resilience of Deep Learning Based Edge Devices

David Atienza Alonso, Andreas Peter Burg, Miguel Peon Quiros, Martino Ruggiero, Andrea Bonetti, Flavio Ponzina, Benoît Walter Denkinger, Soumya Subhra Basu, Szabolcs Balási

Energy consumption is a significant obstacle to integrate deep learning into edge devices. Two common techniques to curve it are quantization, which reduces the size of the memories (static energy) and the number of accesses (dynamic energy), and voltage s ...

2020

HMM-Based Error Correction Mechanism for Five-Key Chording Keyboards

Pierre Dillenbourg, Bixio Rimoldi, Adrian Dan Tarniceriu

As different text input devices lead to different typing error patterns, considering the device characteristics when designing an error correction mechanism can lead to significantly improved results. In this paper, we propose and evaluate a spelling corre ...

2015

Framework for explicit drift correction in multivariate calibration models

Dominique Bonvin, Michael Amrhein, Pamandeep Singh Gujral

Latent-variable calibrations using principal component regression and partial least-squares regression are often compromised by drift such as systematic disturbances and offsets. This paper presents a two-step framework that facilitates the evaluation and ...

2010