Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.

Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.

A new robust multivariate mode estimator for eye-tracking calibration.

We propose in this work a new method for estimating the main mode of multivariate distributions, with application to eye-tracking calibration. When performing eye-tracking experiments with poorly cooperative subjects, such as infants or monkeys, the calibration data generally suffer from high contamination. Outliers are typically organized in clusters, corresponding to fixations in the time intervals when subjects were not looking at the calibration points.

Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification.

This data manuscript presents a set of signals collected from the Llaima volcano located at the western edge of the Andes in Araucania Region, Chile. The signals were recorded from the LAV station between 2010 and 2016. After individually processing and analyzing every signal, specialists from the Observatorio Vulcanológico de los Andes Sur (OVDAS) classified them into four class according to their event source: i) Volcano-Tectonic (VT); ii) Long Period (LP); iii) Tremor (TR), and iv) Tectonic (TC).