On the use of machine learning algorithms in the measurement of stellar magnetic fields

Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines. In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal magnetic field in stars (H_ eff) from polarized spectra of high resolution, through the inversion of the so-called multi-line profiles. Using synthetic data, we tested the performance of our technique considering different noise levels: In an ideal scenario of noise-free multi-line profiles, the inversion results are excellent; however, the accuracy of the inversions diminish considerably when noise is taken into account. In consequence, we propose a data pre-process in order to reduce the noise impact, which consists in a denoising profile process combined with an iterative inversion methodology. Applying this data pre-process, we have found a considerable improvement of the inversions results, allowing to estimate the errors associated to the measurements of stellar magnetic fields at different noise levels. We have successfully applied our data analysis technique to two different stars, attaining by first time the measurement of H_eff from multi-line profiles beyond the condition of line autosimilarity assumed by other techniques.Comment: Accepted for publication in A&

Transforming triangulations on non planar-surfaces

We consider whether any two triangulations of a polygon or a point set on a non-planar surface with a given metric can be transformed into each other by a sequence of edge flips. The answer is negative in general with some remarkable exceptions, such as polygons on the cylinder, and on the flat torus, and certain configurations of points on the cylinder.Comment: 19 pages, 17 figures. This version has been accepted in the SIAM Journal on Discrete Mathematics. Keywords: Graph of triangulations, triangulations on surfaces, triangulations of polygons, edge fli

N<i>e</i>XOS – the design, development and evaluation of a rehabilitation system for the lower limbs

Recent years have seen the development of a number of automated and semi-automated systems to support for physiotherapy and rehabilitation. These deploy a range of technologies from highly complex purpose built systems to approaches based around the use of industrial robots operating either individually or in combination for applications ranging from stroke to mobility enhancement. The NeXOS project set out to investigate an approach to the rehabilitation of the lower limbs in a way which brought together expertise in engineering design and mechatronics with specilists in rehabilitation and physiotherapy. The resulting system has resulted in a prototype of a system which is capable in operating in a number of modes from fully independent to providing direct support to a physiotherapist during manipulation of the limb. Designed around a low cost approach for an implementation ultimately capable of use in a patients home using web-baased strategies for communication with their support team, the prototype NeXOS system has validated the adoption of an integrated approach to its development. The paper considers this design and development process and provides the results from the initial tests with physiotherapists to establish the operational basis for clinical implementation