Sound Source Separation

This is the author's accepted pre-print of the article, first published as G. Evangelista, S. Marchand, M. D. Plumbley and E. Vincent. Sound source separation. In U. Zölzer (ed.), DAFX: Digital Audio Effects, 2nd edition, Chapter 14, pp. 551-588. John Wiley & Sons, March 2011. ISBN 9781119991298. DOI: 10.1002/9781119991298.ch14file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.2

L’instabilità dei nuovi lavori: un’analisi dei percorsi lavorativi

This paper starts from the hypothesis that an effective measurement of the area stability and in the labour market cannot be based only on a an indicator of labour mobility (number of job to job transitions) but requires to take into consideration the actual amount of time worked by each individual (level of saturation of working time). The evidence presented confirms our hypothesis showing that, in the Italian case, the increasing instability of work histories (of young people and in the entrance stage) is due to a growing difficulty of working on a continuous basis, as well as to the increasing time required to get a stable job. The evidence presented also shows that being an unstable worker has a negative impact on wages both in the short and in the long run. The exception is represented by those flexible workers which are able to combine high levels of mobility with a full saturation of working time. However, this category represents a rather marginal share of total young people accessing the labour market.Work Histories, Stayers and Movers, Tenure; Unstability, Duration models

Partial nutrient balances from agronomic and economic viewpoints: the case of corn cultivation in the acid upland soils of Isabela, the Philippines

Soil propertiesMaizeEconomic aspects

A green prospective for learned post-processing in sparse-view tomographic reconstruction

Deep Learning is developing interesting tools that are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green AI literature, we propose a shallow neural network to perform efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results show that the proposed inexpensive network computes images of comparable (or even higher) quality in about one-fourth of time and is more robust than the widely used and very deep ResUNet for tomographic reconstructions from sparse-view protocols