Earth Observation (EO) mining systems aim at supporting efficient access and exploration of large volumes of image products. In this work, we address the problem of content-based image retrieval via example-based queries from Petabyte-scale EO data archives. To this end, we propose an interactive data mining system that relies on distributing unsupervised ingestion processes onto virtual machine instances in elastic, on-demand computing infrastructures that also support archive-scale content indexing via a "big data" analytics cluster-computing framework.

In document image analysis the task of segmenting images of ancient printed documents in distinct elements is known to be a very complex problem. In general, these documents are of low quality and can present skew and degradations because of old printing or ink stains. To face these problems we will show and discuss the validity of the Mumford and Shah variational method, based on the ? convergence theory, along with its numerical handling.

The purpose of this article is to present a methodology to identify the sources of activity in brain networks from functional magnetic resonance imaging (fMRI) data using the multiset canonical correlation analysis algorithm. The aim is to lay the foundations for a screening marker to be used as indicator of mental diseases. Group analysis blind source separation methods have proved reliable to extract the latent sources underlying the brain activities but currently there is no recognized biomarker for mental disorders.

Variable annuities are investment products offered by Insurance Companies. They allow investors to place assets in mutual funds under the umbrella of a tax-deferred account. The account value of variable annuities fluctuates based on the performance of the selected mutual funds and therefore some risks are involved. Recently there has been a growing interest in using fuzzy numbers to deal with financial uncertainty.