5,857 research outputs found
MAGDA: A Mobile Agent based Grid Architecture
Mobile agents mean both a technology
and a programming paradigm. They allow for a
flexible approach which can alleviate a number
of issues present in distributed and Grid-based
systems, by means of features such as migration,
cloning, messaging and other provided mechanisms.
In this paper we describe an architecture
(MAGDA – Mobile Agent based Grid Architecture)
we have designed and we are currently
developing to support programming and execution
of mobile agent based application upon Grid
systems
Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Continuous monitoring of hydrogen and carbon dioxide at Mt Etna
This study assessed the use of an H2 fuel cell as an H2-selective sensor for volcano monitoring. The resolution,
repeatability, and cross-sensitivity of the sensor were investigated and evaluated under known laboratory
conditions. A tailor-made device was developed and used for continuously monitoring H2 and CO2 at Mt Etna
throughout 2009 and 2010. The temporal variations of both parameters were strongly correlated with the
evolution of the volcanic activity during the monitoring period. In particular, the CO2 flux exhibited long-term
variations, while H2 exhibited pulses immediately before the explosive activity that occurred at Mt Etna during
2010
Characterization of Strombolian events by using independent component analysis
We apply Independent Component Analysis (ICA) to seismic signals recorded at Stromboli volcano. Firstly, we show how ICA works considering synthetic signals, which are generated by dynamical systems. We prove that Strombolian signals, both tremor and explosions, in the high frequency band (>0.5 Hz), are similar in time domain. This seems to give some insights to the organ pipe model generation for the source of these events. Moreover, we are able to recognize in the tremor signals a low frequency component (<0.5 Hz), with a well defined peak corresponding to 30s
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