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

A HIERARCHICAL DISTRIBUTED SHARED MEMORY PARALLEL BRANCH & BOUND APPLICATION WITH PVM AND OPENMP FOR MULTIPROCESSOR CLUSTERS

Branch&Bound (B&B) is a technique widely used to solve combinatorial optimization problems in physics and engineering science. In this paper we show how the combined use of PVM and OpenMP libraries can be a promising approach to exploit the intrinsic parallel nature of this class of application and to obtain efficient code for hybrid computational architectures. We described how both the shared memory and the distributed memory programming models can be applied to implement the same algorithm for the inter-nodes and intra-node parallelization. Some experimental tests on a local area network (LAN) of workstations are finally discussed

Costruire apprendimento e partecipazione a scuola: da studenti unteachables a comunità di motivated learners

Education of the future is a central theme in many debates nowadays. This paper focuses on the question what knowledge and skills are most relevant to prepare students for a rapidly changing society and if schools are able to respond effectively to costant transformation driven by technological innovation. While the school systemis involved in a modification process that concerns its core contents, new “wired” generations of students from lower secondary education are labelled as difficult to teach or even “unteachables”, students who are losing interest in academic learning, who are not motivated or responsible for their learning processes. The contribution examines the activities of an ongoing Erasmus+ research project - Unteachables. Helping the new generations of school teachers turn increasingly unteachable young students into young Learnables -, which involves school students (aged 12-16), university master students, in-training teachers and university researchers from seven European countries, aimed at experimenting teacher training activities designed to involve students in creating communities of motivated learners. This paper, profiling unteachables students, highlights the need of innovative teaching methods that can enhance motivation and participation of students

Near-field light emission from semiconductor macroatoms

We present a microscopic theoretical analysis of time and spatially resolved photoluminescence of naturally occurring quantum dots induced by monolayer fluctuations in the thickness of semiconductor quantum wells. In particular we study the carrier dynamics and the emission properties of a semiconductor quantum dot under both continuous-wave and pulsed excitations resonant with the barrier energy levels. We show that collection-mode near-field optical microscopy allows the detection of light emission from excitonic dark states. We find that, at low temperature, the second (dark) energy level displays a carrier density significantly larger than that of the lowest energy level. This behaviour is a consequence of carrier trapping due to the symmetry-induced suppression of radiative recombination

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