Dynamics of a particle confined in a two-dimensional dilating and deforming domain

Some recent results concerning a particle confined in a one-dimensional box with moving walls are briefly reviewed. By exploiting the same techniques used for the 1D problem, we investigate the behavior of a quantum particle confined in a two-dimensional box (a 2D billiard) whose walls are moving, by recasting the relevant mathematical problem with moving boundaries in the form of a problem with fixed boundaries and time-dependent Hamiltonian. Changes of the shape of the box are shown to be important, as it clearly emerges from the comparison between the "pantographic", case (same shape of the box through all the process) and the case with deformation.Comment: 13 pages, 2 figure

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

A critical analysis of happiness and well-being. Where we stand now, where we need to go

This paper aims to critically analyse happiness and well-being to find novel ways for theorizing and promoting better life conditions for individuals and societies. The necessity to shift from a subjective view of individual well-being to a more social and contextual version of these constructs is the common thread running throughout the whole work. To this end, the first part introduces the reader into the complexity of the happiness and well-being scholarship by outlining some of the most relevant approaches developed by the psychological and economic literature. After highlighting the limitations of both disciplines, the second part of the paper presents some alternative models, namely the Feminist Economics, the Capabilities Approach, and the model of Four Qualities of Life. In addition to these, we will draw attention, in the last section, to the Critical Community Psychology approach to happiness and well-being. Our main argument is that this emerging discipline bears the potential to frame the pursuit of the good life in a whole new fashion that takes into account a) contextual features, in particular the recourses that a given environment offers and the opportunity to access them, b) the role of power, justice, and liberation, and c) the value of participation, reciprocity, and ethics of care. Current limitations of CCP are also discussed and future directions outlined

Observation of quantum interference in the plasmonic Hong-Ou-Mandel effect

We report direct evidence of the bosonic nature of surface plasmon polaritons (SPPs) in a scattering-based beamsplitter. A parametric down-conversion source is used to produce two indistinguishable photons, each of which is converted into a SPP on a metal-stripe waveguide and then made to interact through a semi-transparent Bragg mirror. In this plasmonic analog of the Hong-Ou-Mandel experiment, we measure a coincidence dip with a visibility of 72%, a key signature that SPPs are bosons and that quantum interference is clearly involved.Comment: 5 pages, 3 figure

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

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

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

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