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

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

Using tabu search to configure support vector regression for effort estimation

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Photopolymerized thermosensitive poly(HPMAlactate)-PEG-based hydrogels : effect of network design on mechanical properties, degradation, and release behavior

Photopolymerized thermosensitive A-B-A triblock copolymer hydrogels composed of poly(N-(2-hydroxypropyl)-methacrylamide lactate) A-blocks, partly derivatizal with methacrylate groups to different extents (10, 20, and 30%) and hydrophilic poly(ethylene glycol) B-blocks of different molecular weights (4, 10, and 20 kDa) were synthesized. The aim of the present study was to correlate the polymer architecture with the hydrogel properties, particularly rheological, swelling, degradation properties and release behavior. It was found that an increasing methacrylation extent and a decreasing PEG molecular weight resulted in increasing gel strength and cross-link density, which tailored the degradation profiles from 25 to more than 300 days. Polymers having small PEG blocks showed a remarkable phase separation into polymer- and water-rich domains, as demonstrated by confocal microscopy. Depending on the hydrophobic domain density, the loaded protein resides in the hydrophilic pores or is partitioned into hydrophilic and hydrophobic domains, and its release from these compartments is tailored by the extent of methacrylation and by PEG length, respectively. As the mechanical properties, degradation, and release profiles can be fully controlled by polymer design and concentration, these hydrogels are suitable for controlled protein release

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