Graph-Based Analysis and Visualisation of Mobility Data

Urban mobility forecast and analysis can be addressed through grid-based and graph-based models. However, graph-based representations have the advantage of more realistically depicting the mobility networks and being more robust since they allow the implementation of Graph Theory machinery, enhancing the analysis and visualisation of mobility flows. We define two types of mobility graphs: Region Adjacency graphs and Origin-Destination graphs. Several node centrality metrics of graphs are applied to identify the most relevant nodes of the network in terms of graph connectivity. Additionally, the Perron vector associated with a strongly connected graph is applied to define a circulation function on the mobility graph. Such node values are visualised in the geographically embedded graphs, showing clustering patterns within the network. Since mobility graphs can be directed or undirected, we define several Graph Laplacian for both cases and show that these matrices and their spectral properties provide insightful information for network analysis. The computation of node centrality metrics and Perron-induced circulation functions for three different geographical regions demonstrate that basic elements from Graph Theory applied to mobility networks can lead to structure analysis for graphs of different connectivity, size, and orientation properties.Comment: 19 pages, 7 figure

LBGS: a smart approach for very large data sets vector quantization

Abstract In this paper, LBGS, a new parallel/distributed technique for Vector Quantization is presented. It derives from the well known LBG algorithm and has been designed for very complex problems where both large data sets and large codebooks are involved. Several heuristics have been introduced to make it suitable for implementation on parallel/distributed hardware. These lead to a slight deterioration of the quantization error with respect to the serial version but a large improvement in computing efficiency

Learning-based Framework for US Signals Super-resolution

We propose a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. We up-sample the acquired low-resolution image through a vision-based interpolation method; then, we train a learning-based model to improve the quality of the up-sampling. We qualitatively and quantitatively test our model on different anatomical districts (e.g., cardiac, obstetric) images and with different up-sampling resolutions (i.e., 2X, 4X). Our method improves the PSNR median value with respect to SOTA methods of $1.7\%$ on obstetric 2X raw images, $6.1\%$ on cardiac 2X raw images, and $4.4\%$ on abdominal raw 4X images; it also improves the number of pixels with a low prediction error of $9.0\%$ on obstetric 4X raw images, $5.2\%$ on cardiac 4X raw images, and $6.2\%$ on abdominal 4X raw images. The proposed method is then applied to the spatial super-resolution of 2D videos, by optimising the sampling of lines acquired by the probe in terms of the acquisition frequency. Our method specialises trained networks to predict the high-resolution target through the design of the network architecture and the loss function, taking into account the anatomical district and the up-sampling factor and exploiting a large ultrasound data set. The use of deep learning on large data sets overcomes the limitations of vision-based algorithms that are general and do not encode the characteristics of the data. Furthermore, the data set can be enriched with images selected by medical experts to further specialise the individual networks. Through learning and high-performance computing, our super-resolution is specialised to different anatomical districts by training multiple networks. Furthermore, the computational demand is shifted to centralised hardware resources with a real-time execution of the network's prediction on local devices

US & MR Image-Fusion Based on Skin Co-Registration

The study and development of innovative solutions for the advanced visualisation, representation and analysis of medical images offer different research directions. Current practice in medical imaging consists in combining real-time US with imaging modalities that allow internal anatomy acquisitions, such as CT, MRI, PET or similar. Application of image-fusion approaches can be found in tracking surgical tools and/or needles, in real-time during interventions. Thus, this work proposes a fusion imaging system for the registration of CT and MRI images with real-time US acquisition leveraging a 3D camera sensor. The main focus of the work is the portability of the system and its applicability to different anatomical districts

Adiabatic dynamics in open quantum critical many-body systems

The purpose of this work is to understand the effect of an external environment on the adiabatic dynamics of a quantum critical system. By means of scaling arguments we derive a general expression for the density of excitations produced in the quench as a function of its velocity and of the temperature of the bath. We corroborate the scaling analysis by explicitly solving the case of a one-dimensional quantum Ising model coupled to an Ohmic bath.Comment: 4 pages, 4 figures; revised version to be published in Phys. Rev. Let