Interacting Multiple Try Algorithms with Different Proposal Distributions

We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multiple-try generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. We provide numerical studies which show that the new algorithm can perform better than the basic MTM algorithm and that the interaction mechanism allows the IMTM to efficiently explore the state space

Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View Fusion

As a fundamental part of computational healthcare, Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) provide volumetric data, making the development of algorithms for 3D image analysis a necessity. Despite being computationally cheap, 2D Convolutional Neural Networks can only extract spatial information. In contrast, 3D CNNs can extract three-dimensional features, but they have higher computational costs and latency, which is a limitation for clinical practice that requires fast and efficient models. Inspired by the field of video action recognition we propose a new 2D-based model dubbed Slice SHift UNet (SSH-UNet) which encodes three-dimensional features at 2D CNN's complexity. More precisely multi-view features are collaboratively learned by performing 2D convolutions along the three orthogonal planes of a volume and imposing a weights-sharing mechanism. The third dimension, which is neglected by the 2D convolution, is reincorporated by shifting a portion of the feature maps along the slices' axis. The effectiveness of our approach is validated in Multi-Modality Abdominal Multi-Organ Segmentation (AMOS) and Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) datasets, showing that SSH-UNet is more efficient while on par in performance with state-of-the-art architectures

Bayesian Dynamic Tensor Regression

High- and multi-dimensional array data are becoming increasingly available. They admit a natural representation as tensors and call for appropriate statistical tools. We propose a new linear autoregressive tensor process (ART) for tensor-valued data, that encompasses some well-known time series models as special cases. We study its properties and derive the associated impulse response function. We exploit the PARAFAC low-rank decomposition for providing a parsimonious parametrization and develop a Bayesian inference allowing for shrinking effects. We apply the ART model to time series of multilayer networks and study the propagation of shocks across nodes, layers and time

A flexible predictive density combination for large financial data sets in regular and crisis periods

A flexible predictive density combination is introduced for large financial data sets, which allows for model set incompleteness. Dimension reduction procedures that include learning to allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the representation of the probability model in extended nonlinear state-space form, efficient simulation-based Bayesian inference is proposed using parallel dynamic clustering as well as nonlinear filtering implemented on graphics processing units. The approach is applied to combine predictive densities based on a large number of individual US stock returns of daily observations over a period that includes the Covid-19 crisis period. Evidence on dynamic cluster composition, weight patterns and model set incompleteness gives valuable signals for improved modelling. This enables higher predictive accuracy and better assessment of uncertainty and risk for investment fund management

Enhancing L2 skills through independent learning: the case study of Italian e-magazine

Engaging students in the learning process of a second language can be challenging and the tutor always needs to find new tools to enhance motivation for learner engagement in and outside the classroom. Starting from our linguistic and journalistic background, we started to create an e-magazine in Italian language (dealing with Italian topics, current affairs in Italy, etc.) with the active involvement of the Italian class students from Upper Intermediate to Proficiency level (B1 to C2). The Italian e-magazine is created within the Department of Languages at Leeds Beckett University to allow students to develop academic skills and to embrace a topic-based linguistic challenge of authentic materials. The students have been deeply engaged in research, reading, discussion and writing in Italian and the results of this experiment have been significantly impactful. The practice of the four skills has resulted in an enrichment of the vocabulary, a development of accuracy and the creation of group works with exchange of opinions. The experience has shown how the flipped classroom experience and the independent learning can interchangeably inform each other. All these activities led to: • Developing motivation • Stimulating interest and curiosity • Engagement in research • Flipped learning Students became active participants in planning and delivering learning activities. They have developed and produced tools in a learner-centred approach, showing a great interest in the idea and a significant improvement in their learning process. “The important practical contribution of the students represents the strength of their engagement in the study of the target language and the involvement in writing as a tool for learning and development” (Ferris D., 2008). It has become clear that this project is going to grow more and more thanks to the enthusiasm of our learners and the support in the group work

A successful defence strategy in grapevine cultivar ‘Tocai friulano’ provides compartmentation of grapevine Flavescence dorée phytoplasma

Background: Flavescence dorée (FD) is a grapevine disease caused by phytoplasma and it is one of the most destructive pathologies in Europe. Nowadays, the only strategies used to control the epidemics are insecticides against vector, but more sustainable techniques are required. Completely resistant Vitis vinifera varieties have not been uncovered yet, but differences in susceptibility among cultivars and spontaneous recovery from FD symptoms have been observed. The grapevine cultivar ‘Tocai friulano’ shows very low susceptibility to FD but its defence strategy to counteract the phytoplasma spread has not been deciphered yet. In this work, the mechanisms occurring within ‘Tocai friulano’ FD-infected plants were examined in depth to identify the phytoplasma distribution and the defence pathways involved. Results: In ‘Tocai friulano’ symptoms of FD-infection remained confined near the area where they appeared during all the vegetative season. Analyses of secondary phloem showed a total absence of FD phytoplasma (FDp) in the trunk and its disappearance in 2-year-old arms from July to November, which was different from ‘Pinot gris’, a highly susceptible variety. Diverse modulations of defence genes and accumulation of metabolites were revealed in 1-year-old canes of ‘Tocai friulano’ FD-infected plants, depending on the sanitary status. Symptomatic portions showed high activation of both jasmonate- and salicylate-mediated responses, together with a great accumulation of resveratrol. Whereas activation of jasmonate-mediated response and high content of ε-viniferin were identified in asymptomatic 1-year-old cane portions close to the symptomatic ones. Conclusion: Successful defence mechanisms activated near the symptomatic areas allowed the compartmentation of FD symptoms and phytoplasmas within the infected ‘Tocai friulano’ plants. These results could suggest specific agronomical practices to be adopted during FD management of this variety, and drive research of resistance genes against FD