Stochastic parareal: an application of probabilistic methods to time-parallelisation

Parareal is a well-studied algorithm for numerically integrating systems of time-dependent differential equations by parallelising the temporal domain. Given approximate initial values at each temporal sub-interval, the algorithm locates a solution in a fixed number of iterations using a predictor-corrector, stopping once a tolerance is met. This iterative process combines solutions located by inexpensive (coarse resolution) and expensive (fine resolution) numerical integrators. In this paper, we introduce a stochastic parareal algorithm with the aim of accelerating the convergence of the deterministic parareal algorithm. Instead of providing the predictor-corrector with a deterministically located set of initial values, the stochastic algorithm samples initial values from dynamically varying probability distributions in each temporal sub-interval. All samples are then propagated by the numerical method in parallel. The initial values yielding the most continuous (smoothest) trajectory across consecutive sub-intervals are chosen as the new, more accurate, set of initial values. These values are fed into the predictor-corrector, converging in fewer iterations than the deterministic algorithm with a given probability. The performance of the stochastic algorithm, implemented using various probability distributions, is illustrated on systems of ordinary differential equations. When the number of sampled initial values is large enough, we show that stochastic parareal converges almost certainly in fewer iterations than the deterministic algorithm while maintaining solution accuracy. Additionally, it is shown that the expected value of the convergence rate decreases with increasing numbers of samples

Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

Saliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors

Energetic analysis and optimal design of a CHP plant in a frozen food processing factory through a dynamical simulation model

The proper design of cogeneration plants requires the choice of the technologies that best fits the ratio between heating and power loads. In this paper, a dynamical procedure of selecting and dimensioning a cogeneration plant, using deep and detailed energy, exergy and economic analysis of the entire production process of a frozen food production factory is proposed. The results highlight that a design method, based on a dynamic simulation, optimizes the energy efficiency of the food processing plant involved in the experimental test. Indeed, by considering the overall efficiency of the CHP + National grid system, the energy efficiency is 6% higher in the case of dynamic compared to a static design, resulting in better overall use of resources with a possible lower level of environmental impact. Moreover, the CHP plant designed with the proposed method generates electrical energy which appropriately matches that required by the process, with a surplus/deficit less than 4%, while the classic method never covers the amount required and results in a deficit greater than 20%. Finally, the annual savings of the solution derived from the dynamic method is 12% higher than that obtained with a traditional design technique. Considering the greater absolute cost of the cogeneration plant, this dynamic approach results in more profitable annual investment margins for the company

GParareal: A time-parallel ODE solver using Gaussian process emulation

Sequential numerical methods for integrating initial value problems (IVPs) can be prohibitively expensive when high numerical accuracy is required over the entire interval of integration. One remedy is to integrate in a parallel fashion, "predicting" the solution serially using a cheap (coarse) solver and "correcting" these values using an expensive (fine) solver that runs in parallel on a number of temporal subintervals. In this work, we propose a time-parallel algorithm (GParareal) that solves IVPs by modelling the correction term, i.e. the difference between fine and coarse solutions, using a Gaussian process emulator. This approach compares favourably with the classic parareal algorithm and we demonstrate, on a number of IVPs, that GParareal can converge in fewer iterations than parareal, leading to an increase in parallel speed-up. GParareal also manages to locate solutions to certain IVPs where parareal fails and has the additional advantage of being able to use archives of legacy solutions, e.g. solutions from prior runs of the IVP for different initial conditions, to further accelerate convergence of the method -- something that existing time-parallel methods do not do

Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images

When hyperspectral images are analyzed, a big amount of data, representing the reflectance at hundreds of wavelengths, needs to be processed. Hence, dimensionality reduction techniques are used to discard unnecessary information. In order to detect the so called “saliency”, i.e., the relevant pixels, we propose a bottom-up approach based on three main ingredients: sparse non negative matrix factorization (SNMF), spatial and spectral functions to measure the reconstruction error between the input image and the reconstructed one and a final clustering technique. We introduce novel error functions and show some useful mathematical properties. The method is validated on hyperspectral images and compared with state-of-the-art different approaches

TOXOPLASMOSIS: FOOD SAFETY AND RISK COMMUNICATION

Toxoplasmosis, parasitic pathology supported by Toxoplasma gondii, is a typical example of multi-issue and inter-disciplinary on which, with equal intensity, converge the interests of various branches of human and veterinary medicine. The aim of research was the assessment of risk communication to pregnant women by doctors gynecologists involved in ASL's territorial about toxoplasmosis, which can have serious effects on pregnancy and the unborn child. The results acquired during the investigation showed the need to develop and implement appropriate information campaigns and proper nutrition education

A resources ecosystem for digital and heritage-led holistic knowledge in rural regeneration

This paper presents a digital resources ecosystem prototype of integrated tools and resources to support heritage-led regeneration of rural regions, thanks to a deeper understanding of the complexity of cultural natural landscapes throughout their historical and current development. The ecosystem is conceived as a distributed software platform establishing data ecosystem and open standards for the management of information, aimed at providing different services and applications to address the needs of the various end-users identified. The platform has been conceived and realised in the framework of a Horizon 2020 research project, with a view to building a set of holistic knowledge about rural regions and their cultural and natural heritage and making it available for long-lasting heritage-led territorial processes of change. It is the product of a multidisciplinary collaboration amongst heritage, digital humanities and ICTs experts, and combines data and methodologies from a range of approaches to humanities together with the customisation of effective digital tools. It has been designed for deployment also in cloud systems compliant with the Infrastructure-as-a-Service paradigm. All data is Findable, Accessible, Interoperable, Reusable (FAIR data). It hosts and integrates different tools, making the data gathered with/for local stakeholders usable and making the same data re-usable within the tools’ functions, generating integrated heritage knowledge. It comprises data on 19 rural pilot territories, where the tools and their integration have been developed and tested, while 62 more are partially included as additional territories which participate in certain activities within the project. The main testers for this platform and its functions are the local stakeholders of these territories. The paper describes and analyses the platform and its impact, discussing the integration of tools as an innovative approach that goes beyond the use of individual tools in shaping a multidimensional vision. It also offers an analysis of the potential of an integrated digital ecosystem in evidence-based and place-based regeneration strategies. Some reflections for developments and cooperation during the pandemic are also presented

Classification of hyperspectral images with copulas

In the last decade, supervised learning methods for the classification of remotely sensed images (RSI) have grown significantly, especially for hyper-spectral (HS) images. Recently, deep learning-based approaches have produced encouraging results for the land cover classification of HS images. In particular, the Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have shown good performance. However, these methods suffer for the problem of the hyperparameter optimization or tuning that requires a high computational cost; moreover, they are sensitive to the number of observations in the learning phase. In this work we propose a novel supervised learning algorithm based on the use of copula functions for the classification of hyperspectral images called CopSCHI (Copula Supervised Classification of Hyperspectral Images). In particular, we start with a dimensionality reduction technique based on Singular Value Decomposition (SVD) in order to extract a small number of relevant features that best preserve the characteristics of the original image. Afterward, we learn the classifier through a dynamic choice of copulas that allows us to identify the distribution of the different classes within the dataset. The use of copulas proves to be a good choice due to their ability to recognize the probability distribution of classes and hence an accurate final classification with low computational cost can be conducted. The proposed approach was tested on two benchmark datasets widely used in literature. The experimental results confirm that CopSCHI outperforms the state-of-the-art methods considered in this paper as competitors

On the Classification of Hyperspectral Images with Different Copula Family

In the task of remote sensing, the Hyperspectral image (HSI) classification to analyze land cover is an established research topic. However, the nature of remote sensing data still poses several challenges including, the curse of dimensionality, the negligible number of samples during training or the presence of unbalanced data which makes learning difficult. Having a training set of pixels with the label of the assigned class, the operation that is performed in the classification of hyperspectral images is to assign a class label to each pixel in the test set based on the knowledge acquired with the training set. This paper discusses a new approach in the supervised classification of HS images considering the statistical tool of Copulas. Comparison with well-established techniques shows the good behaviour of this technique