Age class structure in SIRD models for the COVID-19 - An analysis of Tennessee data

The COVID-19 pandemic is bringing disruptive effects on the healthcare system, economy and social life of countries all over the world. Even though the elder portion of the population is the most severely affected by the coronavirus disease, the counter-measures introduced so far by the governments do not take into account age structure, and the restrictions act uniformly on the population irrespectively of age. In this paper, we introduce a SIRD model with age classes for studying the impact on the epidemic evolution of lockdown policies applied heterogeneously on the different age groups of the population. The proposed model is then applied to COVID-19 data from the state of Tennessee. The simulation results suggest that a selective lockdown, while having a lighter socioeconomic impact, may bring benefits in terms of reduction of the mortality rate that are comparable to the ones obtained by a uniform lockdown

Learning Localized Representations of Point Clouds with Graph-Convolutional Generative Adversarial Networks

Point clouds are an important type of geometric data generated by 3D acquisition devices, and have widespread use in computer graphics and vision. However, learning representations for point clouds is particularly challenging due to their nature as being an unordered collection of points irregularly distributed in 3D space. Recently, supervised and semisupervised problems for point clouds leveraged graph convolution, a generalization of the convolution operation for data defined over graphs. This operation has been shown to be very successful at extracting localized features from point clouds. In this paper, we study the unsupervised problem of a generative model exploiting graph convolution. Employing graph convolution operations in generative models is not straightforward and it poses some unique challenges. In particular, we focus on the generator of a GAN, where the graph is not known in advance as it is the very output of the generator. We show that the proposed architecture can learn to generate the graph and the features simultaneously. We also study the problem of defining an upsampling layer in the graph-convolutional generator, proposing two methods that respectively learn to exploit a multi-resolution or self-similarity prior to sample the data distribution

RAN-GNNs: Breaking the Capacity Limits of Graph Neural Networks

Graph neural networks (GNNs) have become a staple in problems addressing learning and analysis of data defined over graphs. However, several results suggest an inherent difficulty in extracting better performance by increasing the number of layers. Recent works attribute this to a phenomenon peculiar to the extraction of node features in graph-based tasks, i.e., the need to consider multiple neighborhood sizes at the same time and adaptively tune them. In this article, we investigate the recently proposed randomly wired architectures in the context of GNNs. Instead of building deeper networks by stacking many layers, we prove that employing a randomly wired architecture can be a more effective way to increase the capacity of the network and obtain richer representations. We show that such architectures behave like an ensemble of paths, which are able to merge contributions from receptive fields of varied size. Moreover, these receptive fields can also be modulated to be wider or narrower through the trainable weights over the paths. We also provide extensive experimental evidence of the superior performance of randomly wired architectures over multiple tasks and five graph convolution definitions, using recent benchmarking frameworks that address the reliability of previous testing methodologies

NIR image colorization with graph-convolutional neural networks

Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations

Multiclass Sparse Centroids With Application to Fast Time Series Classification

In this article, we propose an efficient multiclass classification scheme based on sparse centroids classifiers. The proposed strategy exhibits linear complexity with respect to both the number of classes and the cardinality of the feature space. The classifier we introduce is based on binary space partitioning, performed by a decision tree where the assignation law at each node is defined via a sparse centroid classifier. We apply the presented strategy to the time series classification problem, showing by experimental evidence that it achieves performance comparable to that of state-of-the-art methods, but with a significantly lower classification time. The proposed technique can be an effective option in resource-constrained environments where the classification time and the computational cost are critical or, in scenarios, where real-time classification is necessary

Signal Compression via Neural Implicit Representations

Existing end-to-end signal compression schemes using neural networks are largely based on an autoencoder-like structure, where a universal encoding function creates a compact latent space and the signal representation in this space is quantized and stored. Recently, advances from the field of 3D graphics have shown the possibility of building implicit representation networks, i.e., neural networks returning the value of a signal at a given query coordinate. In this paper, we propose using neural implicit representations as a novel paradigm for signal compression with neural networks, where the compact representation of the signal is defined by the very weights of the network. We discuss how this compression framework works, how to include priors in the design, and highlight interesting connections with transform coding. While the framework is general, and still lacks maturity, we already show very competitive performance on the task of compressing point cloud attributes, which is notoriously challenging due to the irregularity of the domain, but becomes trivial in the proposed framework

Learning Robust Graph-Convolutional Representations for Point Cloud Denoising

Point clouds are an increasingly relevant geometric data type but they are often corrupted by noise and affected by the presence of outliers. We propose a deep learning method that can simultaneously denoise a point cloud and remove outliers in a single model. The core of the proposed method is a graph-convolutional neural network able to efficiently deal with the irregular domain and the permutation invariance problem typical of point clouds. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs from similarity among the high-dimensional feature representations of the points. The proposed approach outperforms state-of-the-art denoising methods showing robust performance in the challenging setup of high noise levels and in presence of structured noise

optimized low pressure solar dec with zeolite based adsorption

Abstract This paper presents a new concept of hybrid/natural air conditioning system with a high level of architectural integration. A solar DEC (Desiccant Evaporative Cooling) open cycle with very low pressure drops, drastically reduces the electricity consumption for driving fans. The supply air is dehumidified by an innovative zeolite coated adsorption bed and cooled indirectly by an evaporative cooler, through a low pressure drop heat exchanger. The adsorption bed is a finned coil heat exchanger coated with a SAPO-34 zeolite layer realizing both heat and mass transfer in one component. Low thermal grade heat is used to regenerate the adsorbent material, showing high compatibility with low temperature solar systems such as flat plate or evacuated tubes solar collectors. Experimental data have been used for validating a CFD model of the coated coil. The possibility to remove the adsorption heat during dehumidification reduces the air temperature with a positive effect on cooling power

DeepSUM: Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images

Recently, convolutional neural networks (CNNs) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution (SR) from multitemporal unregistered imagery have received little attention so far. This article proposes a novel CNN-based technique that exploits both spatial and temporal correlations to combine multiple images. This novel framework integrates the spatial registration task directly inside the CNN, and allows one to exploit the representation learning capabilities of the network to enhance registration accuracy. The entire SR process relies on a single CNN with three main stages: shared 2-D convolutions to extract high-dimensional features from the input images; a subnetwork proposing registration filters derived from the high-dimensional feature representations; 3-D convolutions for slow fusion of the features from multiple images. The whole network can be trained end-to-end to recover a single high-resolution image from multiple unregistered low-resolution images. The method presented in this article is the winner of the PROBA-V SR challenge issued by the European Space Agency (ESA)