Wavelet-Based Entropy Measures to Characterize Two-Dimensional Fractional Brownian Fields

The aim of this work was to extend the results of Perez et al. (Physica A (2006), 365 (2), 282–288) to the two-dimensional (2D) fractional Brownian field. In particular, we defined Shannon entropy using the wavelet spectrum from which the Hurst exponent is estimated by the regression of the logarithm of the square coefficients over the levels of resolutions. Using the same methodology. we also defined two other entropies in 2D: Tsallis and the Rényi entropies. A simulation study was performed for showing the ability of the method to characterize 2D (in this case, α = 2) self-similar processes

Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures

Breast cancer is the second leading cause of death in women in the United States. Mammography is currently the most eective method for detecting breast cancer early; however, radiological inter- pretation of mammogram images is a challenging task. Many medical images demonstrate a certain degree of self-similarity over a range of scales. This scaling can help us to describe and classify mammograms. In this work, we generalize the scale-mixing wavelet spectra to the complex wavelet domain. In this domain, we estimate Hurst parameter and image phase and use them as discriminatory descriptors to clas- sify mammographic images to benign and malignant. The proposed methodology is tested on a set of images from the University of South Florida Digital Database for Screening Mammography (DDSM). Keywords: Scaling; Complex Wavelets; Self-similarity; 2-D Wavelet Scale-Mixing Spectra

Assessing Seismic Hazard in Chile Using Deep Neural Networks

Earthquakes represent one of the most destructive yet unpredictable natural disasters around the world, with a massive physical, psychological, and economical impact in the population. Earthquake events are, in some cases, explained by some empirical laws such as Omori’s law, Bath’s law, and Gutenberg-Richter’s law. However, there is much to be studied yet; due to the high complexity associated with the process, nonlinear correlations among earthquake occurrences and also their occurrence depend on a multitude of variables that in most cases are yet unidentified. Therefore, having a better understanding on occurrence of each seismic event, and estimating the seismic hazard risk, would represent an invaluable tool for improving earthquake prediction. In that sense, this work consists in the implementation of a machine learning approach for assessing the earthquake risk in Chile, using information from 2012 to 2018. The results show a good performance of the deep neural network models for predicting future earthquake events

Space-Time Forecasting of Seismic Events in Chile

The aim of this work is to study the seismicity in Chile using the ETAS (epidemic type aftershock sequences) space‐time approach. The proposed ETAS model is estimated using a semi‐parametric technique taking into account the parametric and nonparametric components corresponding to the triggered and background seismicity, respectively. The model is then used to predict the temporal and spatial intensity of events for some areas of Chile where recent large earthquakes (with magnitude greater than 8.0 M) occurred

Spatio-temporal prediction of Baltimore crime events using CLSTM neural networks

Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of crime events. Spatio-temporal prediction of crimes activity can help the cities to have a better allocation of police resources and surveillance. Deep learning techniques are considered efficient tools to predict future events analyzing the behavior of past ones; however, they are not usually applied to crime event prediction using a spatio-temporal approach. In this paper, a Convolutional Neural Network (CNN) together with a Long-Short Term Memory (LSTM) network (thus CLSTM-NN) are proposed to predict the presence of crime events over the city of Baltimore (USA). In particular, matrices of past crime events are used as input to a CLSTM-NN to predict the presence of at least one event in future days. The model is implemented on two types of events: ‘‘street robbery’’ and ‘‘larceny’’. The proposed procedure is able to take into account spatial and temporal correlations present in the past data to improve future prediction. The prediction performance of the proposed neural network is assessed under a number of controlled plausible scenarios, using some standard metrics (Accuracy, AUC-ROC, and AUC-PR

Space-Time Integration of Heterogeneous Networks in Air Quality Monitoring

Lo scopo di questo lavoro è di proporre un modello per l'integrazione di dati provenienti da reti eterogenee di monitoraggio al fine di valutare la qualità dell'aria. Per esempio, la rete di monitoraggio del PM10 nel Nord Italia `e composta per la gran parte da centraline che si basano su due sistemi di rilevamento, TEOM and LVG. Mentre i dati rilevati con il metodo TEOM sottostimano il “veroâ€Â? livello di PM10, le centraline LVG sono più precise e per questo sono state scelte dalla Comunità Europea come “strumenti di riferimentoâ€Â?. L’idea su cui si basa il lavoro è di utilizzare le concentrazioni giornaliere dei PM, misurate con gli strumenti più precisi, per correggere le misure meno esatte, rilevate da centraline “non equivalentiâ€Â? a quelle gravimetriche e che, non necessariamente si trovano nella stessa zona in cui sono situati i primi. A tal fine, introduciamo un modello di calibrazione multivariato spazio temporale che abbiamo denominato Geostatistical Dynamical Calibration model (GDC). La principale ipotesi su cui si basa il modello `e che entrambi gli strumenti siano contaminati da errori di misura e che le rilevazioni TEOM siano distorte, rispetto alle “vereâ€Â? concentrazioni, per un fattore addittivo ed uno moltiplicativo.Si assume, inoltre, che “veroâ€Â? livello di PM10 sia un processo spazio temporale latente, rappresentato dall’equazione di stato nella formulazione state space. Le stime dei valori calibrati si ottengono dall’applicazione del filtro di Kalman. Questo approccio può essere considerato un’estensione geostatistica del modello DDC (Dynamical Displaced Calibration) di Fass`o and Nicolis (2004)

Propuesta de modelo de predicción econométrico espacial para el consumo de agua potable en Chile

El consumo de agua potable es un derecho y servicio básico vital para la humanidad debido a que cada persona en el planeta Tierra requiere un consumo promedio entre 20 a 50 litros de agua limpia al día para beber, cocinar o simplemente mantenerse limpios. Este recurso natural es una preocupación a nivel mundial, debido a que se ha ido agotando paulatinamente producto de una serie de factores que están ocurriendo durante el transcurso de los años. En el presente documento se desarrolla un modelo estadístico para la predicción del consumo del agua potable en Chile considerando las distintas ubicaciones geográficas de todas las comunas del país desde el 2012 hasta el 2019 y su correlación espacial. La primera parte de este trabajo inicia con un análisis estadístico con el objetivo de seleccionar las covariables relacionadas directamente con la variable a predecir (como, por ejemplo: población, precios tarifarios, nivel socio económico, tasa de pobreza, entre otros.). Sucesivamente se realiza un análisis estadístico descriptivo y espacial de todas las variables comprobando la existencia de una dependencia espacial global a través del Índice de Moran. Al final se propone la implementación de modelos espacio temporales bayesianos, aplicados a través de la Aproximación de Laplace Anidada Integrada (INLA). Dichos modelos permiten capturar los efectos aleatorios espaciales estructurados (similitudes entre comunas), no estructurados (residuos aleatorios) y los efectos fijos de cada una de las covariables seleccionadas. Los resultados obtenidos demuestran que el consumo de agua potable en Chile es más alto en la zona central del país dependiendo esencialmente de la variable índice de nivel socioeconómico, donde el modelo presentó una mayor precisión al momento de considerar la correlación espacial de las variables en comparación a un modelo de regresión multivariado tradicional.Fil: Calderón, Daniel. Universidad Andrés Bello. Facultad de Ingeniería; Chile.Fil: Leal, Danilo. Universidad Andrés Bello. Facultad de Ingeniería; Chile.Fil: Nicolis, Orietta. Universidad Andrés Bello. Facultad de Ingeniería; Chile