Six papers on computational methods for the analysis of structured and unstructured data in the economic domain

This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events.This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Counter-Terrorism, Ethics and Technology

This open access book brings together a range of contributions that seek to explore the ethical issues arising from the overlap between counter-terrorism, ethics, and technologies. Terrorism and our responses pose some of the most significant ethical challenges to states and people. At the same time, we are becoming increasingly aware of the ethical implications of new and emerging technologies. Whether it is the use of remote weapons like drones as part of counter-terrorism strategies, the application of surveillance technologies to monitor and respond to terrorist activities, or counterintelligence agencies use of machine learning to detect suspicious behavior and hacking computers to gain access to encrypted data, technologies play a significant role in modern counter-terrorism. However, each of these technologies carries with them a range of ethical issues and challenges. How we use these technologies and the policies that govern them have broader impact beyond just the identification and response to terrorist activities. As we are seeing with China, the need to respond to domestic terrorism is one of the justifications for their rollout of the “social credit system.” Counter-terrorism technologies can easily succumb to mission creep, where a technology’s exceptional application becomes normalized and rolled out to society more generally. This collection is not just timely but an important contribution to understand the ethics of counter-terrorism and technology and has far wider implications for societies and nations around the world

Counter-Terrorism, Ethics and Technology

This open access book brings together a range of contributions that seek to explore the ethical issues arising from the overlap between counter-terrorism, ethics, and technologies. Terrorism and our responses pose some of the most significant ethical challenges to states and people. At the same time, we are becoming increasingly aware of the ethical implications of new and emerging technologies. Whether it is the use of remote weapons like drones as part of counter-terrorism strategies, the application of surveillance technologies to monitor and respond to terrorist activities, or counterintelligence agencies use of machine learning to detect suspicious behavior and hacking computers to gain access to encrypted data, technologies play a significant role in modern counter-terrorism. However, each of these technologies carries with them a range of ethical issues and challenges. How we use these technologies and the policies that govern them have broader impact beyond just the identification and response to terrorist activities. As we are seeing with China, the need to respond to domestic terrorism is one of the justifications for their rollout of the “social credit system.” Counter-terrorism technologies can easily succumb to mission creep, where a technology’s exceptional application becomes normalized and rolled out to society more generally. This collection is not just timely but an important contribution to understand the ethics of counter-terrorism and technology and has far wider implications for societies and nations around the world