66 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Essays in the Economics of Innovation

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    Innovation is a crucial determinant of long-run economic growth in advanced economies. This dissertation explores the economic and social determinants of the production and diffusion of innovation in the context of Europe and the United States in the late nineteenth and early twentieth century. The first chapter (jointly authored with Gaia Dossi) documents how out-migration impacts innovation in the country of origin of migrants. During the Age of Mass Migration, nearly four million English migrants settled in the US. We construct a novel individual-level dataset linking English immigrants in the US to the UK census and complement it with the newly digitized universe of UK patents. Using a new shift-share instrument for bilateral migration flows and a triple-differences design, we document a positive, significant, and persistent effect of exposure to US technology through migrant ties on the direction of innovation in Britain in 1870--1940. The individual-level analysis suggests that physical return migration is not the main factor underlying this ``return innovation'' effect. Instead, we find that migration ties generate information flows that facilitate the cross-border diffusion of novel knowledge. Furthermore, our findings suggest that market integration fostered by migration linkages is a crucial driver of information flows. The second chapter (jointly authored with Lorenzo Spadavecchia) interprets out-migration through the lenses of directed technical change and adoption theory. We study the impact of immigration restriction policies on technology adoption in countries sending migrants. Between 1920 and 1921, the number of Italian immigrants to the United States dropped by 85\% after Congress passed the Emergency Quota Act, a severely restrictive immigration law. In a difference-in-differences setting, we exploit variation in exposure across Italian districts to this massive restriction against human mobility. Using novel individual-level data on Italian immigrants to the US and newly digitized historical censuses, we show that this policy substantially hampered technology adoption and capital investment. We interpret this as evidence of directed technical adoption: an increase in the labor supply dampens the incentive for firms to adopt labor-saving technologies. To validate this mechanism, we show that more exposed districts display a sizable increase in overall population and employment in manufacturing. We provide evidence that ``missing migrants,'' whose migration was inhibited by the Act, drive this result. The third chapter (jointly authored with Enrico Berkes, Gaia Dossi, and Mara P. Squicciarini) investigates how societies respond to adversity. After a negative shock, separate strands of research document either an increase in religiosity or a boost in innovation efforts. In this paper, we show that both reactions can occur at the same time, driven by different individuals within the society. The setting of our study is 1918--1919 influenza pandemic in the United States. To measure religiosity, we construct a novel indicator based on the naming patterns of newborns. We measure innovation through the universe of granted patents. Exploiting plausibly exogenous county-level variation in exposure to the pandemic, we provide evidence that more-affected counties become both more religious and more innovative. Looking within counties, we uncover heterogeneous responses: individuals from more religious backgrounds further embrace religion, while those from less religious backgrounds become more likely to choose a scientific occupation. Facing adversity widens the distance in religiosity between science-oriented individuals and the rest of the population, and it increases the polarization of religious beliefs

    Exploring Animal Behavior Through Sound: Volume 1

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    This open-access book empowers its readers to explore the acoustic world of animals. By listening to the sounds of nature, we can study animal behavior, distribution, and demographics; their habitat characteristics and needs; and the effects of noise. Sound recording is an efficient and affordable tool, independent of daylight and weather; and recorders may be left in place for many months at a time, continuously collecting data on animals and their environment. This book builds the skills and knowledge necessary to collect and interpret acoustic data from terrestrial and marine environments. Beginning with a history of sound recording, the chapters provide an overview of off-the-shelf recording equipment and analysis tools (including automated signal detectors and statistical methods); audiometric methods; acoustic terminology, quantities, and units; sound propagation in air and under water; soundscapes of terrestrial and marine habitats; animal acoustic and vibrational communication; echolocation; and the effects of noise. This book will be useful to students and researchers of animal ecology who wish to add acoustics to their toolbox, as well as to environmental managers in industry and government

    Understanding the evolutionary origin and ancestral composition of honey bee (Apis mellifera) populations.

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    The honey bee, Apis mellifera, is arguably the most important managed pollinator globally. Yet despite its economic and ecological importance, there are still several unknowns regarding the species ancestral origin and ancestral complexity. Understanding the genetic composition of native and managed honey bee colonies is imperative for resolving the species life history and elucidating how ancestry may inform management strategies. In this dissertation, I take a deep dive into the evolutionary origins of Apis mellifera and learn how ancestral complexity has shaped the composition of contemporary populations. In Chapter two, I settle a long-standing debate about the ancestral origins of the species. I find that Apis mellifea diverged out of Western Asia via at least three colonization routes, which resulted in the evolution of at least seven genetically distinct lineages. Interesting, I find that these lineages were able to adapt to their current distribution by repeated selection among a core set of genes. In Chapter three, I take a closer look at the genetic complexity of managed Canadian honey bees by estimating the ancestral composition of colonies using the genomic dataset from Chapter two. I find that patterns of ancestry differ between Canadian provinces, and that admixture correlates strongly with levels of genetic diversity. Interestingly, I find that genomic intervals with elevated levels of admixture segregate non-randomly in the genome and are associated with genes related to parasite and xenobiotic tolerance. Though admixture may bear advantages for managed colonies, admixture among honey bee is not always valued. In Chapter four and five I make use of the ancestral composition of invasive Africanized honey bees to develop assays to identify and track populations. This was achieved using machine learning models to choose the most informative single nucleotide polymorphisms (Chapter 4) and insertion-deletion (Chapter 5) markers that best delineate Africanized genetics from managed European colonies. My research addresses many gaps in our understanding of honey bee origins and ancestral complexity

    Understanding Cognitive Variability in Alzheimer’s Disease

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    Alzheimer’s Disease (AD) is highly heterogenous, both clinically and biologically. This variability is exacerbated by the ways within which, the clinical presentation is assessed with cognitive measures. This inhibits clinical trial success and earlier diagnosis of individuals. Marrying the clinical presentation to the pathology of the disease has so far proved troublesome. This thesis will look at how cognitive measures can best capture the clinical presentation of AD and how these measures can link to the underlying pathology using machine learning methods. This thesis studied this problem across four analyses and two cohorts. Each study looked at a different aspect of cognitive testing within AD. This was done with the overarching aim to interrogate the cognitive variability across the spectrum of AD. Study 1 showed a novel discrepancy score is different to memory measures at screening for AD. It also showed it tracks with AD severity, in the same way memory recall does. Studies 2 & 3 uncovered broad psychometric variance within amnestic measurement of impairment due to AD. This was done in two different populations across two different constructs of amnestic measurement, story recall and verbal list learning. These tests are frequently used interchangeably. These two studies show they should not be. Finally, Study 4 built models from cognitive measures to predict AD pathology. The performance of these models was moderate showing that even with novel cognitive measures, further work is needed to link the clinical and amyloid related biological presentations of AD. Bridging the gap between clinical presentation and pathology of AD using clinical and cognitive markers alone is not possible. Even when using a novel measure of discrepancy score. The discrepancy measure shows promise but was limited due to the inability of the MMSE to measure verbal ability. Conceptually a discrepancy score remains a promising avenue of research for screening, but broader language measures, as well as other AD biomarkers are needed to further test the construct validity of this measure

    Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

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    Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

    SIS 2017. Statistics and Data Science: new challenges, new generations

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    The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data

    Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

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    Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging

    Social and Affective Neuroscience of Everyday Human Interaction

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    This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers

    Three Risky Decades: A Time for Econophysics?

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    Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era
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