3,098 research outputs found

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    Human gut microbes’ transmission, persistence, and contribution to lactose tolerance

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    Human genotypes and their environment interact to produce selectable phenotypes. How microbes of the human gut microbiome interact with their host genotype to shape phenotype is not fully understood. Microbiota that inhabit the human body are environmentally acquired, yet many are passed intergenerationally between related family members, raising the possibility that they could act like genes. Here, I present three studies aimed at better understanding how certain gut microbiota contribute to host phenotypes. In a first study, I assessed mother to child transmission in understudied populations. I collected stool samples from 386 mother-infant pairs in Gabon and Vietnam, which are relatively under-studied for microbiome dynamics, and in Germany. Using metagenomic sequencing I characterized microbial strain diversity. I found that 25-50% of strains detected in mother-infant pairs were shared, and that strain-sharing between unrelated individuals was rare overall. These observations indicate that vertical transmission of microbes is widespread in human populations. Second, to test whether strains acquired during infancy persist into adulthood (similar to human genes), I collected stool from an adolescent previously surveyed for microbiome diversity as an infant. This dataset represents the longest follow-up to date for the persistence of strains seeded in infancy. I observed two strains that had persisted in the gut despite over 10 years passing, as well as 5 additional strains shared between the subject and his parents. Taken together, the results of these first two studies suggest that gut microbial strains persist throughout life and transmit between host-generations, dynamics more similar to those of the host’s own genome than of their environment. Third, I tested whether gut microbes could confer a phenotype (lactose tolerance) to individuals lacking the necessary genotypes (lactase persistence). I studied 784 women in Gabon, Vietnam and Germany for lactase persistence (genotype), lactose tolerance (phenotype), and characterized their gut microbiomes through metagenomic sequencing. Despite the genotype, I observed that 13% of participants were lactose tolerant by clinical criteria; I termed this novel phenotype microbially-acquired lactose tolerance (MALT). Those with MALT harbored microbiomes enriched for Bifidobacteria, a known lactose degrader. These results indicate that Bifidobacteria - which is passed intergenerationally - can confer a phenotype previously thought to be under only host genetic control. Taken together, my thesis work lends weight to the concept that specific microbes inhabiting the human gut have the potential to behave as epigenetic factors in evolution

    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

    Air Quality Research Using Remote Sensing

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    Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    The Development of Novel Genomic Technologies that Classify Pathogens of Public Health Significance

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    Recent advances in whole genome sequencing (WGS) have led to the routine sequencing of pathogenic bacteria that burden public health care. Taking advantage of the freely available genomes of tens of thousands of bacterial isolates, this thesis developed novel genomic classification technologies that described the long-and-short-term genomic epidemiology of two pathogens of significant disease burden, Vibrio cholerae and Staphylococcus aureus. V. cholerae is the etiological agent of cholera disease and has circumnavigated the globe in a series of seven pandemics. In Chapter 3, a novel tool named Multilevel Genome Typing (MGT) was developed to classify the V. cholerae species with a focus on the seventh pandemic. The V. cholerae MGT analysed a seventh pandemic dataset (n=4,770), described the seventh pandemic's global population structure, and reconfirmed the origins of the 2016 Yemen outbreak, considered the worst cholera outbreak in modern history. Informed by the successful application of the V. cholerae MGT in Chapter 4, a S. aureus MGT was developed. S. aureus asymptotically colonises approximately 30% of the population and is responsible for the onset of over a dozen diseases. The MGT characterised the global population structure of a clone that emerged in the early 2000s and became the major cause of infections in North America. The S. aureus MGT further investigated the persistent colonisation of patients not associated with hospitals. The MGT described the carriage of multiple isolates colonising the same patient. To gain a high-level view of the population structure consistent with phylogenetic divisions, in Chapter 5, a novel genomic classification tool named the S. aureus Lineage Typer (SaLTy) was developed for the species-level classification of S. aureus. We applied SaLTy to a species dataset (n=50,481) to generate a snapshot of the species population structure and identified six large lineages representing most of the species. To summarise, this thesis developed three novel genomic technologies that, when applied, improved the description of S. aureus and V. cholerae genomic epidemiology. The classifications defined by these technologies can inform the design of prevention and control strategies aiming to lower the disease and economic burdens caused by S. aureus and V. cholerae

    Towards a muon collider

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    A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work

    Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange

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    Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease
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