137 research outputs found

    Jews in East Norse Literature

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    This book explores the portrayal of Jews and Judaism in medieval Danish and Swedish literary and visual culture. Drawing on over 100 manuscripts and incunabula as well as runic inscriptions and religious art, the author describes the various, often contradictory, images ranging from antisemitism and anti-Judaism to the elevation of Jews as morally exemplary figures. It includes new editions of 54 East Norse texts with English translations

    Complex observation processes in ecology and epidemiology: general theory and specific examples

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    Complex observation processes abound in ecology and epidemiology. In order to answer the large-scale, urgent questions that are the focus of modern research, we must rely on indirect and opportunistic observation. Relating these data to the biological processes we are interested in is challenging. Statisticians working in this area need an understanding of both state-of-the-art modelling techniques and the field-specific nuances of how the data were generated. As a result, many methods to deal with complex observation processes are highly bespoke. Bespoke models are hard to translate between contexts and, because they are often presented in field-specific language, hard to learn from. Modelling of observation processes is thus a fractured area of study, leading to duplication of research effort and limiting the rate at which we can make progress. In this thesis, I aim to provide a road-map to how we might achieve some unification in this area. I begin by establishing a conceptual framework that can be used to describe observation processes and identify methods to address them. The framework defines all observation processes as a combination of issues of latency, identifiability, effort or scaling (L.I.E.S.). I illustrate the framework using motivating examples from ecology and epidemiology. The risk with conceptual frameworks is that they can be over-fitted to existing data and may fail when faced with new, real-world problems. To address this, I also approach the problem from a bottom-up perspective by tackling a series of ecological and epidemiological case studies. Each case study requires novel statistical methods to deal with the observation process. By developing new methods, I explore the world of observation processes potentially not well-captured in the literature. I then explore whether these case studies motivate revision or reassessment of my conceptual framework. While the case studies were chosen to challenge the L.I.E.S. framework, I find that they mutually reinforce each other. The framework provides a helpful scaffolding with which to describe the problems in the case studies. The case studies provide useful examples of more complex observation processes and how the four issues encoded in L.I.E.S. interact with one another. These findings illustrate the value of a framework for unifying approaches to observation processes

    Bayesian Methods in Tensor Analysis

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    Tensors, also known as multidimensional arrays, are useful data structures in machine learning and statistics. In recent years, Bayesian methods have emerged as a popular direction for analyzing tensor-valued data since they provide a convenient way to introduce sparsity into the model and conduct uncertainty quantification. In this article, we provide an overview of frequentist and Bayesian methods for solving tensor completion and regression problems, with a focus on Bayesian methods. We review common Bayesian tensor approaches including model formulation, prior assignment, posterior computation, and theoretical properties. We also discuss potential future directions in this field.Comment: 32 pages, 8 figures, 2 table

    Filter-Based Probabilistic Markov Random Field Image Priors: Learning, Evaluation, and Image Analysis

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    Markov random fields (MRF) based on linear filter responses are one of the most popular forms for modeling image priors due to their rigorous probabilistic interpretations and versatility in various applications. In this dissertation, we propose an application-independent method to quantitatively evaluate MRF image priors using model samples. To this end, we developed an efficient auxiliary-variable Gibbs samplers for a general class of MRFs with flexible potentials. We found that the popular pairwise and high-order MRF priors capture image statistics quite roughly and exhibit poor generative properties. We further developed new learning strategies and obtained high-order MRFs that well capture the statistics of the inbuilt features, thus being real maximum-entropy models, and other important statistical properties of natural images, outlining the capabilities of MRFs. We suggest a multi-modal extension of MRF potentials which not only allows to train more expressive priors, but also helps to reveal more insights of MRF variants, based on which we are able to train compact, fully-convolutional restricted Boltzmann machines (RBM) that can model visual repetitive textures even better than more complex and deep models. The learned high-order MRFs allow us to develop new methods for various real-world image analysis problems. For denoising of natural images and deconvolution of microscopy images, the MRF priors are employed in a pure generative setting. We propose efficient sampling-based methods to infer Bayesian minimum mean squared error (MMSE) estimates, which substantially outperform maximum a-posteriori (MAP) estimates and can compete with state-of-the-art discriminative methods. For non-rigid registration of live cell nuclei in time-lapse microscopy images, we propose a global optical flow-based method. The statistics of noise in fluorescence microscopy images are studied to derive an adaptive weighting scheme for increasing model robustness. High-order MRFs are also employed to train image filters for extracting important features of cell nuclei and the deformation of nuclei are then estimated in the learned feature spaces. The developed method outperforms previous approaches in terms of both registration accuracy and computational efficiency

    Diffuse Optical Imaging with Ultrasound Priors and Deep Learning

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    Diffuse Optical Imaging (DOI) techniques are an ever growing field of research as they are noninvasive, compact, cost-effective and can furnish functional information about human tissues. Among others, they include techniques such as Tomography, which solves an inverse reconstruction problem in a tissue volume, and Mapping which only seeks to find values on a tissue surface. Limitations in reliability and resolution, due to the ill-posedness of the underlying inverse problems, have hindered the clinical uptake of this medical imaging modality. Multimodal imaging and Deep Learning present themselves as two promising solutions to further research in DOI. In relation to the first idea, we implement and assess here a set of methods for SOLUS, a combined Ultrasound (US) and Diffuse Optical Tomography (DOT) probe for breast cancer diagnosis. An ad hoc morphological prior is extracted from US B-mode images and utilised for the regularisation of the inverse problem in DOT. Combination of the latter in reconstruction with a linearised forward model for DOT is assessed on specifically designed dual phantoms. The same reconstruction approach with the incorporation of a spectral model has been assessed on meat phantoms for reconstruction of functional properties. A simulation study with realistic digital phantoms is presented for an assessment of a non-linear model in reconstruction for the quantification of optical properties of breast lesions. A set of machine learning tools is presented for diagnosis breast lesions based on the reconstructed optical properties. A preliminary clinical study with the SOLUS probe is presented. Finally, a specifically designed deep learning architecture for diffusion is applied to mapping on the brain cortex or Diffuse Optical Cortical Mapping (DOCM). An assessment of its performances is presented on simulated and experimental data

    Advances in Molecular Simulation

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    Molecular simulations are commonly used in physics, chemistry, biology, material science, engineering, and even medicine. This book provides a wide range of molecular simulation methods and their applications in various fields. It reflects the power of molecular simulation as an effective research tool. We hope that the presented results can provide an impetus for further fruitful studies

    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

    Roman and Medieval Exeter and their Hinterlands

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    This first volume, presenting research carried out through the Exeter: A Place in Time project, provides a synthesis of the development of Exeter within its local, regional, national and international hinterlands. Exeter began life in c. AD 55 as one of the most important legionary bases within early Roman Britain, and for two brief periods in the early and late 60s AD, Exeter was a critical centre of Roman power within the new province. When the legion moved to Wales the fortress was converted into the civitas capital for the Dumnonii. Its development as a town was, however, relatively slow, reflecting the gradual pace at which the region as a whole adapted to being part of the Roman world. The only evidence we have for occupation within Exeter between the 5th and 8th centuries is for a church in what was later to become the Cathedral Close. In the late 9th century, however, Exeter became a defended burh, and this was followed by the revival of urban life. Exeter’s wealth was in part derived from its central role in the south-west’s tin industry, and by the late 10th century Exeter was the fifth most productive mint in England. Exeter’s importance continued to grow as it became an episcopal and royal centre, and excavations within Exeter have revealed important material culture assemblages that reflect its role as an international port
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