Cloud-Based Benchmarking of Medical Image Analysis

Medical imagin

Discriminative Appearance Models for Face Alignment

The proposed face alignment algorithm uses local gradient features as the appearance representation. These features are obtained by pixel value comparison, which provide robustness against changes in illumination, as well as partial occlusion and local deformation due to the locality. The adopted features are modeled in three discriminative methods, which correspond to different alignment cost functions. The discriminative appearance modeling alleviate the generalization problem to some extent

Index ordering by query-independent measures

There is an ever-increasing amount of data that is being produced from various data sources — this data must then be organised effectively if we hope to search though it. Traditional information retrieval approaches search through all available data in a particular collection in order to find the most suitable results, however, for particularly large collections this may be extremely time consuming. Our purposed solution to this problem is to only search a limited amount of the collection at query-time, in order to speed this retrieval process up. Although, in doing this we aim to limit the loss in retrieval efficacy (in terms of accuracy of results). The way we aim to do this is to firstly identify the most “important” documents within the collection, and then sort the documents within the collection in order of their "importance” in the collection. In this way we can choose to limit the amount of information to search through, by eliminating the documents of lesser importance, which should not only make the search more efficient, but should also limit any loss in retrieval accuracy. In this thesis we investigate various different query-independent methods that may indicate the importance of a document in a collection. The more accurate the measure is at determining an important document, the more effectively we can eliminate documents from the retrieval process - improving the query-throughput of the system, as well as providing a high level of accuracy in the returned results. The effectiveness of these approaches are evaluated using the datasets provided by the terabyte track at the Text REtreival Conference (TREC)

No public, no power? Analyzing the importance of public support for constitutional review with novel data and machine learning methods

Constitutional review is a central feature of liberal democracy. However, with neither the power of the purse nor the sword, the mere presence of constitutional courts does not automatically imply the effective exercise of judicial authority. Courts must rely on elected officials for the implementation of their rulings. The ability of a court to ensure that government officials faithfully comply with judicial decisions critically depends on the existence of sufficient public support for the court and the public’s ability to monitor legislative responses to judicial decisions. In this dissertation, I study the importance of public support for the relationship between court-government and court-public. I draw on the judicial politics literature on separation of powers, public support and legislative noncompliance and extend existing theory in two regards. First, I argue that not all courts possess the sufficient level of public support necessary to ensure legislative compliance. Varying degrees of public support strongly affect the leverage that courts possess in judicial-legislative and judicial-public interactions. Second, I argue that courts actively take measures in the form of the institutional tools at their disposal when they expect legislative noncompliance. One such tool is decision language, whose strategic usage allows judges to pressure the government or hide likely noncompliance from public view, if necessary. I test these arguments empirically by combining classical inferential methods such as survey experiments with novel data on court decision-making and methodologies from the field of machine learning and computational linguistic. Throughout all chapters, I employ a comparative perspective and test my arguments using data on the German Federal Constitutional Court, a court with strong and robust levels of public support, and the less popular French Conseil Constitutionnel. My empirical evidence shows that considering varying degrees of public support and the institutional tools of judges indeed helps to generate a more accurate picture of how judges behave in judicial-legislative and judicial-public interactions. Three conclusions are drawn. First, court decisions can legitimize public policies, albeit only if the court itself is perceived as a legitimate institution. Second, courts are more attentive to the political environment of a decision than previously thought: depending on their degree of public support, they actively adapt the language of their decisions as a function of the risk of noncompliance and their institutional support. Third, public support and other political context factors are important for judicial decision-making not only from an inferential but also from a predictive perspective. The results of my analyses confirm that public support plays a crucial role for courts’ ability to effectively exercise constitutional review, as well as highlighting the benefits of increased differentiation of constitutional courts institutional tools and their diffuse support from a comparative view. Therefore, my results have implications for the growing literature on strategic courts using their institutional tools to address potential noncompliance and the general awareness of judges for their institutional reputation. Overall, this project offers new perspectives on the most important resource of judges – their public support – and has important implications not only for research on judicial politics but also for the efficacy of constitutional review in a constitutional state, and thus the sustainability of liberal democracy

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

Species boundaries in bats: a philosophical, morphometric, environmental, and phylogenetic analysis of the genera Anoura, Carollia and Sturnira

Thesis (Ph.D.)--Boston UniversitySpecies are central to evolutionary biology, systematics and taxonomy. However, their precise definition and diagnosis is not straightforward. Species may be purely nominal constructs of the human mind or they may be real entities. Part of the difficulty of defining and diagnosing species lies in the continuous nature of variation from the level of the individual to the population, subspecies and species. It is here where systematics and taxonomy become challenging and exciting tools for understanding life on the planet. For bats, most of the efforts to describe and differentiate species have been qualitative. This may have worked in earlier times, during the first efforts to describe and name species. But, more recently, our perspectives have become sharper and the shortcomings of the qualitative approach have become obvious. This thesis is a collection of published essays, submitted studies, and ongoing research into the boundaries of bat species. In each chapter, I stress that species are not ideas or categories in the mind, but are real entities, based on testable hypotheses about the distribution of character states within multiorganismal entities. Therefore, these hypotheses and distributions of character states should optimally be analyzed through the prism of statistical inference. The dynamics of size and shape in the genus Anoura are discussed in the context of the space occupied by the different species within the genus, with novel insights into the interpretation of the distribution of these species in morphospace. For boundaries in the genus Carollia, I reassess current taxonomical knowledge, analyze morphological variation in relation to the environment, and the statistical uncertainty of species discrimination. In the species-rich genus Sturnira, I analyze a large morphological dataset for several species from Ecuador, describe a new species (S. peria) synonymize an old one (S. luisi), and provide a new perspective on phylogenetic relationships and species boundaries

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives