6,244 research outputs found

    Mapping Critical Practice In A Transdisciplinary Urban Studio

    Get PDF
    Architecture and Planning exist to make positive changes to our environment. Future practitioners in these disciplines will be responsible for how our cities develop and are managed - they will be required to exercise their professional judgement in complex and unpredictable contexts. There is increasing interest in transdisciplinary urbanism, but implementation in academic contexts has to date been relatively limited. This thesis aims to build on these examples, through a detailed account of one academic design studio which operates across architecture and urban planning; in doing so it aims to make the case for transdisciplinary, problem and place-based studio teaching. The study considers how a transdisciplinary studio environment supported students to develop a critical approach to practice through collaborative discourse. It looked at studio methods/practices; what it means to practice ‘critically’ in the context of design; and the role ‘going public’ by sharing ideas in public fora might play in developing critical positions. The study was undertaken in collaboration with nine students, a single cohort undertaking the final year of a hybrid master’s qualification in Architecture with Urban Planning. It adopts socio-material and spatial approaches to follow how the studio environment and the students’ emerging interdisciplinary identities shaped both their individual and their shared work. It mapped how their approach to their practice evolved through observations, interviews, and informal conversations, and through their drawings, models and journals. In carrying out these observations, and their analysis, I have returned to drawing methods common in architecture. This allowed me to explore and record aspects of studio practice which might otherwise be missed and revealed the importance of visual and spatial thinking to my own practice. Observations revealed how material spaces, tools and artefacts acted to structure social relations in the studio, and how these relations shaped individual approaches to critical practice

    Panacea or producer? Analysing the relationship between international Law and disaster risk

    Get PDF
    This thesis seeks to critically analyse the relationship between international law and disaster risk. Despite the increasing global threat that disasters present, international law’s engagement with their prevention remains at a relatively nascent stage compared to the development of other areas of the law. However, the progress that has been made since the United Nation’s International Decade for Natural Disaster Reduction in the 1990s suggests that international law is widely viewed as a valuable tool in addressing the issue and reducing the risk of disasters. In contrast to this, however, relatively little attention has been paid to the ways that international law itself may also play a role in the creation of disaster risk. It is here that the project makes an important and original contribution, by interrogating this presupposition and analysing the ways that international law itself may be culpable in the creation and exacerbation of risk. Through a novel, compound theoretical lens combining Marxist and Third World approaches to international law and insights from disaster theory, the thesis highlights the longstanding complicity of international law in the production of disaster risk. The thesis draws on understandings of disasters as processes that reach back through time, and thus begins its analysis with an examination of the early history of international law and the role of its colonial doctrines in the historic construction of vulnerability and hazards. It then turns to modern international law, particularly within the realm of international economic law, to examine the continuing legacies of these early developments and the ongoing role of international law in disaster risk creation. Overall, the thesis offers an original contribution to conversations on the connection between international law and disaster risk. Rather than focusing only on the positive role that international law can have in the reduction of disaster risk found in the majority of the literature, it seeks to highlight more pathological aspects of the relationship between the two and the implications of this. It ultimately concludes that unless the burgeoning field of international disaster law engages more with such critical accounts of international law and their understandings of the harm the law produces, then it will remain blind to a major source of disaster risk creation and be unsuccessful in achieving its normative aims

    Meta-learning algorithms and applications

    Get PDF
    Meta-learning in the broader context concerns how an agent learns about their own learning, allowing them to improve their learning process. Learning how to learn is not only beneficial for humans, but it has also shown vast benefits for improving how machines learn. In the context of machine learning, meta-learning enables models to improve their learning process by selecting suitable meta-parameters that influence the learning. For deep learning specifically, the meta-parameters typically describe details of the training of the model but can also include description of the model itself - the architecture. Meta-learning is usually done with specific goals in mind, for example trying to improve ability to generalize or learn new concepts from only a few examples. Meta-learning can be powerful, but it comes with a key downside: it is often computationally costly. If the costs would be alleviated, meta-learning could be more accessible to developers of new artificial intelligence models, allowing them to achieve greater goals or save resources. As a result, one key focus of our research is on significantly improving the efficiency of meta-learning. We develop two approaches: EvoGrad and PASHA, both of which significantly improve meta-learning efficiency in two common scenarios. EvoGrad allows us to efficiently optimize the value of a large number of differentiable meta-parameters, while PASHA enables us to efficiently optimize any type of meta-parameters but fewer in number. Meta-learning is a tool that can be applied to solve various problems. Most commonly it is applied for learning new concepts from only a small number of examples (few-shot learning), but other applications exist too. To showcase the practical impact that meta-learning can make in the context of neural networks, we use meta-learning as a novel solution for two selected problems: more accurate uncertainty quantification (calibration) and general-purpose few-shot learning. Both are practically important problems and using meta-learning approaches we can obtain better solutions than the ones obtained using existing approaches. Calibration is important for safety-critical applications of neural networks, while general-purpose few-shot learning tests model's ability to generalize few-shot learning abilities across diverse tasks such as recognition, segmentation and keypoint estimation. More efficient algorithms as well as novel applications enable the field of meta-learning to make more significant impact on the broader area of deep learning and potentially solve problems that were too challenging before. Ultimately both of them allow us to better utilize the opportunities that artificial intelligence presents

    Southern Adventist University Undergraduate Catalog 2023-2024

    Get PDF
    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    UMSL Bulletin 2023-2024

    Get PDF
    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

    Get PDF
    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

    Get PDF
    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

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

    Get PDF
    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
    • …
    corecore